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1. Genome-wide identification of the genetic basis of amyotrophic lateral sclerosis

Author

Zhang, S., Cooper-Knock, J., Weimer, A.K., Shi, M., Moll, T., Marshall, J.N.G., Harvey, C., Nezhad, H.G., Franklin, J., Souza, C.D.S., Ning, K., Wang, C., Li, J., Dilliott, A.A., Farhan, S., Elhaik, E., Pasniceanu, I., Livesey, M.R., Eitan, C., Hornstein, E., Kenna, K.P., Project MinE ALS Sequencing Consortium, (The), Veldink, J.H., Ferraiuolo, L., Shaw, P.J., and Snyder, M.P.

Abstract

Amyotrophic lateral sclerosis (ALS) is a complex disease that leads to motor neuron death. Despite heritability estimates of 52%, genome-wide association studies (GWASs) have discovered relatively few loci. We developed a machine learning approach called RefMap, which integrates functional genomics with GWAS summary statistics for gene discovery. With transcriptomic and epigenetic profiling of motor neurons derived from induced pluripotent stem cells (iPSCs), RefMap identified 690 ALS-associated genes that represent a 5-fold increase in recovered heritability. Extensive conservation, transcriptome, network, and rare variant analyses demonstrated the functional significance of candidate genes in healthy and diseased motor neurons and brain tissues. Genetic convergence between common and rare variation highlighted KANK1 as a new ALS gene. Reproducing KANK1 patient mutations in human neurons led to neurotoxicity and demonstrated that TDP-43 mislocalization, a hallmark pathology of ALS, is downstream of axonal dysfunction. RefMap can be readily applied to other complex diseases.

Published
2022

2. Cartography of opportunistic pathogens and antibiotic resistance genes in a tertiary hospital environment

Author

Chng K. R., Li C., Bertrand D., Ng A. H. Q., Kwah J. S., Low H. M., Tong C., Natrajan M., Zhang M. H., Xu L., Ko K. K. K., Ho E. X. P., Av-Shalom T. V., Teo J. W. P., Khor C. C., Danko D., Bezdan D., Afshinnekoo E., Ahsanuddin S., Bhattacharya C., Butler D. J., De Filippis F., Hecht J., Kahles A., Karasikov M., Kyrpides N. C., Leung M. H. Y., Meleshko D., Mustafa H., Mutai B., Neches R. Y., Ng A., Nieto-Caballero M., Nikolayeva O., Nikolayeva T., Png E., Sanchez J. L., Shaaban H., Sierra M. A., Tong X., Young B., Alicea J., Bhattacharyya M., Blekhman R., Castro-Nallar E., Canas A. M., Chatziefthimiou A. D., Crawford R. W., Deng Y., Desnues C., Dias-Neto E., Donnellan D., Dybwad M., Elhaik E., Ercolini D., Frolova A., Graf A. B., Green D. C., Hajirasouliha I., Hernandez M., Iraola G., Jang S., Jones A., Kelly F. J., Knights K., Labaj P. P., Lee P. K. H., Shawn L., Ljungdahl P., Lyons A., Mason-Buck G., McGrath K., Mongodin E. F., Moraes M. O., Nagarajan N., Noushmehr H., Oliveira M., Ossowski S., Osuolale O. O., Ozcan O., Paez-Espino D., Rascovan N., Richard H., Ratsch G., Schriml L. M., Semmler T., Sezerman O. U., Shi L., Song L. H., Suzuki H., Court D. S., Thomas D., Tighe S. W., Udekwu K. I., Ugalde J. A., Valentine B., Vassilev D. I., Vayndorf E., Velavan T. P., Zambrano M. M., Zhu J., Zhu S., Mason C. E., Chen S. L., Ng O. T., Marimuthu K., Ang B., Genome Institute of Singapore (GIS), Singapore University of Technology and Design (SUTD), Singapore General Hospital, National University Hospital [Singapore] (NUH), Weill Cornell Medicine [Cornell University], Cornell University [New York], Nanyang Technological University [Singapour], Tan Tock Seng Hospital, Department of Computational and Systems Biology [Singapore], Funding for this work was provided by A*STAR (N.N.), and we are grateful for support from NMRC (NMRC CGAug16C005: O.T.N. and K.M.). C.E.M. acknowledges support from the WorldQuant Foundation, the Bill and Melinda Gates Foundation (OPP1151054) and the Alfred P. Sloan Foundation (G-2015-13964). The funders had no role in the study design, data collection and analysis, decision to publish or preparation of the manuscript. We would like to thank J. Gilbert for insightful comments and feedback on this work., MetaSUB Consortium: David Danko, Daniela Bezdan, Ebrahim Afshinnekoo, Sofia Ahsanuddin, Chandrima Bhattacharya, Daniel J. Butler, Kern Rei Chng, Francesca De Filippis, Jochen Hecht, Andre Kahles, Mikhail Karasikov, Nikos C. Kyrpides, Marcus H. Y. Leung, Dmitry Meleshko, Harun Mustafa, Beth Mutai, Russell Y. Neches, Amanda Ng, Marina Nieto-Caballero, Olga Nikolayeva, Tatyana Nikolayeva, Eileen Png, Jorge L. Sanchez, Heba Shaaban, Maria A. Sierra, Xinzhao Tong, Ben Young, Josue Alicea, Malay Bhattacharyya, Ran Blekhman, Eduardo Castro-Nallar, Ana M. Cañas, Aspassia D. Chatziefthimiou, Robert W. Crawford, Youping Deng, Christelle Desnues, Emmanuel Dias-Neto, Daisy Donnellan, Marius Dybwad, Eran Elhaik, Danilo Ercolini, Alina Frolova, Alexandra B. Graf, David C. Green, Iman Hajirasouliha, Mark Hernandez, Gregorio Iraola, Soojin Jang, Angela Jones, Frank J. Kelly, Kaymisha Knights, Paweł P. Łabaj, Patrick K. H. Lee, Levy Shawn, Per Ljungdahl, Abigail Lyons, Gabriella Mason-Buck, Ken McGrath, Emmanuel F. Mongodin, Milton Ozorio Moraes, Niranjan Nagarajan, Houtan Noushmehr, Manuela Oliveira, Stephan Ossowski, Olayinka O. Osuolale, Orhan Özcan, David Paez-Espino, Nicolas Rascovan, Hugues Richard, Gunnar Rätsch, Lynn M. Schriml, Torsten Semmler, Osman U. Sezerman, Leming Shi, Le Huu Song, Haruo Suzuki, Denise Syndercombe Court, Dominique Thomas, Scott W. Tighe, Klas I. Udekwu, Juan A. Ugalde, Brandon Valentine, Dimitar I. Vassilev, Elena Vayndorf, Thirumalaisamy P. Velavan, María M. Zambrano, Jifeng Zhu, Sibo Zhu & Christopher E. Mason, Weill Cornell Medicine [New York], Chng, K. R., Li, C., Bertrand, D., Ng, A. H. Q., Kwah, J. S., Low, H. M., Tong, C., Natrajan, M., Zhang, M. H., Xu, L., Ko, K. K. K., Ho, E. X. P., Av-Shalom, T. V., Teo, J. W. P., Khor, C. C., Danko, D., Bezdan, D., Afshinnekoo, E., Ahsanuddin, S., Bhattacharya, C., Butler, D. J., De Filippis, F., Hecht, J., Kahles, A., Karasikov, M., Kyrpides, N. C., Leung, M. H. Y., Meleshko, D., Mustafa, H., Mutai, B., Neches, R. Y., Ng, A., Nieto-Caballero, M., Nikolayeva, O., Nikolayeva, T., Png, E., Sanchez, J. L., Shaaban, H., Sierra, M. A., Tong, X., Young, B., Alicea, J., Bhattacharyya, M., Blekhman, R., Castro-Nallar, E., Canas, A. M., Chatziefthimiou, A. D., Crawford, R. W., Deng, Y., Desnues, C., Dias-Neto, E., Donnellan, D., Dybwad, M., Elhaik, E., Ercolini, D., Frolova, A., Graf, A. B., Green, D. C., Hajirasouliha, I., Hernandez, M., Iraola, G., Jang, S., Jones, A., Kelly, F. J., Knights, K., Labaj, P. P., Lee, P. K. H., Shawn, L., Ljungdahl, P., Lyons, A., Mason-Buck, G., Mcgrath, K., Mongodin, E. F., Moraes, M. O., Nagarajan, N., Noushmehr, H., Oliveira, M., Ossowski, S., Osuolale, O. O., Ozcan, O., Paez-Espino, D., Rascovan, N., Richard, H., Ratsch, G., Schriml, L. M., Semmler, T., Sezerman, O. U., Shi, L., Song, L. H., Suzuki, H., Court, D. S., Thomas, D., Tighe, S. W., Udekwu, K. I., Ugalde, J. A., Valentine, B., Vassilev, D. I., Vayndorf, E., Velavan, T. P., Zambrano, M. M., Zhu, J., Zhu, S., Mason, C. E., Chen, S. L., Ng, O. T., Marimuthu, K., Ang, B., and Acibadem University Dspace

Subjects
0301 basic medicine, Disease prevention, 030106 microbiology, Geographic Mapping, Drug resistance, Beds, Biology, Opportunistic Infections, Genome, General Biochemistry, Genetics and Molecular Biology, Article, Tertiary Care Centers, 03 medical and health sciences, Plasmid, Antibiotic resistance, Spatio-Temporal Analysis, Drug Resistance, Multiple, Bacterial, Drug Resistance, Bacterial, Patients' Rooms, Humans, Microbiome, Equipment and Supplies, Hospital, Genetics, Cross Infection, Infection Control, Singapore, Microbiota, General Medicine, [SDV.MP.BAC]Life Sciences [q-bio]/Microbiology and Parasitology/Bacteriology, 3. Good health, Resistome, Disinfection, 030104 developmental biology, Metagenomics, Biofilms, Equipment Contamination, Mobilome, Microbial genetics
Abstract

Although disinfection is key to infection control, the colonization patterns and resistomes of hospital-environment microbes remain underexplored. We report the first extensive genomic characterization of microbiomes, pathogens and antibiotic resistance cassettes in a tertiary-care hospital, from repeated sampling (up to 1.5 years apart) of 179 sites associated with 45 beds. Deep shotgun metagenomics unveiled distinct ecological niches of microbes and antibiotic resistance genes characterized by biofilm-forming and human-microbiome-influenced environments with corresponding patterns of spatiotemporal divergence. Quasi-metagenomics with nanopore sequencing provided thousands of high-contiguity genomes, phage and plasmid sequences (>60% novel), enabling characterization of resistome and mobilome diversity and dynamic architectures in hospital environments. Phylogenetics identified multidrug-resistant strains as being widely distributed and stably colonizing across sites. Comparisons with clinical isolates indicated that such microbes can persist in hospitals for extended periods (>8 years), to opportunistically infect patients. These findings highlight the importance of characterizing antibiotic resistance reservoirs in hospitals and establish the feasibility of systematic surveys to target resources for preventing infections., Spatiotemporal characterization of microbial diversity and antibiotic resistance in a tertiary-care hospital reveals broad distribution and persistence of antibiotic-resistant organisms that could cause opportunistic infections in a healthcare setting.

Published
2020

3. The impact of cross-kingdom molecular forensics on genetic privacy

Author

Elhaik, E., Ahsanuddin, S., Robinson, J.M., Foster, E.M., and Mason, C.E.

Abstract

Recent advances in metagenomic technology and computational prediction may inadvertently weaken an individual’s reasonable expectation of privacy. Through cross-kingdom genetic and metagenomic forensics, we can already predict at least a dozen human phenotypes with varying degrees of accuracy. There is also growing potential to detect a “molecular echo” of an individual’s microbiome from cells deposited on public surfaces. At present, host genetic data from somatic or germ cells provide more reliable information than microbiome samples. However, the emerging ability to infer personal details from different microscopic biological materials left behind on surfaces requires in-depth ethical and legal scrutiny. There is potential to identify and track individuals, along with new, surreptitious means of genetic discrimination. This commentary underscores the need to update legal and policy frameworks for genetic privacy with additional considerations for the information that could be acquired from microbiome-derived data. The article also aims to stimulate ubiquitous discourse to ensure the protection of genetic rights and liberties in the post-genomic era.

Published
2021

4. Forensic applications of microbiomics: a review

Author

Robinson, J.M., Pasternak, Z., Mason, C.E., and Elhaik, E.

Abstract

The rise of microbiomics and metagenomics has been driven by advances in genomic sequencing technology, improved microbial sampling methods, and fast-evolving approaches in bioinformatics. Humans are a host to diverse microbial communities in and on their bodies, which continuously interact with and alter the surrounding environments. Since information relating to these interactions can be extracted by analyzing human and environmental microbial profiles, they have the potential to be relevant to forensics. In this review, we analyzed over 100 papers describing forensic microbiome applications with emphasis on geolocation, personal identification, trace evidence, manner and cause of death, and inference of the postmortem interval (PMI). We found that although the field is in its infancy, utilizing microbiome and metagenome signatures has the potential to enhance the forensic toolkit. However, many of the studies suffer from limited sample sizes and model accuracies, and unrealistic environmental settings, leaving the full potential of microbiomics to forensics unexplored. It is unlikely that the information that can currently be elucidated from microbiomics can be used by law enforcement. Nonetheless, the research to overcome these challenges is ongoing, and it is foreseeable that microbiome-based evidence could contribute to forensic investigations in the future.

Published
2021

5. The Diversity of REcent and Ancient huMan (DREAM): A New Microarray for Genetic Anthropology and Genealogy, Forensics, and Personalized Medicine

Author

Elhaik, E., Yusuf, L., Anderson, A.I.J., Pirooznia, M., Arnellos, D., Vilshansky, G., Ercal, G., Lu, Y., Webster, T., Baird, M.L., and Esposito, U.

Subjects
CNVs, Genetic Markers, DNA Copy Number Variations, Genotype, Genome, Human, population genetics, Microarray Analysis, Polymorphism, Single Nucleotide, humanities, Pedigree, Evolution, Molecular, forensics, Genetics, Population, Anthropology, Humans, DNA, Ancient, Precision Medicine, archaic DNA, ancient DNA, biogeography, Research Article
Abstract

The human population displays wide variety in demographic history, ancestry, content of DNA derived from hominins or ancient populations, adaptation, traits, copy number variation, drug response, and more. These polymorphisms are of broad interest to population geneticists, forensics investigators, and medical professionals. Historically, much of that knowledge was gained from population survey projects. Although many commercial arrays exist for genome-wide single-nucleotide polymorphism genotyping, their design specifications are limited and they do not allow a full exploration of biodiversity. We thereby aimed to design the Diversity of REcent and Ancient huMan (DREAM)—an all-inclusive microarray that would allow both identification of known associations and exploration of standing questions in genetic anthropology, forensics, and personalized medicine. DREAM includes probes to interrogate ancestry informative markers obtained from over 450 human populations, over 200 ancient genomes, and 10 archaic hominins. DREAM can identify 94% and 61% of all known Y and mitochondrial haplogroups, respectively, and was vetted to avoid interrogation of clinically relevant markers. To demonstrate its capabilities, we compared its FST distributions with those of the 1000 Genomes Project and commercial arrays. Although all arrays yielded similarly shaped (inverse J) FST distributions, DREAM’s autosomal and X-chromosomal distributions had the highest mean FST, attesting to its ability to discern subpopulations. DREAM performances are further illustrated in biogeographical, identical by descent, and copy number variation analyses. In summary, with approximately 800,000 markers spanning nearly 2,000 genes, DREAM is a useful tool for genetic anthropology, forensic, and personalized medicine studies.

Published
2017

6. The 10th Conference of the Entomological Society of Israel: Agricultural Entomology February 11,1997 ARO, the Volcani Center, Bet Dagan, Israel

Author

Galun, Rachel, Ishaaya, I., Yablonski, Sara, Mendelson, Zmira, Mansour, Y., Kontsedalov, Svetlana, Pluschkell, U., Horowitz, A. R., Horowitz, A. R., Yablonski, Sara, Mendelson, Zmira, Ishaaya, I., Forer, G., Mansour, Y., Horowitz, A. R., Ishaaya, I., Ben-Yakir, D., Chen, M., Gokkes, M., Eitan, S., Klein, Z., Kuzlitzky, W., Ben-David, Z., Lavi, A., Ben-Yakir, D., Chen, M., Freidberg, A., Hadar, Ester, Shaaya, E., Kostjukovsky, M., Carmi, Y., Binenboim, I., Shaaya, E., Kostjukovsky, M., Ravid, U., Gamliel, A., Austerweil, Miriam, Riven, Yehudit, Steiner, B., Zilberg, V., Ovadia, S., Cohen, S., Yechezkely, Y., Masika, Y., Lapidot, M., Antignus, Y., Hadar, D., Nestel, D., Weintraub, Phyllis G., Horowitz, A. R., Zvieli, E., Ganmor, B., Gamliel, A., Regev, R., Arazi, Y., Berlinger, M. J., Lebiush-Mordechi, Sara, Fridja, Dvora, Khasdan, V., Rubin, A., Navarro, S., Mansour, F. A., Karchi, Z., Herschenhorn, J., Edelstein, M., Abo-Moch, F., Meisner, J., Ascher, K. R. S., Klein, M., Davidovich, M., Caspy, Immy, Kontsedalov, Svetlana, Ben-Moshe, E., Meisner, J., Ascher, K. R. S., Klein, M., Saphir, Nitza, Madar, Z., Zahavi, A., Mendel, Z., Ofek, G., Huberman, Gili, Izhar, Y., Wysoki, M., Kuzlitzky, W., Reneh, S., Gavish, S., Coll, M., Dory, I., Kadmon, A., Berlinger, M. J., Lebiush-Mordechi, Sara, Fridja, Dvora, Khasdan, V., Elhaik, E., Rodman, R., Argov, Yael, Rossler, Y., Wysoki, M., Swirski, E., Reneh, S., Kuzlitzky, W., Izhar, Y., Ben-Yehuda, S., Hadar, D., Mendel, Z., Assael, Fabienne, Zeidan, S., Zehavi, A., Gross, S., Dreishpon, Y., Steinberg, S., Cohen, M., Blumberg, D., Assael, Fabienne, Mendel, Z., Berlinger, M. J., Lebiush-Mordechi, Sara, Fridja, Dvora, Khasdan, V., Schwartz, B., Rodman, R., Glazer, I., Glazer, I., Rössler, Y., Gazit, Y., Chejanovsky, N., Gershburg, E., Lehavi, D., Rivkin, H., Faktor, O., Toister-Achituv, Mira, Faruchi, S., Kamensky, Batya, Faktor, O., Navon, A., Keren, Sara, Levski, Shlomit, Sachs, Y., Nakache, Y., Lazare, M., Gottlieb, Yuval, Zchori-Fein, Einat, Faktor, O., Rosen, D., Coll, M., Nevo, E., Coll, M., Harari, Ali R., Ben-Yehuda, S., Assae, Fabienne, Mende, Z., Klein, M., Chyzik, Raisa, Caspy, Immy, Davidovich, M., Ascher, K. R. S., Meisner, J., Izhar, Y., Ben-Yehuda, S., Wysoki, M., Swirski, E., Kuslitzky, W., Nestel, D., Klein, M., Adar, K., Nestel, D., Dunkelblum, E., Assael, Fabienne, Tarn, S., Harel, Miriam, Mendel, Z., Yarom, I., Malihi, Y., Svechkov, Anna, Freidberg, A., Horowitz, A. R., Ishaaya, I., Gazit, Y., Rossler, Y., Israely, N., Yuval, B., Nestel, D., Kitron, U., Perry, Y., Yuval, B., Kaspi, R., Yuval, B., Cohen, Hadass, Yuval, B., Dunkelblum, E., Mendel, Z., Altstein, Miriam, Ben-Aziz, Orna, Schafler, Irit, Seltzer, I., Gilon, C., Rafaeli, A., Gileadi, C., Dorchin, Netta, and Freidberg, A.

Published
1997
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7. A genomic dating tool for ancient genomes resolves the origins of hundreds of Eurasian genomes

Author

Esposito, U., Holland, G., Alshehab, G., Dobre, A. M., Pirooznia, M., Brimacombe, C. S., and Elhaik, E.

Abstract

Radiocarbon dating is the gold-standard in archaeology to estimate the age of skeletons, a key to studying their origins. Half of all published ancient human genomes lack reliable and direct dates, which results in obscure and contradictory reports. We developed Temporal Population Structure (TPS), the first DNA-based dating method for ancient genomes ranging from the Upper Palaeolithic to modern-day samples and applied it to all 961 ancient Eurasians. We show that TPS predictions for radiocarbon-dated skeletons align with their known dates and correctly account for kin relationships. The TPS-dating of 359 poorly dated Eurasian samples resolves conflicts and sheds new light on disputed findings as illustrated by five test cases. We discuss the phenotypic traits of the Time Informative Markers (TIMs) that underlie TPS. Summary TPS is a novel method to date humans from the Upper Palaeolithic to modern time from their DNA sequences.

Published
2019
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8. Pair Matcher (PaM): fast model-based optimisation of treatment/case-control matches

Author

Elhaik, E. and Ryan, D.

Abstract

Motivation: In clinical trials, individuals are matched using\ud demographic criteria, paired, and then randomly assigned to treatment and control groups to determine a drug’s efficacy. A chief cause for the \ud irreproducibility of results across pilot to Phase III trials is population stratification bias caused by the uneven distribution of ancestries in the treatment and control groups.\ud Results: Pair Matcher (PaM) addresses stratification bias by optimising pairing assignments a priori and/or a posteriori to the trial using both genetic and demographic criteria. Using simulated and real datasets, we show that PaM\ud identifies ideal and near-ideal pairs that are more genetically homogeneous than those identified based on competing methods, including the commonly used principal component analysis (PCA). Homogenising the treatment (or case) and control groups can be expected to improve the accuracy and reproducibility of the trial or genetic study. PaM’s ancestral inferences also \ud allow characterizing responders and developing a precision medicine approach to treatment.

Published
2019

9. Neonatal circumcision and prematurity are associated with sudden infant death syndrome (SIDS)

Author

Elhaik, E.

Subjects
medicine.medical_specialty, unexplained mortality, neonatal circumcision, stress, 03 medical and health sciences, 0302 clinical medicine, 030225 pediatrics, Epidemiology, Medicine, pain, 030212 general & internal medicine, business.industry, Obstetrics, Mortality rate, prematurity, lilith, Sudden infant death syndrome, Allostatic load, Infant mortality, 3. Good health, trauma, Clinical evidence, Original Article, business, allostatic load, sudden infant death syndrome (SIDS), Cohort study
Abstract

Background: Sudden Infant Death Syndrome (SIDS) is the most common cause of postneonatal unexplained infant death. The allostatic load hypothesis posits that SIDS is the result of cumulative perinatal painful, stressful, or traumatic exposures that tax neonatal regulatory systems. \ud \ud Aims: To test the predictions of the allostatic load hypothesis we explored the relationships between SIDS and two common phenotypes, male neonatal circumcision (MNC) and prematurity. \ud \ud Methods: We collated latitudinal data from 15 countries and 40 US states sampled during 2009 and 2013. We used linear regression analyses and likelihood ratio tests to calculate the association between SIDS and the phenotypes. \ud \ud Results: SIDS mortality rate was significantly and positively correlated with MNC. Globally (weighted): Increase of 0.6 (95% CI=0.01–0.1, t=2.86, p=0.01) per 1000 live-births SIDS mortality per 10% increase in circumcision rate. US (weighted): Increase of 0.1 (95% CI=0.03–0.16, t=2.81, p=0.01) per 1000 live-births unexplained mortality per 10% increase in circumcision rate. US states in which Medicaid covers MNC had significantly higher MNC rates (X ̃=0.72 vs 0.49; p=0.007) and male/female ratio of SIDS deaths (X ̃=1.48 vs 1.125; p=0.015) than other US states. Prematurity was also significantly and positively correlated with MNC. Globally: Increase of 0.5 (weighted: 95% CI: 0.02–0.086, t=3.37, p =0.004) per 1000 SIDS mortality per 10% increase in the prematurity rates. US: Increase of 1.9 (weighted: 95% CI: 0.06–0.32, t=3.13, p=0.004) per 1000 unexplained mortalities per 10% increase in the prematurity rates. Combined, the phenotypes increased the likelihood of SIDS. \ud \ud Conclusions: Epidemiological analyses are useful to generate hypotheses but cannot provide strong evidence of causality. Biological plausibility is provided by a growing body of experimental and clinical evidence linking aversive preterm and early-life SIDS events. Together with historical and anthropological evidence, our findings emphasize the necessity of cohort studies that consider these phenotypes with the aim of improving the identification of at-risk infants and reducing infant mortality. \ud \ud Relevance for patients: Preterm birth and neonatal circumcision are associated with a greater risk of SIDS, and efforts should be focused on reducing their rates.

Published
2018

11. The Origins of Ashkenaz, Ashkenazic Jews, and Yiddish

Author

Das, R., Wexler, P., Pirooznia, M., and Elhaik, E.

Subjects
Ashkenazic Jews, lcsh:Genetics, Archaeogenetics, lcsh:QH426-470, Ashkenaz, Perspective, Genetics, Molecular Medicine, geographic population structure (GPS), Yiddish, Rhineland hypothesis, ancient DNA, Genetics (clinical)
Abstract

Recently, the geographical origins of Ashkenazic Jews (AJs) and their native language Yiddish were investigated by applying the Geographic Population Structure (GPS) to a cohort of exclusively Yiddish-speaking and multilingual AJs. GPS localized most AJs along major ancient trade routes in northeastern Turkey adjacent to primeval villages with names that resemble the word "Ashkenaz." These findings were compatible with the hypothesis of an Irano-Turko-Slavic origin for AJs and a Slavic origin for Yiddish and at odds with the Rhineland hypothesis advocating a Levantine origin for AJs and German origins for Yiddish. We discuss how these findings advance three ongoing debates concerning (1) the historical meaning of the term "Ashkenaz;" (2) the genetic structure of AJs and their geographical origins as inferred from multiple studies employing both modern and ancient DNA and original ancient DNA analyses; and (3) the development of Yiddish. We provide additional validation to the non-Levantine origin of AJs using ancient DNA from the Near East and the Levant. Due to the rising popularity of geo-localization tools to address questions of origin, we briefly discuss the advantages and limitations of popular tools with focus on the GPS approach. Our results reinforce the non-Levantine origins of AJs.

Published
2017
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12. Reconstructing Druze population\ud history

Author

Marshall, S., Das, R., Pirooznia, M., and Elhaik, E.

Abstract

The Druze are an aggregate of communities in the Levant and Near East living almost exclusively in the mountains of Syria, Lebanon and Israel whose ~1000 year old religion formally opposes mixed marriages and conversions. Despite increasing interest in genetics of the population structure of the Druze, their population history remains unknown. We investigated the genetic relationships between Israeli Druze and both modern and ancient populations. We evaluated our findings in light of three hypotheses purporting to explain Druze history that posit Arabian, Persian or mixed Near Eastern-Levantine roots. The biogeographical analysis localised proto-Druze to the mountainous regions of southeastern Turkey, northern Iraq and southeast Syria and their descendants clustered along a trajectory between these two regions. The mixed Near Eastern-Middle Eastern localisation of the Druze, shown using both modern and ancient DNA data, is distinct from that of neighbouring Syrians, Palestinians and most of the Lebanese, who exhibit a high affinity to the Levant. Druze biogeographic affinity, migration patterns, time of emergence and genetic similarity to Near Eastern populations are highly suggestive of Armenian-Turkish ancestries for the proto-Druze.

Published
2016

13. A 'Wear and Tear' Hypothesis to Explain Sudden Infant Death Syndrome

Author

Elhaik, E.

Subjects
sudden infant death syndrome, neonatal circumcision, stress, trauma, pain, allostatic load, Neuroscience
Abstract

Sudden infant death syndrome (SIDS) is the leading cause of death among USA infants under 1 year of age accounting for ~2,700 deaths per year. Although formally SIDS dates back at least 2,000 years and was even mentioned in the Hebrew Bible (Kings 3:19), its etiology remains unexplained prompting the CDC to initiate a sudden unexpected infant death case registry in 2010. Due to their total dependence, the ability of the infant to allostatically regulate stressors and stress responses shaped by genetic and environmental factors is severely constrained. We propose that SIDS is the result of cumulative painful, stressful, or traumatic exposures that begin in utero and tax neonatal regulatory systems incompatible with allostasis. We also identify several putative biochemical mechanisms involved in SIDS. We argue that the important characteristics of SIDS, namely male predominance (60:40), the significantly different SIDS rate among USA Hispanics (80% lower) compared to whites, 50% of cases occurring between 7.6 and 17.6 weeks after birth with only 10% after 24.7 weeks, and seasonal variation with most cases occurring during winter, are all associated with common environmental stressors, such as neonatal circumcision and seasonal illnesses. We predict that neonatal circumcision is associated with hypersensitivity to pain and decreased heart rate variability, which increase the risk for SIDS. We also predict that neonatal male circumcision will account for the SIDS gender bias and that groups that practice high male circumcision rates, such as USA whites, will have higher SIDS rates compared to groups with lower circumcision rates. SIDS rates will also be higher in USA states where Medicaid covers circumcision and lower among people that do not practice neonatal circumcision and/or cannot afford to pay for circumcision. We last predict that winter-born premature infants who are circumcised will be at higher risk of SIDS compared to infants who experienced fewer nociceptive exposures. All these predictions are testable experimentally using animal models or cohort studies in humans. Our hypothesis provides new insights into novel risk factors for SIDS that can reduce its risk by modifying current infant care practices to reduce nociceptive exposures.

Published
2016
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14. In Search of the jüdische Typus: A Proposed Benchmark to Test the Genetic Basis of Jewishness Challenges Notions of 'Jewish Biomarkers'

Author

Elhaik, E.

Subjects
Jewish Urtypus, ancestry, Jews, genetic genealogy, Genetics, Jewishness, Original Research
Abstract

The debate as to whether Jewishness is a biological trait inherent from an "authentic" "Jewish type" (jüdische Typus) ancestor or a system of beliefs has been raging for over two centuries. While the accumulated biological and anthropological evidence support the latter argument, recent genetic findings, bolstered by the direct-to-consumer genetic industry, purport to identify Jews or quantify one's Jewishness from genomic data. To test the merit of claims that Jews and non-Jews are genetically distinguishable, we propose a benchmark where genomic data of Jews and non-Jews are hybridized over two generations and the observed and predicted Jewishness of the terminal offspring according to either the Orthodox religious law (Halacha) or the Israeli Law of Return are compared. Members of academia, the public, and 23andMe were invited to use the benchmark to test claims that Jews are genetically distinct from non-Jews. Here, we report the findings from these trials. We also compare the genomic similarity of ∼300 individuals from nearly thirty Afro-Eurasian Jewish communities to a simulated jüdische Typus population. The results are discussed in light of modern trends in the genetics of Jews and related fields and provide a tentative answer to the ageless question "who is a Jew?"

Published
2016

15. Localizing Ashkenazic Jews to Primeval Villages in the Ancient Iranian Lands of Ashkenaz

Author

Das, R., Wexler, P., Pirooznia, M., and Elhaik, E.

Abstract

TheYiddishlanguageisover1,000yearsoldandincorporatesGerman,Slavic,andHebrewelements.TheprevalentviewclaimsYiddish\ud hasaGermanorigin,whereastheopposingviewpositsaSlavicoriginwithstrongIranianandweakTurkicsubstrata.Oneofthemajor\ud difficulties in deciding between these hypotheses is the unknown geographical origin of Yiddish speaking Ashkenazic Jews (AJs). An\ud analysis of 393 Ashkenazic, Iranian, and mountain Jews and over 600 non-Jewish genomes demonstrated that Greeks, Romans,\ud Iranians,andTurksexhibitthehighestgeneticsimilaritywithAJs.TheGeographicPopulationStructureanalysislocalizedmostAJsalong\ud major primeval trade routes in northeastern Turkey adjacent to primeval villages with names that may be derived from “Ashkenaz.”\ud IranianandmountainJewswerelocalizedalongtraderoutesontheTurkey’seasternborder.Lossofmaternalhaplogroupswasevident\ud in non-Yiddish speaking AJs. Our results suggest that AJs originated from a Slavo-Iranian confederation, which the Jews call\ud “Ashkenazic” (i.e., “Scythian”), though these Jews probably spoke Persian and/or Ossete. This is compatible with linguistic evidence\ud suggesting that Yiddish is a Slavic language created by Irano-Turko-Slavic Jewish merchants along the Silk Roads as a cryptic trade\ud language, spoken only by its originators to gain an advantage in trade. Later, in the 9th century, Yiddish underwent relexification by\ud adoptinganewvocabularythatconsistsofaminorityofGermanandHebrewandamajorityofnewlycoinedGermanoidandHebroid\ud elements that replaced most of the original Eastern Slavic and Sorbian vocabularies, while keeping the original grammars intact.

Published
2016

16. Toward high-resolution population genomics using archaeological samples

Author

Morozova, I., Flegontov, P., Mikheyev, A.S., Bruskin, S., Asgharian, H., Ponomarenko, P., Klyuchnikov, V., ArunKumar, G., Prokhortchouk, E., Gankin, Y., Rogaev, E., Nikolsky, Y., Baranova, A., Elhaik, E., Tatarinova, T.V., University of Zurich, and Morozova, Irina

Subjects
1311 Genetics, 11294 Institute of Evolutionary Medicine, 1312 Molecular Biology, 570 Life sciences, biology, 610 Medicine & health
Abstract

The term ‘ancient DNA’ (aDNA) is coming of age, with over 1,200 hits in the PubMed database,\ud beginning in the early 1980s with the studies of ‘molecular paleontology’. Rooted in cloning\ud and limited sequencing of DNA from ancient remains during the pre-PCR era, the field has\ud made incredible progress since the introduction of PCR and next-generation sequencing. Over\ud the last decade, aDNA analysis ushered in a new era in genomics and became the method of\ud choice for reconstructing the history of organisms, their biogeography, and migration routes,\ud with applications in evolutionary biology, population genetics, archaeogenetics, paleoepidemiology,\ud and many other areas. This change was brought by development of new strategies\ud for coping with the challenges in studying aDNA due to damage and fragmentation, scarce\ud samples, significant historical gaps, and limited applicability of population genetics methods. In this review, we describe the state-of-the-art achievements in aDNA studies, with particular focus\ud on human evolution and demographic history. We present the current experimental and theoretical\ud procedures for handling and analysing highly degraded aDNA. We also review the challenges\ud in the rapidly growing field of ancient epigenomics. Advancement of aDNA tools and\ud methods signifies a new era in population genetics and evolutionary medicine research.

Published
2016

17. The metagenomics and metadesign of the subways and Urban biomes (MetaSUB) international consortium inaugural meeting report

Author

Afshinnekoo, E, Ahsanuddin, S, Ghedin, E, Read, T, Fraser, C, Dudley, J, Bowler, C, Mason, CE, Chernomoretz, A, Stolovitzky, G, Łabaj, PP, Graf, AB, Darling, A, Burke, C, Noushmehr, H, Dias-Neto, E, Guo, Y, Xie, Z, Lee, PKH, Shi, L, Ruiz-Perez, CA, Zambrano, MM, Siam, R, Ouf, A, Richard, H, Lafontaine, I, Wieler, LH, Semmler, T, Prithiviraj, B, Nedunuri, N, Mehr, S, Banihashemi, K, Lista, F, Anselmo, A, Suzuki, H, Kuroda, M, Yamashita, R, Sato, Y, Kaminuma, E, Aranda, CMA, Martinez, J, Dada, C, Dybwad, M, Oliveira, M, Schuster, S, Siwo, GH, Jang, S, Seo, SC, Hwang, SH, Ossowski, S, Bezdan, D, Chaker, S, Chatziefthimiou, AD, Udekwu, K, Liungdahl, P, Sezerman, U, Meydan, C, Elhaik, E, Gonnet, G, Schriml, LM, Mongodin, E, Huttenhower, C, Gilbert, J, Eisen, J, Hirschberg, D, Hernandez, M, McGrath, K, McGrath, L, Gray, A, Osuolale, O, Segata, N, Fillo, S, Iraola, G, Zhou, Y, Chang, Y, Li, Y, Zhend, Y, Hou, W, Ramirez, A, Cepeda, M, Desnues, C, Rascovan, N, Baron, C, Nagarajan, N, Ercolini, D, Menary, W, Tighe, S, Donia, M, Levy, S, Benito, J, Jones, A, Kasarskis, A, Maritz, J, Jorgensen, E, Neches, R, Livelli, T, Barnetche, JM, Pasolli, E, Greenfield, N, and Hasan, N

Subjects
Research Design, Databases, Genetic, Humans, Public Health, Metagenomics, City Planning, Ecosystem
Abstract

© 2016 The MetaSUB International Consortium. The Metagenomics and Metadesign of the Subways and Urban Biomes (MetaSUB) International Consortium is a novel, interdisciplinary initiative comprised of experts across many fields, including genomics, data analysis, engineering, public health, and architecture. The ultimate goal of the MetaSUB Consortium is to improve city utilization and planning through the detection, measurement, and design of metagenomics within urban environments. Although continual measures occur for temperature, air pressure, weather, and human activity, including longitudinal, cross-kingdom ecosystem dynamics can alter and improve the design of cities. The MetaSUB Consortium is aiding these efforts by developing and testing metagenomic methods and standards, including optimized methods for sample collection, DNA/RNA isolation, taxa characterization, and data visualization. The data produced by the consortium can aid city planners, public health officials, and architectural designers. In addition, the study will continue to lead to the discovery of new species, global maps of antimicrobial resistance (AMR) markers, and novel biosynthetic gene clusters (BGCs). Finally, we note that engineered metagenomic ecosystems can help enable more responsive, safer, and quantified cities.

Published
2016

18. Geographic population structure analysis of worldwide human populations infers their biogeographical origins

Author

Elhaik, E., Tatarinova, T., Chebotarev, D., Piras, I.S., Maria Calò, C., De Montis, A., Atzori, M., Marini, M., Tofanelli, S., Francalacci, P., Pagani, L., Tyler-Smith, C., Xue, Y., Cucca, F., Schurr, T.G., Gaieski, J.B., Melendez, C., Vilar, M.G., Owings, A.C., Gómez, R., Fujita, R., Santos, F.R., Comas, D., Balanovsky, O., Balanovska, E., Zalloua, P., Soodyall, H., Pitchappan, R., GaneshPrasad, A., Hammer, M., Matisoo-Smith, L., Wells, R.S., Acosta, O., Adhikarla, S., Adler, C.J., Bertranpetit, J., Clarke, A.C., Cooper, A., Der Sarkissian, C.S.I., Haak, W., Haber, M., Jin, L., Kaplan, M.E., Li, H., Li, S., Martínez-Cruz, B., Merchant, N.C., Mitchell, J.R., Parida, L., Platt, D.E., Quintana-Murci, L., Renfrew, C., Lacerda, D.R., Royyuru, A.K., Sandoval, J.R., Santhakumari, A.V., Soria Hernanz, D.F., Swamikrishnan, P., and Ziegle, J.S.

Subjects
Structure analysis, Genetic genealogy, Biogeography, Sequencing data, Genética de población, General Physics and Astronomy, Geographic population, MED/03 Genetica medica, Variación genética, Biology, Bioinformatics, Polymorphism, Single Nucleotide, General Biochemistry, Genetics and Molecular Biology, Article, Genètica de poblacions humanes, Genetics, Humans, Características de la población, 576 - Genética y evolución, Multidisciplinary, business.industry, Genome, Human, BIO/18 Genetica, General Chemistry, Quarter (United States coin), Corrigenda, Country of origin, Europe, Biological sciences, Genetics, Population, Global Positioning System, Biogeografía, Fitogeografía, business, Genètica humana -- Variació, Cartography, Pruebas genéticas, Algorithms
Abstract

The search for a method that utilizes biological information to predict humans’ place of origin has occupied scientists for millennia. Over the past four decades, scientists have employed genetic data in an effort to achieve this goal but with limited success. While biogeographical algorithms using next-generation sequencing data have achieved an accuracy of 700 km in Europe, they were inaccurate elsewhere. Here we describe the Geographic Population Structure (GPS) algorithm and demonstrate its accuracy with three data sets using 40,000–130,000 SNPs. GPS placed 83% of worldwide individuals in their country of origin. Applied to over 200 Sardinians villagers, GPS placed a quarter of them in their villages and most of the rest within 50 km of their villages. GPS’s accuracy and power to infer the biogeography of worldwide individuals down to their country or, in some cases, village, of origin, underscores the promise of admixture-based methods for biogeography and has ramifications for genetic ancestry testing., Current methods to identify the geographical origin of humans based on DNA data present limited accuracy. Here, the authors develop a new algorithm, the Genographic Population Structure (GPS), and demonstrate its ability to place worldwide individuals within their country or, in some cases, village of origin.

Published
2014

19. Finding the missing honey bee genes: lessons learned from a genome upgrade

Author

Elsik, C. G., Worley, K. C., Bennett, A. K., Beye, M., Camara, F., Childers, C. P., de Graaf, D. C., Debyser, G., Deng, J., Devreese, B., Elhaik, E., Evans, J. D., Foster, L. J., Graur, D., Guigo, R., Hoff, K. J., Holder, M. E., Hudson, M. E., Hunt, G. J., Jiang, H., Joshi, V., Khetani, R. S., Kosarev, P., Kovar, C. L., Ma, J., Maleszka, R., Moritz, R. F. A., Munoz-Torres, M. C., Murphy, T. D., Muzny, D. M., Newsham, I. F., Reese, J. T., Robertson, H. M., Robinson, G. E., Rueppell, O., Solovyev, V., Stanke, M., Stolle, E., Tsuruda, J. M., Van Vaerenbergh, M., Waterhouse, R. M., Weaver, D. B., Whitfield, C. W., Wu, Y., Zdobnov, E. M., Zhang, L., Zhu, D., Gibbs, R. A., Teams, H. G. S. C. P., and Consor, H. B. G. S.

Subjects
fungi
Abstract

Background\ud The first generation of genome sequence assemblies and annotations have had a significant impact upon our understanding of the biology of the sequenced species, the phylogenetic relationships among species, the study of populations within and across species, and have informed the biology of humans. As only a few Metazoan genomes are approaching finished quality (human, mouse, fly and worm), there is room for improvement of most genome assemblies. The honey bee (Apis mellifera) genome, published in 2006, was noted for its bimodal GC content distribution that affected the quality of the assembly in some regions and for fewer genes in the initial gene set (OGSv1.0) compared to what would be expected based on other sequenced insect genomes.\ud \ud Results\ud Here, we report an improved honey bee genome assembly (Amel_4.5) with a new gene annotation set (OGSv3.2), and show that the honey bee genome contains a number of genes similar to that of other insect genomes, contrary to what was suggested in OGSv1.0. The new genome assembly is more contiguous and complete and the new gene set includes ~5000 more protein-coding genes, 50% more than previously reported. About 1/6 of the additional genes were due to improvements to the assembly, and the remaining were inferred based on new RNAseq and protein data.\ud \ud Conclusions\ud Lessons learned from this genome upgrade have important implications for future genome sequencing projects. Furthermore, the improvements significantly enhance genomic resources for the honey bee, a key model for social behavior and essential to global ecology through pollination.

Published
2014

20. The GenoChip: A New Tool for Genetic Anthropology

Author

Elhaik, E, Greenspan, E, Staats, S, Krahn, T, Tyler-Smith, C, Xue, Y, Tofanelli, S, Francalacci, P, Cucca, F, Pagani, Luca, Jin, L, Li, H, Schurr, Tg, Greenspan, B, Spencer Wells, R, Acosta, O, Adhikarla, S, Adler, Cj, Balanovska, E, Balanovsky, O, Bertranpetit, J, Clarke, Ac, Comas, D, Cooper, A, Dulik, Mc, Gaieski, Jb, Ganesh Prasad AK, Haak, W, Haber, M, Kaplan, Me, Lacerda, Dr, Li, S, Martinez-Cruz, B, Matisoo-Smith, Ea, Merchant, Nc, Mitchell, Jr, Owings, Ac, Parida, L, Pitchappan, R, Platt, De, Quintana-Murci, L, Renfrew, C, Royyuru, Ak, Sandoval, Jr, Santhakumari, Av, Santos, Fr, Der Sarkissian CSI, Soodyall, S, Soria Hernanz DF, Swamikrishnan, P, Vieira, Pp, Vilar, Mg, Zalloua, Pa, and Ziegle, Js

Subjects
Letter, Anthropology, Human Migration, Population genetics, Genética de población, Ancestry-informative marker, Human genetic variation, MED/03 Genetica medica, Genética humana, DNA, Mitochondrial, Polymorphism, Single Nucleotide, Haplogroup, genetic anthropology, AimsFinder, 03 medical and health sciences, 0302 clinical medicine, Genes, Y-Linked, Human Genome Project, Genetics, Humans, Quantitative Biology - Genomics, Genographic Project, Quantitative Biology - Populations and Evolution, Genotyping, Denisovan, Antropologia, Ecology, Evolution, Behavior and Systematics, Anthropology, Cultural, History, Ancient, 030304 developmental biology, GenoChip, Oligonucleotide Array Sequence Analysis, Genomics (q-bio.GN), 0303 health sciences, Genètica humana, Genètica de poblacions, biology, BIO/18 Genetica, Populations and Evolution (q-bio.PE), population genetics, Especiación genética, biology.organism_classification, FOS: Biological sciences, haplogroups, Haplotipos, 030217 neurology & neurosurgery, Human mitochondrial DNA haplogroup
Abstract

The Genographic Project is an international effort using genetic data to chart human migratory history. The project is non-profit and non-medical, and through its Legacy Fund supports locally led efforts to preserve indigenous and traditional cultures. In its second phase, the project is focusing on markers from across the entire genome to obtain a more complete understanding of human genetic variation. Although many commercial arrays exist for genome-wide SNP genotyping, they were designed for medical genetic studies and contain medically related markers that are not appropriate for global population genetic studies. GenoChip, the Genographic Project's new genotyping array, was designed to resolve these issues and enable higher-resolution research into outstanding questions in genetic anthropology. We developed novel methods to identify AIMs and genomic regions that may be enriched with alleles shared with ancestral hominins. Overall, we collected and ascertained AIMs from over 450 populations. Containing an unprecedented number of Y-chromosomal and mtDNA SNPs and over 130,000 SNPs from the autosomes and X-chromosome, the chip was carefully vetted to avoid inclusion of medically relevant markers. The GenoChip results were successfully validated. To demonstrate its capabilities, we compared the FST distributions of GenoChip SNPs to those of two commercial arrays for three continental populations. While all arrays yielded similarly shaped (inverse J) FST distributions, the GenoChip autosomal and X-chromosomal distributions had the highest mean FST, attesting to its ability to discern subpopulations. The GenoChip is a dedicated genotyping platform for genetic anthropology and promises to be the most powerful tool available for assessing population structure and migration history., Comment: 11 pages, 5 Figures, 2 supplementary notes

Published
2013

21. GC₃ Biology in Eukaryotes and Prokaryotes

Author

Elhaik, E., Tatarinova, T., Tatarinova, T., and Kerton, O.

Abstract

We describe the distribution of Guanine and Cytosine (GC) content in the third codon position (GC3) distributions in different species, analyze evolutionary trends and discuss differences between genes and organisms with distinct GC3 levels. We scrutinize previously published theoretical frameworks and construct a unified view of GC3 biology in eukaryotes and prokaryotes.

Published
2012

22. Empirical distributions of F(ST) from large-scale human polymorphism data

Author

Elhaik, E.

Subjects
0106 biological sciences, lcsh:Medicine, Population genetics, 01 natural sciences, Linkage Disequilibrium, Gene Frequency, Natural Selection, Additive genetic effects, lcsh:Science, Genetics, 0303 health sciences, education.field_of_study, Evolutionary Theory, Multidisciplinary, Research Article, Gene Flow, Population, HapMap Project, Biology, 010603 evolutionary biology, Polymorphism, Single Nucleotide, White People, 03 medical and health sciences, Genetic drift, Asian People, Population Groups, Humans, Allele, Selection, Genetic, education, Allele frequency, Alleles, 030304 developmental biology, Evolutionary Biology, Balding–Nichols model, Models, Genetic, Population Biology, lcsh:R, Genetic Drift, Computational Biology, Genetics, Population, Haplotypes, F-statistics, Neutral Theory, Genetic Polymorphism, lcsh:Q, human activities, Population Genetics
Abstract

Studies of the apportionment of human genetic variation have long established that most human variation is within population groups and that the additional variation between population groups is small but greatest when comparing different continental populations. These studies often used Wright's F(ST) that apportions the standardized variance in allele frequencies within and between population groups. Because local adaptations increase population differentiation, high-F(ST) may be found at closely linked loci under selection and used to identify genes undergoing directional or heterotic selection. We re-examined these processes using HapMap data. We analyzed 3 million SNPs on 602 samples from eight worldwide populations and a consensus subset of 1 million SNPs found in all populations. We identified four major features of the data: First, a hierarchically F(ST) analysis showed that only a paucity (12%) of the total genetic variation is distributed between continental populations and even a lesser genetic variation (1%) is found between intra-continental populations. Second, the global F(ST) distribution closely follows an exponential distribution. Third, although the overall F(ST) distribution is similarly shaped (inverse J), F(ST) distributions varies markedly by allele frequency when divided into non-overlapping groups by allele frequency range. Because the mean allele frequency is a crude indicator of allele age, these distributions mark the time-dependent change in genetic differentiation. Finally, the change in mean-F(ST) of these groups is linear in allele frequency. These results suggest that investigating the extremes of the F(ST) distribution for each allele frequency group is more efficient for detecting selection. Consequently, we demonstrate that such extreme SNPs are more clustered along the chromosomes than expected from linkage disequilibrium for each allele frequency group. These genomic regions are therefore likely candidates for natural selection.

Published
2012

23. Exonic DNA Sequencing of ERBB4 in Bipolar Disorder

Author

Goes, F. S., Rongione, M., Chen, Y-C., Karchin, R., Elhaik, E., Potash, J. B., and Study, BG

Abstract

The Neuregulin-ErbB4 pathway plays a crucial role in brain development and constitutes one of the most biologically plausible signaling pathways implicated in schizophrenia and, to a lesser extent, in bipolar disorder (BP). However, recent genome-wide association analyses have not provided evidence for common variation in NRG1 or ERBB4 influencing schizophrenia or bipolar disorder susceptibility. In this study, we investigate the role of rare coding variants in ERBB4 in BP cases with mood-incongruent psychotic features, a form of BP with arguably the greatest phenotypic overlap with schizophrenia. We performed Sanger sequencing of all 28 exons in ERBB4, as well as part of the promoter and part of the 3′UTR sequence, hypothesizing that rare deleterious variants would be found in 188 cases with mood-incongruent psychosis from the GAIN BP study. We found 42 variants, of which 16 were novel, although none were non-synonymous or clearly deleterious. One of the novel variants, present in 11.2% of cases, is located next to an alternative stop codon, which is associated with a shortened transcript of ERBB4 that is not translated. We genotyped this variant in the GAIN BP case-control samples and found a marginally significant association with mood-incongruent psychotic BP compared with controls (additive model: OR = 1.64, P-value = 0.055; dominant model: OR = 1.73. P-value = 0.039). In conclusion, we found no rare variants of clear deleterious effect, but did uncover a modestly associated novel variant that could affect alternative splicing of ERBB4. However, the modest sample size in this study cannot definitively rule out a role for rare variants in bipolar disorder and studies with larger sample sizes are needed to confirm the observed association.

Published
2011

24. Functional and evolutionary insights from the genomes of three parasitoid Nasonia species

Author

Werren, J. H., Richards, S., Desjardins, C. A., Niehuis, O., Gadau, J., Colbourne, J. K., Beukeboom, L. W., Desplan, C., Elsik, C. G., Grimmelikhuijzen, C. J., Kitts, P., Lynch, J. A., Murphy, T., Oliveira, D. C., Smith, C. D., van de Zande, L., Worley, K. C., Zdobnov, E. M., Aerts, M., Albert, S., Anaya, V. H., Anzola, J. M., Barchuk, A. R., Behura, S. K., Bera, A. N., Berenbaum, M. R., Bertossa, R. C., Bitondi, M. M., Bordenstein, S. R., Bork, P., Bornberg-Bauer, E., Brunain, M., Cazzamali, G., Chaboub, L., Chacko, J., Chavez, D., Childers, C. P., Choi, J. H., Clark, M. E., Claudianos, C., Clinton, R. A., Cree, A. G., Cristino, A. S., Dang, P. M., Darby, A. C., de Graaf, D. C., Devreese, B., Dinh, H. H., Edwards, R., Elango, N., Elhaik, E., Ermolaeva, O., Evans, J. D., Foret, S., Fowler, G. R., Gerlach, D., Gibson, J. D., Gilbert, D. G., Graur, D., Gr�nder, S., Hagen, D. E., Han, Y., Hauser, F., Hultmark, D., Hunter, H. C., Hurst, G. D., Jhangian, S. N., Jiang, H., Johnson, R. M., Jones, A. K., Junier, T., Kadowaki, T., Kamping, A., Kapustin, Y., Kechavarzi, B., Kim, J., Kiryutin, B., Koevoets, T., Kovar, C. L., Kriventseva, E. V., Kucharski, R., Lee, H., Lee, S. L., Lees, K., Lewis, L. R., Loehlin, D. W., Logsdon, J. M., Lopez, J. A., Lozado, R. J., Maglott, D., Maleszka, R., Mayampurath, A., Mazur, D. J., McClure, M. A., Moore, A. D., Morgan, M. B., Muller, J., Munoz-Torres, M. C., Muzny, D. M., Nazareth, L. V., Neupert, S., Nguyen, N. B., Nunes, F. M., Oakeshott, J. G., Okwuonu, G. O., Pannebakker, B. A., Pejaver, V. R., Peng, Z., Pratt, S. C., Predel, R., Pu, L. L., Ranson, H., Raychoudhury, R., Rechtsteiner, A., Reese, J. T., Reid, J. G., Riddle, M., Robertson, H. M., Romero-Severson, J., Rosenberg, M., Sackton, T. B., Sattelle, D. B., Schl�ns, H., Schmitt, T., Schneider, M., Sch�ler, A., Schurko, A. M., Shuker, D. M., Sims, Z. L., Sinha, S., Smith, Z., Solovyev, V., Souvorov, A., Springauf, A., Stafflinger, E., Stage, D. E., Stanke, M., Tanaka, Y., Telschow, A., Trent, C., Vattathil, S., Verhulst, E. C., Viljakainen, L., Wanner, K. W., Waterhouse, R. M., Whitfield, J. B., Wilkes, T. E., Williamson, M., Willis, J. H., Wolschin, F., Wyder, S., Yamada, Takuji, Yi, S. V., Zecher, C. N., Zhang, L., Gibbs, R. A., Group, Nasonia Genome Working, Zdobnov, Evgeny, Gerlach, Daniel, Junier, Thomas, Muller, Jean, Beukeboom lab, and Van de Zande lab

Subjects
0106 biological sciences, Male, Wasp Venoms/chemistry/toxicity, Insecta, Insect Viruses/genetics, PARASITOLOGIA, VITRIPENNIS, Wasps, Genome, Insect, HYMENOPTERA, Wasp Venoms, Genes, Insect, 01 natural sciences, Genome, Nasonia vitripennis, HONEYBEE, PTEROMALIDAE, Wasps/ genetics/physiology, Arthropods/parasitology, Pteromalidae, DNA METHYLATION, ddc:616, Recombination, Genetic, 0303 health sciences, Multidisciplinary, biology, Ecology, WASP NASONIA, Biological Evolution, 3. Good health, Insects, DROSOPHILA, APIS-MELLIFERA, Insect Proteins, Wolbachia, Female, Wolbachia/genetics, Nasonia, GENES, Gene Transfer, Horizontal, Evolution, Genetic Speciation, Molecular Sequence Data, Quantitative Trait Loci, Insect Viruses, Quantitative trait locus, 010603 evolutionary biology, Article, Host-Parasite Interactions, 03 medical and health sciences, Genetic model, Animals, Life Science, Arthropods, 030304 developmental biology, fungi, Genetic Variation, Sequence Analysis, DNA, DNA Methylation, biology.organism_classification, SOCIAL INSECTS, Insects/genetics, Evolutionary biology, DNA Transposable Elements, Insect Proteins/genetics/metabolism
Abstract

Parasitoid Wasp Genomes Parasitoid wasps, which prey on and reproduce in host insect species, play important roles in plant herbivore interactions, and may provide valuable tools in the biological control of pest species. The Nasonia Genome Working Group (p. 343 ; see the news story by Pennisi ) presents the genome of three very closely related species: Nasonia vitripennis, N. giraulti , and N. longicornis . The findings document rapid evolution between a host and endosymbiont that can cause nuclear-cytoplasmic incompatibilities that may affect speciation.

Published
2010

25. Finding the missing honey bee genes: Lessons learned from a genome upgrade

Author

Elsik, CG, Worley, KC, Bennett, AK, Beye, M, Camara, F, Childers, CP, de Graaf, DC, Debyser, G, Deng, J, Devreese, B, Elhaik, E, Evans, JD, Foster, LJ, Graur, D, Guigo, R, Hoff, KJ, Holder, ME, Hudson, ME, Hunt, GJ, Jiang, H, Joshi, V, Khetani, RS, Kosarev, P, Kovar, CL, Ma, J, Maleszka, R, Moritz, RFA, Munoz-Torres, MC, Murphy, TD, Muzny, DM, Newsham, IF, Reese, JT, Robertson, HM, Robinson, GE, Rueppell, O, Solovyev, V, Stanke, M, Stolle, E, Tsuruda, JM, Vaerenbergh, MV, Waterhouse, RM, Weaver, DB, Whitfield, CW, Wu, Y, Zdobnov, EM, Zhang, L, Zhu, D, Gibbs, RA, Patil, S, Gubbala, S, Aqrawi, P, Arias, F, Bess, C, Blankenburg, KB, Brocchini, M, Buhay, C, Challis, D, Chang, K, Chen, D, Coleman, P, Drummond, J, English, A, Evani, U, Francisco, L, Fu, Q, Goodspeed, R, Haessly, TH, Hale, W, Han, H, Hu, Y, Jackson, L, Jakkamsetti, A, Jayaseelan, JC, Kakkar, N, Kalra, D, Kandadi, H, Lee, S, Li, H, Liu, Y, Macmil, S, Mandapat, CM, Mata, R, Mathew, T, Matskevitch, T, Munidasa, M, Nagaswamy, U, Najjar, R, Nguyen, N, Niu, J, Opheim, D, Palculict, T, Paul, S, Pellon, M, Perales, L, Pham, C, Pham, P, Pu, LL, Qi, S, Qu, J, Ren, Y, Elsik, CG, Worley, KC, Bennett, AK, Beye, M, Camara, F, Childers, CP, de Graaf, DC, Debyser, G, Deng, J, Devreese, B, Elhaik, E, Evans, JD, Foster, LJ, Graur, D, Guigo, R, Hoff, KJ, Holder, ME, Hudson, ME, Hunt, GJ, Jiang, H, Joshi, V, Khetani, RS, Kosarev, P, Kovar, CL, Ma, J, Maleszka, R, Moritz, RFA, Munoz-Torres, MC, Murphy, TD, Muzny, DM, Newsham, IF, Reese, JT, Robertson, HM, Robinson, GE, Rueppell, O, Solovyev, V, Stanke, M, Stolle, E, Tsuruda, JM, Vaerenbergh, MV, Waterhouse, RM, Weaver, DB, Whitfield, CW, Wu, Y, Zdobnov, EM, Zhang, L, Zhu, D, Gibbs, RA, Patil, S, Gubbala, S, Aqrawi, P, Arias, F, Bess, C, Blankenburg, KB, Brocchini, M, Buhay, C, Challis, D, Chang, K, Chen, D, Coleman, P, Drummond, J, English, A, Evani, U, Francisco, L, Fu, Q, Goodspeed, R, Haessly, TH, Hale, W, Han, H, Hu, Y, Jackson, L, Jakkamsetti, A, Jayaseelan, JC, Kakkar, N, Kalra, D, Kandadi, H, Lee, S, Li, H, Liu, Y, Macmil, S, Mandapat, CM, Mata, R, Mathew, T, Matskevitch, T, Munidasa, M, Nagaswamy, U, Najjar, R, Nguyen, N, Niu, J, Opheim, D, Palculict, T, Paul, S, Pellon, M, Perales, L, Pham, C, Pham, P, Pu, LL, Qi, S, Qu, J, and Ren, Y

Abstract

Background: The first generation of genome sequence assemblies and annotations have had a significant impact upon our understanding of the biology of the sequenced species, the phylogenetic relationships among species, the study of populations within and across species, and have informed the biology of humans. As only a few Metazoan genomes are approaching finished quality (human, mouse, fly and worm), there is room for improvement of most genome assemblies. The honey bee (Apis mellifera) genome, published in 2006, was noted for its bimodal GC content distribution that affected the quality of the assembly in some regions and for fewer genes in the initial gene set (OGSv1.0) compared to what would be expected based on other sequenced insect genomes. Results: Here, we report an improved honey bee genome assembly (Amel_4.5) with a new gene annotation set (OGSv3.2), and show that the honey bee genome contains a number of genes similar to that of other insect genomes, contrary to what was suggested in OGSv1.0. The new genome assembly is more contiguous and complete and the new gene set includes ~5000 more protein-coding genes, 50% more than previously reported. About 1/6 of the additional genes were due to improvements to the assembly, and the remaining were inferred based on new RNAseq and protein data. Conclusions: Lessons learned from this genome upgrade have important implications for future genome sequencing projects. Furthermore, the improvements significantly enhance genomic resources for the honey bee, a key model for social behavior and essential to global ecology through pollination. © 2014 Elsik et al.; licensee BioMed Central Ltd.

Published
2014

26. The genome of the model beetle and pest tribolium castaneum

Author

Richards, S, Gibbs, RA, Weinstock, GM, Brown, SJ, Denell, R, Beeman, RW, Gibbs, R, Bucher, G, Friedrich, M, Grimmelikhuijzen, CJ, Klingler, M, Lorenzen, M, Roth, S, Schröder, R, Tautz, D, Zdobnov, EM, Muzny, D, Attaway, T, Bell, S, Buhay, CJ, Chandrabose, MN, Chavez, D, Clerk-Blankenburg, KP, Cree, A, Dao, M, Davis, C, Chacko, J, Dinh, H, Dugan-Rocha, S, Fowler, G, Garner, TT, Garnes, J, Gnirke, A, Hawes, A, Hernandez, J, Hines, S, Holder, M, Hume, J, Jhangiani, SN, Joshi, V, Khan, ZM, Jackson, L, Kovar, C, Kowis, A, Lee, S, Lewis, LR, Margolis, J, Morgan, M, Nazareth, LV, Nguyen, N, Okwuonu, G, Parker, D, Ruiz, SJ, Santibanez, J, Savard, J, Scherer, SE, Schneider, B, Sodergren, E, Vattahil, S, Villasana, D, White, CS, Wright, R, Park, Y, Lord, J, Oppert, B, Brown, S, Wang, L, Weinstock, G, Liu, Y, Worley, K, Elsik, CG, Reese, JT, Elhaik, E, Landan, G, Graur, D, Arensburger, P, Atkinson, P, Beidler, J, Demuth, JP, Drury, DW, Du, YZ, Fujiwara, H, Maselli, V, Osanai, M, Robertson, HM, Tu, Z, Wang, JJ, Wang, S, Song, H, Zhang, L, Werner, D, Stanke, M, Morgenstern, B, Solovyev, V, Kosarev, P, Brown, G, Chen, HC, Ermolaeva, O, Hlavina, W, Kapustin, Y, Kiryutin, B, Kitts, P, Maglott, D, Pruitt, K, Sapojnikov, V, Souvorov, A, Mackey, AJ, Waterhouse, RM, Wyder, S, Kriventseva, EV, Kadowaki, T, Bork, P, Aranda, M, Bao, R, Beermann, A, Berns, N, Bolognesi, R, Bonneton, F, Bopp, D, Butts, T, Chaumot, A, Denell, RE, Ferrier, DE, Gordon, CM, Jindra, M, Lan, Q, Lattorff, HM, Laudet, V, von Levetsow, C, Liu, Z, Lutz, R, Lynch, JA, da Fonseca, RN, Posnien, N, Reuter, R, Schinko, JB, Schmitt, C, Schoppmeier, M, Shippy, TD, Simonnet, F, Marques-Souza, H, Tomoyasu, Y, Trauner, J, Van der Zee, M, Vervoort, M, Wittkopp, N, Wimmer, EA, Yang, X, Jones, AK, Sattelle, DB, Ebert, PR, Nelson, D, Scott, JG, Muthukrishnan, S, Kramer, KJ, Arakane, Y, Zhu, Q, Hogenkamp, D, Dixit, R, Jiang, H, Zou, Z, Marshall, J, Elpidina, E, Vinokurov, K, Oppert, C, Evans, J, Lu, Z, Zhao, P, Sumathipala, N, Altincicek, B, Vilcinskas, A, Williams, M, Hultmark, D, Hetru, C, Hauser, F, Cazzamali, G, Williamson, M, Li, B, Tanaka, Y, Predel, R, Neupert, S, Schachtner, J, Verleyen, P, Raible, F, Walden, KK, Angeli, S, Forêt, S, Schuetz, S, Maleszka, R, Miller, SC, Grossmann, D, MDC Library, and Zdobnov, Evgeny

Subjects
0106 biological sciences, Repetitive Sequences, Nucleic Acid/genetics, Insecticides, Proteome, Genome, Insect, Cytochrome P-450 Enzyme System/genetics, Genes, Insect, Insect, Receptors, Odorant, 01 natural sciences, Genome, Receptors, G-Protein-Coupled, G-Protein-Coupled Receptors, Genome, Insect/ genetics, Oogenesis, Cytochrome P-450 Enzyme System, RNA interference, Odorant Receptors, Caenorhabditis elegans, Insect Genome, Phylogeny, media_common, Genetics, ddc:616, 0303 health sciences, Base Composition, Neurotransmitter Agents, Tribolium, Multidisciplinary, Neurotransmitter Agents/genetics, Receptors, Odorant/genetics, Vision, Ocular/genetics, Telomere, Insecticides/pharmacology, DNA Transposable Elements/genetics, Proteome/genetics, Genes, Insect/ genetics, Oogenesis/genetics, Taste, RNA Interference, Growth and Development, Drosophila melanogaster, animal structures, Nucleic Acid Repetitive Sequences, Taste/genetics, media_common.quotation_subject, 570 Life Sciences, Biology, 010603 evolutionary biology, 610 Medical Sciences, Medicine, 03 medical and health sciences, Humans, Insect Genes, Ocular Vision, Animals, Tribolium/classification/embryology/ genetics/physiology, Red flour beetle, Gene, Drosophila, Vision, Ocular, 030304 developmental biology, Repetitive Sequences, Nucleic Acid, Growth and Development/genetics, Telomere/genetics, Body Patterning, fungi, biology.organism_classification, Body Patterning/genetics, Cardiovascular and Metabolic Diseases, DNA Transposable Elements, Receptors, G-Protein-Coupled/genetics
Abstract

Tribolium castaneum is a member of the most species-rich eukaryotic order, a powerful model organism for the study of generalized insect development, and an important pest of stored agricultural products. We describe its genome sequence here. This omnivorous beetle has evolved the ability to interact with a diverse chemical environment, as shown by large expansions in odorant and gustatory receptors, as well as P450 and other detoxification enzymes. Development in Tribolium is more representative of other insects than is Drosophila, a fact reflected in gene content and function. For example, Tribolium has retained more ancestral genes involved in cell-cell communication than Drosophila, some being expressed in the growth zone crucial for axial elongation in short-germ development. Systemic RNA interference in T. castaneum functions differently from that in Caenorhabditis elegans, but nevertheless offers similar power for the elucidation of gene function and identification of targets for selective insect control.

Published
2008

29. Responding to an enquiry concerning the geographic population structure (GPS) approach and the origin of Ashkenazic Jews - a reply to Flegontov et al

Author

Das, R., Wexler, P., Pirooznia, M., and Elhaik, E.

Subjects
FOS: Biological sciences, Populations and Evolution (q-bio.PE), Quantitative Biology - Populations and Evolution
Abstract

Recently, we investigated the geographical origins of Ashkenazic Jews (AJs) and their native language Yiddish by applying a biogeographical tool, the Geographic Population Structure (GPS), to a cohort of 367 exclusively Yiddish-speaking and multilingual AJs genotyped on the Genochip microarray. GPS localized most AJs along major ancient trade routes in northeastern Turkey adjacent to primeval villages with names that may be derived from the word "Ashkenaz." These findings were compatible with the hypothesis of an Irano-Turko-Slavic origin for AJs and a Slavic origin for Yiddish and at odds with the Rhineland hypothesis advocating a German origin of both. Our approach has been recently adopted by Flegontov et al. (2016a) to trace the origin of the Siberian Ket people and their language. Recently, Flegontov et al. (2016b) have raised several questions concerning the accuracy of the Genochip microarray and GPS, specifically in relation to AJs and Yiddish. Although many of these issues have been addressed in our previous papers, we take this opportunity to clarify the principles of the GPS approach, review the recent biogeographical and ancient DNA findings regarding AJs, and comment on the origin of Yiddish., 32 pages, 2 figures, 2 tables

30. 'Genome order index' should not be used for defining compositional constraints in nucleotide sequences - a case study of the Z-curve

Author

Josić Krešimir, Graur Dan, and Elhaik Eran

Subjects
Biology (General), QH301-705.5
Abstract

Abstract Background The Z-curve is a three dimensional representation of DNA sequences proposed over a decade ago and has been extensively applied to sequence segmentation, horizontal gene transfer detection, and sequence analysis. Based on the Z-curve, a "genome order index," was proposed, which is defined as S = a2+ c2+t2+g2, where a, c, t, and g are the nucleotide frequencies of A, C, T, and G, respectively. This index was found to be smaller than 1/3 for almost all tested genomes, which was taken as support for the existence of a constraint on genome composition. A geometric explanation for this constraint has been suggested. Each genome was represented by a point P whose distance from the four faces of a regular tetrahedron was given by the frequencies a, c, t, and g. They claimed that an inscribed sphere of radius r = 1/ contains almost all points corresponding to various genomes, implying that S

Published
2010
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31. A global metagenomic map of urban microbiomes and antimicrobial resistance

Author

Nadine Farhat, Tomoki Takeda, Astred Castro, Ken McGrath, Khaliun Sanchir, Iman Hajirasouliha, Eunice So, Laraib Zafar, Diana N. Nunes, Harun Mustafa, Amy Zhang, Priscilla Lisboa, Christian Schori, Marisano James, Jasna Chalangal, Sebastien Halary, Shahryar Rana, Yunmi Lee, Oli Schacher, Liliana Godoy, David A. Coil, Phanthira Pugdeethosal, Michelle D. Williams, German Marchandon, Angela Cantillo, Naoya Takahashi, Christopher Mozsary, Juana Gonzalez, Patrick K. H. Lee, Gerardo de Lamotte, Alessandro Robertiello, Steven Du, Fabienne Velter, Stefan G. Stark, Miguel Carbajo, Vincent Matthys, David A. Westfall, Julia Boeri, Irène Mauricette Mendy, Jonathan Cedillo, Francesco Oteri, Robert W. Crawford, Takayuki Ito, Tina Wunderlin, Maureen Muscat, David Paez-Espino, Carmen Urgiles, Aida Nesimi, Steffen Schaaf, Adan Ramirez-Rojas, Kunihiko Miyake, Christopher E. Mason, Anais Cardenas, Sharah Islam, Diego Benítez, Melissa Pool Pizzi, Kianna Ciaramella, Ciro Borrelli, Riham Islam, Dorottya Nagy-Szakal, Abd-Manaaf Bakere, Ait-hamlat Adel, Olha Lakhneko, Badamnyambuu Iderzorig, Ana Valeria Castro, Adam Phillips, Robert A. Petit, Flavia Corsi, Romain Conte, Krista Ryon, Soojin Jang, Joseph Benson, Fernanda de Souza Gomes Kehdy, Cindy Wang, Nicole Mathews, Jenn-Wei Chen, Rachel Paras, Paulina Pastuszek, Abigail Lyons, Paul Roldán, Muntaha Munia, Pierre Nicolas, Cassie L. Ettinger, Kyrylo Pyrshev, Katterinne N. Mendez, Eduardo Castro-Nallar, Valeriia Dotsenko, Michelle Tuz, Krizzy Mallari, Eileen Png, Yuya Sonohara, Tanja Miketic, Stéphane Delmas, Shu Zhang, Masaki Sato, Yuanting Zheng, Jifeng Zhu, Roland Häusler, Lucie Bittner, Savlatjon Rahmatulloev, Jonathan Foox, Bruno D'Alessandro, Alketa Plaku, Faisal Alquaddoomi, Yang Zhang, Kern Rei Chng, Juliana Lago, Allaeddine Chettouh, Tamera Henry, Houtan Noushmehr, Tranette Gregory, Sara Abdul Majid, Frank J. Kelly, Benjamin Pulatov, Laurie Casalot, Takema Kajita, Lennard Epping, Thais Fernanda Bartelli, Eftar Moniruzzaman, Renee Vivancos-Koopman, Thirumalaisamy P. Velavan, Tracy W. Liu, Yelyzaveta Tymoshenko, Alma Plaku, Nika Gurianova, Ambar Mendez, Anna Tomaselli, Sonia Dorado, Donato Giovannelli, Hira Choudhry, Synti Ng, Sheelta S. Kumar, Jennifer Q. Lu, Weijun Liang, Ellen Koag, Dennis Gankin, Maria João Amorim, Gwenola Simon, Kiyoshi Suganuma, Mikhail Karasikov, Christos A. Ouzounis, Madelyn May, Eran Elhaik, Stephan Ossowski, Kevin Bolzli, Matthew Arthur, Yuya Oto, Jananan Pathmanathan, Salah Mahmoud, Kou Takahashi, Brunna Marques, Kelly French, Felipe Sepúlveda, Shusei Yoshikawa, Paulo Thiago de Souza Santos, Andrew N. Gray, Juliana S Bernardes, Felipe Segato, Björn Brindefalk, George C. Yeh, Jhovana L. Velasco Flores, Jill Sullivan, Silva Baburyan, Denisse Flores, Russell Y. Neches, Sabrina Persaud, Rasheena Wright, Takumi Togashi, Verónica Antelo, Nao Kato, Skye Felice, Tatjana Mustac, Daisy Donnellan, Katerine Carrillo, Anna Litskevitch, Catalina García, Sota Ito, Naya Eady, Andrew Wan, Irene Meng, Sophie Guasco, Danilo Ercolini, Francesca De Filippis, Vincent Lemaire, Luice Fan, Lothar H. Wieler, Mariia Rybak, Jorge Sanchez, Jonathan S. Gootenberg, Itsuki Tomita, Maritza S Mosella, Laura Garcia, Natalka Makogon, Daisy Cheung, Hitler Francois Vasquez Arevalo, Freddy Asenjo, Gabriela P. Branco, Erika Cifuentes, Chloé Dequeker, Aspassia D. Chatziefthimiou, Alexis Terrero, Roy Meoded, Isabelle de Oliveira Moraes, Shaleni K. Singh, Orgil-Erdene Molomjamts, Karishma Miah, Laurent David, Wolfgang Haehr, Dao Phuong Giang, Romain Lannes, Prashanthi Ratnanandan, Ryota Yamanaka, Riccardo Vicedomini, Sadaf Ayaz, Oluwatosin M. Osuolale, Laura E. Vann, Gregory Chem, Andrea Gonzalez, Aszia Burrell, Ariel Chernomoretz, Sakura Ishizuka, Michelle Rivera, Avigdor Nosrati, Michelle B. Chen, Juliette Auvinet, Nils Ordioni, Tomoro Warashina, Guillaume Blanc, Tomislav Ivankovic, Christina Black, Lauren E. Hittle, David Hess-Homeier, Michael Kozhar, Hamood Suliman, Karobi Moitra, Saher Rahiel, Spyridon Gkotzis, Jenny Arevalo, Shaikh B. Iqbal, Beth Mutai, Mohammed Mohsin, Scott Tighe, Sylvie Collin, Yoshitaka Saito, Wayne Menary, Youping Deng, Lucy Lee, Esmeralda Jiminez, Ayuki Watanabe, Nikos C. Kyrpides, Natasha Mohan, Angelika Pupiec, Dedan Githae, Simone Cawthorne, Jonathan A. Eisen, Tomoki Iwashiro, Chiaki Homma, Thomas Saw Aung, Laura Molina, Marcus H. Y. Leung, Ophélie Da Silva, Yan Ling Wong, Hosna Noorzi, Mario Moreno, Alina Butova, Leming Shi, Brian W. Wong, Sarah S. Jackson, Moses Lin, Annabelle Meagher, Pujita Das, Catherine Burke, Mitsuki Ota, Maria Domenica Moccia, Nicolas Sprinsky, Catherine E. Pugh, David C. Green, Fazlina Fauzi, Erdenetsetseg Batdelger, Annie Geiger, Valeria Ventorino, Tolulope Oluwadare, Delisia Cuebas, Catalina Truong, Leonardo Posada, Michael Angelov, Tathiane M. Malta, Amanda Ng, Francesca Nadalin, Arya Hawkins-Zafarnia, Yuh Shiwa, Athena Mitsios, Milton Ozório Moraes, Manolo Laiola, Kalyn Ali, Jaden J.A. Hastings, Ikuto Saito, Maheen Shakil, Chisato Suzuki, Elena M. Vayndorf, Hubert Rehrauer, Ajay Menon, Kaitlan Russell, Aliyah Shari, Rebecca Smith, Gregorio Iraola, Max Priestman, Alan Briones, Silver A. Wolf, Camila Gonzalez-Poblete, Eleonora De Lazzari, Shirley Chiu, Michelle Ki, Irene Hoxie, Marianne Jaubert, Ayantu Jinfessa, Ryan J. King, Nghiem Xuan Hoan, Jalia Bynoe, Jacob Friedman, Aneisa Ramcharan, Pablo Fresia, Cristina Muñoz, Muhammad Afaq, Anyi Tang, Médine Benchouaia, Isabella Kuniko T. Takenaka, Anastasia Chasapi, Areeg Naeem, Hannah Benisty, Cecilia N. Cossio, Nathalie Hüsser, Mahfuza Sabina, Thais S. Sabedot, JoAnn Jacobs, Camila P. E. de Souza, Manuela Oliveira, Jean-Pierre Bouly, Mariko Usui, Wilson Miranda, Natalia Marciniak, Hiram Caballero, Samuel Weekes, Alexandra B. Graf, Emily Leong, Tatyana Nikolayeva, Dominique Thomas, Charlotte Greselle, Cecilia Salazar, Sreya Ray Chaudhuri, Kevin Becher, Sandra Roth, Ryusei Miura, Kari Oline Bøifot, Dimitri Manoir, Oliver Toth, Chandrima Bhattacharya, Manuel Perez, Isha Lamba, Takafumi Tsurumaki, Timothy D. Read, Anna-Lena M. Schinke, Ryan Sankar, Le Huu Song, Narasimha Rao Nedunuri, Emmanuel Dias-Neto, Ana Flávia Costa, Adiell Melamed, Christelle Desnues, Natalie R. Davidson, Aaron E. Darling, Hyung Jun Kim, Josephine Galipon, Jacqueline Orrego, Dimitar Vassilev, Michael Huber, Nur Hazlin Hazrin-Chong, Gaston H. Gonnet, Kaymisha Knights, Osman U. Sezerman, Dmitry Meleshko, Eunice Thambiraja, Jingcheng Yang, Aubin Fleiss, Gloria Nguyen, Katelyn Jackson, Nuria Aventin, Stephanie L. Hyland, Andrea Hässig, Catharine Aquino, Simona Lysakova, Israel O. Osuolale, Kasia Sluzek, Rania Siam, Alina Frolova, Samuel Hernandez, Yui Him Lo, Bazartseren Boldgiv, Ben Young, Maryna Korshevniuk, Majelia Ampadu, Yuk Man Tang, Amanda L. Muehlbauer, Sade Thomas, Gabriel Figueroa, Alexis Rivera, Lisbeth Pineda, Alexandra Dutan, Jennifer M. Tran, Chris K. Deng, Vedbar S. Khadka, Paola Florez de Sessions, Elizabeth Humphries, Hugues Richard, Hiba Naveed, Nora C. Toussaint, Mahshid Khavari, Maria del Mar Vivanco Ruiz, Antonin Thiébaut, Nicolás Rascovan, Marius Dybwad, Orhan Özcan, Lawrence Kwong, David Danko, Shaira Khan, Andrea Tassinari, Silvia Beurmann, Tsoi Ying Lai, Nanami Kubota, Tieliu Shi, Diana Chicas, Evan E. Afshin, Hirokazu Yano, Jonas Krebs, Mayuko Nakagawa, Hyun Jung Lee, Irene González Navarrete, Rachid Ounit, Lucia E. Alvarado-Arnez, Masaki Nasu, Allison Chan, Harilanto Andrianjakarivony, Jennifer Amachee, Mahdi Taye, Wan Chiew Ng, Kathryn O’Brien, Shino Ishikawa, Tristan Bitard-Feildel, Sora Takagi, Felix Hartkopf, Niamh B. O’Hara, Marcos A. S. Fonseca, Subhamitra Pakrashi, Amrit Kaur, Eva Hell, Patricia Vera-Wolf, Naimah Munim, Luiza Ferreira de Araújo, Mizuki Igarashi, Brianna Pompa-Hogan, Alessandra Carbone, Anne-Sophie Benoiston, Eric Helfrich, Michael A. Suarez-Villamil, Omar O. Abudayyeh, Natasha Abdullah, Jaime J. Fuentes, Juan Carlos Forero, Tetiana Yeskova, Denis Bertrand, Sambhawa Priya, Denisse Maldonado, Agier Nicolas, Ana Valeria B Castro, Starr Chatziefthimiou, André Kahles, Aaishah Francis, Fernanda Arredondo, Emilio Tarcitano, Irvind Buttar, Alex Alexiev, Jennifer Molinet, Sarah Shalaby, Itunu A. Oluwadare, Jason Sperry, Katrin Bakhl, Ana M. Cañas, Sofia Ahsanuddin, Miar Elaskandrany, Elodie Laine, Sven Bönigk, Johannes Werner, Stephen Eduard Boja Ruiz, Gargi Dayama, Paulina Buczansla, Brandon Valentine, Bharath Prithiviraj, Toni Bode, Stas Zubenko, Jake Cohen, Guilllaume Jospin, Zulena Saravi, Per O. Ljungdahl, Inderjit Kaur, Mauricio Moldes, Giuseppe KoLoMonaco, Denise Syndercombe Court, Sonia Bouchard, Sonia Losim, Sookwon Moon, Heba Shaaban, Suraj Patel, Sibo Zhu, Sarh Aly, Arif Asyraf Md Supie, LaShonda Dorsey, Juan Guerra, François Baudon, Rantimi A. Olawoyin, Alexia Bordigoni, Iqra Faiz, Mathilde Garcia, Gabriella Mason-Buck, María Gabriela Portilla, Niranjan Nagarajan, Fumie Takahara, Nancy Merino, Watson Andrew, Gina Kim, Yuma Sato, Hyenah Shim, Marie-Laure Jerier, Affifah Saadah Ahmad Kassim, Katerina Kuchin, Daniel Butler, Paweł P. Łabaj, Nadezhda Kobko-Litskevitch, Emmanuel F. Mongodin, Yuto Togashi, Paula Rodríguez, Pilar Lopez Hernandez, Xiaoqing Chen, Maria A. Sierra, Olga Nikolayeva, Manon Loubens, Colleen Conger, Hikaru Shirahata, Chenhao Li, Timothy Donahoe, Youngja Park, Lucia Elena Alvarado Arnez, Salama Chaker, Francisco Chavez, Alessandra Breschi, Jorge L. Sanchez, Kaung Myat San, Nayra Aguilar Rojas, Marcos Abraao, Kai Sasaki, Bryan Nazario, Olena Yemets, Klas I. Udekwu, Lynn M. Schriml, Anisia Peters, Aliaksei Holik, Mark Hernandez, Emile Faure, Malay Bhattacharyya, Josef W. Moser, Núria Andreu Somavilla, María Mercedes Zambrano, Kannan Rajendran, Gabriela E. Albuquerque, Tao Qing, Kazutoshi Tsuda, Ymke De Jong, Princess Osma, Mayra Arauco Livia, Javier Quilez Oliete, Carl Chrispin, Hyun Woo Joo, Ingrid Lafontaine, Nala An, Seisuke Sato, Felipe Segato Dezem, Andrew Maltez Thomas, Alexandre Desert, Xiao Wen Cai, O. Osuolale, Jun Wu, Coral Pardo-Esté, Courtney Robinson, Yuri Matsuzaki, Marina Nieto-Caballero, Cem Meydan, Ralph Schlapbach, Mark Menor, Sofia Castro, Rachel Kwong, Brittany Blyther, Olexandr Lykhenko, Jason R. Schriml, Christian Brion, Jenessa Orpilla, Juan A. Ugalde, Elsy Mankah Ngwa, Álvaro Aranguren, Lauren Mak, Matías Giménez, Ashanti Narce, Torsten Semmler, Stefan I. Tsonev, Abdollahi Nika, Katherine E. Dahlhausen, Monika Devi, Gunnar Rätsch, Oasima Muner, Carla Bello, Muhammad Al-Fath Amran, Anyelic Rosario, Melissa Ortega, Andrea Patrignani, Ante Peros, Elias McComb, Ryo Sato, Ireen Alam, Clara N. Dias, Soma Tanaka, Dayana Calderon, Ran Blekhman, Mathilde Mignotte, Alicia Boyd, Jochen Hecht, Thomas Neff, Xinzhao Tong, Josue Alicea, Kiara Olmeda, Sonia Marinovic, Carme Arnan, Kohei Ito, Samantha L. Goldman, Marianna S. Serpa, Renee Richer, Kaisei Sato, Jordana M. Silva, Akash Keluth Chavan, Sangwan Kim, Laís Pereira Ferreira, Sophie Vacant, Nowshin Sayara, Haruo Suzuki, Madeline Leahy, Juan C. Severyn, Sierra Vincent, Masaru Tomita, Maliha Mamun, Lucinda B. Davenport, Gabriella Oken, Dagmara Lewandowska, Gustavo Adolfo Malca Salas, Andrii Kuklin, Tyler Wong, Charlie Feigin, Eric Minwei Liu, Sonia L. Ghose, Daniela Bezdan, Antonietta La Storia, Juan P. Escalera-Antezana, Nuno Rufino de Sousa, Samuel M. Gerner, Weill Cornell Medicine [New York], Icahn School of Medicine at Mount Sinai [New York] (MSSM), Genome Institute of Singapore (GIS), Centre for Genomic Regulation [Barcelona] (CRG), Universitat Pompeu Fabra [Barcelona] (UPF)-Centro Nacional de Analisis Genomico [Barcelona] (CNAG), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Lawrence Berkeley National Laboratory [Berkeley] (LBNL), AUTRES, Massachusetts Institute of Technology (MIT), Indian Statistical Institute [Kolkata], University of Minnesota System, Universidad Andrés Bello [Santiago] (UNAB), California State University [Sacramento], University of Naples Federico II, University of Hawaii, Institut méditerranéen d'océanologie (MIO), Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Toulon (UTLN), Medical Genomics Group, University College of London [London] (UCL)-UCL Cancer Institute, Norwegian Defence Research Establishment (FFI), Lund University [Lund], Institute of Molecular Biology and Genetics of National Academy of Sciences of Ukraine, University of Vienna [Vienna], King‘s College London, University of Colorado [Boulder], Institut Pasteur de Montevideo, Réseau International des Instituts Pasteur (RIIP), Institut Pasteur Korea - Institut Pasteur de Corée, Fudan University [Shanghai], City University of Hong Kong [Hong Kong] (CUHK), Stockholm University, University of Maryland School of Medicine, University of Maryland System, Fundação Oswaldo Cruz (FIOCRUZ), University of São Paulo (USP), Instituto de Patologia e Imunologia Molecular da Universidade do Porto (IPATIMUP), Barcelona Institute of Science and Technology (BIST), Elizade University, Acibadem Mehmet Ali Aydınlar University, Paléogénomique microbienne - Microbial paleogenomics, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU), Robert Koch Institute [Berlin] (RKI), East China Normal University [Shangaï] (ECNU), Cairo University, Vietnamese-German Center for Medical Research, Keio University, Université du Vermont, Universidad del Desarrollo, University of Sofia, University of Alaska [Fairbanks] (UAF), Universitätsklinikum Tübingen - University Hospital of Tübingen, Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen, Corporación Corpogen-Research Center, Biologie Computationnelle et Quantitative = Laboratory of Computational and Quantitative Biology (LCQB), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de Biologie Paris Seine (IBPS), Sorbonne Université (SU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Weill Cornell Medicine [Cornell University], Cornell University [New York], University of Naples Federico II = Università degli studi di Napoli Federico II, Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Fundação Oswaldo Cruz / Oswaldo Cruz Foundation (FIOCRUZ), Universidade de São Paulo = University of São Paulo (USP), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Софийски университет = Sofia University, Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Universidad Andrés Bello - UNAB (CHILE), Acibadem University Dspace, Danko, D., Bezdan, D., Afshin, E. E., Ahsanuddin, S., Bhattacharya, C., Butler, D. J., Chng, K. R., Donnellan, D., Hecht, J., Jackson, K., Kuchin, K., Karasikov, M., Lyons, A., Mak, L., Meleshko, D., Mustafa, H., Mutai, B., Neches, R. Y., Ng, A., Nikolayeva, O., Nikolayeva, T., Png, E., Ryon, K. A., Sanchez, J. L., Shaaban, H., Sierra, M. A., Thomas, D., Young, B., Abudayyeh, O. O., Alicea, J., Bhattacharyya, M., Blekhman, R., Castro-Nallar, E., Canas, A. M., Chatziefthimiou, A. D., Crawford, R. W., De Filippis, F., Deng, Y., Desnues, C., Dias-Neto, E., Dybwad, M., Elhaik, E., Ercolini, D., Frolova, A., Gankin, D., Gootenberg, J. S., Graf, A. B., Green, D. C., Hajirasouliha, I., Hastings, J. J. A., Hernandez, M., Iraola, G., Jang, S., Kahles, A., Kelly, F. J., Knights, K., Kyrpides, N. C., Labaj, P. P., Lee, P. K. H., Leung, M. H. Y., Ljungdahl, P. O., Mason-Buck, G., Mcgrath, K., Meydan, C., Mongodin, E. F., Moraes, M. O., Nagarajan, N., Nieto-Caballero, M., Noushmehr, H., Oliveira, M., Ossowski, S., Osuolale, O. O., Ozcan, O., Paez-Espino, D., Rascovan, N., Richard, H., Ratsch, G., Schriml, L. M., Semmler, T., Sezerman, O. U., Shi, L., Shi, T., Siam, R., Song, L. H., Suzuki, H., Court, D. S., Tighe, S. W., Tong, X., Udekwu, K. I., Ugalde, J. A., Valentine, B., Vassilev, D. I., Vayndorf, E. M., Velavan, T. P., Wu, J., Zambrano, M. M., Zhu, J., Zhu, S., Mason, C. E., Abdullah, N., Abraao, M., Adel, A. -H., Afaq, M., Al-Quaddoomi, F. S., Alam, I., Albuquerque, G. E., Alexiev, A., Ali, K., Alvarado-Arnez, L. E., Aly, S., Amachee, J., Amorim, M. G., Ampadu, M., Amran, M. A. -F., An, N., Andrew, W., Andrianjakarivony, H., Angelov, M., Antelo, V., Aquino, C., Aranguren, A., Araujo, L. F., Vasquez Arevalo, H. F., Arevalo, J., Arnan, C., Alvarado Arnez, L. E., Arredondo, F., Arthur, M., Asenjo, F., Aung, T. S., Auvinet, J., Aventin, N., Ayaz, S., Baburyan, S., Bakere, A. -M., Bakhl, K., Bartelli, T. F., Batdelger, E., Baudon, F., Becher, K., Bello, C., Benchouaia, M., Benisty, H., Benoiston, A. -S., Benson, J., Benitez, D., Bernardes, J., Bertrand, D., Beurmann, S., Bitard-Feildel, T., Bittner, L., Black, C., Blanc, G., Blyther, B., Bode, T., Boeri, J., Boldgiv, B., Bolzli, K., Bordigoni, A., Borrelli, C., Bouchard, S., Bouly, J. -P., Boyd, A., Branco, G. P., Breschi, A., Brindefalk, B., Brion, C., Briones, A., Buczansla, P., Burke, C. M., Burrell, A., Butova, A., Buttar, I., Bynoe, J., Bonigk, S., Boifot, K. O., Caballero, H., Cai, X. W., Calderon, D., Cantillo, A., Carbajo, M., Carbone, A., Cardenas, A., Carrillo, K., Casalot, L., Castro, S., Castro, A. V., Castro, A., Castro, A. V. B., Cawthorne, S., Cedillo, J., Chaker, S., Chalangal, J., Chan, A., Chasapi, A. I., Chatziefthimiou, S., Chaudhuri, S. R., Chavan, A. K., Chavez, F., Chem, G., Chen, X., Chen, M., Chen, J. -W., Chernomoretz, A., Chettouh, A., Cheung, D., Chicas, D., Chiu, S., Choudhry, H., Chrispin, C., Ciaramella, K., Cifuentes, E., Cohen, J., Coil, D. A., Collin, S., Conger, C., Conte, R., Corsi, F., Cossio, C. N., Costa, A. F., Cuebas, D., D'Alessandro, B., Dahlhausen, K. E., Darling, A. E., Das, P., Davenport, L. B., David, L., Davidson, N. R., Dayama, G., Delmas, S., Deng, C. K., Dequeker, C., Desert, A., Devi, M., Dezem, F. S., Dias, C. N., Donahoe, T. R., Dorado, S., Dorsey, L., Dotsenko, V., Du, S., Dutan, A., Eady, N., Eisen, J. A., Elaskandrany, M., Epping, L., Escalera-Antezana, J. P., Ettinger, C. L., Faiz, I., Fan, L., Farhat, N., Faure, E., Fauzi, F., Feigin, C., Felice, S., Ferreira, L. P., Figueroa, G., Fleiss, A., Flores, D., Velasco Flores, J. L., Fonseca, M. A. S., Foox, J., Forero, J. C., Francis, A., French, K., Fresia, P., Friedman, J., Fuentes, J. J., Galipon, J., Garcia, M., Garcia, L., Garcia, C., Geiger, A., Gerner, S. M., Ghose, S. L., Giang, D. P., Gimenez, M., Giovannelli, D., Githae, D., Gkotzis, S., Godoy, L., Goldman, S., Gonnet, G. H., Gonzalez, J., Gonzalez, A., Gonzalez-Poblete, C., Gray, A., Gregory, T., Greselle, C., Guasco, S., Guerra, J., Gurianova, N., Haehr, W., Halary, S., Hartkopf, F., Hawkins-Zafarnia, A., Hazrin-Chong, N. H., Helfrich, E., Hell, E., Henry, T., Hernandez, S., Hernandez, P. L., Hess-Homeier, D., Hittle, L. E., Hoan, N. X., Holik, A., Homma, C., Hoxie, I., Huber, M., Humphries, E., Hyland, S., Hassig, A., Hausler, R., Husser, N., Petit, R. A., Iderzorig, B., Igarashi, M., Iqbal, S. B., Ishikawa, S., Ishizuka, S., Islam, S., Islam, R., Ito, K., Ito, S., Ito, T., Ivankovic, T., Iwashiro, T., Jackson, S., Jacobs, J., James, M., Jaubert, M., Jerier, M. -L., Jiminez, E., Jinfessa, A., De Jong, Y., Joo, H. W., Jospin, G., Kajita, T., Ahmad Kassim, A. S., Kato, N., Kaur, A., Kaur, I., de Souza Gomes Kehdy, F., Khadka, V. S., Khan, S., Khavari, M., Ki, M., Kim, G., Kim, H. J., Kim, S., King, R. J., Kolomonaco, G., Koag, E., Kobko-Litskevitch, N., Korshevniuk, M., Kozhar, M., Krebs, J., Kubota, N., Kuklin, A., Kumar, S. S., Kwong, R., Kwong, L., Lafontaine, I., Lago, J., Lai, T. Y., Laine, E., Laiola, M., Lakhneko, O., Lamba, I., de Lamotte, G., Lannes, R., De Lazzari, E., Leahy, M., Lee, H., Lee, Y., Lee, L., Lemaire, V., Leong, E., Lewandowska, D., Li, C., Liang, W., Lin, M., Lisboa, P., Litskevitch, A., Liu, E. M., Liu, T., Livia, M. A., Lo, Y. H., Losim, S., Loubens, M., Lu, J., Lykhenko, O., Lysakova, S., Mahmoud, S., Majid, S. A., Makogon, N., Maldonado, D., Mallari, K., Malta, T. M., Mamun, M., Manoir, D., Marchandon, G., Marciniak, N., Marinovic, S., Marques, B., Mathews, N., Matsuzaki, Y., Matthys, V., May, M., Mccomb, E., Meagher, A., Melamed, A., Menary, W., Mendez, K. N., Mendez, A., Mendy, I. M., Meng, I., Menon, A., Menor, M., Meoded, R., Merino, N., Miah, K., Mignotte, M., Miketic, T., Miranda, W., Mitsios, A., Miura, R., Miyake, K., Moccia, M. D., Mohan, N., Mohsin, M., Moitra, K., Moldes, M., Molina, L., Molinet, J., Molomjamts, O. -E., Moniruzzaman, E., Moon, S., de Oliveira Moraes, I., Moreno, M., Mosella, M. S., Moser, J. W., Mozsary, C., Muehlbauer, A. L., Muner, O., Munia, M., Munim, N., Muscat, M., Mustac, T., Munoz, C., Nadalin, F., Naeem, A., Nagy-Szakal, D., Nakagawa, M., Narce, A., Nasu, M., Navarrete, I. G., Naveed, H., Nazario, B., Nedunuri, N. R., Neff, T., Nesimi, A., Ng, W. C., Ng, S., Nguyen, G., Ngwa, E., Nicolas, A., Nicolas, P., Nika, A., Noorzi, H., Nosrati, A., Nunes, D. N., O'Brien, K., O'Hara, N. B., Oken, G., Olawoyin, R. A., Oliete, J. Q., Olmeda, K., Oluwadare, T., Oluwadare, I. A., Ordioni, N., Orpilla, J., Orrego, J., Ortega, M., Osma, P., Osuolale, I. O., Osuolale, O. M., Ota, M., Oteri, F., Oto, Y., Ounit, R., Ouzounis, C. A., Pakrashi, S., Paras, R., Pardo-Este, C., Park, Y. -J., Pastuszek, P., Patel, S., Pathmanathan, J., Patrignani, A., Perez, M., Peros, A., Persaud, S., Peters, A., Phillips, A., Pineda, L., Pizzi, M. P., Plaku, A., Pompa-Hogan, B., Portilla, M. G., Posada, L., Priestman, M., Prithiviraj, B., Priya, S., Pugdeethosal, P., Pugh, C. E., Pulatov, B., Pupiec, A., Pyrshev, K., Qing, T., Rahiel, S., Rahmatulloev, S., Rajendran, K., Ramcharan, A., Ramirez-Rojas, A., Rana, S., Ratnanandan, P., Read, T. D., Rehrauer, H., Richer, R., Rivera, A., Rivera, M., Robertiello, A., Robinson, C., Rodriguez, P., Rojas, N. A., Roldan, P., Rosario, A., Roth, S., Ruiz, M., Boja Ruiz, S. E., Russell, K., Rybak, M., Sabedot, T. S., Sabina, M., Saito, I., Saito, Y., Malca Salas, G. A., Salazar, C., San, K. M., Sanchez, J., Sanchir, K., Sankar, R., de Souza Santos, P. T., Saravi, Z., Sasaki, K., Sato, Y., Sato, M., Sato, S., Sato, R., Sato, K., Sayara, N., Schaaf, S., Schacher, O., Schinke, A. -L. M., Schlapbach, R., Schori, C., Schriml, J. R., Segato, F., Sepulveda, F., Serpa, M. S., De Sessions, P. F., Severyn, J. C., Shakil, M., Shalaby, S., Shari, A., Shim, H., Shirahata, H., Shiwa, Y., Da Silva, O., Silva, J. M., Simon, G., Singh, S. K., Sluzek, K., Smith, R., So, E., Andreu Somavilla, N., Sonohara, Y., Rufino de Sousa, N., Souza, C., Sperry, J., Sprinsky, N., Stark, S. G., La Storia, A., Suganuma, K., Suliman, H., Sullivan, J., Supie, A. A. M., Suzuki, C., Takagi, S., Takahara, F., Takahashi, N., Takahashi, K., Takeda, T., Takenaka, I. K., Tanaka, S., Tang, A., Man Tang, Y., Tarcitano, E., Tassinari, A., Taye, M., Terrero, A., Thambiraja, E., Thiebaut, A., Thomas, S., Thomas, A. M., Togashi, Y., Togashi, T., Tomaselli, A., Tomita, M., Tomita, I., Toth, O., Toussaint, N. C., Tran, J. M., Truong, C., Tsonev, S. I., Tsuda, K., Tsurumaki, T., Tuz, M., Tymoshenko, Y., Urgiles, C., Usui, M., Vacant, S., Vann, L. E., Velter, F., Ventorino, V., Vera-Wolf, P., Vicedomini, R., Suarez-Villamil, M. A., Vincent, S., Vivancos-Koopman, R., Wan, A., Wang, C., Warashina, T., Watanabe, A., Weekes, S., Werner, J., Westfall, D., Wieler, L. H., Williams, M., Wolf, S. A., Wong, B., Wong, Y. L., Wong, T., Wright, R., Wunderlin, T., Yamanaka, R., Yang, J., Yano, H., Yeh, G. C., Yemets, O., Yeskova, T., Yoshikawa, S., Zafar, L., Zhang, Y., Zhang, S., Zhang, A., Zheng, Y., and Zubenko, S.

Subjects
Urban Population, Drug Resistance, Sequence assembly, Microbiologia, microbiome, global health, computer.software_genre, Medical and Health Sciences, shotgun sequencing, BGC, 0302 clinical medicine, Databases, Genetic, 11. Sustainability, Global health, AMR, 11 Medical and Health Sciences, ComputingMilieux_MISCELLANEOUS, 0303 health sciences, built environment, metagenome, antimicrobial resistance, NGS, de novo assembly, biology, Shotgun sequencing, Microbiota, built Environment, Bacterial, Biodiversity, Biological Sciences, 3. Good health, [SDV.MP]Life Sciences [q-bio]/Microbiology and Parasitology, Infection, Biotechnology, Geospatial analysis, [SDV.BID]Life Sciences [q-bio]/Biodiversity, Article, General Biochemistry, Genetics and Molecular Biology, Databases, 03 medical and health sciences, Antibiotic resistance, Genetic, Drug Resistance, Bacterial, International MetaSUB Consortium, Genetics, Humans, Microbiome, 030304 developmental biology, Human Genome, 06 Biological Sciences, 15. Life on land, biology.organism_classification, Resistènica als medicaments antiinfecciosos, SAÚDE PÚBLICA, Genòmica, 13. Climate action, Evolutionary biology, Metagenomics, Antimicrobial Resistance, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, computer, 030217 neurology & neurosurgery, Archaea, Developmental Biology
Abstract

Summary We present a global atlas of 4,728 metagenomic samples from mass-transit systems in 60 cities over 3 years, representing the first systematic, worldwide catalog of the urban microbial ecosystem. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics, antimicrobial resistance (AMR) markers, and genetic elements, including 10,928 viruses, 1,302 bacteria, 2 archaea, and 838,532 CRISPR arrays not found in reference databases. We identified 4,246 known species of urban microorganisms and a consistent set of 31 species found in 97% of samples that were distinct from human commensal organisms. Profiles of AMR genes varied widely in type and density across cities. Cities showed distinct microbial taxonomic signatures that were driven by climate and geographic differences. These results constitute a high-resolution global metagenomic atlas that enables discovery of organisms and genes, highlights potential public health and forensic applications, and provides a culture-independent view of AMR burden in cities., Graphical abstract, Highlights • Cities possess a consistent “core” set of non-human microbes • Urban microbiomes echo important features of cities and city-life • Antimicrobial resistance genes are widespread in cities • Cities contain many novel bacterial and viral species, This systematic, worldwide catalog of urban microbiomes represents a metagenomic atlas important for understanding the ecology, virulence, and antibiotic resistance of city-specific microbial communities.

Published
2021

32. The Genome of the Sea Urchin Strongylocentrotus purpuratus

Author

Amro Hamdoun, Virginia Brockton, Huyen Dinh, Qiang Tu, Richard O. Hynes, Maria Ina Arnone, Wratko Hlavina, L. Courtney Smith, Mariano A. Loza, David R. Burgess, Matthew P. Hoffman, Florian Raible, Qiu Autumn Yuan, Geoffrey Okwuonu, Mark Y. Tong, Jennifer Hume, Donna Maglott, Manisha Goel, Olivier Fedrigo, Manuel L. Gonzalez-Garay, Celina E. Juliano, Judith Hernandez, Gary M. Wessel, William F. Marzluff, Audrey J. Majeske, Christian Gache, Louise Duloquin, Xingzhi Song, François Lapraz, Fowler J, Alexandre Souvorov, Jared V. Goldstone, Georgia Panopoulou, Sandra Hines, Kyle M. Judkins, Clay Davis, Christine G. Elsik, Paul Kitts, Mariano Loza-Coll, Greg Wray, Taku Hibino, Eric Röttinger, Allison M. Churcher, Annamaria Locascio, Arcady Mushegian, Masashi Kinukawa, Anna Reade, Katherine M. Buckley, I. R. Gibbons, Bert Gold, Aleksandar Milosavljevic, David Epel, Victor D. Vacquier, Ling Ling Pu, Vincenzo Cavalieri, Erin L. Allgood, Lan Zhang, Lynne V. Nazareth, Constantin N. Flytzanis, Ian Bosdet, Yi-Hsien Su, Zeev Pancer, Matthew L. Rowe, Robert C. Angerer, David R. McClay, William H. Klein, Rachel F. Gray, Julian L. Wong, Shunsuke Yaguchi, Robert Bellé, Aaron J. Mackey, Herath Jayantha Gunaratne, Karl Frederik Bergeron, Bruce P. Brandhorst, Greg Murray, Avis H. Cohen, Stephanie Bell, Kristin Tessmar-Raible, Ian K. Townley, Bertrand Cosson, Thomas D. Glenn, Jongmin Nam, Cynthia A. Bradham, Michael Dean, Joseph Chacko, Anthony J. Robertson, Margherita Branno, Valeria Matranga, K. James Durbin, Esther Miranda, Lili Chen, Eran Elhaik, Robert D. Burke, Rita A. Wright, Paola Oliveri, Sandra L. Lee, Gary W. Moy, Alexander E Primus, Shawn S. McCafferty, Cristina Calestani, David A. Garfield, Erica Sodergren, Karen Wilson, Joel Smith, Marco A. Marra, Cynthia Messier, Julia Morales, Kim D. Pruitt, Rachel Thorn, Rachel Gill, John S. Taylor, Mark E. Hahn, Victor Sapojnikov, Meredith Howard-Ashby, Lynne M. Angerer, Maurice R. Elphick, Kathy R. Foltz, Anne Marie Genevière, Justin T. Reese, Blanca E. Galindo, Kim C. Worley, Andrew Leone, Glen Humphrey, Kevin Berney, Olga Ermolaeva, George Miner, David P. Terwilliger, Elly Suk Hen Chow, Lora Lewis, Dan Graur, C. Titus Brown, Gerard Manning, Kevin J. Peterson, Angela Jolivet, Michele K. Anderson, Francesca Rizzo, Ekaterina Voronina, Thierry Lepage, Giorgio Matassi, Antonio Fernandez-Guerra, Mamoru Nomura, Charles A. Whittaker, James R.R. Whittle, James A. Coffman, George M. Weinstock, Mohammed M. Idris, Ashlan M. Musante, Sebastian D. Fugmann, Katherine D. Walton, Sorin Istrail, Shu-Yu Wu, Cerrissa Hamilton, Jonah Cool, Jacqueline E. Schein, Stacey M. Curry, Athula Wikramanayke, Seth Carbonneau, Blair J. Rossetti, Christopher E. Killian, Melissa J. Landrum, Amanda P. Rawson, Jenifer C. Croce, Ryan C. Range, Rahul Satija, John J. Stegeman, Yufeng Shen, Cavit Agca, Terry Gaasterland, Rocky Cheung, Takae Kiyama, Nikki Adams, Jonathan P. Rast, Robert Piotr Olinski, Andrew Cree, Mark Scally, Shuguang Liang, David A. Parker, Rebecca Thomason, Gretchen E. Hofmann, Michelle M. Roux, Ronghui Xu, Robert A. Obar, Enrique Arboleda, Odile Mulner-Lorillon, Shannon Dugan-Rocha, David J. Bottjer, Gabriele Amore, Manoj P. Samanta, Waraporn Tongprasit, Véronique Duboc, La Ronda Jackson, Fred H. Wilt, Viktor Stolc, Anna T. Neill, Michael Raisch, Pei Yun Lee, Jia L. Song, Margaret Morgan, Brian T. Livingston, Sofia Hussain, Zheng Wei, Bryan J. Cole, Tonya F. Severson, Victor V. Solovyev, Finn Hallböök, Donna M. Muzny, Christine A. Byrum, Albert J. Poustka, Xiuqian Mu, Andrew R. Jackson, Shin Heesun, Euan R. Brown, Nansheng Chen, Patrick Cormier, Ralph Haygood, Pedro Martinez, R. Andrew Cameron, D. Wang, Wendy S. Beane, Eric H. Davidson, Christie Kovar, Hemant Kelkar, Charles A. Ettensohn, Sham V. Nair, Robert L. Morris, Stefan C. Materna, Michael C. Thorndyke, Richard A. Gibbs, Dan O Mellott, Department of Physiology and Biophysics, Stony Brook University [The State University of New York] ( SBU ), Astronomy Unit ( AU ), Queen Mary University of London ( QMUL ), Urban and Industrial Air Quality Group, CSIRO Energy Technology, Commonwealth Scientific and Industrial Research Organisation Energy Technology ( CSIRO Energy Technology ), Commonwealth Scientific and Industrial Research Organisation, Center for Polymer Studies ( CPS ), Boston University [Boston] ( BU ), Physics Department [Boston] ( BU-Physics ), Max Planck Institute for Psycholinguistics, Max-Planck-Institut, Department of Biology [Norton], Wheaton College [Norton], Mathematical Institute [Oxford] ( MI ), University of Oxford [Oxford], Centre for the Analysis of Time Series ( CATS ), London School of Economics and Political Science ( LSE ), Thomas Jefferson National Accelerator Facility ( Jefferson Lab ), Thomas Jefferson National Accelerator Facility, Laboratoire d'Energétique et de Mécanique Théorique Appliquée ( LEMTA ), Université de Lorraine ( UL ) -Centre National de la Recherche Scientifique ( CNRS ), Laboratoire Evolution, Génomes et Spéciation ( LEGS ), Centre National de la Recherche Scientifique ( CNRS ), Department of Geology, University of Illinois at Urbana-Champaign [Urbana], Department of Electrical and Computer Engineering [Portland] ( ECE ), Portland State University [Portland] ( PSU ), Saint-Gobain Crystals [USA], SAINT-GOBAIN, Institute for Animal Health ( IAH ), Biotechnology and Biological Sciences Research Council, Center for Agricultural Resources Research, Chinese Academy of Sciences [Changchun Branch] ( CAS ), Ipsen Inc. [Milford] ( Ipsen ), IPSEN, Department of Physics [Berkeley], University of California [Berkeley], Institute for Climate and Atmospheric Science [Leeds] ( ICAS ), University of Leeds, Chung-Ang University ( CAU ), Chung-Ang University [Seoul], Antarctic Climate and Ecosystems Cooperative Research Center ( ACE-CRC ), Institute of Aerodynamics and Fluid Mechanics ( AER ), Technische Universität München [München] ( TUM ), Mer et santé ( MS ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Centre National de la Recherche Scientifique ( CNRS ), Imperial College London, Radio and Atmospheric Sciences Division, National Physical Laboratory [Teddington] ( NPL ), International Research Institute for Climate and Society ( IRI ), Earth Institute at Columbia University, Columbia University [New York]-Columbia University [New York], Soils Group, The Macaulay Institute, Department of Haematology, University of Cambridge [UK] ( CAM ), School of Biology and Biochemistry, Queen's University, Leslie Hill Institute for Plant Conservation ( PCU ), University of Cape Town, Institute for Microelectronics and Microsystems/ Istituto per la Microelettronica e Microsistemi ( IMM ), Consiglio Nazionale delle Ricerche ( CNR ), Laboratoire d'acoustique de l'université du Mans ( LAUM ), Le Mans Université ( UM ) -Centre National de la Recherche Scientifique ( CNRS ), Interactive Systems Labs ( ISL ), Carnegie Mellon University [Pittsburgh] ( CMU ), Dalian Institute of Chemical Physics ( DICP ), Architectures, Languages and Compilers to Harness the End of Moore Years ( ALCHEMY ), Laboratoire de Recherche en Informatique ( LRI ), Université Paris-Sud - Paris 11 ( UP11 ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ) -Université Paris-Sud - Paris 11 ( UP11 ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ) -Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique ( Inria ), Clean Air Task Force ( CATF ), Clean Air Task Force, Space Physics Laboratory, Indian Space Research Organisation ( ISRO ), Centre d'études et de recherches appliquées à la gestion ( CERAG ), Université Pierre Mendès France - Grenoble 2 ( UPMF ) -Centre National de la Recherche Scientifique ( CNRS ), Department of Microbiology and Immunology, College of Medicine and Health Sciences-Sultan Qaboos University, European Molecular Biology Laboratory [Heidelberg] ( EMBL ), Department of Biostatistics, University of Michigan [Ann Arbor], Department of Radiation Oncology [Michigan] ( Radonc ), Department of Physics and Astronomy [Leicester], University of Leicester, Informatique, Biologie Intégrative et Systèmes Complexes ( IBISC ), Université d'Évry-Val-d'Essonne ( UEVE ) -Centre National de la Recherche Scientifique ( CNRS ), Institut für Meteorologie und Klimaforschung ( IMK ), Karlsruher Institut für Technologie ( KIT ), Physics Department [UNB], University of New Brunswick ( UNB ), Laboratoire Parole et Langage ( LPL ), Centre National de la Recherche Scientifique ( CNRS ) -Aix Marseille Université ( AMU ), Institut des Sciences Chimiques de Rennes ( ISCR ), Université de Rennes 1 ( UR1 ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Ecole Nationale Supérieure de Chimie de Rennes-Institut National des Sciences Appliquées ( INSA ) -Centre National de la Recherche Scientifique ( CNRS ), Biogéosciences [Dijon] ( BGS ), Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique ( CNRS ), Bioprojet, Laboratoire de Matériaux à Porosité Contrôlée ( LMPC ), Université de Haute-Alsace (UHA) Mulhouse - Colmar ( Université de Haute-Alsace (UHA) ) -Ecole Nationale Supérieure de Chimie de Mulhouse-Centre National de la Recherche Scientifique ( CNRS ), School of Information Engineering [USTB] ( SIE ), University of Science and Technology Beijing [Beijing] ( USTB ), Laboratory for Atmospheric and Space Physics [Boulder] ( LASP ), University of Colorado Boulder [Boulder], Department of Applied Mathematics [Sheffield], University of Sheffield [Sheffield], School of Mathematics and Statistics [Sheffield] ( SoMaS ), Laboratoire de Mécanique de Lille - FRE 3723 ( LML ), Université de Lille, Sciences et Technologies-Ecole Centrale de Lille-Centre National de la Recherche Scientifique ( CNRS ), Computer Science Department [UCLA] ( CSD ), University of California at Los Angeles [Los Angeles] ( UCLA ), Développement et évolution ( DE ), Université Paris-Sud - Paris 11 ( UP11 ) -Centre National de la Recherche Scientifique ( CNRS ), Laboratoire de Biologie du Développement de Villefranche sur mer ( LBDV ), Laboratoire Pierre Aigrain ( LPA ), Fédération de recherche du Département de physique de l'Ecole Normale Supérieure - ENS Paris ( FRDPENS ), Centre National de la Recherche Scientifique ( CNRS ) -École normale supérieure - Paris ( ENS Paris ) -Centre National de la Recherche Scientifique ( CNRS ) -École normale supérieure - Paris ( ENS Paris ) -Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Université Paris Diderot - Paris 7 ( UPD7 ) -Centre National de la Recherche Scientifique ( CNRS ), Department of Mathematics and Statistics [Mac Gill], McGill University, Departamento de Botánica [Comahue], Universidad nacional del Comahue, Bioénergétique Cellulaire et Pathologique ( BECP ), Université Joseph Fourier - Grenoble 1 ( UJF ) -Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ), Environnements et Paléoenvironnements OCéaniques ( EPOC ), Observatoire aquitain des sciences de l'univers ( OASU ), Université Sciences et Technologies - Bordeaux 1-Institut national des sciences de l'Univers ( INSU - CNRS ) -Centre National de la Recherche Scientifique ( CNRS ) -Université Sciences et Technologies - Bordeaux 1-Institut national des sciences de l'Univers ( INSU - CNRS ) -Centre National de la Recherche Scientifique ( CNRS ) -École pratique des hautes études ( EPHE ) -Centre National de la Recherche Scientifique ( CNRS ), Institut Jacques Monod ( IJM ), Université Paris Diderot - Paris 7 ( UPD7 ) -Centre National de la Recherche Scientifique ( CNRS ), Laboratori Nazionali del Sud ( LNS ), National Institute for Nuclear Physics ( INFN ), Departament de Matemàtiques [Barcelona], Universitat Autònoma de Barcelona [Barcelona] ( UAB ), Max-Planck-Institut für Kohlenforschung (coal research), Institute of Oceanology [CAS] ( IOCAS ), National Chiao Tung University ( NCTU ), Department of Hydrology and Water Resources ( HWR ), University of Arizona, Centre for Educational Technology, Environment Department [York], University of York [York, UK], State Key Laboratory of Nuclear Physics and Technology ( SKL-NPT ), Peking University [Beijing], Department of Physics and Astronomy [Iowa City], University of Iowa [Iowa], NASA Ames Research Center ( ARC ), Department of Materials, Digital Language & Knowledge Contents Research Association ( DICORA ), Hankuk University of Foreign Studies, Department of Physics [Coventry], University of Warwick [Coventry], Space Science and Technology Department [Didcot] ( RAL Space ), STFC Rutherford Appleton Laboratory ( RAL ), Science and Technology Facilities Council ( STFC ) -Science and Technology Facilities Council ( STFC ), Institut de biologie et chimie des protéines [Lyon] ( IBCP ), Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique ( CNRS ), H M Nautical Almanac Office [RAL] ( HMNAO ), Rutherford Appleton Laboratory, United Kingdom Met Office [Exeter], University College of London [London] ( UCL ), Department of Pathology and Laboratory Medicine [UCLA], University of California at Los Angeles [Los Angeles] ( UCLA ) -School of Medicine, School of Earth and Environmental Sciences [Seoul] ( SEES ), Seoul National University [Seoul], Department of Chemistry, Seoul Women's University, MicroMachines Centre ( MMC ), Nanyang Technological University [Singapour], Regroupement Québécois sur les Matériaux de Pointe ( RQMP ), École Polytechnique de Montréal ( EPM ) -Université de Sherbrooke [Sherbrooke]-McGill University-Université de Montréal-Fonds Québécois de Recherche sur la Nature et les Technologies ( FQRNT ), Département de Physique [Montréal], Université de Montréal, School of Earth and Environment [Leeds] ( SEE ), Centre for Ecology and Hydrology ( CEH ), Natural Environment Research Council ( NERC ), Norwegian Institute for Water Research ( NIVA ), Norwegian Institute for Water Research, Stony Brook University [SUNY] (SBU), State University of New York (SUNY)-State University of New York (SUNY), Astronomy Unit [London] (AU), Queen Mary University of London (QMUL), Commonwealth Scientific and Industrial Research Organisation Energy Technology (CSIRO Energy Technology), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Department of Biochemistry and Molecular Biology [Houston], The University of Texas Medical School at Houston, Mathematical Institute [Oxford] (MI), University of Oxford, Centre for the Analysis of Time Series (CATS), London School of Economics and Political Science (LSE), Thomas Jefferson National Accelerator Facility (Jefferson Lab), Laboratoire Énergies et Mécanique Théorique et Appliquée (LEMTA ), Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Evolution, Génomes et Spéciation (LEGS), Centre National de la Recherche Scientifique (CNRS), University of Illinois System-University of Illinois System, Department of Electrical and Computer Engineering [Portland] (ECE), Portland State University [Portland] (PSU), Saint-Gobain, Institute for Animal Health (IAH), Biotechnology and Biological Sciences Research Council (BBSRC), Chinese Academy of Sciences [Changchun Branch] (CAS), Ipsen Inc. [Milford] (Ipsen), University of California [Berkeley] (UC Berkeley), University of California (UC)-University of California (UC), Institute for Climate and Atmospheric Science [Leeds] (ICAS), School of Earth and Environment [Leeds] (SEE), University of Leeds-University of Leeds, Chung-Ang University (CAU), Antarctic Climate and Ecosystems Cooperative Research Centre (ACE-CRC), Institute of Aerodynamics and Fluid Mechanics (AER), Technische Universität Munchen - Université Technique de Munich [Munich, Allemagne] (TUM), Mer et santé (MS), Station biologique de Roscoff [Roscoff] (SBR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), National Physical Laboratory [Teddington] (NPL), International Research Institute for Climate and Society (IRI), Macaulay Institute, University of Cambridge [UK] (CAM), Queen's University [Kingston, Canada], Leslie Hill Institute for Plant Conservation (PCU), Istituto per la Microelettronica e Microsistemi [Catania] (IMM), National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR), Laboratoire d'Acoustique de l'Université du Mans (LAUM), Le Mans Université (UM)-Centre National de la Recherche Scientifique (CNRS), Interactive Systems Labs (ISL), Carnegie Mellon University [Pittsburgh] (CMU), Dalian Institute of Chemical Physics (DICP), Architectures, Languages and Compilers to Harness the End of Moore Years (ALCHEMY), Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Clean Air Task Force (CATF), Indian Space Research Organisation (ISRO), Centre d'études et de recherches appliquées à la gestion (CERAG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Centre National de la Recherche Scientifique (CNRS), Sultan Qaboos University (SQU)-College of Medicine and Health Sciences [Baylor], Baylor University-Baylor University, European Molecular Biology Laboratory [Heidelberg] (EMBL), University of Michigan System-University of Michigan System, Department of Radiation Oncology [Michigan] (Radonc), Informatique, Biologie Intégrative et Systèmes Complexes (IBISC), Université d'Évry-Val-d'Essonne (UEVE)-Centre National de la Recherche Scientifique (CNRS), Institute for Meteorology and Climate Research (IMK), Karlsruhe Institute of Technology (KIT), University of New Brunswick (UNB), Laboratoire Parole et Langage (LPL), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Institut des Sciences Chimiques de Rennes (ISCR), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Ecole Nationale Supérieure de Chimie de Rennes (ENSCR)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Biogéosciences [UMR 6282] (BGS), Université de Bourgogne (UB)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Matériaux à Porosité Contrôlée (LMPC), Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS), School of Information Engineering [USTB] (SIE), University of Science and Technology Beijing [Beijing] (USTB), Laboratory for Atmospheric and Space Physics [Boulder] (LASP), University of Colorado [Boulder], School of Mathematics and Statistics [Sheffield] (SoMaS), Laboratoire de Mécanique de Lille - FRE 3723 (LML), Université de Lille, Sciences et Technologies-Centrale Lille-Centre National de la Recherche Scientifique (CNRS), Computer Science Department [UCLA] (CSD), University of California [Los Angeles] (UCLA), Développement et évolution (DE), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Biologie du Développement de Villefranche sur mer (LBDV), Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Pierre Aigrain (LPA), Fédération de recherche du Département de physique de l'Ecole Normale Supérieure - ENS Paris (FRDPENS), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Department of Mathematics and Statistics [Montréal], McGill University = Université McGill [Montréal, Canada], Departamento de Botánica [Bariloche], Centro Regional Universitario Bariloche [Bariloche] (CRUB), Universidad Nacional del Comahue [Neuquén] (UNCOMA)-Universidad Nacional del Comahue [Neuquén] (UNCOMA), Bioénergétique Cellulaire et Pathologique (BECP), Université Joseph Fourier - Grenoble 1 (UJF)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Environnements et Paléoenvironnements OCéaniques (EPOC), Observatoire aquitain des sciences de l'univers (OASU), Université Sciences et Technologies - Bordeaux 1 (UB)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Sciences et Technologies - Bordeaux 1 (UB)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Institut Jacques Monod (IJM (UMR_7592)), Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Laboratori Nazionali del Sud (LNS), Istituto Nazionale di Fisica Nucleare (INFN), Departament de Matemàtiques [Barcelona] (UAB), Universitat Autònoma de Barcelona (UAB), Max-Planck-Institut für Kohlenforschung (Coal Research), Max-Planck-Gesellschaft, CAS Institute of Oceanology (IOCAS), Chinese Academy of Sciences [Beijing] (CAS), National Chiao Tung University (NCTU), Department of Hydrology and Water Resources (HWR), State Key Laboratory of Nuclear Physics and Technology (SKL-NPT), University of Iowa [Iowa City], NASA Ames Research Center (ARC), Digital Language & Knowledge Contents Research Association (DICORA), Space Science and Technology Department [Didcot] (RAL Space), STFC Rutherford Appleton Laboratory (RAL), Science and Technology Facilities Council (STFC)-Science and Technology Facilities Council (STFC), Institut de biologie et chimie des protéines [Lyon] (IBCP), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), H M Nautical Almanac Office [RAL] (HMNAO), University College of London [London] (UCL), University of California (UC)-University of California (UC)-School of Medicine, School of Earth and Environmental Sciences [Seoul] (SEES), Seoul National University [Seoul] (SNU), MicroMachines Centre (MMC), Regroupement Québécois sur les Matériaux de Pointe (RQMP), École Polytechnique de Montréal (EPM)-Université de Sherbrooke (UdeS)-McGill University = Université McGill [Montréal, Canada]-Université de Montréal (UdeM)-Fonds Québécois de Recherche sur la Nature et les Technologies (FQRNT), Université de Montréal (UdeM), Centre for Ecology and Hydrology (CEH), Natural Environment Research Council (NERC), Norwegian Institute for Water Research (NIVA), SEA URCHIN GENOME SEQUENCING CONSORTIUM, SODERGREN E, WEINSTOCK GM, DAVIDSON EH, CAMERON RA, GIBBS RA, ANGERER RC, ANGERER LM, ARNONE MI, BURGESS DR, BURKE RD, COFFMAN JA, DEAN M, ELPHICK MR, ETTENSOHN CA, FOLTZ KR, HAMDOUN A, HYNES RO, KLEIN WH, MARZLUFF W, MCCLAY DR, MORRIS RL, MUSHEGIAN A, RAST JP, SMITH LC, THORNDYKE MC, VACQUIER VD, WESSEL GM, WRAY G, ZHANG L, ELSIK CG, ERMOLAEVA O, HLAVINA W, HOFMANN G, KITTS P, LANDRUM MJ, MACKEY AJ, MAGLOTT D, PANOPOULOU G, POUSTKA AJ, PRUITT K, SAPOJNIKOV V, SONG X, SOUVOROV A, SOLOVYEV V, WEI Z, WHITTAKER CA, WORLEY K, DURBIN KJ, SHEN Y, FEDRIGO O, GARFIELD D, HAYGOOD R, PRIMUS A, SATIJA R, SEVERSON T, GONZALEZ-GARAY ML, JACKSON AR, MILOSAVLJEVIC A, TONG M, KILLIAN CE, LIVINGSTON BT, WILT FH, ADAMS N, BELLE R, CARBONNEAU S, CHEUNG R, CORMIER P, COSSON B, CROCE J, FERNANDEZ-GUERRA A, GENEVIERE AM, GOEL M, KELKAR H, MORALES J, MULNER-LORILLON O, ROBERTSON AJ, GOLDSTONE JV, COLE B, EPEL D, GOLD B, HAHN ME, HOWARD-ASHBY M, SCALLY M, STEGEMAN JJ, ALLGOOD EL, COOL J, JUDKINS KM, MCCAFFERTY SS, MUSANTE AM, OBAR RA, RAWSON AP, ROSSETTI BJ, GIBBONS IR, HOFFMAN MP, LEONE A, ISTRAIL S, MATERNA SC, SAMANTA MP, STOLC V, TONGPRASIT W, TU Q, BERGERON KF, BRANDHORST BP, WHITTLE J, BERNEY K, BOTTJER DJ, CALESTANI C, PETERSON K, CHOW E, YUAN QA, ELHAIK E, GRAUR D, REESE JT, BOSDET I, HEESUN S, MARRA MA, SCHEIN J, ANDERSON MK, BROCKTON V, BUCKLEY KM, COHEN AH, FUGMANN SD, HIBINO T, LOZA-COLL M, MAJESKE AJ, MESSIER C, NAIR SV, PANCER Z, TERWILLIGER DP, AGCA C, ARBOLEDA E, CHEN N, CHURCHER AM, HALLBOOK F, HUMPHREY GW, IDRIS MM, KIYAMA T, LIANG S, MELLOTT D, MU X, MURRAY G, OLINSKI RP, RAIBLE F, ROWE M, TAYLOR JS, TESSMAR-RAIBLE K, WANG D, WILSON KH, YAGUCHI S, GAASTERLAND T, GALINDO BE, GUNARATNE HJ, JULIANO C, KINUKAWA M, MOY GW, NEILL AT, NOMURA M, RAISCH M, READE A, ROUX MM, SONG JL, SU YH, TOWNLEY IK, VORONINA E, WONG JL, AMORE G, BRANNO M, BROWN ER, CAVALIERI, V, DUBOC V, DULOQUIN L, FLYTZANIS C, GACHE C, LAPRAZ F, LEPAGE T, LOCASCIO A, MART, University of California-University of California, Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC), Consiglio Nazionale delle Ricerche (CNR), Centre National de la Recherche Scientifique (CNRS)-Le Mans Université (UM), Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Ecole Nationale Supérieure de Chimie de Rennes (ENSCR)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Biogéosciences [UMR 6282] [Dijon] (BGS), Centre National de la Recherche Scientifique (CNRS)-Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Ecole Nationale Supérieure de Chimie de Mulhouse-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Fédération de recherche du Département de physique de l'Ecole Normale Supérieure - ENS Paris (FRDPENS), Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Sciences et Technologies - Bordeaux 1-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Sciences et Technologies - Bordeaux 1-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-École pratique des hautes études (EPHE), University of California-University of California-School of Medicine, Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Ecole Nationale Supérieure de Chimie de Rennes (ENSCR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA), Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS), Université de Lille, Sciences et Technologies-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille, Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Joseph Fourier - Grenoble 1 (UJF), University of Manchester Institute of Science and Technology (UMIST), Condensed Matter Physics and Materials Science Department, Brookhaven National Laboratory, Brookhaven National Laboratory [Upton, NY] (BNL), UT-Battelle, LLC-Stony Brook University [SUNY] (SBU), State University of New York (SUNY)-State University of New York (SUNY)-U.S. Department of Energy [Washington] (DOE)-UT-Battelle, LLC-Stony Brook University [SUNY] (SBU), State University of New York (SUNY)-State University of New York (SUNY)-U.S. Department of Energy [Washington] (DOE), Baylor College of Medicine (BCM), Baylor University, Laboratoire de Traitement de l'Information Medicale (LaTIM), Université européenne de Bretagne - European University of Brittany (UEB)-Université de Brest (UBO)-Télécom Bretagne-Institut Mines-Télécom [Paris] (IMT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest), Laboratoire de Modélisation et Simulation Multi Echelle (MSME), Université Paris-Est Marne-la-Vallée (UPEM)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Centre National de la Recherche Scientifique (CNRS), Duke University [Durham], Instituto Andaluz de Geofísica y Prevención de Desastres Sísmicos [Granada] (IAGPDS), Universidad de Granada (UGR), Laboratoire d'Ingénierie des Matériaux de Bretagne (LIMATB), Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université de Brest (UBO), University of New South Wales [Sydney] (UNSW), Celera Genomics (CRA), Celera Genomics, Paléobiodiversité et paléoenvironnements, Muséum national d'Histoire naturelle (MNHN)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Università degli Studi di Roma Tor Vergata [Roma], Unité de recherches forestières (BORDX PIERR UR ), Institut National de la Recherche Agronomique (INRA), Deptartment of Neuroscience, Uppsala University, State Key Laboratory of Palaeobiology and Stratigraphy, Nanjing Institute of Geology and Palaeontology (NIGPAS-CAS), Chinese Academy of Sciences [Nanjing Branch]-Chinese Academy of Sciences [Nanjing Branch], Institut Méditerranéen d'Ecologie et de Paléoécologie (IMEP), Université Paul Cézanne - Aix-Marseille 3-Université de Provence - Aix-Marseille 1-Avignon Université (AU)-Centre National de la Recherche Scientifique (CNRS), Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China, Université Paris Diderot - Paris 7 (UPD7), Department of Physical and Environmental Sciences [Toronto], University of Toronto at Scarborough, inconnu temporaire UPEMLV, Inconnu, Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Department of Atmospheric Sciences [Seattle], University of Washington [Seattle], National Institute of Advanced Industrial Science and Technology (AIST), Department of Pharmacy, Università degli studi di Genova = University of Genoa (UniGe), Interdisciplinary Arts and Sciences Department, St. Vincent's Hospital, Sydney, Laboratoire des Sciences de l'Environnement Marin (LEMAR) (LEMAR), Institut de Recherche pour le Développement (IRD)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Université de Brest (UBO)-Institut Universitaire Européen de la Mer (IUEM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Department of Electrical Engineering (DEE-POSTECH), Pohang University of Science and Technology (POSTECH), Centre Suisse d'Electronique et de Microtechnique SA [Neuchatel] (CSEM), Centre Suisse d'Electronique et Microtechnique SA (CSEM), Human Genome Sequencing Center [Houston] (HGSC), Brookhaven National Laboratory, Meteorological Service of Canada, 4905 Dufferin Street, Université européenne de Bretagne - European University of Brittany (UEB)-Télécom Bretagne-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Mines-Télécom [Paris] (IMT), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Université Paris-Est Marne-la-Vallée (UPEM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Unité de Recherches Forestières, Department of Physical and Environmental Sciences, University of Toronto [Scarborough, Canada], National Institute for Nuclear Physics (INFN), University of Genoa (UNIGE), Institut de Recherche pour le Développement (IRD)-Institut Universitaire Européen de la Mer (IUEM), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Université de Brest (UBO)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS), Universidad de Granada = University of Granada (UGR), Laboratoire d'Energétique et de Mécanique Théorique Appliquée (LEMTA ), Technische Universität München [München] (TUM), Queen's University [Kingston], Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Grenoble Alpes (UGA), Institut für Meteorologie und Klimaforschung (IMK), Karlsruher Institut für Technologie (KIT), Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU), Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Ecole Nationale Supérieure de Chimie de Rennes-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES), Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Ecole Centrale de Lille-Université de Lille, Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Université Sciences et Technologies - Bordeaux 1-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Sciences et Technologies - Bordeaux 1-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-École pratique des hautes études (EPHE)-Centre National de la Recherche Scientifique (CNRS), Universitat Autònoma de Barcelona [Barcelona] (UAB), École Polytechnique de Montréal (EPM)-Université de Sherbrooke [Sherbrooke]-Université de Montréal [Montréal]-McGill University-Fonds Québécois de Recherche sur la Nature et les Technologies (FQRNT), Université de Montréal [Montréal], U.S. Department of Energy [Washington] (DOE)-UT-Battelle, LLC-Stony Brook University [SUNY] (SBU), Université de Bretagne Sud (UBS)-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université de Brest (UBO)-Université de Brest (UBO), Muséum national d'Histoire naturelle (MNHN)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC), Université Paul Cézanne - Aix-Marseille 3-Centre National de la Recherche Scientifique (CNRS)-Avignon Université (AU)-Université de Provence - Aix-Marseille 1, Institut Universitaire Européen de la Mer (IUEM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Centre National de la Recherche Scientifique (CNRS)-Université de Brest (UBO), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Université de Lille, Sciences et Technologies-Ecole Centrale de Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), École normale supérieure - Paris (ENS Paris), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Male, MESH: Signal Transduction, MESH: Sequence Analysis, DNA, MESH : Transcription Factors, MESH : Calcification, Physiologic, Genome, MESH : Proteins, 0302 clinical medicine, MESH : Embryonic Development, MESH: Gene Expression Regulation, Developmental, Innate, MESH: Embryonic Development, Developmental, Nervous System Physiological Phenomena, MESH: Animals, MESH: Proteins, [SDV.BDD]Life Sciences [q-bio]/Development Biology, Complement Activation, ComputingMilieux_MISCELLANEOUS, MESH: Evolution, Molecular, MESH : Strongylocentrotus purpuratus, Genetics, 0303 health sciences, MESH: Nervous System Physiological Phenomena, Multidisciplinary, biology, Medicine (all), MESH: Immunologic Factors, Gene Expression Regulation, Developmental, Genome project, MESH: Transcription Factors, MESH : Immunity, Innate, MESH : Complement Activation, MESH: Genes, Bacterial artificial chromosome (BAC)DeuterostomesStrongylocentrotus purpuratusVertebrate innovations, Echinoderm, MESH : Nervous System Physiological Phenomena, embryonic structures, MESH: Cell Adhesion Molecules, MESH : Genes, MESH: Immunity, Innate, Sequence Analysis, Signal Transduction, MESH: Computational Biology, Genome evolution, MESH: Complement Activation, Sequence analysis, Evolution, MESH: Strongylocentrotus purpuratus, MESH : Male, Embryonic Development, MESH : Immunologic Factors, Article, MESH: Calcification, Physiologic, Calcification, MESH : Cell Adhesion Molecules, Evolution, Molecular, 03 medical and health sciences, Calcification, Physiologic, Animals, Immunologic Factors, MESH: Genome, [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, MESH : Evolution, Molecular, Physiologic, Gene, Strongylocentrotus purpuratus, [ SDV.BBM ] Life Sciences [q-bio]/Biochemistry, Molecular Biology, 030304 developmental biology, MESH : Signal Transduction, Bacterial artificial chromosome, Immunity, Molecular, Computational Biology, Proteins, Cell Adhesion Molecules, Genes, Immunity, Innate, Transcription Factors, Sequence Analysis, DNA, DNA, biology.organism_classification, MESH: Male, Gene Expression Regulation, MESH : Animals, MESH : Gene Expression Regulation, Developmental, MESH : Genome, 030217 neurology & neurosurgery, MESH : Computational Biology, MESH : Sequence Analysis, DNA
Abstract

We report the sequence and analysis of the 814-megabase genome of the sea urchin Strongylocentrotus purpuratus , a model for developmental and systems biology. The sequencing strategy combined whole-genome shotgun and bacterial artificial chromosome (BAC) sequences. This use of BAC clones, aided by a pooling strategy, overcame difficulties associated with high heterozygosity of the genome. The genome encodes about 23,300 genes, including many previously thought to be vertebrate innovations or known only outside the deuterostomes. This echinoderm genome provides an evolutionary outgroup for the chordates and yields insights into the evolution of deuterostomes.

Published
2006

33. Microbiome Geographic Population Structure (mGPS) Detects Fine-Scale Geography.

Author

Zhang Y, McCarthy L, Ruff E, and Elhaik E

Subjects
Soil Microbiology, Machine Learning, Bacteria genetics, Bacteria classification, Geography, Microbiota
Abstract

Over the past decade, sequencing data generated by large microbiome projects showed that taxa exhibit patchy geographical distribution, raising questions about the geospatial dynamics that shape natural microbiomes and the spread of antimicrobial resistance genes. Answering these questions requires distinguishing between local and nonlocal microorganisms and identifying the source sites for the latter. Predicting the source sites and migration routes of microbiota has been envisioned for decades but was hampered by the lack of data, tools, and understanding of the processes governing biodiversity. State-of-the-art biogeographical tools suffer from low resolution and cannot predict biogeographical patterns at a scale relevant to ecological, medical, or epidemiological applications. Analyzing urban, soil, and marine microorganisms, we found that some taxa exhibit regional-specific composition and abundance, suggesting they can be used as biogeographical biomarkers. We developed the microbiome geographic population structure, a machine learning-based tool that utilizes microbial relative sequence abundances to yield a fine-scale source site for microorganisms. Microbiome geographic population structure predicted the source city for 92% of the samples and the within-city source for 82% of the samples, though they were often only a few hundred meters apart. Microbiome geographic population structure also predicted soil and marine sampling sites for 86% and 74% of the samples, respectively. We demonstrated that microbiome geographic population structure differentiated local from nonlocal microorganisms and used it to trace the global spread of antimicrobial resistance genes. Microbiome geographic population structure's ability to localize samples to their water body, country, city, and transit stations opens new possibilities in tracing microbiomes and has applications in forensics, medicine, and epidemiology., (© The Author(s) 2024. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution.)

Published
2024
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34. Functional variant rs9344 at 11q13.3 regulates CCND1 expression in multiple myeloma with t(11;14).

Author

Tang H, Yan H, Shivaram S, Lehman S, Sharma N, Smadbeck J, Zepeda-Mendoza C, Tian S, Asmann Y, Vachon C, Gaspar Maia A, Keats J, Bergsagel PL, Fonseca R, Stewart AK, Hsu JS, Kandasamy RK, Pandey A, Kaddoura MA, Maura F, Mitra A, Rajkumar SV, Kumar SK, Elhaik E, Braggio E, and Baughn LB

Abstract

Multiple myeloma (MM) is a plasma cell (PC) malignancy characterized by cytogenetic abnormalities, such as t(11;14)(q13;q32), resulting in CCND1 overexpression. The rs9344 G allele within CCND1 is the most significant susceptibility allele for t(11;14). Sequencing data from 2 independent cohorts, CoMMpass (n = 698) and Mayo Clinic (n = 661), confirm the positive association between the G allele and t(11;14). Among 80% of individuals heterozygous for rs9344 with t(11;14), the t(11;14) event occurs on the G allele, demonstrating a biological preference for the G allele in t(11;14). Within t(11;14), the G allele is associated with higher CCND1 expression and elevated H3K27ac and H3K4me3. CRISPR/Cas9 mediated A to G conversion resulted in increased H3K27ac over CCND1 and elevated CCND1 expression. ENCODE ChIP-seq data supported a PAX5 binding site within the enhancer region covering rs9344, showing preferential binding to the G allele. Overexpression of PAX5 resulted in increased CCND1 expression. These results support the importance of rs9344 G enhancer in increasing CCND1 expression in MM., (© 2024. The Author(s).)

Published
2024
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35. CAMP: A modular metagenomics analysis system for integrated multi-step data exploration.

Author

Mak L, Tierney B, Ronkowski C, Toscan RB, Turhan B, Toomey M, Martinez JSA, Fu C, Lucaci AG, Solano AHB, Setubal JC, Henriksen JR, Zimmerman S, Kopbayeva M, Noyvert A, Iwan Z, Kar S, Nakazawa N, Meleshko D, Horyslavets D, Kantsypa V, Frolova A, Kahles A, Danko D, Elhaik E, Labaj P, Mangul S, Mason CE, and Hajirasouliha I

Abstract

Motivation: Computational analysis of large-scale metagenomics sequencing datasets have proven to be both incredibly valuable for extracting isolate-level taxonomic, and functional insights from complex microbial communities. However, due to an ever-expanding ecosystem of metagenomics-specific methods and file-formats, designing studies which implement seamless and scalable end-to-end workflows, and exploring the massive amounts of output data have become studies unto themselves. One-click bioinformatics pipelines have helped to organize these tools into targeted workflows, but they suffer from general compatibility and maintainability issues., Methods: To address the gap in easily extensible yet robustly distributable metagenomics workflows, we have developed a module-based metagenomics analysis system: "Core Analysis Metagenomics Pipeline" (CAMP), written in Snakemake, a popular workflow management system, along with a standardized module and working directory architecture. Each module can be run independently or conjointly with a series of others to produce the target data format (ex. short-read preprocessing alone, or short-read preprocessing followed by de novo assembly), and outputs aggregated summary statistics reports and semi-guided Jupyter notebook-based visualizations., Results: We have applied CAMP to a set of ten metagenomics samples to demonstrate how a modular analysis system with built-in data visualization at intermediate steps facilitates rich and seamless inter-communication between output data from different analytic purposes., Availability: The module template as well as the modules described below can be found at https://github.com/MetaSUB-CAMP., Competing Interests: Competing Interests No competing interest is declared.

Published
2024
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36. predicTTE: An accessible and optimal tool for time-to-event prediction in neurological diseases.

Author

Weinreich M, McDonough H, Yacovzada N, Magen I, Cohen Y, Harvey C, Gornall S, Boddy S, Alix J, Mohseni N, Kurz JM, Kenna KP, Zhang S, Iacoangeli A, Al-Khleifat A, Snyder MP, Hobson E, Al-Chalabi A, Hornstein E, Elhaik E, Shaw PJ, McDermott C, and Cooper-Knock J

Abstract

Time-to-event prediction is a key task for biological discovery, experimental medicine, and clinical care. This is particularly true for neurological diseases where development of reliable biomarkers is often limited by difficulty visualising and sampling relevant cell and molecular pathobiology. To date, much work has relied on Cox regression because of ease-of-use, despite evidence that this model includes incorrect assumptions. We have implemented a set of deep learning and spline models for time-to-event modelling within a fully customizable 'app' and accompanying online portal, both of which can be used for any time-to-event analysis in any disease by a non-expert user. Our online portal includes capacity for end-users including patients, Neurology clinicians, and researchers, to access and perform predictions using a trained model, and to contribute new data for model improvement, all within a data-secure environment. We demonstrate a pipeline for use of our app with three use-cases including imputation of missing data, hyperparameter tuning, model training and independent validation. We show that predictions are optimal for use in downstream applications such as genetic discovery, biomarker interpretation, and personalised choice of medication. We demonstrate the efficiency of an ensemble configuration, including focused training of a deep learning model. We have optimised a pipeline for imputation of missing data in combination with time-to-event prediction models. Overall, we provide a powerful and accessible tool to develop, access and share time-to-event prediction models; all software and tutorials are available at www.predictte.org.

Published
2024
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37. The microbial biodiversity at the archeological site of Tel Megiddo (Israel).

Author

Zhang Y, Ruff SE, Oskolkov N, Tierney BT, Ryon K, Danko D, Mason CE, and Elhaik E

Abstract

Introduction: The ancient city of Tel Megiddo in the Jezreel Valley (Israel), which lasted from the Neolithic to the Iron Age, has been continuously excavated since 1903 and is now recognized as a World Heritage Site. The site features multiple ruins in various areas, including temples and stables, alongside modern constructions, and public access is allowed in designated areas. The site has been studied extensively since the last century; however, its microbiome has never been studied. We carried out the first survey of the microbiomes in Tel Megiddo. Our objectives were to study (i) the unique microbial community structure of the site, (ii) the variation in the microbial communities across areas, (iii) the similarity of the microbiomes to urban and archeological microbes, (iv) the presence and abundance of potential bio-corroding microbes, and (v) the presence and abundance of potentially pathogenic microbes., Methods: We collected 40 swab samples from ten major areas and identified microbial taxa using next-generation sequencing of microbial genomes. These genomes were annotated and classified taxonomically and pathogenetically., Results: We found that eight phyla, six of which exist in all ten areas, dominated the site (>99%). The relative sequence abundance of taxa varied between the ruins and the sampled materials and was assessed using all metagenomic reads mapping to a respective taxon. The site hosted unique taxa characteristic of the built environment and exhibited high similarity to the microbiome of other monuments. We identified acid-producing bacteria that may pose a risk to the site through biocorrosion and staining and thus pose a danger to the site's preservation. Differences in the microbiomes of the publicly accessible or inaccessible areas were insignificant; however, pathogens were more abundant in the former., Discussion: We found that Tel Megiddo combines microbiomes of arid regions and monuments with human pathogens. The findings shed light on the microbial community structures and have relevance for bio-conservation efforts and visitor health., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Zhang, Ruff, Oskolkov, Tierney, Ryon, Danko, Mason and Elhaik.)

Published
2023
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38. Glucocorticoid receptor regulates protein chaperone, circadian clock and affective disorder genes in the zebrafish brain.

Author

Eachus H, Oberski L, Paveley J, Bacila I, Ashton JP, Esposito U, Seifuddin F, Pirooznia M, Elhaik E, Placzek M, Krone NP, and Cunliffe VT

Subjects
Animals, Adult, Humans, Zebrafish genetics, Zebrafish metabolism, Brain metabolism, Mood Disorders metabolism, Receptors, Glucocorticoid genetics, Receptors, Glucocorticoid metabolism, Circadian Clocks genetics
Abstract

Glucocorticoid resistance is commonly observed in depression, and has been linked to reduced expression and/or function of the glucocorticoid receptor (NR3C1 in human, hereafter referred to as GR). Previous studies have shown that GR-mutant zebrafish exhibit behavioural abnormalities that are indicative of an affective disorder, suggesting that GR plays a role in brain function. We compared the brain methylomes and brain transcriptomes of adult wild-type and GR-mutant zebrafish, and identified 249 differentially methylated regions (DMRs) that are regulated by GR. These include a cluster of CpG sites within the first intron of fkbp5, the gene encoding the glucocorticoid-inducible heat shock protein co-chaperone Fkbp5. RNA-sequencing analysis revealed that genes associated with chaperone-mediated protein folding, the regulation of circadian rhythm and the regulation of metabolism are particularly sensitive to loss of GR function. In addition, we identified subsets of genes exhibiting GR-regulated transcription that are known to regulate behaviour, and are linked to unipolar depression and anxiety. Taken together, our results identify key biological processes and novel molecular mechanisms through which the GR is likely to mediate responses to stress in the adult zebrafish brain, and they provide further support for the zebrafish GR mutant as a model for the study of affective disorders., Competing Interests: Competing interests The authors declare no competing or financial interests., (© 2023. Published by The Company of Biologists Ltd.)

Published
2023
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39. Interplay between human STING genotype and bacterial NADase activity regulates inter-individual disease variability.

Author

Movert E, Bolarin JS, Valfridsson C, Velarde J, Skrede S, Nekludov M, Hyldegaard O, Arnell P, Svensson M, Norrby-Teglund A, Cho KH, Elhaik E, Wessels MR, Råberg L, and Carlsson F

Subjects
Humans, Bacterial Proteins genetics, Genotype, Macrophages microbiology, NAD+ Nucleosidase genetics, Streptococcus pyogenes genetics
Abstract

Variability in disease severity caused by a microbial pathogen is impacted by each infection representing a unique combination of host and pathogen genomes. Here, we show that the outcome of invasive Streptococcus pyogenes infection is regulated by an interplay between human STING genotype and bacterial NADase activity. S. pyogenes-derived c-di-AMP diffuses via streptolysin O pores into macrophages where it activates STING and the ensuing type I IFN response. However, the enzymatic activity of the NADase variants expressed by invasive strains suppresses STING-mediated type I IFN production. Analysis of patients with necrotizing S. pyogenes soft tissue infection indicates that a STING genotype associated with reduced c-di-AMP-binding capacity combined with high bacterial NADase activity promotes a 'perfect storm' manifested in poor outcome, whereas proficient and uninhibited STING-mediated type I IFN production correlates with protection against host-detrimental inflammation. These results reveal an immune-regulating function for bacterial NADase and provide insight regarding the host-pathogen genotype interplay underlying invasive infection and interindividual disease variability., (© 2023. The Author(s).)

Published
2023
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40. A history of the MetaSUB consortium: Tracking urban microbes around the globe.

Author

Ryon KA, Tierney BT, Frolova A, Kahles A, Desnues C, Ouzounis C, Gibas C, Bezdan D, Deng Y, He D, Dias-Neto E, Elhaik E, Afshin E, Grills G, Iraola G, Suzuki H, Werner J, Udekwu K, Schriml L, Bhattacharyya M, Oliveira M, Zambrano MM, Hazrin-Chong NH, Osuolale O, Łabaj PP, Tiasse P, Rapuri S, Borras S, Pozdniakova S, Shi T, Sezerman U, Rodo X, Sezer ZH, and Mason CE

Abstract

The MetaSUB Consortium, founded in 2015, is a global consortium with an interdisciplinary team of clinicians, scientists, bioinformaticians, engineers, and designers, with members from more than 100 countries across the globe. This network has continually collected samples from urban and rural sites including subways and transit systems, sewage systems, hospitals, and other environmental sampling. These collections have been ongoing since 2015 and have continued when possible, even throughout the COVID-19 pandemic. The consortium has optimized their workflow for the collection, isolation, and sequencing of DNA and RNA collected from these various sites and processing them for metagenomics analysis, including the identification of SARS-CoV-2 and its variants. Here, the Consortium describes its foundations, and its ongoing work to expand on this network and to focus its scope on the mapping, annotation, and prediction of emerging pathogens, mapping microbial evolution and antibiotic resistance, and the discovery of novel organisms and biosynthetic gene clusters., (© 2022 The Authors.)

Published
2022
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41. Principal Component Analyses (PCA)-based findings in population genetic studies are highly biased and must be reevaluated.

Author

Elhaik E

Subjects
Animals, Artifacts, Humans, Principal Component Analysis, Algorithms, Genetics, Population
Abstract

Principal Component Analysis (PCA) is a multivariate analysis that reduces the complexity of datasets while preserving data covariance. The outcome can be visualized on colorful scatterplots, ideally with only a minimal loss of information. PCA applications, implemented in well-cited packages like EIGENSOFT and PLINK, are extensively used as the foremost analyses in population genetics and related fields (e.g., animal and plant or medical genetics). PCA outcomes are used to shape study design, identify, and characterize individuals and populations, and draw historical and ethnobiological conclusions on origins, evolution, dispersion, and relatedness. The replicability crisis in science has prompted us to evaluate whether PCA results are reliable, robust, and replicable. We analyzed twelve common test cases using an intuitive color-based model alongside human population data. We demonstrate that PCA results can be artifacts of the data and can be easily manipulated to generate desired outcomes. PCA adjustment also yielded unfavorable outcomes in association studies. PCA results may not be reliable, robust, or replicable as the field assumes. Our findings raise concerns about the validity of results reported in the population genetics literature and related fields that place a disproportionate reliance upon PCA outcomes and the insights derived from them. We conclude that PCA may have a biasing role in genetic investigations and that 32,000-216,000 genetic studies should be reevaluated. An alternative mixed-admixture population genetic model is discussed., (© 2022. The Author(s).)

Published
2022
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42. Temporal population structure, a genetic dating method for ancient Eurasian genomes from the past 10,000 years.

Author

Behnamian S, Esposito U, Holland G, Alshehab G, Dobre AM, Pirooznia M, Brimacombe CS, and Elhaik E

Subjects
Skeleton, Archaeology methods, Radiometric Dating methods, Genetic Techniques
Abstract

Radiocarbon dating is the gold standard in archeology to estimate the age of skeletons, a key to studying their origins. Many published ancient genomes lack reliable and direct dates, which results in obscure and contradictory reports. We developed the temporal population structure (TPS), a DNA-based dating method for genomes ranging from the Late Mesolithic to today, and applied it to 3,591 ancient and 1,307 modern Eurasians. TPS predictions aligned with the known dates and correctly accounted for kin relationships. TPS dating of poorly dated Eurasian samples resolved conflicting reports in the literature, as illustrated by one test case. We also demonstrated how TPS improved the ability to study phenotypic traits over time. TPS can be used when radiocarbon dating is unfeasible or uncertain or to develop alternative hypotheses for samples younger than 10,000 years ago, a limitation that may be resolved over time as ancient data accumulate., Competing Interests: The authors declare no competing interests., (© 2022 The Authors.)

Published
2022
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43. Genome-wide identification of the genetic basis of amyotrophic lateral sclerosis.

Author

Zhang S, Cooper-Knock J, Weimer AK, Shi M, Moll T, Marshall JNG, Harvey C, Nezhad HG, Franklin J, Souza CDS, Ning K, Wang C, Li J, Dilliott AA, Farhan S, Elhaik E, Pasniceanu I, Livesey MR, Eitan C, Hornstein E, Kenna KP, Veldink JH, Ferraiuolo L, Shaw PJ, and Snyder MP

Subjects
Adaptor Proteins, Signal Transducing genetics, Cell Death genetics, Cytoskeletal Proteins genetics, Genome-Wide Association Study, Humans, Motor Neurons pathology, Amyotrophic Lateral Sclerosis genetics, Amyotrophic Lateral Sclerosis pathology, Induced Pluripotent Stem Cells pathology
Abstract

Amyotrophic lateral sclerosis (ALS) is a complex disease that leads to motor neuron death. Despite heritability estimates of 52%, genome-wide association studies (GWASs) have discovered relatively few loci. We developed a machine learning approach called RefMap, which integrates functional genomics with GWAS summary statistics for gene discovery. With transcriptomic and epigenetic profiling of motor neurons derived from induced pluripotent stem cells (iPSCs), RefMap identified 690 ALS-associated genes that represent a 5-fold increase in recovered heritability. Extensive conservation, transcriptome, network, and rare variant analyses demonstrated the functional significance of candidate genes in healthy and diseased motor neurons and brain tissues. Genetic convergence between common and rare variation highlighted KANK1 as a new ALS gene. Reproducing KANK1 patient mutations in human neurons led to neurotoxicity and demonstrated that TDP-43 mislocalization, a hallmark pathology of ALS, is downstream of axonal dysfunction. RefMap can be readily applied to other complex diseases., Competing Interests: Declaration of interests M.P.S. is a co-founder and member of the scientific advisory board of Personalis, Qbio, January, SensOmics, Protos, Mirvie, NiMo, Onza, and Oralome. He is also on the scientific advisory board of Danaher, Genapsys, and Jupiter. J.L. is a co-founder of SensOmics. No other authors have competing interests., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

Published
2022
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44. Systems biology analysis of human genomes points to key pathways conferring spina bifida risk.

Author

Aguiar-Pulido V, Wolujewicz P, Martinez-Fundichely A, Elhaik E, Thareja G, Abdel Aleem A, Chalhoub N, Cuykendall T, Al-Zamer J, Lei Y, El-Bashir H, Musser JM, Al-Kaabi A, Shaw GM, Khurana E, Suhre K, Mason CE, Elemento O, Finnell RH, and Ross ME

Subjects
Case-Control Studies, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Systems Biology, Transcription Factors genetics, Genome, Human, Spinal Dysraphism genetics
Abstract

Spina bifida (SB) is a debilitating birth defect caused by multiple gene and environment interactions. Though SB shows non-Mendelian inheritance, genetic factors contribute to an estimated 70% of cases. Nevertheless, identifying human mutations conferring SB risk is challenging due to its relative rarity, genetic heterogeneity, incomplete penetrance, and environmental influences that hamper genome-wide association studies approaches to untargeted discovery. Thus, SB genetic studies may suffer from population substructure and/or selection bias introduced by typical candidate gene searches. We report a population based, ancestry-matched whole-genome sequence analysis of SB genetic predisposition using a systems biology strategy to interrogate 298 case-control subject genomes (149 pairs). Genes that were enriched in likely gene disrupting (LGD), rare protein-coding variants were subjected to machine learning analysis to identify genes in which LGD variants occur with a different frequency in cases versus controls and so discriminate between these groups. Those genes with high discriminatory potential for SB significantly enriched pathways pertaining to carbon metabolism, inflammation, innate immunity, cytoskeletal regulation, and essential transcriptional regulation consistent with their having impact on the pathogenesis of human SB. Additionally, an interrogation of conserved noncoding sequences identified robust variant enrichment in regulatory regions of several transcription factors critical to embryonic development. This genome-wide perspective offers an effective approach to the interrogation of coding and noncoding sequence variant contributions to rare complex genetic disorders., Competing Interests: Competing interest statement. R.H.F. formerly held a leadership position with the now dissolved TeratOmic Consulting LLC. He also receives travel funds to attend editorial board meetings of the Journal of Reproductive and Developmental Medicine published out of the Red Hospital of Fudan University. E.E. consults for the DNA Diagnostics Center. P.S. and R.H.F. are coauthors on a 2020 paper resulting from an NIH workshop: Maruvada P et al., Knowledge gaps in understanding the metabolic and clinical effects of excess folates/folic acid: a summary, and perspectives, from an NIH workshop. Am J Clin Nutr. 2020 Nov 11;112(5):1390-1403. doi: 10.1093/ajcn/nqaa259. PMID: 33022704; PMCID: PMC7657327., (Copyright © 2021 the Author(s). Published by PNAS.)

Published
2021
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45. Loop-Mediated Isothermal Amplification Detection of SARS-CoV-2 and Myriad Other Applications.

Author

Moore KJM, Cahill J, Aidelberg G, Aronoff R, Bektaş A, Bezdan D, Butler DJ, Chittur SV, Codyre M, Federici F, Tanner NA, Tighe SW, True R, Ware SB, Wyllie AL, Afshin EE, Bendesky A, Chang CB, Dela Rosa R 2nd, Elhaik E, Erickson D, Goldsborough AS, Grills G, Hadasch K, Hayden A, Her SY, Karl JA, Kim CH, Kriegel AJ, Kunstman T, Landau Z, Land K, Langhorst BW, Lindner AB, Mayer BE, McLaughlin LA, McLaughlin MT, Molloy J, Mozsary C, Nadler JL, D'Silva M, Ng D, O'Connor DH, Ongerth JE, Osuolale O, Pinharanda A, Plenker D, Ranjan R, Rosbash M, Rotem A, Segarra J, Schürer S, Sherrill-Mix S, Solo-Gabriele H, To S, Vogt MC, Yu AD, and Mason CE

Subjects
COVID-19 Nucleic Acid Testing, Humans, Molecular Diagnostic Techniques, Pandemics, RNA, Viral, COVID-19 diagnosis, Nucleic Acid Amplification Techniques, SARS-CoV-2 isolation & purification
Abstract

As the second year of the COVID-19 pandemic begins, it remains clear that a massive increase in the ability to test for SARS-CoV-2 infections in a myriad of settings is critical to controlling the pandemic and to preparing for future outbreaks. The current gold standard for molecular diagnostics is the polymerase chain reaction (PCR), but the extraordinary and unmet demand for testing in a variety of environments means that both complementary and supplementary testing solutions are still needed. This review highlights the role that loop-mediated isothermal amplification (LAMP) has had in filling this global testing need, providing a faster and easier means of testing, and what it can do for future applications, pathogens, and the preparation for future outbreaks. This review describes the current state of the art for research of LAMP-based SARS-CoV-2 testing, as well as its implications for other pathogens and testing. The authors represent the global LAMP (gLAMP) Consortium, an international research collective, which has regularly met to share their experiences on LAMP deployment and best practices; sections are devoted to all aspects of LAMP testing, including preanalytic sample processing, target amplification, and amplicon detection, then the hardware and software required for deployment are discussed, and finally, a summary of the current regulatory landscape is provided. Included as well are a series of first-person accounts of LAMP method development and deployment. The final discussion section provides the reader with a distillation of the most validated testing methods and their paths to implementation. This review also aims to provide practical information and insight for a range of audiences: for a research audience, to help accelerate research through sharing of best practices; for an implementation audience, to help get testing up and running quickly; and for a public health, clinical, and policy audience, to help convey the breadth of the effect that LAMP methods have to offer., (© 2021 ABRF.)

Published
2021
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46. A global metagenomic map of urban microbiomes and antimicrobial resistance.

Author

Danko D, Bezdan D, Afshin EE, Ahsanuddin S, Bhattacharya C, Butler DJ, Chng KR, Donnellan D, Hecht J, Jackson K, Kuchin K, Karasikov M, Lyons A, Mak L, Meleshko D, Mustafa H, Mutai B, Neches RY, Ng A, Nikolayeva O, Nikolayeva T, Png E, Ryon KA, Sanchez JL, Shaaban H, Sierra MA, Thomas D, Young B, Abudayyeh OO, Alicea J, Bhattacharyya M, Blekhman R, Castro-Nallar E, Cañas AM, Chatziefthimiou AD, Crawford RW, De Filippis F, Deng Y, Desnues C, Dias-Neto E, Dybwad M, Elhaik E, Ercolini D, Frolova A, Gankin D, Gootenberg JS, Graf AB, Green DC, Hajirasouliha I, Hastings JJA, Hernandez M, Iraola G, Jang S, Kahles A, Kelly FJ, Knights K, Kyrpides NC, Łabaj PP, Lee PKH, Leung MHY, Ljungdahl PO, Mason-Buck G, McGrath K, Meydan C, Mongodin EF, Moraes MO, Nagarajan N, Nieto-Caballero M, Noushmehr H, Oliveira M, Ossowski S, Osuolale OO, Özcan O, Paez-Espino D, Rascovan N, Richard H, Rätsch G, Schriml LM, Semmler T, Sezerman OU, Shi L, Shi T, Siam R, Song LH, Suzuki H, Court DS, Tighe SW, Tong X, Udekwu KI, Ugalde JA, Valentine B, Vassilev DI, Vayndorf EM, Velavan TP, Wu J, Zambrano MM, Zhu J, Zhu S, and Mason CE

Subjects
Biodiversity, Databases, Genetic, Humans, Drug Resistance, Bacterial genetics, Metagenomics, Microbiota genetics, Urban Population
Abstract

We present a global atlas of 4,728 metagenomic samples from mass-transit systems in 60 cities over 3 years, representing the first systematic, worldwide catalog of the urban microbial ecosystem. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics, antimicrobial resistance (AMR) markers, and genetic elements, including 10,928 viruses, 1,302 bacteria, 2 archaea, and 838,532 CRISPR arrays not found in reference databases. We identified 4,246 known species of urban microorganisms and a consistent set of 31 species found in 97% of samples that were distinct from human commensal organisms. Profiles of AMR genes varied widely in type and density across cities. Cities showed distinct microbial taxonomic signatures that were driven by climate and geographic differences. These results constitute a high-resolution global metagenomic atlas that enables discovery of organisms and genes, highlights potential public health and forensic applications, and provides a culture-independent view of AMR burden in cities., Competing Interests: Declaration of interests C.E.M. is co-founder of Biotia and Onegevity Health. D.B. is co-founder and CSO of Poppy Health Inc. The other authors declare they have no competing interests that impacted this study., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

Published
2021
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47. The impact of cross-kingdom molecular forensics on genetic privacy.

Author

Elhaik E, Ahsanuddin S, Robinson JM, Foster EM, and Mason CE

Subjects
Genomics, Humans, Metagenome, Metagenomics, Genetic Privacy, Privacy
Abstract

Recent advances in metagenomic technology and computational prediction may inadvertently weaken an individual's reasonable expectation of privacy. Through cross-kingdom genetic and metagenomic forensics, we can already predict at least a dozen human phenotypes with varying degrees of accuracy. There is also growing potential to detect a "molecular echo" of an individual's microbiome from cells deposited on public surfaces. At present, host genetic data from somatic or germ cells provide more reliable information than microbiome samples. However, the emerging ability to infer personal details from different microscopic biological materials left behind on surfaces requires in-depth ethical and legal scrutiny. There is potential to identify and track individuals, along with new, surreptitious means of genetic discrimination. This commentary underscores the need to update legal and policy frameworks for genetic privacy with additional considerations for the information that could be acquired from microbiome-derived data. The article also aims to stimulate ubiquitous discourse to ensure the protection of genetic rights and liberties in the post-genomic era. Video abstract.

Published
2021
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48. Population genetic considerations for using biobanks as international resources in the pandemic era and beyond.

Author

Carress H, Lawson DJ, and Elhaik E

Subjects
Genetics, Population, Humans, Privacy, Biological Specimen Banks, Pandemics
Abstract

The past years have seen the rise of genomic biobanks and mega-scale meta-analysis of genomic data, which promises to reveal the genetic underpinnings of health and disease. However, the over-representation of Europeans in genomic studies not only limits the global understanding of disease risk but also inhibits viable research into the genomic differences between carriers and patients. Whilst the community has agreed that more diverse samples are required, it is not enough to blindly increase diversity; the diversity must be quantified, compared and annotated to lead to insight. Genetic annotations from separate biobanks need to be comparable and computable and to operate without access to raw data due to privacy concerns. Comparability is key both for regular research and to allow international comparison in response to pandemics. Here, we evaluate the appropriateness of the most common genomic tools used to depict population structure in a standardized and comparable manner. The end goal is to reduce the effects of confounding and learn from genuine variation in genetic effects on phenotypes across populations, which will improve the value of biobanks (locally and internationally), increase the accuracy of association analyses and inform developmental efforts.

Published
2021
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49. On the Unfounded Enthusiasm for Soft Selective Sweeps III: The Supervised Machine Learning Algorithm That Isn't.

Author

Elhaik E and Graur D

Subjects
Evolution, Molecular, Genetic Drift, Humans, Adaptation, Physiological, Algorithms, Artificial Intelligence, Genetics, Population, Genome, Human, Machine Learning, Selection, Genetic
Abstract

In the last 15 years or so, soft selective sweep mechanisms have been catapulted from a curiosity of little evolutionary importance to a ubiquitous mechanism claimed to explain most adaptive evolution and, in some cases, most evolution. This transformation was aided by a series of articles by Daniel Schrider and Andrew Kern. Within this series, a paper entitled "Soft sweeps are the dominant mode of adaptation in the human genome" (Schrider and Kern, Mol. Biol. Evolut . 2017 , 34 (8), 1863-1877) attracted a great deal of attention, in particular in conjunction with another paper (Kern and Hahn, Mol. Biol. Evolut . 2018 , 35 (6), 1366-1371), for purporting to discredit the Neutral Theory of Molecular Evolution (Kimura 1968). Here, we address an alleged novelty in Schrider and Kern's paper, i.e., the claim that their study involved an artificial intelligence technique called supervised machine learning (SML). SML is predicated upon the existence of a training dataset in which the correspondence between the input and output is known empirically to be true. Curiously, Schrider and Kern did not possess a training dataset of genomic segments known a priori to have evolved either neutrally or through soft or hard selective sweeps. Thus, their claim of using SML is thoroughly and utterly misleading. In the absence of legitimate training datasets, Schrider and Kern used: (1) simulations that employ many manipulatable variables and (2) a system of data cherry-picking rivaling the worst excesses in the literature. These two factors, in addition to the lack of negative controls and the irreproducibility of their results due to incomplete methodological detail, lead us to conclude that all evolutionary inferences derived from so-called SML algorithms (e.g., S/HIC) should be taken with a huge shovel of salt.

Published
2021
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50. COVID-19 drug practices risk antimicrobial resistance evolution.

Author

Afshinnekoo E, Bhattacharya C, Burguete-García A, Castro-Nallar E, Deng Y, Desnues C, Dias-Neto E, Elhaik E, Iraola G, Jang S, Łabaj PP, Mason CE, Nagarajan N, Poulsen M, Prithiviraj B, Siam R, Shi T, Suzuki H, Werner J, Zambrano MM, and Bhattacharyya M

Subjects
Anti-Bacterial Agents adverse effects, Drug Resistance, Bacterial, Humans, SARS-CoV-2, COVID-19 Drug Treatment
Published
2021
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