Search

As SARS-CoV-2 continues to spread and evolve, detecting emerging variants early is critical for public health interventions. Inferring lineage prevalence by clinical testing is infeasible at scale, especially in areas with limited resources, participation, or testing and/or sequencing capacity, which can also introduce biases1–3. SARS-CoV-2 RNA concentration in wastewater successfully tracks regional infection dynamics and provides less biased abundance estimates than clinical testing4,5. Tracking virus genomic sequences in wastewater would improve community prevalence estimates and detect emerging variants. However, two factors limit wastewater-based genomic surveillance: low-quality sequence data and inability to estimate relative lineage abundance in mixed samples. Here we resolve these critical issues to perform a high-resolution, 295-day wastewater and clinical sequencing effort, in the controlled environment of a large university campus and the broader context of the surrounding county. We developed and deployed improved virus concentration protocols and deconvolution software that fully resolve multiple virus strains from wastewater. We detected emerging variants of concern up to 14 days earlier in wastewater samples, and identified multiple instances of virus spread not captured by clinical genomic surveillance. Our study provides a scalable solution for wastewater genomic surveillance that allows early detection of SARS-CoV-2 variants and identification of cryptic transmission.

SummaryThe maturation of genomic surveillance in the past decade has enabled tracking of the emergence and spread of epidemics at an unprecedented level. During the COVID-19 pandemic, for example, genomic data revealed that local epidemics varied considerably in the frequency of SARS-CoV-2 lineage importation and persistence, likely due to a combination of COVID-19 restrictions and changing connectivity. Here, we show that local COVID-19 epidemics are driven by regional transmission, including across international boundaries, but can become increasingly connected to distant locations following the relaxation of public health interventions. By integrating genomic, mobility, and epidemiological data, we find abundant transmission occurring between both adjacent and distant locations, supported by dynamic mobility patterns. We find that changing connectivity significantly influences local COVID-19 incidence. Our findings demonstrate a complex meaning of ‘local’ when investigating connected epidemics and emphasize the importance of collaborative interventions for pandemic prevention and mitigation.

Nucleotide-binding oligomerization domain-like receptor-family pyrin domain-containing 3 (NLRP3) inflammasome activation has been shown to result in liver fibrosis. Mechanisms and downstream signaling remain incompletely understood. Here, we studied the role of IL-18 in hepatic stellate cells (HSCs), and its impact on liver fibrosis.We observed significantly increased serum levels of IL-18 (128.4 pg/ml vs. 74.9 pg/ml) and IL-18 binding protein (BP; 46.50 ng/ml vs. 15.35 ng/ml) in patients with liver cirrhosis compared with healthy controls. Single cell RNA sequencing data showed that an immunoregulatory subset of murine HSCs highly expresses Il18 and Il18r1. Treatment of cultured primary murine HSC with recombinant mouse IL-18 accelerated their transdifferentiation into myofibroblasts. In vivo, IL-18 receptor-deficient mice had reduced liver fibrosis in a model of fibrosis induced by HSC-specific NLRP3 overactivation. Whole liver RNA sequencing analysis from a murine model of severe NASH-induced fibrosis by feeding a choline-deficient, L-amino acid-defined, high fat diet showed that genes related to IL-18 and its downstream signaling were significantly upregulated, and Il18Our study highlights the role of IL-18 in the development of liver fibrosis by its direct effect on HSC activation identifying IL-18 as a target to treat liver fibrosis.

Summary Despite the great diversity of vertebrate limb proportion and our deep understanding of the genetic mechanisms that drive skeletal elongation, little is known about how individual bones reach different lengths in any species. Here, we directly compare the transcriptomes of homologous growth cartilages of the mouse (Mus musculus) and bipedal jerboa (Jaculus jaculus), the latter of which has “mouse-like” arms but extremely long metatarsals of the feet. Intersecting gene-expression differences in metatarsals and forearms of the two species revealed that about 10% of orthologous genes are associated with the disproportionately rapid elongation of neonatal jerboa feet. These include genes and enriched pathways not previously associated with endochondral elongation as well as those that might diversify skeletal proportion in addition to their known requirements for bone growth throughout the skeleton. We also identified transcription regulators that might act as “nodes” for sweeping differences in genome expression between species. Among these, Shox2, which is necessary for proximal limb elongation, has gained expression in jerboa metatarsals where it has not been detected in other vertebrates. We show that Shox2 is sufficient to increase mouse distal limb length, and a nearby putative cis-regulatory region is preferentially accessible in jerboa metatarsals. In addition to mechanisms that might directly promote growth, we found evidence that jerboa foot elongation may occur in part by de-repressing latent growth potential. The genes and pathways that we identified here provide a framework to understand the modular genetic control of skeletal growth and the remarkable malleability of vertebrate limb proportion.

SummaryAs SARS-CoV-2 continues to spread and evolve, detecting emerging variants early is critical for public health interventions. Inferring lineage prevalence by clinical testing is infeasible at scale, especially in areas with limited resources, participation, or testing/sequencing capacity, which can also introduce biases. SARS-CoV-2 RNA concentration in wastewater successfully tracks regional infection dynamics and provides less biased abundance estimates than clinical testing. Tracking virus genomic sequences in wastewater would improve community prevalence estimates and detect emerging variants. However, two factors limit wastewater-based genomic surveillance: low-quality sequence data and inability to estimate relative lineage abundance in mixed samples. Here, we resolve these critical issues to perform a high-resolution, 295-day wastewater and clinical sequencing effort, in the controlled environment of a large university campus and the broader context of the surrounding county. We develop and deploy improved virus concentration protocols and deconvolution software that fully resolve multiple virus strains from wastewater. We detect emerging variants of concern up to 14 days earlier in wastewater samples, and identify multiple instances of virus spread not captured by clinical genomic surveillance. Our study provides a scalable solution for wastewater genomic surveillance that allows early detection of SARS-CoV-2 variants and identification of cryptic transmission.

Throughout the COVID-19 pandemic, massive sequencing and data sharing efforts enabled the real-time surveillance of novel SARS-CoV-2 strains throughout the world, the results of which provided public health officials with actionable information to prevent the spread of the virus. However, with great sequencing comes great computation, and while cloud computing platforms bring high-performance computing directly into the hands of all who seek it, optimal design and configuration of a cloud compute cluster requires significant system administration expertise. We developed ViReflow, a user-friendly viral consensus sequence reconstruction pipeline enabling rapid analysis of viral sequence datasets leveraging Amazon Web Services (AWS) cloud compute resources and the Reflow system. ViReflow was developed specifically in response to the COVID-19 pandemic, but it is general to any viral pathogen. Importantly, when utilized with sufficient compute resources, ViReflow can trim, map, call variants, and call consensus sequences from amplicon sequence data from 1000 SARS-CoV-2 samples at 1000X depth in

BackgroundSchools are high-risk settings for SARS-CoV-2 transmission, but necessary for children’s educational and social-emotional wellbeing. Previous research suggests that wastewater monitoring can detect SARS-CoV-2 infections in controlled residential settings with high levels of accuracy. However, its effective accuracy, cost, and feasibility in non-residential community settings is unknown.MethodsThe objective of this study was to determine the effectiveness and accuracy of community-based passive wastewater and surface (environmental) surveillance to detect SARS-CoV-2 infection in neighborhood schools compared to weekly diagnostic (PCR) testing. We implemented an environmental surveillance system in nine elementary schools with 1700 regularly present staff and students in southern California. The system was validated from November 2020 – March 2021.FindingsIn 447 data collection days across the nine sites 89 individuals tested positive for COVID-19, and SARS-CoV-2 was detected in 374 surface samples and 133 wastewater samples. Ninety-three percent of identified cases were associated with an environmental sample (95% CI: 88% - 98%); 67% were associated with a positive wastewater sample (95% CI: 57% - 77%), and 40% were associated with a positive surface sample (95% CI: 29% - 52%). The techniques we utilized allowed for near-complete genomic sequencing of wastewater and surface samples.InterpretationPassive environmental surveillance can detect the presence of COVID-19 cases in non-residential community school settings with a high degree of accuracy.FundingCounty of San Diego, Health and Human Services Agency, National Institutes of Health, National Science Foundation, Centers for Disease Control

QIIME 2 is a completely re‐engineered microbiome bioinformatics platform based on the popular QIIME platform, which it has replaced. QIIME 2 facilitates comprehensive and fully reproducible microbiome data science, improving accessibility to diverse users by adding multiple user interfaces. QIIME 2 can be combined with Qiita, an open‐source web‐based platform, to re‐use available data for meta‐analysis. The following basic protocol describes how to install QIIME 2 on a single computer and analyze microbiome sequence data, from processing of raw DNA sequence reads through generating publishable interactive figures. These interactive figures allow readers of a study to interact with data with the same ease as its authors, advancing microbiome science transparency and reproducibility. We also show how plug‐ins developed by the community to add analysis capabilities can be installed and used with QIIME 2, enhancing various aspects of microbiome analyses—e.g., improving taxonomic classification accuracy. Finally, we illustrate how users can perform meta‐analyses combining different datasets using readily available public data through Qiita. In this tutorial, we analyze a subset of the Early Childhood Antibiotics and the Microbiome (ECAM) study, which tracked the microbiome composition and development of 43 infants in the United States from birth to 2 years of age, identifying microbiome associations with antibiotic exposure, delivery mode, and diet. For more information about QIIME 2, see https://qiime2.org. To troubleshoot or ask questions about QIIME 2 and microbiome analysis, join the active community at https://forum.qiime2.org., Current protocols in bioinformatics, 70 (1)

We developed a systematic approach to map human genetic networks by combinatorial CRISPR-Cas9 perturbations coupled to robust analysis of growth kinetics. We targeted all pairs of 73 cancer genes with dual-guide RNAs in three cell lines, altogether comprising 141,912 tests of interaction. Numerous therapeutically relevant interactions were identified and these patterns replicated with combinatorial drugs at 75% precision. Based on these results we anticipate cellular context will be critical to synthetic-lethal therapies.

Summary With the growing availability of population-scale whole-exome and whole-genome sequencing, demand for reproducible, scalable variant analysis has spread within genomic research communities. To address this need, we introduce the Python package Variant Analysis and Prioritization (VAPr). VAPr leverages existing annotation tools ANNOVAR and MyVariant.info with MongoDB-based flexible storage and filtering functionality. It offers biologists and bioinformatics generalists easy-to-use and scalable analysis and prioritization of genomic variants from large cohort studies. Availability and implementation VAPr is developed in Python and is available for free use and extension under the MIT License. An install package is available on PyPi at https://pypi.python.org/pypi/VAPr, while source code and extensive documentation are on GitHub at https://github.com/ucsd-ccbb/VAPr.

Despite the great diversity of vertebrate limb proportion and our deep understanding of the genetic mechanisms that drive skeletal elongation, little is known about how individual bones reach different lengths in any species. Here, we directly compare the transcriptomes of homologous growth cartilages of the mouse (Mus musculus) and bipedal jerboa (Jaculus jaculus), which has extremely long metatarsals of the feet and ‘mouse-like’ arms. When we intersected gene expression differences in metatarsals of the two species with expression differences in forearms, we found that about 10% of all orthologous genes are associated with disproportionate elongation of jerboa feet. Among these,Shox2, has gained expression in jerboa metatarsals where it is not expressed in other vertebrates that have been assessed. This transcription factor is necessary for proximal limb elongation, and we show that it is sufficient to increase mouse distal limb length. Unexpectedly, we also found evidence that jerboa foot elongation occurs in part by releasing latent growth potential that is repressed in mouse feet. In jerboa metatarsals, we observed higher expression ofCrabp1, an antagonist of growth inhibitory retinoic acid, lower expression ofGdf10, an inhibitory TGFβ ligand, and lower expression ofMab21L2, a BMP signaling inhibitor that we show is sufficient to reduce limb bone elongation. By intersecting our data with prior expression analyses in other systems, we identify mechanisms that may both establish limb proportion during development and diversify proportion during evolution. The genes we identified here therefore provide a framework to understand the modular genetic control of skeletal growth and the remarkable malleability of vertebrate limb proportion.

Inflammation-dependent base deaminases promote therapeutic resistance in many malignancies. However, their roles in human pre-leukemia stem cell (pre-LSC) evolution to acute myeloid leukemia stem cells (LSCs) had not been elucidated. Comparative whole-genome and whole-transcriptome sequencing analyses of FACS-purified pre-LSCs from myeloproliferative neoplasm (MPN) patients reveal APOBEC3C upregulation, an increased C-to-T mutational burden, and hematopoietic stem and progenitor cell (HSPC) proliferation during progression, which can be recapitulated by lentiviral APOBEC3C overexpression. In pre-LSCs, inflammatory splice isoform overexpression coincides with APOBEC3C upregulation and ADAR1p150-induced A-to-I RNA hyper-editing. Pre-LSC evolution to LSCs is marked by STAT3 editing, STAT3β isoform switching, elevated phospho-STAT3, and increased ADAR1p150 expression, which can be prevented by JAK2/STAT3 inhibition with ruxolitinib or fedratinib or lentiviral ADAR1 shRNA knockdown. Conversely, lentiviral ADAR1p150 expression enhances pre-LSC replating and STAT3 splice isoform switching. Thus, pre-LSC evolution to LSCs is fueled by primate-specific APOBEC3C-induced pre-LSC proliferation and ADAR1-mediated splicing deregulation.

Motivation Network biology is widely used to elucidate mechanisms of disease and biological processes. The ability to interact with biological networks is important for hypothesis generation and to give researchers an intuitive understanding of the data. We present visJS2jupyter, a tool designed to embed interactive networks in Jupyter notebooks to streamline network analysis and to promote reproducible research. Results The tool provides functions for performing and visualizing useful network operations in biology, including network overlap, network propagation around a focal set of genes, and co-localization of two sets of seed genes. visJS2jupyter uses the JavaScript library vis.js to create interactive networks displayed within Jupyter notebook cells with features including drag, click, hover, and zoom. We demonstrate the functionality of visJS2jupyter applied to a biological question, by creating a network propagation visualization to prioritize risk-related genes in autism. Availability and implementation The visJS2jupyter package is distributed under the MIT License. The source code, documentation and installation instructions are freely available on GitHub at https://github.com/ucsd-ccbb/visJS2jupyter. The package can be downloaded at https://pypi.python.org/pypi/visJS2jupyter. Supplementary information Supplementary data are available at Bioinformatics online.

While inflammation induced ADAR1 RNA deaminases protect the human genome from retroviral integration, deregulation promotes therapeutic resistance in many malignancies. However, the combinatorial role of these deaminases in pre-leukemia stem cell (pre-LSC) evolution to therapy resistant LSC had not been elucidated. Thus, we performed whole genome sequencing (WGS) analysis of 43 CD34+pre-leukemic myeloproliferative neoplasm (MPN) samples compared with matched saliva and non-MPN controls andwhole transcriptome sequencing (RNA-seq) analysis of 113 FACS-purified hematopoietic stem cells (HSC) and hematopoietic progenitor cells (HPC) from MPN, acute myeloid leukemia (AML) and healthy young and aged samples. During MPN progression, inflammation-driven ADAR1 isoform p150 upregulation corresponded with increased Adenosine-to-Inosine (A-to-I) transitions in both MPN stem and progenitor populations. We identified unique A-to-I editing events in coding regions that are associated with either normal stem progenitors or with MPN progenitors. In addition, STAT3 transcript hyper-editing leads to STAT3beta splice isoform expression. Moreover, lentiviral ADAR1p150 overexpression enhanced replating as well as beta-catenin activation, which was reversed by lentiviral shRNA ADAR1 knockdown. In summary, innate immune deaminase deregulation fuels pre-LSC evolution to LSC and may represent a vital LSC therapeutic vulnerability. Citation Format: Qingfei Jiang, Frida Holm, Jane Isquith, Adam Mark, Cayla Mason, Eduardo Reynoso, Isabella Morris, Wenxue Ma, Raymond Diep, Jessica Pham, Chanond Nasamran, Guorong Xu, Roman Sasik, Sara Brin Rosenthal, Amanda Birmingham, Leslie Crews, Gabriel Pineda, Thomas Whisenant, Kathleen Fisch, Catriona Jamieson. A-to-I RNA deaminase deregulation in pre-leukemia stem cell evolution [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5726.

Innate immune anti-viral adenosine to inosine (A-to-I) base editing enzymes (editases) promote hematopoietic stem cell (HSC) self-renewal and protect the human genome from retroviral integration in response to inflammatory cytokine signaling. However, hyper-editing has been linked to therapeutic resistance and cancer progression. Because myeloproliferative neoplasm (MPN) progression is typified by increased JAK2/STAT-mediated cytokine signaling, we investigated the cell type and context specific role of adenosine deaminase acting on RNA1 (ADAR1) editaseactivity in MPN pre-leukemia stem cell (pre-LSC) evolution into acute myeloid leukemia stem cells (LSCs). Here we show by whole transcriptome sequencing (RNA-seq) of 113 FACS-purified hematopoietic stem cells and progenitors from 78 individuals, including 54 MPN and AML patients and 24healthy young and aged individuals, that anti-viral signaling pathway activation and splice isoform switching from ADAR1p110 to JAK2/STAT-inducible ADAR1p150 RNA editase activation contributes to MPN progression. Pre-LSC evolution to LSC was characterized by ADAR1p150 upregulation, distinctive RNA editome patterns, STAT3 hyper-editing, increased replating as a measure of self-renewal. Moreover, LSC generation was typified by beta-catenin self-renewal pathway upregulation, which was recapitulated by lentiviral ADAR1p150 overexpression and reversed by lentiviral ADAR1p150 shRNA knockdown. Our studyunderscores the importance of inflammatory-cytokine fueled enzymatic mutagenesis in human MPN pre-LSC evolution to LSC. Thus, this study sets the stage for developing predictive RNA editome biomarkers of LSC generation to guidetherapeutic strategies aimed at preventing progression of hematopoietic malignancies. Disclosures Crews: Ionis Pharmaceuticals: Research Funding.

Methamphetamine abuse is common among humans with immunodeficiency virus (HIV). The HIV-1 regulatory protein TAT induces dysfunction of mesolimbic dopaminergic systems which may result in impaired reward processes and contribute to methamphetamine abuse. These studies investigated the impact of TAT expression on methamphetamine-induced locomotor sensitization, underlying changes in dopamine function and adenosine receptors in mesolimbic brain areas and neuroinflammation (microgliosis). Transgenic mice with doxycycline-induced TAT protein expression in the brain were tested for locomotor activity in response to repeated methamphetamine injections and methamphetamine challenge after a 7-day abstinence period. Dopamine function in the nucleus accumbens (Acb) was determined using high performance liquid chromatography. Expression of dopamine and/or adenosine A receptors (ADORA) in the Acb and caudate putamen (CPu) was assessed using RT-PCR and immunohistochemistry analyses. Microarrays with pathway analyses assessed dopamine and adenosine signaling in the CPu. Activity-dependent neurotransmitter switching of a reserve pool of non-dopaminergic neurons to a dopaminergic phenotype in the ventral tegmental area (VTA) was determined by immunohistochemistry and quantified with stereology. TAT expression enhanced methamphetamine-induced sensitization. TAT expression alone decreased striatal dopamine (D1, D2, D4, D5) and ADORA1A receptor expression, while increasing ADORA2A receptors expression. Moreover, TAT expression combined with methamphetamine exposure was associated with increased adenosine A receptors (ADORA1A) expression and increased recruitment of dopamine neurons in the VTA. TAT expression and methamphetamine exposure induced microglia activation with the largest effect after combined exposure. Our findings suggest that dopamine-adenosine receptor interactions and reserve pool neuronal recruitment may represent potential targets to develop new treatments for methamphetamine abuse in individuals with HIV.

RNA interference screening using pooled, short hairpin RNA (shRNA) is a powerful, high-throughput tool for determining the biological relevance of genes for a phenotype. Assessing an shRNA pooled screen’s performance is difficult in practice; one can estimate the performance only by using reproducibility as a proxy for power or by employing a large number of validated positive and negative controls. Here, we develop an open-source software tool, the Power Decoder simulator, for generating shRNA pooled screening experiments in silico that can be used to estimate a screen’s statistical power. Using the negative binomial distribution, it models both the relative abundance of multiple shRNAs within a single screening replicate and the biological noise between replicates for each individual shRNA. We demonstrate that this simulator can successfully model the data from an actual laboratory experiment. We then use it to evaluate the effects of biological replicates and sequencing counts on the performance of a pooled screen, without the necessity of gathering additional data. The Power Decoder simulator is written in R and Python and is available for download under the GNU General Public License v3.0.

The discovery that the machinery of the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-Cas9 bacterial immune system can be re-purposed to easily create deletions, insertions and replacements in the mammalian genome has revolutionized the field of genome engineering and re-invigorated the field of gene therapy. Many parallels have been drawn between the newly discovered CRISPR-Cas9 system and the RNA interference (RNAi) pathway in terms of their utility for understanding and interrogating gene function in mammalian cells. Given this similarity, the CRISPR-Cas9 field stands to benefit immensely from lessons learned during the development of RNAi technology. We examine how the history of RNAi can inform today's challenges in CRISPR-Cas9 genome engineering such as efficiency, specificity, high-throughput screening and delivery for in vivo and therapeutic applications.

Background Data from 16S ribosomal RNA (rRNA) amplicon sequencing present challenges to ecological and statistical interpretation. In particular, library sizes often vary over several ranges of magnitude, and the data contains many zeros. Although we are typically interested in comparing relative abundance of taxa in the ecosystem of two or more groups, we can only measure the taxon relative abundance in specimens obtained from the ecosystems. Because the comparison of taxon relative abundance in the specimen is not equivalent to the comparison of taxon relative abundance in the ecosystems, this presents a special challenge. Second, because the relative abundance of taxa in the specimen (as well as in the ecosystem) sum to 1, these are compositional data. Because the compositional data are constrained by the simplex (sum to 1) and are not unconstrained in the Euclidean space, many standard methods of analysis are not applicable. Here, we evaluate how these challenges impact the performance of existing normalization methods and differential abundance analyses. Results Effects on normalization: Most normalization methods enable successful clustering of samples according to biological origin when the groups differ substantially in their overall microbial composition. Rarefying more clearly clusters samples according to biological origin than other normalization techniques do for ordination metrics based on presence or absence. Alternate normalization measures are potentially vulnerable to artifacts due to library size. Effects on differential abundance testing: We build on a previous work to evaluate seven proposed statistical methods using rarefied as well as raw data. Our simulation studies suggest that the false discovery rates of many differential abundance-testing methods are not increased by rarefying itself, although of course rarefying results in a loss of sensitivity due to elimination of a portion of available data. For groups with large (~10×) differences in the average library size, rarefying lowers the false discovery rate. DESeq2, without addition of a constant, increased sensitivity on smaller datasets (20 samples per group) but also critically the only method tested that has a good control of false discovery rate. Conclusions These findings guide which normalization and differential abundance techniques to use based on the data characteristics of a given study. Electronic supplementary material The online version of this article (doi:10.1186/s40168-017-0237-y) contains supplementary material, which is available to authorized users.

This report summarizes the proceedings of the structure mapping working group meeting of the RNA Ontology Consortium (ROC), held in Kona, Hawaii on January 8-9, 2011. The ROC hosted this workshop to facilitate collaborations among those researchers formalizing con-cepts in RNA, those developing RNA-related software, and those performing genome annota-tion and standardization. The workshop included three software presentations, extended round-table discussions, and the constitution of two new working groups, the first to address the need for better software integration and the second to discuss standardization and ben-chmarking of existing RNA annotation pipelines. These working groups have subsequently pursued concrete implementation of actions suggested during the discussion. Further infor-mation about the ROC and its activities can be found at http://roc.bgsu.edu/.

Genetic interactions, in particular negative or "synthetic-lethal" interactions for which simultaneous disruption of two genes causes cell killing, have implications for therapeutic development as has been demonstrated by the clinical success of PARP inhibitors specifically for tumors with loss-of-function mutations in BRCA1/2. However, further applications of synthetic-lethal cancer therapy have been limited by poor understanding of the important genetic interactions in a cancer cell, and how these vary from one cancer type to another or from patient to patient. To enable systematic mapping of these genetic interaction networks, we recently developed a CRISPR-Cas9 screening methodology for knocking out single and pairs of genes in high throughput. Critical to this method is the precise determination of single-gene knockout effects, which is accomplished by serial measurement of the relative changes in gRNAs at days 3, 14, 21 and 28 post-transduction. To robustly quantify gene fitness and genetic interactions we developed a novel computational analysis framework that integrates all samples across the multiple days of the experiment; with said method we achieve Pearson correlation of 0.95 or greater between biologic replicates in the same cell line (p < 1x10-30). Additionally we demonstrate that our analysis method is robust to the compositional effects inherent in a pooled knockout experiment. To facilitate reproducibility of analyses and distribution to the scientific community, the code has been packaged into a modular series of python notebooks freely available on github. Evaluating all pairwise gene knockout combinations among a panel of 73 genes divided between tumor-suppressor genes (TSG) and cancer-relevant drug targets (DT) in a total of 5 cancer cell lines from diverse lineages (HeLa, A549, 293T, U2OS, LN229), we identified 226 synthetic lethal and 14 epistatic interactions at a Z-score cut-off of -3 (FDR ~0.3). Of the synthetic lethal interactions 203 (89.8%) were private to a single cell line, and no interaction was seen in more than 3 of 5 five cell lines. Thus far 10 (out of 16 tested) therapeutically relevant interactions have been replicated in low-throughput assays using either combinatorial drugs or CRISPR knockout of a TSG paired with a drug (71% precision or positive predictive value). The cell line specificity of interactions was also confirmed in low-throughput assays (75% negative predictive value). In summary, we have discovered many therapeutically relevant genetic interactions in cancer and identified the great importance of cellular context on the architecture of the genetic interaction network. Recognizing that there will be great diversity in genetic interaction between different tumors, it will be important to perform future studies across a large number of samples, which is enabled by the high-throughput method we have developed. Citation Format: John Paul Shen, Dongxin Zhao, Brenton Munson, Amanda Birmingham, Roman Sasik, Ana Bojorquez-Gomez, Katherine Licon, Kristin Klepper, Alex Beckett, Kyle Salinas Sanchez, Prashant Mali, Trey Ideker. High-throughput combinatorial CRISPR-Cas9 gene knockout reveals most genetic interactions are context dependent [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3299.

The metabolic pathways fueling tumor growth have been well characterized, but the specific impact of transforming events on network topology and enzyme essentiality remains poorly understood. To this end, we performed combinatorial CRISPR-Cas9 screens on a set of 51 carbohydrate metabolism genes that represent glycolysis and the pentose phosphate pathway. This high-throughput methodology enabled systems-level interrogation of metabolic gene dispensability, interactions, and compensation across multiple cell types. The metabolic impact of specific combinatorial knockouts were validated using 13C and 2H isotope tracing, and, these assays together revealed key nodes controlling redox homeostasis along the KEAP1-NRF2 signaling axis. Specifically, targeting KEAP1 in combination with oxidative PPP enzymes mitigated the deleterious effects of these knockouts on growth rates. These results demonstrate how our integrated framework, combining genetic, transcriptomic, and flux measurements, can improve elucidation of metabolic network alterations, and guide precision targeting of metabolic vulnerabilities based on tumor genetics.

MiRNAs are regulatory molecules that can be packaged into exosomes and secreted from cells. Here, we show that adipose tissue macrophages (ATMs) in obese mice secrete miRNA-containing exosomes (Exos), which cause glucose intolerance and insulin resistance when administered to lean mice. Conversely, ATM Exos obtained from lean mice improve glucose tolerance and insulin sensitivity when administered to obese recipients. miR-155 is one of the miRNAs overexpressed in obese ATM Exos, and earlier studies have shown that PPARγ is a miR-155 target. Our results show that miR-155KO animals are insulin sensitive and glucose tolerant compared to controls. Furthermore, transplantation of WT bone marrow into miR-155KO mice mitigated this phenotype. Taken together, these studies show that ATMs secrete exosomes containing miRNA cargo. These miRNAs can be transferred to insulin target cell types through mechanisms of paracrine or endocrine regulation with robust effects on cellular insulin action, in vivo insulin sensitivity, and overall glucose homeostasis.

Effective gene silencing by the RNA interference (RNAi) pathway requires a comprehensive understanding of the elements that influence small interfering RNA (siRNA) functionality and specificity. These include (i) sequence space restrictions that define the boundaries of siRNA targeting, (ii) structural and sequence features required for efficient siRNA performance, (iii) mechanisms that underlie nonspecific gene modulation and (iv) additional features specific to the intended use (i.e., inclusion of native sugar or base chemical modifications for increased stability or specificity, vector design, etc.). Attention to each of these factors enhances siRNA performance and heightens overall confidence in the output of RNAi-mediated functional genomic studies. Here, we provide a detailed protocol explaining the methodologies used for manual and web-based design of siRNAs.

Disruption of healthy microbial communities has been linked to numerous diseases, yet microbial interactions are little understood. This is due in part to the large number of bacteria, and the much larger number of interactions (easily in the millions), making experimental investigation very difficult at best and necessitating the nascent field of computational exploration through microbial correlation networks. We benchmark the performance of eight correlation techniques on simulated and real data in response to challenges specific to microbiome studies: fractional sampling of ribosomal RNA sequences, uneven sampling depths, rare microbes and a high proportion of zero counts. Also tested is the ability to distinguish signals from noise, and detect a range of ecological and time-series relationships. Finally, we provide specific recommendations for correlation technique usage. Although some methods perform better than others, there is still considerable need for improvement in current techniques.

Background: Data from 16S amplicon sequencing present challenges to ecological and statistical interpretation. In particular, library sizes often vary over several ranges of magnitude, and the data contains many zeroes. Also, since researchers sample a small fraction of the ecosystem, the observed sequences are relative abundances and therefore the data is compositional. Here we evaluate methods developed in the literature to address these three challenges in the context of normalization and ordination analysis, which is commonly used to visualize overall differences in bacterial composition between sample groups, and differential abundance analysis, which tests for significant differences in the abundances of microbes between sample groups. Results. Effects of normalization on ordination: Most normalization methods successfully cluster samples according to biological origin when many microbes differ between the groups. For datasets in which clusters are subtle and/or sequence depth varies greatly between samples, or for metrics in which rare microbes play an important role, rarefying outperforms other techniques. For abundance-based metrics, rarefying as well as alternatives like DESeq and metagenomeSeq’s cumulative sum scaling (CSS), seem to correctly cluster samples according to biological origin. With these normalization alternatives, clustering by sequence depth as a confounding variable must be checked for, especially for low library sizes. Effects of differential abundance testing model choice: We build on previous work to evaluate each statistical method using rarefied as well as unrarefied data. When the mean library sizes in the differential abundance groups differ by more than 2-3x, or the library sizes differ in distribution, our simulation studies reveal that each statistical method improved in its false positive rate when samples were rarefied. However, when the difference in library size mean is less than 2-3x, and the library sizes are similarly distributed, rarefying results in a loss of power for all methods. In this case, DESeq2 has the highest power to compare groups, especially for less than 20 samples per group. MetagenomeSeq’s fitZIG is a faster alternative to DESeq2, although it does worse for smaller sample sizes (50 samples), rarefying paired with a non-parametric test, such as the Mann-Whitney test, can also yield equally high sensitivity. Based on these results, we recommend a stepwise procedure in which sample groups are first tested for significant differences in library size. If there is a significant difference, we recommend rarefying with a non-parametric test. Otherwise, DESeq2 and/or fitZIG offer increased sensitivity, especially for rare OTUs and small sample numbers. Conclusions. These findings help guide which technique to use depending on the data characteristics of a given study.

We present an optical and near-infrared study of a 45' × 45' field in NGC 2264, which includes both S Mon and the Cone Nebula. We report photometry at optical (UBVRCIC) and near-infrared (JHK) wavelengths for ~5600 stars and spectroscopic classifications for ~400 of these stars. We identify circumstellar disk candidates using three techniques: excess ultraviolet (U-V) emission, excess near-IR (I-K and H-K) emission, and Hα emission-line equivalent widths for those stars with spectra. We find generally good correlation between disk indicators thought to originate from different physical processes. We find little if any evolution of disk fraction with stellar age or mass. However, when we derive mass accretion rates () from the excess emission at U, we find that decreases with age over the age range spanned by our data, ~0.1–5 Myr, and increases with mass over the range ~0.25–1 M⊙. These findings are comparable to results found previously by us in the Orion Nebula cluster flanking fields.

Nucleic acids are particularly amenable to structural characterization using chemical and enzymatic probes. Each individual structure mapping experiment reveals specific information about the structure and/or dynamics of the nucleic acid. Currently, there is no simple approach for making these data publically available in a standardized format. We therefore developed a standard for reporting the results of single nucleotide resolution nucleic acid structure mapping experiments, or SNRNASMs. We propose a schema for sharing nucleic acid chemical probing data that uses generic public servers for storing, retrieving, and searching the data. We have also developed a consistent nomenclature (ontology) within the Ontology of Biomedical Investigations (OBI), which provides unique identifiers (termed persistent URLs, or PURLs) for classifying the data. Links to standardized data sets shared using our proposed format along with a tutorial and links to templates can be found at http://snrnasm.bio.unc.edu.

International audience; Multiple sequence alignments are powerful tools for understanding the structures, functions, and evolutionary histories of linear biological macromolecules (DNA, RNA, and proteins), and for finding homologs in sequence databases. We address several ontological issues related to RNA sequence alignments that are informed by structure. Multiple sequence alignments are usually shown as two-dimensional (2D) matrices, with rows representing individual sequences, and columns identifying nucleotides from different sequences that correspond structurally, functionally, and/or evolutionarily. However, the requirement that sequences and structures correspond nucleotide-by-nucleotide is unrealistic and hinders representation of important biological relationships. High-throughput sequencing efforts are also rapidly making 2D alignments unmanageable because of vertical and horizontal expansion as more sequences are added. Solving the shortcomings of traditional RNA sequence alignments requires explicit annotation of the meaning of each relationship within the alignment. We introduce the notion of "correspondence," which is an equivalence relation between RNA elements in sets of sequences as the basis of an RNA alignment ontology. The purpose of this ontology is twofold: first, to enable the development of new representations of RNA data and of software tools that resolve the expansion problems with current RNA sequence alignments, and second, to facilitate the integration of sequence data with secondary and three-dimensional structural information, as well as other experimental information, to create simultaneously more accurate and more exploitable RNA alignments.

RNA interference (RNAi) has become a powerful technique for reverse genetics and drug discovery, and in both of these areas large-scale high-throughput RNAi screens are commonly performed. The statistical techniques used to analyze these screens are frequently borrowed directly from small-molecule screening; however, small-molecule and RNAi data characteristics differ in meaningful ways. We examine the similarities and differences between RNAi and small-molecule screens, highlighting particular characteristics of RNAi screen data that must be addressed during analysis. Additionally, we provide guidance on selection of analysis techniques in the context of a sample workflow.

Pairing between the hexamer seed region of a small interfering RNA (siRNA) guide strand (nucleotides 2–7) and complementary sequences in the 3′ UTR of mature transcripts has been implicated as an important element in off-target gene regulation and false positive phenotypes. To better understand the association between seed sequences and off-target profiles we performed an analysis of all possible (4096) hexamers and identified a nonuniform distribution of hexamer frequencies across the 3′ UTR transcriptome. Subsequent microarray analysis of cells transfected with siRNAs having seeds with low, medium, or high seed complement frequencies (SCFs) revealed that duplexes with low SCFs generally induced fewer off-targets and off-target phenotypes than molecules with more abundant 3′ UTR complements. These findings provide the first experimentally validated strategy for designing siRNAs with enhanced specificity and allow for more accurate interpretation of high throughput screening data generated with existing siRNA/shRNA collections.

Background: Advanced intercross lines (AIL) are segregating populations created using a multi-generation breeding protocol for fine mapping complex trait loci (QTL) in mice and other organisms. Applying QTL mapping methods for intercross and backcross populations, often followed by na ve permutation of individuals and phenotypes, does not account for the effect of AIL family structure in which final generations have been expanded and leads to inappropriately low significance thresholds. The critical problem with na ve mapping approaches in AIL populations is that the individual is not an exchangeable unit. Methodology/Principal Findings: The effect of family structure has immediate implications for the optimal AIL creation (many crosses, few animals per cross, and population expansion before the final generation) and we discuss these and the utility of AIL populations for QTL fine mapping. We also describe Genome Reshuffling for Advanced Intercross Permutation, (GRAIP) a method for analyzing AIL data that accounts for family structure. GRAIP permutes a more interchangeable unit in the final generation crosses - the parental genome - and simulating regeneration of a permuted AIL population based on exchanged parental identities. GRAIP determines appropriate genome-wide significance thresholds and locus-specific Pvalues for AILs and other populations with similar family structures. Wemore » contrast GRAIP with na ve permutation using a large densely genotyped mouse AIL population (1333 individuals from 32 crosses). A na ve permutation using coat color as a model phenotype demonstrates high false-positive locus identification and uncertain significance levels, which are corrected using GRAIP. GRAIP also detects an established hippocampus weight locus and a new locus, Hipp9a. Conclusions and Significance: GRAIP determines appropriate genome-wide significance thresholds and locus-specific Pvalues for AILs and other populations with similar family structures. The effect of family structure has immediate implications for the optimal AIL creation and we discuss these and the utility of AIL populations.« less

We have employed various sequence alignment algorithms and scoring techniques to determine whether current computational tools accurately predict genes that will be off-targeted by the RNA interference (RNAi) pathway. Our studies show that distributions of maximum alignment scores for off-targeted and untargeted genes are statistically indistinguishable, indicating that maximum complementarity by itself is an unsatisfactory predictor of off-targeting. Interestingly, a highly significant association was observed between off-targeting and exact complementarity between the seed region (bases 2-7) of siRNA and their off-targeted genes. This pattern has been previously recognized in microRNA-mediated gene knockdown and suggests a distinctive role for the 5 terminus of these strands in RNAi-triggered gene suppression.

Although recent microarray studies have provided evidence of RNA interference (RNAi)-mediated off-target gene modulation, little is known about whether these changes induce observable phenotypic outcomes. Here we show that a fraction of randomly selected small inhibitory RNAs (siRNAs) can induce changes in cell viability in a target-independent fashion. The observed toxicity requires an intact RNAi pathway and can be eliminated by the addition of chemical modifications that reduce off-target effects. Furthermore, an analysis of toxic and nontoxic duplexes identifies a strong correlation between the toxicity and the presence of a 4-base-pair motif (UGGC) in the RISC-entering strand of toxic siRNA. This article provides further evidence of siRNA-induced off-target effects generating a measurable phenotype and also provides an example of how such undesirable phenotypes can be mitigated by addition of chemical modifications to the siRNA.

Off-target gene silencing can present a notable challenge in the interpretation of data from large-scale RNA interference (RNAi) screens. We performed a detailed analysis of off-targeted genes identified by expression profiling of human cells transfected with small interfering RNA (siRNA). Contrary to common assumption, analysis of the subsequent off-target gene database showed that overall identity makes little or no contribution to determining whether the expression of a particular gene will be affected by a given siRNA, except for near-perfect matches. Instead, off-targeting is associated with the presence of one or more perfect 3' untranslated region (UTR) matches with the hexamer or heptamer seed region (positions 2-7 or 2-8) of the antisense strand of the siRNA. These findings have strong implications for future siRNA design and the application of RNAi in high-throughput screening and therapeutic development.

BayesFold is a Web application that folds an alignment of closely related sequences and evaluates hypotheses about their shared structure. It uses Bayes’s Theorem to combine information from several sources, including chemical mapping (if available), thermodynamic folding, and observed sequence variations. Its method provides a rational basis for integrating results, even when these methods conflict. On a gapped alignment of 86 tRNAPhe sequences each 77 bases long, BayesFold takes 31 sec to perform the calculations; the best structure contained 95% of the base pairs in the true structure, and the true structure was ranked second. Notably, similar results come from random samples of only 10 sequences from the alignment (running time 3 sec), suggesting that remarkably few sequences are required for good results. In contrast, folding single sequences with BayesFold produced structures 9.6 bp different, or with the Vienna package, 13.4 bp different, from the true structure . Similar results were obtained for other families of tRNAs. We especially recommend BayesFold for alignments of 3–50 closely related sequences, such as the sequence families frequently found in SELEX. In addition to providing a convenient way to explore the effects of each of the criteria on the plausibility of different structures, BayesFold also makes it easy to produce publication-quality secondary-structure graphics. The Web interface, available at http://bayes.colorado.edu/fold/, includes the flexibility to thread any of the sequences (or the consensus sequence) through any of the structures, including the one judged most probable.