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The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish "gold standard" protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory 'omics' features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices.

A growing body of evidence links gut microbiota changes with inflammatory bowel disease (IBD), raising the potential benefit of exploiting metagenomics data for non-invasive IBD diagnostics. The sbv IMPROVER Metagenomics Diagnosis for Inflammatory Bowel Disease Challenge (MEDIC) investigated computational metagenomics methods for discriminating IBD and nonIBD subjects. Participants in this challenge were given independent training and test metagenomics data from IBD and nonIBD subjects, which could be wither either raw read data (sub-challenge 1, SC1) or processed taxonomy- and function-based profiles (sub-challenge 2, SC2). A total of 81 anonymized submissions were received between September 2019 and March 2020. Most participants’ predictions performed better than random predictions in classifying IBD vs nonIBD, Ulcerative Colitis (UC) vs nonIBD, and Crohn’s Disease (CD) vs nonIBD. However, discrimination between UC and CD remains challenging, with the classification quality similar to the set of random predictions. We analyzed the class prediction accuracy, the metagenomics features by the teams, and computational methods used. These results will be openly shared with the scientific community to help advance IBD research and illustrate the application of a range of computational methodologies for effective metagenomic classification.

A growing body of evidence links gut microbiota changes with inflammatory bowel disease (IBD), raising the potential benefit of exploiting metagenomics data for non-invasive IBD diagnostics. The sbv IMPROVER Metagenomics Diagnosis for Inflammatory Bowel Disease Challenge (MEDIC) investigated computational metagenomics methods for discriminating IBD and nonIBD subjects. Participants in this challenge were given independent training and test metagenomics data from IBD and nonIBD subjects, which could be wither either raw read data (sub-challenge 1, SC1) or processed taxonomy- and function-based profiles (sub-challenge 2, SC2). A total of 81 anonymized submissions were received between September 2019 and March 2020. Most participants’ predictions performed better than random predictions in classifying IBD vs nonIBD, Ulcerative Colitis (UC) vs nonIBD, and Crohn’s Disease (CD) vs nonIBD. However, discrimination between UC and CD remains challenging, with the classification quality similar to the set of random predictions. We analyzed the class prediction accuracy, the metagenomics features by the teams, and computational methods used. These results will be openly shared with the scientific community to help advance IBD research and illustrate the application of a range of computational methodologies for effective metagenomic classification.

A growing body of evidence links gut microbiota changes with inflammatory bowel disease (IBD), raising the potential benefit of exploiting metagenomics data for non-invasive IBD diagnostics. The sbv IMPROVER Metagenomics Diagnosis for Inflammatory Bowel Disease Challenge (MEDIC) investigated computational metagenomics methods for discriminating IBD and nonIBD subjects. Participants in this challenge were given independent training and test metagenomics data from IBD and nonIBD subjects, which could be wither either raw read data (sub-challenge 1, SC1) or processed taxonomy- and function-based profiles (sub-challenge 2, SC2). A total of 81 anonymized submissions were received between September 2019 and March 2020. Most participants’ predictions performed better than random predictions in classifying IBD vs nonIBD, Ulcerative Colitis (UC) vs nonIBD, and Crohn’s Disease (CD) vs nonIBD. However, discrimination between UC and CD remains challenging, with the classification quality similar to the set of random predictions. We analyzed the class prediction accuracy, the metagenomics features by the teams, and computational methods used. These results will be openly shared with the scientific community to help advance IBD research and illustrate the application of a range of computational methodologies for effective metagenomic classification.

A growing body of evidence links gut microbiota changes with inflammatory bowel disease (IBD), raising the potential benefit of exploiting metagenomics data for non-invasive IBD diagnostics. The sbv IMPROVER Metagenomics Diagnosis for Inflammatory Bowel Disease Challenge (MEDIC) investigated computational metagenomics methods for discriminating IBD and nonIBD subjects. Participants in this challenge were given independent training and test metagenomics data from IBD and nonIBD subjects, which could be wither either raw read data (sub-challenge 1, SC1) or processed taxonomy- and function-based profiles (sub-challenge 2, SC2). A total of 81 anonymized submissions were received between September 2019 and March 2020. Most participants’ predictions performed better than random predictions in classifying IBD vs nonIBD, Ulcerative Colitis (UC) vs nonIBD, and Crohn’s Disease (CD) vs nonIBD. However, discrimination between UC and CD remains challenging, with the classification quality similar to the set of random predictions. We analyzed the class prediction accuracy, the metagenomics features by the teams, and computational methods used. These results will be openly shared with the scientific community to help advance IBD research and illustrate the application of a range of computational methodologies for effective metagenomic classification.

The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 “ML4Microbiome” that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.

The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 “ML4Microbiome” that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.

The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 “ML4Microbiome” that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.

The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 “ML4Microbiome” that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.

The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 “ML4Microbiome” that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.

Whole genome sequencing (WGS) enables high resolution typing of bacteria up to the single nucleotide polymorphism (SNP) level. WGS is used in clinical microbiology laboratories for infection control, molecular surveillance and outbreak analyses. Given the large palette of WGS reagents and bioinformatics tools, the Swiss clinical bacteriology community decided to conduct a ring trial (RT) to foster harmonization of NGS-based bacterial typing. The RT aimed at assessing methicillin-susceptible Staphylococcus aureus strain relatedness from WGS and epidemiological data. The RT was designed to disentangle the variability arising from differences in sample preparation, SNP calling and phylogenetic methods. Nine laboratories participated. The resulting phylogenetic tree and cluster identification were highly reproducible across the laboratories. Cluster interpretation was, however, more laboratory dependent, suggesting that an increased sharing of expertise across laboratories would contribute to further harmonization of practices. More detailed bioinformatic analyses unveiled that while similar clusters were found across laboratories, these were actually based on different sets of SNPs, differentially retained after sample preparation and SNP calling procedures. Despite this, the observed number of SNP differences between pairs of strains, an important criterion to determine strain relatedness given epidemiological information, was similar across pipelines for closely related strains when restricting SNP calls to a common core genome defined by S. aureus cgMLST schema. The lessons learned from this pilot study will serve the implementation of larger-scale RT, as a mean to have regular external quality assessments for laboratories performing WGS analyses in a clinical setting.

The draft genome sequences of the strains Acinetobacter baumannii 107m, Acinetobacter nosocomialis 28F, and Acinetobacter pittii 42F, isolated from Colombian hospitals, are reported here. These isolates are causative of nosocomial infections and are classified as multidrug resistant, as they showed resistance to four different antibiotic groups.

Understanding how the nervous system bridges sensation and behavior requires the elucidation of complex neural and molecular networks. Forward genetic approaches, such as screens conducted in C. elegans, have successfully identified genes required to process natural sensory stimuli. However, functional redundancy within the underlying neural circuits, which are often organized with multiple parallel neural pathways, limits our ability to identify ‘neural pathway-specific genes’, i.e. genes that are essential for the function of some, but not all of these redundant neural pathways. To overcome this limitation, we developed a ‘forward optogenetics’ screening strategy in which natural stimuli are initially replaced by the selective optogenetic activation of a specific neural pathway. We used this strategy to address the function of the polymodal FLP nociceptors mediating avoidance of noxious thermal and mechanical stimuli. According to our expectations, we identified both mutations in ‘general’ avoidance genes that broadly impact avoidance responses to a variety of natural noxious stimuli (unc-4, unc-83, and eat-4) and mutations that produce a narrower impact, more restricted to the FLP pathway (syd-2, unc-14 and unc-68). Through a detailed follow-up analysis, we further showed that the Ryanodine receptor UNC-68 acts cell-autonomously in FLP to adjust heat-evoked calcium signals and aversive behaviors. As a whole, our work (i) reveals the importance of properly regulated ER calcium release for FLP function, (ii) provides new entry points for new nociception research and (iii) demonstrates the utility of our forward optogenetic strategy, which can easily be transposed to analyze other neural pathways.

Clostridium chauvoei is the etiological agent of blackleg, a disease of cattle and sheep with high mortality rates, causing severe economic losses in livestock production. Here, we report the draft genome sequence of the virulent C. chauvoei strain JF4335 (2.8 Mbp and 28% G+C content) and the annotation of the genome.

Rapid evolution of weed biological control agents (BCAs) to new biotic and abiotic conditions is poorly understood and so far only little considered both in pre-release and post-release studies, despite potential major negative or positive implications for risks of nontargeted attacks or for colonizing yet unsuitable habitats, respectively. Provision of genetic resources, such as assembled and annotated genomes, is essential to assess potential adaptive processes by identifying underlying genetic mechanisms. Here, we provide the first sequenced genome of a phytophagous insect used as a BCA, that is, the leaf beetle Ophraella communa, a promising BCA of common ragweed, recently and accidentally introduced into Europe. A total 33.98 Gb of raw DNA sequences, representing ∼43-fold coverage, were obtained using the PacBio SMRT-Cell sequencing approach. Among the five different assemblers tested, the SMARTdenovo assembly displaying the best scores was then corrected with Illumina short reads. A final genome of 774 Mb containing 7,003 scaffolds was obtained. The reliability of the final assembly was then assessed by benchmarking universal single-copy orthologous genes (>96.0% of the 1,658 expected insect genes) and by remapping tests of Illumina short reads (average of 98.6 ± 0.7% without filtering). The number of protein-coding genes of 75,642, representing 82% of the published antennal transcriptome, and the phylogenetic analyses based on 825 orthologous genes placing O. communa in the monophyletic group of Chrysomelidae, confirm the relevance of our genome assembly. Overall, the genome provides a valuable resource for studying potential risks and benefits of this BCA facing environmental novelties.

Introduction: There is compositional overlap between the maternal intestinal microbiome, the breast milk microbiome and the infant oral and intestinal microbiome. Antibiotics cause profound changes in the microbiome. However, the effect of intrapartum and early-life antibiotics on the maternal intestinal and breast milk microbiome, and the infant oral and intestinal microbiome, and whether effects are only short term or persist long term remain uncertain. Methods and analyses: In this prospective cohort study, we will use metagenomic sequencing to determine: (1) the effect of intrapartum antibiotics on the composition of the breast milk, and the infant oral and intestinal microbiome, including the development and persistence of antibiotic resistance; (2) the effect of antibiotic exposure in the first year of life on the composition of the infant oral and intestinal microbiome, including the development and persistence of antibiotic resistance; (3) the effect of disruption of the infant oral and intestinal microbiome on health outcomes and (4) the compositional overlap between the maternal intestinal microbiome, the breast milk microbiome and the infant oral and intestinal microbiome. Ethics and dissemination: The ABERRANT study has been approved by the commission cantonale d’éthique de la recherche sur l’être humain (CER-VD) du Canton de Vaud (#2019–01567). Outcomes will be disseminated through publication and will be presented at scientific conferences. Trial registration number NCT04091282.

Mycoplasma feriruminatoris is a fast-growing and genetically tractable mycoplasma species. We sequenced the Swiss strain IVB14/OD_0535, isolated from an Alpine ibex. This strain has a circular genome of 1,027,435 bp with a G+C content of 24.3%. It encodes 835 open reading frames (ORFs), 2 rRNA operons, and 30 tRNAs

Toxin–antitoxin systems (TASs) are widely distributed in prokaryotes and encode pairs of genes involved in many bacterial biological processes and mechanisms, including pathogenesis. The TASs have not been extensively studied in Listeria monocytogenes (Lm), a pathogenic bacterium of the Firmicutes phylum causing infections in animals and humans. Using our recently published TASmania database, we focused on the known and new putative TASs in 352 Listeria monocytogenes genomes and identified the putative core gene TASs (cgTASs) with the Pasteur BIGSdb-Lm database and, by complementarity, the putative accessory gene TAS (acTASs). We combined the cgTASs with those of an additional 227 L. monocytogenes isolates from our previous studies containing metadata information. We discovered that the differences in 14 cgTAS alleles are sufficient to separate the four main lineages of Listeria monocytogenes. Analyzing these differences in more details, we uncovered potentially co-evolving residues in some pairs of proteins in cgTASs, probably essential for protein–protein interactions within the TAS complex.

Bacterial Toxin-Antitoxin systems (TAS) are involved in key biological functions including plasmid maintenance, defense against phages, persistence and virulence. They are found in nearly all phyla and classified into 6 different types based on the mode of inactivation of the toxin, with the type II TAS being the best characterized so far. We have herein developed a new in silico discovery pipeline named TASmania, which mines the >41K assemblies of the EnsemblBacteria database for known and uncharacterized protein components of type I to IV TAS loci. Our pipeline annotates the proteins based on a list of curated HMMs, which leads to >2.106 loci candidates, including orphan toxins and antitoxins, and organises the candidates in pseudo- operon structures in order to identify new TAS candidates based on a guilt-by- association strategy. In addition, we classify the two-component TAS with an unsupervised method on top of the pseudo-operon (pop) gene structures, leading to 1567 “popTA” models offering a more robust classification of the TAs families. These results give valuable clues in understanding the toxin/antitoxin modular structures and the TAS phylum specificities. Preliminary in vivo work confirmed six putative new hits in Mycobacterium tuberculosis as promising candidates. The TASmania database is available on the following server https://shiny.bioinformatics.unibe.ch/apps/tasmania/.

After a peripheral nerve lesion, distal ends of injured axons disintegrate into small fragments that are subsequently cleared by Schwann cells and later by macrophages. Axonal debris clearing is an early step of the repair process that facilitates regeneration. We show here that Schwann cells promote distal cut axon disintegration for timely clearing. By combining cell-based and in vivo models of nerve lesion with mouse genetics, we show that this mechanism is induced by distal cut axons, which signal to Schwann cells through PlGF mediating the activation and upregulation of VEGFR1 in Schwann cells. In turn, VEGFR1 activates Pak1, leading to the formation of constricting actomyosin spheres along unfragmented distal cut axons to mediate their disintegration. Interestingly, oligodendrocytes can acquire a similar behavior as Schwann cells by enforced expression of VEGFR1. These results thus identify controllable molecular cues of a neuron-glia crosstalk essential for timely clearing of damaged axons.

Mycoplasma bovis is an important pathogen of cattle causing bovine mycoplasmosis. Clinical manifestations are numerous, but pneumonia, mastitis, and arthritis cases are mainly reported. Currently, no efficient vaccine is available and antibiotic treatments are not always satisfactory. The design of new, efficient prophylactic and therapeutic approaches requires a better understanding of the molecular mechanisms responsible for M. bovis pathogenicity. Random transposon mutagenesis has been widely used in Mycoplasma species to identify potential gene functions. Such an approach can also be used to screen genomes and search for essential and non-essential genes for growth. Here, we generated a random transposon mutant library of M. bovis strain JF4278 containing approximately 4000 independent insertion sites. We then coupled high-throughput screening of this mutant library to transposon sequencing and bioinformatic analysis to identify M. bovis non-essential, adhesion- and virulence- related genes. Three hundred and fifty-two genes of M. bovis were assigned as essential for growth in rich medium. Among the remaining non-essential genes, putative virulence-related factors were subsequently identified. The complete mutant library was screened for adhesion using primary bovine mammary gland epithelial cells. Data from this assay resulted in a list of conditional-essential genes with putative adhesion-related functions by identifying non-essential genes for growth that are essential for host cell-adhesion. By individually assessing the adhesion capacity of six selected mutants, two previously unknown factors and the adhesin TrmFO were associated with a reduced adhesion phenotype. Overall, our study (i) uncovers new, putative virulence-related genes; (ii) offers a list of putative adhesion-related factors; and (iii) provides valuable information for vaccine design and for exploring M. bovis biology, pathogenesis, and host-interaction.

Mycoplasmas are the smallest free-living organisms and cause a number of economically important diseases affecting humans, animals, insects, and plants. Here, we demonstrate that highly virulent Mycoplasma mycoides subspecies capri (Mmc) can be fully attenuated via targeted deletion of non-essential genes encoding, among others, potential virulence traits. Five genomic regions, representing approximately 10% of the original Mmc genome, were successively deleted using Saccharomyces cerevisiae as an engineering platform. Specifically, a total of 68 genes out of the 432 genes verified to be individually non-essential in the JCVI-Syn3.0 minimal cell, were excised from the genome. In vitro characterization showed that this mutant was similar to its parental strain in terms of its doubling time, even though 10% of the genome content were removed. A novel in vivo challenge model in goats revealed that the wild-type parental strain caused marked necrotizing inflammation at the site of inoculation, septicemia and all animals reached endpoint criteria within 6 days after experimental infection. This is in contrast to the mutant strain, which caused no clinical signs nor pathomorphological lesions. These results highlight, for the first time, the rational design, construction and complete attenuation of a Mycoplasma strain via synthetic genomics tools. Trait addition using the yeast-based genome engineering platform and subsequent in vitro or in vivo trials employing the Mycoplasma chassis will allow us to dissect the role of individual candidate Mycoplasma virulence factors and lead the way for the development of an attenuated designer vaccine.

Listeria monocytogenes is a foodborne pathogen that causes abortion, septicemia, gastroenteritis and central nervous system (CNS) infections in ruminants and humans. L. monocytogenes strains mainly belong to two distinct phylogenetic groups, named lineages I and II. In general, clinical cases in humans and animals, in particular CNS infections, are caused by lineage I strains, while most of the environmental and food strains belong to lineage II. Little is known about why lineage I is more virulent than lineage II, even though various molecular factors and mechanisms associated with pathogenesis are known. In this study, we have used a variety of whole genome sequence analyses and comparative genomic tools in order to find characteristics that distinguish lineage I from lineage II strains and CNS infection strains from non-CNS strains. We analyzed 225 strains and identified single nucleotide variants between lineages I and II, as well as differences in the gene content. Using a novel approach based on Reads Per Kilobase per Million Mapped (RPKM), we identified 167 genes predominantly absent in lineage II but present in lineage I. These genes are mostly encoding for membrane-associated proteins. Additionally, we found 77 genes that are largely absent in the non-CNS associated strains, while 39 genes are especially lacking in our defined “non-clinical” group. Based on the RPKM analysis and the metadata linked to the L. monocytogenes strains, we identified 6 genes potentially associated with CNS cases, which include a transcriptional regulator, an ABC transporter and a non-coding RNA. Although there is not a clear separation between pathogenic and non-pathogenic strains based on phylogenetic lineages, the presence of the genes identified in our study reveals potential pathogenesis traits in ruminant L. monocytogenes strains. Ultimately, the differences that we have found in our study will help steer future studies in understanding the virulence mechanisms of the most p

The parasite Plasmodium falciparum causes the most severe form of malaria. Cell communication between parasites is an important mechanism to control population density and differentiation. The infected red blood cells (iRBCs) release small extracellular vesicles (EVs) that transfer cargoes between cells. The EVs synchronize the differentiation of the asexual parasites into gametocytes to initiate the transmission to the mosquito. Beside their role in parasite communication, EVs regulate vascular function. So far, the exact cargoes responsible for cellular communication remain unknown. We isolated EVs from cultured iRBCs to determine their small RNA content. We identified several types of human and plasmodial regulatory RNAs. While the miRNAs and tRNA-derived fragments were the most abundant human RNAs, we also found Y-RNAs, vault RNAs, snoRNAs and piRNAs. Interestingly, we found about 120 plasmodial RNAs, including mRNAs coding for exported proteins and proteins involved in drug resistance, as well as non-coding RNAs, such as rRNAs, small nuclear (snRNAs) and tRNAs. These data show, that iRBC-EVs carry small regulatory RNAs. A role in cellular communication is possible since the RNAs were transferred to endothelial cells. Furthermore, the presence of Plasmodium RNAs, in EVs suggests that they may be used as biomarker to track and detect disease.

PROSITE [Bairoch and Bucher (1994) Nucleic Acids Res., 22, 3583-3589; Hofmann et al. (1999) Nucleic Acids Res., 27, 215-219] is a method of identifying the functions of uncharacterized proteins translated from genomic or cDNA sequences. The PROSITE database (http://www.expasy.org/prosite/) consists of biologically significant patterns and profiles designed in such a way that with appropriate computational tools it can rapidly and reliably help to determine to which known family of proteins (if any) a new sequence belongs, or which known domain(s) it contains

EMBnet is a consortium of collaborating bioinformatics groups located mainly within Europe (http://www.embnet.org). Each member country is represented by a ‘node', a group responsible for the maintenance of local services for their users (e.g. education, training, software, database distribution, technical support, helpdesk). Among these services a web portal with links and access to locally developed and maintained software is essential and different for each node. Our web portal targets biomedical scientists in Switzerland and elsewhere, offering them access to a collection of important sequence analysis tools mirrored from other sites or developed locally. We describe here the Swiss EMBnet node web site (http://www.ch.embnet.org), which presents a number of original services not available anywhere else

The PROSITE database (http://www.expasy.ch/sprot/prosite.html) consists of biologically significant patterns and profiles formulated in such a way that with appropriate computational tools it can help to determine to which known family of protein (if any) a new sequence belongs, or which known domain(s) it contains

Summary: Recent technological progress has greatly facilitated de novo genome sequencing. However, de novo assemblies consist in many pieces of contiguous sequence (contigs) arranged in thousands of scaffolds instead of small numbers of chromosomes. Confirming and improving the quality of such assemblies is critical for subsequent analysis. We present a method to evaluate genome scaffolding by aligning independently obtained transcriptome sequences to the genome and visually summarizing the alignments using the Cytoscape software. Applying this method to the genome of the red fire ant Solenopsis invicta allowed us to identify inconsistencies in 7%, confirm contig order in 20% and extend 16% of scaffolds. Contact: oksana.ribagrognuz@unil.ch; yannick.wurm@unil.ch Availability: Scripts that generate tables for visualization in Cytoscape from FASTA sequence and scaffolding information files are publicly available at https://github.com/ksanao/TGNet. Supplementary Information: Supplementary data are available at Bioinformatics online

Peroxidases (EC 1.11.1.x), which are encoded by small or large multigenic families, are involved in several important physiological and developmental processes. They use various peroxides as electron acceptors to catalyse a number of oxidative reactions and are present in almost all living organisms. We have created a peroxidase database (http://peroxibase.isb-sib.ch) that contains all identified peroxidase-encoding sequences (about 6000 sequences in 940 organisms). They are distributed between 11 superfamilies and about 60 subfamilies. All the sequences have been individually annotated and checked. PeroxiBase can be consulted using six major interlink sections ‘Classes', ‘Organisms', ‘Cellular localisations', ‘Inducers', ‘Repressors' and ‘Tissue types'. General documentation on peroxidases and PeroxiBase is accessible in the ‘Documents' section containing ‘Introduction', ‘Class description', ‘Publications' and ‘Links'. In addition to the database, we have developed a tool to classify peroxidases based on the PROSITE profile methodology. To improve their specificity and to prevent overlaps between closely related subfamilies the profiles were built using a new strategy based on the silencing of residues. This new profile construction method and its discriminatory capacity have been tested and validated using the different peroxidase families and subfamilies present in the database. The peroxidase classification tool called PeroxiScan is accessible at the following address: http://peroxibase.isb-sib.ch/peroxiscan.php

For many crops, wild relatives constitute an extraordinary resource for cultivar improvement [1, 2] and also help to better understand the history of their domestication [3]. However, the wild ancestor species of several perennial crops have not yet been identified. Perennial crops generally present a weak domestication syndrome allowing cultivated individuals to establish feral populations difficult to distinguish from truly wild populations, and there is frequently ongoing gene flow between wild relatives and the crop that might erode most genetic differences [4]. Here we report the discovery of populations of the wild ancestor species of the date palm (Phoenix dactylifera L.), one of the oldest and most important cultivated fruit plants in hot and arid regions of the Old World. We discovered these wild individuals in remote and isolated mountainous locations of Oman. They are genetically more diverse than and distinct from a representative sample of Middle Eastern cultivated date palms and exhibit rounded seed shapes resembling those of a close sister species and archeological samples, but not modern cultivars. Whole-genome sequencing of several wild and cultivated individuals revealed a complex domestication history involving the contribution of at least two wild sources to African cultivated date palms. The discovery of wild date palms offers a unique chance to further elucidate the history of this iconic crop that has constituted the cornerstone of traditional oasis polyculture systems for several thousand years [5].

Full genome sequences of 20 strains of Clostridium chauvoei, the etiological agent of blackleg of cattle and sheep, isolated from four different continents over a period of 64 years (1951–2015) were determined and analyzed. The study reveals that the genome of the species C. chauvoei is highly homogeneous compared to the closely related species C. perfringens, a widespread pathogen that affects human and many animal species. Analysis of the CRISPR locus is sufficient to differentiate most C. chauvoei strains and is the most heterogenous region in the genome, containing in total 187 different spacer elements that are distributed as 30 – 77 copies in the various strains. Some genetic differences are found in the 3 allelic variants of fliC1, fliC2 and fliC3 genes that encode structural flagellin proteins, and certain strains do only contain one or two alleles. However, the major virulence genes including the highly toxic C. chauvoei toxin A, the sialidase and the two hyaluronidases are fully conserved as are the metabolic and structural genes of C. chauvoei. These data indicate that C. chauvoei is a strict ruminant-associated pathogen that has reached a dead end in its evolution.

Contagious caprine pleuropneumonia (CCPP) is a highly contagious disease caused by Mycoplasma capricolum subsp. capripneumoniae that affects goats in Africa and Asia. Current available methods for the diagnosis of Mycoplasma infection, including cultivation, serological assays, and PCR, are time-consuming and require fully equipped stationary laboratories, which make them incompatible with testing in the resource-poor settings that are most relevant to this disease. We report a rapid, specific, and sensitive assay employing isothermal DNA amplification using recombinase polymerase amplification (RPA) for the detection of M. capricolum subsp. capripneumoniae. We developed the assay using a specific target sequence in M. capricolum subsp. capripneumoniae, as found in the genome sequence of the field strain ILRI181 and the type strain F38 and that was further evidenced in 10 field strains from different geographical regions. Detection limits corresponding to 5 × 103 and 5 × 104 cells/ml were obtained using genomic DNA and bacterial culture from M. capricolum subsp. capripneumoniae strain ILRI181, while no amplification was obtained from 71 related Mycoplasma isolates or from the Acholeplasma or the Pasteurella isolates, demonstrating a high degree of specificity. The assay produces a fluorescent signal within 15 to 20 min and worked well using pleural fluid obtained directly from CCPP-positive animals without prior DNA extraction. We demonstrate that the diagnosis of CCPP can be achieved, with a short sample preparation time and a simple read-out device that can be powered by a car battery, in < 45 min in a simulated field setting.

We have explored the importance of the phyllosphere microbiome in plant resistance in the cuticle mutants bdg (BODYGUARD) or lacs2.3 (LONG CHAIN FATTY ACID SYNTHASE 2) that are strongly resistant to the fungal pathogen Botrytis cinerea. The study includes infection of plants under sterile conditions, 16S ribosomal DNA sequencing of the phyllosphere microbiome, and isolation and high coverage sequencing of bacteria from the phyllosphere. When inoculated under sterile conditions bdg became as susceptible as wild-type (WT) plants whereas lacs2.3 mutants retained the resistance. Adding washes of its phyllosphere microbiome could restore the resistance of bdg mutants, whereas the resistance of lacs2.3 results from endogenous mechanisms. The phyllosphere microbiome showed distinct populations in WT plants compared to cuticle mutants. One species identified as Pseudomonas sp isolated from the microbiome of bdg provided resistance to B. cinerea on Arabidopsis thaliana as well as on apple fruits. No direct activity was observed against B. cinerea and the action of the bacterium required the plant. Thus, microbes present on the plant surface contribute to the resistance to B. cinerea. These results open new perspectives on the function of the leaf microbiome in the protection of plants.

Many tools exist to predict structural variants (SVs), utilizing a variety of algorithms. However, they have largely been developed and tested on human germline or somatic (e.g. cancer) variation. It seems appropriate to exploit this wealth of technology available for humans also for other species. Objectives of this work included: a) Creating an automated, standardized pipeline for SV prediction. b) Identifying the best tool(s) for SV prediction through benchmarking. c) Providing a statistically sound method for merging SV calls.

Human identification has played a prominent role in forensic science for the past two decades. Identification based on unique genetic traits is driving the field. However, this may have limitations, for instance, for twins. Moreover, high-throughput sequencing techniques are now available and may provide a high amount of data likely useful in forensic science.This study investigates the potential for bacteria found in the salivary microbiome to be used to differentiate individuals. Two different targets (16S rRNA and rpoB) were chosen to maximise coverage of the salivary microbiome and when combined, they increase the power of differentiation (identification). Paired-end Illumina high-throughput sequencing was used to analyse the bacterial composition of saliva from two different people at four different time points (t = 0 and t = 28 days and then one year later at t = 0 and t = 28 days). Five major phyla dominate the samples: Firmicutes, Proteobacteria, Actinobacteria, Bacteroidetes and Fusobacteria. Streptococcus, a Firmicutes, is one of the most abundant aerobic genera found in saliva and targeting Streptococcus rpoB has enabled a deeper characterisation of the different streptococci species, which cannot be differentiated using 16S rRNA alone. We have observed that samples from the same person group together regardless of time of sampling. The results indicate that it is possible to distinguish two people using the bacterial microbiota present in their saliva.

Ribosomes are the highly complex macromolecular assemblies dedicated to the synthesis of all cellular proteins from mRNA templates. The main principles underlying the making of ribosomes are conserved across eukaryotic organisms and this process has been studied in most detail in the yeast Saccharomyces cerevisiae. Yeast ribosomes are composed of four ribosomal RNAs (rRNAs) and 79 ribosomal proteins (r-proteins). Most r-proteins need to be transported from the cytoplasm to the nucleus where they get incorporated into the evolving pre-ribosomal particles. Due to the high abundance and difficult physicochemical properties of r-proteins, their correct folding and fail-safe targeting to the assembly site depends largely on general, as well as highly specialized, chaperone and transport systems. Many r-proteins contain universally conserved or eukaryote-specific internal loops and/or terminal extensions, which were shown to mediate their nuclear targeting and association with dedicated chaperones in a growing number of cases. The 60S r-protein Rpl4 is particularly interesting since it harbours a conserved long internal loop and a prominent Cterminal eukaryote-specific extension. Here we show that both the long internal loop and the C-terminal eukaryote-specific extension are strictly required for the functionality of Rpl4. While Rpl4 contains at least five distinct nuclear localization signals (NLS), the C-terminal part of the long internal loop associates with a specific binding partner, termed Acl4. Absence of Acl4 confers a severe slow-growth phenotype and a deficiency in the production of 60S subunits. Genetic and biochemical evidence indicates that Acl4 can be considered as a dedicated chaperone of Rpl4. Notably, Acl4 localizes to both the cytoplasm and nucleus and it has the capacity to capture nascent Rpl4 in a co-translational manner. Taken together, our findings indicate that the dedicated chaperone Acl4 accompanies Rpl4 from the cytoplasm to its pre-60S assembl

Ribosomes are the highly complex macromolecular assemblies dedicated to the synthesis of all cellular proteins from mRNA templates. The main principles underlying the making of ribosomes are conserved across eukaryotic organisms and this process has been studied in most detail in the yeast Saccharomyces cerevisiae. Yeast ribosomes are composed of four ribosomal RNAs (rRNAs) and 79 ribosomal proteins (r-proteins). Most r-proteins need to be transported from the cytoplasm to the nucleus where they get incorporated into the evolving pre-ribosomal particles. Due to the high abundance and difficult physicochemical properties of r-proteins, their correct folding and fail-safe targeting to the assembly site depends largely on general, as well as highly specialized, chaperone and transport systems. Many r-proteins contain universally conserved or eukaryote-specific internal loops and/or terminal extensions, which were shown to mediate their nuclear targeting and association with dedicated chaperones in a growing number of cases. The 60S r-protein Rpl4 is particularly interesting since it harbours a conserved long internal loop and a prominent Cterminal eukaryote-specific extension. Here we show that both the long internal loop and the C-terminal eukaryote-specific extension are strictly required for the functionality of Rpl4. While Rpl4 contains at least five distinct nuclear localization signals (NLS), the C-terminal part of the long internal loop associates with a specific binding partner, termed Acl4. Absence of Acl4 confers a severe slow-growth phenotype and a deficiency in the production of 60S subunits. Genetic and biochemical evidence indicates that Acl4 can be considered as a dedicated chaperone of Rpl4. Notably, Acl4 localizes to both the cytoplasm and nucleus and it has the capacity to capture nascent Rpl4 in a co-translational manner. Taken together, our findings indicate that the dedicated chaperone Acl4 accompanies Rpl4 from the cytoplasm to its pre-60S assembl

Ribosomes are the highly complex macromolecular assemblies dedicated to the synthesis of all cellular proteins from mRNA templates. The main principles underlying the making of ribosomes are conserved across eukaryotic organisms and this process has been studied in most detail in the yeast Saccharomyces cerevisiae. Yeast ribosomes are composed of four ribosomal RNAs (rRNAs) and 79 ribosomal proteins (r-proteins). Most r-proteins need to be transported from the cytoplasm to the nucleus where they get incorporated into the evolving pre-ribosomal particles. Due to the high abundance and difficult physicochemical properties of r-proteins, their correct folding and fail-safe targeting to the assembly site depends largely on general, as well as highly specialized, chaperone and transport systems. Many r-proteins contain universally conserved or eukaryote-specific internal loops and/or terminal extensions, which were shown to mediate their nuclear targeting and association with dedicated chaperones in a growing number of cases. The 60S r-protein Rpl4 is particularly interesting since it harbours a conserved long internal loop and a prominent Cterminal eukaryote-specific extension. Here we show that both the long internal loop and the C-terminal eukaryote-specific extension are strictly required for the functionality of Rpl4. While Rpl4 contains at least five distinct nuclear localization signals (NLS), the C-terminal part of the long internal loop associates with a specific binding partner, termed Acl4. Absence of Acl4 confers a severe slow-growth phenotype and a deficiency in the production of 60S subunits. Genetic and biochemical evidence indicates that Acl4 can be considered as a dedicated chaperone of Rpl4. Notably, Acl4 localizes to both the cytoplasm and nucleus and it has the capacity to capture nascent Rpl4 in a co-translational manner. Taken together, our findings indicate that the dedicated chaperone Acl4 accompanies Rpl4 from the cytoplasm to its pre-60S assembl

Background: Many tools exist to predict structural variants (SVs), utilizing a variety of algorithms. However, they have largely been developed and tested on human germline or somatic (e.g. cancer) variation. It seems appropriate to exploit this wealth of technology available for humans also for other species. Objectives of this work included: a) Creating an automated, standardized pipeline for SV prediction. Results: The SV-AUTOPILOT meta-tool platform is an automated pipeline for standardization of SV prediction and SV tool development in paired-end next-generation sequencing (NGS) analysis. SV-AUTOPILOT comes in the form of a virtual machine, which includes all datasets, tools and algorithms presented here. The virtual machine easily allows one to add, replace and update genomes, SV callers and post-processing routines and therefore provides an easy, out-of-the-box environment for complex SV discovery tasks. SV-AUTOPILOT was used to make a direct comparison between 7 popular SV tools on the Arabidopsis thaliana genome using the Landsberg (Ler) ecotype as a standardized dataset. Recall and precision measurements suggest that Pindel and Clever were the most adaptable to this dataset across all size ranges while Delly performed well for SVs larger than 250 nucleotides. A novel, statistically-sound merging process, which can control the false discovery rate, reduced the false positive rate on the Arabidopsis benchmark dataset used here by >60%. Conclusion: SV-AUTOPILOT provides a meta-tool platform for future SV tool development and the benchmarking of tools on other genomes using a standardized pipeline. It optimizes detection of SVs in non-human genomes using statistically robust merging. The benchmarking in this study has demonstrated the power of 7 different SV tools for analyzing different size classes and types of structural variants. The optional merge feature enriches the call set and reduces false positives providing added benefit to researchers planning to

Ribosomes are the highly complex macromolecular assemblies dedicated to the synthesis of all cellular proteins from mRNA templates. The main principles underlying the making of ribosomes are conserved across eukaryotic organisms and this process has been studied in most detail in the yeast Saccharomyces cerevisiae. Yeast ribosomes are composed of four ribosomal RNAs (rRNAs) and 79 ribosomal proteins (r-proteins). Most r-proteins need to be transported from the cytoplasm to the nucleus where they get incorporated into the evolving pre-ribosomal particles. Due to the high abundance and difficult physicochemical properties of r-proteins, their correct folding and fail-safe targeting to the assembly site depends largely on general, as well as highly specialized, chaperone and transport systems. Many r-proteins contain universally conserved or eukaryote-specific internal loops and/or terminal extensions, which were shown to mediate their nuclear targeting and association with dedicated chaperones in a growing number of cases. The 60S r-protein Rpl4 is particularly interesting since it harbours a conserved long internal loop and a prominent C-terminal eukaryote-specific extension. Here we show that both the long internal loop and the C-terminal eukaryote-specific extension are strictly required for the functionality of Rpl4. While Rpl4 contains at least five distinct nuclear localization signals (NLS), the C-terminal part of the long internal loop associates with a specific binding partner, termed Acl4. Absence of Acl4 confers a severe slow-growth phenotype and a deficiency in the production of 60S subunits. Genetic and biochemical evidence indicates that Acl4 can be considered as a dedicated chaperone of Rpl4. Notably, Acl4 localizes to both the cytoplasm and nucleus and it has the capacity to capture nascent Rpl4 in a co-translational manner. Taken together, our findings indicate that the dedicated chaperone Acl4 accompanies Rpl4 from the cytoplasm to its pre-60S assemb

Pseudomonas knackmussii B13 was the first strain to be isolated in 1974 that could degrade chlorinated aromatic hydrocarbons. This discovery was the prologue for subsequent characterization of numerous bacterial metabolic pathways, for genetic and biochemical studies, and which spurred ideas for pollutant bioremediation. In this study, we determined the complete genome sequence of B13 using next generation sequencing technologies and optical mapping. Genome annotation indicated that B13 has a variety of metabolic pathways for degrading monoaromatic hydrocarbons including chlorobenzoate, aminophenol, anthranilate and hydroxyquinol, but not polyaromatic compounds. Comparative genome analysis revealed that B13 is closest to Pseudomonas denitrificans and Pseudomonas aeruginosa. The B13 genome contains at least eight genomic islands [prophages and integrative conjugative elements (ICEs)], which were absent in closely related pseudomonads. We confirm that two ICEs are identical copies of the 103 kb self-transmissible element ICEclc that carries the genes for chlorocatechol metabolism. Comparison of ICEclc showed that it is composed of a variable and a ‘core’ region, which is very conserved among proteobacterial genomes, suggesting a widely distributed family of so far uncharacterized ICE. Resequencing of two spontaneous B13 mutants revealed a number of single nucleotide substitutions, as well as excision of a large 220 kb region and a prophage that drastically change the host metabolic capacity and survivability.

The genomic sequence of Clostridium chauvoei, the etiological agent of blackleg, a severe disease of ruminants with high mortality specified by a myonecrosis reveals a chromosome of 2.8 million base-pairs and a cryptic plasmid of 5.5 kilo base-pairs. The chromosome contains the main pathways like glycolysis/gluconeogenesis, sugar metabolism, purine and pyrimidine metabolisms, but the notable absence of genes of the citric acid cycle and deficient or partially deficient amino acid metabolism for Histidine, Tyrosine, Phenylalanine, and Tryptophan. These essential amino acids might be acquired from host tissue damage caused by various toxins and by protein metabolism that includes 57 genes for peptidases, and several ABC transporters for amino acids import.

Arbuscular mycorrhiza (AM) is a mutual symbiosis that involves a complex symbiotic interface over which nutrients are exchanged between the plant host and the AM fungus. Dozens of genes in the host are required for the establishment and functioning of the interaction, among them nutrient transporters that mediate the uptake of mineral nutrients delivered by the fungal arbuscules. We have isolated in a genetic mutant screen a petunia GRAS-type transcription factor, ATYPICAL ARBUSCULE (ATA), that acts as the central regulator of AM-related genes and is required for the morphogenesis of arbuscules. Forced mycorrhizal inoculations from neighbouring wild type plants revealed an additional role of ATA in restricting mycorrhizal colonization of the root meristem. The lack of ATA, which represents the orthologue of RAM1 in Medicago truncatula, renders the interaction completely ineffective, hence demonstrating the central role of AM-related genes for arbuscule development and function.

Genes involved in arbuscular mycorrhizal (AM) symbiosis have been identified primarily by mutant screens, followed by identification of the mutated genes (forward genetics). In addition, a number of AM-related genes has been identified by their AM-related expression patterns, and their function has subsequently been elucidated by knock-down or knock-out approaches (reverse genetics). However, genes that are members of functionally redundant gene families, or genes that have a vital function and therefore result in lethal mutant phenotypes, are difficult to identify. If such genes are constitutively expressed and therefore escape differential expression analyses, they remain elusive. The goal of this study was to systematically search for AM-related genes with a bioinformatics strategy that is insensitive to these problems. The central element of our approach is based on the fact that many AM-related genes are conserved only among AM-competent species.Results: Our approach involves genome-wide comparisons at the proteome level of AM-competent host species with non-mycorrhizal species. Using a clustering method we first established orthologous/paralogous relationships and subsequently identified protein clusters that contain members only of the AM-competent species. Proteins of these clusters were then analyzed in an extended set of 16 plant species and ranked based on their relatedness among AM-competent monocot and dicot species, relative to non-mycorrhizal species. In addition, we combined the information on the protein-coding sequence with gene expression data and with promoter analysis. As a result we present a list of yet uncharacterized proteins that show a strongly AM-related pattern of sequence conservation, indicating that the respective genes may have been under selection for a function in AM. Among the top candidates are three genes that encode a small family of similar receptor-like kinases that are related to the S-locus receptor kinases involved in spor

With the widespread availability of high-throughput sequencing technologies, sequencing projects have become pervasive in the molecular life sciences. The huge bulk of data generated daily must be analyzed further by biologists with skills in bioinformatics and by “embedded bioinformaticians,” i.e., bioinformaticians integrated in wet lab research groups. Thus, students interested in molecular life sciences must be trained in the main steps of genomics: sequencing, assembly, annotation and analysis. To reach that goal, a practical course has been set up for master students at the University of Lausanne: the “Sequence a genome” class. At the beginning of the academic year, a few bacterial species whose genome is unknown are provided to the students, who sequence and assemble the genome(s) and perform manual annotation. Here, we report the progress of the first class from September 2010 to June 2011 and the results obtained by seven master students who specifically assembled and annotated the genome of Estrella lausannensis, an obligate intracellular bacterium related to Chlamydia. The draft genome of Estrella is composed of 29 scaffolds encompassing 2,819,825 bp that encode for 2233 putative proteins. Estrella also possesses a 9136 bp plasmid that encodes for 14 genes, among which we found an integrase and a toxin/antitoxin module. Like all other members of the Chlamydiales order, Estrella possesses a highly conserved type III secretion system, considered as a key virulence factor. The annotation of the Estrella genome also allowed the characterization of the metabolic abilities of this strictly intracellular bacterium. Altogether, the students provided the scientific community with the Estrella genome sequence and a preliminary understanding of the biology of this recently-discovered bacterial genus, while learning to use cutting-edge technologies for sequencing and to perform bioinformatics analyses.

Título en español: Análisis genómico del resistoma de la cepa de Acinetobacter baumannii ABIBUN 107m multi-resistente y persistente en hospitales colombianosTítulo en ingles: Genomic analysis of the resistoma of the strain of Acinetobacter baumannii ABIBUN 107m multi-resistant and persistent in Colombian hospitalsTítulo corto: Análisis genómico del resistoma de la cepa de Acinetobacter baumannii Resumen: Acinetobacter baumannii es una bacteria, causante de infecciones asociadas a la atención en salud como neumonía, septicemia, meningitis e infecciones urinarias entre otras. Se caracteriza por su capacidad para desarrollar y acumular rápidamente una gran variedad de mecanismos de resistencia a antibióticos. En esta investigación se realizó el análisis genómico de una cepa de A. baumannii ABIBUN 107m que forma parte de un clon persistente en hospitales colombianos, resistente a los antibióticos carbapenémicos (imipenem y meropenem), antibióticos de elección en el tratamiento infecciones causadas por este microorganismo. El genoma de esta bacteria fue secuenciado utilizando técnicas de alto rendimiento, ensamblado y anotado, obteniéndose un genoma constituido por 3954000 pb con 56 contigs; consta de 4256 genes con un tamaño promedio de 912 pb; 3796 CDS de los cuales por anotación 2884 se asignaron a COG; 57 tRNA y un porcentaje de GC de 38,74%. A. baumannii ABIBUN 107m es resistente a b-lactámicos, aminoglicósidos, quinolonas, tetraciclina, sulfonamida y colistina. En su genoma se localizaron genes asociados con el perfil de resistencia ya que presenta serin b-lactamasas (blaADC-38, blaOXA-64, blaOXA-23, blaampC-like, blaamp(H)-like), metalo b-lactamasa_B; proteínas de unión a penicilina de elevada masa molecular, secuencias de inserción tipo ISAba1; mutaciones en los genes de DNA girasa y topoisomerasa IV subunidad A (gyrA y parC); enzimas modificadoras de aminoglicósidos (aphA-like, aadA-like); cloranfenicol aciltransferasa (cat) y dehidropteroato