Increased Brucella abortus asRNA_0067 expression under intraphagocytic stressors is associated with enhanced virB2 transcription.

Intracellular pathogens like Brucella face challenges during the intraphagocytic adaptation phase, where the modulation of gene expression plays an essential role in taking advantage of stressors to persist inside the host cell. This study aims to explore the expression of antisense virB2 RNA strand and related genes under intracellular simulation media. Sense and antisense virB2 RNA strands increased expression when nutrient deprivation and acidification were higher, being starvation more determinative. Meanwhile, bspB, one of the T4SS effector genes, exhibited the highest expression during the exposition to pH 4.5 and nutrient abundance. Based on RNA-seq analysis and RACE data, we constructed a regional map depicting the 5' and 3' ends of virB2 and the cis-encoded asRNA_0067. Without affecting the CDS or a possible autonomous RBS, we generate the deletion mutant ΔasRNA_0067, significantly reducing virB2 mRNA expression and survival rate. These results suggest that the antisense asRNA_0067 expression is promoted under exposure to the intraphagocytic adaptation phase stressors, and its deletion is associated with a lower transcription of the virB2 gene. Our findings illuminate the significance of these RNA strands in modulating the survival strategy of Brucella within the host and emphasize the role of nutrient deprivation in gene expression.

N-3 PUFA deficiency disrupts oligodendrocyte maturation and myelin integrity during brain development.

Westernization of dietary habits has led to a progressive reduction in dietary intake of n-3 polyunsaturated fatty acids (n-3 PUFAs). Low maternal intake of n-3 PUFAs has been linked to neurodevelopmental disorders, conditions in which myelination processes are abnormal, leading to defects in brain functional connectivity. Only little is known about the role of n-3 PUFAs in oligodendrocyte physiology and white matter development. Here, we show that lifelong n-3 PUFA deficiency disrupts oligodendrocytes maturation and myelination processes during the postnatal period in mice. This has long-term deleterious consequences on white matter organization and hippocampus-prefrontal functional connectivity in adults, associated with cognitive and emotional disorders. Promoting developmental myelination with clemastine, a first-generation histamine antagonist and enhancer of oligodendrocyte precursor cell differentiation, rescues memory deficits in n-3 PUFA deficient animals. Our findings identify a novel mechanism through which n-3 PUFA deficiency alters brain functions by disrupting oligodendrocyte maturation and brain myelination during the neurodevelopmental period.

Development of a fixed module repertoire for the analysis and interpretation of blood transcriptome data.

As the capacity for generating large-scale molecular profiling data continues to grow, the ability to extract meaningful biological knowledge from it remains a limitation. Here, we describe the development of a new fixed repertoire of transcriptional modules, BloodGen3, that is designed to serve as a stable reusable framework for the analysis and interpretation of blood transcriptome data. The construction of this repertoire is based on co-clustering patterns observed across sixteen immunological and physiological states encompassing 985 blood transcriptome profiles. Interpretation is supported by customized resources, including module-level analysis workflows, fingerprint grid plot visualizations, interactive web applications and an extensive annotation framework comprising functional profiling reports and reference transcriptional profiles. Taken together, this well-characterized and well-supported transcriptional module repertoire can be employed for the interpretation and benchmarking of blood transcriptome profiles within and across patient cohorts. Blood transcriptome fingerprints for the 16 reference cohorts can be accessed interactively via:  https://drinchai.shinyapps.io/BloodGen3Module/ .

Consensus clustering applied to multi-omics disease subtyping.

BACKGROUND: Facing the diversity of omics data and the difficulty of selecting one result over all those produced by several methods, consensus strategies have the potential to reconcile multiple inputs and to produce robust results. RESULTS: Here, we introduce ClustOmics, a generic consensus clustering tool that we use in the context of cancer subtyping. ClustOmics relies on a non-relational graph database, which allows for the simultaneous integration of both multiple omics data and results from various clustering methods. This new tool conciliates input clusterings, regardless of their origin, their number, their size or their shape. ClustOmics implements an intuitive and flexible strategy, based upon the idea of evidence accumulation clustering. ClustOmics computes co-occurrences of pairs of samples in input clusters and uses this score as a similarity measure to reorganize data into consensus clusters. CONCLUSION: We applied ClustOmics to multi-omics disease subtyping on real TCGA cancer data from ten different cancer types. We showed that ClustOmics is robust to heterogeneous qualities of input partitions, smoothing and reconciling preliminary predictions into high-quality consensus clusters, both from a computational and a biological point of view. The comparison to a state-of-the-art consensus-based integration tool, COCA, further corroborated this statement. However, the main interest of ClustOmics is not to compete with other tools, but rather to make profit from their various predictions when no gold-standard metric is available to assess their significance. AVAILABILITY: The ClustOmics source code, released under MIT license, and the results obtained on TCGA cancer data are available on GitHub: https://github.com/galadrielbriere/ClustOmics .

ART-Treated Patients Exhibit an Adaptive Immune Response against the HFVAC Peptides, a Potential HIV-1 Therapeutic Vaccine (Provir/Latitude45 Study).

We proposed a new HIV-1 therapeutic vaccine based on conserved cytotoxic T lymphocyte (CTL) epitopes of archived HIV-1 DNA according to their affinity to the dominant HLA-A and -B alleles of the population investigated. Our proposal (Hla Fitted VAC, HFVAC) was composed of 15 peptides originating from the RT, gag and nef parts of proviral DNA. Our aim was to investigate baseline immune reactivity to the vaccine in HIV-1 chronically infected patients at success of antiretroviral therapy (ART) who would be eligible for a therapeutic vaccine. Forty-one patients were tested. Most of them had been infected with HIV-1 subtype B and all had been receiving successful ART for 2 to 20 years. The predominant HLA-A and -B alleles were those of a Caucasian population. ELISPOT was carried out using the HFVAC peptides. In 22 patients, the PD-1 marker was investigated on CD4+ and CD8+ T cells by flow cytometry in order to evaluate global T cell exhaustion. ELISPOT positivity was 65% overall and 69% in patients exhibiting at least one HLA allele fitting with HFVAC. The percentages of CD4+ and CD8+ T cells expressing PD-1 were high (median values 23.70 and 32.60, respectively), but did not seem to be associated with an impairment of the immune response investigated in vitro. In conclusion, reactivity to HFVAC was high in this ART-treated population with dominant HLA alleles, despite potential cellular exhaustion associated with the PD-1 marker.

GSAn: an alternative to enrichment analysis for annotating gene sets.

The revolution in new sequencing technologies is greatly leading to new understandings of the relations between genotype and phenotype. To interpret and analyze data that are grouped according to a phenotype of interest, methods based on statistical enrichment became a standard in biology. However, these methods synthesize the biological information by a priori selecting the over-represented terms and may suffer from focusing on the most studied genes that represent a limited coverage of annotated genes within a gene set. Semantic similarity measures have shown great results within the pairwise gene comparison by making advantage of the underlying structure of the Gene Ontology. We developed GSAn, a novel gene set annotation method that uses semantic similarity measures to synthesize a priori Gene Ontology annotation terms. The originality of our approach is to identify the best compromise between the number of retained annotation terms that has to be drastically reduced and the number of related genes that has to be as large as possible. Moreover, GSAn offers interactive visualization facilities dedicated to the multi-scale analysis of gene set annotations. Compared to enrichment analysis tools, GSAn has shown excellent results in terms of maximizing the gene coverage while minimizing the number of terms.

Multiple Origins and Specific Evolution of CRISPR/Cas9 Systems in Minimal Bacteria (Mollicutes).

CRISPR/Cas systems provide adaptive defense mechanisms against invading nucleic acids in prokaryotes. Because of its interest as a genetic tool, the Type II CRISPR/Cas9 system from Streptococcus pyogenes has been extensively studied. It includes the Cas9 endonuclease that is dependent on a dual-guide RNA made of a tracrRNA and a crRNA. Target recognition relies on crRNA annealing and the presence of a protospacer adjacent motif (PAM). Mollicutes are currently the bacteria with the smallest genome in which CRISPR/Cas systems have been reported. Many of them are pathogenic to humans and animals (mycoplasmas and ureaplasmas) or plants (phytoplasmas and some spiroplasmas). A global survey was conducted to identify and compare CRISPR/Cas systems found in the genome of these minimal bacteria. Complete or degraded systems classified as Type II-A and less frequently as Type II-C were found in the genome of 21 out of 52 representative mollicutes species. Phylogenetic reconstructions predicted a common origin of all CRISPR/Cas systems of mycoplasmas and at least two origins were suggested for spiroplasmas systems. Cas9 in mollicutes were structurally related to the S. aureus Cas9 except the PI domain involved in the interaction with the PAM, suggesting various PAM might be recognized by Cas9 of different mollicutes. Structure of the predicted crRNA/tracrRNA hybrids was conserved and showed typical stem-loop structures pairing the Direct Repeat part of crRNAs with the 5' region of tracrRNAs. Most mollicutes crRNA/tracrRNAs showed G + C% significantly higher than the genome, suggesting a selective pressure for maintaining stability of these secondary structures. Examples of CRISPR spacers matching with mollicutes phages were found, including the textbook case of Mycoplasma cynos strain C142 having no prophage sequence but a CRISPR/Cas system with spacers targeting prophage sequences that were found in the genome of another M. cynos strain that is devoid of a CRISPR system. Despite their small genome size, mollicutes have maintained protective means against invading DNAs, including restriction/modification and CRISPR/Cas systems. The apparent lack of CRISPR/Cas systems in several groups of species including main pathogens of humans, ruminants, and plants suggests different evolutionary routes or a lower risk of phage infection in specific ecological niches.

A new method for evaluating the impacts of semantic similarity measures on the annotation of gene sets.

MOTIVATION: The recent revolution in new sequencing technologies, as a part of the continuous process of adopting new innovative protocols has strongly impacted the interpretation of relations between phenotype and genotype. Thus, understanding the resulting gene sets has become a bottleneck that needs to be addressed. Automatic methods have been proposed to facilitate the interpretation of gene sets. While statistical functional enrichment analyses are currently well known, they tend to focus on well-known genes and to ignore new information from less-studied genes. To address such issues, applying semantic similarity measures is logical if the knowledge source used to annotate the gene sets is hierarchically structured. In this work, we propose a new method for analyzing the impact of different semantic similarity measures on gene set annotations. RESULTS: We evaluated the impact of each measure by taking into consideration the two following features that correspond to relevant criteria for a "good" synthetic gene set annotation: (i) the number of annotation terms has to be drastically reduced and the representative terms must be retained while annotating the gene set, and (ii) the number of genes described by the selected terms should be as large as possible. Thus, we analyzed nine semantic similarity measures to identify the best possible compromise between both features while maintaining a sufficient level of details. Using Gene Ontology to annotate the gene sets, we obtained better results with node-based measures that use the terms' characteristics than with measures based on edges that link the terms. The annotation of the gene sets achieved with the node-based measures did not exhibit major differences regardless of the characteristics of terms used.

Visualizing omics and clinical data: Which challenges for dealing with their variety?

Life sciences are currently going through a great number of transformations raised by the in-going revolution in high-throughput technologies for the acquisition of data. The integration of their high dimensionality, ranging from omics to clinical data, is becoming one of the most challenging stages. It involves inter-disciplinary developments with the aim to move towards an enhanced understanding of human physiology for caring purposes. Biologists, bioinformaticians, physicians and other experts related to the healthcare domain have to accompany each step of the analysis process in order to investigate and expertise these various data. In this perspective, methods related to information visualization are gaining increasing attention within life sciences. The softwares based on these methods are now well recognized to facilitate expert users' success in carrying out their data analysis tasks. This article aims at reviewing the current methods and techniques dedicated to information visualisation and their current use in software development related to omics or/and clinical data.

rNAV 2.0: a visualization tool for bacterial sRNA-mediated regulatory networks mining.

BACKGROUND: Bacterial sRNA-mediated regulatory networks has been introduced as a powerful way to analyze the fast rewiring capabilities of a bacteria in response to changing environmental conditions. The identification of mRNA targets of bacterial sRNAs is essential to investigate their functional activities. However, this step remains challenging with the lack of knowledge of the topological and biological constraints behind the formation of sRNA-mRNA duplexes. Even with the most sophisticated bioinformatics target prediction tools, the large proportion of false predictions may be prohibitive for further analyses. To deal with this issue, sRNA target analyses can be carried out from the resulting gene lists given by RNA-SEQ experiments when available. However, the number of resulting target candidates may be still huge and cannot be easily interpreted by domain experts who need to confront various biological features to prioritize the target candidates. Therefore, novel strategies have to be carried out to improve the specificity of computational prediction results, before proposing new candidates for an expensive experimental validation stage. RESULT: To address this issue, we propose a new visualization tool rNAV 2.0, for detecting and filtering bacterial sRNA targets for regulatory networks. rNAV is designed to cope with a variety of biological constraints, including the gene annotations, the conserved regions of interaction or specific patterns of regulation. Depending on the application, these constraints can be variously combined to analyze the target candidates, prioritized for instance by a known conserved interaction region, or because of a common function. CONCLUSION: The standalone application implements a set of known algorithms and interaction techniques, and applies them to the new problem of identifying reasonable sRNA target candidates.

Advantages of mixing bioinformatics and visualization approaches for analyzing sRNA-mediated regulatory bacterial networks.

The revolution in high-throughput sequencing technologies has enabled the acquisition of gigabytes of RNA sequences in many different conditions and has highlighted an unexpected number of small RNAs (sRNAs) in bacteria. Ongoing exploitation of these data enables numerous applications for investigating bacterial transacting sRNA-mediated regulation networks. Focusing on sRNAs that regulate mRNA translation in trans, recent works have noted several sRNA-based regulatory pathways that are essential for key cellular processes. Although the number of known bacterial sRNAs is increasing, the experimental validation of their interactions with mRNA targets remains challenging and involves expensive and time-consuming experimental strategies. Hence, bioinformatics is crucial for selecting and prioritizing candidates before designing any experimental work. However, current software for target prediction produces a prohibitive number of candidates because of the lack of biological knowledge regarding the rules governing sRNA-mRNA interactions. Therefore, there is a real need to develop new approaches to help biologists focus on the most promising predicted sRNA-mRNA interactions. In this perspective, this review aims at presenting the advantages of mixing bioinformatics and visualization approaches for analyzing predicted sRNA-mediated regulatory bacterial networks.

Draft Genome Sequences of Mycoplasma auris and Mycoplasma yeatsii, Two Species of the Ear Canal of Caprinae.

We report here the draft genome sequences of Mycoplasma auris and Mycoplasma yeatsii, two species commonly isolated from the external ear canal of Caprinae.

Draft Genome Sequences of Mycoplasma alkalescens, Mycoplasma arginini, and Mycoplasma bovigenitalium, Three Species with Equivocal Pathogenic Status for Cattle.

We report here the draft genome sequences of Mycoplasma alkalescens, Mycoplasma arginini, and Mycoplasma bovigenitalium. These three species are regularly isolated from bovine clinical specimens, although their role in disease is unclear.

Complete Genome Sequence of Mycoplasma putrefaciens Strain 9231, One of the Agents of Contagious Agalactia in Goats.

Mycoplasma putrefaciens is one of the etiologic agents of contagious agalactia in goats. We report herein the complete genome sequence of Mycoplasma putrefaciens strain 9231.

Flux Analysis of the Trypanosoma brucei Glycolysis Based on a Multiobjective-Criteria Bioinformatic Approach.

Trypanosoma brucei is a protozoan parasite of major of interest in discovering new genes for drug targets. This parasite alternates its life cycle between the mammal host(s) (bloodstream form) and the insect vector (procyclic form), with two divergent glucose metabolism amenable to in vitro culture. While the metabolic network of the bloodstream forms has been well characterized, the flux distribution between the different branches of the glucose metabolic network in the procyclic form has not been addressed so far. We present a computational analysis (called Metaboflux) that exploits the metabolic topology of the procyclic form, and allows the incorporation of multipurpose experimental data to increase the biological relevance of the model. The alternatives resulting from the structural complexity of networks are formulated as an optimization problem solved by a metaheuristic where experimental data are modeled in a multiobjective function. Our results show that the current metabolic model is in agreement with experimental data and confirms the observed high metabolic flexibility of glucose metabolism. In addition, Metaboflux offers a rational explanation for the high flexibility in the ratio between final products from glucose metabolism, thsat is, flux redistribution through the malic enzyme steps.

Evolutionary history of contagious bovine pleuropneumonia using next generation sequencing of Mycoplasma mycoides Subsp. mycoides "Small Colony".

Mycoplasma mycoides subsp. mycoides "Small Colony" (MmmSC) is responsible for contagious bovine pleuropneumonia (CBPP) in bovidae, a notifiable disease to the World Organization for Animal Health (OIE). Although its origin is not documented, the disease was known in Europe in 1773. It reached nearly world-wide distribution in the 19(th) century through the cattle trade and was eradicated from most continents by stamping-out policies. During the 20(th) century it persisted in Africa, and it reappeared sporadically in Southern Europe. Yet, classical epidemiology studies failed to explain the re-occurrence of the disease in Europe in the 1990s. The objectives of this study were to obtain a precise phylogeny of this pathogen, reconstruct its evolutionary history, estimate the date of its emergence, and determine the origin of the most recent European outbreaks. A large-scale genomic approach based on next-generation sequencing technologies was applied to construct a robust phylogeny of this extremely monomorphic pathogen by using 20 representative strains of various geographical origins. Sixty two polymorphic genes of the MmmSC core genome were selected, representing 83601 bp in total and resulting in 139 SNPs within the 20 strains. A robust phylogeny was obtained that identified a lineage specific to European strains; African strains were scattered in various branches. Bayesian analysis allowed dating the most recent common ancestor for MmmSC around 1700. The strains circulating in Sub-Saharan Africa today, however, were shown to descend from a strain that existed around 1810. MmmSC emerged recently, about 300 years ago, and was most probably exported from Europe to other continents, including Africa, during the 19(th) century. Its diversity is now greater in Africa, where CBPP is enzootic, than in Europe, where outbreaks occurred sporadically until 1999 and where CBPP may now be considered eradicated unless MmmSC remains undetected.

Emergence of atypical Mycoplasma agalactiae strains harboring a new prophage and associated with an alpine wild ungulate mortality episode.

The bacterium Mycoplasma agalactiae is responsible for contagious agalactia (CA) in small domestic ruminants, a syndrome listed by the World Organization for Animal Health and responsible for severe damage to the dairy industry. Recently, we frequently isolated this pathogen from lung lesions of ibexes during a mortality episode in the French Alps. This situation was unusual in terms of host specificity and tissue tropism, raising the question of M. agalactiae emergence in wildlife. To address this issue, the ibex isolates were characterized using a combination of approaches that included antigenic profiles, molecular typing, optical mapping, and whole-genome sequencing. Genome analyses showed the presence of a new, large prophage containing 35 coding sequences (CDS) that was detected in most but not all ibex strains and has a homolog in Mycoplasma conjunctivae, a species causing keratoconjunctivitis in wild ungulates. This and the presence in all strains of large integrated conjugative elements suggested highly dynamic genomes. Nevertheless, M. agalactiae strains circulating in the ibex population were shown to be highly related, most likely originating from a single parental clone that has also spread to another wild ungulate species of the same geographical area, the chamois. These strains clearly differ from strains described in Europe so far, including those found nearby, before CA eradication a few years ago. While M. agalactiae pathogenicity in ibexes remains unclear, our data showed the emergence of atypical strains in Alpine wild ungulates, raising the question of a role for the wild fauna as a potential reservoir of pathogenic mycoplasmas.

A novel substitution matrix fitted to the compositional bias in Mollicutes improves the prediction of homologous relationships.

BACKGROUND: Substitution matrices are key parameters for the alignment of two protein sequences, and consequently for most comparative genomics studies. The composition of biological sequences can vary importantly between species and groups of species, and classical matrices such as those in the BLOSUM series fail to accurately estimate alignment scores and statistical significance with sequences sharing marked compositional biases. RESULTS: We present a general and simple methodology to build matrices that are especially fitted to the compositional bias of proteins. Our approach is inspired from the one used to build the BLOSUM matrices and is based on learning substitution and amino acid frequencies on real sequences with the corresponding compositional bias. We applied it to the large scale comparison of Mollicute AT-rich genomes. The new matrix, MOLLI60, was used to predict pairwise orthology relationships, as well as homolog families among 24 Mollicute genomes. We show that this new matrix enables to better discriminate between true and false orthologs and improves the clustering of homologous proteins, with respect to the use of the classical matrix BLOSUM62. CONCLUSIONS: We show in this paper that well-fitted matrices can improve the predictions of orthologous and homologous relationships among proteins with a similar compositional bias. With the ever-increasing number of sequenced genomes, our approach could prove valuable in numerous comparative studies focusing on atypical genomes.

An introduction to metabolic networks and their structural analysis.

There has been a renewed interest for metabolism in the computational biology community, leading to an avalanche of papers coming from methodological network analysis as well as experimental and theoretical biology. This paper is meant to serve as an initial guide for both the biologists interested in formal approaches and the mathematicians or computer scientists wishing to inject more realism into their models. The paper is focused on the structural aspects of metabolism only. The literature is vast enough already, and the thread through it difficult to follow even for the more experienced worker in the field. We explain methods for acquiring data and reconstructing metabolic networks, and review the various models that have been used for their structural analysis. Several concepts such as modularity are introduced, as are the controversies that have beset the field these past few years, for instance, on whether metabolic networks are small-world or scale-free, and on which model better explains the evolution of metabolism. Clarifying the work that has been done also helps in identifying open questions and in proposing relevant future directions in the field, which we do along the paper and in the conclusion.

The Cm56 tRNA modification in archaea is catalyzed either by a specific 2'-O-methylase, or a C/D sRNP.

We identified the first archaeal tRNA ribose 2'-O-methylase, aTrm56, belonging to the Cluster of Orthologous Groups (COG) 1303 that contains archaeal genes only. The corresponding protein exhibits a SPOUT S-adenosylmethionine (AdoMet)-dependent methyltransferase domain found in bacterial and yeast G18 tRNA 2'-O-methylases (SpoU, Trm3). We cloned the Pyrococcus abyssi PAB1040 gene belonging to this COG, expressed and purified the corresponding protein, and showed that in vitro, it specifically catalyzes the AdoMet-dependent 2'-O-ribose methylation of C at position 56 in tRNA transcripts. This tRNA methylation is present only in archaea, and the gene for this enzyme is present in all the archaeal genomes sequenced up to now, except in the crenarchaeon Pyrobaculum aerophilum. In this archaea, the C56 2'-O-methylation is provided by a C/D sRNP. Our work is the first demonstration that, within the same kingdom, two different mechanisms are used to modify the same nucleoside in tRNAs.