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One of the difficulties encountered in the statistical analysis of metaproteomics data is the high proportion of missing values, which are usually treated by imputation. Nevertheless, imputation methods are based on restrictive assumptions regarding missingness mechanisms, namely “at random” or “not at random”. To circumvent these limitations in the context of feature selection in a multi-class comparison, we propose a univariate selection method that combines a test of association between missingness and classes, and a test for difference of observed intensities between classes. This approach implicitly handles both missingness mechanisms. We performed a quantitative and qualitative comparison of our procedure with imputation-based feature selection methods on two experimental data sets, as well as simulated data with various scenarios regarding the missingness mechanisms and the nature of the difference of expression (differential intensity or differential presence). Whereas we observed similar performances in terms of prediction on the experimental data set, the feature ranking and selection from various imputation-based methods were strongly divergent. We showed that the combined test reaches a compromise by correlating reasonably with other methods, and remains efficient in all simulated scenarios unlike imputation-based feature selection methods.

Bacillus subtilis has been extensively used to study the molecular mechanisms behind the development and dispersal of surface bacterial multicellular communities. Well-structured spatially organised communities (colony, pellicle, and submerged biofilm) share some similarities, but also display considerable differences at the structural, chemical and biological levels. To unveil the spatial transcriptional heterogeneity between the different communities, we analysed by RNA-seq nine spatio-physiological populations selected from planktonic and spatially organised communities. This led to a global landscape characterisation of gene expression profiles uncovering genes specifically expressed in each compartmental population. From this mesoscale analysis and using fluorescent transcriptional reporter fusions, 17 genes were selected and their patterns of expression reported at single cell scale with time-lapse confocal laser scanning microscopy (CLSM). Derived kymographs allowed to emphasise spectacular mosaic gene expression patterns within a biofilm. A special emphasis on oppositely regulated carbon metabolism genes (gapA and gapB) permitted to pinpoint the coexistence of spatially segregated bacteria under either glycolytic or gluconeogenic regime in a same biofilm population. Altogether, this study gives novel insights on the development and dispersal of B. subtilis surface-associated communities.

Considering human decision-making is essential for understanding the mechanisms underlying the propagation of real-life diseases. We present an extension of a model for pathogen spread that considers farmers’ dynamic decision-making regarding the adoption of a control measure in their own herd. Farmers can take into account the decisions and observed costs of their trade partners or of their geographic neighbours. The model and construction of such costs are adapted to the case of bovine viral diarrhoea, for which an individual-based stochastic model is considered. Simulation results suggest that obtaining information from geographic neighbours might lead to a better control of bovine viral diarrhoea than considering information from trade partners. In particular, using information from all geographic neighbours at each decision time seems to be more beneficial than considering only the information from one geographic neighbour or trade partner at each time. This study highlights the central role that social dynamics among farmers can take in the spread and control of bovine viral diarrhoea, providing insights into how public policy efforts could be targeted in order to increase voluntary vaccination uptake against this disease in endemic areas.

To better understand the energetic status of proliferating cells, we have measured the intracellular pH (pHi) and concentrations of key metabolites, such as adenosine triphosphate (ATP), nicotinamide adenine dinucleotide (NAD), and nicotinamide adenine dinucleotide phosphate (NADP) in normal and cancer cells, extracted from fresh human colon tissues. Cells were sorted by elutriation and segregated in different phases of the cell cycle (G0/G1/S/G2/M) in order to study their redox (NAD, NADP) and bioenergetic (ATP, pHi) status. Our results show that the average ATP concentration over the cell cycle is higher and the pHi is globally more acidic in normal proliferating cells. The NAD+/NADH and NADP+/NADPH redox ratios are, respectively, five times and ten times higher in cancer cells compared to the normal cell population. These energetic differences in normal and cancer cells may explain the well-described mechanisms behind the Warburg effect. Oscillations in ATP concentration, pHi, NAD+/NADH, and NADP+/NADPH ratios over one cell cycle are reported and the hypothesis addressed. We also investigated the mitochondrial membrane potential (MMP) of human and mice normal and cancer cell lines. A drastic decrease of the MMP is reported in cancer cell lines compared to their normal counterparts. Altogether, these results strongly support the high throughput aerobic glycolysis, or Warburg effect, observed in cancer cells.

Abstract Mobile Genetic Elements (MGEs) are integrated in bacterial genomes and key elements that drive prokaryote genome evolution. Among them are Integrative and Conjugative Elements (ICEs) and Integrative Mobilizable Elements (IMEs) which are important for bacterial fitness since they frequently carry genes participating in important bacterial adaptation phenotypes such as antibiotic resistance, virulence or specialized metabolic pathways. Although ICEs and IMEs are widespread, they are as yet almost never annotated in public bacterial genomes. To address the need of dedicated strategies for the annotation of these elements, we developed ICEscreen, a tool that introduces two new features to detect ICEs and IMEs in Firmicute genomes. First, ICEscreen uses an efficient strategy to detect Signature Proteins of ICEs and IMEs based on a database dedicated to Firmicutes and composed of manually curated proteins and Hidden Markov Models (HMM) profiles. Second, ICEscreen includes a new original algorithm that detects composite structures of ICEs and IMEs that are frequent in genomes of Firmicutes but are currently not resolved by any other tool. We benchmarked ICEscreen on experimentally supported elements and on a public dataset of 246 manually annotated elements including the genomes of 40 Firmicutes and demonstrate its efficiency to detect ICEs and IMEs.

Isogenic bacterial populations can consist of cells displaying heterogeneous physiological traits. Small regulatory RNAs (sRNAs) could affect this heterogeneity since they act by fine-tuning mRNA or protein levels to coordinate the appropriate cellular behavior. Here we show that the sRNA RnaC/S1022 from the Gram-positive bacterium Bacillus subtilis can suppress exponential growth by modulation of the transcriptional regulator AbrB. Specifically, the post-transcriptional abrB-RnaC/S1022 interaction allows B. subtilis to increase the cell-to-cell variation in AbrB protein levels, despite strong negative autoregulation of the abrB promoter. This behavior is consistent with existing mathematical models of sRNA action, thus suggesting that induction of protein expression noise could be a new general aspect of sRNA regulation. Importantly, we show that the sRNA-induced diversity in AbrB levels generates heterogeneity in growth rates during the exponential growth phase. Based on these findings, we hypothesize that the resulting subpopulations of fast-and slow-growing B. subtilis cells reflect a bet-hedging strategy for enhanced survival of unfavorable conditions.

Streptococcus salivarius is a significant contributor to the human oral, pharyngeal and gut microbiomes that contribute to the maintenance of health. The high genomic diversity observed in this species is mainly caused by horizontal gene transfer. This work aimed to evaluate the contribution of integrative and conjugative elements (ICEs) and integrative and mobilizable elements (IMEs) in S. salivarius genome diversity. For this purpose, we performed an in-depth analysis of 75 genomes of S. salivarius and searched for signature genes of conjugative and mobilizable elements. This analysis led to the retrieval of 69 ICEs, 165 IMEs and many decayed elements showing their high prevalence in S. salivarius genomes. The identification of almost all ICE and IME boundaries allowed the identification of the genes in which these elements are inserted. Furthermore, the exhaustive analysis of the adaptation genes carried by these elements showed that they encode numerous functions such as resistance to stress, to antibiotics or to toxic compounds, and numerous enzymes involved in diverse cellular metabolic pathways. These data support the idea that not only ICEs but also IMEs and decayed elements play an important role in S. salivarius adaptation to the environment.

Bacillus subtilis has been extensively used to study the molecular mechanisms behind the development and dispersal of surface bacterial multicellular communities. Well-structured spatially organised communities (colony, pellicle, and submerged biofilm) share some similarities, but also display considerable differences at the structural, chemical and biological levels. To unveil the spatial transcriptional heterogeneity between the different communities, we analysed by RNA-seq nine spatio-physiological populations selected from planktonic and spatially organised communities. This led to a global landscape characterisation of gene expression profiles uncovering genes specifically expressed in each compartmental population. From this mesoscale analysis and using fluorescent transcriptional reporter fusions, 17 genes were selected and their patterns of expression reported at single cell scale with time-lapse confocal laser scanning microscopy (CLSM). Derived kymographs allowed to emphasise spectacular mosaic gene expression patterns within a biofilm. A special emphasis on oppositely regulated carbon metabolism genes (gapA and gapB) permitted to pinpoint the coexistence of spatially segregated bacteria under either glycolytic or gluconeogenic regime in a same biofilm population. Altogether, this study gives novel insights on the development and dispersal of B. subtilis surface-associated communities.Competing Interest StatementThe authors have declared no competing interest.Footnotes* https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE214964* https://entrepot.recherche.data.gouv.fr/dataset.xhtml?persistentId=doi:10.57745/Z511A6