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While metagenomic sequencing has become the tool of preference to study host-associated microbial communities, downstream analyses and clinical interpretation of microbiome data remains challenging due to the sparsity and compositionality of sequence matrices. Here, we evaluate both computational and experimental approaches proposed to mitigate the impact of these outstanding issues. Generating fecal metagenomes drawn from simulated microbial communities, we benchmark the performance of thirteen commonly used analytical approaches in terms of diversity estimation, identification of taxon-taxon associations, and assessment of taxon-metadata correlations under the challenge of varying microbial ecosystem loads. We find quantitative approaches including experimental procedures to incorporate microbial load variation in downstream analyses to perform significantly better than computational strategies designed to mitigate data compositionality and sparsity, not only improving the identification of true positive associations, but also reducing false positive detection. When analyzing simulated scenarios of low microbial load dysbiosis as observed in inflammatory pathologies, quantitative methods correcting for sampling depth show higher precision compared to uncorrected scaling. Overall, our findings advocate for a wider adoption of experimental quantitative approaches in microbiome research, yet also suggest preferred transformations for specific cases where determination of microbial load of samples is not feasible. Here, the authors use simulated quantitative gut microbial communities to benchmark the performance of 13 common data transformations in determining diversity as well as microbe-microbe and microbe-metadata associations, finding that quantitative approaches incorporating microbial load variation outperform computational strategies in downstream analyses, urging for a widespread adoption of quantitative approaches, or recommending specific computational transformations whenever determination of microbial load of samples is not feasible.

Identifying all essential genomic components is critical for the assembly of minimal artificial life. In the genome‐reduced bacterium Mycoplasma pneumoniae , we found that small ORFs (smORFs; < 100 residues), accounting for 10% of all ORFs, are the most frequently essential genomic components (53%), followed by conventional ORFs (49%). Essentiality of smORFs may be explained by their function as members of protein and/or DNA/RNA complexes. In larger proteins, essentiality applied to individual domains and not entire proteins, a notion we could confirm by expression of truncated domains. The fraction of essential non‐coding RNAs (ncRNAs) non‐overlapping with essential genes is 5% higher than of non‐transcribed regions (0.9%), pointing to the important functions of the former. We found that the minimal essential genome is comprised of 33% (269,410 bp) of the M. pneumoniae genome. Our data highlight an unexpected hidden layer of smORFs with essential functions, as well as non‐coding regions, thus changing the focus when aiming to define the minimal essential genome. Synopsis A genome essentiality analysis in the genome‐reduced bacterium Mycoplasma pneumoniae , reveals that protein essentiality should be considered at the domain level and that small proteins (< 100 aa) and ncRNAs are frequently essential genomic elements. A genome essentiality analysis is performed using two mini‐transposon mutant libraries of M. pneumoniae . The results indicate that ORF essentiality should be considered at the protein domain level. Small ORFs are as essential as conventional ORFs and they can interact with DNA. Some essential antisense ncRNAs are involved in the regulation of essential ORF expression. Graphical Abstract A genome essentiality analysis in the genome‐reduced bacterium Mycoplasma pneumoniae , reveals that protein essentiality should be considered at the domain level and that small proteins (< 100 aa) and ncRNAs are frequently essential genomic elements.

The composition of the human gut microbiome is well resolved, but predictive understanding of its dynamics is still lacking. Here, we followed a bottom-up strategy to explore human gut community dynamics: we established a synthetic community composed of three representative human gut isolates (Roseburia intestinalis L1-82, Faecalibacterium prausnitzii A2-165 and Blautia hydrogenotrophica S5a33) and explored their interactions under well-controlled conditions in vitro. Systematic mono- and pair-wise fermentation experiments confirmed competition for fructose and cross-feeding of formate. We quantified with a mechanistic model how well tri-culture dynamics was predicted from mono-culture data. With the model as reference, we demonstrated that strains grown in co-culture behaved differently than those in mono-culture and confirmed their altered behavior at the transcriptional level. In addition, we showed with replicate tri-cultures and simulations that dominance in tri-culture sensitively depends on the initial conditions. Our work has important implications for gut microbial community modeling as well as for ecological interaction detection from batch cultures. Our gut is home to trillions of microorganisms, most of them bacteria, which have an important impact on our body. During healthy periods, these microorganisms help our digestion, protect our cells, and compete against disease-causing bacteria. But specific communities of gut bacteria are linked to many diseases. We already have a good knowledge of the bacterial composition present in a wide range of human guts, but how the different bacterial species within such communities affect each other, has so far been unclear. Future disease treatments may be able to steer ‘bad’ communities to healthier mixtures. For this to happen we need to know how species interact and how these interactions change the behavior of the whole community. To investigate this further, D'hoe, Vet, Faust et al. studied three common species of gut bacteria under controlled conditions in the laboratory. The different species were either grown alone, in pairs or together, and the number of bacteria and the concentration of nutrients were measured over time. The results showed that when grown alone or together, their behavior changed. D'hoe et al. then used a mathematical model to estimate the rates at which species multiplied and consumed nutrients. This model was able to predict the dynamics of each of the species grown alone. However, the data from bacteria grown in pairs was needed to predict the dynamics of bacteria grown as a group of three. Next, D'hoe et al. compared the activity of genes between bacteria grown alone or together, and discovered several differences. This suggests that bacterial species affect each other greatly, and community behavior cannot be predicted from knowledge of its members alone. Therefore, studying bacteria in isolation is not enough to understand the complex environments of our guts, which are inhabited not by three but hundreds of bacterial species. In future, interactions between bacteria will need to be studied to ultimately be able to shift the gut community into better shapes.

Understanding the pathology of COVID-19 is a global research priority. Early evidence suggests that the respiratory microbiome may be playing a role in disease progression, yet current studies report contradictory results. Here, we examine potential confounders in COVID-19 respiratory microbiome studies by analyzing the upper ( n  = 58) and lower ( n  = 35) respiratory tract microbiome in well-phenotyped COVID-19 patients and controls combining microbiome sequencing, viral load determination, and immunoprofiling. We find that time in the intensive care unit and type of oxygen support, as well as associated treatments such as antibiotic usage, explain the most variation within the upper respiratory tract microbiome, while SARS-CoV-2 viral load has a reduced impact. Specifically, mechanical ventilation is linked to altered community structure and significant shifts in oral taxa previously associated with COVID-19. Single-cell transcriptomics of the lower respiratory tract of COVID-19 patients identifies specific oral bacteria in physical association with proinflammatory immune cells, which show higher levels of inflammatory markers. Overall, our findings suggest confounders are driving contradictory results in current COVID-19 microbiome studies and careful attention needs to be paid to ICU stay and type of oxygen support, as bacteria favored in these conditions may contribute to the inflammatory phenotypes observed in severe COVID-19 patients. Here, the authors profile the respiratory microbiome of COVID-19 patients and link clinical practices, such as mechanical ventilation, with vast changes in the microbiota. In the lungs, oral bacteria are found physically associated with proinflammatory immune cells, thus possibly contributing to exacerbated immune responses in severe disease

In the bacterial world, methylation is most commonly associated with restriction-modification systems that provide a defense mechanism against invading foreign genomes. In addition, it is known that methylation plays functionally important roles, including timing of DNA replication, chromosome partitioning, DNA repair, and regulation of gene expression. However, full DNA methylome analyses are scarce due to a lack of a simple methodology for rapid and sensitive detection of common epigenetic marks (ie N(6)-methyladenine (6 mA) and N(4)-methylcytosine (4 mC)), in these organisms. Here, we use Single-Molecule Real-Time (SMRT) sequencing to determine the methylomes of two related human pathogen species, Mycoplasma genitalium G-37 and Mycoplasma pneumoniae M129, with single-base resolution. Our analysis identified two new methylation motifs not previously described in bacteria: a widespread 6 mA methylation motif common to both bacteria (5'-CTAT-3'), as well as a more complex Type I m6A sequence motif in M. pneumoniae (5'-GAN(7)TAY-3'/3'-CTN(7)ATR-5'). We identify the methyltransferase responsible for the common motif and suggest the one involved in M. pneumoniae only. Analysis of the distribution of methylation sites across the genome of M. pneumoniae suggests a potential role for methylation in regulating the cell cycle, as well as in regulation of gene expression. To our knowledge, this is one of the first direct methylome profiling studies with single-base resolution from a bacterial organism.

Despite substantial progress in cancer microbiome research, recognized confounders and advances in absolute microbiome quantification remain underused; this raises concerns regarding potential spurious associations. Here we study the fecal microbiota of 589 patients at different colorectal cancer (CRC) stages and compare observations with up to 15 published studies (4,439 patients and controls total). Using quantitative microbiome profiling based on 16S ribosomal RNA amplicon sequencing, combined with rigorous confounder control, we identified transit time, fecal calprotectin (intestinal inflammation) and body mass index as primary microbial covariates, superseding variance explained by CRC diagnostic groups. Well-established microbiome CRC targets, such as Fusobacterium nucleatum , did not significantly associate with CRC diagnostic groups (healthy, adenoma and carcinoma) when controlling for these covariates. In contrast, the associations of Anaerococcus vaginalis , Dialister pneumosintes , Parvimonas micra , Peptostreptococcus anaerobius , Porphyromonas asaccharolytica and Prevotella intermedia remained robust, highlighting their future target potential. Finally, control individuals (age 22–80 years, mean 57.7 years, standard deviation 11.3) meeting criteria for colonoscopy (for example, through a positive fecal immunochemical test) but without colonic lesions are enriched for the dysbiotic Bacteroides2 enterotype, emphasizing uncertainties in defining healthy controls in cancer microbiome research. Together, these results indicate the importance of quantitative microbiome profiling and covariate control for biomarker identification in CRC microbiome studies. Controlling for confounders calls into question the robustness of some associations of microbiota with colorectal cancer stages.

Many driver mutations in cancer are specific in that they occur at significantly higher rates than – presumably – functionally alternative mutations. For example, V600E in the BRAF hydrophobic activation segment (AS) pocket accounts for >95% of all kinase mutations. While many hypotheses tried to explain such significant mutation patterns, conclusive explanations are lacking. Here, we use experimental and in silico structure-energy statistical analyses, to elucidate why the V600E mutation, but no other mutation at this, or any other positions in BRAF’s hydrophobic pocket, is predominant. We find that BRAF mutation frequencies depend on the equilibrium between the destabilization of the hydrophobic pocket, the overall folding energy, the activation of the kinase and the number of bases required to change the corresponding amino acid. Using a random forest classifier, we quantitatively dissected the parameters contributing to BRAF AS cancer frequencies. These findings can be applied to genome-wide association studies and prediction models. Mutations in the gene that encodes a protein called BRAF are commonly found in certain cancers, such as melanomas. The same BRAF mutation is found in nearly all of these cancers. This mutation causes the 600th amino acid in the BRAF protein – an amino acid called a valine – to be replaced with another amino acid, a glutamate. BRAF is a type of enzyme called a kinase, and it transmits signals inside cells to promote cell growth. Kinases work by adding a phosphate group to other proteins to alter their activity. The structure of the BRAF kinase contains a pocket-like shape, and the valine at position 600 sits buried inside this pocket when the enzyme is inactive. The “valine-to-glutamate” mutation (often called V600E for short) disrupts the interactions that create this pocket. This in turn results in a permanently active form of BRAF and uncontrolled cell growth. However, it remains unclear why the valine-to-glutamate mutation is so much more common in cancer cells than any other mutation that could affect the pocket in BRAF. To address this question, Kiel et al. used a computational tool to generate three-dimensional models for all the different amino acid substitutions that could occur in BRAF’s pocket. Each mutation was then assessed to see how it might destabilize the structure of BRAF. Only the mutations that affected the 600th amino acid were predicted to be able to open the pocket without destabilizing the part of the enzyme that adds phosphate groups to other proteins. Kiel et al. validated their computational predictions by introducing normal or mutant versions of the BRAF-encoding gene into human cells grown in the laboratory. These experiments showed that a mutation that introduced an amino acid called histidine into position 600 could activate BRAF as much the valine-to-glutamate mutation. Kiel et al. suggest that this “valine-to-histidine” substitution is not found in cancers because it requires three changes to the DNA sequence of the BRAF gene, whereas the valine-to-glutamate substitution only requires one. The results underscore the importance of considering changes at both the DNA and protein level when attempting to understand why certain cancer-causing mutations are more common than others.

Background RNA sequencing methods have already altered our view of the extent and complexity of bacterial and eukaryotic transcriptomes, revealing rare transcript isoforms (circular RNAs, RNA chimeras) that could play an important role in their biology. Results We performed an analysis of chimera formation by four different computational approaches, including a custom designed pipeline, to study the transcriptomes of M. pneumoniae and P. aeruginosa , as well as mixtures of both. We found that rare transcript isoforms detected by conventional pipelines of analysis could be artifacts of the experimental procedure used in the library preparation, and that they are protocol-dependent. Conclusion By using a customized pipeline we show that optimal library preparation protocol and the pipeline to analyze the results are crucial to identify real chimeric RNAs.

The human respiratory tract pathogen M. pneumoniae is one of the best characterized minimal bacterium. Until now, two main groups of clinical isolates of this bacterium have been described (types 1 and 2), differing in the sequence of the P1 adhesin gene. Here, we have sequenced the genomes of 23 clinical isolates of M. pneumoniae. Studying SNPs, non-synonymous mutations, indels and genome rearrangements of these 23 strains and 4 previously sequenced ones, has revealed new subclasses in the two main groups, some of them being associated with the country of isolation. Integrative analysis of in vitro gene essentiality and mutation rates enabled the identification of several putative virulence factors and antigenic proteins; revealing recombination machinery, glycerol metabolism and peroxide production as possible factors in the genetics and physiology of these pathogenic strains. Additionally, the transcriptomes and proteomes of two representative strains, one from each of the two main groups, have been characterized to evaluate the impact of mutations on RNA and proteins levels. This study has revealed that type 2 strains show higher expression levels of CARDS toxin, a protein recently shown to be one of the major factors of inflammation. Thus, we propose that type 2 strains could be more toxigenic than type 1 strains of M. pneumoniae.