Extensive strain-level copy-number variation across human gut microbiome species

A single rare σ70 variant establishes a unique gene expression pattern in the E. coli pathobiont LF82

LF82, an adherent-invasive Escherichia coli (AIEC) pathobiont, is associated with Crohn's disease, an inflammatory bowel disease of unknown etiology. Although AIEC phenotypes differ from those of 'commensal' or pathogenic E. coli, work has failed to identify genetic features accounting for these differences. We have investigated a natural, but rare, single nucleotide polymorphism (SNP) in LF82 present within the highly conserved rpoD gene, encoding σ70 [primary sigma factor, RNA polymerase (RNAP)]. We demonstrate that σ70 D445V results in transcriptomic and phenotypic changes consistent with LF82 phenotypes, including increased antibiotic resistance and biofilm formation and increased capacity for methionine biosynthesis. RNA-seq analyses comparing σ70 V445 versus σ70 D445 identified 24 genes upregulated by σ70 V445 in both LF82 and the laboratory E. coli K-12 strain MG1655. Using in vitro transcription, we demonstrate that σ70 D445V directly increases transcription from promoters for several of the up-regulated genes and that the presence of a 16 bp spacer and -14 G:C is associated with this increase. The position of D445V within RNAP suggests that it could affect RNAP/spacer interaction. Our work represents the first identification of a distinguishing SNP for this pathobiont and suggests an underrecognized mechanism by which pathobionts and strain variants can emerge.

PUPpy: a primer design pipeline for substrain-level microbial detection and absolute quantification

Characterizing microbial communities at high resolution and with absolute quantification is crucial to unravel the complexity and diversity of microbial ecosystems. This can be achieved with PCR assays, which enable highly selective detection and absolute quantification of microbial DNA. However, a major challenge that has hindered PCR applications in microbiome research is the design of highly specific primer sets that exclusively amplify intended targets. Here, we introduce Phylogenetically Unique Primers in python (PUPpy), a fully automated pipeline to design microbe- and group-specific primers within a given microbial community. PUPpy can be executed from a user-friendly graphical user interface, or two simple terminal commands, and it only requires coding sequence files of the community members as input. PUPpy-designed primers enable the detection of individual microbes and quantification of absolute microbial abundance in defined communities below the strain level. We experimentally evaluated the performance of PUPpy-designed primers using two bacterial communities as benchmarks. Each community comprises 10 members, exhibiting a range of genetic similarities that spanned from different phyla to substrains. PUPpy-designed primers also enable the detection of groups of bacteria in an undefined community, such as the detection of a gut bacterial family in a complex stool microbiota sample. Taxon-specific primers designed with PUPpy showed 100% specificity to their intended targets, without unintended amplification, in each community tested. Lastly, we show the absolute quantification of microbial abundance using PUPpy-designed primers in droplet digital PCR, benchmarked against 16S rRNA and shotgun sequencing. Our data shows that PUPpy-designed microbe-specific primers can be used to quantify substrain-level absolute counts, providing more resolved and accurate quantification in defined communities than short-read 16S rRNA and shotgun sequencing.

IMPORTANCE

Profiling microbial communities at high resolution and with absolute quantification is essential to uncover hidden ecological interactions within microbial ecosystems. Nevertheless, achieving resolved and quantitative investigations has been elusive due to methodological limitations in distinguishing and quantifying highly related microbes. Here, we describe Phylogenetically Unique Primers in python (PUPpy), an automated computational pipeline to design taxon-specific primers within defined microbial communities. Taxon-specific primers can be used to selectively detect and quantify individual microbes and larger taxa within a microbial community. PUPpy achieves substrain-level specificity without the need for computationally intensive databases and prioritizes user-friendliness by enabling both terminal and graphical user interface applications. Altogether, PUPpy enables fast, inexpensive, and highly accurate perspectives into microbial ecosystems, supporting the characterization of bacterial communities in both in vitro and complex microbiota settings.

Direct prediction of antimicrobial resistance in Pseudomonas aeruginosa by metagenomic next-generation sequencing

Objective

Pseudomonas aeruginosa has strong drug resistance and can tolerate a variety of antibiotics, which is a major problem in the management of antibiotic-resistant infections. Direct prediction of multi-drug resistance (MDR) resistance phenotypes of P. aeruginosa isolates and clinical samples by genotype is helpful for timely antibiotic treatment.

Methods

In the study, whole genome sequencing (WGS) data of 494 P. aeruginosa isolates were used to screen key anti-microbial resistance (AMR)-associated genes related to imipenem (IPM), meropenem (MEM), piperacillin/tazobactam (TZP), and levofloxacin (LVFX) resistance in P. aeruginosa by comparing genes with copy number differences between resistance and sensitive strains. Subsequently, for the direct prediction of the resistance of P. aeruginosa to four antibiotics by the AMR-associated features screened, we collected 74 P. aeruginosa positive sputum samples to sequence by metagenomics next-generation sequencing (mNGS), of which 1 sample with low quality was eliminated. Then, we constructed the resistance prediction model.

Results

We identified 93, 88, 80, 140 AMR-associated features for IPM, MEM, TZP, and LVFX resistance in P. aeruginosa. The relative abundance of AMR-associated genes was obtained by matching mNGS and WGS data. The top 20 features with importance degree for IPM, MEM, TZP, and LVFX resistance were used to model, respectively. Then, we used the random forest algorithm to construct resistance prediction models of P. aeruginosa, in which the areas under the curves of the IPM, MEM, TZP, and LVFX resistance prediction models were all greater than 0.8, suggesting these resistance prediction models had good performance.

Conclusion

In summary, mNGS can predict the resistance of P. aeruginosa by directly detecting AMR-associated genes, which provides a reference for rapid clinical detection of drug resistance of pathogenic bacteria.

Duplicated antibiotic resistance genes reveal ongoing selection and horizontal gene transfer in bacteria

Horizontal gene transfer (HGT) and gene duplication are often considered as separate mechanisms driving the evolution of new functions. However, the mobile genetic elements (MGEs) implicated in HGT can copy themselves, so positive selection on MGEs could drive gene duplications. Here, we use a combination of modeling and experimental evolution to examine this hypothesis and use long-read genome sequences of tens of thousands of bacterial isolates to examine its generality in nature. Modeling and experiments show that antibiotic selection can drive the evolution of duplicated antibiotic resistance genes (ARGs) through MGE transposition. A key implication is that duplicated ARGs should be enriched in environments associated with antibiotic use. To test this, we examined the distribution of duplicated ARGs in 18,938 complete bacterial genomes with ecological metadata. Duplicated ARGs are highly enriched in bacteria isolated from humans and livestock. Duplicated ARGs are further enriched in an independent set of 321 antibiotic-resistant clinical isolates. Our findings indicate that duplicated genes often encode functions undergoing positive selection and horizontal gene transfer in microbial communities.

Temporal colonization and metabolic regulation of the gut microbiome in neonatal oxen at single nucleotide resolution

The colonization of microbes in the gut is key to establishing a healthy host-microbiome symbiosis for newborns. We longitudinally profiled the gut microbiome in a model consisting of 36 neonatal oxen from birth up to 2 months postpartum and carried out microbial transplantation to reshape their gut microbiome. Genomic reconstruction of deeply sequenced fecal samples resulted in a total of 3931 metagenomic-assembled genomes from 472 representative species, of which 184 were identified as new species when compared with existing databases of oxen. Single nucleotide level metagenomic profiling shows a rapid influx of microbes after birth, followed by dynamic shifts during the first few weeks of life. Microbial transplantation was found to reshape the genetic makeup of 33 metagenomic-assembled genomes (FDR < 0.05), mainly from Prevotella and Bacteroides species. We further linked over 20 million microbial single nucleotide variations to 736 plasma metabolites, which enabled us to characterize 24 study-wide significant associations (P < 4.4 × 10-9) that identify the potential microbial genetic regulation of host immune and neuro-related metabolites, including glutathione and L-dopa. Our integration analyses further revealed that microbial genetic variations may influence the health status and growth performance by modulating metabolites via structural regulation of their encoded proteins. For instance, we found that the albumin levels and total antioxidant capacity were correlated with L-dopa, which was determined by single nucleotide variations via structural regulations of metabolic enzymes. The current results indicate that temporal colonization and transplantation-driven strain replacement are crucial for newborn gut development, offering insights for enhancing newborn health and growth.

Cultivated Enterococcus faecium B6 from children with obesity promotes nonalcoholic fatty liver disease by the bioactive metabolite tyramine

Gut microbiota plays an essential role in nonalcoholic fatty liver disease (NAFLD). However, the contribution of individual bacterial strains and their metabolites to childhood NAFLD pathogenesis remains poorly understood. Herein, the critical bacteria in children with obesity accompanied by NAFLD were identified by microbiome analysis. Bacteria abundant in the NAFLD group were systematically assessed for their lipogenic effects. The underlying mechanisms and microbial-derived metabolites in NAFLD pathogenesis were investigated using multi-omics and LC-MS/MS analysis. The roles of the crucial metabolite in NAFLD were validated in vitro and in vivo as well as in an additional cohort. The results showed that Enterococcus spp. was enriched in children with obesity and NAFLD. The patient-derived Enterococcus faecium B6 (E. faecium B6) significantly contributed to NAFLD symptoms in mice. E. faecium B6 produced a crucial bioactive metabolite, tyramine, which probably activated PPAR-γ, leading to lipid accumulation, inflammation, and fibrosis in the liver. Moreover, these findings were successfully validated in an additional cohort. This pioneering study elucidated the important functions of cultivated E. faecium B6 and its bioactive metabolite (tyramine) in exacerbating NAFLD. These findings advance the comprehensive understanding of NAFLD pathogenesis and provide new insights for the development of microbe/metabolite-based therapeutic strategies.

Effect of diet on the evolution of gut commensal bacteria

The gut microbiota, comprising trillions of diverse microorganisms inhabiting the intestines of animals, forms a complex and indispensable ecosystem with profound implications for the host's well-being. Its functions include contributing to developing the host's immune response, aiding in nutrient digestion, synthesizing essential compounds, acting as a barrier against pathogen invasion, and influencing the development or regression of various pathologies. The dietary habits of the host directly impact this intricate community of gut microbes. Diet influences the composition and function of the gut microbiota through alterations in gene expression, enzymatic activity, and metabolome. While the impact of diet on gut ecology is well-established, the investigation into the relationship between dietary consumption and microbial genotypic diversity has been limited. This review provides an overview of the relationship between diet and gut microbiota, emphasizing the impact of host nutrition on both short- and long-term evolution in the mammalian gut. It is evident that the evolution of the gut microbiota occurs even on short timescales through the acquisition of novel mutations, within the gut bacteria of individual hosts. Consequently, we discuss the importance of considering alterations in bacterial genomic diversity when analyzing microbiota-dependent effects on host physiology. Future investigations into the various microbiota-related traits shall greatly benefit from a deeper understanding of commensal bacterial evolutionary adaptation.

Turnover of strain-level diversity modulates functional traits in the honeybee gut microbiome between nurses and foragers

BACKGROUND

Strain-level diversity is widespread among bacterial species and can expand the functional potential of natural microbial communities. However, to what extent communities undergo consistent shifts in strain composition in response to environmental/host changes is less well understood.

RESULTS

Here, we used shotgun metagenomics to compare the gut microbiota of two behavioral states of the Western honeybee (Apis mellifera), namely nurse and forager bees. While their gut microbiota is composed of the same bacterial species, we detect consistent changes in strain-level composition between nurses and foragers. Single nucleotide variant profiles of predominant bacterial species cluster by behavioral state. Moreover, we identify strain-specific gene content related to nutrient utilization, vitamin biosynthesis, and cell-cell interactions specifically associated with the two behavioral states.

CONCLUSIONS

Our findings show that strain-level diversity in host-associated communities can undergo consistent changes in response to host behavioral changes modulating the functional potential of the community.

Cryptic community structure and metabolic interactions among the heritable facultative symbionts of the pea aphid

Most insects harbour influential, yet non-essential heritable microbes in their hemocoel. Communities of these symbionts exhibit low diversity. But their frequent multi-species nature raises intriguing questions on roles for symbiont-symbiont synergies in host adaptation, and on the stability of the symbiont communities, themselves. In this study, we build on knowledge of species-defined symbiont community structure across US populations of the pea aphid, Acyrthosiphon pisum. Through extensive symbiont genotyping, we show that pea aphids' microbiomes can be more precisely defined at the symbiont strain level, with strain variability shaping five out of nine previously reported co-infection trends. Field data provide a mixture of evidence for synergistic fitness effects and symbiont hitchhiking, revealing causes and consequences of these co-infection trends. To test whether within-host metabolic interactions predict common versus rare strain-defined communities, we leveraged the high relatedness of our dominant, community-defined symbiont strains vs. 12 pea aphid-derived Gammaproteobacteria with sequenced genomes. Genomic inference, using metabolic complementarity indices, revealed high potential for cooperation among one pair of symbionts-Serratia symbiotica and Rickettsiella viridis. Applying the expansion network algorithm, through additional use of pea aphid and obligate Buchnera symbiont genomes, Serratia and Rickettsiella emerged as the only symbiont community requiring both parties to expand holobiont metabolism. Through their joint expansion of the biotin biosynthesis pathway, these symbionts may span missing gaps, creating a multi-party mutualism within their nutrient-limited, phloem-feeding hosts. Recent, complementary gene inactivation, within the biotin pathways of Serratia and Rickettsiella, raises further questions on the origins of mutualisms and host-symbiont interdependencies.

Oxytocin and the microbiome

The mammalian host microbiome affects many targets throughout the body, at least in part through an integrated gut-brain-immune axis and neuropeptide hormone oxytocin. It was discovered in animal models that microbial symbionts, such as Lactobacillus reuteri, leverage perinatal niches to promote multigenerational good health and reproductive fitness. While roles for oxytocin were once limited to women, such as giving birth and nurturing offspring, oxytocin is now also proposed to have important roles linking microbial symbionts with overall host fitness and survival throughout the evolutionary journey.

Bacterial SNPs in the human gut microbiome associate with host BMI

Genome-wide association studies (GWASs) have provided numerous associations between human single-nucleotide polymorphisms (SNPs) and health traits. Likewise, metagenome-wide association studies (MWASs) between bacterial SNPs and human traits can suggest mechanistic links, but very few such studies have been done thus far. In this study, we devised an MWAS framework to detect SNPs and associate them with host phenotypes systematically. We recruited and obtained gut metagenomic samples from a cohort of 7,190 healthy individuals and discovered 1,358 statistically significant associations between a bacterial SNP and host body mass index (BMI), from which we distilled 40 independent associations. Most of these associations were unexplained by diet, medications or physical exercise, and 17 replicated in a geographically independent cohort. We uncovered BMI-associated SNPs in 27 bacterial species, and 12 of them showed no association by standard relative abundance analysis. We revealed a BMI association of an SNP in a potentially inflammatory pathway of Bilophila wadsworthia as well as of a group of SNPs in a region coding for energy metabolism functions in a Faecalibacterium prausnitzii genome. Our results demonstrate the importance of considering nucleotide-level diversity in microbiome studies and pave the way toward improved understanding of interpersonal microbiome differences and their potential health implications.

Gene-by-environment interactions influence the fitness cost of gene copy-number variation in yeast

Variation in gene copy number can alter gene expression and influence downstream phenotypes; thus copy-number variation provides a route for rapid evolution if the benefits outweigh the cost. We recently showed that genetic background significantly influences how yeast cells respond to gene overexpression, revealing that the fitness costs of copy-number variation can vary substantially with genetic background in a common-garden environment. But the interplay between copy-number variation tolerance and environment remains unexplored on a genomic scale. Here, we measured the tolerance to gene overexpression in four genetically distinct Saccharomyces cerevisiae strains grown under sodium chloride stress. Overexpressed genes that are commonly deleterious during sodium chloride stress recapitulated those commonly deleterious under standard conditions. However, sodium chloride stress uncovered novel differences in strain responses to gene overexpression. West African strain NCYC3290 and North American oak isolate YPS128 are more sensitive to sodium chloride stress than vineyard BC187 and laboratory strain BY4743. Consistently, NCYC3290 and YPS128 showed the greatest sensitivities to overexpression of specific genes. Although most genes were deleterious, hundreds were beneficial when overexpressed-remarkably, most of these effects were strain specific. Few beneficial genes were shared between the sodium chloride-sensitive isolates, implicating mechanistic differences behind their sodium chloride sensitivity. Transcriptomic analysis suggested underlying vulnerabilities and tolerances across strains, and pointed to natural copy-number variation of a sodium export pump that likely contributes to strain-specific responses to overexpression of other genes. Our results reveal extensive strain-by-environment interactions in the response to gene copy-number variation, raising important implications for the accessibility of copy-number variation-dependent evolutionary routes under times of stress.

Gene-by-environment interactions influence the fitness cost of gene copy-number variation in yeast

Variation in gene copy number can alter gene expression and influence downstream phenotypes; thus copy-number variation (CNV) provides a route for rapid evolution if the benefits outweigh the cost. We recently showed that genetic background significantly influences how yeast cells respond to gene over-expression (OE), revealing that the fitness costs of CNV can vary substantially with genetic background in a common-garden environment. But the interplay between CNV tolerance and environment remains unexplored on a genomic scale. Here we measured the tolerance to gene OE in four genetically distinct Saccharomyces cerevisiae strains grown under sodium chloride (NaCl) stress. OE genes that are commonly deleterious during NaCl stress recapitulated those commonly deleterious under standard conditions. However, NaCl stress uncovered novel differences in strain responses to gene OE. West African strain NCYC3290 and North American oak isolate YPS128 are more sensitive to NaCl stress than vineyard BC187 and laboratory strain BY4743. Consistently, NCYC3290 and YPS128 showed the greatest sensitivities to gene OE. Although most genes were deleterious, hundreds were beneficial when overexpressed - remarkably, most of these effects were strain specific. Few beneficial genes were shared between the NaCl-sensitive isolates, implicating mechanistic differences behind their NaCl sensitivity. Transcriptomic analysis suggested underlying vulnerabilities and tolerances across strains, and pointed to natural CNV of a sodium export pump that likely contributes to strain-specific responses to OE of other genes. Our results reveal extensive strain-by-environment interaction in the response to gene CNV, raising important implications for the accessibility of CNV-dependent evolutionary routes under times of stress.

Pitfalls of genotyping microbial communities with rapidly growing genome collections

Detecting genetic variants in metagenomic data is a priority for understanding the evolution, ecology, and functional characteristics of microbial communities. Many tools that perform this metagenotyping rely on aligning reads of unknown origin to a database of sequences from many species before calling variants. In this synthesis, we investigate how databases of increasingly diverse and closely related species have pushed the limits of current alignment algorithms, thereby degrading the performance of metagenotyping tools. We identify multi-mapping reads as a prevalent source of errors and illustrate a trade-off between retaining correct alignments versus limiting incorrect alignments, many of which map reads to the wrong species. Then we evaluate several actionable mitigation strategies and review emerging methods showing promise to further improve metagenotyping in response to the rapid growth in genome collections. Our results have implications beyond metagenotyping to the many tools in microbial genomics that depend upon accurate read mapping.

The E. coli pathobiont LF82 encodes a unique variant of σ70 that results in specific gene expression changes and altered phenotypes

LF82, an adherent invasive Escherichia coli pathobiont, is associated with ileal Crohn's disease, an inflammatory bowel disease of unknown etiology. Although LF82 contains no virulence genes, it carries several genetic differences, including single nucleotide polymorphisms (SNPs), that distinguish it from nonpathogenic E. coli. We have identified and investigated an extremely rare SNP that is within the highly conserved rpoD gene, encoding σ70, the primary sigma factor for RNA polymerase. We demonstrate that this single residue change (D445V) results in specific transcriptome and phenotypic changes that are consistent with multiple phenotypes observed in LF82, including increased antibiotic resistance and biofilm formation, modulation of motility, and increased capacity for methionine biosynthesis. Our work demonstrates that a single residue change within the bacterial primary sigma factor can lead to multiple alterations in gene expression and phenotypic changes, suggesting an underrecognized mechanism by which pathobionts and other strain variants with new phenotypes can emerge.

Genomic and phenotypic comparison of Prevotella intermedia strains possessing different virulence in vivo

Prevotella intermedia readily colonizes healthy dental biofilm and is associated with periodontal diseases. The viscous exopolysaccharide (EPS)-producing capability is known as a major virulence factor of P. intermedia 17 (Pi17). However, the inter-strain difference in P. intermedia regarding virulence-associated phenotype is not well studied. We compared in vivo virulence and whole genome sequences using five wild-type strains: ATCC 49046 (Pi49046), ATCC 15032 (Pi15032), ATCC 15033 (Pi15033), ATCC 25611 (Pi25611), and Pi17. Non-EPS producing Pi25611 was the least virulent in insect and mammalian models. Unexpectedly, Pi49046 did not produce viscous EPS but was the most virulent, followed by Pi17. Genomes of the five strains were quite similar but revealed subtle differences such as copy number variations and single nucleotide polymorphisms. Variations between strains were found in genes encoding glycosyltransferases and genes involved in the acquisition of carbohydrates and iron/haem. Based on these genetic variations, further analyses were performed. Phylogenetic and structural analyses discovered phosphoglycosyltransferases of Pi49046 and Pi17 have evolved to contain additional loops that may confer substrate specificity. Pi17, Pi15032, and Pi15033 displayed increased growth by various carbohydrates. Meanwhile, Pi49046 exhibited the highest activities for haemolysis and haem accumulation, as well as co-aggregation with Porphyromonas gingivalis harbouring fimA type II, which is more tied to periodontitis than other fimA types. Collectively, subtle genetic differences related to glycosylation and acquisition of carbohydrates and iron/haem may contribute to the diversity of virulence and phenotypic traits among P. intermedia strains. These variations may also reflect versatile strategies for within-host adaptation of P. intermedia.

Cytomegalovirus infection disrupts the influence of short-chain fatty acid producers on Treg/Th17 balance

BACKGROUND

Both the gut microbiota and chronic viral infections have profound effects on host immunity, but interactions between these influences have been only superficially explored. Cytomegalovirus (CMV), for example, infects approximately 80% of people globally and drives significant changes in immune cells. Similarly, certain gut-resident bacteria affect T-cell development in mice and nonhuman primates. It is unknown if changes imposed by CMV on the intestinal microbiome contribute to immunologic effects of the infection.

RESULTS

We show that rhesus cytomegalovirus (RhCMV) infection is associated with specific differences in gut microbiota composition, including decreased abundance of Firmicutes, and that the extent of microbial change was associated with immunologic changes including the proliferation, differentiation, and cytokine production of CD8+ T cells. Furthermore, RhCMV infection disrupted the relationship between short-chain fatty acid producers and Treg/Th17 balance observed in seronegative animals, showing that some immunologic effects of CMV are due to disruption of previously existing host-microbe relationships.

CONCLUSIONS

Gut microbes have an important influence on health and disease. Diet is known to shape the microbiota, but the influence of concomitant chronic viral infections is unclear. We found that CMV influences gut microbiota composition to an extent that is correlated with immunologic changes in the host. Additionally, pre-existing correlations between immunophenotypes and gut microbes can be subverted by CMV infection. Immunologic effects of CMV infection on the host may therefore be mediated by two different mechanisms involving gut microbiota. Video Abstract.

Strain-level profiling with picodroplet microfluidic cultivation reveals host-specific adaption of honeybee gut symbionts

BACKGROUND

Symbiotic gut microbes have a rich genomic and metabolic pool and are closely related to hosts' health. Traditional sequencing profiling masks the genomic and phenotypic diversity among strains from the same species. Innovative droplet-based microfluidic cultivation may help to elucidate the inter-strain interactions. A limited number of bacterial phylotypes colonize the honeybee gut, while individual strains possess unique genomic potential and critical capabilities, which provides a particularly good model for strain-level analyses.

RESULTS

Here, we construct a droplet-based microfluidic platform and generated ~ 6 × 108 droplets encapsulated with individual bacterial cells from the honeybee gut and cultivate in different media. Shotgun metagenomic analysis reveals significant changes in community structure after droplet-based cultivation, with certain species showing higher strain-level diversity than in gut samples. We obtain metagenome-assembled genomes, and comparative analysis reveal a potential novel cluster from Bifidobacterium in the honeybee. Interestingly, Lactobacillus panisapium strains obtained via droplet cultivation from Apis mellifera contain a unique set of genes encoding L-arabinofuranosidase, which is likely important for the survival of bacteria in competitive environments.

CONCLUSIONS

By encapsulating single bacteria cells inside microfluidic droplets, we exclude potential interspecific competition for the enrichment of rare strains by shotgun sequencing at high resolution. The comparative genomic analysis reveals underlying mechanisms for host-specific adaptations, providing intriguing insights into microbe-microbe interactions. The current approach may facilitate the hunting for elusive bacteria and paves the way for large-scale studies of more complex animal microbial communities. Video Abstract.

Incipient Sympatric Speciation and Evolution of Soil Bacteria Revealed by Metagenomic and Structured Non-Coding RNAs Analysis

Simple Summary

The microevolutionary dynamics of soil bacteria under microclimatic differences are largely unexplored in contrast to our improving knowledge of their vast diversity. In this study, we performed a comparative metagenomic analysis of two sharply divergent rocks and soil types at the Evolution Plateau (EP) in eastern Upper Galilee, Israel. We have identified the significant differences in bacterial taxonomic diversity, functions, and patterns of RNA-based gene regulation between the bacteria from two different soil types. Furthermore, we have identified several species with a significant genetic divergence of the same species between the two soil types, highlighting the soil bacteria’s incipient sympatric speciation.

Abstract

Soil bacteria respond rapidly to changes in new environmental conditions. For adaptation to the new environment, they could mutate their genome, which impacts the alternation of the functional and regulatory landscape. Sometimes, these genetic and ecological changes may drive the bacterial evolution and sympatric speciation. Although sympatric speciation has been controversial since Darwin suggested it in 1859, there are several strong theoretical or empirical evidences to support it. Sympatric speciation associated with soil bacteria remains largely unexplored. Here, we provide potential evidence of sympatric speciation of soil bacteria by comparison of metagenomics from two sharply contrasting abutting divergence rock and soil types (Senonian chalk and its rendzina soil, and abutting Pleistocene basalt rock and basalt soil). We identified several bacterial species with significant genetic differences in the same species between the two soil types and ecologies. We show that the bacterial community composition has significantly diverged between the two soils; correspondingly, their functions were differentiated in order to adapt to the local ecological stresses. The ecologies, such as water availability and pH value, shaped the adaptation and speciation of soil bacteria revealed by the clear-cut genetic divergence. Furthermore, by a novel analysis scheme of riboswitches, we highlight significant differences in structured non-coding RNAs between the soil bacteria from two divergence soil types, which could be an important driver for functional adaptation. Our study provides new insight into the evolutionary divergence and incipient sympatric speciation of soil bacteria under microclimatic ecological differences.

Promises of microbiome-based therapies

Humans harbour large quantities of microbes, including bacteria, fungi, viruses and archaea, in the gut. Patients with liver disease exhibit changes in the intestinal microbiota and gut barrier dysfunction. Preclinical models demonstrate the importance of the gut microbiota in the pathogenesis of various liver diseases. In this review, we discuss how manipulation of the gut microbiota can be used as a novel treatment approach for liver disease. We summarise current data on untargeted approaches, including probiotics and faecal microbiota transplantation, and precision microbiome-centered therapies, including engineered bacteria, postbiotics and phages, for the treatment of liver diseases.

Neural networks enable efficient and accurate simulation-based inference of evolutionary parameters from adaptation dynamics

The rate of adaptive evolution depends on the rate at which beneficial mutations are introduced into a population and the fitness effects of those mutations. The rate of beneficial mutations and their expected fitness effects is often difficult to empirically quantify. As these 2 parameters determine the pace of evolutionary change in a population, the dynamics of adaptive evolution may enable inference of their values. Copy number variants (CNVs) are a pervasive source of heritable variation that can facilitate rapid adaptive evolution. Previously, we developed a locus-specific fluorescent CNV reporter to quantify CNV dynamics in evolving populations maintained in nutrient-limiting conditions using chemostats. Here, we use CNV adaptation dynamics to estimate the rate at which beneficial CNVs are introduced through de novo mutation and their fitness effects using simulation-based likelihood-free inference approaches. We tested the suitability of 2 evolutionary models: a standard Wright-Fisher model and a chemostat model. We evaluated 2 likelihood-free inference algorithms: the well-established Approximate Bayesian Computation with Sequential Monte Carlo (ABC-SMC) algorithm, and the recently developed Neural Posterior Estimation (NPE) algorithm, which applies an artificial neural network to directly estimate the posterior distribution. By systematically evaluating the suitability of different inference methods and models, we show that NPE has several advantages over ABC-SMC and that a Wright-Fisher evolutionary model suffices in most cases. Using our validated inference framework, we estimate the CNV formation rate at the GAP1 locus in the yeast Saccharomyces cerevisiae to be 10-4.7 to 10-4 CNVs per cell division and a fitness coefficient of 0.04 to 0.1 per generation for GAP1 CNVs in glutamine-limited chemostats. We experimentally validated our inference-based estimates using 2 distinct experimental methods-barcode lineage tracking and pairwise fitness assays-which provide independent confirmation of the accuracy of our approach. Our results are consistent with a beneficial CNV supply rate that is 10-fold greater than the estimated rates of beneficial single-nucleotide mutations, explaining the outsized importance of CNVs in rapid adaptive evolution. More generally, our study demonstrates the utility of novel neural network-based likelihood-free inference methods for inferring the rates and effects of evolutionary processes from empirical data with possible applications ranging from tumor to viral evolution.

Early indicators of microbial strain dysbiosis in the human gastrointestinal microbial community of certain healthy humans and hospitalized COVID-19 patients

Dysbiosis in the human gastrointestinal microbial community could functionally impact microbial metabolism and colonization resistance to pathogens. To further elucidate the indicators of microbial strain dysbiosis, we have developed an analytic method that detects patterns of presence/absence of selected KEGG metabolic pathways for a selected strain (PKS). Using a metagenomic data set consisting of multiple high-density fecal samples from six normal individuals, we found three had unique PKS for important gut commensal microbes, Bacteroides vulgatus and Bacteroides uniformis, at all sample times examined. Two individuals had multiple shared PKS clusters of B. vulgatus or B. uniformis over time. Analysis of a data set of high-density fecal samples from eight COVID-19 hospitalized patients taken over a short period revealed that two patients had shared PKS clusters for B. vulgatus and one shared cluster for B. uniformis. Our analysis demonstrates that while the majority of normal individuals with no B. vulgatus or B. uniformis strain change over time have unique PKS, in some healthy humans and patients hospitalized with COVID-19, we detected shared PKS clusters at the different times suggesting a slowing down of the intrinsic rates of strain variation that could eventually lead to a dysbiosis in the microbial strain community.

MetaPop: a pipeline for macro- and microdiversity analyses and visualization of microbial and viral metagenome-derived populations

BACKGROUND

Microbes and their viruses are hidden engines driving Earth's ecosystems from the oceans and soils to humans and bioreactors. Though gene marker approaches can now be complemented by genome-resolved studies of inter-(macrodiversity) and intra-(microdiversity) population variation, analytical tools to do so remain scattered or under-developed.

RESULTS

Here, we introduce MetaPop, an open-source bioinformatic pipeline that provides a single interface to analyze and visualize microbial and viral community metagenomes at both the macro- and microdiversity levels. Macrodiversity estimates include population abundances and α- and β-diversity. Microdiversity calculations include identification of single nucleotide polymorphisms, novel codon-constrained linkage of SNPs, nucleotide diversity (π and θ), and selective pressures (pN/pS and Tajima's D) within and fixation indices (FST) between populations. MetaPop will also identify genes with distinct codon usage. Following rigorous validation, we applied MetaPop to the gut viromes of autistic children that underwent fecal microbiota transfers and their neurotypical peers. The macrodiversity results confirmed our prior findings for viral populations (microbial shotgun metagenomes were not available) that diversity did not significantly differ between autistic and neurotypical children. However, by also quantifying microdiversity, MetaPop revealed lower average viral nucleotide diversity (π) in autistic children. Analysis of the percentage of genomes detected under positive selection was also lower among autistic children, suggesting that higher viral π in neurotypical children may be beneficial because it allows populations to better "bet hedge" in changing environments. Further, comparisons of microdiversity pre- and post-FMT in autistic children revealed that the delivery FMT method (oral versus rectal) may influence viral activity and engraftment of microdiverse viral populations, with children who received their FMT rectally having higher microdiversity post-FMT. Overall, these results show that analyses at the macro level alone can miss important biological differences.

CONCLUSIONS

These findings suggest that standardized population and genetic variation analyses will be invaluable for maximizing biological inference, and MetaPop provides a convenient tool package to explore the dual impact of macro- and microdiversity across microbial communities. Video abstract.

Strain-level fitness in the gut microbiome is an emergent property of glycans and a single metabolite

The human gut microbiota resides within a diverse chemical environment challenging our ability to understand the forces shaping this ecosystem. Here, we reveal that fitness of the Bacteroidales, the dominant order of bacteria in the human gut, is an emergent property of glycans and one specific metabolite, butyrate. Distinct sugars serve as strain-variable fitness switches activating context-dependent inhibitory functions of butyrate. Differential fitness effects of butyrate within the Bacteroides are mediated by species-level variation in Acyl-CoA thioesterase activity and nucleotide polymorphisms regulating an Acyl-CoA transferase. Using in vivo multi-omic profiles, we demonstrate Bacteroides fitness in the human gut is associated together, but not independently, with Acyl-CoA transferase expression and butyrate. Our data reveal that each strain of the Bacteroides exists within a unique fitness landscape based on the interaction of chemical components unpredictable by the effect of each part alone mediated by flexibility in the core genome.

Studies of xenobiotic-induced gut microbiota dysbiosis: from correlation to mechanisms

Environmental chemicals can alter gut microbial community composition, known as dysbiosis. However, the gut microbiota is a highly dynamic system and its functions are still largely underexplored. Likewise, it is unclear whether xenobiotic exposure affects host health through impairing host-microbiota interactions. Answers to this question not only can lead to a more precise understanding of the toxic effects of xenobiotics but also can provide new targets for the development of new therapeutic strategies. Here, we aim to identify the major challenges in the field of microbiota-exposure research and highlight the need to exam the health effects of xenobiotic-induced gut microbiota dysbiosis in host bodies. Although the changes of gut microbiota frequently co-occur with the xenobiotic exposure, the causal relationship of xenobiotic-induced microbiota dysbiosis and diseases is rarely established. The high dynamics of the gut microbiota and the complex interactions among exposure, microbiota, and host, are the major challenges to decipher the specific health effects of microbiota dysbiosis. The next stage of study needs to combine various technologies to precisely assess the xenobiotic-induced gut microbiota perturbation and the subsequent health effects in host bodies. The exposure, gut microbiota dysbiosis, and disease outcomes have to be causally linked. Many microbiota-host interactions are established by previous studies, including signaling metabolites and response pathways in the host, which may use as start points for future research to examine the mechanistic interactions of exposure, gut microbiota, and host health. In conclusion, to precisely understand the toxicity of xenobiotics and develop microbiota-based therapies, the causal and mechanistic links of exposure and microbiota dysbiosis have to be established in the next stage study.

KOMB: K-core based de novo characterization of copy number variation in microbiomes

Characterizing metagenomes via kmer-based, database-dependent taxonomic classification has yielded key insights into underlying microbiome dynamics. However, novel approaches are needed to track community dynamics and genomic flux within metagenomes, particularly in response to perturbations. We describe KOMB, a novel method for tracking genome level dynamics within microbiomes. KOMB utilizes K-core decomposition to identify Structural variations (SVs), specifically, population-level Copy Number Variation (CNV) within microbiomes. K-core decomposition partitions the graph into shells containing nodes of induced degree at least K, yielding reduced computational complexity compared to prior approaches. Through validation on a synthetic community, we show that KOMB recovers and profiles repetitive genomic regions in the sample. KOMB is shown to identify functionally-important regions in Human Microbiome Project datasets, and was used to analyze longitudinal data and identify keystone taxa in Fecal Microbiota Transplantation (FMT) samples. In summary, KOMB represents a novel graph-based, taxonomy-oblivious, and reference-free approach for tracking CNV within microbiomes. KOMB is open source and available for download at https://gitlab.com/treangenlab/komb.

What is unknown in using microbiota as a therapeutic?

Fecal microbiota transplantation (FMT) has been used extensively in the treatment of various gastrointestinal and extraintestinal conditions, despite that there are still a lot of missing gaps in our knowledge in the gut microbiota and its behavior. This article describes the unknowns in microbiota biology (undetected microbes, uncertain colonization, unclear mechanisms of action, uncertain indications, unsure long-term efficacy, or side effects). We discuss how these unknowns may affect the therapeutic uses of FMT, and the potentials and caveats of other related microbiota-based therapies. When used as an experimental therapy or last resort in difficult conditions, caution should be taken against inadvertent complications. Clear documentations of post-treatment events should be made mandatory, classified, and graded as in clinical trials. Further robust scientific experiments and properly designed clinical studies are needed.

Bacterial genomic epidemiology with mixed samples

Genomic epidemiology is a tool for tracing transmission of pathogens based on whole-genome sequencing. We introduce the mGEMS pipeline for genomic epidemiology with plate sweeps representing mixed samples of a target pathogen, opening the possibility to sequence all colonies on selective plates with a single DNA extraction and sequencing step. The pipeline includes the novel mGEMS read binner for probabilistic assignments of sequencing reads, and the scalable pseudoaligner Themisto. We demonstrate the effectiveness of our approach using closely related samples in a nosocomial setting, obtaining results that are comparable to those based on single-colony picks. Our results lend firm support to more widespread consideration of genomic epidemiology with mixed infection samples.

High-resolution sweep metagenomics using fast probabilistic inference

Determining the composition of bacterial communities beyond the level of a genus or species is challenging because of the considerable overlap between genomes representing close relatives. Here, we present the mSWEEP pipeline for identifying and estimating the relative sequence abundances of bacterial lineages from plate sweeps of enrichment cultures. mSWEEP leverages biologically grouped sequence assembly databases, applying probabilistic modelling, and provides controls for false positive results. Using sequencing data from major pathogens, we demonstrate significant improvements in lineage quantification and detection accuracy. Our pipeline facilitates investigating cultures comprising mixtures of bacteria, and opens up a new field of plate sweep metagenomics.

Captivity and the co-diversification of great ape microbiomes

Wild great apes harbor clades of gut bacteria that are restricted to each host species. Previous research shows the evolutionary relationships among several host-restricted clades mirror those of great-ape species. However, processes such as geographic separation, host-shift speciation, and host-filtering based on diet or gut physiology can generate host-restricted bacterial clades and mimic patterns of co-diversification across host species. To gain insight into the distribution of host-restricted taxa, we examine captive great apes living under conditions where sharing of bacterial strains is readily possible. Here, we show that increased sampling of wild and captive apes identifies additional host-restricted lineages whose relationships are not concordant with the host phylogeny. Moreover, the gut microbiomes of captive apes converge through the displacement of strains that are restricted to their wild conspecifics by human-restricted strains. We demonstrate that host-restricted and co-diversifying bacterial strains in wild apes lack persistence and fidelity in captive environments.

Infants with cystic fibrosis have altered fecal functional capacities with potential clinical and metabolic consequences

Background

Infants with cystic fibrosis (CF) suffer from gastrointestinal (GI) complications, including pancreatic insufficiency and intestinal inflammation, which have been associated with impaired nutrition and growth. Recent evidence identified altered fecal microbiota taxonomic compositions in infants with CF relative to healthy infants that were characterized by differences in the abundances of taxa associated with GI health and nutrition. Furthermore, these taxonomic differences were more pronounced in low length infants with CF, suggesting a potential link to linear growth failure. We hypothesized that these differences would entail shifts in the microbiome’s functional capacities that could contribute to inflammation and nutritional failure in infants with CF.

Results

To test this hypothesis, we compared fecal microbial metagenomic content between healthy infants and infants with CF, supplemented with an analysis of fecal metabolomes in infants with CF. We identified notable differences in CF fecal microbial functional capacities, including metabolic and environmental response functions, compared to healthy infants that intensified during the first year of life. A machine learning-based longitudinal metagenomic age analysis of healthy and CF fecal metagenomic functional profiles further demonstrated that these differences are characterized by a CF-associated delay in the development of these functional capacities. Moreover, we found metagenomic differences in functions related to metabolism among infants with CF that were associated with diet and antibiotic exposure, and identified several taxa as potential drivers of these functional differences. An integrated metagenomic and metabolomic analysis further revealed that abundances of several fecal GI metabolites important for nutrient absorption, including three bile acids, correlated with specific microbes in infants with CF.

Conclusions

Our results highlight several metagenomic and metabolomic factors, including bile acids and other microbial metabolites, that may impact nutrition, growth, and GI health in infants with CF. These factors could serve as promising avenues for novel microbiome-based therapeutics to improve health outcomes in these infants.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-021-02305-z.

Genetic identity and genotype × genotype interactions between symbionts outweigh species level effects in an insect microbiome

Microbial symbionts often alter the phenotype of their host. Benefits and costs to hosts depend on many factors, including host genotype, symbiont species and genotype, and environmental conditions. Here, we present a study demonstrating genotype-by-genotype (G×G) interactions between multiple species of endosymbionts harboured by an insect, and the first to quantify the relative importance of G×G interactions compared with species interactions in such systems. In the most extensive study to date, we microinjected all possible combinations of five Hamiltonella defensa and five Fukatsuia symbiotica (X-type; PAXS) isolates into the pea aphid, Acyrthosiphon pisum. We applied several ecological challenges: a parasitoid wasp, a fungal pathogen, heat shock, and performance on different host plants. Surprisingly, genetic identity and genotype × genotype interactions explained far more of the phenotypic variation (on average 22% and 31% respectively) than species identity or species interactions (on average 12% and 0.4%, respectively). We determined the costs and benefits associated with co-infection, and how these compared to corresponding single infections. All phenotypes were highly reliant on individual isolates or interactions between isolates of the co-infecting partners. Our findings highlight the importance of exploring the eco-evolutionary consequences of these highly specific interactions in communities of co-inherited species.

Embracing Metagenomic Complexity with a Genome-Free Approach

A central paradigm in microbiome data analysis, which we term the genome-centric paradigm, is that a linear (non-branching) DNA sequence is the ideal representation of a microbial genome. This representation is natural, as microbes indeed have non-branching genomes. Tremendous discoveries in microbiology were made under this paradigm, but is it always optimal for microbiome research? In this Commentary, we claim that the realization of this paradigm in metagenomic assembly, a fundamental step in the “metagenomics analysis pipeline,” suboptimally models the extensive genomic variability present in the microbiome. We outline our efforts to address these issues with a “genome-free” approach that eschews linear genomic representations in favor of a pan-metagenomic graph.

Genetic divergence and functional convergence of gut bacteria between the Eastern honey bee Apis cerana and the Western honey bee Apis mellifera

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The inter-species diversity of A. cerana and A. mellifera core gut bacteria was revealed.

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Core bacterial species of A. cerana and A. mellifera are distinctive in function.

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Functional profile of overall gut community of A. cerana and A. mellifera are similar.

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Metabolome showed that A. cerana and A. mellifera gut bacteria have similar metabolic capability.

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A. cerana and A. mellifera core gut bacteria have no strict host specificity.

Introduction

The functional relevance of intra-species diversity in natural microbial communities remains largely unexplored. The guts of two closely related honey bee species, Apis cerana and A. mellifera, are colonised by a similar set of core bacterial species composed of host-specific strains, thereby providing a good model for an intra-species diversity study.

Objectives

We aim to assess the functional relevance of intra-species diversity of A. cerana and A. mellifera gut microbiota.

Methods

Honey bee workers were collected from four regions of China. Their gut microbiomes were investigated by shotgun metagenomic sequencing, and the bacterial compositions were compared at the species level. A cross-species colonisation assay was conducted, with the gut metabolomes being characterised by LC-MS/MS.

Results

Comparative analysis showed that the strain composition of the core bacterial species was host-specific. These core bacterial species presented distinctive functional profiles between the hosts. However, the overall functional profiles of the A. cerana and A. mellifera gut microbiomes were similar; this was further supported by the consistency of the honey bees’ gut metabolome, as the gut microbiota of different honey bee species showed rather similar metabolic profiles in the cross-species colonisation assay. Moreover, this experiment also demonstrated that the gut microbiota of A. cerana and A. mellifera could cross colonise between the two honey bee species.

Conclusion

Our findings revealed functional differences in most core gut bacteria between the guts of A. cerana and A. mellifera, which may be associated with their inter-species diversity. However, the functional profiles of the overall gut microbiomes between the two honey bee species converge, probably as a result of the overlapping ecological niches of the two species. Our findings provide critical insights into the evolution and functional roles of the mutualistic microbiota of honey bees and reveal that functional redundancy could stabilise the gene content diversity at the strain-level within the gut community.

A Cutibacterium acnes antibiotic modulates human skin microbiota composition in hair follicles

The composition of the skin microbiota varies widely among individuals when sampled at the same body site. A key question is which molecular factors determine strain-level variability within sub-ecosystems of the skin microbiota. Here, we used a genomics-guided approach to identify an antibacterial biosynthetic gene cluster in Cutibacterium acnes (formerly Propionibacterium acnes), a human skin commensal bacterium that is widely distributed across individuals and skin sites. Experimental characterization of this biosynthetic gene cluster resulted in identification of a new thiopeptide antibiotic, cutimycin. Analysis of individual human skin hair follicles revealed that cutimycin contributed to the ecology of the skin hair follicle microbiota and helped to reduce colonization of skin hair follicles by Staphylococcus species.

Avenanthramide Metabotype from Whole-Grain Oat Intake is Influenced by Faecalibacterium prausnitzii in Healthy Adults

BACKGROUND

Oat has been widely accepted as a key food for human health. It is becoming increasingly evident that individual differences in metabolism determine how different individuals benefit from diet. Both host genetics and the gut microbiota play important roles on the metabolism and function of dietary compounds.

OBJECTIVES

To investigate the mechanism of individual variations in response to whole-grain (WG) oat intake.

METHODS

We used the combination of in vitro incubation assays with human gut microbiota, mouse and human S9 fractions, chemical analyses, germ-free (GF) mice, 16S rRNA sequencing, gnotobiotic techniques, and a human feeding study.

RESULTS

Avenanthramides (AVAs), the signature bioactive polyphenols of WG oat, were not metabolized into their dihydro forms, dihydro-AVAs (DH-AVAs), by both human and mouse S9 fractions. DH-AVAs were detected in the colon and the distal regions but not in the proximal and middle regions of the perfused mouse intestine, and were in specific pathogen-free (SPF) mice but not in GF mice. A kinetic study of humans fed oat bran showed that DH-AVAs reached their maximal concentrations at much later time points than their corresponding AVAs (10.0-15.0 hours vs. 4.0-4.5 hours, respectively). We observed interindividual variations in the metabolism of AVAs to DH-AVAs in humans. Faecalibacterium prausnitzii was identified as the individual bacterium to metabolize AVAs to DH-AVAs by 16S rRNA sequencing analysis. Moreover, as opposed to GF mice, F. prausnitzii-monocolonized mice were able to metabolize AVAs to DH-AVAs.

CONCLUSIONS

These findings demonstrate that the presence of intestinal F. prausnitzii is indispensable for proper metabolism of AVAs in both humans and mice. We propose that the abundance of F. prausnitzii can be used to subcategorize individuals into AVA metabolizers and nonmetabolizers after WG oat intake. This study was registered at clinicaltrials.gov as NCT04335435.

Microbial driven genetic variation in holobionts

Genetic variation in holobionts, (host and microbiome), occurring by changes in both host and microbiome genomes, can be observed from two perspectives: observable variations and the processes that bring about the variation. The observable includes the enormous genetic diversity of prokaryotes, which gave rise to eukaryotic organisms. Holobionts then evolved a rich microbiome with a stable core containing essential genes, less so common taxa, and a more diverse non-core enabling considerable genetic variation. The result being that, the human gut microbiome, for example, contains 1,000 times more unique genes than are present in the human genome. Microbial driven genetic variation processes in holobionts include: (1) Acquisition of novel microbes from the environment, which bring in multiple genes in one step, (2) amplification/reduction of certain microbes in the microbiome, that contribute to holobiont` s adaptation to changing conditions, (3) horizontal gene transfer between microbes and between microbes and host, (4) mutation, which plays an important role in optimizing interactions between different microbiota and between microbiota and host. We suggest that invertebrates and plants, where microbes can live intracellularly, have a greater chance of genetic exchange between microbiota and host, thus a greater chance of vertical transmission and a greater effect of microbiome on evolution of host than vertebrates. However, even in vertebrates the microbiome can aid in environmental fluctuations by amplification/reduction and by acquisition of novel microorganisms.

Taxonomic composition and variation in the gut microbiota of laboratory mice

The gut microbiota can affect host health, including humans. Mouse models have been used extensively to study the relationships between the host and the gut microbiota. With the development of cost-effective high-throughput DNA sequencing, several methods have been used to identify members of the gut microbiota of laboratory mice. In recent years, the amount of research and knowledge about the mouse gut microbiota has exploded, leading to significant breakthroughs in understanding of the taxonomic composition of and variation in this community. In addition, the rapidly increasing volume of data has allowed the development of public resources for exploring the mouse gut microbiota. In this review, we describe the concepts and pros and cons of basic methodologies that can be used to determine the gut bacterial profile in laboratory mice. We also present the key bacterial components of the mouse gut microbiota from the phylum to the species level and then compare them with those identified in other references. Additionally, we discuss variations in the mouse gut microbiota and their association with experiments using mice. Finally, we summarize the properties and functions of currently available public resources for exploring the mouse gut microbiota.

The long-term genetic stability and individual specificity of the human gut microbiome

By following up the gut microbiome, 51 human phenotypes and plasma levels of 1,183 metabolites in 338 individuals after 4 years, we characterize microbial stability and variation in relation to host physiology. Using these individual-specific and temporally stable microbial profiles, including bacterial SNPs and structural variations, we develop a microbial fingerprinting method that shows up to 85% accuracy in classifying metagenomic samples taken 4 years apart. Application of our fingerprinting method to the independent HMP cohort results in 95% accuracy for samples taken 1 year apart. We further observe temporal changes in the abundance of multiple bacterial species, metabolic pathways, and structural variation, as well as strain replacement. We report 190 longitudinal microbial associations with host phenotypes and 519 associations with plasma metabolites. These associations are enriched for cardiometabolic traits, vitamin B, and uremic toxins. Finally, mediation analysis suggests that the gut microbiome may influence cardiometabolic health through its metabolites.

Interactions of host-associated multispecies bacterial communities

The oral microbiome comprises microbial communities colonizing biotic (epithelia, mucosa) and abiotic (enamel) surfaces. Different communities are associated with health (eg, immune development, pathogen resistance) and disease (eg, tooth loss and periodontal disease). Like any other host-associated microbiome, colonization and persistence of both beneficial and dysbiotic oral microbiomes are dictated by successful utilization of available nutrients and defense against host and competitor assaults. This chapter will explore these general features of microbe-host interactions through the lens of symbiotic (mutualistic and antagonistic/pathogenic) associations with nonmammalian animals. Investigations in such systems across a broad taxonomic range have revealed conserved mechanisms and processes that underlie the complex associations among microbes and between microbes and hosts.

Long-term evolution and short-term adaptation of microbiota strains and sub-strains in mice

Isobiotic mice, with an identical stable microbiota composition, potentially allow models of host-microbial mutualism to be studied over time and between different laboratories. To understand microbiota evolution in these models, we carried out a 6-year experiment in mice colonized with 12 representative taxa. Increased non-synonymous to synonymous mutation rates indicate positive selection in multiple taxa, particularly for genes annotated for nutrient acquisition or replication. Microbial sub-strains that evolved within a single taxon can stably coexist, consistent with niche partitioning of ecotypes in the complex intestinal environment. Dietary shifts trigger rapid transcriptional adaptation to macronutrient and micronutrient changes in individual taxa and alterations in taxa biomass. The proportions of different sub-strains are also rapidly altered after dietary shift. This indicates that microbial taxa within a mouse colony adapt to changes in the intestinal environment by long-term genomic positive selection and short-term effects of transcriptional reprogramming and adjustments in sub-strain proportions.

The structure and diversity of strain-level variation in vaginal bacteria

The vaginal microbiome plays an important role in human health and species of vaginal bacteria have been associated with reproductive disease. Strain-level variation is also thought to be important, but the diversity, structure and evolutionary history of vaginal strains is not as well characterized. We developed and validated an approach to measure strain variation from metagenomic data based on SNPs within the core genomes for six species of vaginal bacteria: Gardnerella vaginalis, Lactobacillus crispatus, Lactobacillus iners, Lactobacillus jensenii, Lactobacillus gasseri and Atopobium vaginae. Despite inhabiting the same environment, strain diversity and structure varies across species. All species except L. iners are characterized by multiple distinct groups of strains. Even so, strain diversity is lower in the Lactobacillus species, consistent with a more recent colonization of the human vaginal microbiome. Both strain diversity and the frequency of multi-strain samples is related to species-level diversity of the microbiome in which they occur, suggesting similar ecological factors influencing diversity within the vaginal niche. We conclude that the structure of strain-level variation provides both the motivation and means of testing whether strain-level differences contribute to the function and health consequences of the vaginal microbiome.

Conducting research on diet-microbiome interactions: A review of current challenges, essential methodological principles, and recommendations for best practice in study design

Diet is one of the strongest modulators of the gut microbiome. However, the complexity of the interactions between diet and the microbial community emphasises the need for a robust study design and continued methodological development. This review aims to summarise considerations for conducting high-quality diet-microbiome research, outline key challenges unique to the field, and provide advice for addressing these in a practical manner useful to dietitians, microbiologists, gastroenterologists and other diet-microbiome researchers. Searches of databases and references from relevant articles were conducted using the primary search terms 'diet', 'diet intervention', 'dietary analysis', 'microbiome' and 'microbiota', alone or in combination. Publications were considered relevant if they addressed methods for diet and/or microbiome research, or were a human study relevant to diet-microbiome interactions. Best-practice design in diet-microbiome research requires appropriate consideration of the study population and careful choice of trial design and data collection methodology. Ongoing challenges include the collection of dietary data that accurately reflects intake at a timescale relevant to microbial community structure and metabolism, measurement of nutrients in foods pertinent to microbes, improving ability to measure and understand microbial metabolic and functional properties, adequately powering studies, and the considered analysis of multivariate compositional datasets. Collaboration across the disciplines of nutrition science and microbiology is crucial for high-quality diet-microbiome research. Improvements in our understanding of the interaction between nutrient intake and microbial metabolism, as well as continued methodological innovation, will facilitate development of effective evidence-based personalised dietary treatments.

Copy number variation: Characteristics, evolutionary and pathological aspects

Copy number variants (CNVs) were the subject of extensive research in the past years. They are common features of the human genome that play an important role in evolution, contribute to population diversity, development of certain diseases, and influence host–microbiome interactions. CNVs have found application in the molecular diagnosis of many diseases and in non-invasive prenatal care, but their full potential is only emerging. CNVs are expected to have a tremendous impact on screening, diagnosis, prognosis, and monitoring of several disorders, including cancer and cardiovascular disease. Here, we comprehensively review basic definitions of the term CNV, outline mechanisms and factors involved in CNV formation, and discuss their evolutionary and pathological aspects. We suggest a need for better defined distinguishing criteria and boundaries between known types of CNVs.

From Binary Model Systems to the Human Microbiome: Factors That Drive Strain Specificity in Host-Symbiont Associations

Microbial symbionts are ubiquitous and can have significant impact on hosts. These impacts can vary in the sign (positive or negative) and degree depending on the identity of the interacting partners. Studies on host-symbiont associations indicate that subspecies (strain) genetic variation can influence interaction outcomes, making it necessary to go beyond species-level distinction to understand host-symbiont dynamics. In this review, we discuss examples of strain specificity found in host-symbiont associations, from binary model systems to the human microbiome. Although host and bacterial factors identified as mediators for specificity could be distinct at the molecular level, they generally fall into two broad functional categories: (1) those that contribute a required activity in support of the association and (2) those involved in antagonistic interactions with organisms outside of the association. We argue here based on current literature that factors from these two categories can work in concert to drive strain specificity and that this strain specificity must be considered to fully understand the molecular and ecological dynamics of host-symbiont associations, including the human microbiome.

Copy Number Variation: Methods and Clinical Applications

Gains and losses of large segments of genomic DNA, known as copy number variants (CNVs) gained considerable interest in clinical diagnostics lately, as particular forms may lead to inherited genetic diseases. In recent decades, researchers developed a wide variety of cytogenetic and molecular methods with different detection capabilities to detect clinically relevant CNVs. In this review, we summarize methodological progress from conventional approaches to current state of the art techniques capable of detecting CNVs from a few bases up to several megabases. Although the recent rapid progress of sequencing methods has enabled precise detection of CNVs, determining their functional effect on cellular and whole-body physiology remains a challenge. Here, we provide a comprehensive list of databases and bioinformatics tools that may serve as useful assets for researchers, laboratory diagnosticians, and clinical geneticists facing the challenge of CNV detection and interpretation.

PStrain: An Iterative Microbial Strains Profiling Algorithm for Shotgun Metagenomic Sequencing Data

MOTIVATION

The microbial community plays an essential role in human diseases and physiological activities. The functions of microbes can differ due to strain-level differences in the genome sequences. Shotgun metagenomic sequencing allows us to profile the strains in microbial communities practically. However, current methods are underdeveloped due to the highly similar sequences among strains. We observe that strains genotypes at the same single nucleotide variant (SNV) locus can be speculated by the genotype frequencies. Also, the variants in different loci covered by the same reads can provide evidence that they reside on the same strain.

RESULTS

These insights inspire us to design PStrain, an optimization method that utilizes genotype frequencies and the reads which cover multiple SNV loci to profile strains iteratively based on SNVs in a set of MetaPhlAn2 marker genes. Compared to the state-of-art methods, PStrain, on average, improved the performance of inferring strains abundances and genotypes by 87.75% and 59.45%, respectively. We have applied the PStrain package to the dataset with two cohorts of colorectal cancer (CRC) and found that the sequences of Bacteroides coprocola strains are significantly different between CRC and control samples, which is the first time to report the potential role of B. coprocola in the gut microbiota of CRC.

AVAILABILITY

https://github.com/wshuai294/PStrain.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

How wide is the application of genetic big data in biomedicine

In the era of big data, massive genetic data, as a new industry, has quickly swept almost all industries, especially the pharmaceutical industry. As countries around the world start to build their own gene banks, scientists study the data to explore the origins and migration of humans. Moreover, big data encourage the development of cancer therapy and bring good news to cancer patients. Big datum has been involved in the study of many diseases, and it has been found that analyzing diseases at the gene level can lead to more beneficial treatment options than ordinary treatments. This review will introduce the development of extensive data in medical research from the perspective of big data and tumor, neurological and psychiatric diseases, cardiovascular diseases, other applications and the development direction of big data in medicine.

Phenotypic and Genomic Analysis of Clostridium beijerinckii NRRL B-598 Mutants With Increased Butanol Tolerance

N-Butanol, a valuable solvent and potential fuel extender, can be produced via acetone-butanol-ethanol (ABE) fermentation. One of the main drawbacks of ABE fermentation is the high toxicity of butanol to producing cells, leading to cell membrane disruption, low culture viability and, consequently, low produced concentrations of butanol. The goal of this study was to obtain mutant strains of Clostridium beijerinckii NRRL B-598 with improved butanol tolerance using random chemical mutagenesis, describe changes in their phenotypes compared to the wild-type strain and reveal changes in the genome that explain improved tolerance or other phenotypic changes. Nine mutant strains with stable improved features were obtained by three different approaches and, for two of them, ethidium bromide (EB), a known substrate of efflux pumps, was used for either selection or as a mutagenic agent. It is the first utilization of this approach for the development of butanol-tolerant mutants of solventogenic clostridia, for which generally there is a lack of knowledge about butanol efflux or efflux mechanisms and their regulation. Mutant strains exhibited increase in butanol tolerance from 36% up to 127% and the greatest improvement was achieved for the strains for which EB was used as a mutagenic agent. Additionally, increased tolerance to other substrates of efflux pumps, EB and ethanol, was observed in all mutants and higher antibiotic tolerance in some of the strains. The complete genomes of mutant strains were sequenced and revealed that improved butanol tolerance can be attributed to mutations in genes encoding typical stress responses (chemotaxis, autolysis or changes in cell membrane structure), but, also, to mutations in genes X276_07980 and X276_24400, encoding efflux pump regulators. The latter observation confirms the importance of efflux in butanol stress response of the strain and offers new targets for rational strain engineering.