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Clostridioides difficile infection (CDI) is the fifth leading cause of death from nonmalignant gastrointestinal disease in the United States. The contribution of resistance to C. difficile-active antibiotics to the outcomes of CDI is unclear. We evaluated the antimicrobial susceptibility of C. difficile isolates in a U.S. hospital and determined associations of clinical variables and binary toxin positivity with antibiotic resistance. C. difficile spores were cultured from fecal specimens of adult patients with CDI for genotyping and antimicrobial susceptibility assay (for clindamycin [CLI], fidaxomicin [FDX], metronidazole [MTZ], moxifloxacin [MXF], tigecycline [TGC], and vancomycin [VAN]). Electronic medical records were reviewed for clinical data extraction. Ninety-seven of 130 (75%) fecal samples grew toxigenic C. difficile in culture. Most of the isolates were tcdA(+) tcdB(+) cdtB(−) (80.4%), and 18.6% and 1% were tcdA(+) tcdB(+) cdtB(+) and tcdA(−) tcdB(+) cdtB(+), respectively. Susceptibility to VAN, MTZ, FDX, TGC, MXF, and CLI was 96%, 94%, 100%, 100%, 8%, and 79%, respectively. Six isolates, all cdtB positive and belonging to the 027 ribotype, were resistant to VAN and/or MTZ. Higher MICs were found in isolates with a mutation in the VAN-related resistance gene vanR, but not vanS. In addition, cdtB(+) isolates exhibited higher MICs of VAN, MTZ, TGC, CLI, and MXF compared to cdtB(−) strains. Patients with greater intestinal inflammation or severe disease were more likely to be infected with cdtB(+) strains. Decreased susceptibility to antibiotics is not directly associated with either severe or recurrent CDI. However, antimicrobial susceptibility of C. difficile is decreased in strains positive for the binary toxin gene.

On the macroevolutionary timescale, does trait evolution proceed gradually or by rapid bursts (pulses) separated by prolonged periods of stasis or slow evolution? Although studies have shown pulsed evolution is prevalent in animals, our knowledge about the tempo and mode of evolution across the tree of life is very limited. This long-standing debate calls for a test in bacteria and archaea, the most ancient and diverse forms of life with unique population genetic properties (asexual reproduction, large population sizes, short generation times, high dispersal rates and extensive lateral gene transfers). Using a likelihood-based framework, we analyzed evolutionary patterns of four microbial genomic traits (genome size, genome GC%, 16S rRNA GC% and the nitrogen use in proteins) on a broad macroevolutionary timescale. Our model fitting of phylogenetic comparative data shows that pulsed evolution is not only present, but also prevalent and predominant in microbial genomic trait evolution. Interestingly, for the first time, we detected two distinct types of pulsed evolution (small frequent and large rare jumps) that are predicted by the punctuated equilibrium and quantum evolution theories. Our findings suggest that major bacterial lineages could have originated in quick bursts and pulsed evolution is a common theme across the tree of life despite the drastically different population genetic properties of bacteria, archaea and eukaryotes.

Early detection of coronavirus disease 2019 (COVID-19) is critical for both initiating appropriate treatment and preventing the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent. A simple and rapid diagnostic test that can be performed without any expensive equipment would be valuable for clinicians working in a low-resource setting. Here, we report a point-of-care detection technique for COVID-19 that combines the power of isothermal amplification (reverse transcription helicase-dependent amplification, RT-HDA) and dipstick technologies. The limit of detection of this diagnostic test is six copies of SARS-CoV-2 µl−1 in clinical specimens. Of the 22 clinical specimens tested, RT-HDA-coupled dipstick correctly identified all positive and negative specimens. The RT-HDA can be performed over a heating block and the results can be interpreted visually with the dipstick technology without any specialized equipment. Furthermore, the RT-HDA-coupled dipstick could be performed in a short turnaround time of ~2 h. Thus, the RT-HDA-coupled dipstick could serve as a point-of-care diagnostic test for COVID-19 in a low-resource environment.

Aim To investigate the role of the gut microbiome in regulating key insulin homeostasis traits (insulin sensitivity, insulin secretion and insulin clearance) whose dysfunction leads to type 2 diabetes (T2D). Materials and methods The Microbiome and Insulin Longitudinal Evaluation Study (MILES) focuses on African American and non-Hispanic white participants aged 40-80 years without diabetes. Three study visits are planned (at baseline, 15 and 30 months). Baseline measurements include assessment of the stool microbiome and administration of an oral glucose tolerance test, which will yield indexes of insulin sensitivity, insulin secretion and insulin clearance. The gut microbiome profile (composition and function) will be determined using whole metagenome shotgun sequencing along with analyses of plasma short chain fatty acids. Additional data collected include dietary history, sociodemographic factors, health habits, anthropometry, medical history, medications and family history. Most assessments are repeated 15 and 30 months following baseline. Results After screening 875 individuals, 129 African American and 224 non-Hispanic white participants were enrolled. At baseline, African American participants have higher blood pressure, weight, body mass index, waist and hip circumferences but similar waist-hip ratio compared with the non-Hispanic white participants. On average, African American participants are less insulin-sensitive and have higher acute insulin secretion and lower insulin clearance. Conclusions The longitudinal design and robust characterization of potential mediators will allow for the assessment of glucose and insulin homeostasis and gut microbiota as they change over time, improving our ability to discern causal relationships between the microbiome and the insulin homeostasis traits whose deterioration determines T2D, setting the stage for future microbiome-directed therapies to prevent and treat T2D.

The interaction between airway microbiome and host in chronic obstructive pulmonary disease (COPD) is poorly understood. Here we used a multi-omic meta-analysis approach to characterize the functional signature of airway microbiome in COPD. We retrieved all public COPD sputum microbiome datasets, totaling 1640 samples from 16S rRNA gene datasets and 26 samples from metagenomic datasets from across the world. We identified microbial taxonomic shifts using random effect meta-analysis and established a global classifier for COPD using 12 microbial genera. We inferred the metabolic potentials for the airway microbiome, established their molecular links to host targets, and explored their effects in a separate meta-analysis on 1340 public human airway transcriptome samples for COPD. 29.6% of differentially expressed human pathways were predicted to be targeted by microbiome metabolism. For inferred metabolite-host interactions, the flux of disease-modifying metabolites as predicted from host transcriptome was generally concordant with their predicted metabolic turnover in microbiome, suggesting a synergistic response between microbiome and host in COPD. The meta-analysis results were further validated by a pilot multi-omic study on 18 COPD patients and 10 controls, in which airway metagenome, metabolome, and host transcriptome were simultaneously characterized. 69.9% of the proposed "microbiome-metabolite-host" interaction links were validated in the independent multi-omic data. Butyrate, homocysteine, and palmitate were the microbial metabolites showing strongest interactions with COPD-associated host genes. Our meta-analysis uncovered functional properties of airway microbiome that interacted with COPD host gene signatures, and demonstrated the possibility of leveraging public multi-omic data to interrogate disease biology.

Gut microbiome studies have documented depletion of butyrate-producing taxa in type 2 diabetes. We analyzed associations between butyrate-producing taxa and detailed measures of insulin homeostasis whose dysfunction underlies diabetes in 224 non-Hispanic Whites and 129 African Americans, all of whom completed an oral glucose tolerance test. Stool microbiome was assessed by whole metagenome shotgun sequencing with taxonomic profiling. We examined associations between 36 butyrate-producing taxa (7 genera, 29 species) and insulin sensitivity, insulin secretion, disposition index, insulin clearance, and prevalence of dysglycemia (prediabetes plus diabetes, 46% of cohort), adjusting for age, sex, body mass index, and race. Genus Coprococcus was associated with higher insulin sensitivity (β=0.14, P=0.002) and disposition index (β=0.12, P=0.012) and a lower rate of dysglycemia (odds ratio 0.91, 95% confidence interval (CI) 0.85-0.97, P=0.0025); in contrast, Flavonifractor was associated with lower insulin sensitivity (β=-0.13, P=0.004) and disposition index (β=-0.11, P=0.04) and higher prevalence of dysglycemia (OR 1.22, 95% CI 1.08-1.38, P=0.0013). Species-level analyses found 10 bacteria associated with beneficial directions of effects and two bacteria with adverse associations on insulin homeostasis and dysglycemia. While most butyrate-producers analyzed appear to be metabolically beneficial, this is not the case for all such bacteria, suggesting that microbiome-directed therapeutic measures to prevent or treat diabetes should be targeted to specific butyrate-producing taxa rather than all butyrate producers.

Background: 16S rRNA gene has been widely used in microbial diversity studies to determine the community composition and structure. 16S rRNA gene copy number (16S GCN) varies among microbial species and this variation introduces biases to the relative cell abundance estimated using 16S rRNA read counts. To correct the biases, methods (e.g., PICRUST2) have been developed to predict 16S GCN. 16S GCN predictions come with inherent uncertainty, which is often ignored in the downstream analyses. However, a recent study suggests that the uncertainty can be so great that copy number correction is not justified in practice. Despite the significant implications in 16S rRNA based microbial diversity studies, the uncertainty associated with 16S GCN predictions has not been well characterized and its impact on microbial diversity studies needs to be investigated. Results: Here we develop RasperGade16S, a novel method and software to better model and capture the inherent uncertainty in 16S rRNA GCN prediction. RasperGade16S implements a maximum likelihood framework of pulsed evolution model and explicitly accounts for intraspecific GCN variation and heterogeneous GCN evolution rates among species. Using cross validation, we show that our method provides robust confidence estimates for the GCN predictions and outperforms PICRUST2 in both precision and recall. We have predicted GCN for 592605 OTUs in the SILVA database and tested 113842 bacterial communities that represent an exhaustive and diverse list of engineered and natural environments. We found that the prediction uncertainty is small enough for 99% of the communities that 16S GCN correction should improve their compositional and functional profiles estimated using 16S rRNA reads. On the other hand, we found that GCN variation has limited impacts on beta-diversity analyses such as PCoA, PERMANOVA and random forest test. Conclusion: We have developed a method to accurately account for uncertainty in 16S rRNA GCN predictions and the downstream analyses. For almost all 16S rRNA surveyed bacterial communities, correction of 16S GCN should improve the results when estimating their compositional and functional profiles. However, such correction is not necessary for beta-diversity analyses.

Ancestral state reconstruction is not only a fundamental tool for studying trait evolution, but also very useful for predicting the unknown trait values (hidden states) of extant species. A well-known problem in ancestral and hidden state predictions is that the uncertainty associated with predictions can be so large that predictions themselves are of little use. Therefore, for meaningful interpretation of predicted traits and hypothesis testing, it is prudent to accurately assess the uncertainty of the predictions. Commonly used constant-rate Brownian motion (BM) model fails to capture the complexity of tempo and mode of trait evolution in nature, making predictions under the BM model vulnerable to lack-of-fit errors from model misspecification. Using empirical data (mammalian body size and bacterial genome size), we show that the distribution of residual Z-scores under the BM model is neither homoscedastic nor normal as expected. Consequently, the 95% confidence intervals of predicted traits are so unreliable that the actual coverage probability ranges from 33% (strongly permissive) to 100% (strongly conservative). Alternative methods such as BayesTraits and StableTraits that allow variable rates in evolution improve the predictions but are computationally expensive. Here, we develop Reconstructing Ancestral State under Pulsed Evolution in R by Gaussian Decomposition (RasperGade), a method of ancestral and hidden state prediction that uses the Levy process to explicitly model gradual evolution, pulsed evolution, and time-independent variation. Using the same empirical data, we show that RasperGade outperforms both BayesTraits and StableTraits in providing reliable confidence estimates and is orders-of-magnitude faster. Our results suggest that, when predicting the ancestral and hidden states of continuous traits, the rate variation should always be assessed and the quality of confidence estimates should always be examined. [Bacterial genomic traits; model misspecification; trait evolution.]

Ancestral state reconstruction is a fundamental tool for studying trait evolution. It is also very useful for predicting the unknown trait values (hidden states) of extant species. A well-known problem in ancestral and hidden state predictions is that the uncertainty associated with predictions can be so large that predictions themselves are of little use. Therefore, for meaningful interpretation of predicted traits and hypothesis testing, it is prudent to accurately assess the uncertainty of the predictions. Commonly used Brownian motion (BM) model fails to capture the complexity of tempo and mode of trait evolution in nature, making predictions under the BM model vulnerable to lack-of-fit errors from model misspecification. Using simulations and empirical data (bacterial genomic traits and vertebrate body size), we show that the presence of pulsed evolution and time-independent variation significantly undermines the confidence level of continuous traits predicted under the BM model. The residual Z-scores are neither homoscedastic nor normally distributed. Consequently, the 95% confidence intervals of predicted traits are so unreliable that the actual coverage probability ranges from 29% (strongly permissive) to 100% (strongly conservative). To remedy the model misspecification problem, we develop RasperGade that accounts for both pulsed evolution and time-independent variation. When applied to simulated and empirical data, RasperGade outperforms commonly used tools such as ape. It restores the normality and homoscedasticity of the Z-score distributions. Accordingly, RasperGade greatly improves the reliability of confidence intervals of predictions and reduces the deviation of their actual coverage probabilities from the 95% expectation by as much as 99%.

Automatic coreference resolution is understudied in Danish even though most of the Danish Dependency Treebank (Buch-Kromann, 2003) is annotated with coreference relations. This paper describes a conversion of its partial, yet well-documented, coreference relations into coreference clusters and the training and evaluation of coreference models on this data. To the best of our knowledge, these are the first publicly available, neural coreference models for Danish. We also present a new entity linking annotation on the dataset using WikiData identifiers, a named entity disambiguation (NED) dataset, and a larger automatically created NED dataset enabling wikily supervised NED models. The entity linking annotation is benchmarked using a state-of-the-art neural entity disambiguation model.

A fundamental question in studying microbial diversity is whether there is a species boundary and if the boundary can be delineated by a universal genetic discontinuity. To address this question, Jain et al. computed the pairwise average nucleotide identity (ANI) of 91,761 microbial (bacterial and archaeal) genomes (the 90K genome dataset) and found that the ANI values from the 8 billion comparisons follow a strong bimodal distribution1. The authors concluded that a clear genetic discontinuum and species boundary were evident from the unprecedented large-scale ANI analysis. As a result, the researchers advocated that an ANI of 95% can be used to accurately demarcate all currently named microbial species. While the FastANI program described in the paper is useful, we argue that the paper’s conclusion of a universal genetic boundary is questionable and resulted from the substantial biased sampling in genome sequencing. We also caution against being overly confident in using 95% ANI for microbial species delineation as the high benchmarks reported in the paper were inflated by using highly redundant genomes.

Summary The widespread availability of energy-dense, rewarding foods is correlated with the increased incidence of obesity across the globe. Overeating during mealtimes and unscheduled snacking disrupts timed metabolic processes, which further contribute to weight gain. The neuronal mechanism by which the consumption of energy-dense food restructures the timing of feeding is poorly understood. Here, we demonstrate that dopaminergic signaling within the suprachiasmatic nucleus (SCN), the central circadian pacemaker, disrupts the timing of feeding, resulting in overconsumption of food. D1 dopamine receptor (Drd1)-null mice are resistant to diet-induced obesity, metabolic disease, and circadian disruption associated with energy-dense diets. Conversely, genetic rescue of Drd1 expression within the SCN restores diet-induced overconsumption, weight gain, and obesogenic symptoms. Access to rewarding food increases SCN dopamine turnover, and elevated Drd1-signaling decreases SCN neuronal activity, which we posit disinhibits downstream orexigenic responses. These findings define a connection between the reward and circadian pathways in the regulation of pathological calorie consumption.

Our objective was to investigate alterations in the cecal microbial composition during the development of type 1 diabetes (T1D) with or without IgM therapy, and correlate these alterations with the corresponding immune profile.(1) Female nonobese diabetic (NOD) mice treated with IgM or saline (n = 20/group) were divided into 5-week-old nondiabetic; 9 to 12-week-old prehyperglycemic stage-1; ≥13-week-old prehyperglycemic stage-2; and diabetic groups. 16S rRNA libraries were prepared from bacterial DNA and deep-sequenced. (2) New-onset diabetic mice were treated with IgM (200 μg on Days 1, 3, and 5) and their blood glucose monitored for 2 months.Significant dysbiosis was observed in the cecal microbiome with the progression of T1D development. The alteration in microbiome composition was characterized by an increase in the bacteroidetes:firmicutes ratio. In contrast, IgM conserved normal bacteroidetes:firmicutes ratio and this effect was long-lasting. Furthermore, oral gavage using cecal content from IgM-treated mice significantly diminished the incidence of diabetes compared with controls, indicating that IgM specifically affected mucosa-associated microbes, and that the affect was causal and not an epiphenomenon. Also, regulatory immune cell populations (myeloid-derived suppressor cells and regulatory T cells) were expanded and insulin autoantibody production diminished in the IgM-treated mice. In addition, IgM therapy reversed hyperglycemia in 70% of new-onset diabetic mice (n = 10) and the mice remained normoglycemic for the entire post-treatment observation period.The cecal microbiome appears to be important in maintaining immune homeostasis and normal immune responses.

Background Our understanding of gut microbiota has been limited primarily to findings from human and laboratory animals, but what shapes the gut microbiota in nature remains largely unknown. To fill this gap, we conducted a comprehensive study of gut microbiota of a well-studied North American red squirrel (Tamiasciurus hudsonicus) population. Red squirrels are territorial, solitary, and live in a highly seasonal environment and therefore represent a very attractive system to study factors that drive the temporal and spatial dynamics of gut microbiota. Result For the first time, this study revealed significant spatial patterns of gut microbiota within a host population, suggesting limited dispersal could play a role in shaping and maintaining the structure of gut microbial communities. We also found a remarkable seasonal rhythm in red squirrel’s gut microbial composition manifested by a tradeoff between relative abundance of two genera Oscillospira and Corpococcus and clearly associated with seasonal variation in diet availability. Our results show that in nature, environmental factors exert a much stronger influence on gut microbiota than host-associated factors including age and sex. Despite strong environmental effects, we found clear evidence of individuality and maternal effects, but host genetics did not seem to be a significant driver of the gut microbial communities in red squirrels. Conclusion Taken together, the results of this study emphasize the importance of external ecological factors rather than host attributes in driving temporal and spatial patterns of gut microbiota in natural environment. Electronic supplementary material The online version of this article (10.1186/s40168-017-0382-3) contains supplementary material, which is available to authorized users.

Amoebae have been considered as a genetic “melting pot” for its symbionts, facilitating genetic exchanges of the bacteria that co-inhabit the same host. To test the “melting pot” hypothesis, we analyzed six genomes of amoeba endosymbionts within Rickettsiales, four of which belong to Holosporaceae family and two to Candidatus Midichloriaceae. For the first time, we identified plasmids in obligate amoeba endosymbionts, which suggests conjugation as a potential mechanism for lateral gene transfers (LGTs) that underpin the “melting pot” hypothesis. We found strong evidence of recent LGTs between the Rickettsiales amoeba endosymbionts, suggesting that the LGTs are continuous and ongoing. In addition, comparative genomic and phylogenomic analyses revealed pervasive and recurrent LGTs between Rickettsiales and distantly related amoeba-associated bacteria throughout the Rickettsiales evolution. Many of these exchanged genes are important for amoeba–symbiont interactions, including genes in transport system, antibiotic resistance, stress response, and bacterial virulence, suggesting that LGTs have played important roles in the adaptation of endosymbionts to their intracellular habitats. Surprisingly, we found little evidence of LGTs between amoebae and their bacterial endosymbionts. Our study strongly supports the “melting pot” hypothesis and highlights the role of amoebae in shaping the Rickettsiales evolution.

Introduction Atopic dermatitis is a common skin disease characterized by altered cutaneous immunity in which patients often exhibit lower skin microbiota diversity compared to healthy skin and are prone to colonization by Staphylococcus aureus. Apple cider vinegar has been shown to have antibacterial effects; however, its effects on the skin microbiome have not previously been well-described. Objectives We aimed to examine the effects of topical dilute apple cider vinegar soaks on Staphylococcus aureus abundance, skin bacterial microbiome composition, and skin bacterial microbiome diversity in atopic dermatitis participants compared to healthy skin. Methods Eleven subjects with atopic dermatitis and 11 healthy controls were enrolled in this randomized, non-blinded, single-institution, split-arm pilot study. Subjects soaked one forearm in dilute apple cider vinegar (0.5% acetic acid) and the other forearm in tap water for 10 minutes daily. Skin bacteria samples were collected from subjects’ volar forearms before and after 14 days of treatment. 16S sequencing was used to analyze Staphylococcus aureus abundance and skin bacterial microbiome composition, and alpha diversity of microbiota were determined using Shannon diversity index. Results There was no difference in skin bacterial microbiome in atopic dermatitis subjects after 2 weeks of daily water or apple cider vinegar treatments (p = 0.056 and p = 0.22, respectively), or in mean abundance of S. aureus on apple cider vinegar-treated forearms (p = 0.60). At 2 weeks, the skin bacterial microbiomes of healthy control subjects were not significantly different from the skin bacterial microbiome of atopic dermatitis subjects (p = 0.14, 0.21, 0.12, and 0.05). Conclusions Our results suggest that daily soaks in 0.5% apple cider vinegar are not an effective method of altering the skin bacterial microbiome in atopic dermatitis. Further studies are needed to explore the effects of different concentrations of apple cider vinegar on skin microflora and disease severity. Trial number UVA IRB-HSR #19906.

Dopamine (DA) is a neurotransmitter with conserved behavioral roles between invertebrate and vertebrate animals. In addition to its neural functions, in insects DA is a critical substrate for cuticle pigmentation and hardening. Drosophila tyrosine hydroxylase (DTH) is the rate limiting enzyme for DA biosynthesis. Viable brain DA-deficient flies were previously generated using tissue-selective GAL4-UAS binary expression rescue of a DTH null mutation and these flies show specific behavioral impairments. To circumvent the limitations of rescue via binary expression, here we achieve rescue utilizing genomically integrated mutant DTH. As expected, our DA-deficient flies have no detectable DTH or DA in the brain, and show reduced locomotor activity. This deficit can be rescued by l-DOPA/carbidopa feeding, similar to human Parkinson's disease treatment. Genetic rescue via GAL4/UAS-DTH was also successful, although this required the generation of a new UAS-DTH1 transgene devoid of most untranslated regions, as existing UAS-DTH transgenes express in the brain without a Gal4 driver via endogenous regulatory elements. A surprising finding of our newly constructed UAS-DTH1m is that it expresses DTH at an undetectable level when regulated by dopaminergic GAL4 drivers even when fully rescuing DA, indicating that DTH immunostaining is not necessarily a valid marker for DA expression. This finding necessitated optimizing DA immunohistochemistry, showing details of DA innervation to the mushroom body and the central complex. When DA rescue is limited to specific DA neurons, DA does not diffuse beyond the DTH-expressing terminals, such that DA signaling can be limited to very specific brain regions.

Adaptive radiations provide unique opportunities to test whether and how recent ecological and evolutionary diversification of host species structures the composition of entire bacterial communities. We used 16S rRNA gene sequencing of faecal samples to test for differences in the gut microbiota of six species of Puerto Rican Anolis lizards characterized by the evolution of distinct 'ecomorphs' related to differences in habitat use. We found substantial variation in the composition of the microbiota within each species and ecomorph (trunk-crown, trunk-ground, grass-bush), but no differences in bacterial alpha diversity among species or ecomorphs. Beta diversity analyses revealed subtle but significant differences in bacterial composition related to host phylogeny and species, but these differences were not consistently associated with Anolis ecomorph. Comparison of a trunk-ground species from this clade (A. cristatellus) with a distantly related member of the same ecomorph class (A. sagrei) where the two species have been introduced and are now sympatric in Florida revealed pronounced differences in the alpha diversity and beta diversity of their microbiota despite their ecological similarity. Comparisons of these populations with allopatric conspecifics also revealed geographic differences in bacterial alpha diversity and beta diversity within each species. Finally, we observed high intraindividual variation over time and strong effects of a simplified laboratory diet on the microbiota of A. sagrei. Collectively, our results indicate that bacterial communities are only weakly shaped by the diversification of their lizard hosts due to the strikingly high levels of bacterial diversity and variation observed within Anolis species.

In response to resource availability, bacteria have evolved two distinct ecological strategies. Copiotrophic bacteria grow fast and are heavily favored by selection where the resource is abundant. In contrast, oligotrophic bacteria grow slowly but more efficiently and are highly adaptive in nutrient-poor environments (Koch, 2001). Although oligotrophs and copiotrophs are ubiquitous, except for a few well-characterized environments like the open ocean and animal gut, the relative abundance of oligotrophic and copiotrophic bacteria and their importance in the global ecosystem are still unclear. In addition, although several studies have demonstrated the impact of nutrient availability on the bacterial community structure under experimental conditions (Klappenbach et al., 2000, Nemergut et al., 2016), the role of nutrients in shaping the structures of bacterial communities in their natural habitats remains largely unknown. Using the ribosomal RNA operon (rrn) copy number to capture the bacterial ecological strategy, we analyzed 44,045 samples from two large bacterial community repositories that cover 78 environmental types. Here we show that animal-associated microbiota are dominated by copiotrophs while plant-associated and free-living bacterial communities are mostly dominated by oligotrophs. Our results suggest that nutrient availability plays an important role in determining the structure and ecological strategy of bacterial communities in nature. We demonstrate that the average and distribution of rrn copy number are simple yet robust predictors of the ecological strategy of bacterial communities that can be applied to all sequence-based microbial surveys to link the community structure and function.

Gut bacterial communities play essential roles in host biology, but to date we lack information on the forces that shape gut microbiota between hosts and over time in natural populations. Understanding these forces in wild primates provides a valuable comparative context that enriches scientific perspectives on human gut microbiota. To this end, we tested predictors of gut microbial composition in a well-studied population of wild baboons. Using cross-sectional and longitudinal samples collected over 13 years, we found that baboons harbour gut microbiota typical of other omnivorous primates, albeit with an especially high abundance of Bifidobacterium. Similar to previous work in humans and other primates, we found strong effects of both developmental transitions and diet on gut microbial composition. Strikingly, baboon gut microbiota appeared to be highly dynamic such that samples collected from the same individual only a few days apart were as different from each other as samples collected over 10 years apart. Despite the dynamic nature of baboon gut microbiota, we identified a set of core taxa that is common among primates, supporting the hypothesis that microbiota codiversify with their host species. Our analysis identified two tentative enterotypes in adult baboons that differ from those of humans and chimpanzees.

Molecular evolutionary rate varies significantly among species and a strict global molecular clock has been rejected across the tree of life. Generation time is one primary life-history trait that influences the molecular evolutionary rate. Theory predicts that organisms with shorter generation times evolve faster because of the accumulation of more DNA replication errors per unit time. Although the generation-time effect has been demonstrated consistently in plants and animals, the evidence of its existence in bacteria is lacking. The bacterial phylum Firmicutes offers an excellent system for testing generation-time effect because some of its members can enter a dormant, nonreproductive endospore state in response to harsh environmental conditions. It follows that spore-forming bacteria would--with their longer generation times--evolve more slowly than their nonspore-forming relatives. It is therefore surprising that a previous study found no generation-time effect in Firmicutes. Using a phylogenetic comparative approach and leveraging on a large number of Firmicutes genomes, we found sporulation significantly reduces the genome-wide spontaneous DNA mutation rate and protein evolutionary rate. Contrary to the previous study, our results provide strong evidence that the evolutionary rates of bacteria, like those of plants and animals, are influenced by generation time.

Background Clostridium difficile infection (CDI) is a serious threat for an aging population. Using an aged mouse model, we evaluated the effect of age and the roles of innate immunity and intestinal microbiota. Methods Aged (18 months) and young (8 weeks) mice were infected with C difficile, and disease severity, immune response, and intestinal microbiome were compared. The same experiment was repeated with intestinal microbiota exchange between aged and young mice before infection. Results Higher mortality was observed in aged mice with weaker neutrophilic mobilization in blood and intestinal tissue and depressed proinflammatory cytokines in early infection. Microbiota exchange improved survival and early immune response in aged mice. Microbiome analysis revealed that aged mice have significant deficiencies in Bacteroidetes phylum and, specifically, Bacteroides, Alistipes, and rc4-4 genera, which were replenished by cage switching. Conclusions Microbiota-dependent alteration in innate immune response early on during infection may explain poor outcome in aged host with CDI.