Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3

Host gut resistome in Gulf War chronic multisymptom illness correlates with persistent inflammation

Chronic multisymptom illness (CMI) affects a subsection of elderly and war Veterans and is associated with systemic inflammation. Here, using a mouse model of CMI and a group of Gulf War (GW) Veterans' with CMI we show the presence of an altered host resistome. Results show that antibiotic resistance genes (ARGs) are significantly altered in the CMI group in both mice and GW Veterans when compared to control. Fecal samples from GW Veterans with persistent CMI show a significant increase of resistance to a wide class of antibiotics and exhibited an array of mobile genetic elements (MGEs) distinct from normal healthy controls. The altered resistome and gene signature is correlated with mouse serum IL-6 levels. Altered resistome in mice also is correlated strongly with intestinal inflammation, decreased synaptic plasticity, reversible with fecal microbiota transplant (FMT). The results reported might help in understanding the risks to treating hospital acquired infections in this population.

Metagenomic assessment of gut microbial communities and risk of severe COVID-19

The gut microbiome is a critical modulator of host immunity and is linked to the immune response to respiratory viral infections. However, few studies have gone beyond describing broad compositional alterations in severe COVID-19, defined as acute respiratory or other organ failure. We profiled 127 hospitalized patients with COVID-19 (n=79 with severe COVID-19 and 48 with moderate) who collectively provided 241 stool samples from April 2020 to May 2021 to identify links between COVID-19 severity and gut microbial taxa, their biochemical pathways, and stool metabolites. 48 species were associated with severe disease after accounting for antibiotic use, age, sex, and various comorbidities. These included significant in-hospital depletions of Fusicatenibacter saccharivorans and Roseburia hominis, each previously linked to post-acute COVID syndrome or “long COVID”, suggesting these microbes may serve as early biomarkers for the eventual development of long COVID. A random forest classifier achieved excellent performance when tasked with predicting whether stool was obtained from patients with severe vs. moderate COVID-19. Dedicated network analyses demonstrated fragile microbial ecology in severe disease, characterized by fracturing of clusters and reduced negative selection. We also observed shifts in predicted stool metabolite pools, implicating perturbed bile acid metabolism in severe disease. Here, we show that the gut microbiome differentiates individuals with a more severe disease course after infection with COVID-19 and offer several tractable and biologically plausible mechanisms through which gut microbial communities may influence COVID-19 disease course. Further studies are needed to validate these observations to better leverage the gut microbiome as a potential biomarker for disease severity and as a target for therapeutic intervention.

Experimental evaluation of ecological principles to understand and modulate the outcome of bacterial strain competition in gut microbiomes

It is unclear if coexistence theory can be applied to gut microbiomes to understand their characteristics and modulate their composition. Through experiments in gnotobiotic mice with complex microbiomes, we demonstrated that strains of Akkermansia muciniphila and Bacteroides vulgatus could only be established if microbiomes were devoid of these species. Strains of A. muciniphila showed strict competitive exclusion, while B. vulgatus strains coexisted but populations were still influenced by competitive interactions. These differences in competitive behavior were reflective of genomic variation within the two species, indicating considerable niche overlap for A. muciniphila strains and a broader niche space for B. vulgatus strains. Priority effects were detected for both species as strains’ competitive fitness increased when colonizing first, which resulted in stable persistence of the A. muciniphila strain colonizing first and competitive exclusion of the strain arriving second. Based on these observations, we devised a subtractive strategy for A. muciniphila using antibiotics and showed that a strain from an assembled community can be stably replaced by another strain. By demonstrating that competitive outcomes in gut ecosystems depend on niche differences and are historically contingent, our study provides novel information to explain the ecological characteristics of gut microbiomes and a basis for their modulation.

Introduction to the principles and methods underlying the recovery of metagenome-assembled genomes from metagenomic data

The rise of metagenomics offers a leap forward for understanding the genetic diversity of microorganisms in many different complex environments by providing a platform that can identify potentially unlimited numbers of known and novel microorganisms. As such, it is impossible to imagine new major initiatives without metagenomics. Nevertheless, it represents a relatively new discipline with various levels of complexity and demands on bioinformatics. The underlying principles and methods used in metagenomics are often seen as common knowledge and often not detailed or fragmented. Therefore, we reviewed these to guide microbiologists in taking the first steps into metagenomics. We specifically focus on a workflow aimed at reconstructing individual genomes, that is, metagenome‐assembled genomes, integrating DNA sequencing, assembly, binning, identification and annotation.

The co-occurrence of antibiotic resistance genes between dogs and their owners in families

The intimate relationship between humans and companion animals causes a unique and critical aspect of antimicrobial resistance in humans. However, a comprehensive analysis of antimicrobial resistance between companion animals and their owners is lacking. Here, we chose 13 owned dogs and 16 owners as well as 22 kennel dogs to analyze the effect of an intimate relationship between owned dogs and owners on their gut microbiome, antibiotic resistance genes (ARGs), and mobile genetic elements (MGEs) and study the correlation of antimicrobial resistance between dogs and their owners in families by metagenomics. Dog gut microbiota had a higher abundance and diversity of ARGs while owners had a higher diversity of taxonomy. In the owned dog gut microbial community, ARG and MGE compositions were significantly more similar to the owner's gut microbiota than those of others. From the perspective of families, there was a strong correlation between macrolide resistance genes between dogs and their owners. In conclusion, our study demonstrated the correlation of ARGs between dogs and their owners at a community-wide level. These findings can alarm the use of antibiotics in companion animals, which implies the potential to harbor antimicrobial resistance and threaten public health.

A Wipe-Based Stool Collection and Preservation Kit for Microbiome Community Profiling

While a range of methods for stool collection exist, many require complicated, self-directed protocols and stool transfer. In this study, we introduce and validate a novel, wipe-based approach to fecal sample collection and stabilization for metagenomics analysis. A total of 72 samples were collected across four different preservation types: freezing at -20°C, room temperature storage, a commercial DNA preservation kit, and a dissolvable wipe used with DESS (dimethyl sulfoxide, ethylenediaminetetraacetic acid, sodium chloride) solution. These samples were sequenced and analyzed for taxonomic abundance metrics, bacterial metabolic pathway classification, and diversity analysis. Overall, the DESS wipe results validated the use of a wipe-based capture method to collect stool samples for microbiome analysis, showing an R2 of 0.96 for species across all kingdoms, as well as exhibiting a maintenance of Shannon diversity (3.1-3.3) and species richness (151-159) compared to frozen samples. Moreover, DESS showed comparable performance to the commercially available preservation kit (R2 of 0.98), and samples consistently clustered by subject across each method. These data support that the DESS wipe method can be used for stable, room temperature collection and transport of human stool specimens.

Metagenomic Analysis Using Phylogenetic Placement-A Review of the First Decade

Phylogenetic placement refers to a family of tools and methods to analyze, visualize, and interpret the tsunami of metagenomic sequencing data generated by high-throughput sequencing. Compared to alternative (e. g., similarity-based) methods, it puts metabarcoding sequences into a phylogenetic context using a set of known reference sequences and taking evolutionary history into account. Thereby, one can increase the accuracy of metagenomic surveys and eliminate the requirement for having exact or close matches with existing sequence databases. Phylogenetic placement constitutes a valuable analysis tool per se, but also entails a plethora of downstream tools to interpret its results. A common use case is to analyze species communities obtained from metagenomic sequencing, for example via taxonomic assignment, diversity quantification, sample comparison, and identification of correlations with environmental variables. In this review, we provide an overview over the methods developed during the first 10 years. In particular, the goals of this review are 1) to motivate the usage of phylogenetic placement and illustrate some of its use cases, 2) to outline the full workflow, from raw sequences to publishable figures, including best practices, 3) to introduce the most common tools and methods and their capabilities, 4) to point out common placement pitfalls and misconceptions, 5) to showcase typical placement-based analyses, and how they can help to analyze, visualize, and interpret phylogenetic placement data.

Microbiome-Independent Effects of Antibiotics in a Murine Model of Nosocomial Infections

Methicillin-resistant Staphylococcus aureus (MRSA) is one of the most common causes of hospital-acquired pneumonia. To better manage patients with MRSA pneumonia, we require a greater understanding of the host-pathogen interactions during infection. MRSA research focuses on highly virulent and cytotoxic strains, which demonstrate robust phenotypes in animal models of infection. However, nosocomial infections are often caused by hospital-acquired MRSA (HA-MRSA) isolates that exhibit low cytotoxicity and few or no phenotypes in mice, thereby confounding mechanistic studies of pathogenesis. Consequently, virulence pathways utilized by HA-MRSA in nosocomial pneumonia are largely unknown. Here, we report that conditioning mice with broad-spectrum antibiotics lowers the barrier to pneumonia, thereby transforming otherwise avirulent HA-MRSA isolates into lethal pathogens. HA-MRSA isolates are avirulent in gnotobiotic mice, mimicking results in conventional animals. Thus, the observed enhanced susceptibility to infection in antibiotic-treated mice is not due to depletion of the microbiota. More generally, we found that antibiotic conditioning leads to increased susceptibility to infection by diverse antimicrobial-resistant (AMR) pathogens of low virulence. Treatment with antibiotics leads to dehydration and malnutrition, suggesting a potential role for these clinically relevant and reducible hospital complications in susceptibility to pathogens. In sum, the model described here mitigates the impact of low virulence in immunocompetent mice, providing a convenient model to gain fundamental insight into the pathogenesis of nosocomial pathogens.

Transcriptomics and metagenomics of common cutworm (Spodoptera litura) and fall armyworm (Spodoptera frugiperda) demonstrate differences in detoxification and development

BACKGROUND

Spodoptera litura is an important polyphagous pest that causes significant damage to the agricultural sector. We performed RNA-seq of 15 S. litura individuals from larval (fifth and sixth instar larvae), chrysalis, and adult developmental stages. We also compared the S. litura transcriptome data with Spodoptera frugiperda across the same developmental stages, which was sequenced in our previous study.

RESULTS

A total of 101,885 differentially expressed transcripts (DETs) were identified in S. litura. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses indicated that S. litura may undergo active xenobiotic and detoxifying metabolism during its larval and adult stages, which may explain difficulties with current population control measures. We also found that DETs of single-copy orthologous genes between S. litura and S. frugiperda were involved in basic metabolism and development. However, energy and metabolic processes genes had a higher expression in S. litura, whereas nervous and olfactory function genes had a higher expression in S. frugiperda. Metagenomics analysis in larval S. litura and S. frugiperda revealed that microbiota participate in the detoxification and metabolism processes, but the relative abundance of detoxification-related microbiota was more abundant in S. frugiperda. Transcriptome results also confirmed the detoxification-related pathway of S. frugiperda was more abundant than in S. litura.

CONCLUSIONS

Significant changes at transcriptional level were identified during the different development stages of S. litura. Importantly, we also identified detoxification associated genes and gut microbiota between S. litura and S. frugiperda at different developmental stages, which will be valuable in revealing possible mechanisms of detoxification and development in these two lepidopterans.

Scalable Microbial Strain Inference in Metagenomic Data Using StrainFacts

While genome databases are nearing a complete catalog of species commonly inhabiting the human gut, their representation of intraspecific diversity is lacking for all but the most abundant and frequently studied taxa. Statistical deconvolution of allele frequencies from shotgun metagenomic data into strain genotypes and relative abundances is a promising approach, but existing methods are limited by computational scalability. Here we introduce StrainFacts, a method for strain deconvolution that enables inference across tens of thousands of metagenomes. We harness a “fuzzy” genotype approximation that makes the underlying graphical model fully differentiable, unlike existing methods. This allows parameter estimates to be optimized with gradient-based methods, speeding up model fitting by two orders of magnitude. A GPU implementation provides additional scalability. Extensive simulations show that StrainFacts can perform strain inference on thousands of metagenomes and has comparable accuracy to more computationally intensive tools. We further validate our strain inferences using single-cell genomic sequencing from a human stool sample. Applying StrainFacts to a collection of more than 10,000 publicly available human stool metagenomes, we quantify patterns of strain diversity, biogeography, and linkage-disequilibrium that agree with and expand on what is known based on existing reference genomes. StrainFacts paves the way for large-scale biogeography and population genetic studies of microbiomes using metagenomic data.

The intestinal and biliary microbiome in autoimmune liver disease-current evidence and concepts

Autoimmune liver diseases are a group of immune-mediated liver diseases with three distinct entities, including autoimmune hepatitis, primary biliary cholangitis, and primary sclerosing cholangitis. The interplay of genetic and environmental factors leads to the breakdown of self-tolerance, resulting in hyper-responsiveness, and auto-aggressive immune activation. Emerging evidence links autoimmune liver diseases with alterations of the commensal microbiome configuration and aberrant immune system activation by microbial signals, mainly via the gut-liver axis. Thus, the microbiome is a new frontier to deepen the pathogenetic understanding, uncover biomarkers, and inspire innovative treatments. Herein, we review the current evidence on the role of the microbiome in autoimmune liver diseases from both clinical and basic research. We highlight recent achievements and also bottlenecks and limitations. Moreover, we give an outlook on future developments and potential for clinical applications.

Close Related Drug-Resistance Beijing Isolates of Mycobacterium tuberculosis Reveal a Different Transcriptomic Signature in a Murine Disease Progression Model

Mycobacterium tuberculosis (MTB) lineage 2/Beijing is associated with high virulence and drug resistance worldwide. In Colombia, the Beijing genotype has circulated since 1997, predominantly on the pacific coast, with the Beijing-Like SIT-190 being more prevalent. This genotype conforms to a drug-resistant cluster and shows a fatal outcome in patients. To better understand virulence determinants, we performed a transcriptomic analysis with a Beijing-Like SIT-190 isolate (BL-323), and Beijing-Classic SIT-1 isolate (BC-391) in progressive tuberculosis (TB) murine model. Bacterial RNA was extracted from mice lungs on days 3, 14, 28, and 60. On average, 0.6% of the total reads mapped against MTB genomes and of those, 90% against coding genes. The strains were independently associated as determined by hierarchical cluster and multidimensional scaling analysis. Gene ontology showed that in strain BL-323 enriched functions were related to host immune response and hypoxia, while proteolysis and protein folding were enriched in the BC-391 strain. Altogether, our results suggested a differential bacterial transcriptional program when evaluating these two closely related strains. The data presented here could potentially impact the control of this emerging, highly virulent, and drug-resistant genotype.

Oral Microbiome of Crohn's Disease Patients With and Without Oral Manifestations

BACKGROUND AND AIMS

Microbiome dysbiosis is associated with inflammatory destruction in Crohn's disease [CD]. Although gut microbiome dysbiosis is well established in CD, the oral microbiome is comparatively under-studied. This study aims to characterize the oral microbiome of CD patients with/without oral manifestations.

METHODS

Patients with CD were recruited with age-, gender- and race-matched controls. Potential confounders such as dental caries and periodontal condition were recorded. The oral microbiome was collected using saliva samples. Microbial DNA was extracted and sequenced using shotgun sequencing. Metagenomic taxonomic and functional profiles were generated and analysed.

RESULTS

The study recruited 41 patients with CD and 24 healthy controls. Within the CD subjects, 39.0% had oral manifestations with the majority presenting with cobblestoning and/or oral ulcers. Principal coordinate analysis demonstrated distinct oral microbiome profiles between subjects with and without CD, with four key variables responsible for overall oral microbiome variance: [1] diagnosis of CD, [2] concomitant use of steroids, [3] concomitant use of azathioprine and 4] presence of oral ulcers. Thirty-two significant differentially abundant microbial species were identified, with the majority associated with the diagnosis of CD. A predictive model based on differences in the oral microbiome found that the oral microbiome has strong discriminatory function to distinguish subjects with and without CD [AUROC 0.84]. Functional analysis found that an increased representation of microbial enzymes [n = 5] in the butyrate pathway was positively associated with the presence of oral ulcers.

CONCLUSIONS

The oral microbiome can aid in the diagnosis of CD and its composition was associated with oral manifestations.

Emergent marine toxins risk assessment using molecular and chemical approaches

Cyanobacteria harmful blooms represent a deviation to the normal equilibrium in planktonic communities involving a rapid and uncontrolled growth. Owing to the capacity to produce toxins as secondary metabolites, cyanobacteria may cause huge economic losses in the fishing and aquaculture industries and poisoning incidents to humans due to their accumulation in the food chain. The conditions which promote toxic blooms have not yet been fully understood, but climate change and anthropogenic intervention are pointed as significant factors. For the detection of toxins in edible marine organisms, the establishment of international regulations and compulsory surveillance has been probed as exceptionally effective. However, not regulation nor monitoring have been settled concerning emergent marine toxins. In the light of this scenario, it becomes essential to apply fast and reliable surveillance methodologies for the early detection of cyanobacterial blooms as well as the occurrence of emergent marine toxins. Shotgun metagenomic sequencing has potential to become a powerful diagnostic tool in the fields of food safety and One Health surveillance. This culture‐independent approach overcomes limitations of traditional microbiological techniques; it allows a quick and accurate assessment of a complex microbial community, including quantitative identification and functional characterisation, in a single experiment. In the framework of the EU‐FORA fellowship, with the final goal of evaluate metagenomics as a promising risk assessment tool, the fellow worked on the development of an innovative workflow through state‐of‐the‐art molecular and chemical analytical procedures. This work programme aims to evaluate the occurrence of emergent marine toxins and the producing organisms in Cabo Verde coastal cyanobacteria blooms. Our results show the outstanding potential of a holistic metagenomic approach for the risk assessment of emergent marine toxins and the producing organisms. Additionally, we have also highlighted its value for the identification and evaluation of secondary metabolites as natural bioactive compounds with biotechnological and industrial interest.

Fecal microbiota transfer between young and aged mice reverses hallmarks of the aging gut, eye, and brain

BACKGROUND

Altered intestinal microbiota composition in later life is associated with inflammaging, declining tissue function, and increased susceptibility to age-associated chronic diseases, including neurodegenerative dementias. Here, we tested the hypothesis that manipulating the intestinal microbiota influences the development of major comorbidities associated with aging and, in particular, inflammation affecting the brain and retina.

METHODS

Using fecal microbiota transplantation, we exchanged the intestinal microbiota of young (3 months), old (18 months), and aged (24 months) mice. Whole metagenomic shotgun sequencing and metabolomics were used to develop a custom analysis workflow, to analyze the changes in gut microbiota composition and metabolic potential. Effects of age and microbiota transfer on the gut barrier, retina, and brain were assessed using protein assays, immunohistology, and behavioral testing.

RESULTS

We show that microbiota composition profiles and key species enriched in young or aged mice are successfully transferred by FMT between young and aged mice and that FMT modulates resulting metabolic pathway profiles. The transfer of aged donor microbiota into young mice accelerates age-associated central nervous system (CNS) inflammation, retinal inflammation, and cytokine signaling and promotes loss of key functional protein in the eye, effects which are coincident with increased intestinal barrier permeability. Conversely, these detrimental effects can be reversed by the transfer of young donor microbiota.

CONCLUSIONS

These findings demonstrate that the aging gut microbiota drives detrimental changes in the gut-brain and gut-retina axes suggesting that microbial modulation may be of therapeutic benefit in preventing inflammation-related tissue decline in later life. Video abstract.

Dysbiosis of the Female Murine Gut Microbiome Exacerbates Neutrophil-Mediated Vascular Allograft Injury by Affecting Immunoregulation by Acetate

BACKGROUND

The gut microbiota affects immune responses that cause organ transplant rejection, but the mechanisms by which this occurs remain poorly understood.

METHODS

We have examined, in a murine model, how disruption of the gut microbiota with antibiotics early in life alters this microbial community later in life to affect immune responses that injure vascular allografts.

RESULTS

Analysis of 16S rRNA and whole genome sequencing of the gut microbiota demonstrated that early life disruption of this microbial community with antibiotics caused a reduction in taxa and enzymatic genes involved in the synthesis of acetate, an immunoregulatory metabolite in mice and humans. When allograft vascular injury was examined, early life disruption of the gut microbiota increased neutrophil accumulation and related medial injury of transplanted arteries. Normalizing the gut microbiota by co-housing and oral administration of acetate prevented neutrophil-mediated vascular allograft injury.

CONCLUSIONS

Dysbiosis of the gut microbiome that reduces its production of the immunoregulatory metabolite acetate exacerbates neutrophil-mediated allograft vascular injury.

Reduced Enterohepatic Recirculation of Mycophenolate and Lower Blood Concentrations are Associated with the Stool Bacterial Microbiome After Hematopoietic Cell Transplantation

BACKGROUND

Mycophenolate mofetil (MMF) is an important immunosuppressant used after allogeneic hematopoietic cell transplant (HCT). MMF has a narrow therapeutic index and blood concentrations of mycophenolic acid (MPA), the active component of MMF, are highly variable. Low MPA concentrations are associated with risk of graft vs host disease (GvHD) while high concentrations are associated with toxicity. Reasons for variability are not well known and may be due, at least in part, to the presence of β-glucuronidase producing bacteria in the gastrointestinal tract which enhance MPA enterohepatic recirculation (EHR) by transforming MPA metabolites formed in the liver back to MPA.

OBJECTIVE

To determine if individuals with high MPA EHR have a greater abundance of β-glucuronidase producing bacteria in their stool and higher MPA concentrations relative to those with low EHR.

STUDY DESIGN

We conducted a pharmacomicrobiomics study in 20 adult HCT recipients receiving a myeloablative or reduced intensity preparative regimen. Participants received MMF 1g IV every 8 hours with tacrolimus. Intensive pharmacokinetic sampling of mycophenolate was conducted before hospital discharge. Total MPA, MPA glucuronide (MPAG) and acylMPAG were measured. EHR was defined as a ratio of MPA area under the concentration-versus-time curve (AUC)_4-8 to MPA AUC_0-8. Differences in stool microbiome diversity and composition, determined by shotgun metagenomic sequencing, were compared above and below the median EHR (22%, range 5-44%).

RESULTS

Median EHR was 12% and 29% in the low and high EHR groups, respectively. MPA troughs, MPA AUC_4-8 and acylMPAG AUC_4-8/AUC_0-8, were greater in the high EHR group vs low EHR group [1.53 vs 0.28 mcg/mL, p = 0.0001], [7.33 vs 1.79 hr*mcg/mL, p = 0.0003] and [0.33 vs 0.24 hr*mcg/mL, p = 0.0007], respectively. MPA AUC_0-8 was greater in the high EHR than the low EHR group and trended towards significance [22.8 vs. 15.3 hr*mcg/mL, p=0.06]. Bacteroides vulgatus, stercoris and thetaiotaomicron were 1.2-2.4 times more abundant (p=0.039, 0.024, 0.046, respectively) in the high EHR group. MPA EHR was positively correlated with B. vulgatus (⍴=0.58, p≤0.01) and B. thetaiotaomicron (⍴=0.46, p<0.05) and negatively correlated with Blautia hydrogenotrophica (⍴=-0.53, p<0.05). Therapeutic MPA troughs were achieved in 80% of patients in the high EHR group and 0% in the low EHR. There was a trend towards differences in MPA AUC_0-8 and MPA C_ss mcg/mL in high vs. low EHR groups (p=0.06).

CONCLUSION

MPA EHR was variable. Patients with high MPA EHR had greater abundance of Bacteroides species in stool and higher MPA exposure than patients with low MPA EHR. Bacteroides may therefore be protective from poor outcomes such as graft vs host disease but in others it may increase the risk of MPA adverse effects. These data need to be confirmed and studied after oral MMF.

Murine Gut Microbiome Meta-analysis Reveals Alterations in Carbohydrate Metabolism in Response to Aging

Compositional and functional alterations to the gut microbiota during aging are hypothesized to potentially impact our health. Thus, determining aging-specific gut microbiome alterations is critical for developing microbiome-based strategies to improve health and promote longevity in the elderly. In this study, we performed a meta-analysis of publicly available 16S rRNA gene sequencing data from studies investigating the effect of aging on the gut microbiome in mice. Aging reproducibly increased gut microbial alpha diversity and shifted the microbial community structure in mice. We applied the bioinformatic tool PICRUSt2 to predict microbial metagenome function and established a random forest classifier to differentiate between microbial communities from young and old hosts and to identify aging-specific metabolic features. In independent validation data sets, this classifier achieved an area under the receiver operating characteristic curve (AUC) of 0.75 to 0.97 in differentiating microbiomes from young and old hosts. We found that 50% of the most important predicted aging-specific metabolic features were involved in carbohydrate metabolism. Furthermore, fecal short-chain fatty acid (SCFA) concentrations were significantly decreased in old mice, and the expression of the SCFA receptor Gpr41 in the colon was significantly correlated with the relative abundances of gut microbes and microbial carbohydrate metabolic pathways. In conclusion, this study identified aging-specific alterations in the composition and function of the gut microbiome and revealed a potential relationship between aging, microbial carbohydrate metabolism, fecal SCFA, and colonic Gpr41 expression. IMPORTANCE Aging-associated microbial alteration is hypothesized to play an important role in host health and longevity. However, investigations regarding specific gut microbes or microbial functional alterations associated with aging have had inconsistent results. We performed a meta-analysis across 5 independent studies to investigate the effect of aging on the gut microbiome in mice. Our analysis revealed that aging increased gut microbial alpha diversity and shifted the microbial community structure. To determine if we could reliably differentiate the gut microbiomes from young and old hosts, we established a random forest classifier based on predicted metagenome function and validated its performance against independent data sets. Alterations in microbial carbohydrate metabolism and decreased fecal short-chain fatty acid (SCFA) concentrations were key features of aging and correlated with host colonic expression of the SCFA receptor Gpr41. This study advances our understanding of the impact of aging on the gut microbiome and proposes a hypothesis that alterations in gut microbiota-derived SCFA-host GPR41 signaling are a feature of aging.

Whole-Genome Shotgun Metagenomic Sequencing Reveals Distinct Gut Microbiome Signatures of Obese Cats

Overweight and obesity are growing health problems in domestic cats, increasing the risks of insulin resistance, lipid dyscrasias, neoplasia, cardiovascular disease, and decreasing longevity. The signature of obesity in the feline gut microbiota has not been studied at the whole-genome metagenomic level. We performed whole-genome shotgun metagenomic sequencing in the fecal samples of eight overweight/obese and eight normal cats housed in the same research environment. We obtained 271 Gbp of sequences and generated a 961-Mbp de novo reference contig assembly, with 1.14 million annotated microbial genes. In the obese cat microbiome, we discovered a significant reduction in microbial diversity (P < 0.01) and Firmicutes abundance (P = 0.005), as well as decreased Firmicutes/Bacteroidetes ratios (P = 0.02), which is the inverse of obese human/mouse microbiota. Linear discriminant analysis and quantitative PCR (qPCR) validation revealed significant increases of Bifidobacterium sp., Olsenella provencensis, Dialister sp.CAG:486, and Campylobacter upsaliensis as the hallmark of obese microbiota among 400 enriched species, whereas 1,525 bacterial species have decreased abundance in the obese microbiome. Phascolarctobacterium succinatutens and an uncharacterized Erysipelotrichaceae bacterium are highly abundant (>0.05%) in the normal gut with over 400-fold depletion in the obese microbiome. Fatty acid synthesis-related pathways are significantly overrepresented in the obese compared with the normal cat microbiome. In conclusion, we discovered dramatically decreased microbial diversity in obese cat gut microbiota, suggesting potential dysbiosis. A panel of seven significantly altered, highly abundant species can serve as a microbiome indicator of obesity. Our findings in the obese cat microbiome composition, abundance, and functional capacities provide new insights into feline obesity.

IMPORTANCE Obesity affects around 45% of domestic cats, and licensed drugs for treating feline obesity are lacking. Physical exercise and calorie restrictions are commonly used for weight loss but with limited efficacy. Through comprehensive analyses of normal and obese cat gut bacteria flora, we identified dramatic shifts in the obese gut microbiome, including four bacterial species significantly enriched and two species depleted in the obese cats. The key bacterial community and functional capacity alterations discovered from this study will inform new weight management strategies for obese cats, such as evaluations of specific diet formulas that alter the microbiome composition, and the development of prebiotics and probiotics that promote the increase of beneficial species and the depletion of obesity-associated species. Interestingly, these bacteria identified in our study were also reported to affect the weight loss success in human patients, suggesting translational potential in human obesity.

Characterization of the Upper Respiratory Bacterial Microbiome in Critically Ill COVID-19 Patients

The upper respiratory tract (URT) microbiome can contribute to the acquisition and severity of respiratory viral infections. The described associations between URT microbiota and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are limited at microbiota genus level and by the lack of functional interpretation. Our study, therefore, characterized the URT bacterial microbiome at species level and their encoded pathways in patients with COVID-19 and correlated these to clinical outcomes. Whole metagenome sequencing was performed on nasopharyngeal samples from hospitalized patients with critical COVID-19 (n = 37) and SARS-CoV-2-negative individuals (n = 20). Decreased bacterial diversity, a reduction in commensal bacteria, and high abundance of pathogenic bacteria were observed in patients compared to negative controls. Several bacterial species and metabolic pathways were associated with better respiratory status and lower inflammation. Strong correlations were found between species biomarkers and metabolic pathways associated with better clinical outcome, especially Moraxella lincolnii and pathways of vitamin K₂ biosynthesis. Our study demonstrates correlations between the URT microbiome and COVID-19 patient outcomes; further studies are warranted to validate these findings and to explore the causal roles of the identified microbiome biomarkers in COVID-19 pathogenesis.

Deciphering mechanisms and implications of bacterial translocation in human health and disease

Significant increases in potential microbial translocation, especially along the oral-gut axis, have been identified in many immune-related and inflammatory diseases, such as inflammatory bowel disease, colorectal cancer, rheumatoid arthritis, and liver cirrhosis, for which we currently have no cure or long-term treatment options. Recent advances in computational and experimental omics approaches now enable strain tracking, functional profiling, and strain isolation in unprecedented detail, which has the potential to elucidate the causes and consequences of microbial translocation. In this review, we discuss current evidence for the detection of bacterial translocation, examine different translocation axes with a primary focus on the oral-gut axis, and outline currently known translocation mechanisms and how they adversely affect the host in disease. Finally, we conclude with an overview of state-of-the-art computational and experimental tools for strain tracking and highlight the required next steps to elucidate the role of bacterial translocation in human health.

Inferring Species Compositions of Complex Fungal Communities from Long- and Short-Read Sequence Data

Our study is unique in that it provides an in-depth comparative study of a real-life complex fungal community analyzed with multiple long- and short-read sequencing approaches. These technologies and their application are currently of great interest to diverse biologists as they seek to characterize the community compositions of microbiomes.

Critical Assessment of Metagenome Interpretation: the second round of challenges

Evaluating metagenomic software is key for optimizing metagenome interpretation and focus of the Initiative for the Critical Assessment of Metagenome Interpretation (CAMI). The CAMI II challenge engaged the community to assess methods on realistic and complex datasets with long- and short-read sequences, created computationally from around 1,700 new and known genomes, as well as 600 new plasmids and viruses. Here we analyze 5,002 results by 76 program versions. Substantial improvements were seen in assembly, some due to long-read data. Related strains still were challenging for assembly and genome recovery through binning, as was assembly quality for the latter. Profilers markedly matured, with taxon profilers and binners excelling at higher bacterial ranks, but underperforming for viruses and Archaea. Clinical pathogen detection results revealed a need to improve reproducibility. Runtime and memory usage analyses identified efficient programs, including top performers with other metrics. The results identify challenges and guide researchers in selecting methods for analyses.

This study presents the results of the second round of the Critical Assessment of Metagenome Interpretation challenges (CAMI II), which is a community-driven effort for comprehensively benchmarking tools for metagenomics data analysis.

The Potential of Gut Microbiota Metabolic Capability to Detect Drug Response in Rheumatoid Arthritis Patients

Gut microbiota plays an essential role in the development of rheumatoid arthritis (RA) and affects drug responses. However, the underlying mechanism remains elusive and urgent to elucidate to explore the pathology and clinical treatment of RA. Therefore, we selected methotrexate (MTX) as an example of RA drugs to explore the interactions between the gut microbiota and drug responses and obtain an in-depth understanding of their correlation from the perspective of the metabolic capability of gut microbiota on drug metabolism. We identified 2,654 proteins and the corresponding genes involved in MTX metabolism and then profiled their abundances in the gut microbiome datasets of four cohorts. We found that the gut microbiota harbored various genes involved in MTX metabolism in healthy individuals and RA patients. Interestingly, the number of genes involved in MTX metabolism was not significantly different between response (R) and non-response (NR) groups to MTX, but the gene composition in the microbial communities significantly differed between these two groups. Particularly, several models were built based on clinical information, as well as data on the gene, taxonomical, and functional biomarkers by using the random forest algorithm and then validated. Our findings provide bases for clinical management not only of RA but also other gut microbiome–related diseases. First, it suggests that the potential metabolic capability of gut microbiota on drug metabolism is important because they affect drug efficiency; as such, clinical treatment strategies should incorporate the gene compositions of gut microbial communities, in particular genes involved in drug metabolism. Second, a suitable model can be developed to determine hosts’ responses to drugs before clinical treatment.

Horizontal transmission enables flexible associations with locally adapted symbiont strains in deep-sea hydrothermal vent symbioses

SignificanceIn marine ecosystems, transmission of microbial symbionts between host generations occurs predominantly through the environment. Yet, it remains largely unknown how host genetics, symbiont competition, environmental conditions, and geography shape the composition of symbionts acquired by individual hosts. To address this question, we applied population genomic approaches to four species of deep-sea hydrothermal vent snails that live in association with chemosynthetic bacteria. Our analyses show that environment is more important to strain-level symbiont composition than host genetics and that symbiont strains show genetic variation indicative of adaptation to the distinct geochemical conditions at each vent site. This corroborates a long-standing hypothesis that hydrothermal vent invertebrates affiliate with locally adapted symbiont strains to cope with the variable conditions characterizing their habitats.

MTD: a unique pipeline for host and meta-transcriptome joint and integrative analyses of RNA-seq data

Ribonucleic acid (RNA)-seq data contain not only host transcriptomes but also nonhost information that comprises transcripts from active microbiota in the host cells. Therefore, joint and integrative analyses of both host and meta-transcriptome can reveal gene expression of the microbial community in a given sample as well as the correlative and interactive dynamics of the host response to the microbiome. However, there are no convenient tools that can systemically analyze host-microbiota interactions through simultaneously quantifying the host and meta-transcriptome in the same sample at the tissue and the single-cell level. This poses a challenge for interested researchers with limited expertise in bioinformatics. Here, we developed a software pipeline that can comprehensively and synergistically analyze and correlate the host and meta-transcriptome in a single sample using bulk and single-cell RNA-seq data. This pipeline, named meta-transcriptome detector (MTD), can extensively identify and quantify microbiome, including viruses, bacteria, protozoa, fungi, plasmids and vectors, in the host cells and correlate the microbiome with the host transcriptome. MTD is easy to install and run, involving only a few lines of simple commands. It offers researchers with unique genomics insights into host responses to microorganisms.

Machine Learning and Deep Learning Applications in Metagenomic Taxonomy and Functional Annotation

Shotgun sequencing of environmental DNA (i.e., metagenomics) has revolutionized the field of environmental microbiology, allowing the characterization of all microorganisms in a sequencing experiment. To identify the microbes in terms of taxonomy and biological activity, the sequenced reads must necessarily be aligned on known microbial genomes/genes. However, current alignment methods are limited in terms of speed and can produce a significant number of false positives when detecting bacterial species or false negatives in specific cases (virus, plasmids, and gene detection). Moreover, recent advances in metagenomics have enabled the reconstruction of new genomes using de novo binning strategies, but these genomes, not yet fully characterized, are not used in classic approaches, whereas machine and deep learning methods can use them as models. In this article, we attempted to review the different methods and their efficiency to improve the annotation of metagenomic sequences. Deep learning models have reached the performance of the widely used k-mer alignment-based tools, with better accuracy in certain cases; however, they still must demonstrate their robustness across the variety of environmental samples and across the rapid expansion of accessible genomes in databases.

Multiomics Analysis Reveals the Impact of Microbiota on Host Metabolism in Hepatic Steatosis

Metabolic dysfunction-associated fatty liver disease (MAFLD) is a complex disease involving alterations in multiple biological processes regulated by the interactions between obesity, genetic background, and environmental factors including the microbiome. To decipher hepatic steatosis (HS) pathogenesis by excluding critical confounding factors including genetic variants and diabetes, 56 heterogenous MAFLD patients are characterized by generating multiomics data including oral and gut metagenomics as well as plasma metabolomics and inflammatory proteomics data. The dysbiosis in the oral and gut microbiome is explored and the host-microbiome interactions based on global metabolic and inflammatory processes are revealed. These multiomics data are integrated using the biological network and HS's key features are identified using multiomics data. HS is finally predicted using these key features and findings are validated in a follow-up cohort, where 22 subjects with varying degree of HS are characterized.

Host phenotype classification from human microbiome data is mainly driven by the presence of microbial taxa

Machine learning-based classification approaches are widely used to predict host phenotypes from microbiome data. Classifiers are typically employed by considering operational taxonomic units or relative abundance profiles as input features. Such types of data are intrinsically sparse, which opens the opportunity to make predictions from the presence/absence rather than the relative abundance of microbial taxa. This also poses the question whether it is the presence rather than the abundance of particular taxa to be relevant for discrimination purposes, an aspect that has been so far overlooked in the literature. In this paper, we aim at filling this gap by performing a meta-analysis on 4,128 publicly available metagenomes associated with multiple case-control studies. At species-level taxonomic resolution, we show that it is the presence rather than the relative abundance of specific microbial taxa to be important when building classification models. Such findings are robust to the choice of the classifier and confirmed by statistical tests applied to identifying differentially abundant/present taxa. Results are further confirmed at coarser taxonomic resolutions and validated on 4,026 additional 16S rRNA samples coming from 30 public case-control studies.

The composition of the human microbiome has been linked to a large number of different diseases. In this context, classification methodologies based on machine learning approaches have represented a promising tool for diagnostic purposes from metagenomics data. The link between microbial population composition and host phenotypes has been usually performed by considering taxonomic profiles represented by relative abundances of microbial species. In this study, we show that it is more the presence rather than the relative abundance of microbial taxa to be relevant to maximize classification accuracy. This is accomplished by conducting a meta-analysis on more than 4,000 shotgun metagenomes coming from 25 case-control studies and in which original relative abundance data are degraded to presence/absence profiles. Findings are also extended to 16S rRNA data and advance the research field in building prediction models directly from human microbiome data.

Short-Term Metformin Treatment Enriches Bacteroides dorei in an Obese Liver Steatosis Zucker Rat Model

Obesity is the leading cause of health-related diseases in the United States and World. Previously, we reported that obesity can change gut microbiota using the Zucker rat model. Metformin is an oral anti-hyperglycemic agent approved by the FDA to treat type 2 diabetes (T2D) in adults and children older than 10 years of age. The correlation of short-term metformin treatment and specific alterations to the gut microbiota in obese models is less known. Short-term metformin has been shown to reduce liver steatosis. Here we investigate the effects of short-term metformin treatment on population of gut microbiota profile in an obese rat model. Five week old obese (n = 12) female Zucker rats after 1 week of acclimation, received AIN-93 G diet for 8 weeks and then rats were randomly assigned into two groups (6 rats/group): (1) obese without metformin (ObC), or (2) obese with metformin (ObMet). Metformin was mixed with AIN-93G diet at 1,000 mg/kg of diet. Rats were weighed twice per week. All rats were sacrificed at the end of metformin treatment at 10 weeks and fecal samples were collected and kept at -80°C. Total microbial DNA was collected directly from the fecal samples used for shotgun-metagenomics sequencing and subsequently analyzed using MetaPlAn and HUMAnN. After stringent data filtering and quality control we found significant differences (p = 0.0007) in beta diversity (Aitchison distances) between the ObC vs. ObMet groups. Supervised and unsupervised analysis of the log-ratios Bacteroides dorei and B. massiliensis vs. all other Bacteroides spp., revealed that B. dorei and B. massiliensis were enriched in the ObMet group, while the remaining Bacteroides spp. where enriched in the ObC group (p = 0.002). The contributional diversity of pathways is also significantly associated by treatment group (p = 0.008). In summary, in the obese Zucker rat model, short-term metformin treatment changes the gut microbiota profile, particularly altering the composition Bacteroides spp. between ObC and ObMet.

Isolation of Leptospira interrogans Serovar Canicola in a Vaccinated Dog without Clinical Symptoms

More than one million cases of leptospirosis occur across the globe annually, resulting in about 59,000 deaths. Dogs are one of the most important reservoirs of Leptospira species and play an important role in transmitting the pathogen to humans. Many of these infections are controlled by routine vaccination that has reduced the possible reintroduction of leptospiral serovars into the human population. However, it is still not clear how a vaccinated dog can become infected with one or more Leptospira serovars contained in the vaccine formulation and thus against which it should be immunized. Here, we present the case of an asymptomatic dog who developed leptospiral infection despite being vaccinated. This unusual case emphasizes the substantial impact of immunization on mitigating the acute signs of the disease, even while providing limited protection against infection. Further studies will be required to better understand the role of dogs in the environmental circulation of leptospiral serovars in Sardinia. Asymptomatic leptospiral infection in vaccinated dogs should be considered to allow for better diagnosis and management of the infection. This will be essential for preventing Leptospira outbreaks in the future.

Metagenomic strain detection with SameStr: identification of a persisting core gut microbiota transferable by fecal transplantation

BACKGROUND

The understanding of how microbiomes assemble, function, and evolve requires metagenomic tools that can resolve microbiota compositions at the strain level. However, the identification and tracking of microbial strains in fecal metagenomes is challenging and available tools variably classify subspecies lineages, which affects their applicability to infer microbial persistence and transfer.

RESULTS

We introduce SameStr, a bioinformatic tool that identifies shared strains in metagenomes by determining single-nucleotide variants (SNV) in species-specific marker genes, which are compared based on a maximum variant profile similarity. We validated SameStr on mock strain populations, available human fecal metagenomes from healthy individuals and newly generated data from recurrent Clostridioides difficile infection (rCDI) patients treated with fecal microbiota transplantation (FMT). SameStr demonstrated enhanced sensitivity to detect shared dominant and subdominant strains in related samples (where strain persistence or transfer would be expected) when compared to other tools, while being robust against false-positive shared strain calls between unrelated samples (where neither strain persistence nor transfer would be expected). We applied SameStr to identify strains that are stably maintained in fecal microbiomes of healthy adults over time (strain persistence) and that successfully engraft in rCDI patients after FMT (strain engraftment). Taxonomy-dependent strain persistence and engraftment frequencies were positively correlated, indicating that a specific core microbiota of intestinal species is adapted to be competitive both in healthy microbiomes and during post-FMT microbiome assembly. We explored other use cases for strain-level microbiota profiling, as a metagenomics quality control measure and to identify individuals based on the persisting core gut microbiota.

CONCLUSION

SameStr provides for a robust identification of shared strains in metagenomic sequence data with sufficient specificity and sensitivity to examine strain persistence, transfer, and engraftment in human fecal microbiomes. Our findings identify a persisting healthy adult core gut microbiota, which should be further studied to shed light on microbiota contributions to chronic diseases. Video abstract.

Metagenomic Approaches Reveal Strain Profiling and Genotyping of Klebsiella pneumoniae from Hospitalized Patients in China

Klebsiella pneumoniae is a leading cause of highly drug-resistant infections in hospitals worldwide. Strain-level bacterial identification on the genetic determinants of multidrug resistance and high pathogenicity is critical for the surveillance and treatment of this clinically relevant pathogen. In this study, metagenomic next-generation sequencing was performed for specimens collected from August 2020 to May 2021 in Ruijin Hospital, Ningbo Women and Children’s Hospital, and the Second Affiliated Hospital of Harbin Medical University. Genome biology of K. pneumoniae prevalent in China was characterized based on metagenomic data. Thirty K. pneumoniae strains derived from 14 sequence types were identified by multilocus sequence typing. The hypervirulent ST11 K. pneumoniae strains carrying the KL64 capsular locus were the most prevalent in the hospital population. The phylogenomic analyses revealed that the metagenome-reconstructed strains and public isolate genomes belonging to the same STs were closely related in the phylogenetic tree. Furthermore, the pangenome structure of the detected K. pneumoniae strains was analyzed, particularly focusing on the distribution of antimicrobial resistance genes and virulence genes across the strains. The genes encoding carbapenemases and extended-spectrum beta-lactamases were frequently detected in the strains of ST11 and ST15. The highest numbers of virulence genes were identified in the well-known hypervirulent strains affiliated to ST23 bearing the K1 capsule. In comparison to traditional cultivation and identification, strain-level metagenomics is advantageous to understand the mechanisms underlying resistance and virulence of K. pneumoniae directly from clinical specimens. Our findings should provide novel clues for future research into culture-independent metagenomic surveillance for bacterial pathogens.

IMPORTANCE Routine culture and PCR-based molecular testing in the clinical microbiology laboratory are unable to recognize pathogens at the strain level and to detect strain-specific genetic determinants involved in virulence and resistance. To address this issue, we explored the strain-level profiling of K. pneumoniae prevalent in China based on metagenome-sequenced patient materials. Genome biology of the targeted bacterium can be well characterized through decoding sequence signatures and functional gene profiles at the single-strain resolution. The in-depth metagenomic analysis on strain profiling presented here shall provide a promising perspective for culture-free pathogen surveillance and molecular epidemiology of nosocomial infections.

A sensitive visual method for onsite detection of quarantine pathogenic bacteria from horticultural crops using an LbCas12a variant system

Pre-planting testing of seeds and plantlets for the existence of quarantine pathogens is an important phytosanitary measure. The CRISPR-mediated molecular diagnostic methodologies are being developed for pathogens detection, but many challenges remain. Here, we profiled an engineered Crispr/LbCas12a variant (LbCas12a-5M) that has more robust trans-cleavage activity and a wider PAM sequences (TNTN) preference than wild type. We developed a procedure for screening specific sequences of bacterial plant pathogens, and the designed species-specific crRNA displayed no cross-reactions with other bacterial species. Combined with a simple extraction of bacterial DNA, an LbCas12a-5M-based visual detection technique was established and optimized for detecting quarantine pathogens Erwinia amylovora and Acidovorax citrulli with detection limits up to 40 CFU/reaction and a sensitivity consistent with qPCR assay. This protocol was faster and simpler than qPCR, requiring 40 min or less from sample preparation. We further validated the potential application of the method by showing that it can be used for rapid and accurate diagnosis of A. citrulli on seeds of watermelon, with 100% agreement with the results of qPCR assay. The developed method simplifies the detection of pathogens and provides cost-effective countermeasures to quarantine interventions.

A commensal-encoded genotoxin drives restriction of Vibrio cholerae colonization and host gut microbiome remodeling

SignificanceIn a polymicrobial battlefield where different species compete for nutrients and colonization niches, antimicrobial compounds are the sword and shield of commensal microbes in competition with invading pathogens and each other. The identification of an Escherichia coli-produced genotoxin, colibactin, and its specific targeted killing of enteric pathogens and commensals, including Vibrio cholerae and Bacteroides fragilis, sheds light on our understanding of intermicrobial interactions in the mammalian gut. Our findings elucidate the mechanisms through which genotoxins shape microbial communities and provide a platform for probing the larger role of enteric multibacterial interactions regarding infection and disease outcomes.

A Metagenomic Approach for Characterizing Antibiotic Resistance Genes in Specific Bacterial Populations: Demonstration with Escherichia coli in Cattle Manure

The high diversity of bacterial antibiotic resistance genes (ARGs) and the different health risks due to their association with different bacterial hosts require environmental ARG risk assessment to have capabilities of both high throughput and host differentiation. Current whole genome sequencing of cultivated isolates is low in throughput, while direct metagenomic next generation sequencing (mNGS) of environmental samples is nonselective with respect to bacterial hosts. This study introduced a population metagenomic approach that combines isolate library construction and mNGS of the population metagenomic DNA, which enables studying ARGs and their association with mobile genetic elements (MGEs) in a specific bacterial population. The population metagenomic approach was demonstrated with the E. coli population in cattle manure, which detected the co-location of multiple ARGs on the same MGEs and their correspondence to the prevalence of resistance phenotypes of the E. coli isolates. When compared with direct mNGS of the cattle manure samples, the E. coli population metagenomes exhibited a significantly different resistome and an overall higher relative abundance of ARGs and horizontal gene transfer risks. IMPORTANCE Bacterial antibiotic resistance genes in the environment are ubiquitous and can pose different levels of human health risks due to their bacterial host association and subsequent mobility. This study introduced a population metagenomic approach to study ARGs and their mobility in specific bacterial populations through a combination of selective cultivation followed by next generation sequencing and bioinformatic analysis of the combined metagenome of isolates. The utility of this approach was demonstrated with the E. coli population in cattle manure samples, which showed that ARGs detected in the E. coli population corresponded to the observed resistance phenotypes, co-location of multiple ARGs on the same mobile genetic elements.

Collective effects of human genomic variation on microbiome function

Studies of the impact of host genetics on gut microbiome composition have mainly focused on the impact of individual single nucleotide polymorphisms (SNPs) on gut microbiome composition, without considering their collective impact or the specific functions of the microbiome. To assess the aggregate role of human genetics on the gut microbiome composition and function, we apply sparse canonical correlation analysis (sCCA), a flexible, multivariate data integration method. A critical attribute of metagenome data is its sparsity, and here we propose application of a Tweedie distribution to accommodate this. We use the TwinsUK cohort to analyze the gut microbiomes and human variants of 250 individuals. Sparse CCA, or sCCA, identified SNPs in microbiome-associated metabolic traits (BMI, blood pressure) and microbiome-associated disorders (type 2 diabetes, some neurological disorders) and certain cancers. Both common and rare microbial functions such as secretion system proteins or antibiotic resistance were found to be associated with host genetics. sCCA applied to microbial species abundances found known associations such as Bifidobacteria species, as well as novel associations. Despite our small sample size, our method can identify not only previously known associations, but novel ones as well. Overall, we present a new and flexible framework for examining host-microbiome genetic interactions, and we provide a new dimension to the current debate around the role of human genetics on the gut microbiome.

Integrated analysis of gut microbiome and host immune responses in COVID-19

Emerging evidence indicates that the gut microbiome contributes to the host immune response to infectious diseases. Here, to explore the role of the gut microbiome in the host immune responses in COVID-19, we conducted shotgun metagenomic sequencing and immune profiling of 14 severe/critical and 24 mild/moderate COVID-19 cases as well as 31 healthy control samples. We found that the diversity of the gut microbiome was reduced in severe/critical COVID-19 cases compared to mild/moderate ones. We identified the abundance of some gut microbes altered post-SARS-CoV-2 infection and related to disease severity, such as Enterococcus faecium, Coprococcus comes, Roseburia intestinalis, Akkermansia muciniphila, Bacteroides cellulosilyticus and Blautia obeum. We further analyzed the correlation between the abundance of gut microbes and host responses, and obtained a correlation map between clinical features of COVID-19 and 16 severity-related gut microbe, including Coprococcus comes that was positively correlated with CD3⁺/CD4⁺/CD8⁺ lymphocyte counts. In addition, an integrative analysis of gut microbiome and the transcriptome of peripheral blood mononuclear cells (PBMCs) showed that genes related to viral transcription and apoptosis were up-regulated in Coprococcus comes low samples. Moreover, a number of metabolic pathways in gut microbes were also found to be differentially enriched in severe/critical or mild/moderate COVID-19 cases, including the superpathways of polyamine biosynthesis II and sulfur oxidation that were suppressed in severe/critical COVID-19. Together, our study highlighted a potential regulatory role of severity related gut microbes in the immune response of host.

MIMOSA2: a metabolic network-based tool for inferring mechanism-supported relationships in microbiome-metabolome data

MOTIVATION

Recent technological developments have facilitated an expansion of microbiome-metabolome studies, in which samples are assayed using both genomic and metabolomic technologies to characterize the abundances of microbial taxa and metabolites. A common goal of these studies is to identify microbial species or genes that contribute to differences in metabolite levels across samples. Previous work indicated that integrating these datasets with reference knowledge on microbial metabolic capacities may enable more precise and confident inference of microbe-metabolite links.

RESULTS

We present MIMOSA2, an R package and web application for model-based integrative analysis of microbiome-metabolome datasets. MIMOSA2 uses genomic and metabolic reference databases to construct a community metabolic model based on microbiome data and uses this model to predict differences in metabolite levels across samples. These predictions are compared with metabolomics data to identify putative microbiome-governed metabolites and taxonomic contributors to metabolite variation. MIMOSA2 supports various input data types and customization with user-defined metabolic pathways. We establish MIMOSA2's ability to identify ground truth microbial mechanisms in simulation datasets, compare its results with experimentally inferred mechanisms in honeybee microbiota, and demonstrate its application in two human studies of inflammatory bowel disease. Overall, MIMOSA2 combines reference databases, a validated statistical framework, and a user-friendly interface to facilitate modeling and evaluating relationships between members of the microbiota and their metabolic products.

AVAILABILITY AND IMPLEMENTATION

MIMOSA2 is implemented in R under the GNU General Public License v3.0 and is freely available as a web server at http://elbo-spice.cs.tau.ac.il/shiny/MIMOSA2shiny/ and as an R package from http://www.borensteinlab.com/software_MIMOSA2.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Host-microbiome protein-protein interactions capture disease-relevant pathways

Background

Host-microbe interactions are crucial for normal physiological and immune system development and are implicated in a variety of diseases, including inflammatory bowel disease (IBD), colorectal cancer (CRC), obesity, and type 2 diabetes (T2D). Despite large-scale case-control studies aimed at identifying microbial taxa or genes involved in pathogeneses, the mechanisms linking them to disease have thus far remained elusive.

Results

To identify potential pathways through which human-associated bacteria impact host health, we leverage publicly-available interspecies protein-protein interaction (PPI) data to find clusters of microbiome-derived proteins with high sequence identity to known human-protein interactors. We observe differential targeting of putative human-interacting bacterial genes in nine independent metagenomic studies, finding evidence that the microbiome broadly targets human proteins involved in immune, oncogenic, apoptotic, and endocrine signaling pathways in relation to IBD, CRC, obesity, and T2D diagnoses.

Conclusions

This host-centric analysis provides a mechanistic hypothesis-generating platform and extensively adds human functional annotation to commensal bacterial proteins.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-022-02643-9.

Metagenomic Assessment of the Pathogenic Risk of Microorganisms in Sputum of Postoperative Patients With Pulmonary Infection

Respiratory infections are complicated biological processes associated with an unbalanced microbial community and a wide range of pathogens. To date, robust approaches are still required for distinguishing the pathogenic microorganisms from the colonizing ones in the clinical specimens with complex infection. In this study, we retrospectively analyzed the data of conventional culture testing and metagenomic next-generation sequencing (mNGS) of the sputum samples collected from 50 pulmonary infected patients after cardiac surgery from December 2020 and June 2021 in Ruijin Hospital. Taxonomic classification of the sputum metagenomes showed that the numbers of species belonging to bacteria, fungi, and viruses were 682, 58, and 21, respectively. The full spectrum of microorganisms present in the sputum microbiome covered all the species identified by culture, including 12 bacterial species and two fungal species. Based on species-level microbiome profiling, a reference catalog of microbial abundance detection limits was constructed to assess the pathogenic risks of individual microorganisms in the specimens. The proposed screening procedure detected 64 bacterial pathogens, 10 fungal pathogens, and three viruses. In particular, certain opportunistic pathogenic strains can be distinguished from the colonizing ones in the individual specimens. Strain-level identification and phylogenetic analysis were further performed to decipher molecular epidemiological characteristics of four opportunistic etiologic agents, including Klebsiella pneumoniae, Corynebacterium striatum, Staphylococcus aureus, and Candida albicans. Our findings provide a novel metagenomic insight into precision diagnosis for clinically relevant microbes, especially for opportunistic pathogens in the clinical setting.

Characterization and Demonstration of Mock Communities as Control Reagents for Accurate Human Microbiome Community Measurements

Standardization and quality assurance of microbiome community analysis by high-throughput DNA sequencing require widely accessible and well-characterized reference materials. Here, we report on newly developed DNA and whole-cell mock communities to serve as control reagents for human gut microbiota measurements by shotgun metagenomics and 16S rRNA gene amplicon sequencing. The mock communities were formulated as near-even blends of up to 20 bacterial species prevalent in the human gut, span a wide range of genomic guanine-cytosine (GC) contents, and include multiple strains with Gram-positive type cell walls. Through a collaborative study, we carefully characterized the mock communities by shotgun metagenomics, using previously developed standardized protocols for DNA extraction and sequencing library construction. Further, we validated fitness of the mock communities for revealing technically meaningful differences among protocols for DNA extraction and metagenome/16S rRNA gene amplicon library construction. Finally, we used the mock communities to reveal varying performance of metagenome-based taxonomic profilers and the impact of trimming and filtering of sequencing reads on observed species profiles. The latter showed that aggressive preprocessing of reads may result in substantial GC-dependent bias and should thus be carefully evaluated to minimize unintended effects on species abundances. Taken together, the mock communities are expected to support a myriad of applications that rely on well-characterized control reagents, ranging from evaluation and optimization of methods to assessment of reproducibility in interlaboratory studies and routine quality control.

IMPORTANCE Application of high-throughput DNA sequencing has greatly accelerated human microbiome research and its translation into new therapeutic and diagnostic capabilities. Microbiome community analyses results can, however, vary considerably across studies or laboratories, and establishment of measurement standards to improve accuracy and reproducibility has become a priority. The here-developed mock communities, which are available from the NITE Biological Resource Center (NBRC) at the National Institute of Technology and Evaluation (NITE, Japan), provide well-characterized control reagents that allow users to judge the accuracy of their measurement results. Widespread and consistent adoption of the mock communities will improve reproducibility and comparability of microbiome community analyses, thereby supporting and accelerating human microbiome research and development.

Cross-cohort gut microbiome associations with immune checkpoint inhibitor response in advanced melanoma

The composition of the gut microbiome has been associated with clinical responses to immune checkpoint inhibitor (ICI) treatment, but there is limited consensus on the specific microbiome characteristics linked to the clinical benefits of ICIs. We performed shotgun metagenomic sequencing of stool samples collected before ICI initiation from five observational cohorts recruiting ICI-naive patients with advanced cutaneous melanoma (n = 165). Integrating the dataset with 147 metagenomic samples from previously published studies, we found that the gut microbiome has a relevant, but cohort-dependent, association with the response to ICIs. A machine learning analysis confirmed the link between the microbiome and overall response rates (ORRs) and progression-free survival (PFS) with ICIs but also revealed limited reproducibility of microbiome-based signatures across cohorts. Accordingly, a panel of species, including Bifidobacterium pseudocatenulatum, Roseburia spp. and Akkermansia muciniphila, associated with responders was identified, but no single species could be regarded as a fully consistent biomarker across studies. Overall, the role of the human gut microbiome in ICI response appears more complex than previously thought, extending beyond differing microbial species simply present or absent in responders and nonresponders. Future studies should adopt larger sample sizes and take into account the complex interplay of clinical factors with the gut microbiome over the treatment course.

Nivolumab plus ipilimumab with or without live bacterial supplementation in metastatic renal cell carcinoma: a randomized phase 1 trial

Previous studies have suggested that the gut microbiome influences the response to checkpoint inhibitors (CPIs) in patients with cancer. CBM588 is a bifidogenic live bacterial product that we postulated could augment CPI response through modulation of the gut microbiome. In this open-label, single-center study (NCT03829111), 30 treatment-naive patients with metastatic renal cell carcinoma with clear cell and/or sarcomatoid histology and intermediate- or poor-risk disease were randomized 2:1 to receive nivolumab and ipilimumab with or without daily oral CBM588, respectively. Stool metagenomic sequencing was performed at multiple timepoints. The primary endpoint to compare the relative abundance of Bifidobacterium spp. at baseline and at 12 weeks was not met, and no significant differences in Bifidobacterium spp. or Shannon index associated with the addition of CBM588 to nivolumab–ipilimumab were detected. Secondary endpoints included response rate, progression-free survival (PFS) and toxicity. PFS was significantly longer in patients receiving nivolumab–ipilimumab with CBM588 than without (12.7 months versus 2.5 months, hazard ratio 0.15, 95% confidence interval 0.05–0.47, P = 0.001). Although not statistically significant, the response rate was also higher in patients receiving CBM588 (58% versus 20%, P = 0.06). No significant difference in toxicity was observed between the study arms. The data suggest that CBM588 appears to enhance the clinical outcome in patients with metastatic renal cell carcinoma treated with nivolumab–ipilimumab. Larger studies are warranted to confirm this clinical observation and elucidate the mechanism of action and the effects on microbiome and immune compartments.

A randomized trial in treatment-naive patients with metastatic renal cell carcinoma shows that the addition of a live bacterial product to an immunotherapy combination elicits promising clinical benefit in association with an enrichment of bacterial species, circulating cytokines and immune cell populations in responders.

Different gut microbial communities correlate with efficacy of albendazole-ivermectin against soil-transmitted helminthiases

Soil-transmitted helminth infections represent a large burden with over a quarter of the world’s population at risk. Low cure rates are observed with standard of care (albendazole); therefore, a more effective combination therapy (albendazole and ivermectin) is being investigated but showed variable treatment efficacies without evidence of intrinsic parasite resistance. Here, we analyzed the microbiome of Trichuris trichiura and hookworm-infected patients and found an association of different enterotypes with treatment efficacy. 80 T. trichiura-infected patients with hookworm co-infections from Pak-Khan, Laos, received either albendazole (n = 41) or albendazole and ivermectin combination therapy (n = 39). Pre-/post-treatment stool samples were collected to monitor treatment efficacy and microbial communities were profiled using 16S rRNA gene sequencing, qPCR, and shotgun sequencing. We identified three bacterial enterotypes and show that pre-treatment enterotype is associated with efficacy of the combination treatment for both T. trichiura (CR_ET1 = 5.8%; CR_ET2 = 16.6%; CR_ET3 = 68.8%) and hookworm (CR_ET1 = 31.3%; CR_ET2 = 16.6%; CR_ET3 = 78.6%). This study shows that pre-treatment enterotype enables predicting treatment outcome of combination therapy for T. trichiura and hookworm infections.

Trial registration: ClinicalTrials.gov, NCT03527732. Registered 17 May 2018, https://clinicaltrials.gov/ct2/show/NCT03527732.

Little is known about the cause of treatment failure of soil-transmitted helminth infections. Here, the authors show that pre-treatment gut microbial community composition enables predicting treatment outcome for Trichuris trichiura and hookworm infections.

Imbalances in TCA, Short Fatty Acids and One-Carbon Metabolisms as Important Features of Homeostatic Disruption Evidenced by a Multi-Omics Integrative Approach of LPS-Induced Chronic Inflammation in Male Wistar Rats

Chronic inflammation is an important risk factor in a broad variety of physical and mental disorders leading to highly prevalent non-communicable diseases (NCDs). However, there is a need for a deeper understanding of this condition and its progression to the disease state. For this reason, it is important to define metabolic pathways and complementary biomarkers associated with homeostatic disruption in chronic inflammation. To achieve that, male Wistar rats were subjected to intraperitoneal and intermittent injections with saline solution or increasing lipopolysaccharide (LPS) concentrations (0.5, 5 and 7.5 mg/kg) thrice a week for 31 days. Biochemical and inflammatory parameters were measured at the end of the study. To assess the omics profile, GC-qTOF and UHPLC-qTOF were performed to evaluate plasma metabolome; 1H-NMR was used to evaluate urine metabolome; additionally, shotgun metagenomics sequencing was carried out to characterize the cecum microbiome. The chronicity of inflammation in the study was evaluated by the monitoring of monocyte chemoattractant protein-1 (MCP-1) during the different weeks of the experimental process. At the end of the study, together with the increased levels of MCP-1, levels of interleukin-6 (IL-6), tumour necrosis factor alpha (TNF-α) and prostaglandin E2 (PGE2) along with 8-isoprostanes (an indicative of oxidative stress) were significantly increased (p-value < 0.05). The leading features implicated in the current model were tricarboxylic acid (TCA) cycle intermediates (i.e., alpha-ketoglutarate, aconitic acid, malic acid, fumaric acid and succinic acid); lipids such as specific cholesterol esters (ChoEs), lysophospholipids (LPCs) and phosphatidylcholines (PCs); and glycine, as well as N, N-dimethylglycine, which are related to one-carbon (1C) metabolism. These metabolites point towards mitochondrial metabolism through TCA cycle, β-oxidation of fatty acids and 1C metabolism as interconnected pathways that could reveal the metabolic effects of chronic inflammation induced by LPS administration. These results provide deeper knowledge concerning the impact of chronic inflammation on the disruption of metabolic homeostasis.

Short- and long-read metagenomics of urban and rural South African gut microbiomes reveal a transitional composition and undescribed taxa

Human gut microbiome research focuses on populations living in high-income countries and to a lesser extent, non-urban agriculturalist and hunter-gatherer societies. The scarcity of research between these extremes limits our understanding of how the gut microbiota relates to health and disease in the majority of the world's population. Here, we evaluate gut microbiome composition in transitioning South African populations using short- and long-read sequencing. We analyze stool from adult females living in rural Bushbuckridge (n = 118) or urban Soweto (n = 51) and find that these microbiomes are taxonomically intermediate between those of individuals living in high-income countries and traditional communities. We demonstrate that reference collections are incomplete for characterizing microbiomes of individuals living outside high-income countries, yielding artificially low beta diversity measurements, and generate complete genomes of undescribed taxa, including Treponema, Lentisphaerae, and Succinatimonas. Our results suggest that the gut microbiome of South Africans does not conform to a simple "western-nonwestern" axis and contains undescribed microbial diversity.

Swine manure facilitates the spread of antibiotic resistome including tigecycline-resistant tet(X) variants to farm workers and receiving environment

The prevalence of antibiotic resistance genes (ARGs) in livestock and poultry manure is a severe threat to human health. However, the comprehensive characterization of antibiotic resistance in swine, workers, and the receiving environment is still lacking in the actual breeding environment. Hence, the ARG profile and the potential bacterial hosts producing among swine manure (including sows, piglets, finishing pigs, and nursery pigs), worker feces, and the receiving environment (including sediment and vegetable soil) were comprehensively analyzed based on the metagenomic method. The results showed that swine manure exhibited the high levels of richness and diversity of ARGs. Inactivating tetracycline resistance genes such as tet(X), tet(X1), and tet(X10) were prevalent on swine farms. Workers and the environment were the primary recipients of ARGs, and shared ARGs accounted for at least 90% of their ARG abundances. Network analysis revealed that Escherichia, Acinetobacter, and Erysipelothrix were the most dominant genera co-occurring with specific shared ARGs. The abundance of coexisting ARGs in swine at different developmental stages accounted for 76.4% to 90.8% of the shared ARGs in swine, workers, and environmental samples. The Mantel test revealed that Firmicutes and Proteobacteria had a significant correlation with the ARG profiles. In addition, variation partitioning analysis (VPA) showed that the joint effects of mobile genetic elements (MGEs) and bacterial communities accounted for 24.7% of the resistome variation and played a significant role in the ARG profiles. These results improve our understanding of the transmission and persistence of ARGs in the actual breeding environment.