Phylogeny-Aware Analysis of Metagenome Community Ecology Based on Matched Reference Genomes while Bypassing Taxonomy

Impact of diet change on the gut microbiome of common marmosets (Callithrix jacchus)

Gastrointestinal diseases are the most frequently reported clinical problems in captive common marmosets (Callithrix jacchus), often affecting the health and welfare of the animal and ultimately their use as a research subject. The microbiome has been shown to be intimately connected to diet and gastrointestinal health. Here, we use shotgun metagenomics and untargeted metabolomics in fecal samples of common marmosets collected before, during, and after a dietary transition from a biscuit to a gel diet. The overall health of marmosets, measured as weight recovery and reproductive outcome, improved after the diet transition. Moreover, each marmoset pair had significant shifts in the microbiome and metabolome after the diet transition. In general, we saw a decrease in Escherichia coli and Prevotella species and an increase in Bifidobacterium species. Untargeted metabolic profiles indicated that polyamine levels, specifically cadaverine and putrescine, were high after diet transition, suggesting either an increase in excretion or a decrease in intestinal reabsorption at the intestinal level. In conclusion, our data suggest that Bifidobacterium species could potentially be useful as probiotic supplements to the laboratory marmoset diet. Future studies with a larger sample size will be beneficial to show that this is consistent with the diet change.

IMPORTANCE

Appropriate diet and health of the common marmoset in captivity are essential both for the welfare of the animal and to improve experimental outcomes. Our study shows that a gel diet compared to a biscuit diet improves the health of a marmoset colony, is linked to increases in Bifidobacterium species, and increases the removal of molecules associated with disease. The diet transition had an influence on the molecular changes at both the pair and time point group levels, but only at the pair level for the microbial changes. It appears to be more important which genes and functions present changed rather than specific microbes. Further studies are needed to identify specific components that should be considered when choosing an appropriate diet and additional supplementary foods, as well as to validate the benefits of providing probiotics. Probiotics containing Bifidobacterium species appear to be useful as probiotic supplements to the laboratory marmoset diet, but additional work is needed to validate these findings.

SkinCom, a synthetic skin microbial community, enables reproducible investigations of the human skin microbiome

Existing models of the human skin have aided our understanding of skin health and disease. However, they currently lack a microbial component, despite microbes' demonstrated connections to various skin diseases. Here, we present a robust, standardized model of the skin microbial community (SkinCom) to support in vitro and in vivo investigations. Our methods lead to the formation of an accurate, reproducible, and diverse community of aerobic and anaerobic bacteria. Subsequent testing of SkinCom on the dorsal skin of mice allowed for DNA and RNA recovery from both the applied SkinCom and the dorsal skin, highlighting its practicality for in vivo studies and -omics analyses. Furthermore, 66% of the responses to common cosmetic chemicals in vitro were in agreement with a human trial. Therefore, SkinCom represents a valuable, standardized tool for investigating microbe-metabolite interactions and facilitates the experimental design of in vivo studies targeting host-microbe relationships.

Unveiling the overlooked small-sized microbiome in river ecosystems

Enriching microorganisms using a 0.22-μm pore size is a general pretreatment procedure in river microbiome research. However, it remains unclear the extent to which this method loses microbiome information. Here, we conducted a comparative metagenomics-based study on microbiomes with sizes over 0.22 μm (large-sized) and between 0.22 μm and 0.1 μm (small-sized) in a subtropical river. Although the absolute concentration of small-sized microbiome was about two orders of magnitude lower than that of large-sized microbiome, sequencing only large-sized microbiome resulted in a significant loss of microbiome diversity. Specifically, the microbial community was different between two sizes, and 347 genera were only detected in small-sized microbiome. Small-sized microbiome had much more diverse viral community than large-sized fraction. The viruses had abundant ecological functions and were hosted by 825 species of 169 families, including pathogen-related families. Small-sized microbiome had distinct antimicrobial resistance risks from large-sized microbiome, showing an enrichment of eight antibiotic resistance gene (ARG) types as well as the detection of 140 unique ARG subtypes and five enriched risk rank I ARGs. Draft genomes of five major resistant pathogens having diverse ecological and pollutant-degrading functions were only assembled in small-sized microbiome. These findings provide novel insights into river ecosystems, and highlight the overlooked small-sized microbiome in the environment.

The impact of breastfeeding on the preterm infant's microbiome and metabolome: a pilot study

BACKGROUND

Human milk is unquestionably beneficial for preterm infants. We investigated how the transition from tube to oral/breastfeeding impacts the preterm infants' oral and gut microbiome and metabolome.

METHODS

We analyzed stool, saliva, and milk samples collected from a cohort of preterm infants enrolled in the MAP Study, a prospective observational trial. The microbiome and metabolome of the samples were analyzed from 4 longitudinal sample time points, 2 during tube feeds only and 2 after the initiation of oral/breastfeeding.

RESULTS

We enrolled 11 mother-infant dyads (gestational age = 27.9 (23.4-32.2)) and analyzed a total of 39 stool, 44 saliva, and 43 milk samples over 4 timepoints. In saliva samples, there was a shift towards increased Streptococcus and decreased Staphylococcus after oral feeding/breastfeeding initiation (p < 0.05). Milk sample metabolites were strongly influenced by the route of feeding and milk type (p < 0.05) and represented the pathways of Vitamin E metabolism, Vitamin B12 metabolism, and Tryptophan metabolism.

CONCLUSION

Our analysis demonstrated that the milk and preterm infant's saliva microbiome and metabolome changed over the course of the first four to 5 months of life, coinciding with the initiation of oral/breastfeeds.

IMPACT

The microbiome and metabolome is altered in the infant's saliva but not their stool, and in mother's milk when feeds are transitioned from tube to oral/breastfeeding. We assessed the relationship between the gut and oral microbiome/metabolome with the milk microbiome/metabolome over a longitudinal period of time in preterm babies. Metabolites that changed in the infants saliva after the initiation of oral feeds have the potential to be used as biomarkers for disease risk.

Unveiling the microbial realm with VEBA 2.0: a modular bioinformatics suite for end-to-end genome-resolved prokaryotic, (micro)eukaryotic and viral multi-omics from either short- or long-read sequencing

The microbiome is a complex community of microorganisms, encompassing prokaryotic (bacterial and archaeal), eukaryotic, and viral entities. This microbial ensemble plays a pivotal role in influencing the health and productivity of diverse ecosystems while shaping the web of life. However, many software suites developed to study microbiomes analyze only the prokaryotic community and provide limited to no support for viruses and microeukaryotes. Previously, we introduced the Viral Eukaryotic Bacterial Archaeal (VEBA) open-source software suite to address this critical gap in microbiome research by extending genome-resolved analysis beyond prokaryotes to encompass the understudied realms of eukaryotes and viruses. Here we present VEBA 2.0 with key updates including a comprehensive clustered microeukaryotic protein database, rapid genome/protein-level clustering, bioprospecting, non-coding/organelle gene modeling, genome-resolved taxonomic/pathway profiling, long-read support, and containerization. We demonstrate VEBA's versatile application through the analysis of diverse case studies including marine water, Siberian permafrost, and white-tailed deer lung tissues with the latter showcasing how to identify integrated viruses. VEBA represents a crucial advancement in microbiome research, offering a powerful and accessible software suite that bridges the gap between genomics and biotechnological solutions.

Dietary iron intake has long-term effects on the fecal metabolome and microbiome

Iron is essential for life, but its imbalances can lead to severe health implications. Iron deficiency is the most common nutrient disorder worldwide, and iron dysregulation in early life has been found to cause long-lasting behavioral, cognitive, and neural effects. However, little is known about the effects of dietary iron on gut microbiome function and metabolism. In this study, we sought to investigate the impact of dietary iron on the fecal metabolome and microbiome by using mice fed with three diets with different iron content: an iron deficient, an iron sufficient (standard), and an iron overload diet for 7 weeks. Additionally, we sought to understand whether any observed changes would persist past the 7-week period of diet intervention. To assess this, all feeding groups were switched to a standard diet, and this feeding continued for an additional 7 weeks. Analysis of the fecal metabolome revealed that iron overload and deficiency significantly alter levels of peptides, nucleic acids, and lipids, including di- and tri-peptides containing branched-chain amino acids, inosine and guanosine, and several microbial conjugated bile acids. The observed changes in the fecal metabolome persist long after the switch back to a standard diet, with the cecal gut microbiota composition and function of each group distinct after the 7-week standard diet wash-out. Our results highlight the enduring metabolic consequences of nutritional imbalances, mediated by both the host and gut microbiome, which persist after returning to the original standard diets.

Serum and CSF metabolomics analysis shows Mediterranean Ketogenic Diet mitigates risk factors of Alzheimer's disease

Alzheimer's disease (AD) is influenced by a variety of modifiable risk factors, including a person's dietary habits. While the ketogenic diet (KD) holds promise in reducing metabolic risks and potentially affecting AD progression, only a few studies have explored KD's metabolic impact, especially on blood and cerebrospinal fluid (CSF). Our study involved participants at risk for AD, either cognitively normal or with mild cognitive impairment. The participants consumed both a modified Mediterranean Ketogenic Diet (MMKD) and the American Heart Association diet (AHAD) for 6 weeks each, separated by a 6-week washout period. We employed nuclear magnetic resonance (NMR)-based metabolomics to profile serum and CSF and metagenomics profiling on fecal samples. While the AHAD induced no notable metabolic changes, MMKD led to significant alterations in both serum and CSF. These changes included improved modifiable risk factors, like increased HDL-C and reduced BMI, reversed serum metabolic disturbances linked to AD such as a microbiome-mediated increase in valine levels, and a reduction in systemic inflammation. Additionally, the MMKD was linked to increased amino acid levels in the CSF, a breakdown of branched-chain amino acids (BCAAs), and decreased valine levels. Importantly, we observed a strong correlation between metabolic changes in the CSF and serum, suggesting a systemic regulation of metabolism. Our findings highlight that MMKD can improve AD-related risk factors, reverse some metabolic disturbances associated with AD, and align metabolic changes across the blood-CSF barrier.

Gut microbiome dynamics and predictive value in hospitalized COVID-19 patients: a comparative analysis of shallow and deep shotgun sequencing

The COVID-19 pandemic caused by SARS-CoV-2 has led to a wide range of clinical presentations, with respiratory symptoms being common. However, emerging evidence suggests that the gastrointestinal (GI) tract is also affected, with angiotensin-converting enzyme 2, a key receptor for SARS-CoV-2, abundantly expressed in the ileum and colon. The virus has been detected in GI tissues and fecal samples, even in cases with negative results of the reverse transcription polymerase chain reaction in the respiratory tract. GI symptoms have been associated with an increased risk of ICU admission and mortality. The gut microbiome, a complex ecosystem of around 40 trillion bacteria, plays a crucial role in immunological and metabolic pathways. Dysbiosis of the gut microbiota, characterized by a loss of beneficial microbes and decreased microbial diversity, has been observed in COVID-19 patients, potentially contributing to disease severity. We conducted a comprehensive gut microbiome study in 204 hospitalized COVID-19 patients using both shallow and deep shotgun sequencing methods. We aimed to track microbiota composition changes induced by hospitalization, link these alterations to clinical procedures (antibiotics administration) and outcomes (ICU referral, survival), and assess the predictive potential of the gut microbiome for COVID-19 prognosis. Shallow shotgun sequencing was evaluated as a cost-effective diagnostic alternative for clinical settings. Our study demonstrated the diverse effects of various combinations of clinical parameters, microbiome profiles, and patient metadata on the precision of outcome prognostication in patients. It indicates that microbiological data possesses greater reliability in forecasting patient outcomes when contrasted with clinical data or metadata. Furthermore, we established that shallow shotgun sequencing presents a viable and cost-effective diagnostic alternative to deep sequencing within clinical environments.

Generation of accurate, expandable phylogenomic trees with uDance

Phylogenetic trees provide a framework for organizing evolutionary histories across the tree of life and aid downstream comparative analyses such as metagenomic identification. Methods that rely on single-marker genes such as 16S rRNA have produced trees of limited accuracy with hundreds of thousands of organisms, whereas methods that use genome-wide data are not scalable to large numbers of genomes. We introduce updating trees using divide-and-conquer (uDance), a method that enables updatable genome-wide inference using a divide-and-conquer strategy that refines different parts of the tree independently and can build off of existing trees, with high accuracy and scalability. With uDance, we infer a species tree of roughly 200,000 genomes using 387 marker genes, totaling 42.5 billion amino acid residues.

Greengenes2 unifies microbial data in a single reference tree

Studies using 16S rRNA and shotgun metagenomics typically yield different results, usually attributed to PCR amplification biases. We introduce Greengenes2, a reference tree that unifies genomic and 16S rRNA databases in a consistent, integrated resource. By inserting sequences into a whole-genome phylogeny, we show that 16S rRNA and shotgun metagenomic data generated from the same samples agree in principal coordinates space, taxonomy and phenotype effect size when analyzed with the same tree.

The microbiota drives diurnal rhythms in tryptophan metabolism in the stressed gut

Chronic stress disrupts microbiota-gut-brain axis function and is associated with altered tryptophan metabolism, impaired gut barrier function, and disrupted diurnal rhythms. However, little is known about the effects of acute stress on the gut and how it is influenced by diurnal physiology. Here, we used germ-free and antibiotic-depleted mice to understand how microbiota-dependent oscillations in tryptophan metabolism would alter gut barrier function at baseline and in response to an acute stressor. Cecal metabolomics identified tryptophan metabolism as most responsive to a 15-min acute stressor, while shotgun metagenomics revealed that most bacterial species exhibiting rhythmicity metabolize tryptophan. Our findings highlight that the gastrointestinal response to acute stress is dependent on the time of day and the microbiome, with a signature of stress-induced functional alterations in the ileum and altered tryptophan metabolism in the colon.

Hackflex library preparation enables low-cost metagenomic profiling

Shotgun metagenomic sequencing provides valuable insights into microbial communities, but the high cost of library preparation with standard kits and protocols is a barrier for many. New methods such as Hackflex use diluted commercially available reagents to greatly reduce library preparation costs. However, these methods have not been systematically validated for metagenomic sequencing. Here, we evaluate Hackflex performance by sequencing metagenomic libraries from known mock communities as well as mouse fecal samples prepared by Hackflex, Illumina DNA Prep, and Illumina TruSeq methods. Hackflex successfully recovered all members of the Zymo mock community, performing best for samples with DNA concentrations <1 ng/uL. Furthermore, Hackflex was able to delineate microbiota of individual inbred mice from the same breeding stock at the same mouse facility, and statistical modeling indicated that mouse ID explained a greater fraction of the variance in metagenomic composition than did library preparation method. These results show that Hackflex is suitable for generating inventories of bacterial communities through metagenomic sequencing.

Dermal injury drives a skin to gut axis that disrupts the intestinal microbiome and intestinal immune homeostasis in mice

The composition of the microbial community in the intestine may influence the functions of distant organs such as the brain, lung, and skin. These microbes can promote disease or have beneficial functions, leading to the hypothesis that microbes in the gut explain the co-occurrence of intestinal and skin diseases. Here, we show that the reverse can occur, and that skin directly alters the gut microbiome. Disruption of the dermis by skin wounding or the digestion of dermal hyaluronan results in increased expression in the colon of the host defense genes Reg3 and Muc2, and skin wounding changes the composition and behavior of intestinal bacteria. Enhanced expression Reg3 and Muc2 is induced in vitro by exposure to hyaluronan released by these skin interventions. The change in the colon microbiome after skin wounding is functionally important as these bacteria penetrate the intestinal epithelium and enhance colitis from dextran sodium sulfate (DSS) as seen by the ability to rescue skin associated DSS colitis with oral antibiotics, in germ-free mice, and fecal microbiome transplantation to unwounded mice from mice with skin wounds. These observations provide direct evidence of a skin-gut axis by demonstrating that damage to the skin disrupts homeostasis in intestinal host defense and alters the gut microbiome.

Robustness of cancer microbiome signals over a broad range of methodological variation

In 2020, we identified cancer-specific microbial signals in The Cancer Genome Atlas (TCGA) [1]. Multiple peer-reviewed papers independently verified or extended our findings [2-12]. Given this impact, we carefully considered concerns by Gihawi et al. [13] that batch correction and database contamination with host sequences artificially created the appearance of cancer type-specific microbiomes. (1) We tested batch correction by comparing raw and Voom-SNM-corrected data per-batch, finding predictive equivalence and significantly similar features. We found consistent results with a modern microbiome-specific method (ConQuR [14]), and when restricting to taxa found in an independent, highly-decontaminated cohort. (2) Using Conterminator [15], we found low levels of human contamination in our original databases (~1% of genomes). We demonstrated that the increased detection of human reads in Gihawi et al. [13] was due to using a newer human genome reference. (3) We developed Exhaustive, a method twice as sensitive as Conterminator, to clean RefSeq. We comprehensively host-deplete TCGA with many human (pan)genome references. We repeated all analyses with this and the Gihawi et al. [13] pipeline, and found cancer type-specific microbiomes. These extensive re-analyses and updated methods validate our original conclusion that cancer type-specific microbial signatures exist in TCGA, and show they are robust to methodology.

CONSULT-II: accurate taxonomic identification and profiling using locality-sensitive hashing

MOTIVATION

Taxonomic classification of short reads and taxonomic profiling of metagenomic samples are well-studied yet challenging problems. The presence of species belonging to groups without close representation in a reference dataset is particularly challenging. While k-mer-based methods have performed well in terms of running time and accuracy, they tend to have reduced accuracy for such novel species. Thus, there is a growing need for methods that combine the scalability of k-mers with increased sensitivity.

RESULTS

Here, we show that using locality-sensitive hashing (LSH) can increase the sensitivity of the k-mer-based search. Our method, which combines LSH with several heuristics techniques including soft lowest common ancestor labeling and voting, is more accurate than alternatives in both taxonomic classification of individual reads and abundance profiling.

AVAILABILITY AND IMPLEMENTATION

CONSULT-II is implemented in C++, and the software, together with reference libraries, is publicly available on GitHub https://github.com/bo1929/CONSULT-II.

Medication Use is Associated with Distinct Microbial Features in Anxiety and Depression

This study investigated the relationship between gut microbiota and neuropsychiatric disorders (NPDs), specifically anxiety disorder (ANXD) and/or major depressive disorder (MDD), as defined by DSM-IV or V criteria. The study also examined the influence of medication use, particularly antidepressants and/or anxiolytics, classified through the Anatomical Therapeutic Chemical (ATC) Classification System, on the gut microbiota. Both 16S rRNA gene amplicon sequencing and shallow shotgun sequencing were performed on DNA extracted from 666 fecal samples from the Tulsa-1000 and NeuroMAP CoBRE cohorts. The results highlight the significant influence of medication use; antidepressant use is associated with significant differences in gut microbiota beta diversity and has a larger effect size than NPD diagnosis. Next, specific microbes were associated with ANXD and MDD, highlighting their potential for non-pharmacological intervention. Finally, the study demonstrated the capability of Random Forest classifiers to predict diagnoses of NPD and medication use from microbial profiles, suggesting a promising direction for the use of gut microbiota as biomarkers for NPD. The findings suggest that future research on the gut microbiota's role in NPD and its interactions with pharmacological treatments are needed.

Whole-body metabolic modelling reveals microbiome and genomic interactions on reduced urine formate levels in Alzheimer's disease

In this study, we aimed to understand the potential role of the gut microbiome in the development of Alzheimer's disease (AD). We took a multi-faceted approach to investigate this relationship. Urine metabolomics were examined in individuals with AD and controls, revealing decreased formate and fumarate concentrations in AD. Additionally, we utilised whole-genome sequencing (WGS) data obtained from a separate group of individuals with AD and controls. This information allowed us to create and investigate host-microbiome personalised whole-body metabolic models. Notably, AD individuals displayed diminished formate microbial secretion in these models. Additionally, we identified specific reactions responsible for the production of formate in the host, and interestingly, these reactions were linked to genes that have correlations with AD. This study suggests formate as a possible early AD marker and highlights genetic and microbiome contributions to its production. The reduced formate secretion and its genetic associations point to a complex connection between gut microbiota and AD. This holistic understanding might pave the way for novel diagnostic and therapeutic avenues in AD management.

Swine farming shifted the gut antibiotic resistome of local people

Antibiotic resistance genes (ARGs) are prevalent in the livestock environment, but little is known about impacts of animal farming on the gut antibiotic resistome of local people. Here we conducted metagenomic sequencing to investigate gut microbiome and resistome of residents in a swine farming village as well as environmental relevance by comparing with a nearby non-farming village. Results showed a shift of gut microbiome towards unhealthy status in the residents of swine farming village, with an increased abundance and diversity in pathogens and ARGs. The resistome composition in human guts was more similar with that in swine feces and air than that in soil and water. Mobile gene elements were closely associated with the prevalence of gut resistome. Some plasmid-borne ARGs were colocalized in similar genetic contexts in gut and environmental samples. Metagenomic binning obtained 47 ARGs-carrying families in human guts, and therein Enterobacteriaceae posed the highest threats in antibiotic resistance and virulence. Several ARGs-carrying families were shared by gut and environmental samples (mainly in swine feces and air), and the ARGs were evolutionarily conservative within genera. The findings highlight that swine farming can shape gut resistome of local people with close linkage to farm environmental exposures.

Polysaccharide preferred minority-dominant community assembly and exoenzyme enrichment in transparent exopolymer particles: Implication for global carbon cycle in water

The ubiquitous transparent exopolymer particles (TEPs) are an important organic carbon pool and an ideal microhabitat for bacteria in aquatic environments. They play a crucial role in the global carbon cycle. Organic matter transformation and carbon turnover in TEPs strongly depend on the assembly of their associated bacterial communities and enzyme activity. However, the mechanisms of bacterial community assembly and their potential effects on the organic carbon cycle in TEPs are still unclear. In this study, we comparatively explored the community assembly of TEP-associated bacteria and bacterioplankton from surface freshwater using metagenomics. It was found that the bacterial community assembly in TEPs followed a minority-dominant rule and was governed by homogeneous selection. Pseudomonadota and Actinomycetota, which are responsible for polysaccharide degradation, serve as taxon-specific biomarkers among the abundant and diverse bacteria in TEPs. The network of TEP-associated bacteria displayed stronger robustness than that of bacterioplankton. Bin 76 (majorly Acinetobacter) was the overwhelmingly dominant taxa in TEPs, whereas there was no clearly dominant taxa in TEP-free water. Exoenzyme analysis showed that 64 out of 71 identified polysaccharide hydrolases were markedly linked with the dominant bin 76 in TEPs, while no such linkage was observed for bacterioplankton. Generally, Acinetobacter, which is capable of utilizing polysaccharides, is preferred to be assembled in TEPs together with high polysaccharide hydrolase activity. This may significantly accelerate the turnover of organic carbon in the giant global TEP pool. These findings are important for a deep understanding of the carbon cycle in water.

Integrating global microbiome data into antibiotic resistance assessment in large rivers

Rivers are important in spreading antimicrobial resistance (AMR). Assessing AMR risk in large rivers is challenged by large spatial scale and numerous contamination sources. Integrating river resistome data into a global framework may help addressing this difficulty. Here, we conducted an omics-based assessment of AMR in a large river (i.e. the Pearl River in China) with global microbiome data. Results showed that antibiotic resistome in river water and sediment was more diversified than that in other rivers, with contamination levels in some river reaches higher than global baselines. Discharge of WWTP effluent and landfill waste drove AMR prevalence in the river, and the resistome level was highly associated with human and animal sources. Detection of 54 risk rank I ARGs and emerging mobilizable mcr and tet(X) highlighted AMR risk in the river reaches with high human population density and livestock pollution. Florfenicol-resistant floR therein deserved priority concerns due to its high detection frequency, dissimilar phylogenetic distance, mobilizable potential, and presence in multiple pathogens. Co-sharing of ARGs across taxonomic ranks implied their transfer potentials in the community. By comparing with global genomic data, we found that Burkholderiaceae, Enterobacteriaceae, Moraxellaceae and Pseudomonadaceae were important potential ARG-carrying bacteria in the river, and WHO priority carbapenem-resistant Enterobacteriaceae, A. baumannii and P. aeruginosa should be included in future surveillance. Collectively, the findings from this study provide an omics-benchmarked assessment strategy for public risk associated with AMR in large rivers.

Evaluation of the safety and efficacy of fecal microbiota transplantations in bottlenose dolphins (Tursiops truncatus) using metagenomic sequencing

AIMS

Gastrointestinal disease is a leading cause of morbidity in bottlenose dolphins (Tursiops truncatus) under managed care. Fecal microbiota transplantation (FMT) holds promise as a therapeutic tool to restore gut microbiota without antibiotic use. This prospective clinical study aimed to develop a screening protocol for FMT donors to ensure safety, determine an effective FMT administration protocol for managed dolphins, and evaluate the efficacy of FMTs in four recipient dolphins.

METHODS AND RESULTS

Comprehensive health monitoring was performed on donor and recipient dolphins. Fecal samples were collected before, during, and after FMT therapy. Screening of donor and recipient fecal samples was accomplished by in-house and reference lab diagnostic tests. Shotgun metagenomics was used for sequencing. Following FMT treatment, all four recipient communities experienced engraftment of novel microbial species from donor communities. Engraftment coincided with resolution of clinical signs and a sustained increase in alpha diversity.

CONCLUSION

The donor screening protocol proved to be safe in this study and no adverse effects were observed in four recipient dolphins. Treatment coincided with improvement in clinical signs.

Mock community taxonomic classification performance of publicly available shotgun metagenomics pipelines

Shotgun metagenomic sequencing comprehensively samples the DNA of a microbial sample. Choosing the best bioinformatics processing package can be daunting due to the wide variety of tools available. Here, we assessed publicly available shotgun metagenomics processing packages/pipelines including bioBakery, Just a Microbiology System (JAMS), Whole metaGenome Sequence Assembly V2 (WGSA2), and Woltka using 19 publicly available mock community samples and a set of five constructed pathogenic gut microbiome samples. Also included is a workflow for labelling bacterial scientific names with NCBI taxonomy identifiers for better resolution in assessing results. The Aitchison distance, a sensitivity metric, and total False Positive Relative Abundance were used for accuracy assessments for all pipelines and mock samples. Overall, bioBakery4 performed the best with most of the accuracy metrics, while JAMS and WGSA2, had the highest sensitivities. Furthermore, bioBakery is commonly used and only requires a basic knowledge of command line usage. This work provides an unbiased assessment of shotgun metagenomics packages and presents results assessing the performance of the packages using mock community sequence data.

Variant of the lactase LCT gene explains association between milk intake and incident type 2 diabetes

Cow's milk is frequently included in the human diet, but the relationship between milk intake and type 2 diabetes (T2D) remains controversial. Here, using data from the Hispanic Community Health Study/Study of Latinos, we show that in both sexes, higher milk intake is associated with lower risk of T2D in lactase non-persistent (LNP) individuals (determined by a variant of the lactase LCT gene, single nucleotide polymorphism rs4988235 ) but not in lactase persistent individuals. We validate this finding in the UK Biobank. Further analyses reveal that among LNP individuals, higher milk intake is associated with alterations in gut microbiota (for example, enriched Bifidobacterium and reduced Prevotella) and circulating metabolites (for example, increased indolepropionate and reduced branched-chain amino acid metabolites). Many of these metabolites are related to the identified milk-associated bacteria and partially mediate the association between milk intake and T2D in LNP individuals. Our study demonstrates a protective association between milk intake and T2D among LNP individuals and a potential involvement of gut microbiota and blood metabolites in this association.

Commercial farmed swine harbour a variety of pathogenic bacteria and antimicrobial resistance genes

Introduction. The northern region of Thailand serves as a crucial area for swine production, contributing to the Thai community food supply. Previous studies have highlighted the presence of foodborne bacterial pathogens originating from swine farms in this region, posing a threat to both human and animal health.Gap statement. Multiple swine bacterial pathogens have been studied at a species level, but the distribution and co-occurrence of bacterial pathogens in agricultural swine has not been well established.Aim. Our study employed the intestinal scraping technique to directly examine the bacterial micro-organisms interacting with the swine host.Methodology. We used shotgun metagenomic sequencing to analyse the bacterial pathogens inhabiting the caecal microbiome of swine from five commercial farms in northern Thailand.Results. A variety of pathogenic and opportunistic bacteria were identified, including Escherichia coli, Clostridium botulinum, Staphylococcus aureus and the Corynebacterium genus. From a One Health perspective, these species are important foodborne and opportunistic pathogens in both humans and agricultural animals, making swine a critical pathogen reservoir that can cause illness in humans, especially farm workers. Additionally, the swine caecal microbiome contains commensal bacteria such as Bifidobacterium, Lactobacillus and Faecalibacterium, which are associated with normal physiology and feed utilization in healthy swine. Antimicrobial resistance genes were also detected in all samples, specifically conferring resistance to tetracycline and aminoglycosides, which have historically been used extensively in swine farming.Conclusion. The findings further support the need for improved sanitation standards in swine farms, and additional monitoring of agricultural animals and farm workers to reduce contamination and improved produce safety for human consumption.

Hackflex library preparation enables low-cost metagenomic profiling

Shotgun metagenomic sequencing provides valuable insights into microbial communities, but the high cost of library preparation with standard kits and protocols is a barrier for many. New methods such as Hackflex use diluted commercially available reagents to greatly reduce library preparation costs. However, these methods have not been systematically validated for metagenomic sequencing. Here, we evaluate Hackflex performance by sequencing metagenomic libraries from known mock communities as well as mouse fecal samples prepared by Hackflex, Illumina DNA Prep, and Illumina TruSeq methods. Hackflex successfully recovered all members of the Zymo mock community, performing best for samples with DNA concentrations <1 ng/μL. Furthermore, Hackflex was able to delineate microbiota of individual inbred mice from the same breeding stock at the same mouse facility, and statistical modeling indicated that mouse ID explained a greater fraction of the variance in metagenomic composition than did library preparation method. These results show that Hackflex is suitable for generating inventories of bacterial communities through metagenomic sequencing.

A Modified Mediterranean Ketogenic Diet mitigates modifiable risk factors of Alzheimer's Disease: a serum and CSF-based metabolic analysis

Alzheimer's disease (AD) is influenced by a variety of modifiable risk factors, including a person's dietary habits. While the ketogenic diet (KD) holds promise in reducing metabolic risks and potentially affecting AD progression, only a few studies have explored KD's metabolic impact, especially on blood and cerebrospinal fluid (CSF). Our study involved participants at risk for AD, either cognitively normal or with mild cognitive impairment. The participants consumed both a modified Mediterranean-ketogenic diet (MMKD) and the American Heart Association diet (AHAD) for 6 weeks each, separated by a 6-week washout period. We employed nuclear magnetic resonance (NMR)-based metabolomics to profile serum and CSF and metagenomics profiling on fecal samples. While the AHAD induced no notable metabolic changes, MMKD led to significant alterations in both serum and CSF. These changes included improved modifiable risk factors, like increased HDL-C and reduced BMI, reversed serum metabolic disturbances linked to AD such as a microbiome-mediated increase in valine levels, and a reduction in systemic inflammation. Additionally, the MMKD was linked to increased amino acid levels in the CSF, a breakdown of branched-chain amino acids (BCAAs), and decreased valine levels. Importantly, we observed a strong correlation between metabolic changes in the CSF and serum, suggesting a systemic regulation of metabolism. Our findings highlight that MMKD can improve AD-related risk factors, reverse some metabolic disturbances associated with AD, and align metabolic changes across the blood-CSF barrier.

Targeting the gut-microbiota-brain axis in irritable bowel disease to improve cognitive function - recent knowledge and emerging therapeutic opportunities

The brain-gut axis forms a bidirectional communication system between the gastrointestinal (GI) tract and cognitive brain areas. Disturbances to this system in disease states such as inflammatory bowel disease have consequences for neuronal activity and subsequent cognitive function. The gut-microbiota-brain axis refers to the communication between gut-resident bacteria and the brain. This circuits exists to detect gut microorganisms and relay information to specific areas of the central nervous system (CNS) that in turn, regulate gut physiology. Changes in both the stability and diversity of the gut microbiota have been implicated in several neuronal disorders, including depression, autism spectrum disorder Parkinson's disease, Alzheimer's disease and multiple sclerosis. Correcting this imbalance with medicinal herbs, the metabolic products of dysregulated bacteria and probiotics have shown hope for the treatment of these neuronal disorders. In this review, we focus on recent advances in our understanding of the intricate connections between the gut-microbiota and the brain. We discuss the contribution of gut microbiota to neuronal disorders and the tangible links between diseases of the GI tract with cognitive function and behaviour. In this regard, we focus on irritable bowel syndrome (IBS) given its strong links to brain function and anxiety disorders. This adds to the growing body of evidence supporting targeted therapeutic strategies to modulate the gut microbiota for the treatment of brain/mental-health-related disease.

Integrative analysis of microbial 16S gene and shotgun metagenomic sequencing data improves statistical efficiency

Background

The most widely used technologies for profiling microbial communities are 16S marker-gene sequencing and shotgun metagenomic sequencing. Interestingly, many microbiome studies have performed both sequencing experiments on the same cohort of samples. The two sequencing datasets often reveal consistent patterns of microbial signatures, highlighting the potential for an integrative analysis to improve power of testing these signatures. However, differential experimental biases, partially overlapping samples, and differential library sizes pose tremendous challenges when combining the two datasets. Currently, researchers either discard one dataset entirely or use different datasets for different objectives.

Methods

In this article, we introduce the first method of this kind, named Com-2seq, that combines the two sequencing datasets for testing differential abundance at the genus and community levels while overcoming these difficulties. The new method is based on our LOCOM model (Hu et al., 2022), which employs logistic regression for testing taxon differential abundance while remaining robust to experimental bias. To benchmark the performance of Com-2seq, we introduce two ad hoc approaches: applying LOCOM to pooled taxa count data and combining LOCOM p-values from analyzing each dataset separately.

Results

Our simulation studies indicate that Com-2seq substantially improves statistical efficiency over analysis of either dataset alone and works better than the two ad hoc approaches. An application of Com-2seq to two real microbiome studies uncovered scientifically plausible findings that would have been missed by analyzing individual datasets.

Conclusions

Com-2seq performs integrative analysis of 16S and metagenomic sequencing data, which improves statistical efficiency and has the potential to accelerate the search of microbial communities and taxa that are involved in human health and diseases.

Paired microbiome and metabolome analyses associate bile acid changes with colorectal cancer progression

Colorectal cancer (CRC) is driven by genomic alterations in concert with dietary influences, with the gut microbiome implicated as an effector in disease development and progression. While meta-analyses have provided mechanistic insight into patients with CRC, study heterogeneity has limited causal associations. Using multi-omics studies on genetically controlled cohorts of mice, we identify diet as the major driver of microbial and metabolomic differences, with reductions in α diversity and widespread changes in cecal metabolites seen in high-fat diet (HFD)-fed mice. In addition, non-classic amino acid conjugation of the bile acid cholic acid (AA-CA) increased with HFD. We show that AA-CAs impact intestinal stem cell growth and demonstrate that Ileibacterium valens and Ruminococcus gnavus are able to synthesize these AA-CAs. This multi-omics dataset implicates diet-induced shifts in the microbiome and the metabolome in disease progression and has potential utility in future diagnostic and therapeutic developments.

Integrative analysis of microbial 16S gene and shotgun metagenomic sequencing data improves statistical efficiency

The most widely used technologies for profiling microbial communities are 16S marker-gene sequencing and shotgun metagenomic sequencing. Interestingly, many microbiome studies have performed both sequencing experiments on the same cohort of samples. The two sequencing datasets often reveal consistent patterns of microbial signatures, highlighting the potential for an integrative analysis to improve power of testing these signatures. However, differential experimental biases, partially overlapping samples, and differential library sizes pose tremendous challenges when combining the two datasets. Currently, researchers either discard one dataset entirely or use different datasets for different objectives. In this article, we introduce the first method of this kind, named Com-2seq, that combines the two sequencing datasets for the objective of testing differential abundance at the genus and community levels while overcoming these difficulties. We demonstrate that Com-2seq substantially improves statistical efficiency over analysis of either dataset alone and works better than two ad hoc approaches.

Multi-level analysis of the gut-brain axis shows autism spectrum disorder-associated molecular and microbial profiles

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by heterogeneous cognitive, behavioral and communication impairments. Disruption of the gut-brain axis (GBA) has been implicated in ASD although with limited reproducibility across studies. In this study, we developed a Bayesian differential ranking algorithm to identify ASD-associated molecular and taxa profiles across 10 cross-sectional microbiome datasets and 15 other datasets, including dietary patterns, metabolomics, cytokine profiles and human brain gene expression profiles. We found a functional architecture along the GBA that correlates with heterogeneity of ASD phenotypes, and it is characterized by ASD-associated amino acid, carbohydrate and lipid profiles predominantly encoded by microbial species in the genera Prevotella, Bifidobacterium, Desulfovibrio and Bacteroides and correlates with brain gene expression changes, restrictive dietary patterns and pro-inflammatory cytokine profiles. The functional architecture revealed in age-matched and sex-matched cohorts is not present in sibling-matched cohorts. We also show a strong association between temporal changes in microbiome composition and ASD phenotypes. In summary, we propose a framework to leverage multi-omic datasets from well-defined cohorts and investigate how the GBA influences ASD.

Comprehensive evaluation of shotgun metagenomics, amplicon sequencing, and harmonization of these platforms for epidemiological studies

In a large cohort of 1,772 participants from the Hispanic Community Health Study/Study of Latinos with overlapping 16SV4 rRNA gene (bacterial amplicon), ITS1 (fungal amplicon), and shotgun sequencing data, we demonstrate that 16SV4 amplicon sequencing and shotgun metagenomics offer the same level of taxonomic accuracy for bacteria at the genus level even at shallow sequencing depths. In contrast, for fungal taxa, we did not observe meaningful agreements between shotgun and ITS1 amplicon results. Finally, we show that amplicon and shotgun data can be harmonized and pooled to yield larger microbiome datasets with excellent agreement (<1% effect size variance across three independent outcomes) using pooled amplicon/shotgun data compared to pure shotgun metagenomic analysis. Thus, there are multiple approaches to study the microbiome in epidemiological studies, and we provide a demonstration of a powerful pooling approach that will allow researchers to leverage the massive amount of amplicon sequencing data generated over the last two decades.

Standardized multi-omics of Earth's microbiomes reveals microbial and metabolite diversity

Despite advances in sequencing, lack of standardization makes comparisons across studies challenging and hampers insights into the structure and function of microbial communities across multiple habitats on a planetary scale. Here we present a multi-omics analysis of a diverse set of 880 microbial community samples collected for the Earth Microbiome Project. We include amplicon (16S, 18S, ITS) and shotgun metagenomic sequence data, and untargeted metabolomics data (liquid chromatography-tandem mass spectrometry and gas chromatography mass spectrometry). We used standardized protocols and analytical methods to characterize microbial communities, focusing on relationships and co-occurrences of microbially related metabolites and microbial taxa across environments, thus allowing us to explore diversity at extraordinary scale. In addition to a reference database for metagenomic and metabolomic data, we provide a framework for incorporating additional studies, enabling the expansion of existing knowledge in the form of an evolving community resource. We demonstrate the utility of this database by testing the hypothesis that every microbe and metabolite is everywhere but the environment selects. Our results show that metabolite diversity exhibits turnover and nestedness related to both microbial communities and the environment, whereas the relative abundances of microbially related metabolites vary and co-occur with specific microbial consortia in a habitat-specific manner. We additionally show the power of certain chemistry, in particular terpenoids, in distinguishing Earth's environments (for example, terrestrial plant surfaces and soils, freshwater and marine animal stool), as well as that of certain microbes including Conexibacter woesei (terrestrial soils), Haloquadratum walsbyi (marine deposits) and Pantoea dispersa (terrestrial plant detritus). This Resource provides insight into the taxa and metabolites within microbial communities from diverse habitats across Earth, informing both microbial and chemical ecology, and provides a foundation and methods for multi-omics microbiome studies of hosts and the environment.

A prebiotic diet modulates microglial states and motor deficits in α-synuclein overexpressing mice

Parkinson's disease (PD) is a movement disorder characterized by neuroinflammation, α-synuclein pathology, and neurodegeneration. Most cases of PD are non-hereditary, suggesting a strong role for environmental factors, and it has been speculated that disease may originate in peripheral tissues such as the gastrointestinal (GI) tract before affecting the brain. The gut microbiome is altered in PD and may impact motor and GI symptoms as indicated by animal studies, although mechanisms of gut-brain interactions remain incompletely defined. Intestinal bacteria ferment dietary fibers into short-chain fatty acids, with fecal levels of these molecules differing between PD and healthy controls and in mouse models. Among other effects, dietary microbial metabolites can modulate activation of microglia, brain-resident immune cells implicated in PD. We therefore investigated whether a fiber-rich diet influences microglial function in α-synuclein overexpressing (ASO) mice, a preclinical model with PD-like symptoms and pathology. Feeding a prebiotic high-fiber diet attenuates motor deficits and reduces α-synuclein aggregation in the substantia nigra of mice. Concomitantly, the gut microbiome of ASO mice adopts a profile correlated with health upon prebiotic treatment, which also reduces microglial activation. Single-cell RNA-seq analysis of microglia from the substantia nigra and striatum uncovers increased pro-inflammatory signaling and reduced homeostatic responses in ASO mice compared to wild-type counterparts on standard diets. However, prebiotic feeding reverses pathogenic microglial states in ASO mice and promotes expansion of protective disease-associated macrophage (DAM) subsets of microglia. Notably, depletion of microglia using a CSF1R inhibitor eliminates the beneficial effects of prebiotics by restoring motor deficits to ASO mice despite feeding a prebiotic diet. These studies uncover a novel microglia-dependent interaction between diet and motor symptoms in mice, findings that may have implications for neuroinflammation and PD.

Zebra: Static and Dynamic Genome Cover Thresholds with Overlapping References

Assigning taxonomy remains a challenging topic in microbiome studies, due largely to ambiguity of reads which overlap multiple reference genomes. With the Web of Life (WoL) reference database hosting 10,575 reference genomes and growing, the percentage of ambiguous reads will only increase. The resulting artifacts create both the illusion of co-occurrence and a long tail end of extraneous reference hits that confound interpretation. We introduce genome cover, the fraction of reference genome overlapped by reads, to distinguish these artifacts. We show how to dynamically predict genome cover by read count and examine our model in Staphylococcus aureus monoculture. Our modeling cleanly separates both S. aureus and true contaminants from the false artifacts of reference overlap. We next introduce saturated genome cover, the true fraction of a reference genome overlapped by sample contents. Genome cover may not saturate for low abundance or low prevalence bacteria. We assuage this worry with examination of a large human fecal data set. By compositing the metric across like samples, genome cover saturates even for rare species. We note that it is a threshold on saturated genome cover, not genome cover itself, which indicates a spurious reference hit or distant relative. We present Zebra, a method to compute and threshold the genome cover metric across like samples, a recurrence to estimate genome cover and confirm saturation, and provide guidance for choosing cover thresholds in real world scenarios. Standalone genome cover and integration into Woltka are available: https://github.com/biocore/zebra_filter, https://github.com/qiyunzhu/woltka. IMPORTANCE Taxonomic assignment, assigning sequences to specific taxonomic units, is a crucial processing step in microbiome analyses. Issues in taxonomic assignment affect interpretation of what microbes are present in each sample and may be associated with specific environmental or clinical conditions. Assigning importance to a particular taxon relies strongly on independence of assigned counts. The false inclusion of thousands of correlated taxa makes interpretation ambiguous, leading to underconstrained results which cannot be reproduced. The importance sometimes attached to implausible artifacts such as anthrax or bubonic plague is especially problematic. We show that the Zebra filter retrieves only the nearest relatives of sample contents enabling more reproducible and biologically plausible interpretation of metagenomic data.

Pan-cancer analyses reveal cancer-type-specific fungal ecologies and bacteriome interactions

Cancer-microbe associations have been explored for centuries, but cancer-associated fungi have rarely been examined. Here, we comprehensively characterize the cancer mycobiome within 17,401 patient tissue, blood, and plasma samples across 35 cancer types in four independent cohorts. We report fungal DNA and cells at low abundances across many major human cancers, with differences in community compositions that differ among cancer types, even when accounting for technical background. Fungal histological staining of tissue microarrays supported intratumoral presence and frequent spatial association with cancer cells and macrophages. Comparing intratumoral fungal communities with matched bacteriomes and immunomes revealed co-occurring bi-domain ecologies, often with permissive, rather than competitive, microenvironments and distinct immune responses. Clinically focused assessments suggested prognostic and diagnostic capacities of the tissue and plasma mycobiomes, even in stage I cancers, and synergistic predictive performance with bacteriomes.

A comparison of six DNA extraction protocols for 16S, ITS and shotgun metagenomic sequencing of microbial communities

Microbial communities contain a broad phylogenetic diversity of organisms; however, the majority of methods center on describing bacteria and archaea. Fungi are important symbionts in many ecosystems and are potentially important members of the human microbiome, beyond those that can cause disease. To expand our analysis of microbial communities to include data from the fungal internal transcribed spacer (ITS) region, five candidate DNA extraction kits were compared against our standardized protocol for describing bacteria and archaea using 16S rRNA gene amplicon- and shotgun metagenomics sequencing. The results are presented considering a diverse panel of host-associated and environmental sample types and comparing the cost, processing time, well-to-well contamination, DNA yield, limit of detection and microbial community composition among protocols. Across all criteria, the MagMAX Microbiome kit was found to perform best. The PowerSoil Pro kit performed comparably but with increased cost per sample and overall processing time. The Zymo MagBead, NucleoMag Food and Norgen Stool kits were included.