Towards standards for human fecal sample processing in metagenomic studies

Distinct alterations of gut microbiota between viral- and non-viral-related hepatocellular carcinoma

Altered gut microbiota has been connected to hepatocellular carcinoma (HCC) occurrence and advancement. This study was conducted to identify a gut microbiota signature in differentiating between viral-related HCC (Viral-HCC) and non-hepatitis B-, non-hepatitis C-related HCC (NBNC-HCC). Fecal specimens were obtained from 16 healthy controls, 33 patients with viral-HCC (17 and 16 cases with hepatitis B virus (HBV) and hepatitis C virus (HCV) infection, respectively), and 18 patients with NBNC-HCC. Compositions of fecal microbiota were assessed by 16S rRNA sequencing. Bioinformatic analysis was performed by the DADA2 pipeline in the R program. Significantly different genera from the top 50 relative abundance were used to classify between subgroups of HCC by the Random Forest algorithm. Our data demonstrated that the HCC group had a significantly decreased alpha-diversity and changed microbial composition in comparison with healthy controls. Within the top 50 relative abundance, there were 11 genera including Faecalibacterium, Agathobacter, and Coprococcus that were significantly enhanced in Viral-HCC, while 5 genera such as Bacteroides, Streptococcus, Ruminococcus gnavus group, Parabacteroides, and Erysipelatoclostridium were enhanced in NBNC-HCC. Compared to Viral-HCC, the NBNC-HCC subgroup significantly reduced various short-chain fatty acid-producing bacteria, as well as declined fecal butyrate but elevated plasma surrogate markers of microbial translocation. Based on the machine learning algorithm, a high diagnostic accuracy to classify HCC subgroups was achieved with an area under the receiver-operating characteristic (ROC) curve (AUC) of 0.94. Collectively, these data revealed that gut dysbiosis was distinct according to etiological factors of HCC, which might play an essential role in hepatocarcinogenesis. These findings underscore the possible use of a gut microbiota signature for the diagnosis and therapeutic approaches regarding different subgroups of HCC. KEY POINTS: • Gut dysbiosis is connected to hepatocarcinogenesis and can be used as a novel biomarker. • Gut microbiota composition is significantly altered in different etiological factors of HCC. • Microbiota-based signature can accurately distinguish between Viral-HCC and NBNC-HCC.

Microbiota and IBD: Current knowledge and future perspectives

Inflammatory Bowel Disease (IBD) is a chronic relapsing-remitting disease with a remarkable increase in incidence worldwide and a substantial disease burden. Although the pathophysiology is not fully elucidated yet an aberrant immune reaction against the intestinal microbiota and the gut microbial dysbiosis have been identified to play a major role. The composition of gut microbiota in IBD patients is distinct from that of healthy individuals, with certain organisms predominating over others. Differences in the microbial dysbiosis have been also observed between Crohn Disease (CD) and Ulcerative Colitis (UC). A disruption of the microbiota's balance can lead to inflammation and intestinal damage. Microbiota composition in IBD can be affected both by endogenous (i.e., interaction with the immune system and intestinal epithelial cells) and exogenous (i.e., medications, surgery, diet) factors. The complex interplay between the gut microbiota and IBD is an area of great interest for understanding disease pathogenesis and developing new treatments. The purpose of this review is to summarize the latest evidence on the role of microbiota in IBD pathogenesis and to explore possible future areas of research.

Current Trends and Challenges of Microbiome Research in Prostate Cancer

PURPOSE OF REVIEW

The role of the gut microbiome in prostate cancer is an emerging area of research interest. However, no single causative organism has yet been identified. The goal of this paper is to examine the role of the microbiome in prostate cancer and summarize the challenges relating to methodology in specimen collection, sequencing technology, and interpretation of results.

RECENT FINDINGS

Significant heterogeneity still exists in methodology for stool sampling/storage, preservative options, DNA extraction, and sequencing database selection/in silico processing. Debate persists over primer choice in amplicon sequencing as well as optimal methods for data normalization. Statistical methods for longitudinal microbiome analysis continue to undergo refinement. While standardization of methodology may help yield more consistent results for organism identification in prostate cancer, this is a difficult task due to considerable procedural variation at each step in the process. Further reproducibility and methodology research is required.

Role of the microbiota in response to and recovery from cancer therapy

Our understanding of how the microbiota affects the balance between response to and failure of cancer treatment by modulating the tumour microenvironment and systemic immune system has advanced rapidly in recent years. Microbiota-targeting interventions in patients with cancer are an area of intensive investigation. Promisingly, phase I-II clinical trials have shown that interventions such as faecal microbiota transplantation can overcome resistance to immune checkpoint blockade in patients with melanoma, improve therapeutic outcomes in treatment-naive patients and reduce therapy-induced immunotoxicities. Here, we synthesize the evidence showing that the microbiota is an important determinant of both cancer treatment efficacy and treatment-induced acute and long-term toxicity, and we discuss the complex and inter-related mechanisms involved. We also assess the potential of microbiota-targeting interventions, including bacterial engineering and phage therapy, to optimize the response to and recovery from cancer therapy.

Comparison of DNA extraction methods on different sample matrices within the same terrestrial ecosystem

Metataxonomic studies of ecosystem microbiotas require the simultaneous processing of samples with contrasting physical and biochemical traits. However, there are no published studies of comparisons of different DNA extraction kits to characterize the microbiotas of the main components of terrestrial ecosystems. Here, and to our knowledge for the first time, five DNA extraction kits were used to investigate the composition and diversity of the microbiota of a subset of samples typically studied in terrestrial ecosystems such as bulk soil, rhizosphere soil, invertebrate taxa and mammalian feces. DNA extraction kit was associated with changes in the relative abundance of hundreds of ASVs, in the same samples, resulting in significant differences in alpha and beta diversity estimates of their microbiotas. Importantly, the impact of DNA extraction kit on sample diversity varies according to sample type, with mammalian feces and soil samples showing the most and least consistent diversity estimates across DNA extraction kits, respectively. We show that the MACHEREY-NAGEL NucleoSpin® Soil kit was associated with the highest alpha diversity estimates, providing the highest contribution to the overall sample diversity, as indicated by comparisons with computationally assembled reference communities, and is recommended to be used for any large-scale microbiota study of terrestrial ecosystems.

Gut bacteriome and mood disorders in women with PCOS

STUDY QUESTION

How does the gut bacteriome differ based on mood disorders (MDs) in women with polycystic ovary syndrome (PCOS), and how can the gut bacteriome contribute to the associations between these two conditions?

SUMMARY ANSWER

Women with PCOS who also have MDs exhibited a distinct gut bacteriome with reduced alpha diversity and a significantly lower abundance of Butyricicoccus compared to women with PCOS but without MDs.

WHAT IS KNOWN ALREADY

Women with PCOS have a 4- to 5-fold higher risk of having MDs compared to women without PCOS. The gut bacteriome has been suggested to influence the pathophysiology of both PCOS and MDs.

STUDY DESIGN, SIZE, DURATION

This population-based cohort study was derived from the Northern Finland Birth Cohort 1966 (NFBC1966), which includes all women born in Northern Finland in 1966. Women with PCOS who donated a stool sample at age 46 years (n = 102) and two BMI-matched controls for each case (n = 205), who also responded properly to the MD criteria scales, were included.

PARTICIPANTS/MATERIALS, SETTING, METHODS

A total of 102 women with PCOS and 205 age- and BMI-matched women without PCOS were included. Based on the validated MD criteria, the subjects were categorized into MD or no-MD groups, resulting in the following subgroups: PCOS no-MD (n = 84), PCOS MD (n = 18), control no-MD (n = 180), and control MD (n = 25). Clinical characteristics were assessed at age 31 years and age 46 years, and stool samples were collected from the women at age 46 years, followed by the gut bacteriome analysis using 16 s rRNA sequencing. Alpha diversity was assessed using observed features and Shannon's index, with a focus on genera, and beta diversity was characterized using principal components analysis (PCA) with Bray-Curtis Dissimilarity at the genus level. Associations between the gut bacteriome and PCOS-related clinical features were explored by Spearman's correlation coefficient. A P-value for multiple testing was adjusted with the Benjamini-Hochberg false discovery rate (FDR) method.

MAIN RESULTS AND THE ROLE OF CHANCE

We observed changes in the gut bacteriome associated with MDs, irrespective of whether the women also had PCOS. Similarly, PCOS MD cases showed a lower alpha diversity (Observed feature, PCOS no-MD, median 272; PCOS MD, median 208, FDR = 0.01; Shannon, PCOS no-MD, median 5.95; PCOS MD, median 5.57, FDR = 0.01) but also a lower abundance of Butyricicoccus (log-fold changeAnalysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC)=-0.90, FDRANCOM-BC=0.04) compared to PCOS no-MD cases. In contrast, in the controls, the gut bacteriome did not differ based on MDs. Furthermore, in the PCOS group, Sutterella showed positive correlations with PCOS-related clinical parameters linked to obesity (BMI, r2=0.31, FDR = 0.01; waist circumference, r2=0.29, FDR = 0.02), glucose metabolism (fasting glucose, r2=0.46, FDR < 0.001; fasting insulin, r2=0.24, FDR = 0.05), and gut barrier integrity (zonulin, r2=0.25, FDR = 0.03).

LIMITATIONS, REASONS FOR CAUTION

Although this was the first study to assess the link between the gut bacteriome and MDs in PCOS and included the largest PCOS dataset for the gut microbiome analysis, the number of subjects stratified by the presence of MDs was limited when contrasted with previous studies that focused on MDs in a non-selected population.

WIDER IMPLICATIONS OF THE FINDINGS

The main finding is that gut bacteriome is associated with MDs irrespective of the PCOS status, but PCOS may also modulate further the connection between the gut bacteriome and MDs.

STUDY FUNDING/COMPETING INTEREST(S)

This research was funded by the European Union's Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie Grant Agreement (MATER, No. 813707), the Academy of Finland (project grants 315921, 321763, 336449), the Sigrid Jusélius Foundation, Novo Nordisk Foundation (NNF21OC0070372), grant numbers PID2021-12728OB-100 (Endo-Map) and CNS2022-135999 (ROSY) funded by MCIN/AEI/10.13039/501100011033 and ERFD A Way of Making Europe. The study was also supported by EU QLG1-CT-2000-01643 (EUROBLCS) (E51560), NorFA (731, 20056, 30167), USA/NIH 2000 G DF682 (50945), the Estonian Research Council (PRG1076, PRG1414), EMBO Installation (3573), and Horizon 2020 Innovation Grant (ERIN, No. EU952516). The funders did not participate in any process of the study. We have no conflicts of interest to declare.

TRIAL REGISTRATION NUMBER

N/A.

Impact of dietary carbohydrate, fat or protein restriction on the human gut microbiome: a systematic review

Restriction of dietary carbohydrates, fat and/or protein is often used to reduce body weight and/or treat (metabolic) diseases. Since diet is a key modulator of the human gut microbiome, which plays an important role in health and disease, this review aims to provide an overview of current knowledge of the effects of macronutrient-restricted diets on gut microbial composition and metabolites. A structured search strategy was performed in several databases. After screening for inclusion and exclusion criteria, thirty-six articles could be included. Data are included in the results only when supported by at least three independent studies to enhance the reliability of our conclusions. Low-carbohydrate (<30 energy%) diets tended to induce a decrease in the relative abundance of several health-promoting bacteria, including Bifidobacterium, as well as a reduction in short-chain fatty acid (SCFA) levels in faeces. In contrast, low-fat diets (<30 energy%) increased alpha diversity, faecal SCFA levels and abundance of some beneficial bacteria, including Faecalibacterium prausnitzii. There were insufficient data to draw conclusions concerning the effects of low-protein (<10 energy%) diets on gut microbiota. Although the data of included studies unveil possible benefits of low-fat and potential drawbacks of low-carbohydrate diets for human gut microbiota, the diversity in study designs made it difficult to draw firm conclusions. Using a more uniform methodology in design, sample processing and sharing raw sequence data could foster our understanding of the effects of macronutrient restriction on gut microbiota composition and metabolic dynamics relevant to health. This systematic review was registered at https://www.crd.york.ac.uk/prospero as CRD42020156929.

Microbial signatures in human periodontal disease: a metatranscriptome meta-analysis

The characterization of oral microbial communities and their functional potential has been shaped by metagenomics and metatranscriptomics studies. Here, a meta-analysis of four geographically and technically diverse oral shotgun metatranscriptomics studies of human periodontitis was performed. In total, 54 subgingival plaque samples, 27 healthy and 27 periodontitis, were analyzed. The core microbiota of the healthy and periodontitis group encompassed 40 and 80 species, respectively, with 38 species being common to both microbiota. The differential abundance analysis identified 23 genera and 26 species, that were more abundant in periodontitis. Our results not only validated previously reported genera and species associated with periodontitis with heightened statistical significance, but also elucidated additional genera and species that were overlooked in the individual studies. Functional analysis revealed a significant up-regulation in the transcription of 50 gene families (UniRef-90) associated with transmembrane transport and secretion, amino acid metabolism, surface protein and flagella synthesis, energy metabolism, and DNA supercoiling in periodontitis samples. Notably, the overwhelming majority of the identified gene families did not exhibit differential abundance when examined across individual datasets. Additionally, 4 bacterial virulence factor genes, including TonB dependent receptor from P. gingivalis, surface antigen BspA from T. forsynthia, and adhesin A (PsaA) and Type I glyceraldehyde-3-phosphate dehydrogenase (GAPDH) from the Streptococcus genus, were also found to be significantly more transcribed in periodontitis group. Microbial co-occurrence analysis demonstrated that the periodontitis microbial network was less dense compared to the healthy network, but it contained more positive correlations between the species. Furthermore, there were discernible disparities in the patterns of interconnections between the species in the two networks, denoting the rewiring of the whole microbial network during the transition to the disease state. In summary, our meta-analysis has provided robust insights into the oral active microbiome and transcriptome in both health and disease.

Comparative characterization of the infant gut microbiome and their maternal lineage by a multi-omics approach

The human gut microbiome establishes and matures during infancy, and dysregulation at this stage may lead to pathologies later in life. We conducted a multi-omics study comprising three generations of family members to investigate the early development of the gut microbiota. Fecal samples from 200 individuals, including infants (0-12 months old; 55% females, 45% males) and their respective mothers and grandmothers, were analyzed using two independent metabolomics platforms and metagenomics. For metabolomics, gas chromatography and capillary electrophoresis coupled to mass spectrometry were applied. For metagenomics, both 16S rRNA gene and shotgun sequencing were performed. Here we show that infants greatly vary from their elders in fecal microbiota populations, function, and metabolome. Infants have a less diverse microbiota than adults and present differences in several metabolite classes, such as short- and branched-chain fatty acids, which are associated with shifts in bacterial populations. These findings provide innovative biochemical insights into the shaping of the gut microbiome within the same generational line that could be beneficial in improving childhood health outcomes.

Gut microbiota and inflammatory mediators differentiate IgE mediated and non-IgE mediated cases of cow's milk protein at diagnosis

OBJECTIVE

Analyze fecal and blood samples at point of diagnosis in IgE mediated cow's milk protein allergy (CMPA) and non-IgE mediated (NIM)-CMPA patients to look for potential new biomarkers.

PATIENTS AND METHODS

Fourteen patients with IgE mediated CMPA and 13 with NIM-CMPA were recruited in three hospitals in the north of Spain, and were compared with 25 infants from a control group of the same age range. To characterize intestinal microbiota, 16S rDNA gene and internal transcribed spacer amplicons of bifidobacteria were sequenced with Illumina technology. Fatty acids were analyzed by gas chromatography, meanwhile intestinal inflammation markers were quantified by enzyme-linked immunosorbent assay and a multiplex system. Immunological analysis of blood was performed by flow cytometry.

RESULTS

The fecal results obtained in the NIM-CMPA group stand out. Among them, a significant reduction in the abundance of Bifidobacteriaceae and Bifidobacterium sequences with respect to controls was observed. Bifidobacterial species were also different, highlighting the lower abundance of Bifidobacterium breve sequences. Fecal calprotectin levels were found to be significantly elevated in relation to IgE mediated patients. Also, a higher excretion of IL-10 and a lower excretion of IL-1ra and platelet derived growth factor-BB was found in NIM-CMPA patients.

CONCLUSIONS

The differential fecal parameters found in NIM-CMPA patients could be useful in the diagnosis of NIM food allergy to CM proteins.

Deep learning methods in metagenomics: a review

The ever-decreasing cost of sequencing and the growing potential applications of metagenomics have led to an unprecedented surge in data generation. One of the most prevalent applications of metagenomics is the study of microbial environments, such as the human gut. The gut microbiome plays a crucial role in human health, providing vital information for patient diagnosis and prognosis. However, analysing metagenomic data remains challenging due to several factors, including reference catalogues, sparsity and compositionality. Deep learning (DL) enables novel and promising approaches that complement state-of-the-art microbiome pipelines. DL-based methods can address almost all aspects of microbiome analysis, including novel pathogen detection, sequence classification, patient stratification and disease prediction. Beyond generating predictive models, a key aspect of these methods is also their interpretability. This article reviews DL approaches in metagenomics, including convolutional networks, autoencoders and attention-based models. These methods aggregate contextualized data and pave the way for improved patient care and a better understanding of the microbiome's key role in our health.

Preliminary characterization of gut mycobiome enterotypes reveals the correlation trends between host metabolic parameter and diet: a case study in the Thai Cohort

The association between the gut mycobiome and its potential influence on host metabolism in the Thai Cohort was assessed. Two distinct predominant enterotypes, Saccharomyces (Sa) and Aspergillus/Penicillium (Ap/Pe) showed differences in gut mycobiota diversity and composition. Notably, the Sa enterotype exhibited lower evenness and richness, likely due to the prevalence of Saccharomyces, while both enterotypes displayed unique metabolic behaviors related to nutrient metabolism and body composition. Fiber consumption was positively correlated with adverse body composition and fasting glucose levels in individuals with the Sa enterotype, whereas in the Ap/Pe enterotype it was positively correlated with fat and protein intake. The metabolic functional analysis revealed the Sa enterotype associated with carbohydrate metabolism, while the Ap/Pe enterotype involved in lipid metabolism. Very interestingly, the genes involved in the pentose and glucuronate interconversion pathway, such as polygalacturonase and L-arabinose-isomerase, were enriched in the Sa enterotype signifying a metabolic capacity for complex carbohydrate degradation and utilization of less common sugars as energy sources. These findings highlight the interplay between gut mycobiome composition, dietary habits, and metabolic outcomes within the Thai cohort studies.

A gut microbial signature for combination immune checkpoint blockade across cancer types

Immune checkpoint blockade (ICB) targeting programmed cell death protein 1 (PD-1) and cytotoxic T lymphocyte protein 4 (CTLA-4) can induce remarkable, yet unpredictable, responses across a variety of cancers. Studies suggest that there is a relationship between a cancer patient's gut microbiota composition and clinical response to ICB; however, defining microbiome-based biomarkers that generalize across cohorts has been challenging. This may relate to previous efforts quantifying microbiota to species (or higher taxonomic rank) abundances, whereas microbial functions are often strain specific. Here, we performed deep shotgun metagenomic sequencing of baseline fecal samples from a unique, richly annotated phase 2 trial cohort of patients with diverse rare cancers treated with combination ICB (n = 106 discovery cohort). We demonstrate that strain-resolved microbial abundances improve machine learning predictions of ICB response and 12-month progression-free survival relative to models built using species-rank quantifications or comprehensive pretreatment clinical factors. Through a meta-analysis of gut metagenomes from a further six comparable studies (n = 364 validation cohort), we found cross-cancer (and cross-country) validity of strain-response signatures, but only when the training and test cohorts used concordant ICB regimens (anti-PD-1 monotherapy or combination anti-PD-1 plus anti-CTLA-4). This suggests that future development of gut microbiome diagnostics or therapeutics should be tailored according to ICB treatment regimen rather than according to cancer type.

Microbiota-gut-brain axis and its therapeutic applications in neurodegenerative diseases

The human gastrointestinal tract is populated with a diverse microbial community. The vast genetic and metabolic potential of the gut microbiome underpins its ubiquity in nearly every aspect of human biology, including health maintenance, development, aging, and disease. The advent of new sequencing technologies and culture-independent methods has allowed researchers to move beyond correlative studies toward mechanistic explorations to shed light on microbiome-host interactions. Evidence has unveiled the bidirectional communication between the gut microbiome and the central nervous system, referred to as the "microbiota-gut-brain axis". The microbiota-gut-brain axis represents an important regulator of glial functions, making it an actionable target to ameliorate the development and progression of neurodegenerative diseases. In this review, we discuss the mechanisms of the microbiota-gut-brain axis in neurodegenerative diseases. As the gut microbiome provides essential cues to microglia, astrocytes, and oligodendrocytes, we examine the communications between gut microbiota and these glial cells during healthy states and neurodegenerative diseases. Subsequently, we discuss the mechanisms of the microbiota-gut-brain axis in neurodegenerative diseases using a metabolite-centric approach, while also examining the role of gut microbiota-related neurotransmitters and gut hormones. Next, we examine the potential of targeting the intestinal barrier, blood-brain barrier, meninges, and peripheral immune system to counteract glial dysfunction in neurodegeneration. Finally, we conclude by assessing the pre-clinical and clinical evidence of probiotics, prebiotics, and fecal microbiota transplantation in neurodegenerative diseases. A thorough comprehension of the microbiota-gut-brain axis will foster the development of effective therapeutic interventions for the management of neurodegenerative diseases.

Choice of DNA extraction method affects stool microbiome recovery and subsequent phenotypic association analyses

The lack of standardization in the methods of DNA extraction from fecal samples represents the major source of experimental variation in the microbiome research field. In this study, we aimed to compare the metagenomic profiles and microbiome-phenotype associations obtained by applying two commercially available DNA extraction kits: the AllPrep DNA/RNA Mini Kit (APK) and the QIAamp Fast DNA Stool Mini Kit (FSK). Using metagenomic sequencing data available from 745 paired fecal samples from two independent population cohorts, Lifelines-DEEP (LLD, n = 292) and the 500 Functional Genomics project (500FG, n = 453), we confirmed significant differences in DNA yield and the recovered microbial communities between protocols, with the APK method resulting in a higher DNA concentration and microbial diversity. Further, we observed a massive difference in bacterial relative abundances at species-level between the APK and the FSK protocols, with > 75% of species differentially abundant between protocols in both cohorts. Specifically, comparison with a standard mock community revealed that the APK method provided higher accuracy in the recovery of microbial relative abundances, with the absence of a bead-beating step in the FSK protocol causing an underrepresentation of gram-positive bacteria. This heterogeneity in the recovered microbial composition led to remarkable differences in the association with anthropometric and lifestyle phenotypes. The results of this study further reinforce that the choice of DNA extraction method impacts the metagenomic profile of human gut microbiota and highlight the importance of harmonizing protocols in microbiome studies.

Investigating the impact of probiotic on neurological outcomes in Rett syndrome: A randomized, double-blind, and placebo-controlled pilot study

LAY ABSTRACT

Rett syndrome often involves gastrointestinal symptoms and gut microbiota imbalances. We conducted a study to explore the feasibility of probiotic Lactobacillus plantarum PS128 and the impact on neurological functions in Rett syndrome. The results of our investigation demonstrated that the supplementation of probiotic L. plantarum PS128 was feasible and well tolerated, with 100% retention rate and 0% withdrawal rate. In addition, there was only one participant who had loose stool after taking L. plantarum PS128. Further, there was a tendency to enhance overall cognitive developmental level, as assessed using Mullen Scales of Early Learning. In addition, it significantly improved dystonia, as assessed using the Burke-Fahn-Marsden Movement Scale, in comparison with the placebo group. This study provides a strong foundation for future research and clinical trials exploring the potential of L. plantarum PS128 probiotics as a complementary therapy for individuals with Rett syndrome.

Processing-bias correction with DEBIAS-M improves cross-study generalization of microbiome-based prediction models

Every step in common microbiome profiling protocols has variable efficiency for each microbe. For example, different DNA extraction kits may have different efficiency for Gram-positive and -negative bacteria. These variable efficiencies, combined with technical variation, create strong processing biases, which impede the identification of signals that are reproducible across studies and the development of generalizable and biologically interpretable prediction models. "Batch-correction" methods have been used to alleviate these issues computationally with some success. However, many make strong parametric assumptions which do not necessarily apply to microbiome data or processing biases, or require the use of an outcome variable, which risks overfitting. Lastly and importantly, existing transformations used to correct microbiome data are largely non-interpretable, and could, for example, introduce values to features that were initially mostly zeros. Altogether, processing bias currently compromises our ability to glean robust and generalizable biological insights from microbiome data. Here, we present DEBIAS-M (Domain adaptation with phenotype Estimation and Batch Integration Across Studies of the Microbiome), an interpretable framework for inference and correction of processing bias, which facilitates domain adaptation in microbiome studies. DEBIAS-M learns bias-correction factors for each microbe in each batch that simultaneously minimize batch effects and maximize cross-study associations with phenotypes. Using benchmarks of HIV and colorectal cancer classification from gut microbiome data, and cervical neoplasia prediction from cervical microbiome data, we demonstrate that DEBIAS-M outperforms batch-correction methods commonly used in the field. Notably, we show that the inferred bias-correction factors are stable, interpretable, and strongly associated with specific experimental protocols. Overall, we show that DEBIAS-M allows for better modeling of microbiome data and identification of interpretable signals that are reproducible across studies.

Microbial-derived imidazole propionate links the heart failure-associated microbiome alterations to disease severity

BACKGROUND

Interactions between the gut microbiota, diet, and host metabolism contribute to the development of cardiovascular disease, but a firm link between disease-specific gut microbiota alterations and circulating metabolites is lacking.

METHODS

We performed shot-gun sequencing on 235 samples from 166 HF patients and 69 healthy control samples. Separate plasma samples from healthy controls (n = 53) were used for the comparison of imidazole propionate (ImP) levels. Taxonomy and functional pathways for shotgun sequencing data was assigned using MetaPhlAn3 and HUMAnN3 pipelines.

RESULTS

Here, we show that heart failure (HF) is associated with a specific compositional and functional shift of the gut microbiota that is linked to circulating levels of the microbial histidine-derived metabolite ImP. Circulating ImP levels are elevated in chronic HF patients compared to controls and associated with HF-related gut microbiota alterations. Contrary to the microbiota composition, ImP levels provide insight into etiology and severity of HF and also associate with markers of intestinal permeability and systemic inflammation.

CONCLUSIONS

Our findings establish a connection between changes in the gut microbiota, the presence, etiology, and severity of HF, and the gut-microbially produced metabolite ImP. While ImP appears promising as a circulating biomarker reflecting gut dysbiosis related to HF, further studies are essential to demonstrate its causal or contributing role in HF pathogenesis.

TRIAL REGISTRATION

NCT02637167, registered December 22, 2015.

High diversity, close genetic relatedness, and favorable living conditions benefit species co-occurrence of gut microbiota in Brandt's vole

Introduction

Revealing factors and mechanisms in determining species co-existence are crucial to community ecology, but studies using gut microbiota data are still lacking.

Methods

Using gut microbiota data of 556 Brandt's voles from 37 treatments in eight experiments, we examined the relationship of species co-occurrence of gut microbiota in Brandt's voles (Lasiopodomys brandtii) with genetic distance (or genetic relatedness), community diversity, and several environmental variables.

Results

We found that the species co-occurrence index (a larger index indicates a higher co-occurrence probability) of gut microbiota in Brandt's voles was negatively associated with the genetic distance between paired ASVs and the number of cohabitating voles in the experimental space (a larger number represents more crowding social stress), but positively with Shannon diversity index, grass diets (representing natural foods), and non-physical contact within an experimental space (representing less stress).

Discussion

Our study demonstrated that high diversity, close genetic relatedness, and favorable living conditions would benefit species co-occurrence of gut microbiota in hosts. Our results provide novel insights into factors and mechanisms that shape the community structure and function of gut microbiota and highlight the significance of preserving the biodiversity of gut microbiota.

YACHT: an ANI-based statistical test to detect microbial presence/absence in a metagenomic sample

MOTIVATION

In metagenomics, the study of environmentally associated microbial communities from their sampled DNA, one of the most fundamental computational tasks is that of determining which genomes from a reference database are present or absent in a given sample metagenome. Existing tools generally return point estimates, with no associated confidence or uncertainty associated with it. This has led to practitioners experiencing difficulty when interpreting the results from these tools, particularly for low-abundance organisms as these often reside in the "noisy tail" of incorrect predictions. Furthermore, few tools account for the fact that reference databases are often incomplete and rarely, if ever, contain exact replicas of genomes present in an environmentally derived metagenome.

RESULTS

We present solutions for these issues by introducing the algorithm YACHT: Yes/No Answers to Community membership via Hypothesis Testing. This approach introduces a statistical framework that accounts for sequence divergence between the reference and sample genomes, in terms of ANI, as well as incomplete sequencing depth, thus providing a hypothesis test for determining the presence or absence of a reference genome in a sample. After introducing our approach, we quantify its statistical power and how this changes with varying parameters. Subsequently, we perform extensive experiments using both simulated and real data to confirm the accuracy and scalability of this approach.

AVAILABILITY AND IMPLEMENTATION

The source code implementing this approach is available via Conda and at https://github.com/KoslickiLab/YACHT. We also provide the code for reproducing experiments at https://github.com/KoslickiLab/YACHT-reproducibles.

Gut microbial ecology and exposome of a healthy Pakistani cohort

BACKGROUND

Pakistan is a multi-ethnic society where there is a disparity between dietary habits, genetic composition, and environmental exposures. The microbial ecology of healthy Pakistani gut in the context of anthropometric, sociodemographic, and dietary patterns holds interest by virtue of it being one of the most populous countries, and also being a Lower Middle Income Country (LMIC).

METHODS

16S rRNA profiling of healthy gut microbiome of normo-weight healthy Pakistani individuals from different regions of residence is performed with additional meta-data collected through filled questionnaires. The current health status is then linked to dietary patterns through [Formula: see text] test of independence and Generalized Linear Latent Variable Model (GLLVM) where distribution of individual microbes is regressed against all recorded sources of variability. To identify the core microbiome signature, a dynamic approach is used that considers into account species occupancy as well as consistency across assumed grouping of samples including organization by gender and province of residence. Fitting neutral modeling then revealed core microbiome that is selected by the environment.

RESULTS

A strong determinant of disparity is by province of residence. It is also established that the male microbiome is better adapted to the local niche than the female microbiome, and that there is microbial taxonomic and functional diversity in different ethnicities, dietary patterns and lifestyle habits. Some microbial genera, such as, Megamonas, Porphyromonas, Haemophilus, Klebsiella and Finegoldia showed significant associations with consumption of pickle, fresh fruits, rice, and cheese. Our analyses suggest current health status being associated with the diet, sleeping patterns, employment status, and the medical history.

CONCLUSIONS

This study provides a snapshot of the healthy core Pakistani gut microbiome by focusing on the most populous provinces and ethnic groups residing in predominantly urban areas. The study serves a reference dataset for exploring variations in disease status and designing personalized dietary and lifestyle interventions to promote gut health, particularly in LMICs settings.

Merging and concatenation of sequencing reads: a bioinformatics workflow for the comprehensive profiling of microbiome from amplicon data

A comprehensive profiling of microbial diversity is essential to understand the ecosystem functions. Universal primer sets such as the 515Y/926R could amplify a part of 16S and 18S rRNA and infer the diversity of prokaryotes and eukaryotes. However, the analyses of mixed sequencing data pose a bioinformatics challenge; the 16S and 18S rRNA sequences need to be separated first and analysed individually/independently due to variations in the amplicon length. This study describes an alternative strategy, a merging and concatenation workflow, to analyse the mixed amplicon data without separating the 16S and 18S rRNA sequences. The workflow was tested with 24 mock community (MC) samples, and the analyses resolved the composition of prokaryotes and eukaryotes adequately. In addition, there was a strong correlation (cor = 0.950; P-value = 4.754e-10) between the observed and expected abundances in the MC samples, which suggests that the computational approach could infer the microbial proportions accurately. Further, 18 samples collected from the Sundarbans mangrove region were analysed as a case study. The analyses identified Proteobacteria, Bacteroidota, Actinobacteriota, Cyanobacteria, and Crenarchaeota as dominant bacterial phyla and eukaryotic divisions such as Metazoa, Gyrista, Cryptophyta, Chlorophyta, and Dinoflagellata were found to be dominant in the samples. Thus, the results support the applicability of the method in environmental microbiome research. The merging and concatenation workflow presented here requires considerably less computational resources and uses widely/commonly used bioinformatics packages, saving researchers analyses time (for equivalent sample numbers, compared to the conventional approach) required to infer the diversity of major microbial domains from mixed amplicon data at comparable accuracy.

SPIRE: a Searchable, Planetary-scale mIcrobiome REsource

Meta'omic data on microbial diversity and function accrue exponentially in public repositories, but derived information is often siloed according to data type, study or sampled microbial environment. Here we present SPIRE, a Searchable Planetary-scale mIcrobiome REsource that integrates various consistently processed metagenome-derived microbial data modalities across habitats, geography and phylogeny. SPIRE encompasses 99 146 metagenomic samples from 739 studies covering a wide array of microbial environments and augmented with manually-curated contextual data. Across a total metagenomic assembly of 16 Tbp, SPIRE comprises 35 billion predicted protein sequences and 1.16 million newly constructed metagenome-assembled genomes (MAGs) of medium or high quality. Beyond mapping to the high-quality genome reference provided by proGenomes3 (http://progenomes.embl.de), these novel MAGs form 92 134 novel species-level clusters, the majority of which are unclassified at species level using current tools. SPIRE enables taxonomic profiling of these species clusters via an updated, custom mOTUs database (https://motu-tool.org/) and includes several layers of functional annotation, as well as crosslinks to several (micro-)biological databases. The resource is accessible, searchable and browsable via http://spire.embl.de.

The impact of gestational diabetes on functional capacity of the infant gut microbiome is modest and transient

Gestational diabetes mellitus (GDM) is a metabolic complication that manifests as hyperglycemia during the later stages of pregnancy. In high resource settings, careful management of GDM limits risk to the pregnancy, and hyperglycemia typically resolves after birth. At the same time, previous studies have revealed that the gut microbiome of infants born to mothers who experienced GDM exhibit reduced diversity and reduction in the abundance of several key taxa, including Lactobacillus. What is not known is what the functional consequences of these changes might be. In this case control study, we applied 16S rRNA sequence surveys and metatranscriptomics to profile the gut microbiome of 30 twelve-month-old infants - 16 from mothers with GDM, 14 from mothers without - to examine the impact of GDM during pregnancy. Relative to the mode of delivery and sex of the infant, maternal GDM status had a limited impact on the structure and function of the developing microbiome. While GDM samples were associated with a decrease in alpha diversity, we observed no effect on beta diversity and no differentially abundant taxa. Further, while the mode of delivery and sex of infant affected the expression of multiple bacterial pathways, much of the impact of GDM status on the function of the infant microbiome appears to be lost by twelve months of age. These data may indicate that, while mode of delivery appears to impact function and diversity for longer than anticipated, GDM may not have persistent effects on the function nor composition of the infant gut microbiome.

From hype to hope: Considerations in conducting robust microbiome science

Microbiome science has been one of the most exciting and rapidly evolving research fields in the past two decades. Breakthroughs in technologies including DNA sequencing have meant that the trillions of microbes (particularly bacteria) inhabiting human biological niches (particularly the gut) can be profiled and analysed in exquisite detail. This microbiome profiling has profound impacts across many fields of research, especially biomedical science, with implications for how we understand and ultimately treat a wide range of human disorders. However, like many great scientific frontiers in human history, the pioneering nature of microbiome research comes with a multitude of challenges and potential pitfalls. These include the reproducibility and robustness of microbiome science, especially in its applications to human health outcomes. In this article, we address the enormous promise of microbiome science and its many challenges, proposing constructive solutions to enhance the reproducibility and robustness of research in this nascent field. The optimisation of microbiome science spans research design, implementation and analysis, and we discuss specific aspects such as the importance of ecological principals and functionality, challenges with microbiome-modulating therapies and the consideration of confounding, alternative options for microbiome sequencing, and the potential of machine learning and computational science to advance the field. The power of microbiome science promises to revolutionise our understanding of many diseases and provide new approaches to prevention, early diagnosis, and treatment.

Microbiota and mucosal gene expression of fecal microbiota transplantation or placebo treated patients with chronic pouchitis

Altered microbiota and impaired host immune function have been linked to the pathogenesis of pouchitis. We used 16S rRNA gene sequencing and RNA sequencing data from a previous randomized clinical trial (RCT) on fecal microbiota transplantation (FMT) therapy in 26 chronic pouchitis patients with one-year follow-up. We analyzed changes in both luminal and mucosal microbiota composition, as well as in host mucosal gene expression to gain insights into the host-microbiota interactions possibly underlying clinical outcomes of the patients. Antibiotic type and pattern of use were significant drivers of the luminal microbiota at baseline. Differential gene expression analysis indicated transition from ileal to colonic gene expression in the pouch, and upregulation in inflammation- and immune system-related pathways in the pouch. At 4 weeks, the non-relapsed FMT patients had a lower microbiota dissimilarity to the donor than the non-relapsed placebo patients (p = .02). While two FMT-treated patients showed a shift toward the donor's microbiota during the one-year follow-up, the overall FMT microbiota modulation effect was low. Patient's luminal and mucosal microbiota profiles were unstable in both FMT and placebo groups. Expression of the chemokine receptor CXCR4 was downregulated at 52 weeks compared to the baseline in the non-relapsed patients in both FMT and placebo groups. Microbiota modulation by FMT seems to be low in this patient group. The microbiota composition or alterations did not explain the relapse status of the patients. Some evidence for remission-related host gene expression pattern was found; specifically, CXCR4 expression may have a role in sustained remission.

Tacrolimus Pharmacokinetics is Associated with Gut Microbiota Diversity in Kidney Transplant Patients: Results from a Pilot Cross-Sectional Study

Clinical use of tacrolimus (TAC), an essential immunosuppressant following transplantation, is complexified by its high pharmacokinetic (PK) variability. The gut microbiota gains growing interest but limited investigations have evaluated its contribution to TAC PKs. Here, we explore the associations between the gut microbiota composition and TAC PKs. In this pilot cross-sectional study (Clinicaltrial.gov NCT04360031), we recruited 93 CYP3A5 non-expressers stabilized kidney transplant recipients. Gut microbiota composition was characterized by 16S rRNA gene sequencing, TAC PK parameters were computed, and additional demographic and medical covariates were collected. Associations between PK parameters or diabetic status and the gut microbiota composition, as reflected by α- and β-diversity metrics, were evaluated. Patients with higher TAC area under the curve AUC/(dose/kg) had higher bacterial richness, and TAC PK parameters were associated with specific bacterial taxa (e.g., Bilophila) and amplicon sequence variant (ASV; e.g., ASV 1508 and ASV 1982 (Veillonella/unclassified Sporomusaceae); ASV 664 (unclassified Oscillospiraceae)). Building a multiple linear regression model showed that ASV 1508 (co-abundant with ASV 1982) and ASV 664 explained, respectively, 16.0% and 4.6% of the interindividual variability in TAC AUC/(dose/kg) in CYP3A5 non-expresser patients, when adjusting for hematocrit and age. Anaerostipes relative abundance was decreased in patients with diabetes. Altogether, this pilot study revealed unprecedented links between the gut microbiota composition and diversity and TAC PKs in stable kidney transplant recipients. It supports the relevance of studying the gut microbiota as an important contributor to TAC PK variability. Elucidating the causal relationship will offer new perspectives to predict TAC inter- and intra-PK variability.

Assessment of DNA extraction methods for human gut mycobiome analysis

The gut mycobiome plays an important role in the health and disease of the human gut, but its exact function is still under investigation. While there is a wealth of information available on the bacterial community of the human gut microbiome, research on the fungal community is still relatively limited. In particular, technical methodologies for mycobiome analysis, especially the DNA extraction method for human faecal samples, varied in different studies. In the current study, two commercial kits commonly used in DNA extraction, the QIAamp® Fast DNA Stool Mini Kit and DNeasy PowerSoil Pro Kit, and one manual method, the International Human Microbiome Standards Protocol Q, were compared. Furthermore, the effectiveness of two different bead-beating machines, the Mini-Beadbeater-16 and FastPrep-24TM 5G, was compared in parallel. A mock fungal community with a known composition of fungal strains was also generated and included to compare different DNA extraction methods. Our results suggested that the method using the DNeasy PowerSoil Pro Kit and Mini-Beadbeater-16 provides the best results to extract DNA from human faecal samples. Based on our data, we propose a standard operating procedure for DNA extraction from human faecal samples for mycobiome analysis.

Multicenter evaluation of gut microbiome profiling by next-generation sequencing reveals major biases in partial-length metabarcoding approach

Next-generation sequencing workflows, using either metabarcoding or metagenomic approaches, have massively contributed to expanding knowledge of the human gut microbiota, but methodological bias compromises reproducibility across studies. Where these biases have been quantified within several comparative analyses on their own, none have measured inter-laboratory reproducibility using similar DNA material. Here, we designed a multicenter study involving seven participating laboratories dedicated to partial- (P1 to P5), full-length (P6) metabarcoding, or metagenomic profiling (MGP) using DNA from a mock microbial community or extracted from 10 fecal samples collected at two time points from five donors. Fecal material was collected, and the DNA was extracted according to the IHMS protocols. The mock and isolated DNA were then provided to the participating laboratories for sequencing. Following sequencing analysis according to the laboratories' routine pipelines, relative taxonomic-count tables defined at the genus level were provided and analyzed. Large variations in alpha-diversity between laboratories, uncorrelated with sequencing depth, were detected among the profiles. Half of the genera identified by P1 were unique to this partner and two-thirds of the genera identified by MGP were not detected by P3. Analysis of beta-diversity revealed lower inter-individual variance than inter-laboratory variances. The taxonomic profiles of P5 and P6 were more similar to those of MGP than those obtained by P1, P2, P3, and P4. Reanalysis of the raw sequences obtained by partial-length metabarcoding profiling, using a single bioinformatic pipeline, harmonized the description of the bacterial profiles, which were more similar to each other, except for P3, and closer to the profiles obtained by MGP. This study highlights the major impact of the bioinformatics pipeline, and primarily the database used for taxonomic annotation. Laboratories need to benchmark and optimize their bioinformatic pipelines using standards to monitor their effectiveness in accurately detecting taxa present in gut microbiota.

An Insight into Functional Metagenomics: A High-Throughput Approach to Decipher Food-Microbiota-Host Interactions in the Human Gut

Our understanding of the symbiotic relationship between the microbiota and its host has constantly evolved since our understanding that the "self" was not only defined by our genetic patrimony but also by the genomes of bugs living in us. The first culture-based methods highlighted the important functions of the microbiota. However, these methods had strong limitations and did not allow for a full understanding of the complex relationships that occur at the interface between the microbiota and the host. The recent development of metagenomic approaches has been a groundbreaking step towards this understanding. Its use has provided new insights and perspectives. In the present chapter, we will describe the advances of functional metagenomics to decipher food-microbiota and host-microbiota interactions. This powerful high-throughput approach allows for the assessment of the microbiota as a whole (including non-cultured bacteria) and enabled the discovery of new signaling pathways and functions involved in the crosstalk between food, the gut microbiota and its host. We will present the pipeline and highlight the most important studies that helped to develop the field. To conclude, we will emphasize the most recent developments and hot topics in functional metagenomics.

Post-stress changes in the gut microbiome composition in rats with different levels of nervous system excitability

The gut-brain axis is a critical communication system influencing the interactions between the gastrointestinal tract (GI) and the central nervous system (CNS). The gut microbiota plays a significant role in this axis, affecting the development and function of the nervous system. Stress-induced psychopathologies, such as depression and anxiety, have been linked to the gut microbiota, but underlying mechanisms and genetic susceptibility remain unclear. In this study, we examined stress-induced changes in the gut microbiome composition in two rat strains with different levels of nervous system excitability: high threshold (HT strain) and low threshold (LT strain). Rats were exposed to long-term emotional and painful stress using the Hecht protocol, and fecal samples were collected at multiple time points before and after stress exposure. Using 16S rRNA amplicon sequencing, we assessed the qualitative and quantitative changes in the gut microbiota. Our results revealed distinct microbial diversity between the two rat strains, with the HT strain displaying higher diversity compared to the LT strain. Notably, under prolonged stress, the HT strain showed an increase in relative abundance of microorganisms from the genera Faecalibacterium and Prevotella in fecal samples. Additionally, both strains exhibited a decrease in Lactobacillus abundance following stress exposure. Our findings provide valuable insights into the impact of hereditary nervous system excitability on the gut microbiome composition under stress conditions. Understanding the gut-brain interactions in response to stress may open new avenues for comprehending stress-related psychopathologies and developing potential therapeutic interventions targeted at the gut microbiota. However, further research is needed to elucidate the exact mechanisms underlying these changes and their implications for stress-induced disorders. Overall, this study contributes to the growing body of knowledge on the gut-brain axis and its significance in stress-related neurobiology.

Transposon-aided capture (TRACA) of plasmids from the human gut

The gut microbiota consists of a vast and diverse assemblage of microorganisms that play a pivotal role in maintaining host health. Nevertheless, a significant portion of the human gut microbiota remains uncultivated. Plasmids, a type of MGE, assume a critical function in the biological evolution and adaptation of bacteria to varying environments. To investigate the plasmids present within the gut microbiota community, we used the transposon-aided capture method (TRACA) to explore plasmids derived from the gut microbiota. In this study, fecal samples were collected from two healthy human volunteers and subsequently subjected to the TRACA method for plasmid isolation. Then, the complete sequence of the plasmids was obtained using the genome walking method, and sequence identity was also analyzed. A total of 15 plasmids were isolated. At last, 13 plasmids were successfully sequenced, of which 12 plasmids were highly identical to the plasmids in the National Center for Biotechnology Information (NCBI) database and were all small plasmids. Furthermore, a putative novel plasmid, named pMRPHD, was isolated, which had mobilized elements (oriT and oriV) and a potential type II restriction-modification (R-M) system encoded by DNA cytosine methyltransferase and type II restriction enzyme (Ban I), whose specific functions and applications warrant further exploration.

Integrated Three-Dimensional Microdevice with a Modified Surface for Enhanced DNA Separation from Biological Samples

Functional interfaces and devices for rapid adsorption and immobilization of nucleic acids (NAs) are significant for relevant bioengineering applications. Herein, a microdevice with poly(acrylic acid) (PAA) photosensitive resin was integrated by three-dimensional (3D) printing, named DPAA for short. Precise microscale structures and abundant surface carboxyl functional groups were fabricated for fast and high-throughput deoxyribonucleic acid (DNA) separation. Surface modification was then done using polydopamine (PDA) and poly(ethylene glycol) (PEG) to obtain modified poly(acrylic acid) (PAA)-based devices DPDA-PAA and DPEG-PAA rich in amino and hydroxyl groups, respectively. The fabricated device DPAA possessed superior printing accuracy (40-50 μm). Functionalization of amino and hydroxyl was successful, and the modified devices DPDA-PAA and DPEG-PAA maintained a high thermal stability like DPAA. Surface potential analysis and molecular dynamics simulation indicated that the affinity for DNA was in the order of DPDA-PAA > DPEG-PAA > DPAA. Further DNA separation experiments confirmed the high throughput and high selectivity of DNA separation performance, consistent with the predicted affinity results. DPDA-PAA showed relatively the highest DNA extraction yield, while DPEG-PAA was the worst. An acidic binding system is more favorable for DNA separation and recovery. DPDA-PAA showed significantly better DNA extraction performance than DPAA in a weakly acidic environment (pH 5.0-7.0), and the average DNA yield of the first elution was 2.16 times that of DPAA. This work validates the possibility of modification on integrated 3D microdevices to improve their DNA separation efficiency effectively. It also provides a new direction for the rational design and functionalization of bioengineering separators based on nonmagnetic methods. It may pave a new path for the highly efficient polymerase chain reaction diagnosis.

Mutual modulation of gut microbiota and the immune system in type 1 diabetes models

The transgenic 116C-NOD mouse strain exhibits a prevalent Th17 phenotype, and reduced type 1 diabetes (T1D) compared to non-obese diabetic (NOD) mice. A cohousing experiment between both models revealed lower T1D incidence in NOD mice cohoused with 116C-NOD, associated with gut microbiota changes, reduced intestinal permeability, shifts in T and B cell subsets, and a transition from Th1 to Th17 responses. Distinct gut bacterial signatures were linked to T1D in each group. Using a RAG-2-/- genetic background, we found that T cell alterations promoted segmented filamentous bacteria proliferation in young NOD and 116C-NOD, as well as in immunodeficient NOD.RAG-2-/- and 116C-NOD.RAG-2-/- mice across all ages. Bifidobacterium colonization depended on lymphocytes and thrived in a non-diabetogenic environment. Additionally, 116C-NOD B cells in 116C-NOD.RAG-2-/- mice enriched the gut microbiota in Adlercreutzia and reduced intestinal permeability. Collectively, these results indicate reciprocal modulation between gut microbiota and the immune system in rodent T1D models.

Gut microbiota profiles in two New Zealand cohorts with overweight and prediabetes: a Tū Ora/PREVIEW comparative study

Obesity-related metabolic diseases such as type 2 diabetes (T2D) are major global health issues, affecting hundreds of millions of people worldwide. The underlying factors are both diverse and complex, incorporating biological as well as cultural considerations. A role for ethnicity - a measure of self-perceived cultural affiliation which encompasses diet, lifestyle and genetic components - in susceptibility to metabolic diseases such as T2D is well established. For example, Asian populations may be disproportionally affected by the adverse 'TOFI' (Thin on the Outside, Fat on the Inside) profile, whereby outwardly lean individuals have increased susceptibility due to excess visceral and ectopic organ fat deposition. A potential link between the gut microbiota and metabolic disease has more recently come under consideration, yet our understanding of the interplay between ethnicity, the microbiota and T2D remains incomplete. We present here a 16S rRNA gene-based comparison of the fecal microbiota of European-ancestry and Chinese-ancestry cohorts with overweight and prediabetes, residing in New Zealand. The cohorts were matched for mean fasting plasma glucose (FPG: mean ± SD, European-ancestry: 6.1 ± 0.4; Chinese-ancestry: 6.0 ± 0.4 mmol/L), a consequence of which was a significantly higher mean body mass index in the European group (BMI: European-ancestry: 37.4 ± 6.8; Chinese-ancestry: 27.7 ± 4.0 kg/m2; p < 0.001). Our findings reveal significant microbiota differences between the two ethnicities, though we cannot determine the underpinning factors. In both cohorts Firmicutes was by far the dominant bacterial phylum (European-ancestry: 93.4 ± 5.5%; Chinese-ancestry: 79.6 ± 10.4% of 16S rRNA gene sequences), with Bacteroidetes and Actinobacteria the next most abundant. Among the more abundant (≥1% overall relative sequence abundance) genus-level taxa, four zero-radius operational taxonomic units (zOTUs) were significantly higher in the European-ancestry cohort, namely members of the Subdoligranulum, Blautia, Ruminoclostridium, and Dorea genera. Differential abundance analysis further identified a number of additional zOTUs to be disproportionately overrepresented across the two ethnicities, with the majority of taxa exhibiting a higher abundance in the Chinese-ancestry cohort. Our findings underscore a potential influence of ethnicity on gut microbiota composition in the context of individuals with overweight and prediabetes.

Mothers' Breast Milk Composition and Their Respective Infant's Gut Microbiota Differ between Five Distinct Rural and Urban Regions in Vietnam

Microbiota colonization and development in early life is impacted by various host intrinsic (genetic) factors, but also diet, lifestyle, as well as environmental and residential factors upon and after birth. To characterize the impact of maternal nutrition and environmental factors on vaginally born infant gut microbiota composition, we performed an observational study in five distinct geographical areas in Vietnam. Fecal samples of infants (around 39 days old) and fecal and breast milk samples of their mothers (around 28 years) were collected. The microbiota composition of all samples was analyzed by 16S rRNA gene Illumina sequencing and a bioinformatics workflow based on QIIME. In addition, various breast milk components were determined. Strong associations between the geographically determined maternal diet and breast milk composition as well as infant fecal microbiota were revealed. Most notable was the association of urban Ha Noi with relatively high abundances of taxa considered pathobionts, such as Klebsiella and Citrobacter, at the expense of Bifidobacterium. Breast milk composition was most distinct in rural Ha Long Bay, characterized by higher concentrations of, e.g., docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA), selenium, and vitamin B12, while it was characterized by, e.g., iron, zinc, and α-linolenic acid (ALA) in Ha Noi. Breast milk iron levels were positively associated with infant fecal Klebsiella and negatively with Bifidobacterium, while the EPA and DHA levels were positively associated with Bifidobacterium. In conclusion, differences between five regions in Vietnam with respect to both maternal breast milk and infant gut microbiota composition were revealed, most likely in part due to maternal nutrition. Thus, there could be opportunities to beneficially steer infant microbiota development in a more desired (rural instead of urban) direction through the mother's diet.

Analysis of microbial composition and sharing in low-biomass human milk samples: a comparison of DNA isolation and sequencing techniques

Human milk microbiome studies are currently hindered by low milk bacterial/human cell ratios and often rely on 16S rRNA gene sequencing, which limits downstream analyses. Here, we aimed to find a method to study milk bacteria and assess bacterial sharing between maternal and infant microbiota. We tested four DNA isolation methods, two bacterial enrichment methods and three sequencing methods on mock communities, milk samples and negative controls. Of the four DNA isolation kits, the DNeasy PowerSoil Pro (PS) and MagMAX Total Nucleic Acid Isolation (MX) kits provided consistent 16S rRNA gene sequencing results with low contamination. Neither enrichment method substantially decreased the human metagenomic sequencing read-depth. Long-read 16S-ITS-23S rRNA gene sequencing biased the mock community composition but provided consistent results for milk samples, with little contamination. In contrast to 16S rRNA gene sequencing, 16S-ITS-23S rRNA gene sequencing of milk, infant oral, infant faecal and maternal faecal DNA from 14 mother-infant pairs provided sufficient resolution to detect significantly more frequent sharing of bacteria between related pairs compared to unrelated pairs. In conclusion, PS or MX kit-DNA isolation followed by 16S rRNA gene sequencing reliably characterises human milk microbiota, and 16S-ITS-23S rRNA gene sequencing enables studies of bacterial transmission in low-biomass samples.

Gut microbiota alterations induced by intensive chemotherapy in acute myeloid leukaemia patients are associated with gut barrier dysfunction and body weight loss

BACKGROUND & AIMS

Acute myeloid leukaemia (AML) chemotherapy has been reported to impact gut microbiota composition. In this study, we investigated using a multi -omics strategy the changes in the gut microbiome induced by AML intense therapy and their association with gut barrier function and cachectic hallmarks.

METHODS

10 AML patients, allocated to standard induction chemotherapy (SIC), were recruited. Samples and data were collected before any therapeutic intervention (T0), at the end of the SIC (T1) and at discharge (T4). Gut microbiota composition and function, markers of inflammation, metabolism, gut barrier function and cachexia, as well as faecal, blood and urine metabolomes were assessed.

RESULTS

AML patients demonstrated decreased appetite, weight loss and muscle wasting during hospitalization, with an incidence of cachexia of 50%. AML intensive treatment transiently impaired the gut barrier function and led to a long-lasting change of gut microbiota composition characterized by an important loss of diversity. Lactobacillaceae and Campylobacter concisus were increased at T1 while Enterococcus faecium and Staphylococcus were increased at T4. Metabolomics analyses revealed a reduction in urinary hippurate and faecal bacterial amino acid metabolites (bAAm) (2-methylbutyrate, isovalerate, phenylacetate). Integration using DIABLO revealed a deep interconnection between all the datasets. Importantly, we identified bacteria which disappearance was associated with impaired gut barrier function (Odoribacter splanchnicus) and body weight loss (Gemmiger formicilis), suggesting these bacteria as actionable targets.

CONCLUSION

AML intensive therapy transiently impairs the gut barrier function while inducing enduring alterations in the composition and metabolic activity of the gut microbiota that associate with body weight loss.

TRIAL REGISTRATION

NCT03881826, https://clinicaltrials.gov/ct2/show/NCT03881826.

Challenges and opportunities in sharing microbiome data and analyses

Microbiome data, metadata and analytical workflows have become 'big' in terms of volume and complexity. Although the infrastructure and technologies to share data have been established, the interdisciplinary and multi-omic nature of the field can make resources difficult to identify and use. Following best practices for data deposition requires substantial effort, with sometimes little obvious reward. Gaps remain where microbiome-specific resources for data sharing or reproducibility do not yet exist. We outline available best practices, challenges to their adoption and opportunities in data sharing in microbiome research. We showcase examples of best practices and advocate for their enforcement and incentivization for data sharing. This includes recognition of data curation and sharing endeavours by individuals, institutions, journals and funders. Opportunities for progress include enabling microbiome-specific databases to incorporate future methods for data analysis, integration and reuse.

Establishing reference material for the quest towards standardization in environmental microbial metagenomic studies

Breakthroughs in DNA-based technologies, especially in metagenomic sequencing, have drastically enhanced researchers' ability to explore environmental microbiome and the associated interplays within. However, as new methodologies are being actively developed for improvements in different aspects, metagenomic workflows become diversified and heterogeneous. Through a single-variable control approach, we quantified the microbial profiling variations arising from 6 common technical variables associated with metagenomic workflows for both simple and complex samples. The incurred variations were constantly the lowest in replicates of DNA isolation and DNA sequencing library construction. Different DNA extraction kits often caused the highest variation among all the tested variables. Additionally, sequencing run batch was an important source of variability for targeted platforms. As such, the development of an environmental reference material for complex environmental samples could be beneficial in benchmarking accrued non-biological variability within and between protocols and insuring reliable and reproducible sequencing outputs immediately upstream of bioinformatic analysis. To develop an environment reference material, sequencing of a well-homogenized environmental sample composed of activated sludge was performed using different pre-analytical assays in replications. In parallel, a certified mock community was processed and sequenced. Assays were ranked based on the reconstruction of the theoretical mock community profile. The reproducibility of the best-performing assay and the microbial profile of the reference material were further ascertained. We propose the adoption of our complex environmental reference material, which could reflect the degree of diversity in environmental microbiome studies, to facilitate accurate, reproducible, and comparable environmental metagenomics-based studies.

Interactions between environmental sensitivity and gut microbiota are associated with biomarkers of stress-related psychiatric symptoms

BACKGROUND

Humans vary in their sensitivity to stressful and supportive environments and experiences. Such individual differences in environmental sensitivity are associated with mechanisms of stress-related psychiatric symptoms. In recent years, researchers have focused on bidirectional interactions in the brain-gut-microbiota axis as a neurophysiological pathway contributing to the mechanisms of stress-related psychiatric symptoms, and evidence is rapidly accumulating.

METHODS

Data on environmental sensitivity, gut microbiota, gut permeability (lipopolysaccharide-binding protein; LBP) and inflammation (C-reactive protein; CRP) were collected from 90 adults (50 % female; Mage = 42.1; SDage = 10.0). Environmental sensitivity was measured using a self-report questionnaire. Study participants' feces were analyzed, and observed operational taxonomic units for richness, Shannon's index for evenness, and phylogenetic diversity for biodiversity were evaluated as indicators of gut microbiota. In addition, participants' serum was analyzed for CRP and LBP. We investigated whether the interaction between environmental sensitivity and gut microbiota is associated with biomarkers of inflammation and gut permeability.

RESULTS

The interaction between environmental sensitivity and gut microbiota (excluding the Shannon's index) explained the levels of these biomarkers. Individuals with high environmental sensitivity displayed higher levels of CRP and LBP, when the richness and diversity of the gut microbiota was low. However, even highly susceptible individuals had lower levels of CRP and LBP, when the richness and diversity of the gut microbiota was high.

CONCLUSIONS

Our study indicates that high environmental sensitivity can be a risk factor for inflammation and gut permeability, when the gut microbiota diversity is low, suggesting a brain-gut-microbiota axis interaction.

A Pipeline for Constructing Reference Genomes for Large Cohort-Specific Metagenome Compression

Metagenomic data compression is very important as metagenomic projects are facing the challenges of larger data volumes per sample and more samples nowadays. Reference-based compression is a promising method to obtain a high compression ratio. However, existing microbial reference genome databases are not suitable to be directly used as references for compression due to their large size and redundancy, and different metagenomic cohorts often have various microbial compositions. We present a novel pipeline that generated simplified and tailored reference genomes for large metagenomic cohorts, enabling the reference-based compression of metagenomic data. We constructed customized reference genomes, ranging from 2.4 to 3.9 GB, for 29 real metagenomic datasets and evaluated their compression performance. Reference-based compression achieved an impressive compression ratio of over 20 for human whole-genome data and up to 33.8 for all samples, demonstrating a remarkable 4.5 times improvement than the standard Gzip compression. Our method provides new insights into reference-based metagenomic data compression and has a broad application potential for faster and cheaper data transfer, storage, and analysis.

Evaluation of an Adapted Semi-Automated DNA Extraction for Human Salivary Shotgun Metagenomics

Recent attention has highlighted the importance of oral microbiota in human health and disease, e.g., in Parkinson's disease, notably using shotgun metagenomics. One key aspect for efficient shotgun metagenomic analysis relies on optimal microbial sampling and DNA extraction, generally implementing commercial solutions developed to improve sample collection and preservation, and provide high DNA quality and quantity for downstream analysis. As metagenomic studies are today performed on a large number of samples, the next evolution to increase study throughput is with DNA extraction automation. In this study, we proposed a semi-automated DNA extraction protocol for human salivary samples collected with a commercial kit, and compared the outcomes with the DNA extraction recommended by the manufacturer. While similar DNA yields were observed between the protocols, our semi-automated DNA protocol generated significantly higher DNA fragment sizes. Moreover, we showed that the oral microbiome composition was equivalent between DNA extraction methods, even at the species level. This study demonstrates that our semi-automated protocol is suitable for shotgun metagenomic analysis, while allowing for improved sample treatment logistics with reduced technical variability and without compromising the structure of the oral microbiome.

Drug-microbiota interactions: an emerging priority for precision medicine

Individual variability in drug response (IVDR) can be a major cause of adverse drug reactions (ADRs) and prolonged therapy, resulting in a substantial health and economic burden. Despite extensive research in pharmacogenomics regarding the impact of individual genetic background on pharmacokinetics (PK) and pharmacodynamics (PD), genetic diversity explains only a limited proportion of IVDR. The role of gut microbiota, also known as the second genome, and its metabolites in modulating therapeutic outcomes in human diseases have been highlighted by recent studies. Consequently, the burgeoning field of pharmacomicrobiomics aims to explore the correlation between microbiota variation and IVDR or ADRs. This review presents an up-to-date overview of the intricate interactions between gut microbiota and classical therapeutic agents for human systemic diseases, including cancer, cardiovascular diseases (CVDs), endocrine diseases, and others. We summarise how microbiota, directly and indirectly, modify the absorption, distribution, metabolism, and excretion (ADME) of drugs. Conversely, drugs can also modulate the composition and function of gut microbiota, leading to changes in microbial metabolism and immune response. We also discuss the practical challenges, strategies, and opportunities in this field, emphasizing the critical need to develop an innovative approach to multi-omics, integrate various data types, including human and microbiota genomic data, as well as translate lab data into clinical practice. To sum up, pharmacomicrobiomics represents a promising avenue to address IVDR and improve patient outcomes, and further research in this field is imperative to unlock its full potential for precision medicine.

Exploring the gut microbiota: lifestyle choices, disease associations, and personal genomics

The gut microbiota is a rich and dynamic ecosystem that actively interacts with the human body, playing a significant role in the state of health and disease of the host. Diet, exercise, mental health, and other factors have exhibited the ability to influence the gut bacterial composition, leading to changes that can prevent and improve, or favor and worsen, both intestinal and extra-intestinal conditions. Altered gut microbial states, or 'dysbiosis', associated with conditions and diseases are often characterized by shifts in bacterial abundance and diversity, including an impaired Firmicutes to Bacteroidetes ratio. By understanding the effect of lifestyle on the gut microbiota, personalized advice can be generated to suit each individual profile and foster the adoption of lifestyle changes that can both prevent and ameliorate dysbiosis. The delivery of effective and reliable advice, however, depends not only on the available research and current understanding of the topic, but also on the methods used to assess individuals and to discover the associations, which can introduce bias at multiple stages. The aim of this review is to summarize how human gut microbial variability is defined and what lifestyle choices and diseases have shown association with gut bacterial composition. Furthermore, popular methods to investigate the human gut microbiota are outlined, with a focus on the possible bias caused by the lack of use of standardized methods. Finally, an overview of the current state of personalized advice based on gut microbiota testing is presented, underlining its power and limitations.

Homemade Nucleic Acid Preservation Buffer Proves Effective in Preserving the Equine Faecal Microbiota over Time at Ambient Temperatures

The equine faecal microbiota is often assessed as a proxy of the microbial community in the distal colon, where the microbiome has been linked to states of health and disease in the horse. However, the microbial community structure may change over time if samples are not adequately preserved. This study stored equine faecal samples from n = 10 horses in four preservation treatments at room temperature for up to 150 h and assessed the resulting impact on microbial diversity and the differential abundance of taxa. Treatments included "COLD" (samples packaged with a cool pack), "CLX" (2% chlorhexidine digluconate solution), "NAP" (nucleic acid preservation buffer), and "FTA" (Whatman FTA™ cards). The samples were assessed using 16S rRNA gene sequencing after storage for 0, 24, 72, and 150 h at room temperature under the different treatments. The results showed effective preservation of diversity and community structure with NAP buffer but lower diversity (p = 0.001) and the under-representation of Fibrobacterota in the FTA card samples. The NAP treatment inhibited the overgrowth of bloom taxa that occurred by 72 h at room temperature. The COLD, CLX, and NAP treatments were effective in preserving the faecal microbiota for up to 24 h at room temperature, and the CLX and NAP treatments improved the yield of Patescibacteria and Fibrobacterota in some cases. The cold and CLX treatments were ineffective in preventing community shifts that occurred by 72 h at room temperature. These findings demonstrate the suitability of the COLD, NAP, and CLX treatments for the room temperature storage of equine faeces for up to 24 h and of NAP buffer for up to 150 h prior to processing.

Visceral hypersensitivity induced by mild traumatic brain injury via the corticotropin-releasing hormone receptor: An animal model

BACKGROUND

Mild blast-induced traumatic brain injury (bTBI) induces various gut symptoms resembling human irritable bowel syndrome (IBS) as one of mental and behavioral disorders. However, the underlying mechanisms remain unclear. We investigated whether the extremely localized brain impact extracranially induced by laser-induced shock wave (LISW) evoked IBS-like phenomenon including visceral hypersensitivity and intestinal hyperpermeability in rats.

METHODS

The rats were subjected to LISW on the scalp to shock the entire brain. Visceral hypersensitivity was evaluated by the threshold pressure of abdominal withdrawal reflex (AWR) using a colorectal distension test. Permeability was evaluated by the concentration of penetrating FITC-dextran from intestine and the mRNA expression levels of tight junction family proteins. Involvement of corticotropin-releasing factor receptor (CRFR) 1 and 2 was examined by evaluating mRNA expression and modulating CRFR function with agonist, recombinant CRF (10 μg/kg), and antagonist, astressin (33 μg/kg). High-throughput sequencing of the gut microbiota was performed by MiSeqIII instrument and QIIME tool.

KEY RESULTS

The thresholds of the AWR were significantly lowered after LISW. Permeability was increased in small intestine by LISW along with decreased expression of tight junction ZO-1. LISW significantly increased CRFR1 expression and decreased CRFR2 expression. Visceral hypersensitivity was significantly aggravated by CRFR agonist and suppressed by CRFR antagonist. The α- and β-diversity of the fecal microbiota was altered after LISW.

CONCLUSIONS AND INFERENCES

LISW provoked visceral hypersensitivity, small intestinal hyperpermeability, altered expression of CRFRs and changes in the microbiota, suggesting that genuine bTBI caused by LISW can induce a pathophysiology comparable to that of human IBS.

Microbial and immune faecal determinants in infants hospitalized with COVID-19 reflect bifidobacterial dysbiosis and immature intestinal immunity

The coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread rapidly worldwide, seriously endangering human health. Although SARS-CoV-2 had a lower impact on paediatric population, children with COVID-19 have been reported as suffering from gastrointestinal (GI) symptoms at a higher rate than adults. The aim of this work was to evaluate faeces as a source of potential biomarkers of severity in the paediatric population, with an emphasis on intestinal microbiota and faecal immune mediators, trying to identify possible dysbiosis and immune intestinal dysfunction associated with the risk of hospitalization. This study involved 19 patients with COVID-19 under 24 months of age hospitalized during the pandemic at 6 different hospitals in Spain, and it included a comparable age-matched healthy control group (n = 18). Patients and controls were stratified according to their age in two groups: newborns or young infants (from 0 to 3 months old) and toddlers (infants from 6 to 24 months old). To characterize microbial intestinal communities, sequencing with Illumina technology of total 16S rDNA amplicons and internal transcribed spacer (ITS) amplicons of bifidobacteria were used. Faecal calprotectin (FC) and a range of human cytokines, chemokines, and growth factors were measured in faecal samples using ELISA and a multiplex system. Significant reduction in the abundance of sequences belonging to the phylum Actinobacteria was found in those infants with COVID-19, as well as in the Bifidobacteriaceae family. A different pattern of bifidobacteria was observed in patients, mainly represented by lower percentages of Bifidobacterium breve, as compared with controls. In the group of hospitalized young infants, FC was almost absent compared to age-matched healthy controls. A lower prevalence in faecal excretion of immune factors in these infected patients was also observed.

CONCLUSION

 Hospitalized infants with COVID-19 were depleted in some gut bacteria, such as bifidobacteria, in particular Bifidobacterium breve, which is crucial for the proper establishment of a functional intestinal microbiota, and important for the development of a competent immune system. Our results point to a possible immature immune system at intestine level in young infants infected by SARS-CoV2 requiring hospitalization.

WHAT IS KNOWN

• Although SARS-CoV-2 had a lower impact on paediatric population, children with COVID-19 have been reported as suffering from gastrointestinal symptoms at a higher rate than adults. • Changes in microbial composition have been described in COVID-19 adult patients, although studies in children are limited.

WHAT IS NEW

• The first evidence that hospitalized infants with COVID-19 during the pandemic had a depletion in bifidobacteria, particularly in Bifidobacterium breve, beneficial gut bacteria in infancy that are crucial for the proper establishment of a competent immune system. • In young infants (under 3 months of age) hospitalized with SARS-CoV2 infection, the aberrant bifidobacterial profile appears to overlap with a poor intestinal immune development as seen by calprotectin and the trend of immunological factors excreted in faeces.

From-Toilet-to-Freezer: A Review on Requirements for an Automatic Protocol to Collect and Store Human Fecal Samples for Research Purposes

The composition, viability and metabolic functionality of intestinal microbiota play an important role in human health and disease. Studies on intestinal microbiota are often based on fecal samples, because these can be sampled in a non-invasive way, although procedures for sampling, processing and storage vary. This review presents factors to consider when developing an automated protocol for sampling, processing and storing fecal samples: donor inclusion criteria, urine-feces separation in smart toilets, homogenization, aliquoting, usage or type of buffer to dissolve and store fecal material, temperature and time for processing and storage and quality control. The lack of standardization and low-throughput of state-of-the-art fecal collection procedures promote a more automated protocol. Based on this review, an automated protocol is proposed. Fecal samples should be collected and immediately processed under anaerobic conditions at either room temperature (RT) for a maximum of 4 h or at 4 °C for no more than 24 h. Upon homogenization, preferably in the absence of added solvent to allow addition of a buffer of choice at a later stage, aliquots obtained should be stored at either -20 °C for up to a few months or -80 °C for a longer period-up to 2 years. Protocols for quality control should characterize microbial composition and viability as well as metabolic functionality.

Field and laboratory guidelines for reliable bioinformatic and statistical analysis of bacterial shotgun metagenomic data

Shotgun metagenomics is an increasingly cost-effective approach for profiling environmental and host-associated microbial communities. However, due to the complexity of both microbiomes and the molecular techniques required to analyze them, the reliability and representativeness of the results are contingent upon the field, laboratory, and bioinformatic procedures employed. Here, we consider 15 field and laboratory issues that critically impact downstream bioinformatic and statistical data processing, as well as result interpretation, in bacterial shotgun metagenomic studies. The issues we consider encompass intrinsic properties of samples, study design, and laboratory-processing strategies. We identify the links of field and laboratory steps with downstream analytical procedures, explain the means for detecting potential pitfalls, and propose mitigation measures to overcome or minimize their impact in metagenomic studies. We anticipate that our guidelines will assist data scientists in appropriately processing and interpreting their data, while aiding field and laboratory researchers to implement strategies for improving the quality of the generated results.