A comprehensive evaluation of microbial differential abundance analysis methods: current status and potential solutions

Utilizing the biosimulator to analyze the environmental microbiome within the intensive care units of a hospital

Hospital-acquired infections (HAIs), also known as nosocomial infections, are illnesses contracted during treatment at a healthcare facility and can result in severe or life-threatening complications. HAIs are caused by microorganisms that exhibit resistance to standard antibiotics. HAIs can lead to severe complications, longer stays, and increased mortality, particularly in vulnerable patients. In our previous study, we demonstrated the ability of an engraved Petri dish, referred to as a "biosimulator," to induce adhesion of non-adherent cells and the microbiome. This paper explores the use of the biosimulator to elucidate the microbiome composition within intensive care units (ICUs) in a hospital setting. The biosimulator, with a nutrient-rich bacterial growth medium, was placed in ICUs for 24 h, then incubated for three days under aerobic and anaerobic conditions. Using 16S rRNA sequencing, we profiled the ICU microbiome from multiple samples. Our findings showed that ICU microbiomes closely mirrored those of patients, with microorganisms in the ICU exhibiting stronger interrelationships than in control conditions. The combined use of the biosimulator and profiling offers an effective approach for analyzing and understanding microbiome changes in healthcare settings, particularly in high-risk areas, such as ICUs.

Exploring the composition of placental microbiome and its potential origin in preterm birth

Introduction

For years, the placenta was believed to be sterile, but recent studies reveal it hosts a unique microbiome. Despite these findings, significant questions remain about the origins of the placental microbiome and its effects on pregnancy and fetal health. Some studies suggest it may originate from the vaginal tract, while others indicate that oral bacteria can enter the maternal bloodstream and seed the placenta. However, research analyzing the vaginal, oral, and placental microbiomes within the same cohort is lacking. Additionally, it's unclear whether the placental microbiome differs between healthy pregnancies and those with complications like preterm birth (PTB), which remains a leading cause of neonatal morbidity and mortality worldwide.

Methods

In this study, we performed 16S rRNA gene sequencing to investigate the composition of the oral and placental microbiome in samples collected from 18 women who experienced PTB and 36 matched controls who delivered at term (TB), all of whom were part of the Molecular Signature in Pregnancy (MSP) study. We leveraged on the multisite microbiome sampling from the MSP participants and on our previously published vaginal microbiome data to investigate the potential origins of the placental microbiome and assess whether its composition varies between healthy and complicated pregnancies.

Results and Discussion

Our analysis revealed distinct profiles in the oral microbiome of PTB subjects compared to those who delivered at term. Specifically, we observed an increased abundance of Treponema maltophilum, Bacteroides sp, Mollicutes, Prevotella buccae, Leptotrichia, Prevotella_sp_Alloprevotella, in the PTB group. Importantly, Treponema maltophilum species showed higher abundance in the PTB group during the second trimester, suggesting its potential use as biomarkers. When we assessed the placenta microbiome composition, we found that Firmicutes, Bacteroidetes, Actinobacteria, and Proteobacteria were the most dominant phyla. Interestingly, microorganisms such as Ureaplasma urealyticum were more abundant in PTB placenta samples. Our findings suggest that the placenta microbiome could originate from the oral or vaginal cavities, with a notable increase in the crosstalk between the vaginal and placental sites in cases of PTB. Specifically, our data revealed that in PTB cases, the placental microbiome exhibited a closer resemblance to the vaginal microbiome, whereas in term pregnancies, the placental microbiome was similar to the oral microbiome.

Computational Study Protocol: Leveraging Synthetic Data to Validate a Benchmark Study for Differential Abundance Tests for 16S Microbiome Sequencing Data

Background

Synthetic data's utility in benchmark studies depends on its ability to closely mimic real-world conditions and reproduce results obtained from experimental data. Building on Nearing et al.'s study (1), who assessed 14 differential abundance tests using 38 experimental 16S rRNA datasets in a case-control design, we are generating synthetic datasets that mimic the experimental data to verify their findings. We will employ statistical tests to rigorously assess the similarity between synthetic and experimental data and to validate the conclusions on the performance of these tests drawn by Nearing et al. (1). This protocol adheres to the SPIRIT guidelines, demonstrating how established reporting frameworks can support robust, transparent, and unbiased study planning.

Methods

We replicate Nearing et al.'s (1) methodology, incorporating synthetic data simulated using two distinct tools, mirroring the 38 experimental datasets. Equivalence tests will be conducted on a non-redundant subset of 46 data characteristics comparing synthetic and experimental data, complemented by principal component analysis for overall similarity assessment. The 14 differential abundance tests will be applied to synthetic and experimental datasets, evaluating the consistency of significant feature identification and the number of significant features per tool. Correlation analysis and multiple regression will explore how differences between synthetic and experimental data characteristics may affect the results.

Conclusions

Synthetic data enables the validation of findings through controlled experiments. We assess how well synthetic data replicates experimental data, try to validate previous findings with the most recent versions of the DA methods and delineate the strengths and limitations of synthetic data in benchmark studies. Moreover, to our knowledge this is the first computational benchmark study to systematically incorporate synthetic data for validating differential abundance methods while strictly adhering to a pre-specified study protocol following SPIRIT guidelines, contributing to transparency, reproducibility, and unbiased research.

MaAsLin 3: Refining and extending generalized multivariable linear models for meta-omic association discovery

A key question in microbial community analysis is determining which microbial features are associated with community properties such as environmental or health phenotypes. This statistical task is impeded by characteristics of typical microbial community profiling technologies, including sparsity (which can be either technical or biological) and the compositionality imposed by most nucleotide sequencing approaches. Many models have been proposed that focus on how the relative abundance of a feature (e.g. taxon or pathway) relates to one or more covariates. Few of these, however, simultaneously control false discovery rates, achieve reasonable power, incorporate complex modeling terms such as random effects, and also permit assessment of prevalence (presence/absence) associations and absolute abundance associations (when appropriate measurements are available, e.g. qPCR or spike-ins). Here, we introduce MaAsLin 3 (Microbiome Multivariable Associations with Linear Models), a modeling framework that simultaneously identifies both abundance and prevalence relationships in microbiome studies with modern, potentially complex designs. MaAsLin 3 also newly accounts for compositionality with experimental (spike-ins and total microbial load estimation) or computational techniques, and it expands the space of biological hypotheses that can be tested with inference for new covariate types. On a variety of synthetic and real datasets, MaAsLin 3 outperformed current state-of-the-art differential abundance methods in testing and inferring associations from compositional data. When applied to the Inflammatory Bowel Disease Multi-omics Database, MaAsLin 3 corroborated many previously reported microbial associations with the inflammatory bowel diseases, but notably 77% of associations were with feature prevalence rather than abundance. In summary, MaAsLin 3 enables researchers to identify microbiome associations with higher accuracy and more specific association types, especially in complex datasets with multiple covariates and repeated measures.

Gut Microbiome Signatures During Acute Infection Predict Long COVID

Long COVID (LC), manifests in 10-30% of non-hospitalized individuals post-SARS-CoV-2 infection leading to significant morbidity. The predictive role of gut microbiome composition during acute infection in the development of LC is not well understood, partly due to the heterogeneous nature of disease. We conducted a longitudinal study of 799 outpatients tested for SARS-CoV-2 (380 positive, 419 negative) and found that individuals who later developed LC harbored distinct gut microbiome compositions during acute infection, compared with both SARS-CoV-2-positive individuals who did not develop LC and negative controls with similar symptomatology. However, the temporal changes in gut microbiome composition between the infectious (0-1 month) and post-infectious (1-2 months) phases was not different between study groups. Using machine learning, we showed that microbiome composition alone more accurately predicted LC than clinical variables. Including clinical data only marginally enhanced this prediction, suggesting that microbiome profiles during acute infection may reflect underlying health status and immune responses thus, help predicting individuals at risk for LC. Finally, we identified four LC symptom clusters, with gastrointestinal and fatigue-only groups most strongly linked to gut microbiome alterations.

Revisiting microgenderome: detecting and cataloguing sexually unique and enriched species in human microbiomes

BACKGROUND

Microgenderome or arguably more accurately microsexome refers to studies on sexual dimorphism of human microbiomes aimed at investigating bidirectional interactions between human microbiomes, sex hormones, and immune systems. It is important because of its implications to disease susceptibility and therapy, in which men and women demonstrate divergence in many diseases especially autoimmune diseases. In a previous report [1], we presented analyses of several key ecological aspects of microgenderome by leveraging the large datasets of the HMP (human microbiome project) but failed to offer species-level composition differences such as sexually unique species (US) and enriched species (ES). Existing approaches, for such tasks, including differential species relative abundance analysis and differential network analysis, possess certain limitations given that virtually all rely on species abundance alone or are univariate, while ignoring species distribution information across samples. Obviously, it is both species abundance and distribution that shape/drive the structure and dynamics of human microbiomes, and both should be equally responsible for the universal heterogeneity of microbiomes including the sexual dimorphism.

RESULTS

Here, we fill the gap by taking advantages of a recently developed computational algorithm, species specificity, and specificity diversity (SSD) framework (refer to the companion article) to reanalyze the HMP and complementary seminovaginal microbiome datasets. The SSD framework can randomly search and catalogue the sexually specific unique/enriched species with statistical rigor, guided by species specificity (a synthetic metric of abundance and distribution) and specificity diversity (SD). The SSD framework reveals that men seem to have more unique species than women in their gut and reproductive system microbiomes, but women seem to have more unique species than men in the airway, oral, and skin microbiomes, which is likely due to sexual dimorphism in the hormone and immune systems. We further investigate co-dependency and heterogeneity of those sexually unique/enriched species across 15 body sites, with core/periphery network analyses.

CONCLUSIONS

This study not only produced sexually unique/enriched species in the human microbiomes and analyzed their codependency and heterogeneity but also further validated the robustness of the SSD framework presented in the companion article, by performing all negative control tests based on the HMP gut microbiome samples.

Species specificity and specificity diversity (SSD) framework: a novel method for detecting the unique and enriched species associated with disease by leveraging the microbiome heterogeneity

BACKGROUND

Differentiating the microbiome changes associated with diseases is challenging but critically important. Majority of existing efforts have been focused on a community level, but the discerning power of community or holistic metrics such as diversity analysis seems limited. This prompts many researchers to believe that the promise should be downward to species or even strain level-effectively and efficiently identifying unique or enriched species in diseased microbiomes with statistical rigor. Nevertheless, virtually, all species-level approaches such as differential abundance and differential network analysis methods exclusively rely on species abundances without considering species distribution information, while it can be said that distribution is equally, if not more, important than abundance in shaping the spatiotemporal heterogeneity of community compositions.

RESULTS

Here, we fill the gap by developing a novel framework-species specificity and specificity diversity (SSD)-that synthesizes both abundance and distribution information to differentiate microbiomes, at both species and community scales, under different environmental gradients such as the healthy and diseased treatments. The proposed SSD framework consists of three essential elements. The first is species specificity (SS), a concept that reincarnates the traditional specialist-generalist continuum and is defined by Mariadassou et al. (Ecol Lett 18:974-82, 2015). The SS synthesizes a species' local prevalence (distribution) and global abundance information and attaches specificity measure to each species in a specific habitat (e.g., healthy or diseased treatment). The second element is a new concept to introduce here, the (species) specificity diversity (SD), which is inspired by traditional species (abundance) diversity in community ecology and measures the diversity of specificity (a proxy for metacommunity heterogeneity, essentially) with Renyi's entropy. The third element is a pair of statistical tests based on the principle of permutation tests.

CONCLUSIONS

The SSD framework can (i) identify and catalogue lists of unique species (US), significantly enriched species (ES) in each treatment based on SS and specificity permutation (SP) test and (ii) measure the holistic differences between assemblages (or treatments) based on SD and specificity diversity permutation (SDP) test. Both capacities can be enabling technologies for general comparative microbiome research including risk assessment, diagnosis, and treatment of microbiome-associated diseases.

Review and revamp of compositional data transformation: A new framework combining proportion conversion and contrast transformation

Due to the development of next-generation sequencing technology and an increased appreciation of their role in modulating host immunity and their potential as therapeutic agents, the human microbiome has emerged as a key area of interest in various biological investigations of human health and disease. However, microbiome data present a number of statistical challenges not addressed by existing methods, such as the varying sequencing depth, the compositionality, and zero inflation. Solutions like scaling and transformation methods help to mitigate heterogeneity and release constraints, but often introduce biases and yield inconsistent results on the same data. To address these issues, we conduct a systematic review of compositional data transformation, with a particular focus on the connection and distinction of existing techniques. Additionally, we create a new framework that enables the development of new transformations by combining proportion conversion with contrast transformations. This framework includes well-known methods such as Additive Log Ratio (ALR) and Centered Log Ratio (CLR) as special cases. Using this framework, we develop two novel transformations-Centered Arcsine Contrast (CAC) and Additive Arcsine Contrast (AAC)-which show enhanced performance in scenarios with high zero-inflation. Moreover, our findings suggest that ALR and CLR transformations are more effective when zero values are less prevalent. This comprehensive review and the innovative framework provide microbiome researchers with a significant direction to enhance data transformation procedures and improve analytical outcomes.

Semisynthetic simulation for microbiome data analysis

High-throughput sequencing data lie at the heart of modern microbiome research. Effective analysis of these data requires careful preprocessing, modeling, and interpretation to detect subtle signals and avoid spurious associations. In this review, we discuss how simulation can serve as a sandbox to test candidate approaches, creating a setting that mimics real data while providing ground truth. This is particularly valuable for power analysis, methods benchmarking, and reliability analysis. We explain the probability, multivariate analysis, and regression concepts behind modern simulators and how different implementations make trade-offs between generality, faithfulness, and controllability. Recognizing that all simulators only approximate reality, we review methods to evaluate how accurately they reflect key properties. We also present case studies demonstrating the value of simulation in differential abundance testing, dimensionality reduction, network analysis, and data integration. Code for these examples is available in an online tutorial (https://go.wisc.edu/8994yz) that can be easily adapted to new problem settings.

Structure-adaptive canonical correlation analysis for microbiome multi-omics data

Sparse canonical correlation analysis (sCCA) has been a useful approach for integrating different high-dimensional datasets by finding a subset of correlated features that explain the most correlation in the data. In the context of microbiome studies, investigators are always interested in knowing how the microbiome interacts with the host at different molecular levels such as genome, methylol, transcriptome, metabolome and proteome. sCCA provides a simple approach for exploiting the correlation structure among multiple omics data and finding a set of correlated omics features, which could contribute to understanding the host-microbiome interaction. However, existing sCCA methods do not address compositionality, and its application to microbiome data is thus not optimal. This paper proposes a new sCCA framework for integrating microbiome data with other high-dimensional omics data, accounting for the compositional nature of microbiome sequencing data. It also allows integrating prior structure information such as the grouping structure among bacterial taxa by imposing a "soft" constraint on the coefficients through varying penalization strength. As a result, the method provides significant improvement when the structure is informative while maintaining robustness against a misspecified structure. Through extensive simulation studies and real data analysis, we demonstrate the superiority of the proposed framework over the state-of-the-art approaches.

Synthetic communities derived from the core endophytic microbiome of hyperaccumulators and their role in cadmium phytoremediation

BACKGROUND

Although numerous endophytic bacteria have been isolated and characterized from cadmium (Cd) hyperaccumulators, the contribution and potential application of the core endophytic microbiomes on facilitating phytoremediation were still lack of intensive recognition. Therefore, a 2-year field sampling in different location were firstly conducted to identify the unique core microbiome in Cd hyperaccumulators, among which the representative cultivable bacteria of different genera were then selected to construct synthetic communities (SynComs). Finally, the effects and mechanisms of the optimized SynCom in regulating Cd accumulation in different ecotypes of Sedum alfredii were studied to declare the potential application of the bacterial agents based on core microbiome.

RESULTS

Through an innovative network analysis workflow, 97 core bacterial taxa unique to hyperaccumulator Sedum was identified based on a 2-year field 16S rRNA sequencing data. A SynCom comprising 13 selected strains belonging to 6 different genera was then constructed. Under the combined selection pressure of the plant and Cd contamination, Alcaligenes sp. exhibited antagonistic relationships with other genera and plant Cd concentration. Five representative strains of the other five genera were further conducted genome resequencing and developed six SynComs, whose effects on Cd phytoremediation were compared with single strains by hydroponic experiments. The results showed that SynCom-NS comprising four strains (including Leifsonia shinshuensis, Novosphingobium lindaniclasticum, Ochrobactrum anthropi, and Pseudomonas izuensis) had the greatest potential to enhance Cd phytoremediation. After inoculation with SynCom-NS, genes related to Cd transport, antioxidative defense, and phytohormone signaling pathways were significantly upregulated in both ecotypes of S. alfredii, so as to promote plant growth, Cd uptake, and translocation.

CONCLUSION

In this study, we designed an innovative network analysis workflow to identify the core endophytic microbiome in hyperaccumulator. Based on the cultivable core bacteria, an optimized SynCom-NS was constructed and verified to have great potential in enhancing phytoremediation. This work not only provided a framework for identifying core microbiomes associated with specific features but also paved the way for the construction of functional synthetic communities derived from core microbiomes to develop high efficient agricultural agents. Video Abstract.

Pregnancy during COVID 19 pandemic associated with differential gut microbiome composition as compared to pre-pandemic

The first two years of the COVID-19 pandemic and subsequent health mandates resulted in significant disruptions to daily life, creating a period of heightened psychosocial stress in myriad aspects. Understanding the impact of this period on pregnant individuals' bacteriomes is crucial as pregnancy is a period of heightened vulnerability to stress and its sequelae, anxiety and mood disorders, which have been demonstrated to alter gut microbiome composition. In a prospective cohort study (N = 12-26) conducted from February 2019 to August 2021, we examined psychometric responses and rectal microbiome swabs from pregnant individuals. Full-length 16 S rRNA sequencing followed by calculation of diversity metrics and relative abundance values were used to interrogate fecal microbiome community composition across pandemic groups. Distinct shifts in bacterial diversity and composition were observed during early to late pregnancy in the pandemic group, including lower relative abundance of pathogenic and lesser-known taxa. However, distribution of stress and depressive symptoms did not significantly differ from the pre-pandemic period while the correlation between stress and depressive symptoms dissipated during the pandemic. Our findings suggest that living through the COVID-19 pandemic altered the gut microbiome of pregnant individuals, independent of perceived stress.

ADAPT: Analysis of Microbiome Differential Abundance by Pooling Tobit Models

MOTIVATION

Microbiome differential abundance analysis (DAA) remains a challenging problem despite multiple methods proposed in the literature. The excessive zeros and compositionality of metagenomics data are two main challenges for DAA.

RESULTS

We propose a novel method called "Analysis of Microbiome Differential Abundance by Pooling Tobit Models" (ADAPT) to overcome these two challenges. ADAPT interprets zero counts as left-censored observations to avoid unfounded assumptions and complex models. ADAPT also encompasses a theoretically justified way of selecting non-differentially abundant microbiome taxa as a reference to reveal differentially abundant taxa while avoiding false discoveries. We generate synthetic data using independent simulation frameworks to show that ADAPT has more consistent false discovery rate control and higher statistical power than competitors. We use ADAPT to analyze 16S rRNA sequencing of saliva samples and shotgun metagenomics sequencing of plaque samples collected from infants in the COHRA2 study. The results provide novel insights into the association between the oral microbiome and early childhood dental caries.

AVAILABILITY AND IMPLEMENTATION

The R package ADAPT can be installed from Bioconductor at https://bioconductor.org/packages/release/bioc/html/ADAPT.html or from Github at https://github.com/mkbwang/ADAPT. The source codes for simulation studies and real data analysis are available at https://github.com/mkbwang/ADAPT_example.

Microviridae bacteriophages influence behavioural hallmarks of food addiction via tryptophan and tyrosine signalling pathways

Food addiction contributes to the obesity pandemic, but the connection between how the gut microbiome is linked to food addiction remains largely unclear. Here we show that Microviridae bacteriophages, particularly Gokushovirus WZ-2015a, are associated with food addiction and obesity across multiple human cohorts. Further analyses reveal that food addiction and Gokushovirus are linked to serotonin and dopamine metabolism. Mice receiving faecal microbiota and viral transplantation from human donors with the highest Gokushovirus load exhibit increased food addiction along with changes in tryptophan, serotonin and dopamine metabolism in different regions of the brain, together with alterations in dopamine receptors. Mechanistically, targeted tryptophan analysis shows lower anthranilic acid (AA) concentrations associated with Gokushovirus. AA supplementation in mice decreases food addiction and alters pathways related to the cycle of neurotransmitter synthesis release. In Drosophila, AA regulates feeding behaviour and addiction-like ethanol preference. In summary, this study proposes that bacteriophages in the gut microbiome contribute to regulating food addiction by modulating tryptophan and tyrosine metabolism.

Inflammasome protein scaffolds the DNA damage complex during tumor development

Inflammasome sensors activate cellular signaling machineries to drive inflammation and cell death processes. Inflammasomes also control the development of certain diseases independently of canonical functions. Here, we show that the inflammasome protein NLR family CARD domain-containing protein 4 (NLRC4) attenuated the development of tumors in the Apcmin/+ mouse model. This response was independent of inflammasome signaling by NLRP3, NLRP6, NLR family apoptosis inhibitory proteins, absent in melanoma 2, apoptosis-associated speck-like protein containing a caspase recruitment domain, caspase-1 and caspase-11. NLRC4 interacted with the DNA-damage-sensing ataxia telangiectasia and Rad3-related (ATR)-ATR-interacting protein (ATRIP)-Ewing tumor-associated antigen 1 (ETAA1) complex to promote the recruitment of the checkpoint adapter protein claspin, licensing the activation of the kinase checkpoint kinase-1 (CHK1). Genotoxicity-induced activation of the NLRC4-ATR-ATRIP-ETAA1 complex drove the tumor-suppressing DNA damage response and CHK1 activation, and further attenuated the accumulation of DNA damage. These findings demonstrate a noninflammatory function of an inflammasome protein in promoting the DNA damage response and mediating protection against cancer.

Simplified methods for variance estimation in microbiome abundance count data analysis

The complex nature of microbiome data has made the differential abundance analysis challenging. Microbiome abundance counts are often skewed to the right and heteroscedastic (also known as overdispersion), potentially leading to incorrect inferences if not properly addressed. In this paper, we propose a simple yet effective framework to tackle the challenges by integrating Poisson (log-linear) regression with standard error estimation through the Bootstrap method and Sandwich robust estimation. Such standard error estimates are accurate and yield satisfactory inference even if the distributional assumption or the variance structure is incorrect. Our approach is validated through extensive simulation studies, demonstrating its effectiveness in addressing overdispersion and improving inference accuracy. Additionally, we apply our approach to two real datasets collected from the human gut and vagina, respectively, demonstrating the wide applicability of our methods. The results highlight the efficacy of our covariance estimators in addressing the challenges of microbiome data analysis. The corresponding software implementation is publicly available at https://github.com/yimshi/robustestimates.

The phageome of patients with ulcerative colitis treated with donor fecal microbiota reveals markers associated with disease remission

Bacteriophages are influential within the human gut microbiota, yet they remain understudied relative to bacteria. This is a limitation of studies on fecal microbiota transplantation (FMT) where bacteriophages likely influence outcome. Here, using metagenomics, we profile phage populations - the phageome - in individuals recruited into two double-blind randomized trials of FMT in ulcerative colitis. We leverage the trial designs to observe that phage populations behave similarly to bacterial populations, showing temporal stability in health, dysbiosis in active disease, modulation by antibiotic treatment and by FMT. We identify a donor bacteriophage putatively associated with disease remission, which on genomic analysis was found integrated in a bacterium classified to Oscillospiraceae, previously isolated from a centenarian and predicted to produce vitamin B complex except B12. Our study provides an in-depth assessment of phage populations during different states and suggests that bacteriophage tracking has utility in identifying determinants of disease activity and resolution.

Correction of Batch Effect in Gut Microbiota Profiling of ASD Cohorts from Different Geographical Origins

BACKGROUND

To date, there have been numerous metataxonomic studies on gut microbiota (GM) profiling based on the analyses of data from public repositories. However, differences in study population and wet and dry pipelines have produced discordant results. Herein, we propose a biostatistical approach to remove these batch effects for the GM characterization in the case of autism spectrum disorders (ASDs).

METHODS

An original dataset of GM profiles from patients with ASD was ecologically characterized and compared with GM public digital profiles of age-matched neurotypical controls (NCs). Also, GM data from seven case-control studies on ASD were retrieved from the NCBI platform and exploited for analysis. Hence, on each dataset, conditional quantile regression (CQR) was performed to reduce the batch effects originating from both technical and geographical confounders affecting the GM-related data. This method was further applied to the whole dataset matrix, obtained by merging all datasets. The ASD GM markers were identified by the random forest (RF) model.

RESULTS

We observed a different GM profile in patients with ASD compared with NC subjects. Moreover, a significant reduction of technical- and geographical-dependent batch effects in all datasets was achieved. We identified Bacteroides_H, Faecalibacterium, Gemmiger_A_73129, Blautia_A_141781, Bifidobacterium_388775, and Phocaeicola_A_858004 as robust GM bacterial biomarkers of ASD. Finally, our validation approach provided evidence of the validity of the QCR method, showing high values of accuracy, specificity, sensitivity, and AUC-ROC.

CONCLUSIONS

Herein, we proposed an updated biostatistical approach to reduce the technical and geographical batch effects that may negatively affect the description of bacterial composition in microbiota studies.

Human Herpesvirus-6B Infection and Alterations of Gut Microbiome in Patients with Fibromyalgia: A Pilot Study

Fibromyalgia (FM) is a chronic disorder characterized by widespread musculoskeletal pain often accompanied by fatigue, sleep disturbances, memory issues, and mood disorders. The exact cause of FM remains unknown, and diagnosis is typically based on a history of persistent widespread pain, as there are no objective biomarkers usable in diagnosis of this disorder available. The aim of this study was to identify measurable indicators specific to FM with potential as biomarkers. This study included 17 individuals diagnosed with FM and 24 apparently healthy persons. Using real-time polymerase chain reaction (qPCR), we detected the presence of human herpesvirus (HHV)-6A and B genomic sequences in DNA isolated from peripheral blood mononuclear cells (PBMCs) and buccal swabs. HHV-6-specific IgG and IgM class antibodies, along with proinflammatory cytokine levels, were measured using enzyme-linked immunosorbent assay (ELISA) and bead-based multiplex assays. Additionally, the gut microbiome was analyzed through next-generation sequencing. HHV-6B was more frequently detected in the PBMCs of FM patients. FM patients with a body mass index (BMI) of 30 or higher exhibited elevated cytokine levels compared to the control group with the same BMI range. Gut microbiome analysis revealed significant differences in both α-diversity and β-diversity between the FM and control groups, indicating a shift in species abundance in the FM group.

Rapid species-level metagenome profiling and containment estimation with sylph

Profiling metagenomes against databases allows for the detection and quantification of microorganisms, even at low abundances where assembly is not possible. We introduce sylph, a species-level metagenome profiler that estimates genome-to-metagenome containment average nucleotide identity (ANI) through zero-inflated Poisson k-mer statistics, enabling ANI-based taxa detection. On the Critical Assessment of Metagenome Interpretation II (CAMI2) Marine dataset, sylph was the most accurate profiling method of seven tested. For multisample profiling, sylph took >10-fold less central processing unit time compared to Kraken2 and used 30-fold less memory. Sylph's ANI estimates provided an orthogonal signal to abundance, allowing for an ANI-based metagenome-wide association study for Parkinson disease (PD) against 289,232 genomes while confirming known butyrate-PD associations at the strain level. Sylph took <1 min and 16 GB of random-access memory to profile metagenomes against 85,205 prokaryotic and 2,917,516 viral genomes, detecting 30-fold more viral sequences in the human gut compared to RefSeq. Sylph offers precise, efficient profiling with accurate containment ANI estimation even for low-coverage genomes.

Reproducible gut microbial signatures in bipolar and schizophrenia spectrum disorders: A metagenome-wide study

BACKGROUND

Numerous studies report gut microbiome variations in bipolar disorder (BD) and schizophrenia spectrum disorders (SSD) compared to healthy individuals, though, there is limited consensus on which specific bacteria are associated with these disorders.

METHODS

In this study, we performed a comprehensive metagenomic shotgun sequencing analysis in 103 Dutch patients with BD/SSD and 128 healthy controls matched for age, sex, body mass index and income, while accounting for diet quality, transit time and technical confounders. To assess the replicability of the findings, we used two validation cohorts (total n = 203), including participants from a distinct population with a different metagenomic isolation protocol.

RESULTS

The gut microbiome of the patients had a significantly different β-diversity, but not α-diversity nor neuroactive potential compared to healthy controls. Initially, twenty-six bacterial taxa were identified as differentially abundant in patients. Among these, the previously reported genera Lachnoclostridium and Eggerthella were replicated in the validation cohorts. Employing the CoDaCoRe learning algorithm, we identified two bacterial balances specific to BD/SSD, which demonstrated an area under the receiver operating characteristic curve (AUC) of 0.77 in the test dataset. These balances were replicated in the validation cohorts and showed a positive association with the severity of psychiatric symptoms and antipsychotic use. Last, we showed a positive association between the relative abundance of Klebsiella and Klebsiella pneumoniae with antipsychotic use and between the Anaeromassilibacillus and lithium use.

CONCLUSIONS

Our findings suggest that microbial balances could be a reproducible method for identifying BD/SSD-specific microbial signatures, with potential diagnostic and prognostic applications. Notably, Lachnoclostridium and Eggerthella emerge as frequently occurring bacteria in BD/SSD. Last, our study reaffirms the previously established link between Klebsiella and antipsychotic medication use and identifies a novel association between Anaeromassilibacillus and lithium use.

Novel oil-associated bacteria in Arctic seawater exposed to different nutrient biostimulation regimes

The Arctic Ocean is an oligotrophic ecosystem facing escalating threats of oil spills as ship traffic increases owing to climate change-induced sea ice retreat. Biostimulation is an oil spill mitigation strategy that involves introducing bioavailable nutrients to enhance crude oil biodegradation by endemic oil-degrading microbes. For bioremediation to offer a viable response for future oil spill mitigation in extreme Arctic conditions, a better understanding of the effects of nutrient addition on Arctic marine microorganisms is needed. Controlled experiments tracking microbial populations revealed a significant decline in community diversity along with changes in microbial community composition. Notably, differential abundance analysis highlighted the significant enrichment of the unexpected genera Lacinutrix, Halarcobacter and Candidatus Pseudothioglobus. These groups are not normally associated with hydrocarbon biodegradation, despite closer inspection of genomes from closely related isolates confirming the potential for hydrocarbon metabolism. Co-occurrence analysis further revealed significant associations between these genera and well-known hydrocarbon-degrading bacteria, suggesting potential synergistic interactions during oil biodegradation. While these findings broaden our understanding of how biostimulation promotes enrichment of endemic hydrocarbon-degrading genera, further research is needed to fully assess the suitability of nutrient addition as a stand-alone oil spill mitigation strategy in this sensitive and remote polar marine ecosystem.

Host genetics and microbiota data analysis in colorectal cancer research

Colorectal cancer (CRC) is a heterogeneous disease with a complex aetiology influenced by a myriad of genetic and environmental factors. Despite advances in CRC research, it is a major burden of disease, with the second highest incidence and third leading cause of cancer deaths worldwide. To individualise diagnosis, prognosis, and treatment of CRC, developing new strategies combining precision medicine and bioinformatic procedures is promising. Precision medicine is based on omics technologies and aims to individualise the management of CRC based on patient host genetic characteristics and microbiota. Bioinformatics is central to the application of personalised medicine because it enables the analysis of large datasets generated by these technologies. At the level of host genetics, bioinformatics allows the identification of mutations, genes, molecular pathways, biomarkers and drugs relevant to colorectal carcinogenesis. At the microbiota level, bioinformatics is fundamental to analysing microbial communities' composition and functionality and developing biomarkers and personalised microbiota-based therapies. This paper explores the host and microbiota genetic data analysis in CRC research.

A realistic benchmark for differential abundance testing and confounder adjustment in human microbiome studies

BACKGROUND

In microbiome disease association studies, it is a fundamental task to test which microbes differ in their abundance between groups. Yet, consensus on suitable or optimal statistical methods for differential abundance testing is lacking, and it remains unexplored how these cope with confounding. Previous differential abundance benchmarks relying on simulated datasets did not quantitatively evaluate the similarity to real data, which undermines their recommendations.

RESULTS

Our simulation framework implants calibrated signals into real taxonomic profiles, including signals mimicking confounders. Using several whole meta-genome and 16S rRNA gene amplicon datasets, we validate that our simulated data resembles real data from disease association studies much more than in previous benchmarks. With extensively parametrized simulations, we benchmark the performance of nineteen differential abundance methods and further evaluate the best ones on confounded simulations. Only classic statistical methods (linear models, the Wilcoxon test, t-test), limma, and fastANCOM properly control false discoveries at relatively high sensitivity. When additionally considering confounders, these issues are exacerbated, but we find that adjusted differential abundance testing can effectively mitigate them. In a large cardiometabolic disease dataset, we showcase that failure to account for covariates such as medication causes spurious association in real-world applications.

CONCLUSIONS

Tight error control is critical for microbiome association studies. The unsatisfactory performance of many differential abundance methods and the persistent danger of unchecked confounding suggest these contribute to a lack of reproducibility among such studies. We have open-sourced our simulation and benchmarking software to foster a much-needed consolidation of statistical methodology for microbiome research.

Baseline gut microbiota diversity and composition and albendazole efficacy in hookworm-infected individuals

Soil-transmitted helminth (STH) infections account for a significant global health burden, necessitating mass drug administration with benzimidazole-class anthelmintics, such as albendazole (ALB), for morbidity control. However, ALB efficacy shows substantial variability, presenting challenges for achieving consistent treatment outcomes. We have explored the potential impact of the baseline gut microbiota on ALB efficacy in hookworm-infected individuals through microbiota profiling and machine learning (ML) techniques. Our investigation included 89 stool samples collected from hookworm-infected individuals that were analyzed by microscopy and quantitative PCR (qPCR). Of these, 44 were negative by microscopy for STH infection using the Kato-Katz method and qPCR 21 days after treatment, which entails a cure rate of 49.4%. Microbiota characterization was based on amplicon sequencing of the V3-V4 16S ribosomal RNA gene region. Alpha and beta diversity analyses revealed no significant differences between participants who were cured and those who were not cured, suggesting that baseline microbiota diversity does not influence ALB treatment outcomes. Furthermore, differential abundance analysis at the phylum, family and genus levels yielded no statistically significant associations between bacterial communities and ALB efficacy. Utilizing supervised ML models failed to predict treatment response accurately. Our investigation did not provide conclusive insights into the relationship between gut microbiota and ALB efficacy. However, the results highlight the need for future research to incorporate longitudinal studies that monitor changes in the gut microbiota related to the infection and the cure with ALB, as well as functional metagenomics to better understand the interaction of the microbiome with the drug, and its role, if there is any, in modulating anthelmintic treatment outcomes in STH infections. Interdisciplinary approaches integrating microbiology, pharmacology, genetics and data science will be pivotal in advancing our understanding of STH infections and optimizing treatment strategies globally.

A Novel Slope-Matrix-Graph Algorithm to Analyze Compositional Microbiome Data

Networks are widely used to represent relationships between objects, including microorganisms within ecosystems, based on high-throughput sequencing data. However, challenges arise with appropriate statistical algorithms, handling of rare taxa, excess zeros in compositional data, and interpretation. This work introduces a novel Slope-Matrix-Graph (SMG) algorithm to identify microbiome correlations primarily based on slope-based distance calculations. SMG effectively handles any proportion of zeros in compositional data and involves: (1) searching for correlated relationships (e.g., positive and negative directions of changes) based on a "target of interest" within a setting, and (2) quantifying graph changes via slope-based distances between objects. Evaluations on simulated datasets demonstrated SMG's ability to accurately cluster microbes into distinct positive/negative correlation groups, outperforming methods like Bray-Curtis and SparCC in both sensitivity and specificity. Moreover, SMG demonstrated superior accuracy in detecting differential abundance (DA) compared to ZicoSeq and ANCOM-BC2, making it a robust tool for microbiome analysis. A key advantage is SMG's natural capacity to analyze zero-inflated compositional data without transformations. Overall, this simple yet powerful algorithm holds promise for diverse microbiome analysis applications.

Multiomic Insights into Human Health: Gut Microbiomes of Hunter-Gatherer, Agropastoral, and Western Urban Populations

Societies with exposure to preindustrial diets exhibit improved markers of health. Our study used a comprehensive multi-omic approach to reveal that the gut microbiome of the Ju/'hoansi hunter-gatherers, one of the most remote KhoeSan groups, exhibit a higher diversity and richness, with an abundance of microbial species lost in the western population. The Ju/'hoansi microbiome showed enhanced global transcription and enrichment of complex carbohydrate metabolic and energy generation pathways. The Ju/'hoansi also show high abundance of short-chain fatty acids that are associated with health and optimal immune function. In contrast, these pathways and their respective species were found in low abundance or completely absent in Western populations. Amino acid and fatty acid metabolism pathways were observed prevalent in the Western population, associated with biomarkers of chronic inflammation. Our study provides the first in-depth multi-omic characterization of the Ju/'hoansi microbiome, revealing uncharacterized species and functional pathways that are associated with health.

Metagenome comparison (MC): A new framework for detecting unique/enriched OMUs (operational metagenomic units) derived from whole-genome sequencing reads

BACKGROUND

Current methods for comparing metagenomes, derived from whole-genome sequencing reads, include top-down metrics or parametric models such as metagenome-diversity, and bottom-up, non-parametric, model-free machine learning approaches like Naïve Bayes for k-mer-profiling. However, both types are limited in their ability to effectively and comprehensively identify and catalogue unique or enriched metagenomic genes, a critical task in comparative metagenomics. This challenge is significant and complex due to its NP-hard nature, which means computational time grows exponentially, or even faster, with the problem size, rendering it impractical for even the fastest supercomputers without heuristic approximation algorithms.

METHOD

In this study, we introduce a new framework, MC (Metagenome-Comparison), designed to exhaustively detect and catalogue unique or enriched metagenomic genes (MGs) and their derivatives, including metagenome functional gene clusters (MFGC), or more generally, the operational metagenomic unit (OMU) that can be considered the counterpart of the OTU (operational taxonomic unit) from amplicon sequencing reads. The MC is essentially a heuristic search algorithm guided by pairs of new metrics (termed MG-specificity or OMU-specificity, MG-specificity diversity or OMU-specificity diversity). It is further constrained by statistical significance (P-value) implemented as a pair of statistical tests.

RESULTS

We evaluated the MC using large metagenomic datasets related to obesity, diabetes, and IBD, and found that the proportions of unique and enriched metagenomic genes ranged from 0.001% to 0.08 % and 0.08%-0.82 % respectively, and less than 10 % for the MFGC.

CONCLUSION

The MC provides a robust method for comparing metagenomes at various scales, from baseline MGs to various function/pathway clusters of metagenomes, collectively termed OMUs.

Microcystis abundance is predictable through ambient bacterial communities: A data-oriented approach

The number of cyanobacterial harmful algal blooms (cyanoHABs) has increased, leading to the widespread development of prediction models for cyanoHABs. Although bacteria interact closely with cyanobacteria and directly affect cyanoHABs occurrence, related modeling studies have rarely utilized microbial community data compared to environmental data such as water quality. In this study, we built a machine learning model, the multilayer perceptron (MLP), for the prediction of Microcystis dynamics using both bacterial community and weekly water quality data from the Daechung Reservoir and Nakdong River, South Korea. The modeling performance, indicated by the R2 value, improved to 0.97 in the model combining bacterial community data with environmental factors, compared to 0.78 in the model using only environmental factors. This underscores the importance of microbial communities in cyanoHABs prediction. Through the post-hoc analysis of the MLP models, we revealed that nitrogen sources played a more critical role than phosphorus sources in Microcystis blooms, whereas the bacterial amplicon sequence variants did not have significant differences in their contribution to each other. Similar to the MLP model results, bacterial data also had higher predictability in multiple linear regression (MLR) than environmental data. In both the MLP and MLR models, Microscillaceae showed the strongest association with Microcystis. This modeling approach provides a better understanding of the interactions between bacteria and cyanoHABs, facilitating the development of more accurate and reliable models for cyanoHABs prediction using ambient bacterial data.

Assessment of Distinct Gut Microbiome Signatures in a Diverse Cohort of Patients Undergoing Definitive Treatment for Rectal Cancer

Introduction

Disparities in incidence and outcome of rectal cancer are multifactorial in etiology but may be due, in part, to differences in gut microbiome composition. We used serial robust statistical approaches to assess baseline gut microbiome composition in a diverse cohort of patients with rectal cancer receiving definitive treatment.

Methods

Microbiome composition was compared by age at diagnosis (< 50 vs ≥ 50 years), race and ethnicity (White Hispanic vs non-Hispanic), and response to therapy. Alpha diversity was assessed using the Shannon, Chao1, and Simpson diversity measures. Beta diversity was explored using both Bray-Curtis dissimilarity and Aitchison distance with principal coordinate analysis. To minimize false-positive findings, we used two distinct methods for differential abundance testing: LinDA and MaAsLin2 (all statistics two-sided, Benjamini-Hochberg corrected false discovery rate < 0.05).

Results

Among 64 patients (47% White Hispanic) with median age 51 years, beta diversity metrics showed significant clustering by race and ethnicity (p < 0.001 by both metrics) and by onset (Aitchison p = 0.022, Bray-Curtis p = 0.035). White Hispanic patients had enrichment of bacterial family Prevotellaceae (LinDA fold change 5.32, MaAsLin2 fold change 5.11, combined adjusted p = 0.0007). No significant differences in microbiome composition were associated with neoadjuvant therapy response.

Conclusion

We identified distinct gut microbiome signatures associated with race and ethnicity and age of onset in a diverse cohort of patients undergoing definitive treatment for rectal cancer.

Global compositional and functional states of the human gut microbiome in health and disease

The human gut microbiota is of increasing interest, with metagenomics a key tool for analyzing bacterial diversity and functionality in health and disease. Despite increasing efforts to expand microbial gene catalogs and an increasing number of metagenome-assembled genomes, there have been few pan-metagenomic association studies and in-depth functional analyses across different geographies and diseases. Here, we explored 6014 human gut metagenome samples across 19 countries and 23 diseases by performing compositional, functional cluster, and integrative analyses. Using interpreted machine learning classification models and statistical methods, we identified Fusobacterium nucleatum and Anaerostipes hadrus with the highest frequencies, enriched and depleted, respectively, across different disease cohorts. Distinct functional distributions were observed in the gut microbiomes of both westernized and nonwesternized populations. These compositional and functional analyses are presented in the open-access Human Gut Microbiome Atlas, allowing for the exploration of the richness, disease, and regional signatures of the gut microbiota across different cohorts.

Microeukaryote community coalescence strengthens community stability and elevates diversity

Mixing of entire microbial communities represents a frequent, yet understudied phenomenon. Here, we mimicked estuarine condition in a microcosm experiment by mixing a freshwater river community with a brackish sea community and assessed the effects of both environmental and community coalescences induced by varying mixing processes on microeukaryotic communities. Signs of shifted community composition of coalesced communities towards the sea parent community suggest asymmetrical community coalescence outcome, which, in addition, was generally less impacted by environmental coalescence. Community stability, inferred from community cohesion, differed among river and sea parent communities, and increased following coalescence treatments. Generally, community coalescence increased alpha diversity and promoted competition from the introduction (or emergence) of additional (or rare) species. These competitive interactions in turn had community stabilizing effect as evidenced by the increased proportion of negative cohesion. The fate of microeukaryotes was influenced by mixing ratios and frequencies (i.e. one-time versus repeated coalescence). Namely, diatoms were negatively impacted by coalescence, while fungi, ciliates, and cercozoans were promoted to varying extents, depending on the mixing ratios of the parent communities. Our study suggests that the predictability of coalescence outcomes was greater when the sea parent community dominated the final community, and this predictability was further enhanced when communities collided repeatedly.

Metagenomes from microbial populations beneath a chromium waste tip give insight into the mechanism of Cr (VI) reduction

Dumped Chromium Ore Processing Residue (COPR) at legacy sites poses a threat to health through leaching of toxic Cr(VI) into groundwater. Previous work implicates microbial activity in reducing Cr(VI) to less mobile and toxic Cr(III), but the mechanism has not been explored. To address this question a combined metagenomic and geochemical study was undertaken. Soil samples from below the COPR waste were used to establish anaerobic microcosms which were challenged with Cr(VI), with or without acetate as an electron donor, and incubated for 70 days. Cr was rapidly reduced in both systems, which also reduced nitrate, nitrite then sulfate, but this sequence was accelerated in the acetate amended microcosms. 16S rRNA gene sequencing revealed that the original soil sample was diverse but both microcosm systems became less diverse by the end of the experiment. A high proportion of 16S rRNA gene reads and metagenome-assembled genomes (MAGs) with high completeness could not be taxonomically classified, highlighting the distinctiveness of these alkaline Cr impacted systems. Examination of the coding capacity revealed widespread capability for metal tolerance and Fe uptake and storage, and both populations possessed metabolic capability to degrade a wide range of organic molecules. The relative abundance of genes for fatty acid degradation was 4× higher in the unamended compared to the acetate amended system, whereas the capacity for dissimilatory sulfate metabolism was 3× higher in the acetate amended system. We demonstrate that naturally occurring in situ bacterial populations have the metabolic capability to couple acetate oxidation to sequential reduction of electron acceptors which can reduce Cr(VI) to less mobile and toxic Cr(III), and that microbially produced sulfide may be important in reductive precipitation of chromate. This capability could be harnessed to create a Cr(VI) trap-zone beneath COPR tips without the need to disturb the waste.

Absence of a pancreatic microbiome in intraductal papillary mucinous neoplasm

OBJECTIVE

This study aims to validate the existence of a microbiome within intraductal papillary mucinous neoplasm (IPMN) that can be differentiated from the taxonomically diverse DNA background of next-generation sequencing procedures.

DESIGN

We generated 16S rRNA amplicon sequencing data to analyse 338 cyst fluid samples from 190 patients and 19 negative controls, the latter collected directly from sterile syringes in the operating room. A subset of samples (n=20) and blanks (n=5) were spiked with known concentrations of bacterial cells alien to the human microbiome to infer absolute abundances of microbial traces. All cyst fluid samples were obtained intraoperatively and included IPMNs with various degrees of dysplasia as well as other cystic neoplasms. Follow-up culturing experiments were conducted to assess bacterial growth for microbiologically significant signals.

RESULTS

Microbiome signatures of cyst fluid samples were inseparable from those of negative controls, with no difference in taxonomic diversity, and microbial community composition. In a patient subgroup that had recently undergone invasive procedures, a bacterial signal was evident. This outlier signal was not characterised by higher taxonomic diversity but by an increased dominance index of a gut-associated microbe, leading to lower taxonomic evenness compared with the background signal.

CONCLUSION

The 'microbiome' of IPMNs and other pancreatic cystic neoplasms does not deviate from the background signature of negative controls, supporting the concept of a sterile environment. Outlier signals may appear in a small fraction of patients following recent invasive endoscopic procedures. No associations between microbial patterns and clinical or cyst parameters were apparent.

Interpretable machine learning decodes soil microbiome's response to drought stress

BACKGROUND

Extreme weather events induced by climate change, particularly droughts, have detrimental consequences for crop yields and food security. Concurrently, these conditions provoke substantial changes in the soil bacterial microbiota and affect plant health. Early recognition of soil affected by drought enables farmers to implement appropriate agricultural management practices. In this context, interpretable machine learning holds immense potential for drought stress classification of soil based on marker taxa.

RESULTS

This study demonstrates that the 16S rRNA-based metagenomic approach of Differential Abundance Analysis methods and machine learning-based Shapley Additive Explanation values provide similar information. They exhibit their potential as complementary approaches for identifying marker taxa and investigating their enrichment or depletion under drought stress in grass lineages. Additionally, the Random Forest Classifier trained on a diverse range of relative abundance data from the soil bacterial micobiome of various plant species achieves a high accuracy of 92.3 % at the genus rank for drought stress prediction. It demonstrates its generalization capacity for the lineages tested.

CONCLUSIONS

In the detection of drought stress in soil bacterial microbiota, this study emphasizes the potential of an optimized and generalized location-based ML classifier. By identifying marker taxa, this approach holds promising implications for microbe-assisted plant breeding programs and contributes to the development of sustainable agriculture practices. These findings are crucial for preserving global food security in the face of climate change.

Gut Microbiota Improves Prognostic Prediction in Critically Ill COVID-19 Patients Alongside Immunological and Hematological Indicators

The gut microbiota undergoes substantial changes in COVID-19 patients; yet, the utility of these alterations as prognostic biomarkers at the time of hospital admission, and its correlation with immunological and hematological parameters, remains unclear. The objective of this study is to investigate the gut microbiota's dynamic change in critically ill patients with COVID-19 and evaluate its predictive capability for clinical outcomes alongside immunological and hematological parameters. In this study, anal swabs were consecutively collected from 192 COVID-19 patients (583 samples) upon hospital admission for metagenome sequencing. Simultaneously, blood samples were obtained to measure the concentrations of 27 cytokines and chemokines, along with hematological and biochemical indicators. Our findings indicate a significant correlation between the composition and dynamics of gut microbiota with disease severity and mortality in COVID-19 patients. Recovered patients exhibited a higher abundance of Veillonella and denser interactions among gut commensal bacteria compared to deceased patients. Furthermore, the abundance of gut commensal bacteria exhibited a negative correlation with the concentration of proinflammatory cytokines and organ damage markers. The gut microbiota upon admission showed moderate prognostic prediction ability with an AUC of 0.78, which was less effective compared to predictions based on immunological and hematological parameters (AUC 0.80 and 0.88, respectively). Noteworthy, the integration of these three datasets yielded a higher predictive accuracy (AUC 0.93). Our findings suggest the gut microbiota as an informative biomarker for COVID-19 prognosis, augmenting existing immune and hematological indicators.

ADAPT: Analysis of Microbiome Differential Abundance by Pooling Tobit Models

Microbiome differential abundance analysis remains a challenging problem despite multiple methods proposed in the literature. The excessive zeros and compositionality of metagenomics data are two main challenges for differential abundance analysis. We propose a novel method called "analysis of differential abundance by pooling Tobit models" (ADAPT) to overcome these two challenges. ADAPT uniquely treats zero counts as left-censored observations to facilitate computation and enhance interpretation. ADAPT also encompasses a theoretically justified way of selecting non-differentially abundant microbiome taxa as a reference for hypothesis testing. We generate synthetic data using independent simulation frameworks to show that ADAPT has more consistent false discovery rate control and higher statistical power than competitors. We use ADAPT to analyze 16S rRNA sequencing of saliva samples and shotgun metagenomics sequencing of plaque samples collected from infants in the COHRA2 study. The results provide novel insights into the association between the oral microbiome and early childhood dental caries.

Integrative analysis reveals associations between oral microbiota dysbiosis and host genetic and epigenetic aberrations in oral cavity squamous cell carcinoma

Dysbiosis of the human oral microbiota has been reported to be associated with oral cavity squamous cell carcinoma (OSCC) while the host-microbiota interactions with respect to the potential impact of pathogenic bacteria on host genomic and epigenomic abnormalities remain poorly studied. In this study, the mucosal bacterial community, host genome-wide transcriptome and DNA CpG methylation were simultaneously profiled in tumors and their adjacent normal tissues of OSCC patients. Significant enrichment in the relative abundance of seven bacteria species (Fusobacterium nucleatum, Treponema medium, Peptostreptococcus stomatis, Gemella morbillorum, Catonella morbi, Peptoanaerobacter yurli and Peptococcus simiae) were observed in OSCC tumor microenvironment. These tumor-enriched bacteria formed 254 positive correlations with 206 up-regulated host genes, mainly involving signaling pathways related to cell adhesion, migration and proliferation. Integrative analysis of bacteria-transcriptome and bacteria-methylation correlations identified at least 20 dysregulated host genes with inverted CpG methylation in their promoter regions associated with enrichment of bacterial pathogens, implying a potential of pathogenic bacteria to regulate gene expression, in part, through epigenetic alterations. An in vitro model further confirmed that Fusobacterium nucleatum might contribute to cellular invasion via crosstalk with E-cadherin/β-catenin signaling, TNFα/NF-κB pathway and extracellular matrix remodeling by up-regulating SNAI2 gene, a key transcription factor of epithelial-mesenchymal transition (EMT). Our work using multi-omics approaches explored complex host-microbiota interactions and provided important insights into genetic and functional basis in OSCC tumorigenesis, which may serve as a precursor for hypothesis-driven study to better understand the causational relationship of pathogenic bacteria in this deadly cancer.

Anna Karenina as a promoter of microbial diversity in the cosmopolitan agricultural pest Zeugodacus cucurbitae (Diptera, Tephritidae)

Gut microbial communities are critical in determining the evolutive success of fruit fly phytophagous pests (Diptera, Tephritidae), facilitating their adaptation to suboptimal environmental conditions and to plant allelochemical defences. An important source of variation for the microbial diversity of fruit flies is represented by the crop on which larvae are feeding. However, a "crop effect" is not always the main driver of microbial patterns, and it is often observed in combination with other and less obvious processes. In this work, we aim at verifying if environmental stress and, by extension, changing environmental conditions, can promote microbial diversity in Zeugodacus cucurbitae (Coquillett), a cosmopolitan pest of cucurbit crops. With this objective, 16S rRNA metabarcoding was used to test differences in the microbial profiles of wild fly populations in a large experimental setup in Eastern Central Tanzania. The analysis of 2,973 unique ASV, which were assigned to 22 bacterial phyla, 221 families and 590 putative genera, show that microbial α diversity (as estimated by Abundance Coverage Estimator, Faith's Phylogenetic Diversity, Shannon-Weiner and the Inverse Simpson indexes) as well as β microbial diversity (as estimated by Compositional Data analysis of ASVs and of aggregated genera) significantly change as the species gets closer to its altitudinal limits, in farms where pesticides and agrochemicals are used. Most importantly, the multivariate dispersion of microbial patterns is significantly higher in these stressful environmental conditions thus indicating that Anna Karenina effects contribute to the microbial diversity of Z. cucurbitae. The crop effect was comparably weaker and detected as non-consistent changes across the experimental sites. We speculate that the impressive adaptive potential of polyphagous fruit flies is, at least in part, related to the Anna Karenina principle, which promotes stochastic changes in the microbial diversity of fly populations exposed to suboptimal environmental conditions.

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.

Sexual transmission of urogenital bacteria: whole metagenome sequencing evidence from a sexual network study

Sexual transmission of the urogenital microbiota may contribute to adverse sexual and reproductive health outcomes. The extent of sexual transmission of the urogenital microbiota is unclear as prior studies largely investigated specific pathogens. We used epidemiologic data and whole metagenome sequencing to characterize urogenital microbiota strain concordance between participants of a sexual network study. Individuals who screened positive for genital Chlamydia trachomatis were enrolled and referred their sexual contacts from the prior 60-180 days. Snowball recruitment of sexual contacts continued for up to four waves. Vaginal swabs and penile urethral swabs were collected for whole metagenome sequencing. We evaluated bacterial strain concordance using inStrain and network analysis. We defined concordance as ≥99.99% average nucleotide identity over ≥50% shared coverage; we defined putative sexual transmission as concordance between sexual contacts with <5 single-nucleotide polymorphisms per megabase. Of 138 participants, 74 (54%) were female; 120 (87%) had genital chlamydia; and 43 (31%) were recruited contacts. We identified 115 strain-concordance events among 54 participants representing 25 bacterial species. Seven events (6%) were between sexual contacts including putative heterosexual transmission of Fannyhessea vaginae, Gardnerella leopoldii, Prevotella amnii, Sneathia sanguinegens, and Sneathia vaginalis (one strain each), and putative sexual transmission of Lactobacillus iners between female contacts. Most concordance events (108, 94%) were between non-contacts, including eight female participants connected through 18 Lactobacillus crispatus and 3 Lactobacillus jensenii concordant strains, and 14 female and 2 male participants densely interconnected through 52 Gardnerella swidsinskii concordance events.IMPORTANCEEpidemiologic evidence consistently indicates bacterial vaginosis (BV) is sexually associated and may be sexually transmitted, though sexual transmission remains subject to debate. This study is not capable of demonstrating BV sexual transmission; however, we do provide strain-level metagenomic evidence that strongly supports heterosexual transmission of BV-associated species. These findings strengthen the evidence base that supports ongoing investigations of concurrent male partner treatment for reducing BV recurrence. Our data suggest that measuring the impact of male partner treatment on F. vaginae, G. leopoldii, P. amnii, S. sanguinegens, and S. vaginalis may provide insight into why a regimen does or does not perform well. We also observed a high degree of strain concordance between non-sexual-contact female participants. We posit that this may reflect limited dispersal capacity of vaginal bacteria coupled with individuals' comembership in regional transmission networks where transmission may occur between parent and child at birth, cohabiting individuals, or sexual contacts.

Colonic epithelial hypoxia remains constant during the progression of diabetes in male UC Davis type 2 diabetes mellitus rats

INTRODUCTION

Colonocyte oxidation of bacterial-derived butyrate has been reported to maintain synergistic obligate anaerobe populations by reducing colonocyte oxygen levels; however, it is not known whether this process is disrupted during the progression of type 2 diabetes. Our aim was to determine whether diabetes influences colonocyte oxygen levels in the University of California Davis type 2 diabetes mellitus (UCD-T2DM) rat model.

RESEARCH DESIGN AND METHODS

Age-matched male UCD-T2DM rats (174±4 days) prior to the onset of diabetes (PD, n=15), within 1 month post-onset (RD, n=12), and 3 months post-onset (D3M, n=12) were included in this study. Rats were administered an intraperitoneal injection of pimonidazole (60 mg/kg body weight) 1 hour prior to euthanasia and tissue collection to estimate colonic oxygen levels. Colon tissue was fixed in 10% formalin, embedded in paraffin, and processed for immunohistochemical detection of pimonidazole. The colonic microbiome was assessed by 16S gene rRNA amplicon sequencing and content of short-chain fatty acids was measured by liquid chromatography-mass spectrometry.

RESULTS

HbA1c % increased linearly across the PD (5.9±0.1), RD (7.6±0.4), and D3M (11.5±0.6) groups, confirming the progression of diabetes in this cohort. D3M rats had a 2.5% increase in known facultative anaerobes, Escherichia-Shigella, and Streptococcus (false discovery rate <0.05) genera in colon contents. The intensity of pimonidazole staining of colonic epithelia did not differ across groups (p=0.37). Colon content concentrations of acetate and propionate also did not differ across UCD-T2DM groups; however, colonic butyric acid levels were higher in D3M rats relative to PD rats (p<0.01).

CONCLUSIONS

The advancement of diabetes in UCD-T2DM rats was associated with an increase in facultative anaerobes; however, this was not explained by changes in colonocyte oxygen levels. The mechanisms underlying shifts in gut microbe populations associated with the progression of diabetes in the UCD-T2DM rat model remain to be identified.

Gut microbiota predicts severity and reveals novel metabolic signatures in acute pancreatitis

OBJECTIVE

Early disease prediction is challenging in acute pancreatitis (AP). Here, we prospectively investigate whether the microbiome predicts severity of AP (Pancreatitis-Microbiome As Predictor of Severity; P-MAPS) early at hospital admission.

DESIGN

Buccal and rectal microbial swabs were collected from 424 patients with AP within 72 hours of hospital admission in 15 European centres. All samples were sequenced by full-length 16S rRNA and metagenomic sequencing using Oxford Nanopore Technologies. Primary endpoint was the association of the orointestinal microbiome with the revised Atlanta classification (RAC). Secondary endpoints were mortality, length of hospital stay and severity (organ failure >48 hours and/or occurrence of pancreatic collections requiring intervention) as post hoc analysis. Multivariate analysis was conducted from normalised microbial and corresponding clinical data to build classifiers for predicting severity. For functional profiling, gene set enrichment analysis (GSEA) was performed and normalised enrichment scores calculated.

RESULTS

After data processing, 411 buccal and 391 rectal samples were analysed. The intestinal microbiome significantly differed for the RAC (Bray-Curtis, p value=0.009), mortality (Bray-Curtis, p value 0.006), length of hospital stay (Bray-Curtis, p=0.009) and severity (Bray-Curtis, p value=0.008). A classifier for severity with 16 different species and systemic inflammatory response syndrome achieved an area under the receiving operating characteristic (AUROC) of 85%, a positive predictive value of 67% and a negative predictive value of 94% outperforming established severity scores. GSEA revealed functional pathway units suggesting elevated short-chain fatty acid (SCFA) production in severe AP.

CONCLUSIONS

The orointestinal microbiome predicts clinical hallmark features of AP, and SCFAs may be used for future diagnostic and therapeutic concepts.

TRIAL REGISTRATION NUMBER

NCT04777812.

Metagenomic analysis of gut microbiome illuminates the mechanisms and evolution of lignocellulose degradation in mangrove herbivorous crabs

BACKGROUND

Sesarmid crabs dominate mangrove habitats as the major primary consumers, which facilitates the trophic link and nutrient recycling in the ecosystem. Therefore, the adaptations and mechanisms of sesarmid crabs to herbivory are not only crucial to terrestrialization and its evolutionary success, but also to the healthy functioning of mangrove ecosystems. Although endogenous cellulase expressions were reported in crabs, it remains unknown if endogenous enzymes alone can complete the whole lignocellulolytic pathway, or if they also depend on the contribution from the intestinal microbiome. We attempt to investigate the role of gut symbiotic microbes of mangrove-feeding sesarmid crabs in plant digestion using a comparative metagenomic approach.

RESULTS

Metagenomics analyses on 43 crab gut samples from 23 species of mangrove crabs with different dietary preferences revealed a wide coverage of 127 CAZy families and nine KOs targeting lignocellulose and their derivatives in all species analyzed, including predominantly carnivorous species, suggesting the crab gut microbiomes have lignocellulolytic capacity regardless of dietary preference. Microbial cellulase, hemicellulase and pectinase genes in herbivorous and detritivorous crabs were differentially more abundant when compared to omnivorous and carnivorous crabs, indicating the importance of gut symbionts in lignocellulose degradation and the enrichment of lignocellulolytic microbes in response to diet with higher lignocellulose content. Herbivorous and detritivorous crabs showed highly similar CAZyme composition despite dissimilarities in taxonomic profiles observed in both groups, suggesting a stronger selection force on gut microbiota by functional capacity than by taxonomy. The gut microbiota in herbivorous sesarmid crabs were also enriched with nitrogen reduction and fixation genes, implying possible roles of gut microbiota in supplementing nitrogen that is deficient in plant diet.

CONCLUSIONS

Endosymbiotic microbes play an important role in lignocellulose degradation in most crab species. Their abundance is strongly correlated with dietary preference, and they are highly enriched in herbivorous sesarmids, thus enhancing their capacity in digesting mangrove leaves. Dietary preference is a stronger driver in determining the microbial CAZyme composition and taxonomic profile in the crab microbiome, resulting in functional redundancy of endosymbiotic microbes. Our results showed that crabs implement a mixed mode of digestion utilizing both endogenous and microbial enzymes in lignocellulose degradation, as observed in most of the more advanced herbivorous invertebrates.

2dGBH: Two-dimensional group Benjamini-Hochberg procedure for false discovery rate control in two-way multiple testing of genomic data

MOTIVATION

Emerging omics technologies have introduced a two-way grouping structure in multiple testing, as seen in single-cell omics data, where the features can be grouped by either genes or cell types. Traditional multiple testing methods have limited ability to exploit such two-way grouping structure, leading to potential power loss.

RESULTS

We propose a new 2D Group Benjamini-Hochberg (2dGBH) procedure to harness the two-way grouping structure in omics data, extending the traditional one-way adaptive GBH procedure. Using both simulated and real datasets, we show that 2dGBH effectively controls the false discovery rate across biologically relevant settings, and it is more powerful than the BH or q-value procedure and more robust than the one-way adaptive GBH procedure.

AVAILABILITY AND IMPLEMENTATION

2dGBH is available as an R package at: https://github.com/chloelulu/tdGBH. The analysis code and data are available at: https://github.com/chloelulu/tdGBH-paper.

Evaluation of gut microbiota predictive potential associated with phenotypic characteristics to identify multifactorial diseases

Gut microbiota has been implicated in various clinical conditions, yet the substantial heterogeneity in gut microbiota research results necessitates a more sophisticated approach than merely identifying statistically different microbial taxa between healthy and unhealthy individuals. Our study seeks to not only select microbial taxa but also explore their synergy with phenotypic host variables to develop novel predictive models for specific clinical conditions.

DESIGN

We assessed 50 healthy and 152 unhealthy individuals for phenotypic variables (PV) and gut microbiota (GM) composition by 16S rRNA gene sequencing. The entire modeling process was conducted in the R environment using the Random Forest algorithm. Model performance was assessed through ROC curve construction.

RESULTS

We evaluated 52 bacterial taxa and pre-selected PV (p < 0.05) for their contribution to the final models. Across all diseases, the models achieved their best performance when GM and PV data were integrated. Notably, the integrated predictive models demonstrated exceptional performance for rheumatoid arthritis (AUC = 88.03%), type 2 diabetes (AUC = 96.96%), systemic lupus erythematosus (AUC = 98.4%), and type 1 diabetes (AUC = 86.19%).

CONCLUSION

Our findings underscore that the selection of bacterial taxa based solely on differences in relative abundance between groups is insufficient to serve as clinical markers. Machine learning techniques are essential for mitigating the considerable variability observed within gut microbiota. In our study, the use of microbial taxa alone exhibited limited predictive power for health outcomes, while the integration of phenotypic variables into predictive models substantially enhanced their predictive capabilities.

Methodological Considerations in Longitudinal Analyses of Microbiome Data: A Comprehensive Review

Biological processes underlying health and disease are inherently dynamic and are best understood when characterized in a time-informed manner. In this comprehensive review, we discuss challenges inherent in time-series microbiome data analyses and compare available approaches and methods to overcome them. Appropriate handling of longitudinal microbiome data can shed light on important roles, functions, patterns, and potential interactions between large numbers of microbial taxa or genes in the context of health, disease, or interventions. We present a comprehensive review and comparison of existing microbiome time-series analysis methods, for both preprocessing and downstream analyses, including differential analysis, clustering, network inference, and trait classification. We posit that the careful selection and appropriate utilization of computational tools for longitudinal microbiome analyses can help advance our understanding of the dynamic host-microbiome relationships that underlie health-maintaining homeostases, progressions to disease-promoting dysbioses, as well as phases of physiologic development like those encountered in childhood.

Statistical normalization methods in microbiome data with application to microbiome cancer research

Mounting evidence has shown that gut microbiome is associated with various cancers, including gastrointestinal (GI) tract and non-GI tract cancers. But microbiome data have unique characteristics and pose major challenges when using standard statistical methods causing results to be invalid or misleading. Thus, to analyze microbiome data, it not only needs appropriate statistical methods, but also requires microbiome data to be normalized prior to statistical analysis. Here, we first describe the unique characteristics of microbiome data and the challenges in analyzing them (Section 2). Then, we provide an overall review on the available normalization methods of 16S rRNA and shotgun metagenomic data along with examples of their applications in microbiome cancer research (Section 3). In Section 4, we comprehensively investigate how the normalization methods of 16S rRNA and shotgun metagenomic data are evaluated. Finally, we summarize and conclude with remarks on statistical normalization methods (Section 5). Altogether, this review aims to provide a broad and comprehensive view and remarks on the promises and challenges of the statistical normalization methods in microbiome data with microbiome cancer research examples.

Cultivable Gut Microbiota in Synanthropic Bats: Shifts of Its Composition and Diversity Associated with Hibernation

The role of bats in the global microbial ecology no doubt is significant due to their unique immune responses, ability to fly, and long lifespan, all contributing to pathogen spread. Some of these animals hibernate during winter, which results in the altering of their physiology. However, gut microbiota shifts during hibernation is little studied. In this research, we studied cultivable gut microbiota composition and diversity of Nyctalus noctula before, during, and after hibernation in a bat rehabilitation center. Gut microorganisms were isolated on a broad spectrum of culture media, counted, and identified with mass spectrometry. Linear modeling was used to investigate associations between microorganism abundance and N. noctula physiological status, and alpha- and beta-diversity indexes were used to explore diversity changes. As a result, most notable changes were observed in Serratia liquefaciens, Hafnia alvei, Staphylococcus sciuri, and Staphylococcus xylosus, which were significantly more highly abundant in hibernating bats, while Citrobacter freundii, Klebsiella oxytoca, Providencia rettgeri, Citrobacter braakii, and Pedicoccus pentosaceus were more abundant in active bats before hibernation. The alpha-diversity was the lowest in hibernating bats, while the beta-diversity differed significantly among all studied periods. Overall, this study shows that hibernation contributes to changes in bat cultivable gut microbiota composition and diversity.

Association between vaginal microbiome alteration and povidone iodine use during delivery

BACKGROUND

The vaginal microbiome is a dynamic community of microorganisms in the vagina. Its alteration may be influenced by multiple factors, including gestational status, menstrual cycle, sexual intercourse, hormone levels, hormonal contraceptives, and vaginal drug administration. Povidone iodine has been used before delivery to reduce infection that may be caused by the ascendance of pathogenic and opportunistic bacteria from the vagina to the uterus. This study aimed to elucidate the impact of povidone iodine use during delivery on the vaginal microbiome.

METHODS

This study enrolled a total of 67 women from maternity services in three hospitals. During the delivery process, we have applied povidone iodine in three doses such as low dose, medium dose, and high dose based on the amount of povidone iodine administered, thus, we studied the three groups of women based on the doses applied. Vaginal swab samples were collected both before and immediately after delivery, and the microbial communities were characterized using 16 S rRNA sequencing. The identification of differentially abundant microbial taxa was performed using ZicoSeq software.

RESULTS

Before delivery, the vaginal microbiome was dominated by the genus Lactobacillus, with different percentage observed (86.06%, 85.24%, and 73.42% for the low, medium, and high dose groups, respectively). After delivery, the vaginal microbial community was restructured, with a significant decrease in the relative abundance of Lactobacillus in all three groups (68.06%, 50.08%, and 25.89%), and a significant increase in alpha diversity across all 3 groups (P < 0.01). Furthermore, as the dose of povidone iodine used during delivery increased, there was a corresponding decrease in the relative abundance of Lactobacillus (P < 0.01). Contrary, there was an increase in microbial diversity and the relative abundances of Pseudomonas (0.13%, 0.26%, and 13.04%, P < 0.01) and Ralstonia (0.01%, 0.02%, and 16.07%, P < 0.01) across the groups. Notably, some functional metabolic pathways related to sugar degradation were observed to have significant change with increasing use of povidone iodine.

CONCLUSION

Povidone iodine was associated with the vaginal microbiome alterations after parturition, and its significant change was associated to the dosage of povidone iodine administered. The escalation in iodine dosage was linked to a decrease in Lactobacilli abundance, and elevated prevalence of Pseudomonas and Ralstonia. There is a need for longitudinal studies to clearly understanding the effect of povidone iodine use on maternal and infant microbiome.