An adaptive microbiome α-diversity-based association analysis method

Changes in the gut microbiota of pseudoxanthoma elasticum patients

OBJECTIVE

Pseudoxanthoma elasticum (PXE) is a rare autosomal disorder with a variable phenotype that may be modulated by environmental factors. Plasma vitamin K (VK) levels may be involved in the ectopic calcification process observed in PXE. Since VK2 is predominantly produced by the gut microbiota, we hypothesized that changes in the gut microbiota of PXE patients might exacerbate the calcification process and disease symptoms.

METHODS

Twenty PXE patients were included in the study and 60 gut microbiota profiles from the Biofortis laboratory database were used as controls.

RESULTS

The Rhodospirillaceae family was more abundant in the PXE group while the Sphingomonadaceae family was more abundant in the control group. In a PXE severity subgroup analysis, microbiota dispersion was lower in "severe" than in "non-severe" patients, which was confirmed by permutation multivariate analysis of variance at the phylum, family and genus ranks. However, no significant association was found in a model incorporating relative abundance of bacterial families, severity score, and different blood and fecal VK species.

CONCLUSION

These results suggest slight compositional changes in the gut microbiota of PXE patients. Further studies are needed to substantiate their impact on VK metabolism and the calcification process.

Mix-method toolbox for monitoring greenhouse gas production and microbiome responses to soil amendments

In this study, we adopt an interdisciplinary approach, integrating agronomic field experiments with soil chemistry, molecular biology techniques, and statistics to investigate the impact of organic residue amendments, such as vinasse (a by-product of sugarcane ethanol production), on soil microbiome and greenhouse gas (GHG) production. The research investigates the effects of distinct disturbances, including organic residue application alone or combined with inorganic N fertilizer on the environment. The methods assess soil microbiome dynamics (composition and function), GHG emissions, and plant productivity. Detailed steps for field experimental setup, soil sampling, soil chemical analyses, determination of bacterial and fungal community diversity, quantification of genes related to nitrification and denitrification pathways, measurement and analysis of gas fluxes (N2O, CH4, and CO2), and determination of plant productivity are provided. The outcomes of the methods are detailed in our publications (Lourenço et al., 2018a; Lourenço et al., 2018b; Lourenço et al., 2019; Lourenço et al., 2020). Additionally, the statistical methods and scripts used for analyzing large datasets are outlined. The aim is to assist researchers by addressing common challenges in large-scale field experiments, offering practical recommendations to avoid common pitfalls, and proposing potential analyses, thereby encouraging collaboration among diverse research groups.•Interdisciplinary methods and scientific questions allow for exploring broader interconnected environmental problems.•The proposed method can serve as a model and protocol for evaluating the impact of soil amendments on soil microbiome, GHG emissions, and plant productivity, promoting more sustainable management practices.•Time-series data can offer detailed insights into specific ecosystems, particularly concerning soil microbiota (taxonomy and functions).

A pan-cancer analysis of the microbiome in metastatic cancer

Microbial communities are resident to multiple niches of the human body and are important modulators of the host immune system and responses to anticancer therapies. Recent studies have shown that complex microbial communities are present within primary tumors. To investigate the presence and relevance of the microbiome in metastases, we integrated mapping and assembly-based metagenomics, genomics, transcriptomics, and clinical data of 4,160 metastatic tumor biopsies. We identified organ-specific tropisms of microbes, enrichments of anaerobic bacteria in hypoxic tumors, associations between microbial diversity and tumor-infiltrating neutrophils, and the association of Fusobacterium with resistance to immune checkpoint blockade (ICB) in lung cancer. Furthermore, longitudinal tumor sampling revealed temporal evolution of the microbial communities and identified bacteria depleted upon ICB. Together, we generated a pan-cancer resource of the metastatic tumor microbiome that may contribute to advancing treatment strategies.

Effects of Exercise on Gut Microbiota of Adults: A Systematic Review and Meta-Analysis

BACKGROUND

The equilibrium between gut microbiota (GM) and the host plays a pivotal role in maintaining overall health, influencing various physiological and metabolic functions. Emerging research suggests that exercise modulates the abundance and functionality of gut bacteria, yet the comprehensive effects on GM diversity remain to be synthesized.

OBJECTIVES AND DESIGN

The study aims to quantitatively examine the effect of exercise on the diversity of gut microbiota of adults using a systemic review and meta-analysis approach.

METHODS

PubMed, Ebsco, Embase, Web of Science, Cochrane Central Register of Controlled Trials, the China National Knowledge Infrastructure, and Wanfang Data were searched from their inception to September 2023. Exercise intervention studies with a control group that describe and compare the composition of GM in adults, using 16S rRNA gene sequencing, were included in this meta-analysis.

RESULTS

A total of 25 studies were included in this meta-analysis with a total of 1044 participants. Based on a fixed-effects model [Chi2 = 29.40, df = 20 (p = 0.08); I2 = 32%], the pooled analysis showed that compared with the control group, exercise intervention can significantly increase the alpha diversity of adult GM, using the Shannon index as an example [WMD = 0.05, 95% CI (0.00, 0.09); Z = 1.99 (p = 0.05)]. In addition, exercise interventions were found to significantly alter GM, notably decreasing Bacteroidetes and increasing Firmicutes, indicating a shift in the Firmicutes/Bacteroidetes ratio. The subgroup analysis indicates that females and older adults appear to exhibit more significant changes in the Shannon Index and observed OTUs.

CONCLUSIONS

Exercise may be a promising way to improve GM in adults. In particular, the Shannon index was significantly increased after exercise. Distinct responses in GM diversity to exercise interventions based on gender and age implicated that more research was needed.

MiMultiCat: A Unified Cloud Platform for the Analysis of Microbiome Data with Multi-Categorical Responses

The field of the human microbiome is rapidly growing due to the recent advances in high-throughput sequencing technologies. Meanwhile, there have also been many new analytic pipelines, methods and/or tools developed for microbiome data preprocessing and analytics. They are usually focused on microbiome data with continuous (e.g., body mass index) or binary responses (e.g., diseased vs. healthy), yet multi-categorical responses that have more than two categories are also common in reality. In this paper, we introduce a new unified cloud platform, named MiMultiCat, for the analysis of microbiome data with multi-categorical responses. The two main distinguishing features of MiMultiCat are as follows: First, MiMultiCat streamlines a long sequence of microbiome data preprocessing and analytic procedures on user-friendly web interfaces; as such, it is easy to use for many people in various disciplines (e.g., biology, medicine, public health). Second, MiMultiCat performs both association testing and prediction modeling extensively. For association testing, MiMultiCat handles both ecological (e.g., alpha and beta diversity) and taxonomical (e.g., phylum, class, order, family, genus, species) contexts through covariate-adjusted or unadjusted analysis. For prediction modeling, MiMultiCat employs the random forest and gradient boosting algorithms that are well suited to microbiome data while providing nice visual interpretations. We demonstrate its use through the reanalysis of gut microbiome data on obesity with body mass index categories. MiMultiCat is freely available on our web server.

Babies, bugs and brains: How the early microbiome associates with infant brain and behavior development

Growing evidence is demonstrating the connection between the microbiota gut-brain axis and neurodevelopment. Microbiota colonization occurs before the maturation of many neural systems and is linked to brain health. Because of this it has been hypothesized that the early microbiome interactions along the gut-brain axis evolved to promote advanced cognitive functions and behaviors. Here, we performed a pilot study with a multidisciplinary approach to test if the microbiota composition of infants is associated with measures of early cognitive development, in particular neural rhythm tracking; language (forward speech) versus non-language (backwards speech) discrimination; and social joint attention. Fecal samples were collected from 56 infants between four and six months of age and sequenced by shotgun metagenomic sequencing. Of these, 44 performed the behavioral Point and Gaze test to measure joint attention. Infants were tested on either language discrimination using functional near-infrared spectroscopy (fNIRS; 25 infants had usable data) or neural rhythm tracking using electroencephalogram (EEG; 15 had usable data). Infants who succeeded at the Point and Gaze test tended to have increased Actinobacteria and reduced Firmicutes at the phylum level; and an increase in Bifidobacterium and Eggerthella along with a reduction in Hungatella and Streptococcus at the genus level. Measurements of neural rhythm tracking associated negatively to the abundance of Bifidobacterium and positively to the abundance of Clostridium and Enterococcus for the bacterial abundances, and associated positively to metabolic pathways that can influence neurodevelopment, including branched chain amino acid biosynthesis and pentose phosphate pathways. No associations were found for the fNIRS language discrimination measurements. Although the tests were underpowered due to the small pilot sample sizes, potential associations were identified between the microbiome and measurements of early cognitive development that are worth exploring further.

MiSurv: an Integrative Web Cloud Platform for User-Friendly Microbiome Data Analysis with Survival Responses

Investigators have studied the treatment effects on human health or disease, the treatment effects on human microbiome, and the roles of the microbiome on human health or disease. Especially, in a clinical trial, investigators commonly trace disease status over a lengthy period to survey the sequential disease progression for different treatment groups (e.g., treatment versus placebo, new treatment versus old treatment). Hence, disease responses are often available in the form of survival (i.e., time-to-event) responses stratified by treatment groups. While the recent web cloud platforms have enabled user-friendly microbiome data processing and analytics, there is currently no web cloud platform to analyze microbiome data with survival responses. Therefore, we introduce here an integrative web cloud platform, called MiSurv, for comprehensive microbiome data analysis with survival responses. IMPORTANCE MiSurv consists of a data processing module and its following four data analytic modules: (i) Module 1: Comparative survival analysis between treatment groups, (ii) Module 2: Comparative analysis in microbial composition between treatment groups, (iii) Module 3: Association testing between microbial composition and survival responses, (iv) Module 4: Prediction modeling using microbial taxa on survival responses. We demonstrate its use through an example trial on the effects of antibiotic use on the survival rate against type 1 diabetes (T1D) onset and gut microbiome composition, respectively, and the effects of the gut microbiome on the survival rate against T1D onset. MiSurv is freely available on our web server (http://misurv.micloud.kr) or can alternatively run on the user's local computer (https://github.com/wg99526/MiSurvGit).

multiMiAT: an optimal microbiome-based association test for multicategory phenotypes

Microbes can affect the metabolism and immunity of human body incessantly, and the dysbiosis of human microbiome drives not only the occurrence but also the progression of disease (i.e. multiple statuses of disease). Recently, microbiome-based association tests have been widely developed to detect the association between the microbiome and host phenotype. However, the existing methods have not achieved satisfactory performance in testing the association between the microbiome and ordinal/nominal multicategory phenotypes (e.g. disease severity and tumor subtype). In this paper, we propose an optimal microbiome-based association test for multicategory phenotypes, namely, multiMiAT. Specifically, under the multinomial logit model framework, we first introduce a microbiome regression-based kernel association test for multicategory phenotypes (multiMiRKAT). As a data-driven optimal test, multiMiAT then integrates multiMiRKAT, score test and MiRKAT-MC to maintain excellent performance in diverse association patterns. Massive simulation experiments prove the success of our method. Furthermore, multiMiAT is also applied to real microbiome data experiments to detect the association between the gut microbiome and clinical statuses of colorectal cancer as well as for diverse statuses of Clostridium difficile infections.

The mediating roles of the oral microbiome in saliva and subgingival sites between e-cigarette smoking and gingival inflammation

BACKGROUND

Electronic cigarettes (ECs) have been widely used by young individuals in the U.S. while being considered less harmful than conventional tobacco cigarettes. However, ECs have increasingly been regarded as a health risk, producing detrimental chemicals that may cause, combined with poor oral hygiene, substantial inflammation in gingival and subgingival sites. In this paper, we first report that EC smoking significantly increases the odds of gingival inflammation. Then, through mediation analysis, we seek to identify and explain the mechanism that underlies the relationship between EC smoking and gingival inflammation via the oral microbiome.

METHODS

We collected saliva and subgingival samples from 75 EC users and 75 non-users between 18 and 34 years in age and profiled their microbial compositions via 16S rRNA amplicon sequencing. We conducted raw sequence data processing, denoising and taxonomic annotations using QIIME2 based on the expanded human oral microbiome database (eHOMD). We then created functional annotations (i.e., KEGG pathways) using PICRUSt2.

RESULTS

We found significant increases in α-diversity for EC users and disparities in β-diversity between EC users and non-users. We also found significant disparities between EC users and non-users in the relative abundance of 36 microbial taxa in the saliva site and 71 microbial taxa in the subgingival site. Finally, we found that 1 microbial taxon in the saliva site and 18 microbial taxa in the subgingival site significantly mediated the effects of EC smoking on gingival inflammation. The mediators on the genus level, for example, include Actinomyces, Rothia, Neisseria, and Enterococcus in the subgingival site. In addition, we report significant disparities between EC users and non-users in the relative abundance of 71 KEGG pathways in the subgingival site.

CONCLUSIONS

These findings reveal that continued EC use can further increase microbial dysbiosis that may lead to periodontal disease. Our findings also suggest that continued surveillance for the effect of ECs on the oral microbiome and its transmission to oral diseases is needed.

Integrative web cloud computing and analytics using MiPair for design-based comparative analysis with paired microbiome data

Pairing (or blocking) is a design technique that is widely used in comparative microbiome studies to efficiently control for the effects of potential confounders (e.g., genetic, environmental, or behavioral factors). Some typical paired (block) designs for human microbiome studies are repeated measures designs that profile each subject's microbiome twice (or more than twice) (1) for pre and post treatments to see the effects of a treatment on microbiome, or (2) for different organs of the body (e.g., gut, mouth, skin) to see the disparity in microbiome between (or across) body sites. Researchers have developed a sheer number of web-based tools for user-friendly microbiome data processing and analytics, though there is no web-based tool currently available for such paired microbiome studies. In this paper, we thus introduce an integrative web-based tool, named MiPair, for design-based comparative analysis with paired microbiome data. MiPair is a user-friendly web cloud service that is built with step-by-step data processing and analytic procedures for comparative analysis between (or across) groups or between baseline and other groups. MiPair employs parametric and non-parametric tests for complete or incomplete block designs to perform comparative analyses with respect to microbial ecology (alpha- and beta-diversity) and taxonomy (e.g., phylum, class, order, family, genus, species). We demonstrate its usage through an example clinical trial on the effects of antibiotics on gut microbiome. MiPair is an open-source software that can be run on our web server ( http://mipair.micloud.kr ) or on user's computer ( https://github.com/yj7599/mipairgit ).

Shared Species Analysis, Augmented by Stochasticity Analysis, Is More Effective Than Diversity Analysis in Detecting Variations in the Gut Microbiomes

Diversity analysis is a de facto standard procedure for most existing microbiome studies. Nevertheless, diversity metrics can be insensitive to changes in community composition (identities). For example, if species A (e.g., a beneficial microbe) is replaced by equal number of species B (e.g., an opportunistic pathogen), the diversity metric may not change, but the community composition has changed. The shared species analysis (SSA) is a computational technique that can discern changes of community composition by detecting the increase/decrease of shared species between two sets of microbiome samples, and it should be more sensitive than standard diversity analysis in discerning changes in microbiome structures. Here, we investigated the effects of ethnicity and lifestyles in China on the structure of Chinese gut microbiomes by reanalyzing the datasets of a large Chinese cohort with 300+ individuals covering 7 biggest Chinese ethnic groups (>95% Chinese population). We found: (i) Regarding lifestyles, SSA revealed significant differences between 100% of pair-wise comparisons in community compositions across all but phylum taxon levels (phylum level = 29%), but diversity analysis only revealed 14–29% pair-wise differences in community diversity across all four taxon levels. (ii) Regarding ethnicities, SSA revealed 100% pair-wise differences in community compositions across all but phylum (phylum level = 48–62%) levels, but diversity analysis only revealed 5–57% differences in community diversity across all four taxon levels. (iii) Ethnicity seems to have more prevalent effects on community structures than lifestyle does (iv) Community structures of the gut microbiomes are more stable at the phylum level than at the other three levels. (v) SSA is more powerful than diversity analysis in detecting the changes of community structures; furthermore, SSA can produce lists of unique and shared OTUs. (vi) Finally, we performed stochasticity analysis to mechanistically interpret the observed differences revealed by the SSA and diversity analyses.

An Adaptive and Robust Test for Microbial Community Analysis

In microbiome studies, researchers measure the abundance of each operational taxon unit (OTU) and are often interested in testing the association between the microbiota and the clinical outcome while conditional on certain covariates. Two types of approaches exists for this testing purpose: the OTU-level tests that assess the association between each OTU and the outcome, and the community-level tests that examine the microbial community all together. It is of considerable interest to develop methods that enjoy both the flexibility of OTU-level tests and the biological relevance of community-level tests. We proposed MiAF, a method that adaptively combines p-values from the OTU-level tests to construct a community-level test. By borrowing the flexibility of OTU-level tests, the proposed method has great potential to generate a series of community-level tests that suit a range of different microbiome profiles, while achieving the desirable high statistical power of community-level testing methods. Using simulation study and real data applications in a smoker throat microbiome study and a HIV patient stool microbiome study, we demonstrated that MiAF has comparable or better power than methods that are specifically designed for community-level tests. The proposed method also provides a natural heuristic taxa selection.

Detecting sparse microbial association signals adaptively from longitudinal microbiome data based on generalized estimating equations

The association between the compositions of microbial communities and various host phenotypes is an important research topic. Microbiome association research addresses multiple domains, such as human disease and diet. Statistical methods for testing microbiome-phenotype associations have been studied recently to determine their ability to assess longitudinal microbiome data. However, existing methods fail to detect sparse association signals in longitudinal microbiome data. In this paper, we developed a novel method, namely aGEEMIHC, which is a data-driven adaptive microbiome higher criticism analysis based on generalized estimating equations to detect sparse microbial association signals from longitudinal microbiome data. aGEEMiHC adopts generalized estimating equations framework that fully considers the correlation among different observations from the same subject in longitudinal data. To be robust to diverse correlation structures for longitudinal data, aGEEMiHC integrates multiple microbiome higher criticism analyses based on generalized estimating equations with different working correlation structures. Extensive simulation experiments demonstrate that aGEEMiHC can control the type I error correctly and achieve superior performance according to a statistical power comparison. We also applied it to longitudinal microbiome data with various types of host phenotypes to demonstrate the stability of our method. aGEEMiHC is also utilized for real longitudinal microbiome data, and we found a significant association between the gut microbiome and Crohn's disease. In addition, our method ranks the significant factors associated with the host phenotype to provide potential biomarkers.

Comparative study on fecal flora and blood biochemical indexes in normal and diarrhea British Shorthair cats

In recent years, 16S ribosomal DNA (16S rDNA) sequencing has been widely developed. In the present study, we investigated the changes of fecal flora analyzed by sequencing of 16S rDNA and the alteration of blood biochemical indexes in cats during diarrhea. Seven normal fecal samples and seven fecal samples of British Shorthair cats with bacterial diarrhea about 6 months old were collected. The 16S rDNA V3 region of the bacteria was amplified for high-throughput sequencing. Finally, species analysis at various levels was performed. At the same time, samples of blood were taken to examine the changes of biochemical indexes in cats with diarrhea. The abundance and diversity of microflora in the healthy group were greater than those in the diarrhea group. The normal floras in the feces of healthy cats were Firmicutes, Actinobacteria, Bacteroidetes and Proteobacteria. The content of Proteobacteria and Firmicutes varied greatly in diarrheal cats. In addition, the number of white blood cells, lymphocytes, neutrophils, and globulin were increased in cats with diarrhea, whereas albumin level was decreased in diarrheal cats. In conclusion, the present study suggests 16SrDNA technology showed that the intestinal Proteus was abundant, and the content of Firmicutes was scarce in cats with diarrhea. Escherichia-Shigella was the main pathogens in this sample. Rapid blood biochemical tests may help clinicians to assess the severity and prognosis of cats with diarrhea.

Antidiabetic potential of polysaccharides from Brasenia schreberi regulating insulin signaling pathway and gut microbiota in type 2 diabetic mice

This study aimed to investigate the hypoglycemic activities and gut microbial regulation effects of polysaccharides from Brasenia schreberi (BS) in diabetic mice induced by high-fat diet and streptozotocin. Our data indicated that BS polysaccharides not only improved the symptoms of hyperglycemia and relieved metabolic endotoxemia-related inflammation but also optimized the gut microbiota composition of diabetic mice with significantly decreased Firmicutes/Bacteroidetes ratios. More importantly, altered gut microbiota components may affect liver glycogen and muscle glycogen by increasing the mRNA expression of phosphatidylinositol-3-kinase (PI3K) and protein kinase B (Akt) in the liver of mice through modulated the abundance of beneficial bacteria (Lactobacillus). Altogether, our findings, for the first time, demonstrate that BS polysaccharides may be used as a beneficial probiotic agent that reverses gut microbiota dysbiosis and the hypoglycemic mechanisms of BS polysaccharides may be related to enhancing the abundance of Lactobacillus to activate PI3K/Akt-mediated signaling pathways in T2DM mice.

•

Brasenia schreberi polysaccharides ameliorated hyperglycemia and dyslipidemia in mice.

•

The polysaccharides regulated glucose metabolism through activating PI3K-Akt pathway.

•

The polysaccharides modulated gut microbiota profile of diabetic mice.

Diversity and function of microbial communities in the sand sheath of Agropyron cristatum by metagenomic analysis

The roots of most gramineous plants are surrounded by a variety of microorganisms; however, few studies have focused on the rhizosheath of psammophytes. Therefore, in this study, we used Illumina HiSeq high-throughput sequencing technology to analyse the composition and functional diversity of microbial communities in the rhizosheath of sand-grown Agropyron cristatum (L.) Gaertn. We found that the number of species and functions of microbial communities gradually decreased from the rhizosheath to the bulk soil. Thus, the microbial composition of the rhizosheath was richer and more diverse, and the abundance of bacteria, including Sphingosinicella, Rhizorhabdus, Friedmanniella, Geodermatophilus, Blastococcus, and Oscillatoria, was higher, and the abundance of fungi, such as Mycothermus, was higher. The abundance of CO₂ fixation-related genes (acsA, Pcc, and cbbL) in the carbon cycle; NO₃^-, NO₂^-, NH₂OH, and N₂ transformation genes (nrtP, nirS, hao, and nifK) in the nitrogen cycle; soxB/A/C, Sat, and dsrB genes in the sulphur cycle; and 1-phosphate mannitol dehydrogenase (MtlD) gene and polyketide synthase gene (pks) were higher in the rhizosheath than in the bulk soil, as well as genes related to phosphorus uptake in the phosphorus cycle. Our findings showed that the rhizosheath may host the predominant microbial species related to the formation of a rhizosheath.

Vitamin A Deficiency Exacerbates Gut Microbiota Dysbiosis and Cognitive Deficits in Amyloid Precursor Protein/Presenilin 1 Transgenic Mice

Vitamin A deficiency (VAD) plays an essential role in the pathogenesis of Alzheimer’s disease (AD). However, the specific mechanism by which VAD aggravates cognitive impairment is still unknown. At the intersection of microbiology and neuroscience, the gut-brain axis is undoubtedly contributing to the formation and function of neurological systems, but most of the previous studies have ignored the influence of gut microbiota on the cognitive function in VAD. Therefore, we assessed the effect of VAD on AD pathology and the decline of cognitive function in AD model mice and determined the role played by the intestinal microbiota in the process. Twenty 8-week-old male C57BL/6J amyloid precursor protein/presenilin 1 (APP/PS1) transgenic mice were randomly assigned to either a vitamin A normal (VAN) or VAD diet for 45 weeks. Our results show that VAD aggravated the behavioral learning and memory deficits, reduced the retinol concentration in the liver and the serum, decreased the transcription of vitamin A (VA)-related receptors and VA-related enzymes in the cortex, increased amyloid-β peptides (Aβ40 and Aβ42) in the brain and gut, upregulate the translation of beta-site APP-cleaving enzyme 1 (BACE1) and phosphorylated Tau in the cortex, and downregulate the expression of brain-derived neurotrophic factor (BDNF) and γ-aminobutyric acid (GABA) receptors in the cortex. In addition, VAD altered the composition and functionality of the fecal microbiota as exemplified by a decreased abundance of Lactobacillus and significantly different α- and β-diversity. Of note, the functional metagenomic prediction (PICRUSt analysis) indicated that GABAergic synapse and retinol metabolism decreased remarkably after VAD intervention, which was in line with the decreased expression of GABA receptors and the decreased liver and serum retinol. In summary, the present study provided valuable facts that VAD exacerbated the morphological, histopathological, molecular biological, microbiological, and behavioral impairment in the APP/PS1 transgenic mice, and the intestinal microbiota may play a key mediator role in this mechanism.

Microbiome signature and diversity regulates the level of energy production under anaerobic condition

The microbiome of the anaerobic digester (AD) regulates the level of energy production. To assess the microbiome diversity and composition in different stages of anaerobic digestion, we collected 16 samples from the AD of cow dung (CD) origin. The samples were categorized into four groups (Group-I, Group-II, Group-III and Group-IV) based on the level of energy production (CH₄%), and sequenced through whole metagenome sequencing (WMS). Group-I (n = 2) belonged to initial time of energy production whereas Group-II (n = 5), Group-III (n = 5), and Group-IV (n = 4) had 21-34%, 47-58% and 71-74% of CH₄, respectively. The physicochemical analysis revealed that level of energy production (CH₄%) had significant positive correlation with digester pH (r = 0.92, p < 0.001), O₂ level (%) (r = 0.54, p < 0.05), and environmental temperature (°C) (r = 0.57, p < 0.05). The WMS data mapped to 2800 distinct bacterial, archaeal and viral genomes through PathoScope (PS) and MG-RAST (MR) analyses. We detected 768, 1421, 1819 and 1774 bacterial strains in Group-I, Group-II, Group-III and Group-IV, respectively through PS analysis which were represented by Firmicutes, Bacteroidetes, Proteobacteria, Actinobacteria, Spirochaetes and Fibrobacteres phyla (> 93.0% of the total abundances). Simultaneously, 343 archaeal strains were detected, of which 95.90% strains shared across four metagenomes. We identified 43 dominant species including 31 bacterial and 12 archaeal species in AD microbiomes, of which only archaea showed positive correlation with digester pH, CH₄ concentration, pressure and temperature (Spearman correlation; r > 0.6, p < 0.01). The indicator species analysis showed that the species Methanosarcina vacuolate, Dehalococcoides mccartyi, Methanosarcina sp. Kolksee and Methanosarcina barkeri were highly specific for energy production. The correlation network analysis showed that different strains of Euryarcheota and Firmicutes phyla exhibited significant correlation (p = 0.021, Kruskal-Wallis test; with a cutoff of 1.0) with the highest level (74.1%) of energy production (Group-IV). In addition, top CH₄ producing microbiomes showed increased genomic functional activities related to one carbon and biotin metabolism, oxidative stress, proteolytic pathways, membrane-type-1-matrix-metalloproteinase (MT1-MMP) pericellular network, acetyl-CoA production, motility and chemotaxis. Importantly, the physicochemical properties of the AD including pH, CH₄ concentration (%), pressure, temperature and environmental temperature were found to be positively correlated with these genomic functional potentials and distribution of ARGs and metal resistance pathways (Spearman correlation; r > 0.5, p < 0.01). This study reveals distinct changes in composition and diversity of the AD microbiomes including different indicator species, and their genomic features that are highly specific for energy production.

A powerful adaptive microbiome-based association test for microbial association signals with diverse sparsity levels

The dysbiosis of microbiome may have negative effects on a host phenotype. The microbes related to the host phenotype are regarded as microbial association signals. Recently, statistical methods based on microbiome-phenotype association tests have been extensively developed to detect these association signals. However, the currently available methods do not perform well to detect microbial association signals when dealing with diverse sparsity levels (i.e., sparse, low sparse, non-sparse). Actually, the real association patterns related to different host phenotypes are not unique. Here, we propose a powerful and adaptive microbiome-based association test to detect microbial association signals with diverse sparsity levels, designated as MiATDS. In particular, we define probability degree to measure the associations between microbes and the host phenotype and introduce the adaptive weighted sum of powered score tests by considering both probability degree and phylogenetic information. We design numerous simulation experiments for the task of detecting association signals with diverse sparsity levels to prove the performance of the method. We find that type I error rates can be well-controlled and MiATDS shows superior efficiency on the power. By applying to real data analysis, MiATDS displays reliable practicability too. The R package is available at https://github.com/XiaoyunHuang33/MiATDS.

Diversity and genomic determinants of the microbiomes associated with COVID-19 and non-COVID respiratory diseases

The novel coronavirus disease 2019 (COVID-19) is a rapidly emerging and highly transmissible disease caused by the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). Understanding the microbiomes associated with the upper respiratory tract infection (URTI), chronic obstructive pulmonary disease (COPD) and COVID-19 diseases has clinical interest. We hypothesize that microbiome diversity and composition, and their genomic features are associated with different pathological conditions of these human respiratory tract diseases. To test this hypothesis, we analyzed 21 RNASeq metagenomic data including eleven COVID-19 (BD = 6 and China = 5), six COPD (UK = 6) and four URTI (USA = 4) samples to unravel the microbiome diversity and related genomic metabolic functions. The metagenomic data mapped to 534 bacterial, 60 archaeal and 61 viral genomes with distinct variation in the microbiome composition across the samples (COVID-19 > COPD > URTI). Notably, 94.57%, 80.0% and 24.59% bacterial, archaeal and viral genera shared between the COVID-19 and non-COVID samples, respectively. However, the COVID-19 related samples had sole association with 16 viral genera other than SARS-CoV-2. Strain-level virome profiling revealed 660 and 729 strains in COVID-19 and non-COVID samples, respectively, and of them 34.50% strains shared between the conditions. Functional annotation of the metagenomic data identified the association of several biochemical pathways related to basic metabolism (amino acid and energy), ABC transporters, membrane transport, virulence, disease and defense, regulation of virulence, programmed cell death, and primary immunodeficiency. We also detected 30 functional gene groups/classes associated with resistance to antibiotics and toxic compounds (RATC) in both COVID-19 and non-COVID microbiomes. Furthermore, we detected comparatively higher abundance of cobalt-zinc-cadmium resistance (CZCR) and multidrug resistance to efflux pumps (MREP) genes in COVID-19 metagenome. The profiles of microbiome diversity and associated microbial genomic features found in both COVID-19 and non-COVID (COPD and URTI) samples might be helpful in developing microbiome-based diagnostics and therapeutics for COVID-19 and non-COVID respiratory diseases. However, future studies might be carried out to explore the microbiome dynamics and the cross-talk between host and microbiomes employing larger volume of samples from different ethnic groups and geoclimatic conditions.

Meta-analysis methods for multiple related markers: Applications to microbiome studies with the results on multiple α-diversity indices

Meta-analysis is a practical and powerful analytic tool that enables a unified statistical inference across the results from multiple studies. Notably, researchers often report the results on multiple related markers in each study (eg, various α-diversity indices in microbiome studies). However, univariate meta-analyses are limited to combining the results on a single common marker at a time, whereas existing multivariate meta-analyses are limited to the situations where marker-by-marker correlations are given in each study. Thus, here we introduce two meta-analysis methods, multi-marker meta-analysis (mMeta) and adaptive multi-marker meta-analysis (aMeta), to combine multiple studies throughout multiple related markers with no priori results on marker-by-marker correlations. mMeta is a statistical estimator for a pooled estimate and its SE across all the studies and markers, whereas aMeta is a statistical test based on the test statistic of the minimum P-value among marker-specific meta-analyses. mMeta conducts both effect estimation and hypothesis testing based on a weighted average of marker-specific pooled estimates while estimating marker-by-marker correlations non-parametrically via permutations, yet its power is only moderate. In contrast, aMeta closely approaches the highest power among marker-specific meta-analyses, yet it is limited to hypothesis testing. While their applications can be broader, we illustrate the use of mMeta and aMeta to combine microbiome studies throughout multiple α-diversity indices. We evaluate mMeta and aMeta in silico and apply them to real microbiome studies on the disparity in α-diversity by the status of human immunodeficiency virus (HIV) infection. The R package for mMeta and aMeta is freely available at https://github.com/hk1785/mMeta.

Microbiome dynamics and genomic determinants of bovine mastitis

The milk of lactating cows presents a complex ecosystem of interconnected microbial communities which can influence the pathophysiology of mastitis. We hypothesized possible dynamic shifts of microbiome composition and genomic features with different pathological conditions of mastitis (Clinical Mastitis; CM, Recurrent CM; RCM, Subclinical Mastitis; SCM). To evaluate this hypothesis, we employed whole metagenome sequencing (WMS) in 20 milk samples (CM, 5; RCM, 6; SCM, 4; H, 5) to unravel the microbiome dynamics, interrelation, and relevant metabolic functions. The WMS data mapped to 442 bacterial, 58 archaeal and 48 viral genomes with distinct variation in microbiome composition (CM > H > RCM > SCM). Furthermore, we identified a number of microbial genomic features, including 333, 304, 183 and 50 virulence factors-associated genes (VFGs) and 48, 31, 11 and 6 antibiotic resistance genes (ARGs) in CM, RCM, SCM, and H-microbiomes, respectively. We also detected different metabolic pathway and functional genes associated with mastitis pathogenesis. Therefore, profiling microbiome dynamics in different conditions of mastitis and associated microbial genomic features contributes to developing microbiome-based diagnostics and therapeutics for bovine mastitis.

Insights Into the Resistome of Bovine Clinical Mastitis Microbiome, a Key Factor in Disease Complication

Bovine clinical mastitis (CM) is one of the most prevalent diseases caused by a wide range of resident microbes. The emergence of antimicrobial resistance in CM bacteria is well-known, however, the genomic resistance composition (the resistome) at the microbiome-level is not well characterized. In this study, we applied whole metagenome sequencing (WMS) to characterize the resistome of the CM microbiome, focusing on antibiotics and metals resistance, biofilm formation (BF), and quorum sensing (QS) along with in vitro resistance assays of six selected pathogens isolated from the same CM samples. The WMS generated an average of 21.13 million reads (post-processing) from 25 CM samples that mapped to 519 bacterial strains, of which 30.06% were previously unreported. We found a significant (P = 0.001) association between the resistomes and microbiome composition with no association with cattle breed, despite significant differences in microbiome diversity among breeds. The in vitro investigation determined that 76.2% of six selected pathogens considered "biofilm formers" actually formed biofilms and were also highly resistant to tetracycline, doxycycline, nalidixic acid, ampicillin, and chloramphenicol and remained sensitive to metals (Cr, Co, Ni, Cu, Zn) at varying concentrations. We also found bacterial flagellar movement and chemotaxis, regulation and cell signaling, and oxidative stress to be significantly associated with the pathophysiology of CM. Thus, identifying CM microbiomes, and analyzing their resistomes and genomic potentials will help improve the optimization of therapeutic schemes involving antibiotics and/or metals usage in the prevention and control of bovine CM.

Correlation and association analyses in microbiome study integrating multiomics in health and disease

Correlation and association analyses are one of the most widely used statistical methods in research fields, including microbiome and integrative multiomics studies. Correlation and association have two implications: dependence and co-occurrence. Microbiome data are structured as phylogenetic tree and have several unique characteristics, including high dimensionality, compositionality, sparsity with excess zeros, and heterogeneity. These unique characteristics cause several statistical issues when analyzing microbiome data and integrating multiomics data, such as large p and small n, dependency, overdispersion, and zero-inflation. In microbiome research, on the one hand, classic correlation and association methods are still applied in real studies and used for the development of new methods; on the other hand, new methods have been developed to target statistical issues arising from unique characteristics of microbiome data. Here, we first provide a comprehensive view of classic and newly developed univariate correlation and association-based methods. We discuss the appropriateness and limitations of using classic methods and demonstrate how the newly developed methods mitigate the issues of microbiome data. Second, we emphasize that concepts of correlation and association analyses have been shifted by introducing network analysis, microbe-metabolite interactions, functional analysis, etc. Third, we introduce multivariate correlation and association-based methods, which are organized by the categories of exploratory, interpretive, and discriminatory analyses and classification methods. Fourth, we focus on the hypothesis testing of univariate and multivariate regression-based association methods, including alpha and beta diversities-based, count-based, and relative abundance (or compositional)-based association analyses. We demonstrate the characteristics and limitations of each approaches. Fifth, we introduce two specific microbiome-based methods: phylogenetic tree-based association analysis and testing for survival outcomes. Sixth, we provide an overall view of longitudinal methods in analysis of microbiome and omics data, which cover standard, static, regression-based time series methods, principal trend analysis, and newly developed univariate overdispersed and zero-inflated as well as multivariate distance/kernel-based longitudinal models. Finally, we comment on current association analysis and future direction of association analysis in microbiome and multiomics studies.

A powerful microbial group association test based on the higher criticism analysis for sparse microbial association signals

BACKGROUND

In human microbiome studies, it is crucial to evaluate the association between microbial group (e.g., community or clade) composition and a host phenotype of interest. In response, a number of microbial group association tests have been proposed, which account for the unique features of the microbiome data (e.g., high-dimensionality, compositionality, phylogenetic relationship). These tests generally fall in the class of aggregation tests which amplify the overall group association by combining all the underlying microbial association signals, and, therefore, they are powerful when many microbial species are associated with a given host phenotype (i.e., low sparsity). However, in practice, the microbial association signals can be highly sparse, and this is especially the situation where we have a difficulty to discover the microbial group association.

METHODS

Here, we introduce a powerful microbial group association test for sparse microbial association signals, namely, microbiome higher criticism analysis (MiHC). MiHC is a data-driven omnibus test taken in a search space spanned by tailoring the higher criticism test to incorporate phylogenetic information and/or modulate sparsity levels and including the Simes test for excessively high sparsity levels. Therefore, MiHC robustly adapts to diverse phylogenetic relevance and sparsity levels.

RESULTS

Our simulations show that MiHC maintains a high power at different phylogenetic relevance and sparsity levels with correct type I error controls. We also apply MiHC to four real microbiome datasets to test the association between respiratory tract microbiome and smoking status, the association between the infant's gut microbiome and delivery mode, the association between the gut microbiome and type 1 diabetes status, and the association between the gut microbiome and human immunodeficiency virus status.

CONCLUSIONS

In practice, the true underlying association pattern on the extent of phylogenetic relevance and sparsity is usually unknown. Therefore, MiHC can be a useful analytic tool because of its high adaptivity to diverse phylogenetic relevance and sparsity levels. MiHC can be implemented in the R computing environment using our software package freely available at https://github.com/hk1785/MiHC.

A Distance-Based Kernel Association Test Based on the Generalized Linear Mixed Model for Correlated Microbiome Studies

Researchers have increasingly employed family-based or longitudinal study designs to survey the roles of the human microbiota on diverse host traits of interest (e. g., health/disease status, medical intervention, behavioral/environmental factor). Such study designs are useful to properly control for potential confounders or the sensitive changes in microbial composition and host traits. However, downstream data analysis is challenging because the measurements within clusters (e.g., families, subjects including repeated measures) tend to be correlated so that statistical methods based on the independence assumption cannot be used. For the correlated microbiome studies, a distance-based kernel association test based on the linear mixed model, namely, correlated sequence kernel association test (cSKAT), has recently been introduced. cSKAT models the microbial community using an ecological distance (e.g., Jaccard/Bray-Curtis dissimilarity, unique fraction distance), and then tests its association with a host trait. Similar to prior distance-based kernel association tests (e.g., microbiome regression-based kernel association test), the use of ecological distances gives a high power to cSKAT. However, cSKAT is limited to handling Gaussian traits [e.g., body mass index (BMI)] and a single chosen distance measure at a time. The power of cSKAT differs a lot by which distance measure is used. However, choosing an optimal distance measure is challenging because of the unknown nature of the true association. Here, we introduce a distance-based kernel association test based on the generalized linear mixed model (GLMM), namely, GLMM-MiRKAT, to handle diverse types of traits, such as Gaussian (e.g., BMI), Binomial (e.g., disease status, treatment/placebo) or Poisson (e.g., number of tumors/treatments) traits. We further propose a data-driven adaptive test of GLMM-MiRKAT, namely, aGLMM-MiRKAT, so as to avoid the need to choose the optimal distance measure. Our extensive simulations demonstrate that aGLMM-MiRKAT is robustly powerful while correctly controlling type I error rates. We apply aGLMM-MiRKAT to real familial and longitudinal microbiome data, where we discover significant disparity in microbial community composition by BMI status and the frequency of antibiotic use. In summary, aGLMM-MiRKAT is a useful analytical tool with its broad applicability to diverse types of traits, robust power and valid statistical inference.

Relationship Between MiRKAT and Coefficient of Determination in Similarity Matrix Regression

The Microbiome Regression-based Kernel Association Test (MiRKAT) is widely used in testing for the association between microbiome compositions and an outcome of interest. The MiRKAT statistic is derived as a variance-component score test in a kernel machine regression-based generalized linear mixed model. In this brief report, we show that the MiRKAT statistic is proportional to the R 2 (coefficient of determination) statistic in a similarity matrix regression, which characterizes the fraction of variability in outcome similarity, explained by microbiome similarity (up to a constant).