Data Analysis Strategies for Microbiome Studies in Human Populations-a Systematic Review of Current Practice

Gut microbiota signatures in inflammatory bowel disease

BACKGROUND

Inflammatory bowel diseases (IBD), including Crohn's disease (CD) and ulcerative colitis (UC), affect millions of people worldwide with increasing incidence.

OBJECTIVES

Several studies have shown a link between gut microbiota composition and IBD, but results are often limited by small sample sizes. We aimed to re-analyze publicly available fecal microbiota data from IBD patients.

METHODS

We extracted original fecal 16S rRNA amplicon sequencing data from 45 cohorts of IBD patients and healthy individuals using the BioProject database at the National Center for Biotechnology Information. Unlike previous meta-analyses, we merged all study cohorts into a single dataset, including sex, age, geography, and disease information, based on which microbiota signatures were analyzed, while accounting for varying technical platforms.

RESULTS

Among 2518 individuals in the combined dataset, we discovered a hitherto unseen number of genera associated with IBD. A total of 77 genera associated with CD, of which 38 were novel associations, and a total of 64 genera associated with UC, of which 28 represented novel associations. Signatures were robust across different technical platforms and geographic locations. Reduced alpha diversity in IBD compared to healthy individuals, in CD compared to UC, and altered microbiota composition (beta diversity) in UC and especially in CD as compared to healthy individuals were found.

CONCLUSIONS

Combining original microbiota data from 45 cohorts, we identified a hitherto unseen large number of genera associated with IBD. Identification of microbiota features robustly associated with CD and UC may pave the way for the identification of new treatment targets.

TaxaHFE: a machine learning approach to collapse microbiome datasets using taxonomic structure

Motivation

Biologists increasingly turn to machine learning models not just to predict, but to explain. Feature reduction is a common approach to improve both the performance and interpretability of models. However, some biological datasets, such as microbiome data, are inherently organized in a taxonomy, but these hierarchical relationships are not leveraged during feature reduction. We sought to design a feature engineering algorithm to exploit relationships in hierarchically organized biological data.

Results

We designed an algorithm, called TaxaHFE, to collapse information-poor features into their higher taxonomic levels. We applied TaxaHFE to six previously published datasets and found, on average, a 90% reduction in the number of features (SD = 5.1%) compared to using the most complete taxonomy. Using machine learning to compare the most resolved taxonomic level (i.e. species) against TaxaHFE-preprocessed features, models based on TaxaHFE features achieved an average increase of 3.47% in receiver operator curve area under the curve. Compared to other hierarchical feature engineering implementations, TaxaHFE introduces the novel ability to consider both categorical and continuous response variables to inform the feature set collapse. Importantly, we find TaxaHFE's ability to reduce hierarchically organized features to a more information-rich subset increases the interpretability of models.

Availability and implementation

TaxaHFE is available as a Docker image and as R code at https://github.com/aoliver44/taxaHFE.

Social and psychological adversity are associated with distinct mother and infant gut microbiome variations

Health disparities are driven by underlying social disadvantage and psychosocial stressors. However, how social disadvantage and psychosocial stressors lead to adverse health outcomes is unclear, particularly when exposure begins prenatally. Variations in the gut microbiome and circulating proinflammatory cytokines offer potential mechanistic pathways. Here, we interrogate the gut microbiome of mother-child dyads to compare high-versus-low prenatal social disadvantage, psychosocial stressors and maternal circulating cytokine cohorts (prospective case-control study design using gut microbiomes from 121 dyads profiled with 16 S rRNA sequencing and 89 dyads with shotgun metagenomic sequencing). Gut microbiome characteristics significantly predictive of social disadvantage and psychosocial stressors in the mothers and children indicate that different discriminatory taxa and related pathways are involved, including many species of Bifidobacterium and related pathways across several comparisons. The lowest inter-individual gut microbiome similarity was observed among high-social disadvantage/high-psychosocial stressors mothers, suggesting distinct environmental exposures driving a diverging gut microbiome assembly compared to low-social disadvantage/low-psychosocial stressors controls (P = 3.5 × 10-5 for social disadvantage, P = 2.7 × 10-15 for psychosocial stressors). Children's gut metagenome profiles at 4 months also significantly predicted high/low maternal prenatal IL-6 (P = 0.029), with many bacterial species overlapping those identified by social disadvantage and psychosocial stressors. These differences, based on maternal social and psychological status during a critical developmental window early in life, offer potentially modifiable targets to mitigate health inequities.

Probiotics for constipation and gut microbiota in Parkinson's disease

INTRODUCTION

Constipation is one of the common non-motor symptoms in Parkinson's disease that significantly impacts patient quality of life. Probiotics supplementation may improve constipation symptoms, but its effect on the gut microbiota population is unclear.

METHODS

In this randomized controlled study, 46 PD patients with constipation according to Rome Ⅲ criteria were recruited. The number of complete bowel movements per week, degree of defecation effort, Bristol stool Scale (BSS), Patient Assessment of Constipation symptom (PAC-SYM) and Patient assessment of constipation quality of life questionnaire (PAC-QOL) were collected pre- and post-intervention to evaluate the constipation symptoms. In addition, fresh feces of subjects before and after intervention and healthy controls were collected for 16s rRNA gene V3-V4 region sequencing to compare bacterial flora differences.

RESULTS

Compared with the control group, the treatment group increased the average number of complete bowel movements per week (1.09 ± 1.24 vs. 0.04 ± 0.64, P < 0.001). Probiotics supplementation reduced the BSS score (0.65 ± 0.93 vs. -0.17 ± 0.94, P = 0.004), PAC-SYM score (4.09 ± 6.31 vs. -1.83 ± 4.14, P < 0.001), PAC-QOL score (10.65 ± 16.53 vs. 0.57 ± 12.82, P = 0.042), and degree of defecation effort score (1.00 ± 0.80 vs. 0.00 ± 0.30, P < 0.001). The improvement rate of constipation in the probiotics group was significantly higher than that in the control group (52.2% vs. 8.7%, P = 0.001). PD patients showed intestinal flora disorders compared to healthy controls. After 12 weeks of probiotics treatment, g_Christensenella_sp._Marseille-P2437 significantly increased, while g_Eubacterium_oxidoreducens_group, g_Eubacterium_hallii_group and s_Odoribacter_sp._N54.MGS-14 decreased (p < 0 0.05).

CONCLUSIONS

Probiotics treatment can effectively improve the constipation symptoms of PD patients and positively affected the gut microbiota.

Compositional Data Analysis of 16S rRNA Gene Sequencing Results from Hospital Airborne Microbiome Samples

The compositional analysis of 16S rRNA gene sequencing datasets is applied to characterize the bacterial structure of airborne samples collected in different locations of a hospital infection disease department hosting COVID-19 patients, as well as to investigate the relationships among bacterial taxa at the genus and species level. The exploration of the centered log-ratio transformed data by the principal component analysis via the singular value decomposition has shown that the collected samples segregated with an observable separation depending on the monitoring location. More specifically, two main sample clusters were identified with regards to bacterial genera (species), consisting of samples mostly collected in rooms with and without COVID-19 patients, respectively. Human pathogenic genera (species) associated with nosocomial infections were mostly found in samples from areas hosting patients, while non-pathogenic genera (species) mainly isolated from soil were detected in the other samples. Propionibacterium acnes, Staphylococcus pettenkoferi, Corynebacterium tuberculostearicum, and jeikeium were the main pathogenic species detected in COVID-19 patients' rooms. Samples from these locations were on average characterized by smaller richness/evenness and diversity than the other ones, both at the genus and species level. Finally, the ρ metrics revealed that pairwise positive associations occurred either between pathogenic or non-pathogenic taxa.

PM2.5 drives bacterial functions for carbon, nitrogen, and sulfur cycles in the atmosphere

Airborne bacteria may absorb the substance from the atmospheric particles and play a role in biogeochemical cycling. However, these studies focused on a few culturable bacteria and the samples were usually collected from one site. The metabolic potential of a majority of airborne bacteria on a regional scale and their driving factors remain unknown. In this study, we collected particulates with aerodynamic diameter ≤2.5 μm (PM_2.5) from 8 cities that represent different regions across China and analyzed the samples via high-throughput sequencing of 16S rRNA genes, quantitative polymerase chain reaction (qPCR) analysis, and functional database prediction. Based on the FAPROTAX database, 326 (80.69%), 191 (47.28%) and 45 (11.14%) bacterial genera are possible to conduct the pathways of carbon, nitrogen, and sulfur cycles, respectively. The pathway analysis indicated that airborne bacteria may lead to the decrease in organic carbon while the increase in ammonium and sulfate in PM_2.5 samples, all of which are the important components of PM_2.5. Among the 19 environmental factors studied including air pollutants, meteorological factors, and geographical conditions, PM_2.5 concentration manifested the strongest correlations with the functional genes for the transformation of ammonium and sulfate. Moreover, the PM_2.5 concentration rather than the sampling site will drive the distribution of functional genera. Thus, a bi-directional relationship between PM_2.5 and bacterial metabolism is suggested. Our findings shed light on the potential bacterial pathway for the biogeochemical cycling in the atmosphere and the important role of PM_2.5, offering a new perspective for atmospheric ecology and pollution control.

Gut microbiota features associated with Clostridioides difficile colonization in dairy calves

Diarrheal disease, a major cause of morbidity and mortality in dairy calves, is strongly associated with the health and composition of the gut microbiota. Clostridioides difficile is an opportunistic pathogen that proliferates and can produce enterotoxins when the host experiences gut dysbiosis. However, even asymptomatic colonization with C. difficile can be associated with differing degrees of microbiota disruption in a range of species, including people, swine, and dogs. Little is known about the interaction between C. difficile and the gut microbiota in dairy calves. In this study, we sought to define microbial features associated with C. difficile colonization in pre-weaned dairy calves less than 2 weeks of age. We characterized the fecal microbiota of 80 calves from 23 different farms using 16S rRNA sequencing and compared the microbiota of C. difficile-positive (n = 24) and C. difficile-negative calves (n = 56). Farm appeared to be the greatest source of variability in the gut microbiota. When controlling for calf age, diet, and farm location, there was no significant difference in Shannon alpha diversity (P = 0.50) or in weighted UniFrac beta diversity (P = 0.19) between C. difficile-positive and–negative calves. However, there was a significant difference in beta diversity as assessed using Bray-Curtiss diversity (P = 0.0077), and C. difficile-positive calves had significantly increased levels of Ruminococcus (gnavus group) (Adj. P = 0.052), Lachnoclostridium (Adj. P = 0.060), Butyricicoccus (Adj. P = 0.060), and Clostridium sensu stricto 2 compared to C. difficile-negative calves. Additionally, C. difficile-positive calves had fewer microbial co-occurrences than C. difficile–negative calves, indicating reduced bacterial synergies. Thus, while C. difficile colonization alone is not associated with dysbiosis and is therefore unlikely to result in an increased likelihood of diarrhea in dairy calves, it may be associated with a more disrupted microbiota.