NBZIMM: negative binomial and zero-inflated mixed models, with application to microbiome/metagenomics data analysis

Analyzing microbiome data with taxonomic misclassification using a zero-inflated Dirichlet-multinomial model

The human microbiome is the collection of microorganisms living on and inside of our bodies. A major aim of microbiome research is understanding the role microbial communities play in human health with the goal of designing personalized interventions that modulate the microbiome to treat or prevent disease. Microbiome data are challenging to analyze due to their high-dimensionality, overdispersion, and zero-inflation. Analysis is further complicated by the steps taken to collect and process microbiome samples. For example, sequencing instruments have a fixed capacity for the total number of reads delivered. It is therefore essential to treat microbial samples as compositional. Another complicating factor of modeling microbiome data is that taxa counts are subject to measurement error introduced at various stages of the measurement protocol. Advances in sequencing technology and preprocessing pipelines coupled with our growing knowledge of the human microbiome have reduced, but not eliminated, measurement error. Ignoring measurement error during analysis, though common in practice, can then lead to biased inference and curb reproducibility. We propose a Dirichlet-multinomial modeling framework for microbiome data with excess zeros and potential taxonomic misclassification. We demonstrate how accommodating taxonomic misclassification improves estimation performance and investigate differences in gut microbial composition between healthy and obese children.

Examining emotion reactivity to politically polarizing media in a randomized controlled trial of mindfulness training versus active coping training

Emotional appraisals of political stimuli (e.g., videos) have been shown to drive shared neural encoding, which correspond to shared, yet divisive, interpretations of such stimuli. However, mindfulness practice may entrain a form of emotion regulation that de-automatizes social biases, possibly through alteration of such neural mechanisms. The present study combined a naturalistic neuroimaging paradigm and a randomized controlled trial to examine the effects of short-term mindfulness training (MT) (n = 35) vs structurally equivalent Cognitive Reappraisal training (CT) (n = 37) on politically-situated emotions while evaluating the mechanistic role of prefrontal cortical neural synchrony. Participants underwent functional near-infrared spectroscopy (fNIRS) recording while viewing inflammatory partisan news clips and continuously rating their momentary discrete emotions. MT participants were more likely to respond with extreme levels of anger (odds ratio = 0.12, p < 0.001) and disgust (odds ratio = 0.08, p < 0.001) relative to CT participants. Neural synchrony-based analyses suggested that participants with extreme emotion reactions exhibited greater prefrontal cortical neural synchrony, but that this pattern was less prominent in participants receiving MT relative to CT (CT > MT; channel 1 ISC = 0.040, p = 0.030).

The impact of regular sauerkraut consumption on the human gut microbiota: a crossover intervention trial

BACKGROUND

Sauerkraut is a fermented food that has been suspected to have a beneficial impact on the gut microbiome, but scientific evidence is sparse. In this crossover intervention trial with 87 participants (DRKS00027007), we investigated the impact of daily consumption of fresh or pasteurized sauerkraut for 4 weeks on gut microbial composition and the metabolome in a healthy study population.

RESULTS

Using shotgun metagenomic sequencing, we observed changes in single bacterial species following fresh and pasteurized sauerkraut consumption. More pronounced changes were observed in the pasteurized sauerkraut intervention arm. Only pasteurized sauerkraut consumption increased serum short-chain fatty acids (SCFAs).

CONCLUSIONS

The gut microbiome of healthy individuals is rather resilient to short-term dietary interventions even though single species might be affected by sauerkraut consumption. Video Abstract.

The Composition of the Fecal and Mucosa-adherent Microbiota Varies Based on Age and Disease Activity in Ulcerative Colitis

BACKGROUND

Pediatric-onset ulcerative colitis (pUC) represents a more aggressive disease phenotype compared with adult-onset UC. We hypothesized that this difference can, in part, be explained by the composition of the microbiota.

METHODS

In a prospective, longitudinal study, we included pediatric (N = 30) and adult (N = 30) patients with newly or previously (>1 year) diagnosed UC. We analyzed the microbiota composition in the mucosa-adherent microbiota at baseline, using 16S rRNA gene sequencing, and the fecal microbiota at baseline and at 3-month intervals, using shotgun metagenomics.

RESULTS

For fecal samples, the bacterial composition differed between pUC and aUC in newly diagnosed patients (β-diversity, Bray Curtis: R2 = 0.08, P = .02). In colon biopsies, microbial diversity was higher in aUC compared with pUC (α-diversity, Shannon: estimated difference 0.54, P = .006). In the mucosa-adherent microbiota, Alistipes finegoldii was negatively associated with disease activity in pUC while being positively associated in aUC (estimate: -0.255 and 0.098, P = .003 and P = .02 in pUC and aUC, respectively). Finally, we showed reduced stability of the fecal microbiota in pediatric patients, evidenced by a different composition of the fecal microbiota in newly and previously diagnosed pUC, a pattern not found in adults.

CONCLUSIONS

Our results indicate that pediatric UC patients have a more unstable fecal microbiota and a lower α diversity than adult patients and that the microbiota composition differs between aUC and pUC patients. These findings offer some explanation for the observed differences between pUC and aUC and indicate that individualized approaches are needed if microbiota modifications are to be used in the future treatment of UC.

Spermless males fail to remedy the low fecundity of parthenogenetic females in D. melanogaster

The recent construction of a parthenogenetic strain of D. melanogaster offers new avenues of research, but this potential is limited by the stock's abysmal fecundity. We tried using spermless (placebo) males to "trick" the virgins into producing more offspring, but the boost that we achieved proved to be short-lived due to premature death of the mothers. To explore the cause of this mortality, we compared the lifespans of parthenogenetic vs. wild-type females when mated with spermless males. We found that parthenogenetic females are less robust than wild-females when raised alone but are more resistant to harm from spermless males.

ZINQ-L: a zero-inflated quantile approach for differential abundance analysis of longitudinal microbiome data

Background

Identifying bacterial taxa associated with disease phenotypes or clinical treatments over time is critical for understanding the underlying biological mechanism. Association testing for microbiome data is already challenging due to its complex distribution that involves sparsity, over-dispersion, heavy tails, etc. The longitudinal nature of the data adds another layer of complexity - one needs to account for the within-subject correlations to avoid biased results. Existing longitudinal differential abundance approaches usually depend on strong parametric assumptions, such as zero-inflated normal or negative binomial. However, the complex microbiome data frequently violate these distributional assumptions, leading to inflated false discovery rates. In addition, the existing methods are mostly mean-based, unable to identify heterogeneous associations such as tail events or subgroup effects, which could be important biomedical signals.

Methods

We propose a zero-inflated quantile approach for longitudinal (ZINQ-L) microbiome differential abundance test. A mixed-effects quantile rank-score-based test was proposed for hypothesis testing, which consists of a test in mixed-effects logistic model for the presence-absence status of the investigated taxon, and a series of mixed-effects quantile rank-score tests adjusted for zero inflation given its presence. As a regression method with minimal distributional assumptions, it is robust to the complex microbiome data, controlling false discovery rate, and is flexible to adjust for important covariates. Its comprehensive examination of the abundance distribution enables the identification of heterogeneous associations, improving the testing power.

Results

Extensive simulation studies and an application to a real kidney transplant microbiome study demonstrate the improved power of ZINQ-L in detecting true signals while controlling false discovery rates.

Conclusion

ZINQ-L is a zero-inflated quantile-based approach for detecting individual taxa associated with outcomes or exposures in longitudinal microbiome studies, providing a robust and powerful option to improve and complement the existing methods in the field.

PreLect: Prevalence leveraged consistent feature selection decodes microbial signatures across cohorts

The intricate nature of microbiota sequencing data-high dimensionality and sparsity-presents a challenge in identifying informative and reproducible microbial features for both research and clinical applications. Addressing this, we introduce PreLect, an innovative feature selection framework that harnesses microbes' prevalence to facilitate consistent selection in sparse microbiota data. Upon rigorous benchmarking against established feature selection methodologies across 42 microbiome datasets, PreLect demonstrated superior classification capabilities compared to statistical methods and outperformed machine learning-based methods by selecting features with greater prevalence and abundance. A significant strength of PreLect lies in its ability to reliably identify reproducible microbial features across varied cohorts. Applied to colorectal cancer, PreLect identifies key microbes and highlights crucial pathways, such as lipopolysaccharide and glycerophospholipid biosynthesis, in cancer progression. This case study exemplifies PreLect's utility in discerning clinically relevant microbial signatures. In summary, PreLect's accuracy and robustness make it a significant advancement in the analysis of complex microbiota data.

Association of body index with fecal microbiome in children cohorts with ethnic-geographic factor interaction: accurately using a Bayesian zero-inflated negative binomial regression model

The exponential growth of high-throughput sequencing (HTS) data on the microbial communities presents researchers with an unparalleled opportunity to delve deeper into the association of microorganisms with host phenotype. However, this growth also poses a challenge, as microbial data are complex, sparse, discrete, and prone to zero inflation. Herein, by utilizing 10 distinct counting models for analyzing simulated data, we proposed an innovative Bayesian zero-inflated negative binomial (ZINB) regression model that is capable of identifying differentially abundant taxa associated with distinctive host phenotypes and quantifying the effects of covariates on these taxa. Our proposed model exhibits excellent accuracy compared with conventional Hurdle and INLA models, especially in scenarios characterized by inflation and overdispersion. Moreover, we confirm that dispersion parameters significantly affect the accuracy of model results, with defects gradually alleviating as the number of analyzed samples increases. Subsequently applying our model to amplicon data in real multi-ethnic children cohort, we found that only a subset of taxa were identified as having zero inflation in real data, suggesting that the prevailing understanding and processing of microbial count data in most previous microbiome studies were overly dogmatic. In practice, our pipeline of integrating bacterial differential abundance in microbiome data and relevant covariates is effective and feasible. Taken together, our method is expected to be extended to the microbiota studies of various multi-cohort populations.

IMPORTANCE

The microbiome is closely associated with physical indicators of the body, such as height, weight, age and BMI, which can be used as measures of human health. Accurately identifying which taxa in the microbiome are closely related to indicators of physical development is valuable as microbial markers of regional child growth trajectory. Zero-inflated negative binomial (ZINB) model, a type of Bayesian generalized linear model, can be effectively modeled in complex biological systems. We present an innovative ZINB regression model that is capable of identifying differentially abundant taxa associated with distinctive host phenotypes and quantifying the effects of covariates on these taxa, and demonstrate that its accuracy is superior to traditional Hurdle and INLA models. Our pipeline of integrating bacterial differential abundance in microbiome data and relevant covariates is effective and feasible.

Wise Roles and Future Visionary Endeavors of Current Emperor: Advancing Dynamic Methods for Longitudinal Microbiome Meta-Omics Data in Personalized and Precision Medicine

Understanding the etiological complexity of diseases requires identifying biomarkers longitudinally associated with specific phenotypes. Advanced sequencing tools generate dynamic microbiome data, providing insights into microbial community functions and their impact on health. This review aims to explore the current roles and future visionary endeavors of dynamic methods for integrating longitudinal microbiome multi-omics data in personalized and precision medicine. This work seeks to synthesize existing research, propose best practices, and highlight innovative techniques. The development and application of advanced dynamic methods, including the unified analytical frameworks and deep learning tools in artificial intelligence, are critically examined. Aggregating data on microbes, metabolites, genes, and other entities offers profound insights into the interactions among microorganisms, host physiology, and external stimuli. Despite progress, the absence of gold standards for validating analytical protocols and data resources of various longitudinal multi-omics studies remains a significant challenge. The interdependence of workflow steps critically affects overall outcomes. This work provides a comprehensive roadmap for best practices, addressing current challenges with advanced dynamic methods. The review underscores the biological effects of clinical, experimental, and analytical protocol settings on outcomes. Establishing consensus on dynamic microbiome inter-studies and advancing reliable analytical protocols are pivotal for the future of personalized and precision medicine.

New clinical approach in facial mild-moderate acne: Re-stabilization of skin microbiota balance with a topical biotechnological phytocomplex

OBJECTIVES

Dysbiosis of the skin microbiota has been identified as a key factor in the development of acne. This study was aimed to evaluate the effect of a facial cream gel containing a biotechnological phytocomplex, niacinamide and succinic acid on the bacterial diversity of subjects with mild-moderate acne and its clinical benefits due to microbiota changes.

METHODS

Open-label, clinical study in 44 subjects with mild-moderate acne treated with a facial cream gel for 8 weeks. Bacterial diversity was analyzed by 16S rRNA gene sequencing of skin samples. Clinical effects were evaluated using the IGA acne severity scale, biometric measurements, and safety.

RESULTS

After 56 days of product's use, an increase in alpha and beta diversity was found (p < 0.01), with a decrease in the relative abundance of C. acnes (48.99% vs. 38.83%, p < 0.001). Regarding clinical results, a decrease in acne severity on the IGA scale (27.33%, p < 0.001), number of non-inflammatory and inflammatory lesions (respectively: 31.12%, p = 0.05; 47.27%, p < 0.001), amount of sebum (89.00%, p < 0.01) and erythema (15.35%, p < 0.01), was found. [Correction added on 19 September 2024, after first online publication: In the preceding sentence, "42.27%" has been changed to "47.27%" in this version.] Responder analysis of the IGA score showed that 61.36% of patients improved by at least one point at day 56. The product was well tolerated throughout the study.

CONCLUSIONS

The use of the facial cream gel on skin was effective in rebalancing the microbiota, inhibiting biofilm formation and other virulence factors, reducing the number of mild-moderate acne lesions and sebum secretion, and consequently improving acne's severity.

Impacts of Whole-Grain Soft Red, Whole-Grain Soft White, and Refined Soft White Wheat Flour Crackers on Gastrointestinal Inflammation and the Gut Microbiota of Adult Humans

Consumption of whole-grain wheat has been associated with positive health outcomes, but it remains unclear whether different types of wheat elicit varying effects on the gut microbiome and intestinal inflammation. The objectives of this research were to investigate the effect of two whole-grain wheat flours versus refined wheat flour on the diversity of the human gut microbiota, as well as on butyrate production capacity and gastrointestinal inflammation, using one-week dietary interventions. For this study, 28 participants were recruited, with ages ranging from 18 to 55 years and a mean BMI of 26.0 kg/m2. For four weeks, participants were provided 80 g daily servings of different wheat crackers: Week A was a run-in period of crackers made from soft white wheat flour, Week B crackers were whole-grain soft white wheat flour, Week C crackers were a wash-out period identical to Week A, and Week D crackers were whole-grain soft red wheat flour. At the end of each week, participants provided fecal samples that were analyzed for markers of intestinal inflammation, including lipocalin and calprotectin, using enzyme-linked immunosorbent assays and quantitative real-time PCR. The primary outcome, gut bacterial community alpha and beta diversity, was similar across timepoints. Three taxa significantly differed in abundance following both whole-grain wheat flour interventions: Escherichia/Shigella and Acidaminococcus were significantly depleted, and Lachnospiraceae NK4A136 group was enriched. Secondary outcomes determined that protein markers of intestinal inflammation and genes related to putative butyrate production capacity were similar throughout the study period, with no significant changes. Lipocalin concentrations ranged from 14.8 to 22.6 ng/mL while calprotectin ranged from 33.2 to 62.5 ng/mL across all 4 weeks. The addition of wheat crackers to the adult human subjects' usual diet had a minimal impact on their gastrointestinal inflammation or the gut microbiota.

Sourdough Bread with Different Fermentation Times: A Randomized Clinical Trial in Subjects with Metabolic Syndrome

The Mediterranean diet, featuring sourdough bread, shows promise in managing metabolic syndrome. This study explored the effects of two sourdough breads, with differing fermentation times but similar nutritional profiles, on inflammation, satiety, and gut microbiota composition in adults with metabolic syndrome. In a double-blind clinical trial, participants were randomized to consume either Elias Boulanger® long-fermentation (48 h) sourdough bread (EBLong) or Elias Boulanger® short-fermentation (2 h) sourdough bread (EBShort) over a two-month period. We assessed clinical parameters, inflammatory biomarkers, satiety-related hormones, and the richness and abundance of gut microbiota at baseline and follow-up. The participants included 31 individuals (mean age, 67, 51.6% female). EBShort was associated with reduced levels of soluble intercellular adhesion molecule (sICAM), and all participants, regardless of the intervention, exhibited a decrease in sICAM and diastolic pressure from baseline (p < 0.017). At follow-up, plasminogen activator inhibitor-1 (PAI-1) levels were lower in EBShort (-744 pg/mL; 95%CI: -282 to -1210 pg/mL) compared to EBLong. No differences in microbiota richness or abundance were observed. EBShort bread was effective in reducing some inflammation markers. The consumption of sourdough bread may offer potential benefits in reducing inflammation markers in individuals with metabolic syndrome; however, longer fermentation times did not show additional benefits.

Longitudinal Microbiome Changes in Supragingival Biofilm Transcriptomes Induced by Orthodontics

INTRODUCTION

Common oral diseases are known to be associated with dysbiotic shifts in the supragingival microbiome, yet most oral microbiome associations with clinical end points emanate from cross-sectional studies. Orthodontic treatment is an elective procedure that can be exploited to prospectively examine clinically relevant longitudinal changes in the composition and function of the supragingival microbiome.

METHODS

A longitudinal cohort study was conducted among 24 adolescent orthodontic patients who underwent saliva and plaque sampling and clinical examinations at time points: before fixed appliance bonding and at 1, 6, and 12 wk thereafter. Clinical indices included bleeding on probing (BOP), mean gingival index (GI), probing depths (PDs), and plaque index (PI). To study the biologically (i.e., transcriptionally) active microbial communities, RNA was extracted from plaque and saliva for RNA sequencing and microbiome bioinformatics analysis. Longitudinal changes in microbiome beta diversity were examined using PERMANOVA tests, and the relative abundance of microbial taxa was measured using Kruskal-Wallis tests, Wilcoxon rank-sum tests, and negative binomial and zero-inflated mixed models.

RESULTS

Clinical measures of oral health deteriorated over time-the proportion of sites with GI and PI ≥1 increased by over 70% between prebonding and 12 wk postbonding while the proportion of sites with PD ≥4 mm increased 2.5-fold. Streptococcus sanguinis, a health-associated species that antagonizes cariogenic pathogens, showed a lasting decrease in relative abundance during orthodontic treatment. Contrarily, caries- and periodontal disease-associated taxa, including Selenomonas sputigena, Leptotrichia wadei, and Lachnoanaerobaculum saburreum, increased in abundance after bonding. Relative abundances of Stomatobaculum longum and Mogibacterium diversum in prebonding saliva predicted elevated BOP 12 wk postbonding, whereas Neisseria subflava was associated with lower BOP.

CONCLUSIONS

This study offers insights into longitudinal community and species-specific changes in the supragingival microbiome transcriptome during fixed orthodontic treatment, advancing our understanding of microbial dysbioses and identifying targets of future health-promoting clinical investigations.

KNOWLEDGE TRANSFER STATEMENT

Bonding braces was associated with subsequent changes in the oral microbiome characterized by increases in disease-associated species, decreases in health-associated species, and worsened clinical measures of oral health.

Beneficial Effects of Synbiotics on the Gut Microbiome in Individuals with Low Fiber Intake: Secondary Analysis of a Double-Blind, Randomized Controlled Trial

Insufficient dietary fiber intake can negatively affect the intestinal microbiome and, over time, may result in gut dysbiosis, thus potentially harming overall health. This randomized controlled trial aimed to improve the gut microbiome of individuals with low dietary fiber intake (<25 g/day) during a 7-week synbiotic intervention. The metabolically healthy male participants (n = 117, 32 ± 10 y, BMI 25.66 ± 3.1 kg/m2) were divided into two groups: one receiving a synbiotic supplement (Biotic Junior, MensSana AG, Forchtenberg, Germany) and the other a placebo, without altering their dietary habits or physical activity. These groups were further stratified by their dietary fiber intake into a low fiber group (LFG) and a high fiber group (HFG). Stool samples for microbiome analysis were collected before and after intervention. Statistical analysis was performed using linear mixed effects and partial least squares models. At baseline, the microbiomes of the LFG and HFG were partially separated. After seven weeks of intervention, the abundance of SCFA-producing microbes significantly increased in the LFG, which is known to improve gut health; however, this effect was less pronounced in the HFG. Beneficial effects on the gut microbiome in participants with low fiber intake may be achieved using synbiotics, demonstrating the importance of personalized synbiotics.

Effectiveness, Safety, and Acceptability of Primaquine Mass Drug Administration in Low-Endemicity Areas in Southern Thailand: Proof-of-Concept Study

BACKGROUND

A challenge in achieving the malaria-elimination target in the Greater Mekong Subregion, including Thailand, is the predominance of Plasmodium vivax malaria, which has shown extreme resilience to control measures.

OBJECTIVE

This proof-of-concept study aimed to provide evidence for implementing primaquine mass drug administration (pMDA) as a strategy for P. vivax elimination in low-endemicity settings.

METHODS

The study employed a mixed-methods trial to thoroughly evaluate the effectiveness, safety, acceptability, and community engagement of pMDA. The quantitative part was designed as a 2-period cluster-crossover randomized controlled trial. The intervention was pMDA augmented to the national prevention and control standards with directly observed treatment (DOT) by village health volunteers. The qualitative part employed in-depth interviews and brainstorming discussions. The study involved 7 clusters in 2 districts of 2 southern provinces in Thailand with persistently low P. vivax transmission. In the quantitative part, 5 cross-sectional blood surveys were conducted in both the pMDA and control groups before and 3 months after pMDA. The effectiveness of pMDA was determined by comparing the proportions of P. vivax infections per 1000 population between the 2 groups, with a multilevel zero-inflated negative binomial model adjusted for cluster and time as covariates and the interaction. The safety data comprised adverse events after drug administration. Thematic content analysis was used to assess the acceptability and engagement of stakeholders.

RESULTS

In the pre-pMDA period, the proportions of P. vivax infections in the pMDA (n=1536) and control (n=1577) groups were 13.0 (95% CI 8.2-20.4) and 12.0 (95% CI 7.5-19.1), respectively. At month 3 post-pMDA, these proportions in the pMDA (n=1430) and control (n=1420) groups were 8.4 (95% CI 4.6-15.1) and 5.6 (95% CI 2.6-11.5), respectively. No statistically significant differences were found between the groups. The number of malaria cases reduced in all clusters in both groups, and thus, the impact of pMDA was inconclusive. There were no major safety concerns. Acceptance among the study participants and public health care providers at local and national levels was high, and they believed that pMDA had boosted awareness in the community.

CONCLUSIONS

pMDA was associated with high adherence, safety, and tolerability, but it may not significantly impact P. vivax transmission. As this was a proof-of-concept study, we decided not to scale up the intervention with larger clusters and samples. An alternative approach involving a targeted primaquine treatment strategy with primaquine and DOT is currently being implemented. We experienced success regarding effective health care workforces at point-of-care centers, effective collaborations in the community, and commitment from authorities at local and national levels. Our efforts boosted the acceptability of the malaria-elimination initiative. Community engagement is recommended to achieve elimination targets.

TRIAL REGISTRATION

Thai Clinical Trials Registry TCTR20190806004; https://www.thaiclinicaltrials.org/show/TCTR20190806004.

Protecting the piglet gut microbiota against ETEC-mediated post-weaning diarrhoea using specific binding proteins

Post-weaning diarrhoea (PWD) in piglets presents a widespread problem in industrial pig production and is often caused by enterotoxigenic E. coli (ETEC) strains. Current solutions, such as antibiotics and medicinal zinc oxide, are unsustainable and are increasingly being prohibited, resulting in a dire need for novel solutions. Thus, in this study, we propose and evaluate a protein-based feed additive, comprising two bivalent heavy chain variable domain (VHH) constructs (VHH-(GGGGS)3-VHH, BL1.2 and BL2.2) as an alternative solution to manage PWD. We demonstrate in vitro that these constructs bind to ETEC toxins and fimbriae, whilst they do no affect bacterial growth rate. Furthermore, in a pig study, we show that oral administration of these constructs after ETEC challenge reduced ETEC proliferation when compared to challenged control piglets (1-2 log10 units difference in gene copies and bacterial count/g faeces across day 2-7) and resulted in week 1 enrichment of three bacterial families (Prevotellaceae (estimate: 1.12 ± 0.25, q = 0.0054), Lactobacillaceae (estimate: 2.86 ± 0.52, q = 0.0012), and Ruminococcaceae (estimate: 0.66 ± 0.18, q = 0.049)) within the gut microbiota that appeared later in challenged control piglets, thus pointing to an earlier transition towards a more mature gut microbiota. These data suggest that such VHH constructs may find utility in industrial pig production as a feed additive for tackling ETEC and reducing the risk of PWD in piglet populations.

Influence of Compost Amendments on Soil and Human Gastrointestinal Bacterial Communities during a Single Gardening Season

To measure associations between gardening with different compost amendments and the human gut microbiota composition, gardeners (n = 25) were provided with one of three types of compost: chicken manure (CM), dairy manure and plant material (DMP), or plant-based (P). Stool samples were collected before gardening (T1), after compost amendment (T2), and at peak garden harvest (T3). Compost and soil samples were collected. DNA was extracted, 16S rRNA libraries were established, and libraries were sequenced by Illumina MiSeq. Sequences were processed using mothur, and data were analyzed in R software version 4.2.2. Fast expectation-maximization microbial source tracking analysis was used to determine stool bacteria sources. At T2/T3, the gut microbiotas of P participants had the lowest Shannon alpha diversity, which was also the trend at T1. In stool from T2, Ruminococcus 1 were less abundant in the microbiotas of those using P compost as compared to those using CM or DMP. At T2, Prevotella 9 had the highest abundance in the microbiotas of those using CM compost. In participants who used CM compost to amend their gardening plots, a larger proportion of the human stool bacteria were sourced from CM compared to soil. Soil exposure through gardening was associated with a small but detectable change in the gardeners' gut microbiota composition. These results suggest that human interactions with soil through gardening could potentially impact health through alterations to the gut microbiota.

Impact of Synbiotic Intake on Liver Metabolism in Metabolically Healthy Participants and Its Potential Preventive Effect on Metabolic-Dysfunction-Associated Fatty Liver Disease (MAFLD): A Randomized, Placebo-Controlled, Double-Blinded Clinical Trial

Synbiotics modulate the gut microbiome and contribute to the prevention of liver diseases such as metabolic-dysfunction-associated fatty liver disease (MAFLD). This study aimed to evaluate the effect of a randomized, placebo-controlled, double-blinded seven-week intervention trial on the liver metabolism in 117 metabolically healthy male participants. Anthropometric data, blood parameters, and stool samples were analyzed using linear mixed models. After seven weeks of intervention, there was a significant reduction in alanine aminotransferase (ALT) in the synbiotic group compared to the placebo group (-14.92%, CI: -26.60--3.23%, p = 0.013). A stratified analysis according to body fat percentage revealed a significant decrease in ALT (-20.70%, CI: -40.88--0.53%, p = 0.045) in participants with an elevated body fat percentage. Further, a significant change in microbiome composition (1.16, CI: 0.06-2.25, p = 0.039) in this group was found, while the microbial composition remained stable upon intervention in the group with physiological body fat. The 7-week synbiotic intervention reduced ALT levels, especially in participants with an elevated body fat percentage, possibly due to modulation of the gut microbiome. Synbiotic intake may be helpful in delaying the progression of MAFLD and could be used in addition to the recommended lifestyle modification therapy.

AM, Doster E, Fernandes L, Calles VF, Bauman C, Godden S, Heins B, Pinedo P, Machado VS, Caixeta LS, Noyes NR. Exploring associations between the teat apex metagenome and Staphylococcus aureus intramammary infections in primiparous cows under organic directives

The primary objective of this study was to identify associations between the prepartum teat apex microbiome and the presence of Staphylococcus aureus intramammary infections (IMI) in primiparous cows during the first 5 weeks after calving. We performed a case-control study using shotgun metagenomics of the teat apex and culture-based milk data collected longitudinally from 710 primiparous cows on five organic dairy farms. Cases had higher odds of having S. aureus metagenomic DNA on the teat apex prior to parturition compared to controls (OR = 38.9, 95% CI: 14.84-102.21). Differential abundance analysis confirmed this association, with cases having a 23.8 higher log fold change (LFC) in the abundance of S. aureus in their samples compared to controls. Of the most prevalent microorganisms in controls, those associated with a lower risk of post-calving S. aureus IMI included Microbacterium phage Min 1 (OR = 0.37, 95% CI: 0.25-0.53), Corynebacterium efficiens (OR = 0.53, 95% CI: 0.30-0.94), Kocuria polaris (OR = 0.54, 95% CI: 0.35-0.82), Micrococcus terreus (OR = 0.64, 95% CI: 0.44-0.93), and Dietzia alimentaria (OR = 0.45, 95% CI: 0.26-0.75). Genes encoding for Microcin B17 AMPs were the most prevalent on the teat apex of cases and controls (99.7% in both groups). The predicted abundance of genes encoding for Microcin B17 was also higher in cases compared to controls (LFC 0.26).

IMPORTANCE

Intramammary infections (IMI) caused by Staphylococcus aureus remain an important problem for the dairy industry. The microbiome on the external skin of the teat apex may play a role in mitigating S. aureus IMI risk, in particular the production of antimicrobial peptides (AMPs) by commensal microbes. However, current studies of the teat apex microbiome utilize a 16S approach, which precludes the detection of genomic features such as genes that encode for AMPs. Therefore, further research using a shotgun metagenomic approach is needed to understand what role prepartum teat apex microbiome dynamics play in IMI risk.

Air pollution and age-dependent changes in emotional behavior across early adolescence in the U.S

Recent studies have linked air pollution to increased risk for behavioral problems during development, albeit with inconsistent findings. Additional longitudinal studies are needed that consider how emotional behaviors may be affected when exposure coincides with the transition to adolescence - a vulnerable time for developing mental health difficulties. This study investigates if annual average PM2.5 and NO2 exposure at ages 9-10 years moderates age-related changes in internalizing and externalizing behaviors over a 2-year follow-up period in a large, nationwide U.S. sample of participants from the Adolescent Brain Cognitive Development (ABCD) Study®. Air pollution exposure was estimated based on the residential address of each participant using an ensemble-based modeling approach. Caregivers answered questions from the Child Behavior Checklist (CBCL) at the baseline, 1-year follow-up, and 2-year follow-up visits, for a total of 3 waves of data; from the CBCL we obtained scores on internalizing and externalizing problems plus 5 syndrome scales (anxious/depressed, withdrawn/depressed, rule-breaking behavior, aggressive behavior, and attention problems). Zero-inflated negative binomial models were used to examine both the main effect of age as well as the interaction of age with each pollutant on behavior while adjusting for various socioeconomic and demographic characteristics. Against our hypothesis, there was no evidence that greater air pollution exposure was related to more behavioral problems with age over time.

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.

Host genomic variation shapes gut microbiome diversity in threespine stickleback fish

IMPORTANCE

A major focus of host-microbe research is to understand how genetic differences, of various magnitudes, among hosts translate to differences in their microbiomes. This has been challenging for animal hosts, including humans, because it is difficult to control environmental variables tightly enough to isolate direct genetic effects on the microbiome. Our work in stickleback fish is a significant contribution because our experimental approach allowed strict control over environmental factors, including standardization of the microbiome from the earliest stage of development and unrestricted co-housing of fish in a truly common environment. Furthermore, we measured host genetic variation over 2,000 regions of the stickleback genome, comparing this information and microbiome composition data among fish from very similar and very different genetic backgrounds. Our findings highlight how differences in the host genome influence microbiome diversity and make a case for future manipulative microbiome experiments that use host systems with naturally occurring genetic variation.

Seasonal restructuring facilitates compositional convergence of gut microbiota in free-ranging rodents

Gut microbes provide essential services to their host and shifts in their composition can impact host fitness. However, despite advances in our understanding of how microbes are assembled in the gut, we understand little about the stability of these communities within individuals, nor what factors influence its composition over the life of an animal. For this reason, we conducted a longitudinal survey of the gut microbial communities of individual free-ranging woodrats (Neotoma spp.) across a hybrid zone in the Mojave Desert, USA, using amplicon sequencing approaches to characterize gut microbial profiles and diet. We found that gut microbial communities were individualized and experienced compositional restructuring as a result of seasonal transitions and changes in diet. Turnover of gut microbiota was highest amongst bacterial subspecies and was much lower at the rank of Family, suggesting there may be selection for conservation of core microbial functions in the woodrat gut. Lastly, we identified an abundant core gut bacterial community that may aid woodrats in metabolizing a diet of plants and their specialized metabolites. These results demonstrate that the gut microbial communities of woodrats are highly dynamic and experience seasonal restructuring which may facilitate adaptive plasticity in response to changes in diet.

Intestinal permeability in patients undergoing stem cell transplantation correlates with systemic acute phase responses and dysbiosis

Intestinal permeability may correlate with adverse outcomes during hematopoietic stem cell transplantation (HSCT), but longitudinal quantification with traditional oral mannitol and lactulose is not feasible in HSCT recipients because of mucositis and diarrhea. A modified lactulose:rhamnose (LR) assay is validated in children with environmental enteritis. Our study objective was to quantify peri-HSCT intestinal permeability changes using the modified LR assay. The LR assay was administered before transplant, at day +7 and +30 to 80 pediatric and young adult patients who received allogeneic HSCT. Lactulose and rhamnose were detected using urine mass spectrometry and expressed as an L:R ratio. Metagenomic shotgun sequencing of stool for microbiome analyses and enzyme-linked immunosorbent assay analyses of plasma lipopolysaccharide binding protein (LBP), ST2, REG3α, claudin1, occludin, and intestinal alkaline phosphatase were performed at the same timepoints. L:R ratios were increased at day +7 but returned to baseline at day +30 in most patients (P = .014). Conditioning regimen intensity did not affect the trajectory of L:R (P = .39). Baseline L:R ratios did not vary with diagnosis. L:R correlated with LBP levels (r2 = 0.208; P = .0014). High L:R ratios were associated with lower microbiome diversity (P = .035), loss of anaerobic organisms (P = .020), and higher plasma LBP (P = .0014). No adverse gastrointestinal effects occurred because of LR. Intestinal permeability as measured through L:R ratios after allogeneic HSCT correlates with intestinal dysbiosis and elevated plasma LBP. The LR assay is well-tolerated and may identify transplant recipients who are more likely to experience adverse outcomes.

Temporal changes in fecal microbiota of patients infected with COVID-19: a longitudinal cohort

BACKGROUND

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a multifaceted disease potentially responsible for various clinical manifestations including gastro-intestinal symptoms. Several evidences suggest that the intestine is a critical site of immune cell development, gut microbiota could therefore play a key role in lung immune response. We designed a monocentric longitudinal observational study to describe the gut microbiota profile in COVID-19 patients and compare it to a pre-existing cohort of ventilated non-COVID-19 patients.

METHODS

From March to December 2020, we included patients admitted for COVID-19 in medicine (43 not ventilated) or intensive care unit (ICU) (14 ventilated) with a positive SARS-CoV-2 RT-PCR assay in a respiratory tract sample. 16S metagenomics was performed on rectal swabs from these 57 COVID-19 patients, 35 with one and 22 with multiple stool collections. Nineteen non-COVID-19 ICU controls were also enrolled, among which 14 developed ventilator-associated pneumonia (pneumonia group) and five remained without infection (control group). SARS-CoV-2 viral loads in fecal samples were measured by qPCR.

RESULTS

Although similar at inclusion, Shannon alpha diversity appeared significantly lower in COVID-19 and pneumonia groups than in the control group at day 7. Furthermore, the microbiota composition became distinct between COVID-19 and non-COVID-19 groups. The fecal microbiota of COVID-19 patients was characterized by increased Bacteroides and the pneumonia group by Prevotella. In a distance-based redundancy analysis, only COVID-19 presented significant effects on the microbiota composition. Moreover, patients in ICU harbored increased Campylobacter and decreased butyrate-producing bacteria, such as Lachnospiraceae, Roseburia and Faecalibacterium as compared to patients in medicine. Both the stay in ICU and patient were significant factors affecting the microbiota composition. SARS-CoV-2 viral loads were higher in ICU than in non-ICU patients.

CONCLUSIONS

Overall, we identified distinct characteristics of the gut microbiota in COVID-19 patients compared to control groups. COVID-19 patients were primarily characterized by increased Bacteroides and decreased Prevotella. Moreover, disease severity showed a negative correlation with butyrate-producing bacteria. These features could offer valuable insights into potential targets for modulating the host response through the microbiota and contribute to a better understanding of the disease's pathophysiology.

TRIAL REGISTRATION

CER-VD 2020-00755 (05.05.2020) & 2017-01820 (08.06.2018).

Air pollution and emotional behavior in adolescents across the U.S

Recent studies have linked air pollution to increased risk for behavioral problems during development, albeit with inconsistent findings. Additional longitudinal studies are needed that consider how emotional behaviors may be affected when exposure coincides with the transition to adolescence - a vulnerable time for developing mental health difficulties. This study examines how annual average PM2.5 and NO2 exposure at ages 9-10 years relates to internalizing and externalizing behaviors over a 2-year follow-up period in a large, nationwide U.S. sample of participants from the Adolescent Brain Cognitive Development (ABCD) Study®. Air pollution exposure was estimated based on the residential address of each participant using an ensemble-based modeling approach. Caregivers answered questions from the Child Behavior Checklist (CBCL) at baseline and annually for two follow-up sessions for a total of 3 waves of data; from the CBCL we obtained scores on internalizing and externalizing problems plus 5 syndrome scales (anxious/depressed, withdrawn/depressed, rule-breaking behavior, aggressive behavior, and attention problems). Zero-inflated negative binomial models were used to examine both the main effect of age as well as the interaction of age with each pollutant on behavior while adjusting for various socioeconomic and demographic characteristics. Overall, the pollution effects moderated the main effects of age with higher levels of PM2.5 and NO2 leading to an even greater likelihood of having no behavioral problems (i.e., score of zero) with age over time, as well as fewer problems when problems are present as the child ages. Albeit this was on the order equal to or less than a 1-point change. Thus, one year of annual exposure at 9-10 years is linked with very small change in emotional behaviors in early adolescence, which may be of little clinical relevance.

Compositionality, sparsity, spurious heterogeneity, and other data-driven challenges for machine learning algorithms within plant microbiome studies

The plant-associated microbiome is a key component of plant systems, contributing to their health, growth, and productivity. The application of machine learning (ML) in this field promises to help untangle the relationships involved. However, measurements of microbial communities by high-throughput sequencing pose challenges for ML. Noise from low sample sizes, soil heterogeneity, and technical factors can impact the performance of ML. Additionally, the compositional and sparse nature of these datasets can impact the predictive accuracy of ML. We review recent literature from plant studies to illustrate that these properties often go unmentioned. We expand our analysis to other fields to quantify the degree to which mitigation approaches improve the performance of ML and describe the mathematical basis for this. With the advent of accessible analytical packages for microbiome data including learning models, researchers must be familiar with the nature of their datasets.

Going with the flow - how a stream insect, Pteronarcys californica, exploits local flows to increase oxygen availability

For insects, life in water is challenging because oxygen supply is typically low compared with in air. Oxygen limitation may occur when oxygen levels or water flows are low or when warm temperatures stimulate metabolic demand for oxygen. A potential mechanism for mitigating oxygen shortages is behavior - moving to cooler, more oxygenated or faster flowing microhabitats. Whether stream insects can make meaningful choices, however, depends on: (i) how temperature, oxygen and flow vary at microspatial scales and (ii) the ability of insects to sense and exploit that variation. To assess the extent of microspatial variation in conditions, we measured temperature, oxygen saturation and flow velocity within riffles of two streams in Montana, USA. In the lab, we then examined preferences of nymphs of the stonefly Pteronarcys californica to experimental gradients based on field-measured values. Temperature and oxygen level varied only slightly within stream riffles. By contrast, flow velocity was highly heterogeneous, often varying by more than 125 cm s-1 within riffles and 44 cm s-1 around individual cobbles. Exploiting micro-variation in flow may thus be the most reliable option for altering rates of oxygen transport. In support of this prediction, P. californica showed little ability to exploit gradients in temperature and oxygen but readily exploited micro-variation in flow - consistently choosing higher flows when conditions were warm or hypoxic. These behaviors may help stream insects mitigate low-oxygen stress from climate change and other anthropogenic disturbances.

Age influences the temporal dynamics of microbiome and antimicrobial resistance genes among fecal bacteria in a cohort of production pigs

BACKGROUND

The pig gastrointestinal tract hosts a diverse microbiome, which can serve to select and maintain a reservoir of antimicrobial resistance genes (ARG). Studies suggest that the types and quantities of antimicrobial resistance (AMR) in fecal bacteria change as the animal host ages, yet the temporal dynamics of AMR within communities of bacteria in pigs during a full production cycle remains largely unstudied.

RESULTS

A longitudinal study was performed to evaluate the dynamics of fecal microbiome and AMR in a cohort of pigs during a production cycle; from birth to market age. Our data showed that piglet fecal microbial communities assemble rapidly after birth and become more diverse with age. Individual piglet fecal microbiomes progressed along similar trajectories with age-specific community types/enterotypes and showed a clear shift from E. coli/Shigella-, Fusobacteria-, Bacteroides-dominant enterotypes to Prevotella-, Megaspheara-, and Lactobacillus-dominated enterotypes with aging. Even when the fecal microbiome was the least diverse, the richness of ARGs, quantities of AMR gene copies, and counts of AMR fecal bacteria were highest in piglets at 2 days of age; subsequently, these declined over time, likely due to age-related competitive changes in the underlying microbiome. ARGs conferring resistance to metals and multi-compound/biocides were detected predominately at the earliest sampled ages.

CONCLUSIONS

The fecal microbiome and resistome-along with evaluated descriptors of phenotypic antimicrobial susceptibility of fecal bacteria-among a cohort of pigs, demonstrated opposing trajectories in diversity primarily driven by the aging of pigs.

Rationale and study protocol for a randomized controlled feeding study to determine the structural- and functional-level effects of diet-specific interventions on the gut microbiota of non-Hispanic black and white adults

BACKGROUND

Colorectal cancer (CRC), the third leading cause of cancer-related deaths in the US, has been associated with an overrepresentation or paucity of several microbial taxa in the gut microbiota, but causality has not been established. Black men and women have among the highest CRC incidence and mortality rates of any racial/ethnic group. This study will examine the impact of the Dietary Approaches to Stop Hypertension (DASH) diet on gut microbiota and fecal metabolites associated with CRC risk.

METHODS

A generally healthy sample of non-Hispanic Black and white adults (n = 112) is being recruited to participate in a parallel-arm randomized controlled feeding study. Participants are randomized to receive the DASH diet or a standard American diet for a 28-day period. Fecal samples are collected weekly throughout the study to analyze changes in the gut microbiota using 16 s rRNA and selected metagenomics. Differences in bacterial alpha and beta diversity and taxa that have been associated with CRC (Bacteroides, Fusobacterium, Clostridium, Lactobacillus, Bifidobacterium, Ruminococcus, Porphyromonas, Succinivibrio) are being evaluated. Covariate measures include body mass index, comorbidities, medication history, physical activity, stress, and demographic characteristics.

CONCLUSION

Our findings will provide preliminary evidence for the DASH diet as an approach for cultivating a healthier gut microbiota across non-Hispanic Black and non-Hispanic White adults. These results can impact clinical, translational, and population-level approaches for modification of the gut microbiota to reduce risk of chronic diseases including CRC.

TRIAL REGISTRATION

This study was registered on ClinicalTrials.gov, identifier NCT04538482, on September 4, 2020 (https://clinicaltrials.gov/ct2/show/NCT04538482).

Effect of a temperature gradient on the behaviour of an endangered Mexican topminnow and an invasive freshwater fish

Climate change and biological invasions are two of the major threats to biodiversity. They could act synergistically to the detriment of natives as non-native species may be more plastic and resilient when facing changing environments. The twoline skiffia (Skiffia bilineata) is an endangered Mexican topminnow that cohabits with invasive guppies (Poecilia reticulata) in some areas in central Mexico. Guppies have been found to take advantage from associating with the twoline skiffia and are considered partially responsible for the decline of its populations. Refuge use and exploratory behaviours are trade-offs between being safe from the unknown and the opportunity to explore novel areas in search for better resources or to disperse. The aim of this study is to investigate how a change in temperature affects the refuge use and exploratory behaviours for both species. We found that temperature affects the refuge use of twoline skiffias, and the swimming activity of both species. Skiffias explored the rock more than guppies regardless of the temperature scenario. Also, smaller fish spent more time performing exploratory behaviours than bigger ones. Our study is the first to test the effect of temperature on the refuge use and exploratory behaviour of a goodeid species, and our results contribute to the idea that some natives could be more affected by climate change than some invaders.

Minimal Influence of Cayenne Pepper on the Human Gastrointestinal Microbiota and Intestinal Inflammation in Healthy Adult Humans-A Pilot Study

Diet impacts human gut microbial composition. Phytochemicals in cayenne pepper (CP), such as capsaicin, have anti-inflammatory properties and alter bacterial growth in vitro. However, the evidence that CP impacts the human microbiota and intestinal inflammation in free-living adults is lacking. Thus, the objective of this randomized cross-over study was to determine the influence of CP on human gut microbiota and intestinal inflammation in vivo. A total of 29 participants were randomly allocated to consume two 250 mL servings of tomato juice plus 1.8 g of CP each day or juice only for 5 days before crossing over to the other study arm. Fecal samples were analyzed. CP reduced Oscillibacter and Phascolarctobacterium but enriched Bifidobacterium and Gp6. When stratified by BMI (body mass index), only the increase in Gp6 was observed in all BMI groups during CP treatment. Stool concentrations of lipocalin-2 and calprotectin were similar regardless of CP treatment. However, lipocalin-2 and calprotectin levels were positively correlated in samples taken after CP consumption. Neither lipocalin-2 nor calprotectin levels were related to gut microbial composition. In conclusion, in healthy adult humans under typical living conditions, consumption of CP minimally influenced the gut microbiota and had little impact on intestinal inflammation.

A robust and transformation-free joint model with matching and regularization for metagenomic trajectory and disease onset

BACKGROUND

To identify operational taxonomy units (OTUs) signaling disease onset in an observational study, a powerful strategy was selecting participants by matched sets and profiling temporal metagenomes, followed by trajectory analysis. Existing trajectory analyses modeled individual OTU or microbial community without adjusting for the within-community correlation and matched-set-specific latent factors.

RESULTS

We proposed a joint model with matching and regularization (JMR) to detect OTU-specific trajectory predictive of host disease status. The between- and within-matched-sets heterogeneity in OTU relative abundance and disease risk were modeled by nested random effects. The inherent negative correlation in microbiota composition was adjusted by incorporating and regularizing the top-correlated taxa as longitudinal covariate, pre-selected by Bray-Curtis distance and elastic net regression. We designed a simulation pipeline to generate true biomarkers for disease onset and the pseudo biomarkers caused by compositionality. We demonstrated that JMR effectively controlled the false discovery and pseudo biomarkers in a simulation study generating temporal high-dimensional metagenomic counts with random intercept or slope. Application of the competing methods in the simulated data and the TEDDY cohort showed that JMR outperformed the other methods and identified important taxa in infants' fecal samples with dynamics preceding host disease status.

CONCLUSION

Our method JMR is a robust framework that models taxon-specific trajectory and host disease status for matched participants without transformation of relative abundance, improving the power of detecting disease-associated microbial features in certain scenarios. JMR is available in R package mtradeR at https://github.com/qianli10000/mtradeR.

Addressing overdispersion and zero-inflation for clustered count data via new multilevel heterogenous hurdle models

Unobserved heterogeneity causing overdispersion and the excessive number of zeros take a prominent place in the methodological development on count modeling. An insight into the mechanisms that induce heterogeneity is required for better understanding of the phenomenon of overdispersion. When the heterogeneity is sourced by the stochastic component of the model, the use of a heterogenous Poisson distribution for this part encounters as an elegant solution. Hierarchical design of the study is also responsible for the heterogeneity as the unobservable effects at various levels also contribute to the overdispersion. Zero-inflation, heterogeneity and multilevel nature in the count data present special challenges in their own respect, however the presence of all in one study adds more challenges to the modeling strategies. This study therefore is designed to merge the attractive features of the separate strand of the solutions in order to face such a comprehensive challenge. This study differs from the previous attempts by the choice of two recently developed heterogeneous distributions, namely Poisson-Lindley (PL) and Poisson-Ailamujia (PA) for the truncated part. Using generalized linear mixed modeling settings, predictive performances of the multilevel PL and PA models and their hurdle counterparts were assessed within a comprehensive simulation study in terms of bias, precision and accuracy measures. Multilevel models were applied to two separate real world examples for the assessment of practical implications of the new models proposed in this study.

specificity: an R package for analysis of feature specificity to environmental and higher dimensional variables, applied to microbiome species data

BACKGROUND

Understanding the factors that influence microbes' environmental distributions is important for determining drivers of microbial community composition. These include environmental variables like temperature and pH, and higher-dimensional variables like geographic distance and host species phylogeny. In microbial ecology, "specificity" is often described in the context of symbiotic or host parasitic interactions, but specificity can be more broadly used to describe the extent to which a species occupies a narrower range of an environmental variable than expected by chance. Using a standardization we describe here, Rao's (Theor Popul Biol, 1982. https://doi.org/10.1016/0040-5809(82)90004-1, Sankhya A, 2010. https://doi.org/10.1007/s13171-010-0016-3 ) Quadratic Entropy can be conveniently applied to calculate specificity of a feature, such as a species, to many different environmental variables.

RESULTS

We present our R package specificity for performing the above analyses, and apply it to four real-life microbial data sets to demonstrate its application. We found that many fungi within the leaves of native Hawaiian plants had strong specificity to rainfall and elevation, even though these variables showed minimal importance in a previous analysis of fungal beta-diversity. In Antarctic cryoconite holes, our tool revealed that many bacteria have specificity to co-occurring algal community composition. Similarly, in the human gut microbiome, many bacteria showed specificity to the composition of bile acids. Finally, our analysis of the Earth Microbiome Project data set showed that most bacteria show strong ontological specificity to sample type. Our software performed as expected on synthetic data as well.

CONCLUSIONS

specificity is well-suited to analysis of microbiome data, both in synthetic test cases, and across multiple environment types and experimental designs. The analysis and software we present here can reveal patterns in microbial taxa that may not be evident from a community-level perspective. These insights can also be visualized and interactively shared among researchers using specificity's companion package, specificity.shiny.

A Longitudinal Investigation of the Effects of Age, Dietary Fiber Type and Level and Injectable Antimicrobials on the Fecal Microbiome and Antimicrobial Resistance of Finisher Pigs

Age and diet are among the factors that influence the community composition of the fecal microbiome. Additionally, antimicrobial use can alter the composition of bacterial communities. An 86-d study with finisher pigs aimed to evaluate age-related dynamics (d 98-177 of age), effects of types and levels of dietary fiber, and injectable antimicrobials on the fecal microbiome and antimicrobial resistance (AMR) was conducted. A total of 287 pigs, housed in 36 pens, with 7 to 8 pigs per pen, fed a corn grain and soybean meal-based basal diet, formulated to contain 8.7% neutral detergent fiber (NDF), were randomly assigned to one of three treatments: 1. basal diet with no supplement, 2. basal diet supplemented with 20% distillers dried grains with solubles (DDGS) formulated to contain 13.6% NDF, or 3. basal diet supplemented with 14.5% sugar beet pulp (SBP) formulated to contain 13.6% NDF. Five finisher pigs from each treatment group were selected randomly, and fecal samples were collected on d 98, 110, 144, and 177 of age. In addition, fecal samples were collected from pigs that were injected intramuscularly ceftiofur hydrochloride or penicillin G on d 1 and 3 along with pen-mate untreated controls on d 1. Fecal samples were subjected to 16S rRNA amplicon-based microbiome analysis and culture methods to quantify the abundance of total and AMR coliforms and enterococci populations. The alpha diversity, such as species richness, increased with age, and the overall bacterial composition changed with age (P =0.001) and diet (P = 0.001). Diet-associated shifts in the specific bacterial taxa were observed. The richness, diversity, and evenness of bacterial taxa did not differ between pigs that were injected with ceftiofur versus their untreated pen mates or by dietary treatments, but differed in pigs that received penicillin G injection. Both antimicrobial treatments contributed to changes in the overall fecal bacterial composition at the genus level. Collectively, the data demonstrate that both age and the diet (control vs. DDGS-, control vs. SBP- or DDGS- vs. SBP-based diets) were associated with overall bacterial community composition and the impact of age on variations in fecal microbiome composition was greater than the diet. Antibiotic treatment had minimal effect on bacterial diversity and relative abundance of taxa. Further, diets and antimicrobial treatment had minimal impact on the overall counts of AMR coliforms and enterococci populations in feces.

A Survey of Statistical Methods for Microbiome Data Analysis

In the last decade, numerous statistical methods have been developed for analyzing microbiome data generated from high-throughput next-generation sequencing technology. Microbiome data are typically characterized by zero inflation, overdispersion, high dimensionality, and sample heterogeneity. Three popular areas of interest in microbiome research requiring statistical methods that can account for the characterizations of microbiome data include detecting differentially abundant taxa across phenotype groups, identifying associations between the microbiome and covariates, and constructing microbiome networks to characterize ecological associations of microbes. These three areas are referred to as differential abundance analysis, integrative analysis, and network analysis, respectively. In this review, we highlight available statistical methods for differential abundance analysis, integrative analysis, and network analysis that have greatly advanced microbiome research. In addition, we discuss each method's motivation, modeling framework, and application.

Analyzing the overall effects of the microbiome abundance data with a Bayesian predictive value approach

The microbiome abundance data is known to be over-dispersed and sparse count data. Among various zero-inflated models, zero-inflated negative binomial (ZINB) model and zero-inflated beta binomial (ZIBB) model are the methods to analyze the microbiome abundance data. ZINB and ZIBB have two sets of parameters, which are for modeling the zero-inflation part and the count part separately. Most previous methods have focused on making inferences in terms of separate case-control effect for the zero-inflation part and the count part. However, in a case-control study, the primary interest is normally focused on the inference and a single interpretation of the overall unconditional mean (also known as the overall effect) of the microbiome abundance in microbiome studies. Here, we propose a Bayesian predictive value (BPV) approach to estimate the overall effect of the microbiome abundance. This approach is implemented based on R package brms. Hence, the parameters in the models will be estimated with two Markov chain Monte Carlo (MCMC) algorithms used in Stan. We performed simulations and real data applications to compare the proposed approach and R package glmmTMB with simulation method in the estimation and inference in terms of the ratio function between the overall effects from two groups in a case-control study. The results show that the performance of the BPV approach is better than R package glmmTMB with the simulation method in terms of lower absolute biases and relative absolute biases, and coverage probability being closer to the nominal level especially when the sample size is small and zero-inflation rate is high.

Effectiveness of a COVID-19 Testing Outreach Intervention for Latinx Communities: A Cluster Randomized Trial

Importance

Latinx individuals have been disproportionately affected during the COVID-19 pandemic caused by the spread of SARS-CoV-2. It is imperative to evaluate newly developed preventive interventions to assess their effect on COVID-19 health disparities.

Objective

To examine the effectiveness of a culturally tailored outreach intervention designed to increase SARS-CoV-2 testing rates among Latinx populations.

Design, Setting, and Participants

In this cluster randomized trial performed from February 1 to August 31, 2021, in community settings in 9 Oregon counties, 38 sites were randomized a priori (19 to the community health promoters intervention and 19 to outreach as usual wait-listed controls). Thirty-three sites were activated. A total of 394 SARS-CoV-2 testing events were held and 1851 diagnostic samples collected, of which 919 were from Latinx persons.

Interventions

A culturally informed outreach program was developed that made use of promotores de salud (community health promoters) to increase Latinx SARS-CoV-2 testing. Strategies addressed barriers by disseminating information on testing events in English and Spanish, mitigating misinformation, and increasing trust.

Main Outcomes and Measures

The primary outcomes were the count of sample tests from Latinx persons and the sampled proportion of the Latinx populace. Site-level covariates included census tract Latinx populace, nativity (number of US-born individuals per 100 population), median age, and income inequality. Time-varying covariates included number of new weekly SARS-CoV-2-positive cases and percentage of vaccine coverage at the county level.

Results

A total of 15 clusters (sites) were randomized to the control group and 18 to the community health promoters group. A total of 1851 test samples were collected, of which 995 (53.8%) were from female participants and 919 (49.6%) were from Latinx individuals. The intervention tested 3.84 (95% CI, 2.47-5.97) times more Latinx individuals per event than controls (incident rate ratio, 0.79; 95% CI, 0.46-1.34; Cohen d = 0.74; P < .001). The intervention was associated with a 0.28 increase in the proportion of Latinx populace being tested compared with control sites for the dependent variable scaled as the proportion of the Latinx populace ×100, or a 0.003 proportion of the raw populace count. The use of a standardized scaling of the proportion of Latinx individuals showed that the relative percentage increase was 0.53 (95% CI, 0.21-0.86) in the intervention sites compared with controls, representing a medium effect size.

Conclusions and Relevance

To our knowledge, this was the first randomized evaluation of an outreach intervention designed to increase SARS-CoV-2 testing among Latinx populations. Findings could be used to implement strategies to reduce other health disparities experienced by these groups.

Trial Registration

ClinicalTrials.gov Identifier: NCT04793464.

Gut microbiota composition is associated with SARS-CoV-2 vaccine immunogenicity and adverse events

OBJECTIVE

The gut microbiota plays a key role in modulating host immune response. We conducted a prospective, observational study to examine gut microbiota composition in association with immune responses and adverse events in adults who have received the inactivated vaccine (CoronaVac; Sinovac) or the mRNA vaccine (BNT162b2; BioNTech; Comirnaty).

DESIGN

We performed shotgun metagenomic sequencing in stool samples of 138 COVID-19 vaccinees (37 CoronaVac and 101 BNT162b2 vaccinees) collected at baseline and 1 month after second dose of vaccination. Immune markers were measured by SARS-CoV-2 surrogate virus neutralisation test and spike receptor-binding domain IgG ELISA.

RESULTS

We found a significantly lower immune response in recipients of CoronaVac than BNT162b2 vaccines (p<0.05). Bifidobacterium adolescentis was persistently higher in subjects with high neutralising antibodies to CoronaVac vaccine (p=0.023) and their baseline gut microbiome was enriched in pathways related to carbohydrate metabolism (linear discriminant analysis (LDA) scores >2 and p<0.05). Neutralising antibodies in BNT162b2 vaccinees showed a positive correlation with the total abundance of bacteria with flagella and fimbriae including Roseburia faecis (p=0.028). The abundance of Prevotella copri and two Megamonas species were enriched in individuals with fewer adverse events following either of the vaccines indicating that these bacteria may play an anti-inflammatory role in host immune response (LDA scores>3 and p<0.05).

CONCLUSION

Our study has identified specific gut microbiota markers in association with improved immune response and reduced adverse events following COVID-19 vaccines. Microbiota-targeted interventions have the potential to complement effectiveness of COVID-19 vaccines.

The early life microbiota mediates maternal effects on offspring growth in a nonhuman primate

Maternal parity can impact offspring growth, but the mechanisms driving this effect are unclear. Here, we test the hypothesis that vertically transmitted microbiota may be one potential mechanism. We analyzed 118 fecal and milk samples from mother-offspring vervet monkey dyads across the first 6 months of life. Despite poorer milk production, offspring born to low parity females grew larger than their counterparts. These offspring exhibited reduced alpha diversity in the first days of life, stronger seeding of maternal milk microbiota, Bacteroides fragilis dominance, and a greater abundance of glycan utilization pathways. Moreover, the attainment of greater body mass by 6 months of age was mediated by reduced early life alpha diversity and B. fragilis dominance. This work demonstrates that the establishment of a specialized, milk-oriented gut microbiota promotes infant growth and suggests an evolutionarily conserved developmental role of B. fragilis in primates.

Circulating microRNAs differentiate fast-progressing from slow-progressing and non-progressing knee osteoarthritis in the Osteoarthritis Initiative cohort

Introduction

The objective of this study is to identify circulating microRNAs that distinguish fast-progressing radiographic knee osteoarthritis (OA) in the Osteoarthritis Initiative cohort by applying microRNA-sequencing.

Methods

Participants with Kellgren-Lawrence (KL) grade 0/1 at baseline were included (N = 106). Fast-progressors were defined by an increase to KL 3/4 by 4-year follow-up (N = 20), whereas slow-progressors showed an increase to KL 2/3/4 only at 8-year follow-up (N = 35). Non-progressors remained at KL 0/1 by 8-year follow-up (N = 51). MicroRNA-sequencing was performed on plasma collected at baseline and 4-year follow-up from the same participants. Negative binomial models were fitted to identify differentially expressed (DE) microRNAs. Penalized logistic regression (PLR) analyses were performed to select combinations of DE microRNAs that distinguished fast-progressors. Area under the receiver operating characteristic curves (AUC) were constructed to evaluate predictive ability.

Results

DE analyses revealed 48 microRNAs at baseline and 2 microRNAs at 4-year follow-up [false discovery rate (FDR) < 0.05] comparing fast-progressors with both slow-progressors and non-progressors. Among these were hsa-miR-320b, hsa-miR-320c, hsa-miR-320d, and hsa-miR-320e, which were predicted to target gene families, including members of the 14-3-3 gene family, involved in signal transduction. PLR models included miR-320 members as top predictors of fast-progressors and yielded AUC ranging from 82.6 to 91.9, representing good accuracy.

Conclusion

The miR-320 family is associated with fast-progressing radiographic knee OA and merits further investigation as potential biomarkers and mechanistic drivers of knee OA.

Glucocorticoids coordinate changes in gut microbiome composition in wild North American red squirrels

The gut microbiome impacts host health and fitness, in part through the diversification of gut metabolic function and pathogen protection. Elevations in glucocorticoids (GCs) appear to reduce gut microbiome diversity in experimental studies, suggesting that a loss of microbial diversity may be a negative consequence of increased GCs. However, given that ecological factors like food availability and population density may independently influence both GCs and microbial diversity, understanding how these factors structure the GC-microbiome relationship is crucial to interpreting its significance in wild populations. Here, we used an ecological framework to investigate the relationship between GCs and gut microbiome diversity in wild North American red squirrels (Tamiasciurus hudsonicus). As expected, higher GCs predicted lower gut microbiome diversity and an increase in metabolic taxa. Surprisingly, but in line with prior empirical studies on wild animals, gastrointestinal pathogens decreased as GCs increased. Both dietary heterogeneity and an upcoming food pulse exhibited direct effects on gut microbiome diversity, whereas conspecific density and reproductive activity impacted diversity indirectly via changes in host GCs. Our results provide evidence of a gut-brain axis in wild red squirrels and highlight the importance of situating the GC-gut microbiome relationship within an ecological framework.

The gut microbiome of extremely preterm infants randomized to the early progression of enteral feeding

BACKGROUND

Early progression of feeding could influence the development of the gut microbiome.

METHODS

We collected fecal samples from extremely preterm infants randomized to receive either early (feeding day 2) or delayed (feeding day 5) feeding progression. After study completion, we compared samples obtained at three different time points (week 1, week 2, and week 3) to determine longitudinal differences in specific taxa between the study groups using unadjusted and adjusted negative binomial and zero-inflated mixed models. Analyses were adjusted for a mode of delivery, breastmilk intake, and exposure to antibiotics.

RESULTS

We analyzed 137 fecal samples from 51 infants. In unadjusted and adjusted analyses, we did not observe an early transition to higher microbial diversity within samples (i.e., alpha diversity) or significant differences in microbial diversity between samples (i.e., beta diversity) in the early feeding group. Our longitudinal, single-taxon analysis found consistent differences in the genera Lactococcus, Veillonella, and Bilophila between groups.

CONCLUSIONS

Differences in single-taxon analyses independent of the mode of delivery, exposure to antibiotics, and breastmilk feeding suggest potential benefits of early progression of enteral feeding volumes. However, this dietary intervention does not appear to increase the diversity of the gut microbiome in the first 28 days after birth.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT02915549.

IMPACT

Early progression of enteral feeding volumes with human milk reduces the duration of parenteral nutrition and the need for central venous access among extremely preterm infants. Early progression of enteral feeding leads to single-taxon differences in longitudinal analyses of the gut microbiome, but it does not appear to increase the diversity of the gut microbiome in the first 28 days after birth. Randomization in enteral feeding trials creates appealing opportunities to evaluate the effects of human milk diets on the gut microbiome.

MZINBVA: variational approximation for multilevel zero-inflated negative-binomial models for association analysis in microbiome surveys

As our understanding of the microbiome has expanded, so has the recognition of its critical role in human health and disease, thereby emphasizing the importance of testing whether microbes are associated with environmental factors or clinical outcomes. However, many of the fundamental challenges that concern microbiome surveys arise from statistical and experimental design issues, such as the sparse and overdispersed nature of microbiome count data and the complex correlation structure among samples. For example, in the human microbiome project (HMP) dataset, the repeated observations across time points (level 1) are nested within body sites (level 2), which are further nested within subjects (level 3). Therefore, there is a great need for the development of specialized and sophisticated statistical tests. In this paper, we propose multilevel zero-inflated negative-binomial models for association analysis in microbiome surveys. We develop a variational approximation method for maximum likelihood estimation and inference. It uses optimization, rather than sampling, to approximate the log-likelihood and compute parameter estimates, provides a robust estimate of the covariance of parameter estimates and constructs a Wald-type test statistic for association testing. We evaluate and demonstrate the performance of our method using extensive simulation studies and an application to the HMP dataset. We have developed an R package MZINBVA to implement the proposed method, which is available from the GitHub repository https://github.com/liudoubletian/MZINBVA.

An Intervention With Michigan-Grown Wheat in Healthy Adult Humans to Determine Effect on Gut Microbiota: Protocol for a Crossover Trial

BACKGROUND

Daily fiber intake can increase the diversity of the human gut microbiota as well as the abundance of beneficial microbes and their metabolites. Whole-grain wheat is high in fiber.

OBJECTIVE

This manuscript presents a study protocol designed to understand the effects of different types of wheat on gastrointestinal tract microbes.

METHODS

Human adults will consume crackers made from three types of wheat flour (refined soft white wheat, whole-grain soft white wheat, and whole-grain soft red wheat). In this study, participants will alternate between crackers made from refined soft white wheat flour to those made from whole-grain soft white wheat and whole-grain soft red wheat flour. Survey and stool sample collection will occur after 7-day treatment periods. We will assess how wheat consumption affects gastrointestinal bacteria by sequencing the V4 region of 16S rRNA gene amplicons and the inflammatory state of participants' intestines using enzyme-linked immunosorbent assays. The butyrate production capacity of the gut microbiota will be determined by targeted quantitative real-time polymerase chain reaction.

RESULTS

We will report the treatment effects on alpha and beta diversity of the microbiota and taxa-specific differences. Microbiota results will be analyzed using the vegan package in R. Butyrate production capacity and biomarkers of intestinal inflammation will be analyzed using parametric statistical methods such as analysis of variance or linear regression. We expect whole wheat intake to increase butyrate production capacity, bacterial alpha diversity, and abundance of bacterial taxa responsive to phenolic compounds. Soft red wheat is also expected to decrease the concentration of inflammatory biomarkers in the stool of participants.

CONCLUSIONS

This protocol describes the methods to be used in a study on the impact of wheat types on the human gastrointestinal microbiota and biomarkers of intestinal inflammation. The analysis of intestinal responses to the consumption of two types of whole wheat will expand our understanding of how specific foods affect health-associated outcomes.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/29046.

Effect on gut microbiota of a 1-y lifestyle intervention with Mediterranean diet compared with energy-reduced Mediterranean diet and physical activity promotion: PREDIMED-Plus Study

BACKGROUND

The Mediterranean diet is a well-recognized healthy diet that has shown to induce positive changes in gut microbiota. Lifestyle changes such as diet along with physical activity could aid in weight loss and improve cardiovascular risk factors.

OBJECTIVES

To investigate the effect of an intensive lifestyle weight loss intervention on gut microbiota.

METHODS

This is a substudy of the PREDIMED-Plus (Prevención con Dieta Mediterránea-Plus), a randomized controlled trial conducted in overweight/obese men and women (aged 55-75 y) with metabolic syndrome. The intervention group (IG) underwent an intensive weight loss lifestyle intervention based on an energy-restricted Mediterranean diet (MedDiet) and physical activity promotion, and the control group (CG) underwent a non-energy-restricted MedDiet for 1 y. Anthropometric, biochemical, and gut microbial 16S rRNA sequencing data were analyzed at baseline (n = 362) and 1-y follow-up (n = 343).

RESULTS

IG participants had a weight loss of 4.2 (IQR, -6.8, -2.5) kg compared with 0.2 (IQR, -2.1, 1.4) kg in the CG (P < 0.001). Reductions in BMI, fasting glucose, glycated hemoglobin, and triglycerides and an increase in HDL cholesterol were greater in IG than in CG participants (P < 0.05). We observed a decrease in Butyricicoccus, Haemophilus, Ruminiclostridium 5, and Eubacterium hallii in the IG compared with the CG. Many genera shifted in the same direction within both intervention groups, indicating an overall effect of the MedDiet. Decreases in Haemophilus, Coprococcus 3, and few other genera were associated with a decrease in adiposity parameters in both intervention groups. Changes in Lachnospiraceae NK4A136 were positively associated with changes in MedDiet adherence.

CONCLUSIONS

Weight loss induced by an energy-restricted MedDiet and physical activity induce changes in gut microbiota. The role of MedDiet-induced changes on the host might be via short-chain fatty acid producing bacteria, whereas with energy restriction, these changes might be modulated with other mechanisms, which need to be explored in future studies. This trial was registered at http://www.isrctn.com/ISRCTN89898870 as ISRCT 89898870.