metamicrobiomeR: an R package for analysis of microbiome relative abundance data using zero-inflated beta GAMLSS and meta-analysis across studies using random effects models

Metagenomic insights into resistome, mobilome and virulome in different fecal waste

Fecal waste is a significant source of antimicrobial resistance (AMR) pollution and provides valuable insights into the AMR development in animal and human populations within the "One health" framework. Various genetic elements, including antibiotic resistance genes (ARGs), biocide and metal resistance genes (BMGs), mobile genetic elements (MGEs), and virulence factor genes (VFGs), are crucial AMR risk determinants (ARDs). However, few studies focused on compositional characteristics of ARDs in different feces. Here, we analyzed 753 public metagenomes from human, pig, chicken, and cattle feces, revealing significant differences in ARD richness and abundance across fecal types, notably lowest in cattle samples. Tetracycline, multi-metal, and -biocide resistance genes were dominant resistome. A few core genes contributed to 25.6%-91.1% of gene abundance, and their correlations were stronger in cattle samples. Procrustes analysis showed that microbial composition had higher correlations with ARGs (M2 = 0.579) and BMGs (M2 = 0.519). Gammaproteobacteria was identified as major ARD-hosts especially in human and pig feces, and they mainly carried multi-resistance genes. MGEs exhibited direct positive effects on ARGs and BMGs, indirectly impacting VFGs. Utilizing random forest methods, we identified 42 indicator genes for tracking AMR pollution originating from fecal sources in the environments. This study offers new insights into understanding and controlling the AMR pollution of fecal waste from human and food animals.

Role of socioeconomic factors and interkingdom crosstalk in the dental plaque microbiome in early childhood caries

Early childhood caries (ECC) is influenced by microbial and host factors, including social, behavioral, and oral health. In this cross-sectional study, we analyze interkingdom dynamics in the dental plaque microbiome and its association with host variables. We use 16S rRNA and ITS1 amplicon sequencing on samples collected from preschool children and analyze questionnaire data to examine the social determinants of oral health. The results indicate a significant enrichment of Streptococcus mutans and Candida dubliniensis in ECC samples, in contrast to Neisseria oralis in caries-free children. Our interkingdom correlation analysis reveals that Candida dubliniensis is strongly correlated with both Neisseria bacilliformis and Prevotella veroralis in ECC. Additionally, ECC shows significant associations with host variables, including oral health status, age, place of residence, and mode of childbirth. This study provides empirical evidence associating the oral microbiome with socioeconomic and behavioral factors in relation to ECC, offering insights for developing targeted prevention strategies.

A cross-cohort analysis of dental plaque microbiome in early childhood caries

Early childhood caries (ECC) is a multifactorial disease with a microbiome playing a significant role in caries progression. Understanding changes at the microbiome level in ECC is required to develop diagnostic and preventive strategies. In our study, we combined data from small independent cohorts to compare microbiome composition using a unified pipeline and applied a batch correction to avoid the pitfalls of batch effects. Our meta-analysis identified common biomarker species between different studies. We identified the best machine learning method for the classification of ECC versus caries-free samples and compared the performance of this method using a leave-one-dataset-out approach. Our random forest model was found to be generalizable when used in combination with other studies. While our results highlight the potential microbial species involved in ECC and disease classification, we also mentioned the limitations that can serve as a guide for future researchers to design and use appropriate tools for such analyses.

Harnessing human microbiomes for disease prediction

The human microbiome has been increasingly recognized as having potential use for disease prediction. Predicting the risk, progression, and severity of diseases holds promise to transform clinical practice, empower patient decisions, and reduce the burden of various common diseases, as has been demonstrated for cardiovascular disease or breast cancer. Combining multiple modifiable and non-modifiable risk factors, including high-dimensional genomic data, has been traditionally favored, but few studies have incorporated the human microbiome into models for predicting the prospective risk of disease. Here, we review research into the use of the human microbiome for disease prediction with a particular focus on prospective studies as well as the modulation and engineering of the microbiome as a therapeutic strategy.

Fungicide ingestion reduces net energy gain and microbiome diversity of the solitary mason bee

Fungicides are frequently used during tree fruit bloom and can threaten insect pollinators. However, little is known about how non-honey bee pollinators such as the solitary bee, Osmia cornifrons, respond to contact and systemic fungicides commonly used in apple production during bloom. This knowledge gap limits regulatory decisions that determine safe concentrations and timing for fungicide spraying. We evaluated the effects of two contact fungicides (captan and mancozeb) and four translaminar/plant systemic fungicides (cyprodinil, myclobutanil, penthiopyrad, and trifloxystrobin) on larval weight gain, survival, sex ratio, and bacterial diversity. This assessment was carried out using chronic oral ingestion bioassays where pollen provisions were treated with three doses based on the currently recommended field use dose (1X), half dose (0.5X), and low dose (0.1X). Mancozeb and penthiopyrad significantly reduced larval weight and survival at all doses. We then sequenced the 16S gene to characterize the larvae bacteriome of mancozeb, the fungicide that caused the highest mortality. We found that larvae fed on mancozeb-treated pollen carried significantly lower bacterial diversity and abundance. Our laboratory results suggest that some of these fungicides can be particularly harmful to the health of O. cornifrons when sprayed during bloom. This information is relevant for future management decisions about the sustainable use of fruit tree crop protection products and informing regulatory processes that aim to protect pollinators.

Unraveling the interplay between root exudates, microbiota, and rhizosheath formation in pearl millet

BACKGROUND

The rhizosheath, a cohesive soil layer firmly adhering to plant roots, plays a vital role in facilitating water and mineral uptake. In pearl millet, rhizosheath formation is genetically controlled and influenced by root exudates. Here, we investigated the impact of root exudates on the microbiota composition, interactions, and assembly processes, and rhizosheath structure in pearl millet using four distinct lines with contrasting soil aggregation abilities.

RESULTS

Utilizing 16S rRNA gene and ITS metabarcoding for microbiota profiling, coupled with FTICR-MS metabonomic analysis of metabolite composition in distinct plant compartments and root exudates, we revealed substantial disparities in microbial diversity and interaction networks. The ß-NTI analysis highlighted bacterial rhizosphere turnover driven primarily by deterministic processes, showcasing prevalent homogeneous selection in root tissue (RT) and root-adhering soil (RAS). Conversely, fungal communities were more influenced by stochastic processes. In bulk soil assembly, a combination of deterministic and stochastic mechanisms shapes composition, with deterministic factors exerting a more pronounced role. Metabolic profiles across shoots, RT, and RAS in different pearl millet lines mirrored their soil aggregation levels, emphasizing the impact of inherent plant traits on microbiota composition and unique metabolic profiles in RT and exudates. Notably, exclusive presence of antimicrobial compounds, including DIMBOA and H-DIMBOA, emerged in root exudates and RT of low aggregation lines.

CONCLUSIONS

This research underscores the pivotal influence of root exudates in shaping the root-associated microbiota composition across pearl millet lines, entwined with their soil aggregation capacities. These findings underscore the interconnectedness of root exudates and microbiota, which jointly shape rhizosheath structure, deepening insights into soil-plant-microbe interactions and ecological processes shaping rhizosphere microbial communities. Deciphering plant-microbe interactions and their contribution to soil aggregation and microbiota dynamics holds promise for the advancement of sustainable agricultural strategies. Video Abstract.

Data pre-processing for analyzing microbiome data - A mini review

The human microbiome is an emerging research frontier due to its profound impacts on health. High-throughput microbiome sequencing enables studying microbial communities but suffers from analytical challenges. In particular, the lack of dedicated preprocessing methods to improve data quality impedes effective minimization of biases prior to downstream analysis. This review aims to address this gap by providing a comprehensive overview of preprocessing techniques relevant to microbiome research. We outline a typical workflow for microbiome data analysis. Preprocessing methods discussed include quality filtering, batch effect correction, imputation of missing values, normalization, and data transformation. We highlight strengths and limitations of each technique to serve as a practical guide for researchers and identify areas needing further methodological development. Establishing robust, standardized preprocessing will be essential for drawing valid biological conclusions from microbiome studies.

Agaricus subrufescens fermented rye affects the development of intestinal microbiota, local intestinal and innate immunity in suckling-to-nursery pigs

BACKGROUND

Agaricus subrufescens is considered as one of the most important culinary-medicinal mushrooms around the world. It has been widely suggested to be used for the development of functional food ingredients to promote human health ascribed to the various properties (e.g., anti-inflammatory, antioxidant, and immunomodulatory activities). In this context, the interest in A. subrufescens based feed ingredients as alternatives for antibiotics has also been fuelled during an era of reduced/banned antibiotics use. This study aimed to investigate the effects of a fermented feed additive -rye overgrown with mycelium (ROM) of A. subrufescens-on pig intestinal microbiota, mucosal gene expression and local and systemic immunity during early life. Piglets received ROM or a tap water placebo (Ctrl) perorally every other day from day 2 after birth until 2 weeks post-weaning. Eight animals per treatment were euthanized and dissected on days 27, 44 and 70.

RESULTS

The results showed ROM piglets had a lower inter-individual variation of faecal microbiota composition before weaning and a lower relative abundance of proteobacterial genera in jejunum (Undibacterium and Solobacterium) and caecum (Intestinibacter and Succinivibrionaceae_UCG_001) on day 70, as compared to Ctrl piglets. ROM supplementation also influenced gut mucosal gene expression in both ileum and caecum on day 44. In ileum, ROM pigs showed increased expression of TJP1/ZO1 but decreased expression of CLDN3, CLDN5 and MUC2 than Ctrl pigs. Genes involved in TLR signalling (e.g., TICAM2, IRAK4 and LY96) were more expressed but MYD88 and TOLLIP were less expressed in ROM pigs than Ctrl animals. NOS2 and HIF1A involved in redox signalling were either decreased or increased in ROM pigs, respectively. In caecum, differentially expressed genes between two groups were mainly shown as increased expression (e.g., MUC2, PDGFRB, TOLLIP, TNFAIP3 and MYD88) in ROM pigs. Moreover, ROM animals showed higher NK cell activation in blood and enhanced IL-10 production in ex vivo stimulated MLN cells before weaning.

CONCLUSIONS

Collectively, these results suggest that ROM supplementation in early life modulates gut microbiota and (local) immune system development. Consequently, ROM supplementation may contribute to improving health of pigs during the weaning transition period and reducing antibiotics use.

Out of site, out of mind: Changes in feather moss phyllosphere microbiota in mine offsite boreal landscapes

Plant-microbe interactions play a crucial role in maintaining biodiversity and ecological services in boreal forest biomes. Mining for minerals, and especially the emission of heavy metal-enriched dust from mine sites, is a potential threat to biodiversity in offsite landscapes. Understanding the impacts of mining on surrounding phyllosphere microbiota is especially lacking. To investigate this, we characterized bacterial and fungal communities in the phyllosphere of feather moss Pleurozium schreberi (Brid). Mitt in boreal landscapes near six gold mine sites at different stages of the mine lifecycle. We found that (1) both mining stage and ecosystem type are drivers of the phyllosphere microbial community structure in mine offsite landscapes; (2) Bacterial alpha diversity is more sensitive than fungal alpha diversity to mining stage, while beta diversity of both groups is impacted; (3) mixed and deciduous forests have a higher alpha diversity and a distinct microbial community structure when compared to coniferous and open canopy ecosystems; (4) the strongest effects are detectable within 0.2 km from operating mines. These results confirmed the presence of offsite effects of mine sites on the phyllosphere microbiota in boreal forests, as well as identified mining stage and ecosystem type as drivers of these effects. Furthermore, the footprint was quantified at 0.2 km, providing a reference distance within which mining companies and policy makers should pay more attention during ecological assessment and for the development of mitigation strategies. Further studies are needed to assess how these offsite effects of mines affect the functioning of boreal ecosystems.

A meta-analysis of the effect of Eimeria spp. and/or Clostridium perfringens infection on the microbiota of broiler chickens

Coccidiosis in chickens is caused by Eimeria spp. The infection provides a growth advantage to Clostridium perfringens (CP), frequently leading to necrotic enteritis. One approach to alleviate the negative impacts of the diseases is to improve the bacterial composition in chickens, and many experiments investigating chicken enteric health in recent years include the characterization of the bacterial microbiota. This meta-analysis synthesized the data of studies investigating the intestinal microbiota after infection with coccidia and/or CP to provide a basis for future research. Inclusion criteria were that experiments contained a group infected with one or both pathogens and an uninfected control group, the use of 16SrRNA Illumina sequencing and the availability of raw data. A total of 17 studies could be included. Meta-analyses of 3 different data sets were performed: 1 on data of 9 experiments on chickens infected with coccidia only; the second on data of 4 studies on chickens infected with CP only; the third on raw data of 8 experiments with chickens infected with coccidia and CP. The meta-analysis of relative abundance and alpha diversity of the data sets was performed in R using the SIAMCAT and metafor packages. The number of families of interest identified by the analyses of experiments with infection with coccidia only, CP only and the combined infection were 23, 2, and 29, respectively. There was an overlap of 13 families identified by analyses of experiments with infection with coccidia only and of experiments with the combined infections. Machine learning was not able to find a model to predict changes of the microbiota in either 1 of the 3 analyses. Meta-analyses of functional profiles showed a more uniform reaction to the infections with the relative abundance of many pathways significantly altered. Alpha diversity was not affected by infection with either pathogen or the combination. In conclusion, the heterogeneity of these microbiota studies makes recognizing common trends difficult, although it seems that coccidia infection affects the microbiota more than an infection with CP. Future studies should focus on the bacterial functions that are changed due to these infections using metagenome techniques.

In vitro gut microbiome response to carbohydrate supplementation is acutely affected by a sudden change in diet

BACKGROUND

Interactions between diet, stress and the gut microbiome are of interest as a means to modulate health and performance. Here, in vitro fermentation was used to explore the effects of a sudden change in diet, 21 days sole sustenance on the Meal, Ready-to-Eat (MRE) U.S. military combat ration, on inter-species competition and functional potential of the human gut microbiota. Human fecal samples collected before and after MRE intervention or consuming a habitual diet (HAB) were introduced to nutrient-rich media supplemented with starch for in vitro fermentation under ascending colon conditions. 16S rRNA amplicon and Whole-metagenome sequencing (WMS) were used to measure community composition and functional potential. Specific statistical analyses were implemented to detect changes in relative abundance from taxa, genes and pathways.

RESULTS

Differential changes in relative abundance of 11 taxa, Dorea, Lachnospira, Bacteroides fragilis, Akkermansia muciniphila, Bifidobacterium adolescentis, Betaproteobacteria, Enterobacteriaceae, Bacteroides egerthii, Ruminococcus bromii, Prevotella, and Slackia, and nine Carbohydrate-Active Enzymes, specifically GH13_14, over the 24 h fermentation were observed as a function of the diet intervention and correlated to specific taxa of interest.

CONCLUSIONS

These findings suggest that consuming MRE for 21 days acutely effects changes in gut microbiota structure in response to carbohydrate but may induce alterations in metabolic capacity. Additionally, these findings demonstrate the potential of starch as a candidate supplemental strategy to functionally modulate specific gut commensals during stress-induced states.

Skin microbiota signature distinguishes IBD patients and reflects skin adverse events during anti-TNF therapy

Crohn's disease (CD) and ulcerative colitis (UC) are two forms of inflammatory bowel disease (IBD), where the role of gut but not skin dysbiosis is well recognized. Inhibitors of TNF have been successful in IBD treatment, but up to a quarter of patients suffer from unpredictable skin adverse events (SkAE). For this purpose, we analyzed temporal dynamics of skin microbiota and serum markers of inflammation and epithelial barrier integrity during anti-TNF therapy and SkAE manifestation in IBD patients. We observed that the skin microbiota signature of IBD patients differs markedly from healthy subjects. In particular, the skin microbiota of CD patients differs significantly from that of UC patients and healthy subjects, mainly in the retroauricular crease. In addition, we showed that anti-TNF-related SkAE are associated with specific shifts in skin microbiota profile and with a decrease in serum levels of L-FABP and I-FABP in IBD patients. For the first time, we showed that shifts in microbial composition in IBD patients are not limited to the gut and that skin microbiota and serum markers of the epithelium barrier may be suitable markers of SkAE during anti-TNF therapy.

Meta-analysis of 16S rRNA microbial data identified alterations of the gut microbiota in COVID-19 patients during the acute and recovery phases

Background

Dozens of studies have demonstrated gut dysbiosis in COVID-19 patients during the acute and recovery phases. However, a consensus on the specific COVID-19 associated bacteria is missing. In this study, we performed a meta-analysis to explore whether robust and reproducible alterations in the gut microbiota of COVID-19 patients exist across different populations.

Methods

A systematic review was conducted for studies published prior to May 2022 in electronic databases. After review, we included 16 studies that comparing the gut microbiota in COVID-19 patients to those of controls. The 16S rRNA sequence data of these studies were then re-analyzed using a standardized workflow and synthesized by meta-analysis.

Results

We found that gut bacterial diversity of COVID-19 patients in both the acute and recovery phases was consistently lower than non-COVID-19 individuals. Microbial differential abundance analysis showed depletion of anti-inflammatory butyrate-producing bacteria and enrichment of taxa with pro-inflammatory properties in COVID-19 patients during the acute phase compared to non-COVID-19 individuals. Analysis of microbial communities showed that the gut microbiota of COVID-19 recovered patients were still in unhealthy ecostates.

Conclusions

Our results provided a comprehensive synthesis to better understand gut microbial perturbations associated with COVID-19 and identified underlying biomarkers for microbiome-based diagnostics and therapeutics.

Chitogel following endoscopic sinus surgery promotes a healthy microbiome and reduces postoperative infections

BACKGROUND

Postoperative infections following endoscopic sinus surgery (ESS) impair wound healing and lead to poor outcomes. The aim of this study is to assess the effectiveness of Chitogel to reduce postoperative infections and restore a healthy microbiome following ESS.

METHODS

In this double-blinded randomized control trial, 25 patients undergoing ESS were prospectively recruited. At the end of surgery, patients were randomized to receive Chitogel to one side of the sinuses (allowing the other side to serve as control). Patients underwent routine follow-up with nasoendoscopies performed at 2, 6, and 12 weeks postoperatively. Sinus ostial measurements, microbiology, and microbiome swabs from bilateral sides were collected intraoperatively and at 12 weeks postoperatively. Additional swabs were collected if infection was present.

RESULTS

Improved endoscopic appearance of the sinuses (p = 0.03) and ostial patency were noted on the Chitogel side compared with control at 12 weeks (p < 0.001). A significant decrease in infections on the Chitogel side (12.0%) compared with control (52.0%) (p = 0.005) was evident. Following the use of Chitogel, there was a significant increase in the combined relative abundance of commensals Corynebacterium and Cutibacterium (Propionibacterium) from 30.15% at baseline to 46.62% at 12 weeks compared with control (47.18% to 40.79%) (p.adj = 0.01).

CONCLUSION

Chitogel significantly improved both the nasoendoscopic appearance of the sinuses and sinus ostial patency at 12 weeks postoperatively. Chitogel used following ESS helps restore an improved microbiome resulting in an increase in the relative abundance of commensals Corynebacterium and Cutibacterium (Propionibacterium). A significant decrease in postoperative infections was noted following use of Chitogel.

Dynamic Alterations in the Respiratory Tract Microbiota of Patients with COVID-19 and its Association with Microbiota in the Gut

The role of respiratory tract microbes and the relationship between respiratory tract and gut microbiomes in coronavirus disease 2019 (COVID-19) remain uncertain. Here, the metagenomes of sputum and fecal samples from 66 patients with COVID-19 at three stages of disease progression are sequenced. Respiratory tract, gut microbiome, and peripheral blood mononuclear cell (PBMC) samples are analyzed to compare the gut and respiratory tract microbiota of intensive care unit (ICU) and non-ICU (nICU) patients and determine relationships between respiratory tract microbiome and immune response. In the respiratory tract, significantly fewer Streptococcus, Actinomyces, Atopobium, and Bacteroides are found in ICU than in nICU patients, while Enterococcus and Candida increase. In the gut, significantly fewer Bacteroides are found in ICU patients, while Enterococcus increases. Significant positive correlations exist between relative microbiota abundances in the respiratory tract and gut. Defensin-related pathways in PBMCs are enhanced, and respiratory tract Streptococcus is reduced in patients with COVID-19. A respiratory tract-gut microbiota model identifies respiratory tract Streptococcus and Atopobium as the most prominent biomarkers distinguishing between ICU and nICU patients. The findings provide insight into the respiratory tract and gut microbial dynamics during COVID-19 progression, considering disease severity, potentially contributing to diagnosis, and treatment strategies.

Gut Dysbiosis Has the Potential to Reduce the Sexual Attractiveness of Mouse Female

Increasing evidence has shown that the gut microbiome has significant effects on mate preferences of insects; however, whether gut microbiota composition affects sexual attractiveness and mate preference in mammals remains largely unknown. Here, we showed that antibiotic treatment significantly restructured the gut microbiota composition of both mouse males and females. Males, regardless of antibiotic treatment, exhibited a higher propensity to interact with the control females than the antibiotic-treated females. The data clearly showed that gut microbiota dysbiosis reduced the sexual attractiveness of females to males, implying that commensal gut microbiota influences female attractiveness to males. The reduced sexual attractiveness of the antibiotic-treated females may be beneficial to discriminating males by avoiding disorders of immunity and sociability in offspring that acquire maternal gut microbiota via vertical transmission. We suggest further work should be oriented to increase our understanding of the interactions between gut microbiota dysbiosis, sexual selection, and mate choice of wild animals at the population level.

Human Milk Oligosaccharides and Bacterial Profile Modulate Infant Body Composition during Exclusive Breastfeeding

Human milk is a complex and variable ecosystem fundamental to the development of newborns. This study aimed to investigate relationships between human milk oligosaccharides (HMO) and human milk bacterial profiles and infant body composition. Human milk samples (n = 60) were collected at two months postpartum. Infant and maternal body composition was measured with bioimpedance spectroscopy. Human milk bacterial profiles were assessed using full-length 16S rRNA gene sequencing and 19 HMOs were quantitated using high-performance liquid chromatography. Relative abundance of human milk bacterial taxa were significantly associated with concentrations of several fucosylated and sialylated HMOs. Individual human milk bacteria and HMO intakes and concentrations were also significantly associated with infant anthropometry, fat-free mass, and adiposity. Furthermore, when data were stratified based on maternal secretor status, some of these relationships differed significantly among infants born to secretor vs non-secretor mothers. In conclusion, in this pilot study the human milk bacterial profile and HMO intakes and concentrations were significantly associated with infant body composition, with associations modified by secretor status. Future research designed to increase the understanding of the mechanisms by which HMO and human milk bacteria modulate infant body composition should include intakes in addition to concentrations.

Effects of E scherichia coli Nissle 1917 on the Porcine Gut Microbiota, Intestinal Epithelium and Immune System in Early Life

Early in life and particularly around weaning, piglets are susceptible to infections because of abrupt social, environmental, and dietary changes. Dietary interventions with probiotic bacteria have gained popularity because of the increased awareness of the direct link between diet and health. In this study, piglets received the probiotic strain Escherichia coli Nissle 1917 (EcN) or a control treatment perorally from day 2 after birth until 2 weeks post-weaning. To investigate spatio-temporal effects of EcN on the gut microbiota composition, intestinal epithelial gene expression and immune system, feces, digesta, blood, scraping material and mesenteric lymph node tissue were collected at different time points. In addition, oral vaccinations against Salmonella enterica serovar Typhimurium were administered on days 21 and 45 of the study to assess the immunocompetence. EcN-treated pigs showed a reduced diversity of taxa within the phylum Proteobacteria and a lower relative abundance of taxa within the genus Treponema during the pre-weaning period. Moreover, EcN induced T cell proliferation and Natural Killer cell activation in blood and enhanced IL-10 production in ex vivo stimulated mesenteric lymph node cells, the latter pointing toward a more regulatory or anti-inflammatory state of the local gut-associated immune system. These outcomes were primarily observed pre-weaning. No significant differences were observed between the treatment groups with regards to body weight, epithelial gene expression, and immune response upon vaccination. Differences observed during the post-weaning period between the treatment groups were modest. Overall, this study demonstrates that the pre-weaning period offers a 'window of opportunity' to modulate the porcine gut microbiota and immune system through dietary interventions such as EcN supplementation.

Large-Scale Meta-Longitudinal Microbiome Data with a Known Batch Factor

Data contamination in meta-approaches where multiple biological samples are combined considerably affects the results of subsequent downstream analyses, such as differential abundance tests comparing multiple groups at a fixed time point. Little has been thoroughly investigated regarding the impact of the lurking variable of various batch sources, such as different days or different laboratories, in more complicated time series experimental designs, for instance, repeatedly measured longitudinal data and metadata. We highlight that the influence of batch factors is significant on subsequent downstream analyses, including longitudinal differential abundance tests, by performing a case study of microbiome time course data with two treatment groups and a simulation study of mimic microbiome longitudinal counts.

A random effect regression based on the odd log-logistic generalized inverse Gaussian distribution

In recent decades, the use of regression models with random effects has made great progress. Among these models' attractions is the flexibility to analyze correlated data. In various situations, the distribution of the response variable presents asymmetry or bimodality. In these cases, it is possible to use the normal regression with random effect at the intercept. In light of these contexts, i.e. the desire to analyze correlated data in the presence of bimodality or asymmetry, in this paper we propose a regression model with random effect at the intercept based onthe generalized inverse Gaussian distribution model with correlated data. The maximum likelihood is adopted to estimate the parameters and various simulations are performed for correlated data. A type of residuals for the new regression is proposed whose empirical distribution is close to normal. The versatility of the new regression is demonstrated by estimating the average price per hectare of bare land in 10 municipalities in the state of São Paulo (Brazil). In this context, various databases are constantly emerging, requiring flexible modeling. Thus, it is likely to be of interest to data analysts, and can make a good contribution to the statistical literature.

Rearranging the sugarcane holobiont via plant growth-promoting bacteria and nitrogen input

The development and productivity of plants are governed by their genetic background, nutrient input, and the microbial communities they host, i.e. the holobiont. Accordingly, engineering beneficial root microbiomes has emerged as a novel and sustainable approach to crop production with reduced nutrient input. Here, we tested the effects of six bacterial strains isolated from sugarcane stalks on sugarcane growth and physiology as well as the dynamics of prokaryote community assembly in the rhizosphere and root endosphere under two N fertilization regimes. All six strains, Paraburkholderia caribensis IAC/BECa 88, Kosakonia oryzae IAC/BECa 90, Kosakonia radicincitans IAC/BECa 95, Paraburkholderia tropica IAC/BECa 135, Pseudomonas fluorescens IAC/BECa 141 and Herbaspirillum frisingense IAC/BECa 152, increased in shoot and root dry mass, and influenced the concentration and accumulation of important macro- and micronutrients. However, N input reduced the impact of inoculation by shifting the sugarcane microbiome (rhizosphere and root endosphere) and weakening the co-dependence between soil microbes and sugarcane biomass and nutrients. The results show that these beneficial microbes improved plant nutrient uptake conditioned to a reduced N nutrient input. Therefore, reduced fertilization is not only desirable consequence of bacterial inoculation but essential for higher impact of these beneficial bacteria on the sugarcane microbiome.

The Viable Microbiome of Human Milk Differs from the Metataxonomic Profile

Bacteria in human milk contribute to the establishment of the infant gut microbiome. As such, numerous studies have characterized the human milk microbiome using DNA sequencing technologies, particularly 16S rRNA gene sequencing. However, such methods are not able to differentiate between DNA from viable and non-viable bacteria. The extent to which bacterial DNA detected in human milk represents living, biologically active cells is therefore unclear. Here, we characterized both the viable bacterial content and the total bacterial DNA content (derived from viable and non-viable cells) of fresh human milk (n = 10). In order to differentiate the living from the dead, a combination of propidium monoazide (PMA) and full-length 16S rRNA gene sequencing was used. Our results demonstrate that the majority of OTUs recovered from fresh human milk samples (67.3%) reflected DNA from non-viable organisms. PMA-treated samples differed significantly in their bacterial composition compared to untreated samples (PERMANOVA p < 0.0001). Additionally, an OTU mapping to Cutibacterium acnes had a significantly higher relative abundance in PMA-treated (viable) samples. These results demonstrate that the total bacterial DNA content of human milk is not representative of the viable human milk microbiome. Our findings raise questions about the validity of conclusions drawn from previous studies in which viability testing was not used, and have broad implications for the design of future work in this field.

Impact of Oropharyngeal Administration of Colostrum in Preterm Newborns' Oral Microbiome

The initial colonization of the human microbiota is of paramount importance. In this context, the oropharyngeal administration of colostrum is a safe, viable, and well-tolerated practice even by the smallest preterm infants. Therefore, this study evaluated the effects of oropharyngeal administration of colostrum on the establishment of preterm infants' oral microbiota. A longitudinal observational study was carried out with 20 premature neonates, divided into two groups: one receiving the protocol (Oropharyngeal Administration of Colostrum; OAC) and the other one receiving Standard Caare (SC). Saliva samples were collected from the newborns weekly during the study period (from the day of birth until the 21st day of life) for analysis of oral microbiota through 16S rRNA gene sequencing. We observed that the colonization of the oral microbiota of preterm newborns preseanted a higher relative abundance of Staphylococcus on the 7th day of life, mainly in the OAC group. Additionally, an increased abundance of Bifidobacterium and Bacteroides was observed in the OAC group at the first week of life. Regarding alpha and beta diversity, time was a key factor in the oral modulation of both groups, showing how dynamic this environment is in early life.

Diversity and compositional changes in the gut microbiota of wild and captive vertebrates: a meta-analysis

The gut microbiota is recognised as an essential asset for the normal functioning of animal biology. When wild animals are moved into captivity, the modified environmental pressures are expected to rewire the gut microbiota, yet whether this transition follows similar patterns across vertebrates is still unresolved due to the absence of systematic multi-species analyses. We performed a meta-analysis of gut microbiota profiles of 322 captive and 322 wild specimens from 24 vertebrate species. Our analyses yielded no overall pattern of diversity and compositional variation between wild and captive vertebrates, but a heterogeneous landscape of responses, which differed depending on the components of diversity considered. Captive populations showed enrichment patterns of human-associated microorganisms, and the minimal host phylogenetic signal suggests that changes between wild and captive populations are mainly driven by case-specific captivity conditions. Finally, we show that microbiota differences between wild and captive populations can impact evolutionary and ecological inferences that rely on hierarchical clustering-based comparative analyses of gut microbial communities across species.

Gut Microbiota and Mycobiota Evolution Is Linked to Memory Improvement after Bariatric Surgery in Obese Patients: A Pilot Study

Patients with obesity are known to exhibit gut microbiota dysbiosis and memory deficits. Bariatric surgery (BS) is currently the most efficient anti-obesity treatment and may improve both gut dysbiosis and cognition. However, no study has investigated association between changes of gut microbiota and cognitive function after BS. We prospectively evaluated 13 obese patients on anthropometric data, memory functions, and gut microbiota-mycobiota before and six months after BS. The Rey Auditory Verbal Learning Test (AVLT) and the symbol span (SS) of the Weschler Memory Scale were used to assess verbal and working memory, respectively. Fecal microbiota and mycobiota were longitudinally analyzed by 16S and ITS2 rRNA sequencing respectively. AVLT and SS scores were significantly improved after BS (AVLT scores: 9.7 ± 1.7 vs. 11.2 ± 1.9, p = 0.02, and SS scores: 9.7 ± 23.0 vs. 11.6 ± 2.9, p = 0.05). An increase in bacterial alpha-diversity, and Ruminococcaceae, Prevotella, Agaricus, Rhodotorula, Dipodascus, Malassezia, and Mucor were significantly associated with AVLT score improvement after BS, while an increase in Prevotella and a decrease in Clostridium, Akkermansia,&nbsp;Dipodascus and Candida were linked to SS scores improvement. We identified several changes in the microbial communities that differ according to the improvement of either the verbal or working memories, suggesting a complex gut-brain-axis that evolves after BS.

The composition of lung microbiome in lung cancer: a systematic review and meta-analysis

Background

Although recent studies have indicated that imbalance in the respiratory microbiome composition is linked to several chronic respiratory diseases, the association between the lung microbiome and lung cancer has not been extensively studied. Conflicting reports of individual studies on respiratory microbiome alterations in lung cancer complicate the matter for specifying how the lung microbiome is linked to lung cancer. Consequently, as the first meta-analysis on this topic, we integrate publicly available 16S rRNA gene sequence data on lung tissue samples of lung cancer patients to identify bacterial taxa which differ consistently between case and control groups.

Results

The findings of the current study suggest that the relative abundance of several bacterial taxa including Actinobacteria phylum, Corynebacteriaceae and Halomonadaceae families, and Corynebacterium, Lachnoanaerobaculum, and Halomonas genera is significantly decreased (p < 0.05) in lung tumor tissues of lung cancer patients in comparison with tumor-adjacent normal tissues.

Conclusions

Despite the underlying need for scrutinizing the findings further, the present study lays the groundwork for future research and adds to our limited understanding of the key role of the lung microbiome and its complex interaction with lung cancer. More data on demographic factors and tumor tissue types would help establish a greater degree of accuracy in characterizing the lung microbial community which accords with subtypes and stages of the disease and fully capturing the changes of the lung microbiome in lung cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-021-02375-z.

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.

Soil Layers Matter: Vertical Stratification of Root-Associated Fungal Assemblages in Temperate Forests Reveals Differences in Habitat Colonization

Ectomycorrhizal and saprotrophic fungi play pivotal roles in ecosystem functioning. Here, we studied the vertical differentiation of root-associated fungi (RAF) in temperate forests. We analysed RAF assemblages in the organic and mineral soil from 150 experimental forest plots across three biogeographic regions spanning a distance of about 800 km. Saprotrophic RAF showed the highest richness in organic and symbiotrophic RAF in mineral soil. Symbiotrophic RAF exhibited higher relative abundances than saprotrophic fungi in both soil layers. Beta-diversity of RAF was mainly due to turnover between organic and mineral soil and showed regional differences for symbiotrophic and saprotrophic fungi. Regional differences were also found for different phylogenetic levels, i.e., fungal orders and indicator species in the organic and mineral soil, supporting that habitat conditions strongly influence differentiation of RAF assemblages. Important exceptions were fungal orders that occurred irrespective of the habitat conditions in distinct soil layers across the biogeographic gradient: Russulales and Cantharellales (ectomycorrhizal fungi) were enriched in RAF assemblages in mineral soil, whereas saprotrophic Polyporales and Sordariales and ectomycorrhizal Boletales were enriched in RAF assemblages in the organic layer. These results underpin a phylogenetic signature for niche partitioning at the rank of fungal orders and suggest that RAF assembly entails two strategies encompassing flexible and territorial habitat colonization by different fungal taxa.

Oral iron supplementation after antibiotic exposure induces a deleterious recovery of the gut microbiota

Background

Oral iron supplementation is commonly prescribed for anemia and may play an important role in the gut microbiota recovery of anemic individuals who received antibiotic treatment. This study aims to investigate the effects of iron supplementation on gut microbiota recovery after antibiotics exposure.

Results

Mice were subjected to oral antibiotic treatment with neomycin and metronidazole and were fed diets with different concentrations of iron. The composition of the gut microbiota was followed throughout treatment by 16S rRNA sequencing of DNA extracted from fecal samples. Gut microbiota functions were inferred using PICRUSt2, and short-chain fatty acid concentration in fecal samples was assessed by liquid-chromatography mass spectrometry. Iron supplementation after antibiotic exposure shifted the gut microbiota composition towards a Bacteroidetes phylum-dominant composition. At the genus level, the iron-supplemented diet induced an increase in the abundance of Parasutterella and Bacteroides, and a decrease of Bilophila and Akkermansia. Parasutterella excrementihominis, Bacteroides vulgatus, and Alistipes finegoldii, were more abundant with the iron excess diet. Iron-induced shifts in microbiota composition were accompanied by functional modifications, including an enhancement of the biosynthesis of primary bile acids, nitrogen metabolism, cyanoamino acid metabolism and pentose phosphate pathways. Recovery after antibiotic treatment increased propionate levels independent of luminal iron levels, whereas butyrate levels were diminished by excess iron.

Conclusions

Oral iron supplementation after antibiotic therapy in mice may lead to deleterious changes in the recovery of the gut microbiota. Our results have implications on the use of oral iron supplementation after antibiotic exposure and justify further studies on alternative treatments for anemia in these settings.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-021-02320-0.

Meta-analysis of Clinical Microbiome Studies in Urolithiasis Reveal Age, Stone Composition, and Study Location as the Predominant Factors in Urolithiasis-Associated Microbiome Composition

To determine whether functionally relevant questions associated with the urinary or gut microbiome and urinary stone disease (USD) can be answered from metagenome-wide association studies (MWAS), we performed the most comprehensive meta-analysis of published clinical MWAS in USD to date, using publicly available data published prior to April 2021. Six relevant studies met inclusion criteria. For alpha-diversity, significant differences were noted between USD status, stone composition, sample type, study location, age, diet, and sex. For beta-diversity, significant differences were noted by USD status, stone composition, sample type, study location, antibiotic use (30 days and 12 months before sampling), sex, hypertension, water intake, body habitus, and age. Prevotella and Lactobacillus in the gut and urinary tract, respectively, were associated with healthy individuals, while Enterobacteriaceae was associated with USD in the urine and stones. Paradoxically, other Prevotella strains were also strongly associated with USD in the gut microbiome. When data were analyzed together, USD status, stone composition, age group, and study location were the predominant factors associated with microbiome composition. Meta-analysis showed significant microbiome differences based on USD status, stone composition, age group or study location. However, analyses were limited by a lack of public data from published studies, metadata collected, and differing study protocols. Results highlight the need for field-specific standardization of experimental protocols in terms of sample collection procedures and the anatomical niches to assess, as well as in defining clinically relevant metadata and subphenotypes such as stone composition.

Remodeling of the maternal gut microbiome during pregnancy is shaped by parity

BACKGROUND

The maternal microbiome has emerged as an important factor in gestational health and outcome and is associated with risk of preterm birth and offspring morbidity. Epidemiological evidence also points to successive pregnancies-referred to as maternal parity-as a risk factor for preterm birth, infant mortality, and impaired neonatal growth. Despite the fact that both the maternal microbiome and parity are linked to maternal-infant health, the impact of parity on the microbiome remains largely unexplored, in part due to the challenges of studying parity in humans.

RESULTS

Using synchronized pregnancies and dense longitudinal monitoring of the microbiome in pigs, we describe a microbiome trajectory during pregnancy and determine the extent to which parity modulates this trajectory. We show that the microbiome changes reproducibly during gestation and that this remodeling occurs more rapidly as parity increases. At the time of parturition, parity was linked to the relative abundance of several bacterial species, including Treponema bryantii, Lactobacillus amylovorus, and Lactobacillus reuteri. Strain tracking carried out in 18 maternal-offspring "quadrads"-each consisting of one mother sow and three piglets-linked maternal parity to altered levels of Akkermansia muciniphila, Prevotella stercorea, and Campylobacter coli in the infant gut 10 days after birth.

CONCLUSIONS

Collectively, these results identify parity as an important environmental factor that modulates the gut microbiome during pregnancy and highlight the utility of a swine model for investigating the microbiome in maternal-infant health. In addition, our data show that the impact of parity extends beyond the mother and is associated with alterations in the community of bacteria that colonize the offspring gut early in life. The bacterial species we identified as parity-associated in the mother and offspring have been shown to influence host metabolism in other systems, raising the possibility that such changes may influence host nutrient acquisition or utilization. These findings, taken together with our observation that even subtle differences in parity are associated with microbiome changes, underscore the importance of considering parity in the design and analysis of human microbiome studies during pregnancy and in infants. Video abstract.

Deep Learning for Integrated Analysis of Insulin Resistance with Multi-Omics Data

Technological advances in next-generation sequencing (NGS) have made it possible to uncover extensive and dynamic alterations in diverse molecular components and biological pathways across healthy and diseased conditions. Large amounts of multi-omics data originating from emerging NGS experiments require feature engineering, which is a crucial step in the process of predictive modeling. The underlying relationship among multi-omics features in terms of insulin resistance is not well understood. In this study, using the multi-omics data of type II diabetes from the Integrative Human Microbiome Project, from 10,783 features, we conducted a data analytic approach to elucidate the relationship between insulin resistance and multi-omics features, including microbiome data. To better explain the impact of microbiome features on insulin classification, we used a developed deep neural network interpretation algorithm for each microbiome feature’s contribution to the discriminative model output in the samples.

Implementation of microbiota analysis in clinical trials for cystic fibrosis lung infection: Experience from the OligoG phase 2b clinical trials

Culture-independent microbiota analysis is widely used in research and being increasingly used in translational studies. However, methods for standardisation and application of these analyses in clinical trials are limited. Here we report the microbiota analysis that accompanied two phase 2b clinical trials of the novel, non-antibiotic therapy OligoG CF-5/20 for cystic fibrosis (CF) lung infection. Standardised protocols (DNA extraction, PCR, qPCR and 16S rRNA gene sequencing analysis) were developed for application to the Pseudomonas aeruginosa (NCT02157922) and Burkholderia cepacia complex (NCT02453789) clinical trials involving 45 and 13 adult trial participants, respectively. Microbiota analysis identified that paired sputum samples from an individual participant, taken within 2 h of each other, had reproducible bacterial diversity profiles. Although culture microbiology had identified patients as either colonised by P. aeruginosa or B. cepacia complex species at recruitment, microbiota analysis revealed patient lung infection communities were not always dominated by these key CF pathogens. Microbiota profiles were patient-specific and remained stable over the course of both clinical trials (6 sampling points over the course of 140 days). Within the Burkholderia trial, participants were infected with B. cenocepacia (n = 4), B. multivorans (n = 6), or an undetermined species (n = 3). Colonisation with either B. cenocepacia or B. multivorans influenced the overall bacterial community structure in sputum. Overall, we have shown that sputum microbiota in adults with CF is stable over a 2 h time-frame, suggesting collection of a single sample on a collection day is sufficient to capture the microbiota diversity. Despite the uniform pathogen culture-positivity status at recruitment, trial participants were highly heterogeneous in their lung microbiota. Understanding the microbiota profiles of individuals with CF ahead of future clinical trials would be beneficial in the context of patient stratification and trial design.

Assessing the Relationship Between Nitrate-Reducing Capacity of the Oral Microbiome and Systemic Outcomes

The significance of the oral microbiome in the generation of the nitric oxide (NO) via the enterosalivary nitrate-nitrite-nitric oxide pathway is increasingly recognized, directly linking the oral microbiome to cardiometabolic outcomes influenced by NO. The objective of this chapter is to outline a strategy of identifying pathway-specific bacterial taxa or predicted genes of interest from 16S rRNA data, specifically in the enterosalivary pathway of nitrate reduction, and analyzing their relationship with cardiometabolic outcomes using multivariable regression models.

Impact of Yeast-Derived β-Glucans on the Porcine Gut Microbiota and Immune System in Early Life

Piglets are susceptible to infections in early life and around weaning due to rapid environmental and dietary changes. A compelling target to improve pig health in early life is diet, as it constitutes a pivotal determinant of gut microbial colonization and maturation of the host’s immune system. In the present study, we investigated how supplementation of yeast-derived β-glucans affects the gut microbiota and immune function pre- and post-weaning, and how these complex systems develop over time. From day two after birth until two weeks after weaning, piglets received yeast-derived β-glucans or a control treatment orally and were subsequently vaccinated against Salmonella Typhimurium. Faeces, digesta, blood, and tissue samples were collected to study gut microbiota composition and immune function. Overall, yeast-derived β-glucans did not affect the vaccination response, and only modest effects on faecal microbiota composition and immune parameters were observed, primarily before weaning. This study demonstrates that the pre-weaning period offers a ‘window of opportunity’ to alter the gut microbiota and immune system through diet. However, the observed changes were modest, and any long-lasting effects of yeast-derived β-glucans remain to be elucidated.

Naught all zeros in sequence count data are the same

Genomic studies feature multivariate count data from high-throughput DNA sequencing experiments, which often contain many zero values. These zeros can cause artifacts for statistical analyses and multiple modeling approaches have been developed in response. Here, we apply different zero-handling models to gene-expression and microbiome datasets and show models can disagree substantially in terms of identifying the most differentially expressed sequences. Next, to rationally examine how different zero handling models behave, we developed a conceptual framework outlining four types of processes that may give rise to zero values in sequence count data. Last, we performed simulations to test how zero handling models behave in the presence of these different zero generating processes. Our simulations showed that simple count models are sufficient across multiple processes, even when the true underlying process is unknown. On the other hand, a common zero handling technique known as "zero-inflation" was only suitable under a zero generating process associated with an unlikely set of biological and experimental conditions. In concert, our work here suggests several specific guidelines for developing and choosing state-of-the-art models for analyzing sparse sequence count data.

Oral microbiome and onset of oral mucositis in patients with squamous cell carcinoma of the head and neck

BACKGROUND

Oral mucositis (OM) is a debilitating sequela for patients treated for squamous cell carcinoma of the head and neck (HNSCC). This study investigated whether oral microbial features before treatment or during treatment are associated with the time to onset of severe OM in patients with HNSCC.

METHODS

This was a cohort study of newly diagnosed patients with locoregional HNSCC who received chemotherapy with or without radiotherapy from April 2016 to September 2017. OM was based on the National Cancer Institute's Common Terminology Criteria for Adverse Events, version 4.0. The oral microbiome was characterized on the basis of the 16S ribosomal RNA V4 region with the Illumina platform. A mixture cure model was used to generate hazard ratios for the onset of severe OM.

RESULTS

Eighty-six percent of the patients developed OM (n = 57 [33 nonsevere cases and 24 severe cases]) with a median time to onset of OM of 21 days. With adjustments for age, sex, and smoking status, genera abundance was associated with the hazard for the onset of severe OM as follows: 1) at the baseline (n = 66), Cardiobacterium (P = .03) and Granulicatella (P = .04); 2) immediately before the development of OM (n = 57), Prevotella (P = .03), Fusobacterium (P = .03), and Streptococcus (P = .01); and 3) immediately before the development of severe OM (n = 24), Megasphaera (P = .0001) and Cardiobacterium (P = .03). There were no differences in α-diversity between the baseline samples and Human Microbiome Project data.

CONCLUSIONS

Changes in the abundance of genera over the course of treatment were associated with the onset of severe OM. The mechanism and therapeutic implications of these findings need to be investigated in future studies.

Fecal metagenomics and metabolomics reveal gut microbial changes after bariatric surgery

BACKGROUND

Evidence from longitudinal patient studies regarding gut microbial changes after bariatric surgery is limited.

OBJECTIVE

To examine intraindividual changes in fecal microbiome and metabolites among patients undergoing Roux-en-Y gastric bypass or vertical sleeve gastrectomy.

SETTING

Observational study.

METHODS

Twenty patients were enrolled and provided stool samples before and 1 week, 1 month, and/or 3 months after surgery. Shallow shotgun metagenomics and untargeted fecal metabolomics were performed. Zero-inflated generalized additive models and linear mixed models were applied to identify fecal microbiome and metabolites changes, with adjustment for potential confounders and correction for multiple testing.

RESULTS

We enrolled 16 women and 4 men, including 16 white and 4 black participants (median age = 45 years; presurgery body mass index = 47.7 kg/m²). Ten patients had Roux-en-Y gastric bypass, 10 had vertical sleeve gastrectomy, and 14 patients provided postsurgery stool samples. Of 47 samples, median sequencing depth was 6.3 million reads and 1073 metabolites were identified. Microbiome alpha-diversity increased after surgery, especially at 3 months. Significant genus-level changes included increases in Odoribacter, Streptococcus, Anaerotruncus, Alistipes, Klebsiella, and Bifidobacterium, while decreases in Bacteroides, Coprocosccus, Dorea, and Faecalibacterium. Large increases in Streptococcus, Akkermansia, and Prevotella were observed at 3 months. Beta-diversity and fecal metabolites were also changed, including reduced caffeine metabolites, indoles, and butyrate.

CONCLUSIONS

Despite small sample size and missing repeated samples in some participants, our pilot study showed significant postsurgery changes in fecal microbiome and metabolites among bariatric surgery patients. Future large-scale, longitudinal studies are warranted to investigate gut microbial changes and their associations with metabolic outcomes after bariatric surgery.

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

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

Panel 3: Genomics, precision medicine and targeted therapies

OBJECTIVE

To review the most recent advances in human and bacterial genomics as applied to pathogenesis and clinical management of otitis media.

DATA SOURCES

PubMed articles published since the last meeting in June 2015 up to June 2019.

REVIEW METHODS

A panel of experts in human and bacterial genomics of otitis media was formed. Each panel member reviewed the literature in their respective fields and wrote draft reviews. The reviews were shared with all panel members, and a merged draft was created. The panel met at the 20th International Symposium on Recent Advances in Otitis Media in June 2019, discussed the review and refined the content. A final draft was made, circulated, and approved by the panel members.

CONCLUSION

Trans-disciplinary approaches applying pan-omic technologies to identify human susceptibility to otitis media and to understand microbial population dynamics, patho-adaptation and virulence mechanisms are crucial to the development of novel, personalized therapeutics and prevention strategies for otitis media.

IMPLICATIONS FOR PRACTICE

In the future otitis media prevention strategies may be augmented by mucosal immunization, combination vaccines targeting multiple pathogens, and modulation of the middle ear microbiome. Both treatment and vaccination may be tailored to an individual's otitis media phenotype as defined by molecular profiles obtained by using rapidly developing techniques in microbial and host genomics.