q2-sample-classifier: machine-learning tools for microbiome classification and regression

Nanoplastic pollution changes the intestinal microbiome but not the morphology or behavior of a freshwater turtle

Humans produce 350 million metric tons of plastic waste per year, leading to microplastic pollution and widespread environmental contamination, particularly in aquatic environments. This subsequently impacts aquatic organisms in myriad ways, yet the vast majority of research is conducted in marine, rather than freshwater systems. In this study, we exposed eggs and hatchlings of the Chinese soft-shelled turtle (Pelodiscus sinensis) to 80-nm polystyrene nanoplastics (PS-NPs) and monitored the impacts on development, behavior and the gut microbiome. We demonstrate that 80-nm PS-NPs can penetrate the eggshell and move into developing embryos. This led to metabolic impairments, as evidenced by bradycardia (a decreased heart rate), which persisted until hatching. We found no evidence that nanoplastic exposure affected hatchling morphology, growth rates, or levels of boldness and exploration, yet we discuss some potential caveats here. Exposure to nanoplastics reduced the diversity and homogeneity of gut microbiota in P. sinensis, with the level of disruption correlating to the length of environmental exposure (during incubation only or post-hatching also). Thirteen core genera (with an initial abundance >1 %) shifted after nanoplastic treatment: pathogenic bacteria increased, beneficial probiotic bacteria decreased, and there was an increase in the proportion of negative correlations between bacterial genera. These changes could have profound impacts on the viability of turtles throughout their lives. Our study highlights the toxicity of environmental NPs to the embryonic development and survival of freshwater turtles. We provide insights about population trends of P. sinensis in the wild, and future directions for research.

Exploring bacterial diversity and antimicrobial resistance gene on a southern Brazilian swine farm

The bacterial composition of and the circulation of antimicrobial resistance genes (ARGs) in waste from Brazilian swine farms are still poorly understood. Considering that antimicrobial resistance (AMR) is one of the main threats to human, animal, and environmental health, the need to accurately assess the load of ARGs released into the environment is urgent. Therefore, this study aimed to characterize the microbiota in a swine farm in southern Brazil and the resistome in swine farm wastewater treated in a series of waste stabilization ponds (WSPs). Samples were collected from farm facilities and the surrounding environment, representing all levels of swine manure within the treatment system. Total metagenomic sequencing was performed on samples from WSPs, and 16S-rDNA sequencing was performed on all the collected samples. The results showed increased bacterial diversity in WSPs, characterized by the presence of Caldatribacteriota, Cloacimonadota, Desulfobacterota, Spirochaetota, Synergistota, and Verrucomicrobiota. Furthermore, resistance genes to tetracyclines, lincosamides, macrolides, rifamycin, phenicol, and genes conferring multidrug resistance were detected in WSPs samples. Interestingly, the most abundant ARG was linG, which confers resistance to the lincosamides. Notably, genes conferring macrolide (mphG and mefC) and rifamycin (rpoB_RIF) resistance appeared in greater numbers in the late WSPs. These drugs are among the high-priority antibiotic classes for human health. Moreover, certain mobile genetic elements (MGEs) were identified in the samples, notably tnpA, which was found in high abundance. These elements are of particular concern due to their potential to facilitate the dissemination of ARGs among bacteria. In summary, the results indicate that, in the studied farm, the swine manure treatment system could not eliminate ARGs and MGEs. Our results validate concerns about Brazil's swine production system. The misuse and overuse of antimicrobials during animal production must be avoided to mitigate AMR.

Engineering multi-degrading bacterial communities to bioremediate soils contaminated with pesticides residues

Parallel to the important use of pesticides in conventional agriculture there is a growing interest for green technologies to clear contaminated soil from pesticides and their degradation products. Bioaugmentation i. e. the inoculation of degrading micro-organisms in polluted soil, is a promising method still in needs of further developments. Specifically, improvements in the understanding of how degrading microorganisms must overcome abiotic filters and interact with the autochthonous microbial communities are needed in order to efficiently design bioremediation strategies. Here we designed a protocol aiming at studying the degradation of two herbicides, glyphosate (GLY) and isoproturon (IPU), via experimental modifications of two source bacterial communities. We used statistical methods stemming from genomic prediction to link community composition to herbicides degradation potentials. Our approach proved to be efficient with correlation estimates over 0.8 - between model predictions and measured pesticide degradation values. Multi-degrading bacterial communities were obtained by coalescing bacterial communities with high GLY or IPU degradation ability based on their community-level properties. Finally, we evaluated the efficiency of constructed multi-degrading communities to remove pesticide contamination in a different soil. While results are less clear in the case of GLY, we showed an efficient transfer of degrading capacities towards the receiving soil even at relatively low inoculation levels in the case of IPU. Altogether, we developed an innovative protocol for building multi-degrading simplified bacterial communities with the help of genomic prediction tools and coalescence, and proved their efficiency in a contaminated soil.

Environmental and structural factors associated with bacterial diversity in household dust across the Arizona-Sonora border

We previously reported that asthma prevalence was higher in the United States (US) compared to Mexico (MX) (25.8% vs. 8.4%). This investigation assessed differences in microbial dust composition in relation to demographic and housing characteristics on both sides of the US-MX Border. Forty homes were recruited in the US and MX. Home visits collected floor dust and documented occupants' demographics, asthma prevalence, housing structure, and use characteristics. US households were more likely to have inhabitants who reported asthma when compared with MX households (30% vs. 5%) and had significantly different flooring types. The percentage of households on paved roads, with flushing toilets, with piped water and with air conditioning was higher in the US, while dust load was higher in MX. Significant differences exist between countries in the microbial composition of the floor dust. Dust from Mexican homes was enriched with Alishewanella, Paracoccus, Rheinheimera genera and Intrasporangiaceae family. A predictive metagenomics analysis identified 68 significantly differentially abundant functional pathways between US and MX. This study documented multiple structural, environmental, and demographic differences between homes in the US and MX that may contribute to significantly different microbial composition of dust observed in these two countries.

Traditional Chinese herbal formulas modulate gut microbiome and improve insomnia in patients with distinct syndrome types: insights from an interventional clinical study

Background

Traditional Chinese medicine (TCM) comprising herbal formulas has been used for millennia to treat various diseases, such as insomnia, based on distinct syndrome types. Although TCM has been proposed to be effective in insomnia through gut microbiota modulation in animal models, human studies remain limited. Therefore, this study employs machine learning and integrative network techniques to elucidate the role of the gut microbiome in the efficacies of two TCM formulas - center-supplementing and qi-boosting decoction (CSQBD) and spleen-tonifying and yin heat-clearing decoction (STYHCD) - in treating insomnia patients diagnosed with spleen qi deficiency and spleen qi deficiency with stomach heat.

Methods

Sixty-three insomnia patients with these two specific TCM syndromes were enrolled and treated with CSQBD or STYHCD for 4 weeks. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI) and Insomnia Severity Index (ISI) every 2 weeks. In addition, variations in gut microbiota were evaluated through 16S rRNA gene sequencing. Stress and inflammatory markers were measured pre- and post-treatment.

Results

At baseline, patients exhibiting only spleen qi deficiency showed slightly lesser severe insomnia, lower IFN-α levels, and higher cortisol levels than those with spleen qi deficiency with stomach heat. Both TCM syndromes displayed distinct gut microbiome profiles despite baseline adjustment of PSQI, ISI, and IFN-α scores. The nested stratified 10-fold cross-validated random forest classifier showed that patients with spleen qi deficiency had a higher abundance of Bifidobacterium longum than those with spleen qi deficiency with stomach heat, negatively associated with plasma IFN-α concentration. Both CSQBD and STYHCD treatments significantly improved sleep quality within 2 weeks, which lasted throughout the study. Moreover, the gut microbiome and inflammatory markers were significantly altered post-treatment. The longitudinal integrative network analysis revealed interconnections between sleep quality, gut microbes, such as Phascolarctobacterium and Ruminococcaceae, and inflammatory markers.

Conclusion

This study reveals distinct microbiome profiles associated with different TCM syndrome types and underscores the link between the gut microbiome and efficacies of Chinese herbal formulas in improving insomnia. These findings deepen our understanding of the gut-brain axis in relation to insomnia and pave the way for precision treatment approaches leveraging TCM herbal remedies.

A culture-independent approach, supervised machine learning, and the characterization of the microbial community composition of coastal areas across the Bay of Bengal and the Arabian Sea

BACKGROUND

Coastal areas are subject to various anthropogenic and natural influences. In this study, we investigated and compared the characteristics of two coastal regions, Andhra Pradesh (AP) and Goa (GA), focusing on pollution, anthropogenic activities, and recreational impacts. We explored three main factors influencing the differences between these coastlines: The Bay of Bengal's shallower depth and lower salinity; upwelling phenomena due to the thermocline in the Arabian Sea; and high tides that can cause strong currents that transport pollutants and debris.

RESULTS

The microbial diversity in GA was significantly higher than that in AP, which might be attributed to differences in temperature, soil type, and vegetation cover. 16S rRNA amplicon sequencing and bioinformatics analysis indicated the presence of diverse microbial phyla, including candidate phyla radiation (CPR). Statistical analysis, random forest regression, and supervised machine learning models classification confirm the diversity of the microbiome accurately. Furthermore, we have identified 450 cultures of heterotrophic, biotechnologically important bacteria. Some strains were identified as novel taxa based on 16S rRNA gene sequencing, showing promising potential for further study.

CONCLUSION

Thus, our study provides valuable insights into the microbial diversity and pollution levels of coastal areas in AP and GA. These findings contribute to a better understanding of the impact of anthropogenic activities and climate variations on biology of coastal ecosystems and biodiversity.

Deciphering the Impact of Defecation Frequency on Gut Microbiome Composition and Diversity

This study explores the impact of defecation frequency on the gut microbiome structure by analyzing fecal samples from individuals categorized by defecation frequency: infrequent (1-3 times/week, n = 4), mid-frequent (4-6 times/week, n = 7), and frequent (daily, n = 9). Utilizing 16S rRNA gene-based sequencing and LC-MS/MS metabolome profiling, significant differences in microbial diversity and community structures among the groups were observed. The infrequent group showed higher microbial diversity, with community structures significantly varying with defecation frequency, a pattern consistent across all sampling time points. The Ruminococcus genus was predominant in the infrequent group, but decreased with more frequent defecation, while the Bacteroides genus was more common in the frequent group, decreasing as defecation frequency lessened. The infrequent group demonstrated enriched biosynthesis genes for aromatic amino acids and branched-chain amino acids (BCAAs), in contrast to the frequent group, which had a higher prevalence of genes for BCAA catabolism. Metabolome analysis revealed higher levels of metabolites derived from aromatic amino acids and BCAA metabolism in the infrequent group, and lower levels of BCAA-derived metabolites in the frequent group, consistent with their predicted metagenomic functions. These findings underscore the importance of considering stool consistency/frequency in understanding the factors influencing the gut microbiome.

Metagenomic Analyses Reveal Gut Microbial Profiles of Cnaphalocrocis medinalis Driven by the Infection of Baculovirus CnmeGV

The composition of microbiota in the digestive tract gut is essential for insect physiology, homeostasis, and pathogen infection. Little is known about the interactions between microbiota load and oral infection with baculoviruses. CnmeGV is an obligative baculovirus to Cnaphalocrocis medinalis. We investigated the impact of CnmeGV infection on the structure of intestinal microbes of C. medinalis during the initial infection stage. The results revealed that the gut microbiota profiles were dynamically driven by pathogen infection of CnmeGV. The numbers of all the OTU counts were relatively higher at the early and later stages, while the microbial diversity significantly increased early but dropped sharply following the infection. The compositional abundance of domain bacteria Firmicutes developed substantially higher. The significantly enriched and depleted species can be divided into four groups at the species level. Fifteen of these species were ultimately predicted as the biomarkers of CnmeGV infection. CnmeGV infection induces significant enrichment of alterations in functional genes related to metabolism and the immune system, encompassing processes such as carbohydrate, amino acid, cofactor, and vitamin metabolism. Finally, the study may provide an in-depth analysis of the relationship between host microbiota, baculovirus infection, and pest control of C. medinalis.

Temporal variations in the gut microbial diversity in response to high-fat diet and exercise

High-fat diet-induced obesity is a pandemic caused by an inactive lifestyle and increased consumption of Western diets and is a major risk factor for diabetes and cardiovascular diseases. In contrast, exercise can positively influence gut microbial diversity and is linked to a decreased inflammatory state. To understand the gut microbial variations associated with exercise and high-fat diet over time, we conducted a longitudinal study to examine the effect of covariates on gut microbial diversity and composition. Young mice were divided into four groups: Chow-diet (CHD), high-fat diet (HFD), high-fat diet + exercise (HFX), and exercise only (EXE) and underwent experimental intervention for 12 weeks. Fecal samples at week 0 and 12 were collected for DNA extraction, followed by 16S library preparation and sequencing. Data were analyzed using QIIME 2, R and MicrobiomeAnalyst. The Bacteroidetes-to-Firmicutes ratio decreased fivefold in the HFD and HFX groups compared to that in the CHD and EXE groups and increased in the EXE group over time. Alpha diversity was significantly increased in the EXE group longitudinally (p < 0.02), whereas diversity (Shannon, Faith's PD, and Fisher) and richness (ACE) was significantly reduced in the HFD (p < 0.005) and HFX (p < 0.03) groups over time. Beta diversity, based on the Jaccard, Bray-Curtis, and unweighted UniFrac distance metrics, was significant among the groups. Prevotella, Paraprevotella, Candidatus arthromitus, Lactobacillus salivarius, L. reuteri, Roseburia, Bacteroides uniformis, Sutterella, and Corynebacterium were differentially abundant in the chow-diet groups (CHD and EXE). Exercise significantly reduced the proportion of taxa characteristic of a high-fat diet, including Butyricimonas, Ruminococcus gnavus, and Mucispirillum schaedleri. Diet, age, and exercise significantly contributed to explaining the bacterial community structure and diversity in the gut microbiota. Modulating the gut microbiota and maintaining its stability can lead to targeted microbiome therapies to manage chronic and recurrent diseases and infections.

Review on computer-assisted biosynthetic capacities elucidation to assess metabolic interactions and communication within microbial communities

Microbial communities thrive through interactions and communication, which are challenging to study as most microorganisms are not cultivable. To address this challenge, researchers focus on the extracellular space where communication events occur. Exometabolomics and interactome analysis provide insights into the molecules involved in communication and the dynamics of their interactions. Advances in sequencing technologies and computational methods enable the reconstruction of taxonomic and functional profiles of microbial communities using high-throughput multi-omics data. Network-based approaches, including community flux balance analysis, aim to model molecular interactions within and between communities. Despite these advances, challenges remain in computer-assisted biosynthetic capacities elucidation, requiring continued innovation and collaboration among diverse scientists. This review provides insights into the current state and future directions of computer-assisted biosynthetic capacities elucidation in studying microbial communities.

Machine-learning analysis of cross-study samples according to the gut microbiome in 12 infant cohorts

IMPORTANCE

There are challenges in merging microbiome data from diverse research groups due to the intricate and multifaceted nature of such data. To address this, we utilized a combination of machine-learning (ML) models to analyze 16S sequencing data from a substantial set of gut microbiome samples, sourced from 12 distinct infant cohorts that were gathered prospectively. Our initial focus was on the mode of delivery due to its prior association with changes in infant gut microbiomes. Through ML analysis, we demonstrated the effective merging and comparison of various gut microbiome data sets, facilitating the identification of robust microbiome biomarkers applicable across varied study populations.

Maternal transmission of bacterial microbiota during embryonic development in a viviparous lizard

IMPORTANCE

We investigated the presence and diversity of bacteria in the embryos of the viviparous lizard Sceloporus grammicus and their amniotic environment. We compared this diversity to that found in the maternal intestine, mouth, and cloaca. We detected bacterial DNA in the embryos, albeit with a lower bacterial species diversity than found in maternal tissues. Most of the bacterial species detected in the embryos were also found in the mother, although not all of them. Interestingly, we detected a high similarity in the composition of bacterial species among embryos from different mothers. These findings suggest that there may be a mechanism controlling the transmission of bacteria from the mother to the embryo. Our results highlight the possibility that the interaction between maternal bacteria and the embryo may affect the development of the lizards.

Microbiomes respond predictably to built habitats on the seafloor

The seafloor contains complex ecosystems where habitat heterogeneity influences biodiversity. Natural biological and geological features including vents, seeps and reefs create habitats that select for distinct populations of micro- and macrofauna. While largely studied for macrobiological diversity, built habitats may also select distinct microbiomes. Built habitat density on the seafloor is increasing with ocean sprawl expanding in the continental shelf and slope, potentially having widespread effects on benthic communities. This study addresses one type of built habitat, shipwrecks, on microbiomes in surrounding sediment. Using deep-sea sediment samples (762 total) from the Gulf of Mexico, we report elevated diversity and a predictable core microbiome around nine shipwrecks. We show the sphere of influence of built habitats extends up to 300 m onto the seafloor. Supervised learning made predictions of sample proximity to structures based on frequency of taxa. Strongest predictions occurred in sediments nearest and furthest from sites for archaea and mid-transect for bacteria. The response of archaea to built habitats was consistent across sites, while bacteria showed greater between site variability. The archaeal core shipwreck microbiome was enriched in taxa (e.g., Bathyarchaeia, Lokiarchaeia, Thermoplasmata) not present in the surrounding seafloor. Shipwrecks shaped microbiomes in expected ways, providing insight on how built habitats impact microbiome biodiversity in the Anthropocene.

Functional Redundancy Secures Resilience of Chain Elongation Communities upon pH Shifts in Closed Bioreactor Ecosystems

For anaerobic mixed cultures performing microbial chain elongation, it is unclear how pH alterations affect the abundance of key players, microbial interactions, and community functioning in terms of medium-chain carboxylate yields. We explored pH effects on mixed cultures enriched in continuous anaerobic bioreactors representing closed model ecosystems. Gradual pH increase from 5.5 to 6.5 induced dramatic shifts in community composition, whereas product range and yields returned to previous states after transient fluctuations. To understand community responses to pH perturbations over long-term reactor operation, we applied Aitchison PCA clustering, linear mixed-effects models, and random forest classification on 16S rRNA gene amplicon sequencing and process data. Different pH preferences of two key chain elongation species─one Clostridium IV species related to Ruminococcaceae bacterium CPB6 and one Clostridium sensu stricto species related to Clostridium luticellarii─were determined. Network analysis revealed positive correlations of Clostridium IV with lactic acid bacteria, which switched from Olsenella to Lactobacillus along the pH increase, illustrating the plasticity of the food web in chain elongation communities. Despite long-term cultivation in closed systems over the pH shift experiment, the communities retained functional redundancy in fermentation pathways, reflected by the emergence of rare species and concomitant recovery of chain elongation functions.

Linking soil phosphorus availability and phosphatase functional genes to coastal marsh erosion: Implications for nutrient cycling and wetland restoration

Accelerated marsh erosion caused by climate change and human activity may have important implications for nutrient cycling and availability. However, how erosion affects phosphorus (P) transformation and microbial function in subtropical coastal marshes remains largely unknown. Here we assessed soil P fractions, availability and the phoD-harboring bacterial community along a marsh erosion gradient (non-eroded, lightly eroded, and heavily eroded). We showed that marsh erosion caused a shift in P fractions, leading to a decrease in P availability and a reduction in concentrations of labile P, moderately labile P, and stable P by 20 %, 9 %, and 17 % respectively. The abundance and diversity of phoD phosphatase genes decreased dramatically along the erosion gradient and were lower at heavily eroded sites than at non-eroded sites. Marsh erosion reshaped phoD gene community composition, and Corallococcus, Amycolatopsis, and Phaeobacter were identified as the dominant phoD-harboring microbes. Notably, marsh erosion reduced the complexity and stability of the phoD-harboring bacterial network, and heavily eroded sites have fewer network edges and nodes than non-eroded sites. The dynamics of soil P fractions, availability, and phoD-harboring bacterial communities driven by marsh erosion are largely shaped by substrate availability and soil properties (e.g., nutrients, pH, electrical conductivity, and moisture). Additionally, strong linkages between P availability and the abundance and diversity of phosphatase genes following erosion, suggest that phosphatase drives P mineralization and dissolution, and erosion weakens the regulation of P transformation by reshaping the phoD phosphatase gene community. Our findings indicate that marsh erosion alters soil P fractions and phoD-harboring bacterial communities, which weakens microbial regulation of P transformation and availability, thereby significantly reducing soil P pools and availability. Our findings broaden understanding of the impacts of coastal erosion on nutrient balance and ecosystem function, offering valuable perspectives that could inform wetland restoration and coastal management strategies.

Potential application of Staphylococcus species detection in the specific identification of saliva

When found at crime scenes, saliva constitutes forensically relevant evidence. Although several tests have been developed to effectively identify saliva in such circumstances, most cannot discriminate between saliva and nasal secretion. Recently, studies have developed saliva tests involving oral bacteria as salivary markers. Although the specificity of such tests has been evaluated on most biological specimens, their specificity for nasal secretion samples remains to be tested. Herein, to improve the specificity of the saliva detection tests for nasal secretion samples, we reanalyzed a public microbiome dataset and conducted inhouse 16S rRNA sequencing to identify a new marker to distinguish between saliva and nasal secretions. The sequencing data indicated the existence of oral bacteria such as Streptococcus in nasal secretion samples, which may be responsible for the false positives in the saliva tests. Furthermore, we found that including the 16S rRNA gene of the genus Staphylococcus as a nasal secretion marker may improve the specificity of PCR-based saliva tests for nasal secretion samples. In addition, we assessed the specificity of previously developed salivary bacteria detection tests for nasal secretion samples and oral bacterial markers were detected in two of eight nasal secretion samples, which led to the false positive results for saliva detection. Thus, the specificity of such tests can be improved by adding Staphylococcus as a nasal marker, as revealed by our sequencing analysis.

Fecal Microbial Composition and Predicted Functional Profile in Irritable Bowel Syndrome Differ between Subtypes and Geographical Locations

Increasing evidence suggests a microbial pathogenesis in irritable bowel syndrome (IBS) but the relationship remains elusive. Fecal DNA samples from 120 patients with IBS, 82 Mexican (IBS-C: n = 33, IBS-D: n = 24, IBS-M: n = 25) and 38 British (IBS-C: n = 6, IBS-D: n = 27, IBS-M: n = 5), were available for analysis using 16S rRNA gene sequencing. Firmicutes (mean: 82.1%), Actinobacteria (10.2%), and Bacteroidetes (4.4%) were the most abundant taxa. The analysis of all samples (n = 120), and females (n = 94) only, showed no significant differences in bacterial microbiota, but the analysis of Mexican patients (n = 82) showed several differences in key taxa (e.g., Faecalibacterium) among the different IBS subtypes. In IBS-D there were significantly higher Bacteroidetes in British patients (n = 27) than in Mexican patients (n = 24), suggesting unique fecal microbiota signatures within the same IBS subtype. These differences in IBS-D were also observed at lower phylogenetic levels (e.g., higher Enterobacteriaceae and Streptococcus in Mexican patients) and were accompanied by differences in several alpha diversity metrics. Beta diversity was not different among IBS subtypes when using all samples, but the analysis of IBS-D patients revealed consistent differences between Mexican and British patients. This study suggests that fecal microbiota is different between IBS subtypes and also within each subtype depending on geographical location.

Critical review of 16S rRNA gene sequencing workflow in microbiome studies: From primer selection to advanced data analysis

The multi-batch reanalysis approach of jointly reevaluating gene/genome sequences from different works has gained particular relevance in the literature in recent years. The large amount of 16S ribosomal ribonucleic acid (rRNA) gene sequence data stored in public repositories and information in taxonomic databases of the same gene far exceeds that related to complete genomes. This review is intended to guide researchers new to studying microbiota, particularly the oral microbiota, using 16S rRNA gene sequencing and those who want to expand and update their knowledge to optimise their decision-making and improve their research results. First, we describe the advantages and disadvantages of using the 16S rRNA gene as a phylogenetic marker and the latest findings on the impact of primer pair selection on diversity and taxonomic assignment outcomes in oral microbiome studies. Strategies for primer selection based on these results are introduced. Second, we identified the key factors to consider in selecting the sequencing technology and platform. The process and particularities of the main steps for processing 16S rRNA gene-derived data are described in detail to enable researchers to choose the most appropriate bioinformatics pipeline and analysis methods based on the available evidence. We then produce an overview of the different types of advanced analyses, both the most widely used in the literature and the most recent approaches. Several indices, metrics and software for studying microbial communities are included, highlighting their advantages and disadvantages. Considering the principles of clinical metagenomics, we conclude that future research should focus on rigorous analytical approaches, such as developing predictive models to identify microbiome-based biomarkers to classify health and disease states. Finally, we address the batch effect concept and the microbiome-specific methods for accounting for or correcting them.

Lactiplantibacillus plantarum Regulated Intestinal Microbial Community and Cytokines to Inhibit Salmonella typhimurium Infection

Lactic acid bacteria (LAB) are recognized as food-grade safe microorganisms and have many beneficial effects. LAB could maintain the host intestinal homeostasis and regulate intestinal microbial community to exert antibacterial effects. In this study, Lactiplantibacillus plantarum (L. plantarum, Lp01) strain isolated from pig intestine was orally administered to C57BL/6 mice, and mice were then infected with Salmonella typhimurium (ATCC14028). The protective effects of L. plantarum were evaluated by monitoring body weight loss, survival rates, bacterial loads in tissue, colon histopathology analysis, and cytokine secretion. 16S rRNA gene sequencing was also utilized to detect the dynamics of the blind gut microbial community in mice. We found that L. plantarum could significantly reduce the body weight loss and improve the survival rates. The survival rate in the L. P-Sty group was up to 67.5%, which was much higher than that in the STY group (25%). Counting of bacterial loads displayed that the colony-forming unit (CFU) of S. typhimurium in the spleen (p < 0.05) and the liver (p < 0.05) from L. P-Sty group both decreased, compared with STY group. Intestinal histopathology showed that it alleviated the intestinal injury caused by Salmonella, inhibited the secretion of pro-inflammatory cytokines, and promoted anti-inflammatory cytokines (p < 0. 01). In addition, L. plantarum also significantly ameliorated the intestinal gut microbiome disturbance caused by Salmonella. It displayed an obvious increase of beneficial bacteria including Lactobacillus and Bacteroidetes and reduction of pathogenic bacteria like Proteobacteria. In conclusion, L. plantarum could regulate microbial community to inhibit Salmonella typhimurium infection.

Environmental and Structural Factors Associated with Bacterial Diversity in Household Dust Across the Arizona-Sonora Border

We previously reported that asthma prevalence was higher in the United States (US) compared to Mexico (MX) (25.8% vs 8.4%). This investigation assessed differences in microbial dust composition in relation to demographic and housing characteristics on both sides of the US-MX Border. Forty homes were recruited in the US and MX. Home visits collected floor dust and documented occupants' demographics, asthma prevalence, and housing structure and use characteristics. US households were more likely to have inhabitants who reported asthma when compared with MX households (30% vs 5%) and had significantly different flooring types. The percentage of households on paved roads, with flushing toilets, with piped water and with air conditioning was higher in the US, while dust load was higher in MX. Significant differences exist between countries in the microbial composition of the floor dust. Dust from US homes was enriched with Geodermatophilus, whereas dust from Mexican homes was enriched with Alishewanella and Chryseomicrobium. A predictive metagenomics analysis identified 68 significantly differentially abundant functional pathways between US and MX. This study documented multiple structural, environmental, and demographic differences between homes in the US and MX that may contribute to significantly different microbial composition of dust observed in these two countries.

High-fat diet impact on intestinal cholesterol conversion by the microbiota and serum cholesterol levels

Cholesterol-to-coprostanol conversion by the intestinal microbiota has been suggested to reduce intestinal and serum cholesterol availability, but the relationship between intestinal cholesterol conversion and the gut microbiota, dietary habits, and serum lipids has not been characterized in detail. We measured conserved proportions of cholesterol high and low-converter types in individuals with and without obesity from two distinct, independent low-carbohydrate high-fat (LCHF) dietary intervention studies. Across both cohorts, cholesterol conversion increased in previous low-converters after LCHF diet and was positively correlated with the fecal relative abundance of Eubacterium coprostanoligenes. Lean cholesterol high-converters had increased serum triacylglycerides and decreased HDL-C levels before LCHF diet and responded to the intervention with increased LDL-C, independently of fat, cholesterol, and saturated fatty acid intake. Our findings identify the cholesterol high-converter type as a microbiome marker, which in metabolically healthy lean individuals is associated with increased LDL-C in response to LCHF.

Partial convergence of the human vaginal and rectal maternal microbiota in late gestation and early post-partum

The human vaginal and fecal microbiota change during pregnancy. Because of the proximity of these perineal sites and the evolutionarily conserved maternal-to-neonatal transmission of the microbiota, we hypothesized that the microbiota of these two sites (rectal and vaginal) converge during the last gestational trimester as part of the preparation for parturition. To test this hypothesis, we analyzed 16S rRNA sequences from vaginal introitus and rectal samples in 41 women at gestational ages 6 and 8 months, and at 2 months post-partum. The results show that the human vaginal and rectal bacterial microbiota converged during the last gestational trimester and into the 2nd month after birth, with a significant decrease in Lactobacillus species in both sites, as alpha diversity progressively increased in the vagina and decreased in the rectum. The microbiota convergence of the maternal vaginal-anal sites perinatally might hold significance for the inter-generational transmission of the maternal microbiota.

Bacterial induction of B cell senescence promotes age-related changes in the gut microbiota

The elucidation of the mechanisms of ageing and the identification of methods to control it have long been anticipated. Recently, two factors associated with ageing-the accumulation of senescent cells and the change in the composition of gut microbiota-have been shown to play key roles in ageing. However, little is known about how these phenomena occur and are related during ageing. Here we show that the persistent presence of commensal bacteria gradually induces cellular senescence in gut germinal centre B cells. Importantly, this reduces both the production and diversity of immunoglobulin A (IgA) antibodies that target gut bacteria, thereby changing the composition of gut microbiota in aged mice. These results have revealed the existence of IgA-mediated crosstalk between the gut microbiota and cellular senescence and thus extend our understanding of the mechanism of gut microbiota changes with age, opening up possibilities for their control.

Targeting the gut-lung axis by synbiotic feeding to infants in a randomized controlled trial

BACKGROUND

Formula-fed infants are at increased risk of infections. Due to the cross-talk between the mucosal systems of the gastrointestinal and respiratory tracts, adding synbiotics (prebiotics and probiotics) to infant formula may prevent infections even at distant sites. Infants that were born full term and weaned from breast milk were randomized to prebiotic formula (fructo- and galactooligosaccharides) or the same prebiotic formula with Lactobacillus paracasei ssp. paracasei F19 (synbiotics) from 1 to 6 months of age. The objective was to examine the synbiotic effects on gut microbiota development.

RESULTS

Fecal samples collected at ages 1, 4, 6, and 12 months were analyzed using 16S rRNA gene sequencing and a combination of untargeted gas chromatography-mass spectrometry/liquid chromatography-mass spectrometry. These analyses revealed that the synbiotic group had a lower abundance of Klebsiella, a higher abundance of Bifidobacterium breve compared to the prebiotic group, and increases in the anti-microbial metabolite d-3-phenyllactic acid. We also analyzed the fecal metagenome and antibiotic resistome in the 11 infants that had been diagnosed with lower respiratory tract infection (cases) and 11 matched controls using deep metagenomic sequencing. Cases with lower respiratory tract infection had a higher abundance of Klebsiella species and antimicrobial resistance genes related to Klebsiella pneumoniae, compared to controls. The results obtained using 16S rRNA gene amplicon and metagenomic sequencing were confirmed in silico by successful recovery of the metagenome-assembled genomes of the bacteria of interest.

CONCLUSIONS

This study demonstrates the additional benefit of feeding specific synbiotics to formula-fed infants over prebiotics only. Synbiotic feeding led to the underrepresentation of Klebsiella, enrichment of bifidobacteria, and increases in microbial degradation metabolites implicated in immune signaling and in the gut-lung and gut-skin axes. Our findings support future clinical evaluation of synbiotic formula in the prevention of infections and associated antibiotic treatment as a primary outcome when breastfeeding is not feasible.

TRIAL REGISTRATION

ClinicalTrials.gov NCT01625273 . Retrospectively registered on 21 June 2012.

Higher alpha diversity and Lactobacillus blooms are associated with better engraftment after Fecal Microbiota Transplant in Inflammatory Bowel Disease

Background

Fecal Microbiota Transplant (FMT) has proven effective in treating recurrent Clostridioides difficile infection (rCDI) and has shown some success in treating inflammatory bowel diseases (IBD). There is emerging evidence that host engraftment of donor taxa is a tenet of successful FMT. However, there is little known regarding predictors of engraftment. We undertook a double-blind, randomized, placebo-controlled pilot study to characterize the response to FMT in children and young adults with mild to moderate active Crohn's disease (CD) and ulcerative colitis (UC).

Results

Subjects with CD or UC were randomized to receive antibiotics and weekly FMT or placebo in addition to baseline medications. The treatment arm received seven days of antibiotics followed by FMT enema and then capsules weekly for seven weeks. We enrolled four subjects with CD and 11 with UC, ages 14-29 years. Due to weekly stool sampling, we were able to create a time series of alpha diversity, beta diversity and engraftment as they related to clinical response. Subjects exhibited a wide range of microbial diversity and donor engraftment as FMT progressed. Specifically, engraftment ranged from 26% to 90% at week 2 and 3% to 92% at two months. Consistent with the current literature, increases over time of both alpha diversity (p< 0.05) and donor engraftment (p< 0.05) correlated with improved clinical response. Additionally, our weekly time series enabled an investigation into the clinical and microbial correlates of engraftment at various time points. We discovered that the post-antibiotic but pre-FMT time point, often overlooked in FMT trials, was rich in microbial correlates of eventual engraftment. Greater residual alpha diversity after antibiotic treatment was positively correlated with engraftment and subsequent clinical response. Interestingly, a transient rise in the relative abundance of Lactobacillus was also positively correlated with engraftment, a finding that we recapitulated with our analysis of another FMT trial with publicly available weekly sequencing data.

Conclusions

We found that higher residual alpha diversity and Lactobacillus blooms after antibiotic treatment correlated with improved engraftment and clinical response to FMT. Future studies should closely examine the host microbial communities pre-FMT and the impact of antibiotic preconditioning on engraftment and response.

Interindividual differences contribute to variation in microbiota composition more than hormonal status: A prospective study

AIMS

Ovarian hormone deficiency is one of the main risk factors for osteoporosis and bone fractures in women, and these risks can be mitigated by menopausal hormone therapy. Recent evidence suggests that gut microbiota may link changes in estrogen levels and bone metabolism. This study was conducted to investigate the potential relationship between hormonal and bone changes induced by oophorectomy and subsequent hormonal therapy and shifts in gut microbiota composition.

METHODS

We collected 159 stool and blood samples in several intervals from 58 women, who underwent bilateral oophorectomy. Changes in fecal microbiota were assessed in paired samples collected from each woman before and after oophorectomy or the start of hormone therapy. Bacterial composition was determined by sequencing the 16S rRNA gene on Illumina MiSeq. Blood levels of estradiol, FSH, biomarkers of bone metabolism, and indices of low-grade inflammation were measured using laboratory analytical systems and commercial ELISA. Areal bone mineral density (BMD) of the lumbar spine, proximal femur, and femur neck was measured using dual-energy X-ray absorptiometry.

RESULTS

We found no significant changes in gut microbiota composition 6 months after oophorectomy, despite major changes in hormone levels, BMD, and bone metabolism. A small decrease in bacterial diversity was apparent 18 months after surgery in taxonomy-aware metrics. Hormonal therapy after oophorectomy prevented bone loss but only marginally affected gut microbiota. There were no significant differences in β-diversity related to hormonal status, although several microbes (e.g., Lactococcus lactis) followed estrogen levels. Body mass index (BMI) was the most significantly associated with microbiota variance. Microbiota was not a suitable predictive factor for the state of bone metabolism.

CONCLUSIONS

We conclude that neither the loss of estrogens due to oophorectomy nor their gain due to subsequent hormonal therapy is associated with a specific gut microbiota signature. Sources of variability in microbiota composition are more related to interindividual differences than hormonal status.

Long-term simulated microgravity alters gut microbiota and metabolome in mice

Spaceflight and microgravity has a significant impact on the immune, central nervous, bone, and muscle support and cardiovascular systems. However, limited studies are available on the adverse effects of long-term microgravity on the intestinal microbiota, metabolism, and its relationships. In this study, a ground-based simulated microgravity (SMG) mouse model was established to evaluate the impact of long-term microgravity on gut microbiota and metabolome. After 8 weeks of SMG, alterations of the intestinal microbiota and metabolites were detected using 16S rRNA sequencing and untargeted metabolomics. Compared to the control, no significant differences in α-diversity were observed at weeks 2, 4 and 8. Nevertheless, there were clear differences in community structures at different time points. The phylum Verrucomicrobia significantly declined from 2 to 8 weeks of SMG, yet the relative abundance of Actinobacteria and Deferribacteres expanded remarkably at weeks 8. SMG decreased the genus of Allobaculum and increased Bacteroides significantly throughout the period of 8 weeks. Besides, Genus Akkermansia, Gracilibacter, Prevotella, Odoribacter, Rothia, Sporosarcina, Gracilibacter, Clostridium, and Mucispirillum were identified as biomarkers for SMG group. Desulfovibrio_c21_c20, Akkermansia_muciniphila, and Ruminococcus_gnavus dropped at week 2, which tend to recover at week 4, except for Akkermansia_muciniphila. Bacteroides_uniformis and Faecalibacterium_prausnitzii declined significantly, while Ruminococcus_flavefaciens and Mucispirillum_schaedleri elevated at week 8. Furthermore, intestinal metabolome analysis showed that 129 were upregulated and 146 metabolites were downregulated in SMG. Long-term SMG most affected steroid hormone biosynthesis, tryptophan, cysteine, methionine, arginine, proline metabolism, and histidine metabolism. Correlated analysis suggested that the potential beneficial taxa Allobaculum, Akkermansia, and Faecalibacterium were negatively associated with tryptophan, histidine, arginine, and proline metabolism, but positively with steroid hormone biosynthesis. Yet Bacteroides, Lachnospiraceae_Clostridium, Rothia, Bilophila, and Coprococcus were positively correlated with arginine, proline, tryptophan, and histidine metabolism, while negatively associated with steroid hormone biosynthesis. These results suggest that Long-term SMG altered the community of intestinal microbiota, and then further disturbed intestinal metabolites and metabolic pathways, which have great potential to help understand and provide clues for revealing the mechanisms of long-term SMG involved diseases.

Co-diet supplementation of low density polyethylene and honeybee wax did not influence the core gut bacteria and associated enzymes of Galleria mellonella larvae (Lepidoptera: Pyralidae)

The current plastic pollution throughout the world is a rising concern that demands the optimization of biodegradation processes. One avenue for this is to identify plastic-degrading bacteria and associated enzymes from the gut bacteria of insect models such as Tenebrio molitor, Plodia interpunctella or Galleria mellonella that have the ability to ingest and rapidly degrade polyethylene. Therefore, this study takes part in understanding the role of the gut bacteria by investigating G. mellonella as a biological model feeding with a diet based on honeybee wax mixed or not with low-density polyethylene. Gut microbiome was analyzed by high throughput 16S rRNA sequencing, and Enterococcaceae and Oxalobacteraceae were found to be the major bacterial families. Compared to the control, the supplementation of low-density polyethylene did not cause significant modification of the bacterial microbiota at community and taxa levels, suggesting bacterial microbiome resilience. The bacterial proteome analysis of gut contents was encouraging for the identification of plastic degrading enzymes such as the phenylacetaldehyde dehydrogenase which participate in styrene degradation. This study allowed a better characterization of the gut bacteria of G. mellonella and provided a basis for the further study of biodegradation of polyethylene based on the bacterial microbiota from insect guts.

Reproducible acquisition, management and meta-analysis of nucleotide sequence (meta)data using q2-fondue

MOTIVATION

The volume of public nucleotide sequence data has blossomed over the past two decades and is ripe for re- and meta-analyses to enable novel discoveries. However, reproducible re-use and management of sequence datasets and associated metadata remain critical challenges. We created the open source Python package q2-fondue to enable user-friendly acquisition, re-use and management of public sequence (meta)data while adhering to open data principles.

RESULTS

q2-fondue allows fully provenance-tracked programmatic access to and management of data from the NCBI Sequence Read Archive (SRA). Unlike other packages allowing download of sequence data from the SRA, q2-fondue enables full data provenance tracking from data download to final visualization, integrates with the QIIME 2 ecosystem, prevents data loss upon space exhaustion and allows download of (meta)data given a publication library. To highlight its manifold capabilities, we present executable demonstrations using publicly available amplicon, whole genome and metagenome datasets.

AVAILABILITY AND IMPLEMENTATION

q2-fondue is available as an open-source BSD-3-licensed Python package at https://github.com/bokulich-lab/q2-fondue. Usage tutorials are available in the same repository. All Jupyter notebooks used in this article are available under https://github.com/bokulich-lab/q2-fondue-examples.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Anti-inflammatory activities of Qingfei oral liquid and its influence on respiratory microbiota in mice with ovalbumin-induced asthma

Dysbiosis of respiratory microbiota is closely related to the pathophysiological processes of asthma, including airway inflammation. Previous studies have shown that Qingfei oral liquid (QF) can alleviate airway inflammation and airway hyper-responsiveness in respiratory syncytial virus-infected asthmatic mice, but its effect on the respiratory microbiota is unknown. We therefore aimed to observe the effects of QF on airway inflammation and respiratory microbiota in ovalbumin (OVA)-induced asthmatic mice. We also explored the potential mechanism of QF in reducing airway inflammation by regulating respiratory microbiota. Hematoxylin and eosin as well as periodic acid-Schiff staining were performed to observe the effects of QF on lung pathology in asthmatic mice. Cytokine levels in bronchoalveolar lavage fluid (BALF) specimens were also measured. Changes in respiratory microbiota were analyzed using 16S rRNA gene sequencing, followed by taxonomical analysis. In order to verify the metagenomic function prediction results, the expression of key proteins related to the MAPK and NOD-like receptor signaling pathways in the lung tissues were detected by immunohistochemistry. The current study found that QF had a significant anti-inflammatory effect in the airways of asthmatic mice. This is mainly attributed to a reduction in lung pathology changes and regulating cytokine levels in BALF. Analysis of the respiratory microbiota in asthmatic mice showed that the abundance of Proteobacteria at the phylum level and Pseudomonas at the genus level increased significantly and QF could significantly regulate the dysbiosis of respiratory microbiota in asthmatic mice. Metagenomic functional prediction showed that QF can downregulate the MAPK and Nod-like receptor signaling pathways. Immunohistochemical results showed that QF could downregulate the expression of p-JNK, p-P38, NLRP3, Caspase-1, and IL-1β, which are all key proteins in the signaling pathway of lung tissue. Our study therefore concluded that QF may reduce airway inflammation in asthmatic mice by regulating respiratory microbiota, and to the possibly downregulate MAPK and Nod-like receptor signaling pathways as its underlying mechanism.

Metataxonomic Mapping of the Microbial Diversity of Irish and Eastern Mediterranean Cheeses

The distinct sensorial characteristics of local cheeses influence consumer preferences, and make an essential contribution to the local economy. Microbial diversity in cheese is among the fundamental contributors to sensorial and qualitative characteristics. However, knowledge regarding the existence of microbial patterns associated with regional production practices in ripened cheeses remains limited. The present research was conducted to test the hypothesis that the background metagenome of cheeses could be used as a marker of their origin. We compared Irish versus Eastern Mediterranean cheeses-namely Greek and Cypriot-using High Throughput Sequencing (HTS). The study identified a significantly distinct separation among cheeses originating from the three different countries, in terms of the total microbial community composition. The use of machine learning and biomarkers discovery algorithms defined key microbes that differentiate each geographic region. Finally, the development of interaction networks revealed that the key species developed mostly negative interactions with the other members of the communities, highlighting their dominance in the community. The findings of the present research demonstrate that metagenome could indeed be used as a biological marker of the origin of mature cheeses, and could provide further insight into the dynamics of microbial community composition in ripened cheeses.

Namco: a microbiome explorer

16S rRNA gene profiling is currently the most widely used technique in microbiome research and allows the study of microbial diversity, taxonomic profiling, phylogenetics, functional and network analysis. While a plethora of tools have been developed for the analysis of 16S rRNA gene data, only a few platforms offer a user-friendly interface and none comprehensively covers the whole analysis pipeline from raw data processing down to complex analysis. We introduce Namco, an R shiny application that offers a streamlined interface and serves as a one-stop solution for microbiome analysis. We demonstrate Namco's capabilities by studying the association between a rich fibre diet and the gut microbiota composition. Namco helped to prove the hypothesis that butyrate-producing bacteria are prompted by fibre-enriched intervention. Namco provides a broad range of features from raw data processing and basic statistics down to machine learning and network analysis, thus covering complex data analysis tasks that are not comprehensively covered elsewhere. Namco is freely available at https://exbio.wzw.tum.de/namco/.

Mathematical Modeling and Computational Prediction of High-Risk Types of Human Papillomaviruses

Cervical cancer is one of the main causes of cancer death all over the world. Most diseases such as cervical epithelial atypical hyperplasia and invasive cervical cancer are closely related to the continuous infection of high-risk types of human papillomavirus. Therefore, the high-risk types of human papillomavirus are the key to the prevention and treatment of cervical cancer. With the accumulation of high-throughput and clinical data, the use of systematic and quantitative methods for mathematical modeling and computational prediction has become more and more important. This paper summarizes the mathematical models and prediction methods of the risk types of human papillomavirus, especially around the key steps such as feature extraction, feature selection, and prediction algorithms. We summarized and discussed the advantages and disadvantages of existing algorithms, which provides a theoretical basis for follow-up research.

Predictable Changes in Eelgrass Microbiomes with Increasing Wasting Disease Prevalence across 23° Latitude in the Northeastern Pacific

Predicting outcomes of marine disease outbreaks presents a challenge in the face of both global and local stressors. Host-associated microbiomes may play important roles in disease dynamics but remain understudied in marine ecosystems. Host–pathogen–microbiome interactions can vary across host ranges, gradients of disease, and temperature; studying these relationships may aid our ability to forecast disease dynamics. Eelgrass, Zostera marina, is impacted by outbreaks of wasting disease caused by the opportunistic pathogen Labyrinthula zosterae. We investigated how Z. marina phyllosphere microbial communities vary with rising wasting disease lesion prevalence and severity relative to plant and meadow characteristics like shoot density, longest leaf length, and temperature across 23° latitude in the Northeastern Pacific. We detected effects of geography (11%) and smaller, but distinct, effects of temperature (30-day max sea surface temperature, 4%) and disease (lesion prevalence, 3%) on microbiome composition. Declines in alpha diversity on asymptomatic tissue occurred with rising wasting disease prevalence within meadows. However, no change in microbiome variability (dispersion) was detected between asymptomatic and symptomatic tissues. Further, we identified members of Cellvibrionaceae, Colwelliaceae, and Granulosicoccaceae on asymptomatic tissue that are predictive of wasting disease prevalence across the geographic range (3,100 kilometers). Functional roles of Colwelliaceae and Granulosicoccaceae are not known. Cellvibrionaceae, degraders of plant cellulose, were also enriched in lesions and adjacent green tissue relative to nonlesioned leaves. Cellvibrionaceae may play important roles in disease progression by degrading host tissues or overwhelming plant immune responses. Thus, inclusion of microbiomes in wasting disease studies may improve our ability to understand variable rates of infection, disease progression, and plant survival.

IMPORTANCE The roles of marine microbiomes in disease remain poorly understood due, in part, to the challenging nature of sampling at appropriate spatiotemporal scales and across natural gradients of disease throughout host ranges. This is especially true for marine vascular plants like eelgrass (Zostera marina) that are vital for ecosystem function and biodiversity but are susceptible to rapid decline and die-off from pathogens like eukaryotic slime-mold Labyrinthula zosterae (wasting disease). We link bacterial members of phyllosphere tissues to the prevalence of wasting disease across the broadest geographic range to date for a marine plant microbiome-disease study (3,100 km). We identify Cellvibrionaceae, plant cell wall degraders, enriched (up to 61% relative abundance) within lesion tissue, which suggests this group may be playing important roles in disease progression. These findings suggest inclusion of microbiomes in marine disease studies will improve our ability to predict ecological outcomes of infection across variable landscapes spanning thousands of kilometers.

Fecal microbiome of horses transitioning between warm-season and cool-season grass pasture within integrated rotational grazing systems

BACKGROUND

Diet is a key driver of equine hindgut microbial community structure and composition. The aim of this study was to characterize shifts in the fecal microbiota of grazing horses during transitions between forage types within integrated warm- (WSG) and cool-season grass (CSG) rotational grazing systems (IRS). Eight mares were randomly assigned to two IRS containing mixed cool-season grass and one of two warm-season grasses: bermudagrass [Cynodon dactylon (L.) Pers.] or crabgrass [Digitaria sanguinalis (L.) Scop.]. Fecal samples were collected during transitions from CSG to WSG pasture sections (C-W) and WSG to CSG (W-C) on days 0, 2, 4, and 6 following pasture rotation and compared using 16S rRNA gene sequencing.

RESULTS

Regardless of IRS or transition (C-W vs. W-C), species richness was greater on day 4 and 6 in comparison to day 0 (P < 0.05). Evenness, however, did not differ by day. Weighted UniFrac also did not differ by day, and the most influential factor impacting β-diversity was the individual horse (R² ≥ 0.24; P = 0.0001). Random forest modeling was unable to accurately predict days within C-W and W-C, but could predict the individual horse based on microbial composition (accuracy: 0.92 ± 0.05). Only three differentially abundant bacterial co-abundance groups (BCG) were identified across days within all C-W and W-C for both IRS (W ≥ 126). The BCG differing by day for all transitions included amplicon sequence variants (ASV) assigned to bacterial groups with known fibrolytic and butyrate-producing functions including members of Lachnospiraceae, Clostridium sensu stricto 1, Anaerovorax the NK4A214 group of Oscillospiraceae, and Sarcina maxima. In comparison, 38 BCG were identified as differentially abundant by horse (W ≥ 704). The ASV in these groups were most commonly assigned to genera associated with degradation of structural carbohydrates included Rikenellaceae RC9 gut group, Treponema, Christensenellaceae R-7 group, and the NK4A214 group of Oscillospiraceae. Fecal pH also did not differ by day.

CONCLUSIONS

Overall, these results demonstrated a strong influence of individual horse on the fecal microbial community, particularly on the specific composition of fiber-degraders. The equine fecal microbiota were largely stable across transitions between forages within IRS suggesting that the equine gut microbiota adjusted at the individual level to the subtle dietary changes imposed by these transitions. This adaptive capacity indicates that horses can be managed in IRS without inducing gastrointestinal dysfunction.

Spatio-temporal variation in the root-associated microbiota of orchard-grown apple trees

BACKGROUND

The root-associated microbiome has been of keen research interest especially in the last decade due to the large potential for increasing overall plant performance in agricultural systems. Studies about spatio-temporal variation of the root-associated microbiome focused so far primarily on community-compositional changes of annual plants, while little is known about their perennial counterparts. The aim of this work was to get deep insight into the spatial patterns and temporal dynamics of the root associated microbiota of apple trees.

RESULTS

The bacterial community structure in rhizospheric soil and endospheric root material from orchard-grown apple trees was characterized based on 16S rRNA gene amplicon sequencing. At the small scale, the rhizosphere and endosphere bacterial communities shifted gradually with increasing root size diameter (PERMANOVA R²-values up to 0.359). At the larger scale, bulk soil heterogeneity introduced variation between tree individuals, especially in the rhizosphere microbiota, while the presence of a root pathogen was contributing to tree-to-tree variation in the endosphere microbiota. Moreover, the communities of both compartments underwent seasonal changes and displayed year-to-year variation (PERMANOVA R²-values of 0.454 and 0.371, respectively).

CONCLUSIONS

The apple tree root-associated microbiota can be spatially heterogeneous at field scale due to soil heterogeneities, which particularly influence the microbiota in the rhizosphere soil, resulting in tree-to-tree variation. The presence of pathogens can contribute to this variation, though primarily in the endosphere microbiota. Smaller-scale spatial heterogeneity is observed in the rhizosphere and endosphere microbiota related to root diameter, likely influenced by root traits and processes such as rhizodeposition. The microbiota is also subject to temporal variation, including seasonal effects and annual variation. As a consequence, responses of the tree root microbiota to further environmental cues should be considered in the context of this spatio-temporal variation.

Diet Is a Stronger Covariate than Exercise in Determining Gut Microbial Richness and Diversity

Obesity is a common metabolic disorder caused by a sedentary lifestyle, and a high-fat and a high-glucose diet in the form of fast foods. High-fat diet-induced obesity is a major cause of diabetes and cardiovascular diseases, whereas exercise and physical activity can ameliorate these disorders. Moreover, exercise and the gut microbiota are known to be interconnected, since exercise can increase the gut microbial diversity and contribute to the beneficial health effects. In this context, we analyzed the effect of diet and exercise on the gut microbiota of mice, by next-generation sequencing of the bacterial V4 region of 16S rRNA. Briefly, mice were divided into four groups: chow-diet (CD), high-fat diet (HFD), high-fat diet + exercise (HFX), and exercise-only (EX). The mice underwent treadmill exercise and diet intervention for 8 weeks, followed by the collection of their feces and DNA extraction for sequencing. The data were analyzed using the QIIME 2 bioinformatics platform and R software to assess their gut microbial composition, richness, and diversity. The Bacteroidetes to Firmicutes ratio was found to be decreased manifold in the HFD and HFX groups compared to the CD and EX groups. The gut microbial richness was comparatively lower in the HFD and HFX groups and higher in the CD and EX groups (ACE, Chao1, and observed OTUs). However, the Shannon alpha diversity index was higher in the HFD and HFX groups than in the CD and EX groups. The beta diversity based on Jaccard, Bray-Curtis, and weighted UniFrac distance metrics was significant among the groups, as measured by PERMANOVA. Paraprevotella, Desulfovibrio, and Lactococcus were the differentially abundant/present genera based on the intervention groups and in addition to these three bacteria, Butyricimonas and Desulfovibrio C21c20 were differentially abundant/present based on diet. Hence, diet significantly contributed to the majority of the changes in the gut microbiota.

Ontogeny of the B Cell Receptor Repertoire and Microbiome in Mice

The immune system matures throughout childhood to achieve full functionality in protecting our bodies against threats. The immune system has a strong reciprocal symbiosis with the host bacterial population and the two systems co-develop, shaping each other. Despite their fundamental role in health physiology, the ontogeny of these systems is poorly characterized. In this study, we investigated the development of the BCR repertoire by analyzing high-throughput sequencing of their receptors in several time points of young C57BL/6J mice. In parallel, we explored the development of the gut microbiome. We discovered that the gut IgA repertoires change from birth to adolescence, including an increase in CDR3 lengths and somatic hypermutation levels. This contrasts with the spleen IgM repertoires that remain stable and distinct from the IgA repertoires in the gut. We also discovered that large clones that germinate in the gut are initially confined to a specific gut compartment, then expand to nearby compartments and later on expand also to the spleen and remain there. Finally, we explored the associations between diversity indices of the B cell repertoires and the microbiome, as well as associations between bacterial and BCR clusters. Our results shed light on the ontogeny of the adaptive immune system and the microbiome, providing a baseline for future research.

Inferring Species Compositions of Complex Fungal Communities from Long- and Short-Read Sequence Data

Our study is unique in that it provides an in-depth comparative study of a real-life complex fungal community analyzed with multiple long- and short-read sequencing approaches. These technologies and their application are currently of great interest to diverse biologists as they seek to characterize the community compositions of microbiomes.

The Microbial Quality of Commercial Chopped Romaine Lettuce Before and After the “Use By” Date

In the United States, due to the limited information about the microbial quality and safety of fresh produce after the labeled open dates, unnecessary discarding of fresh produce in good conditions and food loss have been caused. The aim of this study was to address this knowledge gap and evaluate the microbial quality of commercial chopped Romaine lettuce (RL) on the “Use By” dates (UBD) and 5 days after the “Use By” dates (UBD5). The microbial quality was evaluated using culture-dependent and culture-independent methods. Three brands of RL samples, from early and late harvest seasons, were purchased from local grocery stores and stored at 4°C until 5 days after their UBD. On the UBD and UBD5, bagged lettuce was opened, homogenized, diluted, and plated onto plate count agar and anaerobic agar to obtain total aerobic plate counts (APC) and total anaerobic plate counts (AnPC). For the culture-independent method, DNA was extracted from each sample homogenate and used for 16S rRNA gene sequencing. The culture-dependent results showed that there was no significant change in APC or AnPC between UBD and UBD5 samples. The APC and AnPC ranged from 5.71 ± 0.74 to 7.89 ± 0.10 Log CFU/g and 1.75 ± 0.08 to 7.32 ± 0.61 Log CFU/g, respectively. No significant difference in alpha diversity, based on observed features and Shannon index values, was detected between UBD and UBD5 samples using 16S rRNA sequencing. Similarly, no difference was observed in beta diversity based on the Jaccard distance matrixes and the weighted Unifrac distance matrixes. Taxonomic analysis revealed 128 genera in all RL samples. The top five genera were Pseudomonas (with relative abundance ranging from 16.47 to 92.72%), Serratia (0–52.35%), Weissella (0–42.42%), Pantoea (0.17–21.33%), and Lactococcus (0–24.30%). The differential abundance analysis based on the ANCOM test showed that no bacteria were detected to have significantly differential abundance in RL between UBD and UBD5. In summary, both the culture-dependent and culture-independent results showed that there was no significant difference in the microbial quality of RL before and shortly after the UBD.

Towards a robust out-of-the-box neural network model for genomic data

Background

The accurate prediction of biological features from genomic data is paramount for precision medicine and sustainable agriculture. For decades, neural network models have been widely popular in fields like computer vision, astrophysics and targeted marketing given their prediction accuracy and their robust performance under big data settings. Yet neural network models have not made a successful transition into the medical and biological world due to the ubiquitous characteristics of biological data such as modest sample sizes, sparsity, and extreme heterogeneity.

Results

Here, we investigate the robustness, generalization potential and prediction accuracy of widely used convolutional neural network and natural language processing models with a variety of heterogeneous genomic datasets. Mainly, recurrent neural network models outperform convolutional neural network models in terms of prediction accuracy, overfitting and transferability across the datasets under study.

Conclusions

While the perspective of a robust out-of-the-box neural network model is out of reach, we identify certain model characteristics that translate well across datasets and could serve as a baseline model for translational researchers.

Incorporation of Data From Multiple Hypervariable Regions when Analyzing Bacterial 16S rRNA Gene Sequencing Data

Short read 16 S rRNA amplicon sequencing is a common technique used in microbiome research. However, inaccuracies in estimated bacterial community composition can occur due to amplification bias of the targeted hypervariable region. A potential solution is to sequence and assess multiple hypervariable regions in tandem, yet there is currently no consensus as to the appropriate method for analyzing this data. Additionally, there are many sequence analysis resources for data produced from the Illumina platform, but fewer open-source options available for data from the Ion Torrent platform. Herein, we present an analysis pipeline using open-source analysis platforms that integrates data from multiple hypervariable regions and is compatible with data produced from the Ion Torrent platform. We used the ThermoFisher Ion 16 S Metagenomics Kit and a mock community of twenty bacterial strains to assess taxonomic classification of six amplicons from separate hypervariable regions (V2, V3, V4, V6-7, V8, V9) using our analysis pipeline. We report that different amplicons have different specificities for taxonomic classification, which also has implications for global level analyses such as alpha and beta diversity. Finally, we utilize a generalized linear modeling approach to statistically integrate the results from multiple hypervariable regions and apply this methodology to data from a representative clinical cohort. We conclude that examining sequencing results across multiple hypervariable regions provides more taxonomic information than sequencing across a single region. The data across multiple hypervariable regions can be combined using generalized linear models to enhance the statistical evaluation of overall differences in community structure and relatedness among sample groups.

Electronic Cigarette Use Promotes a Unique Periodontal Microbiome

Electronic cigarettes (e-cigs) have become prevalent as an alternative to conventional cigarette smoking, particularly in youth. E-cig aerosols contain unique chemicals which alter the oral microbiome and promote dysbiosis in ways we are just beginning to investigate. We conducted a 6-month longitudinal study involving 84 subjects who were either e-cig users, conventional smokers, or nonsmokers. Periodontal condition, cytokine levels, and subgingival microbial community composition were assessed, with periodontal, clinical, and cytokine measures reflecting cohort habit and positively correlating with pathogenic taxa (e.g., Treponema, Saccharibacteria, and Porphyromonas). α-Diversity increased similarly across cohorts longitudinally, yet each cohort maintained a unique microbiome. The e-cig microbiome shared many characteristics with the microbiome of conventional smokers and some with nonsmokers, yet it maintained a unique subgingival microbial community enriched in Fusobacterium and Bacteroidales (G-2). Our data suggest that e-cig use promotes a unique periodontal microbiome, existing as a stable heterogeneous state between those of conventional smokers and nonsmokers and presenting unique oral health challenges. IMPORTANCE Electronic cigarette (e-cig) use is gaining in popularity and is often perceived as a healthier alternative to conventional smoking. Yet there is little evidence of the effects of long-term use of e-cigs on oral health. Conventional cigarette smoking is a prominent risk factor for the development of periodontitis, an oral disease affecting nearly half of adults over 30 years of age in the United States. Periodontitis is initiated through a disturbance in the microbial biofilm communities inhabiting the unique space between teeth and gingival tissues. This disturbance instigates host inflammatory and immune responses and, if left untreated, leads to tooth and bone loss and systemic diseases. We found that the e-cig user's periodontal microbiome is unique, eliciting unique host responses. Yet some similarities to the microbiomes of both conventional smokers and nonsmokers exist, with strikingly more in common with that of cigarette smokers, suggesting that there is a unique periodontal risk associated with e-cig use.

Multi-omics data integration reveals metabolome as the top predictor of the cervicovaginal microenvironment

Emerging evidence suggests that host-microbe interaction in the cervicovaginal microenvironment contributes to cervical carcinogenesis, yet dissecting these complex interactions is challenging. Herein, we performed an integrated analysis of multiple "omics" datasets to develop predictive models of the cervicovaginal microenvironment and identify characteristic features of vaginal microbiome, genital inflammation and disease status. Microbiomes, vaginal pH, immunoproteomes and metabolomes were measured in cervicovaginal specimens collected from a cohort (n = 72) of Arizonan women with or without cervical neoplasm. Multi-omics integration methods, including neural networks (mmvec) and Random Forest supervised learning, were utilized to explore potential interactions and develop predictive models. Our integrated analyses revealed that immune and cancer biomarker concentrations were reliably predicted by Random Forest regressors trained on microbial and metabolic features, suggesting close correspondence between the vaginal microbiome, metabolome, and genital inflammation involved in cervical carcinogenesis. Furthermore, we show that features of the microbiome and host microenvironment, including metabolites, microbial taxa, and immune biomarkers are predictive of genital inflammation status, but only weakly to moderately predictive of cervical neoplastic disease status. Different feature classes were important for prediction of different phenotypes. Lipids (e.g. sphingolipids and long-chain unsaturated fatty acids) were strong predictors of genital inflammation, whereas predictions of vaginal microbiota and vaginal pH relied mostly on alterations in amino acid metabolism. Finally, we identified key immune biomarkers associated with the vaginal microbiota composition and vaginal pH (MIF), as well as genital inflammation (IL-6, IL-10, MIP-1α).

Altered microbial biogeography in an innate model of colitis

Changes in the spatial organization, or biogeography, of colonic microbes have been observed in human inflammatory bowel disease (IBD) and mouse models of IBD. We have developed a mouse model of IBD that occurs spontaneously and consistently in the absence of adaptive immunity. Mice expressing tumor necrosis factor-induced protein 3 (TNFAIP3) in intestinal epithelial cells (villin-TNFAIP3) develop colitis when interbred with Recombination Activating 1-deficient mice (RAG1^-/-). The colitis in villin-TNFAIP3 × RAG1^-/- (TRAG) mice is prevented by antibiotics, indicating a role for microbes in this innate colitis. We therefore explored the biogeography of microbes and responses to antibiotics in TRAG colitis. Laser capture microdissection and 16S rRNA sequencing revealed altered microbial populations across the transverse axis of the colon as the inner mucus layer of TRAG, but not RAG1^-/-, mice was infiltrated by microbes, which included increased abundance of the classes Gammaproteobacteria and Actinobacteria. Along the longitudinal axis differences in the efficacy of antibiotics to prevent colitis were evident. Neomycin was most effective for prevention of inflammation in the cecum, while ampicillin was most effective in the proximal and distal colon. RAG1^-/-, but not TRAG, mice exhibited a structured pattern of bacterial abundance with decreased Firmicutes and Proteobacteria but increased Bacteroidetes along the proximal to distal axis of the gut. TRAG mice exhibited increased relative abundance of potential pathobionts including <i>Bifidobacterium animalis</i> along the longitudinal axis of the gut whereas others, like <i>Helicobacter hepaticus</i> were increased only in the cecum. Potential beneficial organisms including <i>Roseburia</i> were decreased in the proximal regions of the TRAG colon, while <i>Bifidobacterium pseudolongulum</i> was decreased in the TRAG distal colon. Thus, the innate immune system maintains a structured, spatially organized, gut microbiome along the transverse and longitudinal axis of the gut, and disruption of this biogeography is a feature of innate immune colitis.

A catalogue of 1,167 genomes from the human gut archaeome

The human gut microbiome plays an important role in health, but its archaeal diversity remains largely unexplored. In the present study, we report the analysis of 1,167 nonredundant archaeal genomes (608 high-quality genomes) recovered from human gastrointestinal tract, sampled across 24 countries and rural and urban populations. We identified previously undescribed taxa including 3 genera, 15 species and 52 strains. Based on distinct genomic features, we justify the split of the Methanobrevibacter smithii clade into two separate species, with one represented by the previously undescribed 'Candidatus Methanobrevibacter intestini'. Patterns derived from 28,581 protein clusters showed significant associations with sociodemographic characteristics such as age groups and lifestyle. We additionally show that archaea are characterized by specific genomic and functional adaptations to the host and carry a complex virome. Our work expands our current understanding of the human archaeome and provides a large genome catalogue for future analyses to decipher its impact on human physiology.

RESCRIPt: Reproducible sequence taxonomy reference database management

Nucleotide sequence and taxonomy reference databases are critical resources for widespread applications including marker-gene and metagenome sequencing for microbiome analysis, diet metabarcoding, and environmental DNA (eDNA) surveys. Reproducibly generating, managing, using, and evaluating nucleotide sequence and taxonomy reference databases creates a significant bottleneck for researchers aiming to generate custom sequence databases. Furthermore, database composition drastically influences results, and lack of standardization limits cross-study comparisons. To address these challenges, we developed RESCRIPt, a Python 3 software package and QIIME 2 plugin for reproducible generation and management of reference sequence taxonomy databases, including dedicated functions that streamline creating databases from popular sources, and functions for evaluating, comparing, and interactively exploring qualitative and quantitative characteristics across reference databases. To highlight the breadth and capabilities of RESCRIPt, we provide several examples for working with popular databases for microbiome profiling (SILVA, Greengenes, NCBI-RefSeq, GTDB), eDNA and diet metabarcoding surveys (BOLD, GenBank), as well as for genome comparison. We show that bigger is not always better, and reference databases with standardized taxonomies and those that focus on type strains have quantitative advantages, though may not be appropriate for all use cases. Most databases appear to benefit from some curation (quality filtering), though sequence clustering appears detrimental to database quality. Finally, we demonstrate the breadth and extensibility of RESCRIPt for reproducible workflows with a comparison of global hepatitis genomes. RESCRIPt provides tools to democratize the process of reference database acquisition and management, enabling researchers to reproducibly and transparently create reference materials for diverse research applications. RESCRIPt is released under a permissive BSD-3 license at https://github.com/bokulich-lab/RESCRIPt.

Generating and managing sequence and taxonomy reference data presents a bottleneck to many researchers, whether they are generating custom databases or attempting to format existing, curated reference databases for use with standard sequence analysis tools. Evaluating database quality and choosing the “best” database can be an equally formidable challenge. We developed RESCRIPt to alleviate this bottleneck, supporting reproducible, streamlined generation, curation, and evaluation of reference sequence databases. RESCRIPt uses QIIME 2 artifact file formats, which store all processing steps as data provenance within each file, allowing researchers to retrace the computational steps used to generate any given file. We used RESCRIPt to benchmark several commonly used marker-gene sequence databases for 16S rRNA genes, ITS, and COI sequences, demonstrating both the utility of RESCRIPt to streamline use of these databases, but also to evaluate several qualitative and quantitative characteristics of each database. We show that larger databases are not always best, and curation steps to reduce redundancy and filter out noisy sequences may be beneficial for some applications. We anticipate that RESCRIPt will streamline the use, management, and evaluation/selection of reference database materials for microbiomics, diet metabarcoding, eDNA, and other diverse applications.

Taxonomic and Functional Dysregulation in Salivary Microbiomes During Oral Carcinogenesis

Exploring microbial community compositions in humans with healthy versus diseased states is crucial to understand the microbe-host interplay associated with the disease progression. Although the relationship between oral cancer and microbiome was previously established, it remained controversial, and yet the ecological characteristics and their responses to oral carcinogenesis have not been well studied. Here, using the bacterial 16S rRNA gene amplicon sequencing along with the in silico function analysis by PICRUSt2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States 2), we systematically characterized the compositions and the ecological drivers of saliva microbiome in the cohorts of orally healthy, non-recurrent oral verrucous hyperplasia (a pre-cancer lesion), and oral verrucous hyperplasia–associated oral cancer at taxonomic and function levels, and compared them with the re-analysis of publicly available datasets. Diversity analyses showed that microbiome dysbiosis in saliva was significantly linked to oral health status. As oral health deteriorated, the number of core species declined, and metabolic pathways predicted by PICRUSt2 were dysregulated. Partitioned beta-diversity revealed an extremely high species turnover but low function turnover. Functional beta-diversity in saliva microbiome shifted from turnover to nestedness during oral carcinogenesis, which was not observed at taxonomic levels. Correspondingly, the quantitative analysis of stochasticity ratios showed that drivers of microbial composition and functional gene content of saliva microbiomes were primarily governed by the stochastic processes, yet the driver of functional gene content shifted toward deterministic processes as oral cancer developed. Re-analysis of publicly accessible datasets supported not only the distinctive family taxa of Veillonellaceae and Actinomycetaceae present in normal cohorts but also that Flavobacteriaceae and Peptostreptococcaceae as well as the dysregulated metabolic pathways of nucleotides, amino acids, fatty acids, and cell structure were related to oral cancer. Using predicted functional profiles to elucidate the correlations to the oral health status shows superior performance than using taxonomic data among different studies. These findings advance our understanding of the oral ecosystem in relation to oral carcinogenesis and provide a new direction to the development of microbiome-based tools to study the interplay of the oral microbiome, metabolites, and host health.

Automation of QIIME2 Metagenomic Analysis Platform

QIIME is a widely used, open-source microbiome analysis software package that converts raw sequence data into interpretable visualizations and statistical results. QIIME2 has recently succeeded QIIME1, becoming the most updated platform. The protocols in this article describe our effort in automating core functions of QIIME2, using datasets available at docs.qiime2.org. While these specific examples are microbial 16S rRNA gene sequences, our automation can be easily applied to other types of QIIME2 analysis. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Preparing files and folders Support Protocol 1: Preparing your data for QAP Support Protocol 2: Understanding automated options Basic Protocol 2: Importing into QIIME Basic Protocol 3: DADA2: Filtering, trimming, merging pairs Basic Protocol 4: Performing core metrics Basic Protocol 5: Sample filtering by metadata Basic Protocol 6: Alpha diversity metrics Basic Protocol 7: Cross-sectional beta diversity Basic Protocol 8: Longitudinal feature volatility Basic Protocol 9: Sample classification.