Analysis Of Composition Of Microbiomes Research Articles

Validation of gavage sampling as tool for longitudinal sampling of microbiota of the mouse gastric lumen

BackgroundFecal samples are commonly used for longitudinal studies of the gut lumen microbiome to track the course of response to infection or drug treatment, but no comparable method has been evaluated for longitudinal analysis of the gastric lumen microbiome in mice. Herein, a buffer flush of the stomach with a flexible gavage needle was used to collect gastric contents at one or several time points without harming the mouse. These samples were compared to samples collected by sacrifice and dissection of the mouse stomach. Microbiota from these samples were sequenced and evaluated in two ways: the composition of samples as measured by beta diversity and the richness of samples as measured by alpha diversity. Additionally, the effect of multiple sampling every two days on these metrics were studied. DNA was extracted from each of these samples and Illumina 16S rRNA gene sequencing was performed. ResultsFirst, taxonomic richness of gavage and dissection samples was compared. A greater number of taxa was detected in gavage samples than in dissection samples. Second, taxonomic richness was analyzed over time. No significant difference in taxonomic richness was observed with repeated gavage flushes. Third, a comparison was made of the taxonomic composition of samples collected by gavage versus dissection followed by a comparison of samples collected over multiple samplings. Nonmetric multidimensional scaling analysis revealed no clear differences between collection by gavage flushing or dissection. Using weighted Unifrac and Aitchison taxonomic distances between gavage and dissection samples were not significantly different from distances between gavage samples themselves, and no significant difference was found in the taxonomic composition of mice which were sampled repeatedly. Finally, relative abundances of specific identified taxa were compared, and eleven taxa were found to differ in frequency between collection methods. Using the more stringent Analysis of Composition of Microbiomes (ANCOM), seven was found to differ. Similarly, no significant differences were uncovered using these analyses over multiple samples by gastric flush. ConclusionIn summary, the consistency of the microbiota collected by gastric flushing recommends its use for microbiome analysis of gastric fluid similar to the use of fecal sampling to study the gut lumen microbiome.

Dietary fibre confers therapeutic effects in a preclinical model of Huntington’s disease

Huntington’s disease (HD) is a neurodegenerative disorder involving psychiatric, cognitive and motor deficits, as well as peripheral symptoms, including gastrointestinal dysfunction. The R6/1 HD mouse model expresses a mutant human huntingtin transgene and has been shown to provide an accurate disease model. Recent evidence of gut microbiome disruption was shown in preclinical and clinical HD. Therefore, we aimed to assess the potential role of gut microbial modulation in the treatment of HD. The R6/1 HD mice and wild-type littermate controls were randomised to receive diets containing different amounts of fibre: high-fibre (10 % fibre), control (5 % fibre), or zero-fibre (0 % fibre), from 6 to 20 weeks of age. We characterized the onset and progression of motor, cognitive and affective deficits, as well as gastrointestinal function and gut morphological changes. Faeces were collected for gut microbiome profiling using 16S rRNA sequencing, at 14 and 20 weeks of age. When compared to the control diet, high-fibre diet improved the performance of HD mice in behavioral tests of cognitive and affective function, as well as the gastrointestinal function of both HD and wild-type mice. While the diets changed the beta diversity of wild-type mice, no statistical significance was observed at 14 or 20 weeks of age within the HD mice. Analysis of Composition of Microbiomes with Bias Correction (ANCOM-BC) models were performed to evaluate microbiota composition, which identified differences, including a decreased relative abundance of the phyla Actinobacteriota, Campylobacterota and Proteobacteria and an increased relative abundance of the families Bacteroidaceae, Oscillospiraceae and Ruminococcaceae in HD mice when compared to wild-type mice after receiving high-fibre diet. PICRUSt2 revealed that high-fibre diet also decreased potentially pathogenic functional pathways in HD. In conclusion, high-fibre intake was effective in enhancing gastrointestinal function, cognition and affective behaviors in HD mice. These findings indicate that dietary fibre interventions may have therapeutic potential in Huntington’s disease to delay clinical onset, and have implications for related disorders exhibiting dysfunction of the gut-brain axis.

Evaluating the Ileal and Cecal Microbiota Composition of a 1940 Heritage Genetic Line and a 2016 Commercial Line of white Leghorns Fed Representative Diets from 1940 to 2016

This study identified and evaluated differences between microbiome compositions of the ileum and ceca of 1940 and 2016 white leghorn genetic strains fed representative contemporary diets from those times. Ileal and cecal samples were collected at 69 weeks of age. Alpha and beta diversity metrics were generated, and the Analysis of Composition of Microbiomes (ANCOM) was utilized to determine significantly different taxa. Ileum and ceca alpha diversity were significantly different (p = 0.001; Q = 0.001); however, no differences between genetic lineage were observed (p > 0.05; Q > 0.05). Beta diversity between the ileum and ceca and the genetic lines was significantly different (p = 0.001; Q = 0.001). The ANCOM of the ileum showed significant differences between Proteobacteria and Actinobacteriota phyla (p ˂ 0.05) and significant differences between Pseudomonas, Rhizobiaceae, Leuconostoc, and Aeriscardovia genera (p ˂ 0.05). For ceca ANCOM, Proteobacteria, Firmicutes, Actinobacteriota, and Euryarchaeota phyla were significantly different (p ˂ 0.05), with Firmicutes having the highest relative abundance across all groups, and there were significant differences in genera Pseudomonas, Leuconostoc, Alloprevotella, and Aeri scardovia, with Alloprevotella having the highest relative abundance. The results suggest that genetic makeup in conjunction with the nutritional composition influences the cecal and ileal microbiota of corresponding hens.

LOCOM: A logistic regression model for testing differential abundance in compositional microbiome data with false discovery rate control

Compositional analysis is based on the premise that a relatively small proportion of taxa are differentially abundant, while the ratios of the relative abundances of the remaining taxa remain unchanged. Most existing methods use log-transformed data, but log-transformation of data with pervasive zero counts is problematic, and these methods cannot always control the false discovery rate (FDR). Further, high-throughput microbiome data such as 16S amplicon or metagenomic sequencing are subject to experimental biases that are introduced in every step of the experimental workflow. McLaren et al. [eLife 8, e46923 (2019)] have recently proposed a model for how these biases affect relative abundance data. Motivated by this model, we show that the odds ratios in a logistic regression comparing counts in two taxa are invariant to experimental biases. With this motivation, we propose logistic compositional analysis (LOCOM), a robust logistic regression approach to compositional analysis, that does not require pseudocounts. Inference is based on permutation to account for overdispersion and small sample sizes. Traits can be either binary or continuous, and adjustment for confounders is supported. Our simulations indicate that LOCOM always preserved FDR and had much improved sensitivity over existing methods. In contrast, analysis of composition of microbiomes (ANCOM) and ANCOM with bias correction (ANCOM-BC)/ANOVA-Like Differential Expression tool (ALDEx2) had inflated FDR when the effect sizes were small and large, respectively. Only LOCOM was robust to experimental biases in every situation. The flexibility of our method for a variety of microbiome studies is illustrated by the analysis of data from two microbiome studies. Our R package LOCOM is publicly available.

Insights into the Composition of a Co-Culture of 10 Probiotic Strains (OMNi BiOTiC® AAD10) and Effects of Its Postbiotic Culture Supernatant.

Background: We aimed to gain insights in a co-culture of 10 bacteria and their postbiotic supernatant. Methods: Abundances and gene expression were monitored by shotgun analysis. The supernatant was characterized by liquid chromatography mass spectroscopy (LC-MS) and gas chromatography mass spectroscopy (GC-MS). Supernatant was harvested after 48 h (S48) and 196 h (S196). Susceptibility testing included nine bacteria and C. albicans. Bagg albino (BALBc) mice were fed with supernatant or culture medium. Fecal samples were obtained for 16S analysis. Results: A time-dependent decrease of the relative abundances and gene expression of L. salivarius, L. paracasei, E. faecium and B. longum/lactis and an increase of L. plantarum were observed. Substances in LC-MS were predominantly allocated to groups amino acids/peptides/metabolites and nucleotides/metabolites, relating to gene expression. Fumaric, panthotenic, 9,3-methyl-2-oxovaleric, malic and aspartic acid, cytidine monophosphate, orotidine, phosphoserine, creatine, tryptophan correlated to culture time. Supernatant had no effect against anaerobic bacteria. S48 was reactive against S. epidermidis, L. monocytogenes, P. aeruginosae, E. faecium and C. albicans. S196 against S. epidermidis and Str. agalactiae. In vivo S48/S196 had no effect on alpha/beta diversity. Linear discriminant analysis effect size (LEfSe) and analysis of composition of microbiomes (ANCOM) revealed an increase of Anaeroplasma and Faecalibacterium prausnitzii. Conclusions: The postbiotic supernatant had positive antibacterial and antifungal effects in vitro and promoted the growth of distinct bacteria in vivo.

Intercolony Comparisons of Gut Microbiome Composition From Lab Reared Eastern Subterranean Termites (Blattodea: Rhinotermitidae).

Termites are social insects living in colonies composed of worker, soldier, and reproductive castes. Termite hindguts are inhabited by all three domains of life- Eukarya (protists), Bacteria, and Archaea. These gut microorganisms are horizontally and vertically transferred by nestmates and reproductives, respectively. Prior evidence suggests that every colony potentially has a different gut microbiome that was transferred vertically and horizontally over time. However, we do not know if different colonies reared in the laboratory on the same diet will ultimately demonstrate similar microbial composition and structure. Therefore, we looked at gut bacteria in Eastern subterranean termite (Reticulitermes flavipes) colonies that were reared in the laboratory with identical diets and rearing conditions. Based on16S rRNA gene sequencing, the observed features, and Shannon’s diversity were significantly different between the colonies while differences in Pielou evenness and Faith phylogenetic diversity were not statistically significant. In addition, the microbial community structures were significantly different between colonies. Based on ANCOM (Analysis of Composition of Microbiomes), the taxa Elizabethkingia (Bacteroidetes: Flavobacteriales) and Chryseobacterium (Bacteroidetes: Flavobacteriales) were differentially abundant between the colonies. These results suggest that providing the exact same diet and rearing environment for >2 yr cannot result in identical gut microbiomes between termite colonies.

Presence of distinctive microbiome in the first-pass meconium of newborn infants

We critically evaluated the fetal microbiome concept in 44 neonates with placenta, amniotic fluid, and first-pass meconium samples. Placental histology showed no signs of inflammation. Meconium samples were more often bacterial culture positive after vaginal delivery. In next-generation sequencing of the bacterial 16S gene, before and after removal of extracellular and PCR contaminant DNA, the median number of reads was low in placenta (48) and amniotic fluid (46) and high in meconium samples (14,556 C-section, 24,860 vaginal). In electron microscopy, meconium samples showed extracellular vesicles. Utilizing the analysis of composition of microbiomes (ANCOM) against water, meconium samples had a higher relative abundance of Firmicutes, Lactobacillus, Streptococcus, and Escherichia-Shigella. Our results did not support the existence of the placenta and amniotic fluid microbiota in healthy pregnancies. The first-pass meconium samples, formed in utero, appeared to harbor a microbiome that may be explained by perinatal colonization or intrauterine colonization via bacterial extracellular vesicles.

Comparative Analysis of Fecal Microbiomes From Wild Waterbirds to Poultry, Cattle, Pigs, and Wastewater Treatment Plants for a Microbial Source Tracking Approach.

Fecal pollution in coastal areas is of a high concern since it affects bathing and shellfish harvesting activities. Wild waterbirds are non-negligible in the overall signal of the detectable pollution. Yet, studies on wild waterbirds’ gut microbiota focus on migratory trajectories and feeding impact on their shape, rare studies address their comparison to other sources and develop quantitative PCR (qPCR)-based Microbial Source Tracking (MST) markers to detect such pollution. Thus, by using 16S rRNA amplicon high-throughput sequencing, the aims of this study were (i) to explore and compare fecal bacterial communities from wild waterbirds (i.e., six families and 15 species, n = 275 samples) to that of poultry, cattle, pigs, and influent/effluent of wastewater treatment plants (n = 150 samples) and (ii) to develop new MST markers for waterbirds. Significant differences were observed between wild waterbirds and the four other groups. We identified 7,349 Amplicon Sequence Variants (ASVs) from the hypervariable V3–V4 region. Firmicutes and Proteobacteria and, in a lesser extent, Actinobacteria and Bacteroidetes were ubiquitous while Fusobacteria and Epsilonbacteraeota were mainly present in wild waterbirds. The clustering of samples in non-metric multidimensional scaling (NMDS) ordination indicated a by-group clustering shape, with a high diversity within wild waterbirds. In addition, the structure of the bacterial communities was distinct according to bird and/or animal species and families (Adonis R2 = 0.13, p = 10–4, Adonis R2 = 0.11, p = 10–4, respectively). The Analysis of Composition of Microbiomes (ANCOM) showed that the wild waterbird group differed from the others by the significant presence of sequences from Fusobacteriaceae (W = 566) and Enterococcaceae (W = 565) families, corresponding to the Cetobacterium (W = 1427) and Catellicoccus (W = 1427) genera, respectively. Altogether, our results suggest that some waterbird members present distinct fecal microbiomes allowing the design of qPCR MST markers. For instance, a swan- and an oystercatcher-associated markers (named Swan_2 and Oyscab, respectively) have been developed. Moreover, bacterial genera harboring potential human pathogens associated to bird droppings were detected in our dataset, including enteric pathogens, i.e., Arcobacter, Clostridium, Helicobacter, and Campylobacter, and environmental pathogens, i.e., Burkholderia and Pseudomonas. Future studies involving other wildlife hosts may improve gut microbiome studies and MST marker development, helping mitigation of yet unknown fecal pollution sources.

Effects of Early Life Stress on the Gut Microbiota of Mice

Exposure to early life stress (ELS) is associated with a greater risk of chronic disease development including depression, cardiovascular disease (CVD), and inflammatory bowel disease (IBD). Changes in the gut microbiota have been linked to IBD and CVD. Rodent models of early life neglect are used to characterize the mechanistic links between ELS exposure and the risk of disease later in life. However, little is understood about ELS exposure and the gut microbiota. We used a mouse model of ELS, maternal separation with early weaning (MSEW) and normally-reared (NR) mice to determine whether the neonate microbiota is altered, and if so, whether the MSEW protocol induces changes in the dam microbiota that are then transmitted to their offspring. MSEW mice (n = 12) were subjected to maternal separation for 4 h per day on postnatal days (PDs) 2–5, and 8 h per day on PDs 6–16, and were subsequently weaned at PD17. NR mice (n = 13) remained undisturbed with dams until weaning on PD21. All mice were maintained on standard chow (NIH-31; NSN 8710-01-005-8438) following weaning. Paired-end Illumina sequencing was conducted on colonic content from PD28 mice and DADA2 was utilized to infer and assign taxonomy to individual amplicon sequence variants (ASVs). The fecal microbiota of dams (n= 5) was also sequenced. Analysis of composition of microbiomes (ANCOM) detected 32 ASVs that had statistically different abundance in MSEW compared to NR mice at PD28. Five of the nine ASVs that increased in MSEW mice were classified as Clostridium senso stricto 1 or Lachnoclostridium, while none the of the 23 ASVs enriched in NR animals belonged to these taxa. Fifty-seven percent (13 of 23) of the ASVs that had greater abundance in NR animals were classified as Lachnospiracea (other than Lachnoclostrium) or Ruminococcaceae. Compared to NR animals, MSEW PD28 mice had reduced phylogenetic diversity (18.7 ± 0.3 compared to 20.3 ± 0.3 in NR; P = 0.003) and Chao1ASV richness (175 ± 8 compared to 198 ± 8 in NR; P = 0.04). Microbial community composition was also altered at PD28 based permutational analysis of variance of Aitchison distance (F(1,24) = 2.29, P = 0.002). The composition, levels of diversity, and abundance of individual ASVs in the microbiota of dams were no different between MSEW and NR cohorts. Thus, the observed microbiota shifts in the young pups are likely driven by endogenous effects of MSEW in the offspring and independent of maternal inheritance. MSEW mice had lower microbial diversity and a reduction in Lachnospiraceae and Ruminococcaceae, which are important fermenters of short-chain fatty acids (SCFAs). Nine different SCFAs were measured in plasma of PD28 mice and MSEW animals had reduced butyrate (P = 0.02) compared to NR controls. Butyrate promote colonic homeostasis as well as regulates metabolic health and CVD risk. Studies are on-going to determine the impact of altered SCFA levels with ELS exposure. This study suggests a direct effect of ELS-exposure on the gut microbiota, which may impact the risk of chronic disease development.

Effects of Aleurone Supplementation on Glucose-Insulin Metabolism and Gut Microbiome in Untrained Healthy Horses.

Aleurone, a layer of the bran fraction, is deemed to be responsible for the positive health effects associated with the consumption of whole-grain products. Studies on rodents, pigs, and humans report beneficial effects of aleurone in five main areas: the reduction of oxidative stress, immunomodulatory effects, modulation of energy management, digestive health, and the storage of vitamins and minerals. Our study is the first aleurone supplementation study performed in horses. The aim of this study was to investigate the effect of an increase in the dose levels of aleurone on the postprandial glucose-insulin metabolism and the gut microbiome in untrained healthy horses. Seven adult Standardbred horses were supplemented with four different dose levels of aleurone (50, 100, 200, and 400 g/day for 1 week) by using a Latin square model with a 1-week wash out in between doses. On day 7 of each supplementation week, postprandial blood glucose-insulin was measured and fecal samples were collected. 16S ribosomal RNA (rRNA) gene sequencing was performed and QIIME2 software was used for microbiome analysis. Microbial community function was assessed by using the predictive metagenome analysis tool Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) and using the Metacyc database of metabolic pathways. The relative abundancies of a pathway were analyzed by using analysis of composition of microbiomes (ANCOM) in R. There was a significant dose-dependent increase in the postprandial time to peak of glucose (p = 0.030), a significant delay in the time to peak of insulin (p = 0.025), and a significant decrease in both the insulin peak level (p = 0.049) and insulin area under the curve (AUC) (p = 0.019) with increasing dose levels of aleurone, with a consideration of 200 g being the lowest significant dose. Alpha diversity and beta diversity of the fecal microbiome showed no significant changes. Aleurone significantly decreased the relative abundance of the genera Roseburia, Shuttleworthia, Anaerostipes, Faecalibacter, and Succinovibrionaceae. The most pronounced changes in the relative abundance at phyla level were seen in Firmicutes and Verrucomicrobia (downregulation) and Bacteroidetes and Spirochaetes (upregulation). The PICRUSt analysis shows that aleurone induces a downregulation of the degradation of L-glutamate and taurine and an upregulation of the three consecutive pathways of the phospholipid membrane synthesis of the Archaea domain. The results of this study suggest a multimodal effect of aleurone on glucose-insulin metabolism, which is most likely to be caused by its effect on feed texture and subsequent digestive processing; and a synergistic effect of individual aleurone components on the glucose-insulin metabolism and microbiome composition and function.

Unraveling city-specific signature and identifying sample origin locations for the data from CAMDA MetaSUB challenge

BackgroundComposition of microbial communities can be location-specific, and the different abundance of taxon within location could help us to unravel city-specific signature and predict the sample origin locations accurately. In this study, the whole genome shotgun (WGS) metagenomics data from samples across 16 cities around the world and samples from another 8 cities were provided as the main and mystery datasets respectively as the part of the CAMDA 2019 MetaSUB “Forensic Challenge”. The feature selecting, normalization, three methods of machine learning, PCoA (Principal Coordinates Analysis) and ANCOM (Analysis of composition of microbiomes) were conducted for both the main and mystery datasets.ResultsFeatures selecting, combined with the machines learning methods, revealed that the combination of the common features was effective for predicting the origin of the samples. The average error rates of 11.93 and 30.37% of three machine learning methods were obtained for main and mystery datasets respectively. Using the samples from main dataset to predict the labels of samples from mystery dataset, nearly 89.98% of the test samples could be correctly labeled as “mystery” samples. PCoA showed that nearly 60% of the total variability of the data could be explained by the first two PCoA axes. Although many cities overlapped, the separation of some cities was found in PCoA. The results of ANCOM, combined with importance score from the Random Forest, indicated that the common “family”, “order” of the main-dataset and the common “order” of the mystery dataset provided the most efficient information for prediction respectively.ConclusionsThe results of the classification suggested that the composition of the microbiomes was distinctive across the cities, which could be used to identify the sample origins. This was also supported by the results from ANCOM and importance score from the RF. In addition, the accuracy of the prediction could be improved by more samples and better sequencing depth.

Microbiome Profiling Reveals Gut Dysbiosis in the Metabotropic Glutamate Receptor 5 Knockout Mouse Model of Schizophrenia.

Schizophrenia (SZ) is a psychiatric disorder that constitutes one of the top 10 global causes of disability. More recently, a potential pathogenic role for the gut microbial community (microbiota) has been highlighted, with numerous studies describing dysregulated microbial profiles in SZ patients when compared to healthy controls. However, no animal model of SZ has previously recapitulated the gut dysbiosis observed clinically. Since the metabotropic glutamate receptor 5 (mGlu5) knockout mice provide a preclinical model of SZ with strong face and predictive validity, in the present study we performed gut microbiome profiling of mGlu5 knockout (KO) and wild-type (WT) mice by 16S rRNA sequencing of bacterial genomic DNA from fecal samples, analyzing bacterial diversity and taxonomic composition, as well as gastrointestinal parameters as indicators of gut function. We found a significant genotype difference in microbial beta diversity. Analysis of composition of microbiomes (ANCOM) models were performed to evaluate microbiota compositions, which identified a decreased relative abundance of the Erysipelotrichaceae family and Allobaculum genus in this mouse model of SZ. We also identified a signature of bacteria discriminating between the genotypes (KO and WT), consisting of the Erysipelotrichales, Bacteroidales, and Clostridiales orders and macroscopic gut differences. We thus uncovered global differential community composition in the gut microbiota profile between mGlu5 KO and WT mice, outlining the first evidence for gut dysbiosis in a genetic animal model of SZ. Our findings suggest that this widely used preclinical model of SZ also has substantial utility for investigations of gut dysbiosis and associated signaling via the microbiota–gut–brain axis, as potential modulators of SZ pathogenesis. Our discovery opens up new avenues to explore gut dysbiosis and its proposed links to brain dysfunction in SZ, as well as novel therapeutic approaches to this devastating disorder.

Free Choline, but Not Phosphatidylcholine, Elevates Circulating Trimethylamine-N-oxide and This Response Is Modified by the Gut Microbiota Composition in Healthy Men

Abstract Objectives Trimethylamine-N-oxide (TMAO), a choline-derived gut microbiota-dependent metabolite, is a newly recognized risk marker for cardiovascular disease. However, the contributions of different forms of choline and gut microbiota composition on TMAO production are largely unknown. The objectives of this study were to: 1) compare acute TMAO response to meals containing free choline (choline bitartrate) versus fat-soluble choline (phosphatidylcholine) and 2) to determine the effects of gut microbiota composition on TMAO response. Methods In a controlled, double-blinded, cross-over study, healthy men (n = 37) were provided meals containing (i) 600 mg choline as choline bitartrate (free choline); (ii) 600 mg choline as phosphatidylcholine; or (iii) no choline control in a random order. Blood and urine samples were collected at baseline and throughout the 6-h study period; a one-time stool sample was collected at baseline. Results Compared to no choline and phosphatidylcholine, free choline yielded 295% higher plasma TMAO (P = 0.002) and 250% higher urinary TMAO (P = 0.01), with no difference in TMAO response between phosphatidylcholine and no choline. High-TMAO producers (those with ≥40% increase in urinary TMAO response to free choline) had significantly different beta-diversity measures (unweighted UniFrac; PERMANOVA P = 0.01) compared to low-TMAO producers (those with <40% increase in TMAO response) but showed no difference in alpha-diversity. Analysis of Composition of Microbiomes (ANCOM) revealed that high-TMAO producers had more abundant lineages of Clostridium from Ruminococcaceae and Lachnospiraceae (in phylum Firmicutes) compared to low-TMAO producers (P < 0.05 with the strength of the ANCOM test W = 11 and W = 8, respectively). Conclusions Given that the majority of choline in food is in the form of phosphatidylcholine, the absence of TMAO elevation with phosphatidylcholine counters arguments that dietary choline should be avoided for TMAO-producing characteristics. Further, development of individualized dietary recommendations based on the gut microbiota composition may be a more appropriate strategy to reduce risk of cardiovascular disease. Funding Sources This research was supported by the Utah Agricultural Experiment Station Seed Grants Program.

Docosahexaenoic Acid Supplementation Alters Gut Microbial Populations in Silica-Triggered Lupus-Prone NZBWF1 Mice Fed the Total Western Diet

Abstract Objectives Inhalation of crystalline silica (Si) has been linked to the pathogenesis of human autoimmunity such as lupus. Dietary lipid intervention can delay progression of Si-induced autoimmunity in the lupus-prone female NZBWF1 mice. However, the role of gut microbiome relative to dietary lipids and Si-induced autoimmunity is unknown. We seek to ask whether Si-induced pathogenicity and supplementation of a Western diet with DHA can alter gut microbial populations. Methods Four experimental diets were used in this study: modified Total Western Diet (MTWD), MTWD supplemented with DHA (↑DHA), MTWD with reduced saturated and ω-6 fatty acids (↓SFω6), and MTWD with ↓SFω6 and ↑DHA (↓SFω6/↑DHA). Groups of mice (n = 8/gp) were fed one of the four isocaloric experimental diets beginning at 6 wks of age. After 2 wks, mice were anesthetized and intranasally instilled with phosphate-buffered saline (PBS) or Si in PBS. DNA obtained from fecal pellets for gut microbiome analysis was collected from an individual mouse at 13 (early-phase) and 22 (late-phase) wks of age. DNA was extracted and sequenced on the Illumina MiSeq platform. The relative abundance of microbial taxa was differentiated by pairwise comparisons using ANalysis of COmposition of Microbiomes (ANCOM), Linear Mixed model (LM), and LDA Effect Size (LEfSe). Results Lupus triggering by Si with dietary lipid intervention caused a significant difference in Lachnospiraceae bacterium 609 (Lb609) abundance depending on the stringency of analysis frameworks. Under early-phase data, Lb609 was significantly found to be higher in the group of Si-exposed MTWD (MTWD/Si) than all other groups in LefSe analysis (LDA score >3). LM showed the same results except the comparison between MTWD/Si and ↓SFω6 (FDR-adjust P > 0.05). In the results of ANCOM, Lb609 in MTWD/Si was only significantly different from the groups of PBS-exposed MTWD (MTWD/PBS) and ↓SFω6+↑DHA. Although Lb609 was identified discriminatively between MTWD/Si and MTWD/PBS in all analyses from late-phase data, it was not differential in other comparisons. Conclusions Lupus triggering altered microbial composition reflected in increased Lb609 and decreased when supplementation was applied. Further, the studies revealed that interventions on microbial populations were dependent on time. Funding Sources The USDA National Institute of Food and Federal Appropriations.

Abstract B12: Cross-sectional analysis of grain, gluten, and fiber intakes and gut microbiota in the Food and Microbiome Longitudinal Investigation (FAMiLI) Study

Abstract Whole grains are recommended as part of a healthy dietary pattern for the prevention of chronic diseases, including colorectal cancer. Greater whole grain consumption may beneficially alter gut microbiota, and it is unclear if gluten, a protein component of grains, is also associated with altered microbiota. To examine the association between grain, gluten, and fiber intake with gut microbiota, we analyzed data from the ongoing prospective Food and Microbiome Longitudinal Investigation Study. This cross-sectional analysis included 300 men and 501 women of White, Black, Hispanic, and Asian race/ethnicity who completed a 137-item semiquantitative food frequency questionnaire. Gluten intake was estimated using the protein content of grain products. Bacterial 16S rRNA genes from stool samples collected via RNAlater tubes were amplified, sequenced, and assigned to bacterial taxa. We examined associations of whole and refined grain, gluten, and dietary fiber intakes with gut microbiota diversity and composition using multivariable-adjusted linear regression models and permutational multivariate analysis of variance, respectively, and with microbial taxa abundance using analysis of composition of microbiomes (ANCOM). In this diverse study population, the highest quartiles of energy-adjusted refined grain and gluten intake, when compared to the lowest quartiles, were associated with lower Shannon diversity (P-trend=0.03 and P-trend=0.01, respectively), but no other associations with alpha- or beta-diversity measures were observed. The highest quartile of dietary fiber was not associated with alpha-diversity measures but was associated with shifts in overall gut microbial composition (P-trend=0.02). Whole grain intake was not statistically significantly associated with these microbiome parameters. However, the highest quartile of whole grain intake, compared to the lowest quartile, was associated with greater abundance of several gut taxa, including classes Erysipelotrichi (family Erysipelotrichaceae), Clostridia (genus Faecalibacterium and Lachnospira), and Bacteroidia (Bacteroides plebeius). Higher whole grain intake was also associated with lower abundance of classes Bacteroidia (family Rikenellaceae and Bacteroides uniformis) and Clostridia (genus Oscillospira and Ruminococcus). Similarly, the highest quartile of dietary fiber intake was associated with greater abundance of classes Erysipelotrichi (family Erysipelotrichaceae) and Clostridia (genus Lachnospira) and lower abundance of class Bacteroidia (family Rikenellaceae). Higher intakes of refined grain and gluten were not statistically significantly associated with altered abundance of gut taxa. Though this study is cross-sectional, identification of gut microbes associated with higher intakes of whole grain and fiber may expand our understanding of how these nutritional factors lower risk for colorectal cancer. Citation Format: Caroline Y. Um, Brandilyn A. Peters, Emilia N. Cobbs, Hee Sun Choi, Dia B. Beggs, Richard B. Hayes, Marjorie L. McCullough, Susan M. Gapstur, Jiyoung Ahn. Cross-sectional analysis of grain, gluten, and fiber intakes and gut microbiota in the Food and Microbiome Longitudinal Investigation (FAMiLI) Study [abstract]. In: Proceedings of the AACR Special Conference on the Microbiome, Viruses, and Cancer; 2020 Feb 21-24; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2020;80(8 Suppl):Abstract nr B12.

The effect of linagliptin treatment on gut microbiota in patients with HNF1A-MODY or type 2 diabetes — a preliminary cohort study

Introduction. Many studies have evaluated the relationship between diabetes and microbiota. In animal models, the dipeptidyl peptidase-4 inhibitors altered the gut microbiota. We investigated whether linagliptin alters the gastrointestinal flora in humans. Materials and methods. This prospective cohort study enrolled 24 patients: 5 patients with maturity onset diabetes of the young associated with HNF1A mutation and 19 patients with type 2 diabetes mellitus. Stool samples were collected at baseline and 4 weeks after treatment intensification with either linagliptin or a sulphonylurea alongside current treatment. Faecal 16S rRNA was analysed by next-generation sequencing. Results. Nine patients initiated linagliptin whereas 15 patients initiated or increased the dose of a sulphonylurea. After linagliptin treatment, we did not observe changes in taxa in L2–L7 based on analysis of composition of microbiomes (ANCOM). The same held true for pairwise alpha diversity (Shannon diversity, p = 0.59; Pielou’s measure of evenness, p = 0.68; and observed operational taxonomic units [OTUs], p = 0.77) and beta diversity distances (unweighted UniFrac, p = 0.99; weighted UniFrac, p = 0.93; Bray-Curtis, p = 0.98; and Jaccard, p = 0.99). Similarly, after sulphonylurea intensification, we did not observe changes in taxa in L2–L7 in ANCOM, nor were there changes in alpha diversity (Shannon diversity, p = 0.19; Pielou’s measure of evenness, p = 0.21; and observed OTUs, p = 0.42) or beta diversity distances (unweighted UniFrac, p = 0.99; weighted UniFrac, p = 0.99; Bray-Curtis, p = 1; and Jaccard, p = 0.99). Conclusion. We did not observe changes in colonic microbiota 4 weeks after addition of linagliptin to current diabetes treatment. Further studies are required to determine whether linagliptin influences the colonic microbiota in humans.

The Proteome of Pancreatic Cancer-Derived Exosomes Reveals Signatures Rich in Key Signaling Pathways.

Exosomes are membrane-bound vesicles that traffic small molecular cargos. These cargos participate in cell-cell communication and contribute to the pathogenesis of many disease including cancer. How these mechanisms contribute to communication within the pancreatic adenocarcinoma (PDAC) microenvironment and how they contribute to PDAC biology are poorly understood. Performed in this study are comprehensive, quantitative comparisons of the proteomes of three PDAC cell lines to those of the exosomes they produce. Approximately 35% of whole cell proteins sort into exosomes. Analysis of composition of microbiomes (ANCOM) determined a cluster of 98 enriched pancreatic cancer exosome core proteins (ePC-ECPs). Further, these proteins are predicted by ingenuity pathway analysis (IPA) as actively involved in signaling pathways regulating cell death and survival, cellular movement, and cell-to-cell signaling and interaction in particular (top three p-value significant pathways). Significant enrichment of canonical pathways of acute phase response signaling (inflammatory response signaling pathways) and FXR and RXR activation in biosynthetic pathways are also predicted; 97 ePC-ECPs are associated with cancer and among them, 34 are specifically associated with PDAC. In conclusion, exosomes from PDAC are enriched with cancer-associated signaling proteins. Further assessment of these proteins as PDAC biomarkers or therapeutic targets is warranted.

Characterizing the gut microbiome of patients with triple-negative breast cancer.

e14186 Background: The human microbiome has been postulated to play a role in carcinogenesis, and there is evidence that commensal intestinal bacteria are capable of promoting tumor immunosurveillance and controlling the immune system’s response to therapy by modulating the tumor microenvironment. Methods: We characterized the stool microbiome of 30 patients (pts) with triple-negative breast cancer (TNBC) using 16S rRNA gene sequencing. Analysis of composition of microbiomes (ANCOM) was used to identify differential abundant exact-sequence-variants (ESVs) with p-values adjusted using Benjamini-Hochberg False Discovery Rate (pFDR) correction. We correlated the stool microbiome with clinicopathologic features and response to treatment. Results: The cohort included 7 pts with metastatic breast cancer and 23 pts with early-stage disease. 19 pts were treated with neoadjuvant chemotherapy (NACT). Bacteroides ovatus ESV was enriched (pFDR < 0.05) in black pts (n = 14), whereas ESV from Prevotella copri was abundant (pFDR < 0.05) in white pts (n = 13). ESVs from Clostridium and family Ruminococcaceae were enriched (pFDR < 0.05) in pts with distant metastatic recurrence (n = 6) compared to pts without metastatic disease. Notably, we identified specific taxa that differentiated between pts with a pathological complete response to NACT (pCR, n = 6) and those with residual disease (n = 13). ESVs from Bacteroides and Ruminococcaceae were relatively more abundant in pts with a pCR (pFDR < 0.05). Furthermore, among pts with residual disease after NACT, Bacteroides caccae was significantly enriched in pts with a partial response (n = 4) compared to pts with no response (n = 9). Conclusions: To our knowledge, this is the first time that 16S rRNA amplicon sequencing has been used to identify microbial signatures correlating with clinical phenotypes and response to treatment in pts with TNBC. This small study provides insight into the potential role of microbes in novel therapeutic interventions to correct dysbiosis and shift towards a gut microbial ecosystem that enhances the body’s immune response to TNBC.

Exploration of microbiota targets for major depressive disorder and mood related traits

Growing evidence suggests the link between gut microbiota and mood regulation. The current study aimed to identify microbiota targets for major depressive disorder (MDD) and mood-related traits in Taiwanese samples, while taking into account the influence of dietary patterns. We recruited 36 MDD patients and 37 healthy controls for 16S rRNA gene sequencing. We assessed nutrient content using food frequency questionnaire, and mood related phenotypes, including depressive severity, anxiety, and perceived stress. Analysis of composition of microbiomes (ANCOM) models were performed to evaluate microbiota compositions between patients and controls, while adjusted for fat intake% and sequencing platforms. We found 23 taxa (4 phyla, 7 families and 12 genera) to be associated with depression and beta diversity was differed between groups. Phylum Actinobacteria and Firmicutes were overrepresented in MDD patients. At genus level, Bifidobacterium (7%) and Blautia (8%) had relatively high abundance among MDD patients, while Prevotella (16%) had high abundance in controls. Holdemania exhibited moderate correlation with anxiety (r = 0.65) and perceived stress level (r = 0.49) mainly in MDD patients but not controls. Pathway analyses revealed that pentose phosphate and starch and sucrose metabolism processes were important pathways for depression via microbiota functions. In conclusion, our results revealed microbiota targets for depression that are independent of fat intake. It is worthwhile to conduct further studies to replicate the current findings and to integrate with biochemistry and metabolomics data to better understand the functions of identified targets.

Fine grained compositional analysis of Port Everglades Inlet microbiome using high throughput DNA sequencing.

BackgroundSimilar to natural rivers, manmade inlets connect inland runoff to the ocean. Port Everglades Inlet (PEI) is a busy cargo and cruise ship port in South Florida, which can act as a source of pollution to surrounding beaches and offshore coral reefs. Understanding the composition and fluctuations of bacterioplankton communities (“microbiomes”) in major port inlets is important due to potential impacts on surrounding environments. We hypothesize seasonal microbial fluctuations, which were profiled by high throughput 16S rRNA amplicon sequencing and analysis.Methods & ResultsSurface water samples were collected every week for one year. A total of four samples per month, two from each sampling location, were used for statistical analysis creating a high sampling frequency and finer sampling scale than previous inlet microbiome studies. We observed significant differences in community alpha diversity between months and seasons. Analysis of composition of microbiomes (ANCOM) tests were run in QIIME 2 at genus level taxonomic classification to determine which genera were differentially abundant between seasons and months. Beta diversity results yielded significant differences in PEI community composition in regard to month, season, water temperature, and salinity. Analysis of potentially pathogenic genera showed presence of Staphylococcus and Streptococcus. However, statistical analysis indicated that these organisms were not present in significantly high abundances throughout the year or between seasons.DiscussionSignificant differences in alpha diversity were observed when comparing microbial communities with respect to time. This observation stems from the high community evenness and low community richness in August. This indicates that only a few organisms dominated the community during this month. August had lower than average rainfall levels for a wet season, which may have contributed to less runoff, and fewer bacterial groups introduced into the port surface waters. Bacterioplankton beta diversity differed significantly by month, season, water temperature, and salinity. The 2013–2014 dry season (October–April), was warmer and wetter than historical averages. This may have driven significant differences in beta diversity. Increased nitrogen and phosphorous concentrations were observed in these dry season months, possibly creating favorable bacterial growth conditions. Potentially pathogenic genera were present in the PEI. However their relatively low, non-significant abundance levels highlight their relatively low risk for public health concerns. This study represents the first to sample a large port at this sampling scale and sequencing depth. These data can help establish the inlet microbial community baseline and supplement the vital monitoring of local marine and recreational environments, all the more poignant in context of local reef disease outbreaks and worldwide coral reef collapse in wake of a harsh 2014–16 El Niño event.