scholarly journals A multi-omic cohort as a reference point for promoting a healthy human gut microbiome

2019 ◽  
Author(s):  
Zhuye Jie ◽  
Suisha Liang ◽  
Qiuxia Ding ◽  
Fei Li ◽  
Shanmei Tang ◽  
...  
Keyword(s):  
Gut Microbiome ◽  
The Elderly ◽  
Omics Data ◽  
Sequencing Data ◽  
Healthy Human ◽  
Fecal Microbiome ◽  
Dairy Consumption ◽  
Sleeping Habits ◽  
Study Designs ◽  
High Depth

AbstractMore than a decade of gut microbiome studies have a common goal for human health. As most of the disease studies sample the elderly or the middle-aged, a reference cohort for young individuals has been lacking. It is also not clear what other omics data need to be measured to better understand the gut microbiome. Here we present high-depth metagenomic shotgun sequencing data for the fecal microbiome together with other omics data in a cohort of 2,183 adults, and observe a number of vitamins, hormones, amino acids and trace elements to correlate with the gut microbiome and cluster with T cell receptors. Associations with physical fitness, sleeping habits and dairy consumption are identified in this large multi-omic cohort. Many of the associations are validated in an additional cohort of 1,404 individuals. Our comprehensive data are poised to advise future study designs to better understand and manage our gut microbiome both in population and in mechanistic investigations.

IMMU-09. HUMAN MICROBIOTA INFLUENCE THE EFFICACY OF IMMUNOTHERAPY IN A MOUSE MODEL OF GLIOBLASTOMA

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi93-vi94
Author(s):  
Kory Dees ◽  
Hyunmin Koo ◽  
James Humphreys ◽  
Joseph Hakim ◽  
David Crossman ◽  
...  
Keyword(s):  
Mouse Model ◽  
Microbial Communities ◽  
Mouse Models ◽  
Gut Microbiome ◽  
Positive Response ◽  
Checkpoint Inhibitors ◽  
Metagenomic Sequencing ◽  
Sequencing Data ◽  
Microbial Composition ◽  
Healthy Human

Abstract Although immunotherapy works well in glioblastoma (GBM) pre-clinical mouse models, the therapy has unfortunately not demonstrated efficacy in humans. In melanoma and other cancers, the composition of the gut microbiome has been shown to determine responsiveness or resistance to immune checkpoint inhibitors (anti-PD-1). Most pre-clinical cancer studies have been done in mouse models using mouse gut microbiomes, but there are significant differences between mouse and human microbial gut compositions. To address this inconsistency, we developed a novel humanized microbiome (HuM) model to study the response to immunotherapy in a pre-clinical mouse model of GBM. We used five healthy human donors for fecal transplantation of gnotobiotic mice. After the transplanted microbiomes stabilized, the mice were bred to generate five independent humanized mouse lines (HuM1-HuM5). Analysis of shotgun metagenomic sequencing data from fecal samples revealed a unique microbiome with significant differences in diversity and microbial composition among HuM1-HuM5 lines. Interestingly, we found that the HuM lines responded differently to anti-PD-1. Specifically, we demonstrate that HuM2 and HuM3 mice are responsive to anti-PD-1 and displayed significantly increased survival compared to isotype controls, while HuM1, HuM4, and HuM5 mice are resistant to anti-PD-1. These mice are genetically identical, and only differ in the composition of the gut microbiome. In a correlative experiment, we found that disrupting the responder HuM2 microbiome with antibiotics abrogated the positive response to anti-PD-1, indicating that HuM2 microbiota must be present in the mice to elicit the positive response to anti-PD-1 in the GBM model. The question remains of whether the “responsive” microbial communities in HuM2 and HuM3 can be therapeutically exploited and applicable in other tumor models, or if the “resistant” microbial communities in HuM1, HuM4, and HuM5 can be depleted and/or replaced. Future studies will assess responder microbial transplants as a method of enhancing immunotherapy.

scholarly journals Covariation of the Fecal Microbiome with Diet in Nonpasserine Birds

mSphere ◽  
2021 ◽  
Vol 6 (3) ◽  
Author(s):  
Kangpeng Xiao ◽  
Yutan Fan ◽  
Zhipeng Zhang ◽  
Xuejuan Shen ◽  
Xiaobing Li ◽  
...  
Keyword(s):  
Gut Microbiome ◽  
Dietary Diversity ◽  
Basal Diet ◽  
Metagenomic Sequencing ◽  
Sequencing Data ◽  
Future Health ◽  
Amino Acid Synthesis ◽  
Fecal Samples ◽  
Fecal Microbiome ◽  
Microbiome Diversity

ABSTRACT Opportunistic feeding and multiple other environment factors can modulate the gut microbiome, and bias conclusions, when wild animals are used for studying the influence of phylogeny and diet on their gut microbiomes. Here, we controlled for these other confounding factors in our investigation of the magnitude of the effect of diet on the gut microbiome assemblies of nonpasserine birds. We collected fecal samples, at one point in time, from 35 species of birds in a single zoo as well as 6 species of domestic poultry from farms in Guangzhou city to minimize the influences from interfering factors. Specifically, we describe 16S rRNA amplicon data from 129 fecal samples obtained from 41 species of birds, with additional shotgun metagenomic sequencing data generated from 16 of these individuals. Our data show that diets containing native starch increase the abundance of Lactobacillus in the gut microbiome, while those containing plant-derived fiber mainly enrich the level of Clostridium. Greater numbers of Fusobacteria and Proteobacteria are detected in carnivorous birds, while in birds fed a commercial corn-soybean basal diet, a stronger inner-connected microbial community containing Clostridia and Bacteroidia was enriched. Furthermore, the metagenome functions of the microbes (such as lipid metabolism and amino acid synthesis) were adapted to the different food types to achieve a beneficial state for the host. In conclusion, the covariation of diet and gut microbiome identified in our study demonstrates a modulation of the gut microbiome by dietary diversity and helps us better understand how birds live based on diet-microbiome-host interactions. IMPORTANCE Our study identified food source, rather than host phylogeny, as the main factor modulating the gut microbiome diversity of nonpasserine birds, after minimizing the effects of other complex interfering factors such as weather, season, and geography. Adaptive evolution of microbes to food types formed a dietary-microbiome-host interaction reciprocal state. The covariation of diet and gut microbiome, including the response of microbiota assembly to diet in structure and function, is important for health and nutrition in animals. Our findings help resolve the major modulators of gut microbiome diversity in nonpasserine birds, which had not previously been well studied. The diet-microbe interactions and cooccurrence patterns identified in our study may be of special interest for future health assessment and conservation in birds.

scholarly journals Linking gut microbiome to bone mineral density: a shotgun metagenomic dataset from 361 elderly women

Gigabyte ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Qi Wang ◽  
Qiang Sun ◽  
Xiaoping Li ◽  
Zhefeng Wang ◽  
Haotian Zheng ◽  
...  
Keyword(s):  
Bone Mineral Density ◽  
Bone Mass ◽  
Mass Loss ◽  
Bone Mineral ◽  
Gut Microbiome ◽  
Elderly Women ◽  
The Elderly ◽  
Sequencing Data ◽  
Mineral Density ◽  
Bone Mass Loss

Bone mass loss contributes to the risk of bone fracture in the elderly. Many factors including age, obesity, estrogen and diet, are associated with bone mass loss. Mice studies suggested that the gut microbiome might affect the bone mass by regulating the immune system. However, there has been little evidence from human studies. Bone loss increases after menopause. Therefore, we have recruited 361 Chinese post-menopausal women to collect their fecal samples and metadata to conduct a metagenome-wide association study (MWAS) to investigate the influence of the gut microbiome on bone health. Gut microbiome sequencing data were produced using the BGISEQ-500 sequencer. Bone mineral density (BMD) was calculated using a Hologic dual energy X-ray machine, and body mass index (BMI) and age were also recorded. This collected data allows exploration of the gut microbial diversity and their links to bone mass loss as well as to microbial markers for bone mineral density. In addition, these data are potentially useful in studying the role that the gut microbiota might play in bone mass loss and in exploring the process of bone mass loss.

scholarly journals Associations between Maternal Diet, Body Composition and Gut Microbial Ecology in Pregnancy

Nutrients ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 3295
Author(s):  
Meghan L. Ruebel ◽  
Stephanie P. Gilley ◽  
Clark R. Sims ◽  
Ying Zhong ◽  
Donald Turner ◽  
...  
Keyword(s):  
Body Composition ◽  
Gut Microbiome ◽  
Maternal Obesity ◽  
Bacterial Abundance ◽  
Dietary Factors ◽  
Sequencing Data ◽  
Fecal Microbiome ◽  
Obesity Status ◽  
Α Diversity ◽  
In Pregnancy

Maternal body composition, gestational weight gain (GWG) and diet quality influence offspring obesity risk. While the gut microbiome is thought to play a crucial role, it is understudied in pregnancy. Using a longitudinal pregnancy cohort, maternal anthropometrics, body composition, fecal microbiome and dietary intake were assessed at 12, 24 and 36 weeks of gestation. Fecal samples (n = 101, 98 and 107, at each trimester, respectively) were utilized for microbiome analysis via 16S rRNA amplicon sequencing. Data analysis included alpha- and beta-diversity measures and assessment of compositional changes using MaAsLin2. Correlation analyses of serum metabolic and anthropometric markers were performed against bacterial abundance and predicted functional pathways. α-diversity was unaltered by pregnancy stage or maternal obesity status. Actinobacteria, Lachnospiraceae, Akkermansia, Bifidobacterium, Streptococcus and Anaerotuncus abundances were associated with gestation stage. Maternal obesity status was associated with increased abundance of Lachnospiraceae, Bilophila, Dialister and Roseburia. Maternal BMI, fat mass, triglyceride and insulin levels were positively associated with Bilophila. Correlations of bacterial abundance with diet intake showed that Ruminococcus and Paraprevotella were associated with total fat and unsaturated fatty acid intake, while Collinsella and Anaerostipes were associated with protein intake. While causal relationships remain unclear, collectively, these findings indicate pregnancy- and maternal obesity-dependent interactions between dietary factors and the maternal gut microbiome.

scholarly journals Metabolic Phenotypes as Potential Biomarkers for Linking Gut Microbiome With Inflammatory Bowel Diseases

2021 ◽  
Vol 7 ◽  
Author(s):  
Stanislav N. Iablokov ◽  
Natalia S. Klimenko ◽  
Daria A. Efimova ◽  
Tatiana Shashkova ◽  
Pavel S. Novichkov ◽  
...  
Keyword(s):  
Gut Microbiome ◽  
Large Scale ◽  
Disease Risk ◽  
Good Precision ◽  
Sequencing Data ◽  
Metabolic Potential ◽  
Healthy Human ◽  
Shotgun Metagenomics ◽  
New Findings ◽  
Inflammatory Bowel

The gut microbiome is of utmost importance to human health. While a healthy microbiome can be represented by a variety of structures, its functional capacity appears to be more important. Gene content of the community can be assessed by “shotgun” metagenomics, but this approach is still too expensive. High-throughput amplicon-based surveys are a method of choice for large-scale surveys of links between microbiome, diseases, and diet, but the algorithms for predicting functional composition need to be improved to achieve good precision. Here we show how feature engineering based on microbial phenotypes, an advanced method for functional prediction from 16S rRNA sequencing data, improves identification of alterations of the gut microbiome linked to the disease. We processed a large collection of published gut microbial datasets of inflammatory bowel disease (IBD) patients to derive their community phenotype indices (CPI)—high-precision semiquantitative profiles aggregating metabolic potential of the community members based on genome-wide metabolic reconstructions. The list of selected metabolic functions included metabolism of short-chain fatty acids, vitamins, and carbohydrates. The machine-learning approach based on microbial phenotypes allows us to distinguish the microbiome profiles of healthy controls from patients with Crohn's disease and from ones with ulcerative colitis. The classifiers were comparable in quality to conventional taxonomy-based classifiers but provided new findings giving insights into possible mechanisms of pathogenesis. Feature-wise partial dependence plot (PDP) analysis of contribution to the classification result revealed a diversity of patterns. These observations suggest a constructive basis for defining functional homeostasis of the healthy human gut microbiome. The developed features are promising interpretable candidate biomarkers for assessing microbiome contribution to disease risk for the purposes of personalized medicine and clinical trials.

scholarly journals Retinitis pigmentosa is associated with shifts in the gut microbiome

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Oksana Kutsyr ◽  
Lucía Maestre-Carballa ◽  
Mónica Lluesma-Gomez ◽  
Manuel Martinez-Garcia ◽  
Nicolás Cuenca ◽  
...  
Keyword(s):  
Retinitis Pigmentosa ◽  
Gut Microbiome ◽  
Functional Decline ◽  
Retinal Disease ◽  
Photoreceptor Cells ◽  
Amplicon Sequencing ◽  
Rrna Gene ◽  
Sequencing Data ◽  
Alpha And Beta Diversity ◽  
Potential Biomarker

AbstractThe gut microbiome is known to influence the pathogenesis and progression of neurodegenerative diseases. However, there has been relatively little focus upon the implications of the gut microbiome in retinal diseases such as retinitis pigmentosa (RP). Here, we investigated changes in gut microbiome composition linked to RP, by assessing both retinal degeneration and gut microbiome in the rd10 mouse model of RP as compared to control C57BL/6J mice. In rd10 mice, retinal responsiveness to flashlight stimuli and visual acuity were deteriorated with respect to observed in age-matched control mice. This functional decline in dystrophic animals was accompanied by photoreceptor loss, morphologic anomalies in photoreceptor cells and retinal reactive gliosis. Furthermore, 16S rRNA gene amplicon sequencing data showed a microbial gut dysbiosis with differences in alpha and beta diversity at the genera, species and amplicon sequence variants (ASV) levels between dystrophic and control mice. Remarkably, four fairly common ASV in healthy gut microbiome belonging to Rikenella spp., Muribaculaceace spp., Prevotellaceae UCG-001 spp., and Bacilli spp. were absent in the gut microbiome of retinal disease mice, while Bacteroides caecimuris was significantly enriched in mice with RP. The results indicate that retinal degenerative changes in RP are linked to relevant gut microbiome changes. The findings suggest that microbiome shifting could be considered as potential biomarker and therapeutic target for retinal degenerative diseases.

A zero-inflated non-negative matrix factorization for the deconvolution of mixed signals of biological data

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Yixin Kong ◽  
Ariangela Kozik ◽  
Cindy H. Nakatsu ◽  
Yava L. Jones-Hall ◽  
Hyonho Chun
Keyword(s):  
Matrix Factorization ◽  
Factor Model ◽  
R Package ◽  
Biological Data ◽  
Superior Performance ◽  
Sequencing Data ◽  
Fecal Microbiome ◽  
Brain Gene Expression ◽  
Cell Transcriptome ◽  
Non Negative Matrix Factorization

Abstract A latent factor model for count data is popularly applied in deconvoluting mixed signals in biological data as exemplified by sequencing data for transcriptome or microbiome studies. Due to the availability of pure samples such as single-cell transcriptome data, the accuracy of the estimates could be much improved. However, the advantage quickly disappears in the presence of excessive zeros. To correctly account for this phenomenon in both mixed and pure samples, we propose a zero-inflated non-negative matrix factorization and derive an effective multiplicative parameter updating rule. In simulation studies, our method yielded the smallest bias. We applied our approach to brain gene expression as well as fecal microbiome datasets, illustrating the superior performance of the approach. Our method is implemented as a publicly available R-package, iNMF.

scholarly journals Human-lineage-specific genomic elements are associated with neurodegenerative disease and APOE transcript usage

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Zhongbo Chen ◽  
◽  
David Zhang ◽  
Regina H. Reynolds ◽  
Emil K. Gustavsson ◽  
...  
Keyword(s):  
Neurological Diseases ◽  
Purifying Selection ◽  
Whole Genome Sequencing Data ◽  
Human Lineage ◽  
Sequencing Data ◽  
Protein Coding ◽  
Potential Association ◽  
High Depth ◽  
Specific Sequences ◽  
Human Specific

AbstractKnowledge of genomic features specific to the human lineage may provide insights into brain-related diseases. We leverage high-depth whole genome sequencing data to generate a combined annotation identifying regions simultaneously depleted for genetic variation (constrained regions) and poorly conserved across primates. We propose that these constrained, non-conserved regions (CNCRs) have been subject to human-specific purifying selection and are enriched for brain-specific elements. We find that CNCRs are depleted from protein-coding genes but enriched within lncRNAs. We demonstrate that per-SNP heritability of a range of brain-relevant phenotypes are enriched within CNCRs. We find that genes implicated in neurological diseases have high CNCR density, including APOE, highlighting an unannotated intron-3 retention event. Using human brain RNA-sequencing data, we show the intron-3-retaining transcript to be more abundant in Alzheimer’s disease with more severe tau and amyloid pathological burden. Thus, we demonstrate potential association of human-lineage-specific sequences in brain development and neurological disease.

scholarly journals Gut microbiome is affected by inter-sexual and inter-seasonal variation in diet for thick-billed murres (Uria lomvia)

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Esteban Góngora ◽  
Kyle H. Elliott ◽  
Lyle Whyte
Keyword(s):  
Gut Microbiome ◽  
Sulfur Isotopes ◽  
Trophic Position ◽  
Isotope Analysis ◽  
16S Rrna Gene Sequencing ◽  
Rrna Gene ◽  
Uria Lomvia ◽  
Fecal Microbiome ◽  
Prey Specialization ◽  
Rrna Gene Sequencing

AbstractThe role of the gut microbiome is increasingly being recognized by health scientists and veterinarians, yet its role in wild animals remains understudied. Variations in the gut microbiome could be the result of differential diets among individuals, such as variation between sexes, across seasons, or across reproductive stages. We evaluated the hypothesis that diet alters the avian gut microbiome using stable isotope analysis (SIA) and 16S rRNA gene sequencing. We present the first description of the thick-billed murre (Uria lomvia) fecal microbiome. The murre microbiome was dominated by bacteria from the genus Catellicoccus, ubiquitous in the guts of many seabirds. Microbiome variation was explained by murre diet in terms of proportion of littoral carbon, trophic position, and sulfur isotopes, especially for the classes Actinobacteria, Bacilli, Bacteroidia, Clostridia, Alphaproteobacteria, and Gammaproteobacteria. We also observed differences in the abundance of bacterial genera such as Catellicoccus and Cetobacterium between sexes and reproductive stages. These results are in accordance with behavioural observations of changes in diet between sexes and across the reproductive season. We concluded that the observed variation in the gut microbiome may be caused by individual prey specialization and may also be reinforced by sexual and reproductive stage differences in diet.

scholarly journals Effect of Mixed Nuts on Body Weight and Composition, and Gut Microbiome (P08-054-19)

2019 ◽  
Vol 3 (Supplement_1) ◽  
Author(s):  
Shih Lung Woo ◽  
Dina Ben-Nissan ◽  
Zahra Ezzat-Zadeh ◽  
Jieping Yang ◽  
Lijun Zhang ◽  
...  
Keyword(s):  
Weight Loss ◽  
Body Composition ◽  
Body Weight ◽  
Gut Microbiome ◽  
Plasma Lipids ◽  
Plasma Lipid ◽  
Healthy Weight ◽  
Weight Changes ◽  
Fecal Microbiome ◽  
Tree Nuts

Abstract Objectives This study was designed to assess the effects of mixed nut consumption on body weight and composition, and gut microbiome in obese individuals. Primary outcome was change in body weight and composition. Secondary outcomes include gut microbiome composition, inflammatory markers, and plasma lipids. Methods The reported results are from an interim analysis (n = 50) of a randomized, placebo controlled, parallel study. Total enrollment target is 154 overweight/obese subjects (BMI 27–35 kg/m2). Participants were randomly assigned to consume either 1.5oz mixed tree nuts or pretzels with equal calorie content daily for 24 weeks. The study included a 12-week weight loss phase (500 kcal per day less than total daily energy expenditure), followed by a 12-week weight maintenance phase. Body composition, fasting blood, and stool samples were collected at baseline, week 12 and 24. Body composition, and vitals were analyzed, whereas plasma lipid profile, fecal microbiome, and microbiome metabolites analysis is still pending. Results At week 12, subjects from both the pretzel (n = 15, 10 dropouts; P = 0.009) and nut group (n = 22, 3 dropouts; P = 0.038) lost significant amount of weight. The trend of weight changes did not differ between groups (P = 0.530). Subjects from both groups were able to sustain weight loss through 24 weeks (pretzel: 81.43 ± 3.85 kg at baseline vs. 79.43 ± 4.08 kg at week 24, P = 0.028; nut: 84.26 ± 3.78 kg at baseline vs. 82.38 ± 3.72 kg at week 24, P = 0.026). At week 12, fat mass in both groups was significantly decreased (pretzel: P = 0.002; nut: P = 0.012). The trend of fat changes did not differ between groups (P = 0.547). Subjects from both groups were able to sustain fat loss through 24 weeks (pretzel: 30.84 ± 1.75 kg at baseline vs. 29.25 ± 2.12 kg at week 24, P = 0.024; nut: 31.51 ± 1.56 kg vs 30.21 ± 1.81 kg at week 24, P = 0.04). Muscle mass, and blood pressure were not significantly different between both groups. Conclusions Our data suggested that tree nuts could be consumed as part of a healthy weight loss meal plan without concern of causing weight gain. Further analysis of the remaining samples is needed to confirm results. Due to higher dropouts in the pretzel group, future intention-to-treat analysis is also needed to eliminate bias. Funding Sources This study is supported by the International Tree Nut Council.

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