Comparative transcriptomics reveals key differences in the response to milk oligosaccharides of infant gut-associated bifidobacteria.

Breast milk enhances the predominance of Bifidobacterium species in the infant gut, probably due to its large concentration of human milk oligosaccharides (HMO). Here we screened infant-gut isolates of Bifidobacterium longum subsp. infantis and Bifidobacterium bifidum using individual HMO, and compared the global transcriptomes of representative isolates on major HMO by RNA-seq. While B. infantis displayed homogeneous HMO-utilization patterns, B. bifidum were more diverse and some strains did not use fucosyllactose (FL) or sialyllactose (SL). Transcriptomes of B. bifidum SC555 and B. infantis ATCC 15697 showed that utilization of pooled HMO is similar to neutral HMO, while transcriptomes for growth on FL were more similar to lactose than HMO in B. bifidum. Genes linked to HMO-utilization were upregulated by neutral HMO and SL, but not by FL in both species. In contrast, FL induced the expression of alternative gene clusters in B. infantis. Results also suggest that B. bifidum SC555 does not utilize fucose or sialic acid from HMO. Surprisingly, expression of orthologous genes differed between both bifidobacteria even when grown on identical substrates. This study highlights two major strategies found in Bifidobacterium species to process HMO, and presents detailed information on the close relationship between HMO and infant-gut bifidobacteria

Nutrient Estimation from 24-Hour Food Recalls Using Machine Learning and Database Mapping: A Case Study with Lactose.

The Automated Self-Administered 24-Hour Dietary Assessment Tool (ASA24) is a free dietary recall system that outputs fewer nutrients than the Nutrition Data System for Research (NDSR). NDSR uses the Nutrition Coordinating Center (NCC) Food and Nutrient Database, both of which require a license. Manual lookup of ASA24 foods into NDSR is time-consuming but currently the only way to acquire NCC-exclusive nutrients. Using lactose as an example, we evaluated machine learning and database matching methods to estimate this NCC-exclusive nutrient from ASA24 reports. ASA24-reported foods were manually looked up into NDSR to obtain lactose estimates and split into training (n = 378) and test (n = 189) datasets. Nine machine learning models were developed to predict lactose from the nutrients common between ASA24 and the NCC database. Database matching algorithms were developed to match NCC foods to an ASA24 food using only nutrients ("Nutrient-Only") or the nutrient and food descriptions ("Nutrient + Text"). For both methods, the lactose values were compared to the manual curation. Among machine learning models, the XGB-Regressor model performed best on held-out test data (R2 = 0.33). For the database matching method, Nutrient + Text matching yielded the best lactose estimates (R2 = 0.76), a vast improvement over the status quo of no estimate. These results suggest that computational methods can successfully estimate an NCC-exclusive nutrient for foods reported in ASA24

SAMSA: a comprehensive metatranscriptome analysis pipeline

BackgroundAlthough metatranscriptomics-the study of diverse microbial population activity based on RNA-seq data-is rapidly growing in popularity, there are limited options for biologists to analyze this type of data. Current approaches for processing metatranscriptomes rely on restricted databases and a dedicated computing cluster, or metagenome-based approaches that have not been fully evaluated for processing metatranscriptomic datasets. We created a new bioinformatics pipeline, designed specifically for metatranscriptome dataset analysis, which runs in conjunction with Metagenome-RAST (MG-RAST) servers. Designed for use by researchers with relatively little bioinformatics experience, SAMSA offers a breakdown of metatranscriptome transcription activity levels by organism or transcript function, and is fully open source. We used this new tool to evaluate best practices for sequencing stool metatranscriptomes.ResultsWorking with the MG-RAST annotation server, we constructed the Simple Annotation of Metatranscriptomes by Sequence Analysis (SAMSA) software package, a complete pipeline for the analysis of gut microbiome data. SAMSA can summarize and evaluate raw annotation results, identifying abundant species and significant functional differences between metatranscriptomes. Using pilot data and simulated subsets, we determined experimental requirements for fecal gut metatranscriptomes. Sequences need to be either long reads (longer than 100 bp) or joined paired-end reads. Each sample needs 40-50 million raw sequences, which can be expected to yield the 5-10 million annotated reads necessary for accurate abundance measures. We also demonstrated that ribosomal RNA depletion does not equally deplete ribosomes from all species within a sample, and remaining rRNA sequences should be discarded. Using publicly available metatranscriptome data in which rRNA was not depleted, we were able to demonstrate that overall organism transcriptional activity can be measured using mRNA counts. We were also able to detect significant differences between control and experimental groups in both organism transcriptional activity and specific cellular functions.ConclusionsBy making this new pipeline publicly available, we have created a powerful new tool for metatranscriptomics research, offering a new method for greater insight into the activity of diverse microbial communities. We further recommend that stool metatranscriptomes be ribodepleted and sequenced in a 100 bp paired end format with a minimum of 40 million reads per sample

Lower Diet Quality Associated with Subclinical Gastrointestinal Inflammation in Healthy United States Adults

BackgroundHigher diet quality has been associated with lower risk of developing inflammatory bowel disease, but associations between diet and gastrointestinal (GI) inflammation in healthy adults prior to disease onset are understudied.ObjectivesThe purpose of this project was to examine associations between reported dietary intake and markers of GI inflammation in a healthy adult human cohort.MethodsIn a cross-sectional observational trial of 358 healthy adults, participants completed ≤3 unannounced 24-h dietary recalls using the Automated Self-Administered Dietary Assessment Tool and a Block 2014 Food Frequency Questionnaire to assess recent and habitual intake, respectively. Those who provided a stool sample were included in this analysis. Inflammation markers from stool, including calprotectin, neopterin, and myeloperoxidase, were measured by ELISA along with LPS-binding protein from plasma.ResultsRecent and habitual fiber intake was negatively correlated with fecal calprotectin concentrations (n = 295, P = 0.011, 0.009). Habitual soluble fiber intake was also negatively correlated with calprotectin (P = 0.01). Recent and habitual legume and vegetable intake was negatively correlated with calprotectin (P = 0.013, 0.026, 0.01, 0.009). We observed an inverse correlation between recent Healthy Eating Index (HEI) scores and calprotectin concentrations (n = 295, P = 0.026). Dietary Inflammatory Index scores were calculated and positively correlated with neopterin for recent intake (n = 289, P = 0.015). When participants with clinically elevated calprotectin were excluded, recent and habitual fiber, legume, vegetable, and fruit intake were negatively correlated with calprotectin (n = 253, P = 0.00001, 0.0002, 0.045, 0.001, 0.009, 0.001, 0.004, 0.014). Recent total HEI score was inversely correlated with subclinical calprotectin (P = 0.003).ConclusionsHigher diet quality may be protective against GI inflammation even in healthy adults. This trial was registered at clinicaltrials.gov as NCT02367287

The fecal resistome of dairy cattle is associated with diet during nursing.

Antimicrobial resistance is a global public health concern, and livestock play a significant role in selecting for resistance and maintaining such reservoirs. Here we study the succession of dairy cattle resistome during early life using metagenomic sequencing, as well as the relationship between resistome, gut microbiota, and diet. In our dataset, the gut of dairy calves serves as a reservoir of 329 antimicrobial resistance genes (ARGs) presumably conferring resistance to 17 classes of antibiotics, and the abundance of ARGs declines gradually during nursing. ARGs appear to co-occur with antibacterial biocide or metal resistance genes. Colostrum is a potential source of ARGs observed in calves at day 2. The dynamic changes in the resistome are likely a result of gut microbiota assembly, which is closely associated with diet transition in dairy calves. Modifications in the resistome may be possible via early-life dietary interventions to reduce overall antimicrobial resistance

Gene regulatory networks in lactation: identification of global principles using bioinformatics

<p>Abstract</p> <p>Background</p> <p>The molecular events underlying mammary development during pregnancy, lactation, and involution are incompletely understood.</p> <p>Results</p> <p>Mammary gland microarray data, cellular localization data, protein-protein interactions, and literature-mined genes were integrated and analyzed using statistics, principal component analysis, gene ontology analysis, pathway analysis, and network analysis to identify global biological principles that govern molecular events during pregnancy, lactation, and involution.</p> <p>Conclusion</p> <p>Several key principles were derived: (1) nearly a third of the transcriptome fluctuates to build, run, and disassemble the lactation apparatus; (2) genes encoding the secretory machinery are transcribed prior to lactation; (3) the diversity of the endogenous portion of the milk proteome is derived from fewer than 100 transcripts; (4) while some genes are differentially transcribed near the onset of lactation, the lactation switch is primarily post-transcriptionally mediated; (5) the secretion of materials during lactation occurs not by up-regulation of novel genomic functions, but by widespread transcriptional suppression of functions such as protein degradation and cell-environment communication; (6) the involution switch is primarily transcriptionally mediated; and (7) during early involution, the transcriptional state is partially reverted to the pre-lactation state. A new hypothesis for secretory diminution is suggested – milk production gradually declines because the secretory machinery is not transcriptionally replenished. A comprehensive network of protein interactions during lactation is assembled and new regulatory gene targets are identified. Less than one fifth of the transcriptionally regulated nodes in this lactation network have been previously explored in the context of lactation. Implications for future research in mammary and cancer biology are discussed.</p

Diet, microbiome, and inflammation predictors of fecal and plasma short-chain fatty acids in humans

BackgroundGut microbes produce short-chain fatty acids (SCFAs), which are associated with broad health benefits. However, it is not fully known how diet and/or the gut microbiome could be modulated to improve SCFA production.ObjectivesThe objective of this study was to identify dietary, inflammatory, and/or microbiome predictors of SCFAs in a cohort of healthy adults.MethodsSCFAs were measured in fecal and plasma samples from 359 healthy adults in the United States Department of Agriculture Nutritional Phenotyping Study. Habitual and recent diet was assessed using a food frequency questionnaire and automated self-administered 24-h dietary assessment tool dietary recalls. Markers of systemic and gut inflammation were measured in fecal and plasma samples. The gut microbiome was assessed using shotgun metagenomics. Using statistics and machine learning, we determined how the abundance and composition of SCFAs varied with measures of diet, inflammation, and the gut microbiome.ResultsWe show that fecal pH may be a good proxy for fecal SCFA abundance. A higher healthy eating index for a habitual diet was associated with a compositional increase in fecal butyrate relative to acetate and propionate. SCFAs were associated with markers of subclinical gastrointestinal (GI) inflammation. Fecal SCFA abundance was inversely related to plasma lipopolysaccharide-binding protein. When we analyzed hierarchically organized diet and microbiome data with taxonomy-aware algorithms, we observed that diet and microbiome features were far more predictive of fecal SCFA abundances compared to plasma SCFA abundances. The top diet and microbiome predictors of fecal butyrate included potatoes and the thiamine biosynthesis pathway, respectively.ConclusionsThese results suggest that resistant starch in the form of potatoes and microbially produced thiamine provide a substrate and essential cofactor, respectively, for butyrate synthesis. Thiamine may be a rate-limiting nutrient for butyrate production in adults. Overall, these findings illustrate the complex biology underpinning SCFA production in the gut. This trial was registered at clinicaltrials.gov as NCT02367287