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

BACKGROUND: Higher 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. OBJECTIVES: The purpose of this project was to examine associations between reported dietary intake and markers of GI inflammation in a healthy adult human cohort. METHODS: In 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. RESULTS: Recent 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). CONCLUSIONS: Higher diet quality may be protective against GI inflammation even in healthy adults. This trial was registered at clinicaltrials.gov as NCT02367287.

Association of Estimated Daily Lactose Consumption, Lactase Persistence Genotype (rs4988235), and Gut Microbiota in Healthy Adults in the United States.

BACKGROUND: Lactase persistence (LP) is a heritable trait in which lactose can be digested throughout adulthood. Lactase nonpersistent (LNP) individuals who consume lactose may experience microbial adaptations in response to undigested lactose. OBJECTIVES: The objective of the study was to estimate lactose from foods reported in the Automated Self-Administered 24-Hour Dietary Assessment Tool (ASA24) and determine the interaction between lactose consumption, LP genotype, and gut microbiome in an observational cross-sectional study of healthy adults in the United States (US). METHODS: Average daily lactose consumption was estimated for 279 healthy US adults, genotyped for the lactase gene -13910G>A polymorphism (rs4988235) by matching ASA24-reported foods to foods in the Nutrition Coordinating Center Food and Nutrient Database. Analysis of covariance was used to identify whether the A genotype (LP) influenced lactose and total dairy consumption, with total energy intake and weight as covariates. The 16S rRNA V4/V5 region, amplified from bacterial DNA extracted from each frozen stool sample, was sequenced using Illumina MiSeq (300 bp paired-end) and analyzed using Quantitative Insights Into Microbial Ecology (QIIME)2 (version 2019.10). Differential abundances of bacterial taxa were analyzed using DESeq2 likelihood ratio tests. RESULTS: Across a diverse set of ethnicities, LP subjects consumed more lactose than LNP subjects. Lactobacillaceae abundance was highest in LNP subjects who consumed more than 12.46 g/d (upper tercile). Within Caucasians and Hispanics, family Lachnospiraceae was significantly enriched in the gut microbiota of LNP individuals consuming the upper tercile of lactose across both sexes. CONCLUSIONS: Elevated lactose consumption in individuals with the LNP genotype is associated with increased abundance of family Lactobacillaceae and Lachnospriaceae, taxa that contain multiple genera capable of utilizing lactose. This trial was registered on clinicaltrials.gov as NCT02367287.

Dietary Intake of Monosaccharides from Foods is Associated with Characteristics of the Gut Microbiota and Gastrointestinal Inflammation in Healthy US Adults.

BACKGROUND: Current assessment of dietary carbohydrates does not adequately reflect the nutritional properties and effects on gut microbial structure and function. Deeper characterization of food carbohydrate composition can serve to strengthen the link between diet and gastrointestinal health outcomes. OBJECTIVES: The present study aims to characterize the monosaccharide composition of diets in a healthy US adult cohort and use these features to assess the relationship between monosaccharide intake, diet quality, characteristics of the gut microbiota, and gastrointestinal inflammation. METHODS: This observational, cross-sectional study enrolled males and females across age (18-33 y, 34-49 y, and 50-65 y) and body mass index (normal, 18.5-24.99 kg/m2; overweight, 25-29.99 kg/m2; and obese, 30-44 kg/m2) categories. Recent dietary intake was assessed by the automated self-administered 24-h dietary recall system, and gut microbiota were assessed with shotgun metagenome sequencing. Dietary recalls were mapped to the Davis Food Glycopedia to estimate monosaccharide intake. Participants with >75% of carbohydrate intake mappable to the glycopedia were included (N = 180). RESULTS: Diversity of monosaccharide intake was positively associated with the total Healthy Eating Index score (Pearson's r = 0.520, P = 1.2 × 10-13) and negatively associated with fecal neopterin (Pearson's r = -0.247, P = 3.0 × 10-3). Comparing high with low intake of specific monosaccharides revealed differentially abundant taxa (Wald test, P < 0.05), which was associated with the functional capacity to break down these monomers (Wilcoxon rank-sum test, P < 0.05). CONCLUSIONS: Monosaccharide intake was associated with diet quality, gut microbial diversity, microbial metabolism, and gastrointestinal inflammation in healthy adults. As specific food sources were rich in particular monosaccharides, it may be possible in the future to tailor diets to fine-tune the gut microbiota and gastrointestinal function. This trial is registered at www. CLINICALTRIALS: gov as NCT02367287.

Tree-Based Analysis of Dietary Diversity Captures Associations Between Fiber Intake and Gut Microbiota Composition in a Healthy US Adult Cohort.

BACKGROUND: Diet patterns are a significant and modifiable contributing factor to the composition of the human gut microbiota. OBJECTIVES: We set out to identify reproducible relationships between diet and gut microbial community composition in a diverse, healthy US adult cohort. METHODS: We collected 2 to 3 automated self-administered 24-hour dietary recalls over 10-14 days, together with a single stool sample, from 343 healthy adults in a cross-sectional phenotyping study. This study examined a multi-ethnic cohort balanced for age (18-65 years), sex, and BMI (18.5-45 kg/m2). Dietary data were edited to a tree format according to published methods. The tree structure was annotated with the average total grams of dry weight, fat, protein, carbohydrate, or fiber from each food item reported. The alpha and beta diversity measurements, calculated using the tree structure, were analyzed relative to the microbial community diversity, determined by a Quantitative Insights Into Microbial Ecology (QIIME) 2 analysis of the bacterial 16S ribosomal RNA V4 region, sequenced from stool samples. K-means clustering was used to form groups of individuals consuming similar diets, and gut microbial communities were compared among groups using differential expression analysis for sequence count data. RESULTS: The alpha diversity of diet dry weight was significantly correlated with the gut microbial community alpha diversity (r = 0.171). The correlation improved when diet was characterized using grams of carbohydrates (r = 0.186) or fiber (r = 0.213). Bifidobacterium was enriched with diets containing higher levels of total carbohydrate from cooked grains. Lachnospira, was enriched with diet patterns containing high consumption of fiber from fruits excluding berries. CONCLUSIONS: The tree structure, annotated with grams of carbohydrate, is a robust analysis method for comparing self-reported diet to the gut microbial community composition. This method identified consumption of fiber from fruit robustly associated with an abundance of pectinolytic bacterial genus, Lachnospira, in the guts of healthy adults. This trial was registered at clinicaltrials.gov as NCT02367287.

Machine Learning Identifies Stool pH as a Predictor of Bone Mineral Density in Healthy Multiethnic US Adults.

BACKGROUND: A variety of modifiable and nonmodifiable factors such as ethnicity, age, and diet have been shown to influence bone health. Previous studies are usually limited to analyses focused on the association of a few a priori variables or on a specific subset of the population. OBJECTIVE: Dietary, physiological, and lifestyle data were used to identify directly modifiable and nonmodifiable variables predictive of bone mineral content (BMC) and bone mineral density (BMD) in healthy US men and women using machine-learning models. METHODS: Ridge, lasso, elastic net, and random forest models were used to predict whole-body, femoral neck, and spine BMC and BMD in healthy US men and women ages 18-66 y, with a BMI (kg/m2) of 18-44 (n = 313), using nonmodifiable anthropometric, physiological, and demographic variables; directly modifiable lifestyle (physical activity, tobacco use) and dietary (via FFQ) variables; and variables approximating directly modifiable behavior (circulating 25-hydroxycholecalciferol and stool pH). RESULTS: Machine-learning models using nonmodifiable variables explained more variation in BMC and BMD (highest R2 = 0.75) compared with when using only directly modifiable variables (highest R2 = 0.11). Machine-learning models had better performance compared with multivariate linear regression, which had lower predictive value (highest R2 = 0.06) when using directly modifiable variables only. BMI, body fat percentage, height, and menstruation history were predictors of BMC and BMD. For directly modifiable features, betaine, cholesterol, hydroxyproline, menaquinone-4, dihydrophylloquinone, eggs, cheese, cured meat, refined grains, fruit juice, and alcohol consumption were predictors of BMC and BMD. Low stool pH, a proxy for fermentable fiber intake, was also predictive of higher BMC and BMD. CONCLUSIONS: Modifiable factors, such as diet, explained less variation in the data compared with nonmodifiable factors, such as age, sex, and ethnicity, in healthy US men and women. Low stool pH predicted higher BMC and BMD. This trial was registered at www.clinicaltrials.gov as NCT02367287.

Technician-Scored Stool Consistency Spans the Full Range of the Bristol Scale in a Healthy US Population and Differs by Diet and Chronic Stress Load.

BACKGROUND: Prior studies of adults with constipation or diarrhea suggest that dietary intake, physical activity, and stress may affect stool consistency. However, the influence of these factors is unresolved and has not been investigated in healthy adults. OBJECTIVES: We assessed the relations of technician-scored stool consistency in healthy adults with self-reported diet, objectively monitored physical activity, and quantifiable markers of stress. METHODS: Stool consistency was scored by an independent technician using the Bristol Stool Form Scale (BSFS) to analyze samples provided by healthy adults, aged 18-65 y, BMI 18-44 kg/m2, in the USDA Nutritional Phenotyping Study (n = 364). A subset of participants (n = 109) were also asked to rate their sample using the BSFS. Dietary intake was assessed with two to three 24-h recalls completed at home and energy expenditure from physical activity was monitored using an accelerometer in the 7-d period preceding the stool collection. Stress was measured using the Wheaton Chronic Stress Inventory and allostatic load (AL). Statistical and machine learning analyses were conducted to determine which dietary, physiological, lifestyle, and stress factors differed by stool form. RESULTS: Technician-scored BSFS scores were significantly further (P = 0.003) from the central score (mean ± SEM distance: 1.41 ± 0.089) than the self-reported score (1.06 ± 0.086). Hard stool was associated with higher (P = 0.005) intake of saturated fat (13.8 ± 0.40 g/1000 kcal) than was normal stool (12.5 ± 0.30 g/1000 kcal). AL scores were lower for normal stool (2.49 ± 0.15) than for hard (3.07 ± 0.18) (P = 0.009) or soft stool (2.89 ± 0.18) (P = 0.049). Machine learning analyses revealed that various dietary components, physiological characteristics, and stress hormones predicted stool consistency. CONCLUSIONS: Technician-scored stool consistency differed by dietary intake and stress hormones, but not by physical activity, in healthy adults.This trial was registered at clincialtrials.gov as NCT02367287.

Pre- and post-sequencing recommendations for functional annotation of human fecal metagenomes.

BACKGROUND: Shotgun metagenomes are often assembled prior to annotation of genes which biases the functional capacity of a community towards its most abundant members. For an unbiased assessment of community function, short reads need to be mapped directly to a gene or protein database. The ability to detect genes in short read sequences is dependent on pre- and post-sequencing decisions. The objective of the current study was to determine how library size selection, read length and format, protein database, e-value threshold, and sequencing depth impact gene-centric analysis of human fecal microbiomes when using DIAMOND, an alignment tool that is up to 20,000 times faster than BLASTX. RESULTS: Using metagenomes simulated from a database of experimentally verified protein sequences, we find that read length, e-value threshold, and the choice of protein database dramatically impact detection of a known target, with best performance achieved with longer reads, stricter e-value thresholds, and a custom database. Using publicly available metagenomes, we evaluated library size selection, paired end read strategy, and sequencing depth. Longer read lengths were acheivable by merging paired ends when the sequencing library was size-selected to enable overlaps. When paired ends could not be merged, a congruent strategy in which both ends are independently mapped was acceptable. Sequencing depths of 5 million merged reads minimized the error of abundance estimates of specific target genes, including an antimicrobial resistance gene. CONCLUSIONS: Shotgun metagenomes of DNA extracted from human fecal samples sequenced using the Illumina platform should be size-selected to enable merging of paired end reads and should be sequenced in the PE150 format with a minimum sequencing depth of 5 million merge-able reads to enable detection of specific target genes. Expecting the merged reads to be 180-250 bp in length, the appropriate e-value threshold for DIAMOND would then need to be more strict than the default. Accurate and interpretable results for specific hypotheses will be best obtained using small databases customized for the research question.

SAMSA2: a standalone metatranscriptome analysis pipeline.

BACKGROUND: Complex microbial communities are an area of growing interest in biology. Metatranscriptomics allows researchers to quantify microbial gene expression in an environmental sample via high-throughput sequencing. Metatranscriptomic experiments are computationally intensive because the experiments generate a large volume of sequence data and each sequence must be compared with reference sequences from thousands of organisms. RESULTS: SAMSA2 is an upgrade to the original Simple Annotation of Metatranscriptomes by Sequence Analysis (SAMSA) pipeline that has been redesigned for standalone use on a supercomputing cluster. SAMSA2 is faster due to the use of the DIAMOND aligner, and more flexible and reproducible because it uses local databases. SAMSA2 is available with detailed documentation, and example input and output files along with examples of master scripts for full pipeline execution. CONCLUSIONS: SAMSA2 is a rapid and efficient metatranscriptome pipeline for analyzing large RNA-seq datasets in a supercomputing cluster environment. SAMSA2 provides simplified output that can be examined directly or used for further analyses, and its reference databases may be upgraded, altered or customized to fit the needs of any experiment.

21st century toolkit for optimizing population health through precision nutrition.

Scientific, technological, and economic progress over the last 100 years all but eradicated problems of widespread food shortage and nutrient deficiency in developed nations. But now society is faced with a new set of nutrition problems related to energy imbalance and metabolic disease, which require new kinds of solutions. Recent developments in the area of new analytical tools enable us to systematically study large quantities of detailed and multidimensional metabolic and health data, providing the opportunity to address current nutrition problems through an approach called Precision Nutrition. This approach integrates different kinds of "big data" to expand our understanding of the complexity and diversity of human metabolism in response to diet. With these tools, we can more fully elucidate each individual's unique phenotype, or the current state of health, as determined by the interactions among biology, environment, and behavior. The tools of precision nutrition include genomics, metabolomics, microbiomics, phenotyping, high-throughput analytical chemistry techniques, longitudinal tracking with body sensors, informatics, data science, and sophisticated educational and behavioral interventions. These tools are enabling the development of more personalized and predictive dietary guidance and interventions that have the potential to transform how the public makes food choices and greatly improve population health.

SAMSA: a comprehensive metatranscriptome analysis pipeline.

BACKGROUND: Although 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. RESULTS: Working 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. CONCLUSIONS: By 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.

The human colostrum whey proteome is altered in gestational diabetes mellitus.

Proteomics of human milk has been used to identify the comprehensive cargo of proteins involved in immune and cellular function. Very little is known about the effects of gestational diabetes mellitus (GDM) on lactation and breast milk components. The objective of the current study was to examine the effect of GDM on the expression of proteins in the whey fraction of human colostrum. Colostrum was collected from women who were diagnosed with (n = 6) or without (n = 12) GDM at weeks 24-28 in pregnancy. Colostral whey was analyzed for protein abundances using high-resolution, high-mass accuracy liquid chromatography tandem mass spectrometry. A total of 601 proteins were identified, of which 260 were quantified using label free spectral counting. Orthogonal partial least-squares discriminant analysis identified 27 proteins that best predict GDM. The power law global error model corrected for multiple testing was used to confirm that 10 of the 27 proteins were also statistically significantly different between women with versus without GDM. The identified changes in protein expression suggest that diabetes mellitus during pregnancy has consequences on human colostral proteins involved in immunity and nutrition.

Comparative Proteomics of Human and Macaque Milk Reveals Species-Specific Nutrition during Postnatal Development.

Milk has been well established as the optimal nutrition source for infants, yet there is still much to be understood about its molecular composition. Therefore, our objective was to develop and compare comprehensive milk proteomes for human and rhesus macaques to highlight differences in neonatal nutrition. We developed a milk proteomics technique that overcomes previous technical barriers including pervasive post-translational modifications and limited sample volume. We identified 1606 and 518 proteins in human and macaque milk, respectively. During analysis of detected protein orthologs, we identified 88 differentially abundant proteins. Of these, 93% exhibited increased abundance in human milk relative to macaque and include lactoferrin, polymeric immunoglobulin receptor, alpha-1 antichymotrypsin, vitamin D-binding protein, and haptocorrin. Furthermore, proteins more abundant in human milk compared with macaque are associated with development of the gastrointestinal tract, the immune system, and the brain. Overall, our novel proteomics method reveals the first comprehensive macaque milk proteome and 524 newly identified human milk proteins. The differentially abundant proteins observed are consistent with the perspective that human infants, compared with nonhuman primates, are born at a slightly earlier stage of somatic development and require additional support through higher quantities of specific proteins to nurture human infant maturation.

Changes in intestinal barrier function and gut microbiota in high-fat diet-fed rats are dynamic and region dependent.

A causal relationship between the pathophysiological changes in the gut epithelium and altered gut microbiota with the onset of obesity have been suggested but not defined. The aim of this study was to determine the temporal relationship between impaired intestinal barrier function and microbial dysbiosis in the small and large intestine in rodent high-fat (HF) diet-induced obesity. Rats were fed HF diet (45% fat) or normal chow (C, 10% fat) for 1, 3, or 6 wk; food intake, body weight, and adiposity were measured. Barrier function ex vivo using FITC-labeled dextran (4,000 Da, FD-4) and horseradish peroxidase (HRP) probes in Ussing chambers, gene expression, and gut microbial communities was assessed. After 1 wk, there was an immediate but reversible increase in paracellular permeability, decrease in IL-10 expression, and decrease in abundance of genera within the class Clostridia in the ileum. In the large intestine, HRP flux and abundance of genera within the order Bacteroidales increased with time on the HF diet and correlated with the onset of increased body weight and adiposity. The data show immediate insults in the ileum in response to ingestion of a HF diet, which were rapidly restored and preceded increased passage of large molecules across the large intestinal epithelium. This study provides an understanding of microbiota dysbiosis and gut pathophysiology in diet-induced obesity and has identified IL-10 and Oscillospira in the ileum and transcellular flux in the large intestine as potential early impairments in the gut that might lead to obesity and metabolic disorders.

Bifidobacteria grown on human milk oligosaccharides downregulate the expression of inflammation-related genes in Caco-2 cells.

BACKGROUND: Breastfed human infants are predominantly colonized by bifidobacteria that thrive on human milk oligosaccharides (HMO). Two predominant species of bifidobacteria in infant feces are Bifidobacterium breve (B. breve) and Bifidobacterium longum subsp. infantis (B. infantis), both of which include avid HMO-consumer strains. Our laboratory has previously shown that B. infantis, when grown on HMO, increases adhesion to intestinal cells and increases the expression of the anti-inflammatory cytokine interleukin-10. The purpose of the current study was to investigate the effects of carbon source-glucose, lactose, or HMO-on the ability of B. breve and B. infantis to adhere to and affect the transcription of intestinal epithelial cells on a genome-wide basis. RESULTS: HMO-grown B. infantis had higher percent binding to Caco-2 cell monolayers compared to B. infantis grown on glucose or lactose. B. breve had low adhesive ability regardless of carbon source. Despite differential binding ability, both HMO-grown strains significantly differentially affected the Caco-2 transcriptome compared to their glucose or lactose grown controls. HMO-grown B. breve and B. infantis both downregulated genes in Caco-2 cells associated with chemokine activity. CONCLUSION: The choice of carbon source affects the interaction of bifidobacteria with intestinal epithelial cells. HMO-grown bifidobacteria reduce markers of inflammation, compared to glucose or lactose-grown bifidobacteria. In the future, the design of preventative or therapeutic probiotic supplements may need to include appropriately chosen prebiotics.

Human milk oligosaccharides in premature infants: absorption, excretion, and influence on the intestinal microbiota.

BACKGROUND: Human milk oligosaccharides (HMOs) shape the intestinal microbiota in term infants. In premature infants, alterations in the intestinal microbiota (dysbiosis) are associated with risk of necrotizing enterocolitis (NEC) and sepsis, and the influence of HMOs on the microbiota is unclear. METHODS: Milk, urine, and stool specimens from 14 mother-premature infant dyads were investigated by mass spectrometry for HMO composition. The stools were analyzed by next-generation sequencing to complement a previous analysis. RESULTS: Percentages of fucosylated and sialylated HMOs were highly variable between individuals but similar in urine, feces, and milk within dyads. Differences in urine and fecal HMO composition suggest variability in absorption. Secretor status of the mother correlated with the urine and fecal content of specific HMO structures. Trends toward higher levels of Proteobacteria and lower levels of Firmicutes were noted in premature infants of nonsecretor mothers. Specific HMO structures in the milk, urine, and feces were associated with alterations in fecal Proteobacteria and Firmicutes. CONCLUSION: HMOs may influence the intestinal microbiota in premature infants. Specific HMOs, for example those associated with secretor mothers, may have a protective effect by decreasing pathogens associated with sepsis and NEC, while other HMOs may increase dysbiosis in this population.

Infant age inversely correlates with gut carriage of resistance genes, reflecting modifications in microbial carbohydrate metabolism during early life.

The infant gut microbiome is increasingly recognized as a reservoir of antibiotic resistance genes, yet the assembly of gut resistome in infants and its influencing factors remain largely unknown. We characterized resistome in 4132 metagenomes from 963 infants in six countries and 4285 resistance genes were observed. The inherent resistome pattern of healthy infants (N = 272) could be distinguished by two stages: a multicompound resistance phase (Months 0-7) and a tetracycline-mupirocin-ß-lactam-dominant phase (Months 8-14). Microbial taxonomy explained 40.7% of the gut resistome of healthy infants, with Escherichia (25.5%) harboring the most resistance genes. In a further analysis with all available infants (N = 963), we found age was the strongest influencer on the resistome and was negatively correlated with the overall resistance during the first 3 years (p < 0.001). Using a random-forest approach, a set of 34 resistance genes could be used to predict age (R 2 = 68.0%). Leveraging microbial host inference analyses, we inferred the age-dependent assembly of infant resistome was a result of shifts in the gut microbiome, primarily driven by changes in taxa that disproportionately harbor resistance genes across taxa (e.g., Escherichia coli more frequently harbored resistance genes than other taxa). We performed metagenomic functional profiling and metagenomic assembled genome analyses whose results indicate that the development of gut resistome was driven by changes in microbial carbohydrate metabolism, with an increasing need for carbohydrate-active enzymes from Bacteroidota and a decreasing need for Pseudomonadota during infancy. Importantly, we observed increased acquired resistance genes over time, which was related to increased horizontal gene transfer in the developing infant gut microbiome. In summary, infant age was negatively correlated with antimicrobial resistance gene levels, reflecting a composition shift in the gut microbiome, likely driven by the changing need for microbial carbohydrate metabolism during early life.

Sequencing the transcriptome of milk production: milk trumps mammary tissue.

BACKGROUND: Studies of normal human mammary gland development and function have mostly relied on cell culture, limited surgical specimens, and rodent models. Although RNA extracted from human milk has been used to assay the mammary transcriptome non-invasively, this assay has not been adequately validated in primates. Thus, the objectives of the current study were to assess the suitability of lactating rhesus macaques as a model for lactating humans and to determine whether RNA extracted from milk fractions is representative of RNA extracted from mammary tissue for the purpose of studying the transcriptome of milk-producing cells. RESULTS: We confirmed that macaque milk contains cytoplasmic crescents and that ample high-quality RNA can be obtained for sequencing. Using RNA sequencing, RNA extracted from macaque milk fat and milk cell fractions more accurately represented RNA from mammary epithelial cells (cells that produce milk) than did RNA from whole mammary tissue. Mammary epithelium-specific transcripts were more abundant in macaque milk fat, whereas adipose or stroma-specific transcripts were more abundant in mammary tissue. Functional analyses confirmed the validity of milk as a source of RNA from milk-producing mammary epithelial cells. CONCLUSIONS: RNA extracted from the milk fat during lactation accurately portrayed the RNA profile of milk-producing mammary epithelial cells in a non-human primate. However, this sample type clearly requires protocols that minimize RNA degradation. Overall, we validated the use of RNA extracted from human and macaque milk and provided evidence to support the use of lactating macaques as a model for human lactation.

Lactation and neonatal nutrition: defining and refining the critical questions.

This paper resulted from a conference entitled "Lactation and Milk: Defining and refining the critical questions" held at the University of Colorado School of Medicine from January 18-20, 2012. The mission of the conference was to identify unresolved questions and set future goals for research into human milk composition, mammary development and lactation. We first outline the unanswered questions regarding the composition of human milk (Section I) and the mechanisms by which milk components affect neonatal development, growth and health and recommend models for future research. Emerging questions about how milk components affect cognitive development and behavioral phenotype of the offspring are presented in Section II. In Section III we outline the important unanswered questions about regulation of mammary gland development, the heritability of defects, the effects of maternal nutrition, disease, metabolic status, and therapeutic drugs upon the subsequent lactation. Questions surrounding breastfeeding practice are also highlighted. In Section IV we describe the specific nutritional challenges faced by three different populations, namely preterm infants, infants born to obese mothers who may or may not have gestational diabetes, and infants born to undernourished mothers. The recognition that multidisciplinary training is critical to advancing the field led us to formulate specific training recommendations in Section V. Our recommendations for research emphasis are summarized in Section VI. In sum, we present a roadmap for multidisciplinary research into all aspects of human lactation, milk and its role in infant nutrition for the next decade and beyond.

TaxaHFE: a machine learning approach to collapse microbiome datasets using taxonomic structure.

Motivation: Biologists increasingly turn to machine learning models not just to predict, but to explain. Feature reduction is a common approach to improve both the performance and interpretability of models. However, some biological datasets, such as microbiome data, are inherently organized in a taxonomy, but these hierarchical relationships are not leveraged during feature reduction. We sought to design a feature engineering algorithm to exploit relationships in hierarchically organized biological data. Results: We designed an algorithm, called TaxaHFE, to collapse information-poor features into their higher taxonomic levels. We applied TaxaHFE to six previously published datasets and found, on average, a 90% reduction in the number of features (SD = 5.1%) compared to using the most complete taxonomy. Using machine learning to compare the most resolved taxonomic level (i.e. species) against TaxaHFE-preprocessed features, models based on TaxaHFE features achieved an average increase of 3.47% in receiver operator curve area under the curve. Compared to other hierarchical feature engineering implementations, TaxaHFE introduces the novel ability to consider both categorical and continuous response variables to inform the feature set collapse. Importantly, we find TaxaHFE's ability to reduce hierarchically organized features to a more information-rich subset increases the interpretability of models. Availability and implementation: TaxaHFE is available as a Docker image and as R code at https://github.com/aoliver44/taxaHFE.

Creation of a milk oligosaccharide database, MilkOligoDB, reveals common structural motifs and extensive diversity across mammals.

The carbohydrate fraction of most mammalian milks contains a variety of oligosaccharides that encompass a range of structures and monosaccharide compositions. Human milk oligosaccharides have received considerable attention due to their biological roles in neonatal gut microbiota, immunomodulation, and brain development. However, a major challenge in understanding the biology of milk oligosaccharides across other mammals is that reports span more than 5 decades of publications with varying data reporting methods. In the present study, publications on milk oligosaccharide profiles were identified and harmonized into a standardized format to create a comprehensive, machine-readable database of milk oligosaccharides across mammalian species. The resulting database, MilkOligoDB, includes 3193 entries for 783 unique oligosaccharide structures from the milk of 77 different species harvested from 113 publications. Cross-species and cross-publication comparisons of milk oligosaccharide profiles reveal common structural motifs within mammalian orders. Of the species studied, only chimpanzees, bonobos, and Asian elephants share the specific combination of fucosylation, sialylation, and core structures that are characteristic of human milk oligosaccharides. However, agriculturally important species do produce diverse oligosaccharides that may be valuable for human supplementation. Overall, MilkOligoDB facilitates cross-species and cross-publication comparisons of milk oligosaccharide profiles and the generation of new data-driven hypotheses for future research.