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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.

Advances in natural language processing (NLP) and computer vision are now being applied to many agricultural problems. These techniques take advantage of nontraditional (or nonnumeric) data sources such as text in libraries and images from field operations. However, these techniques could be more powerful if combined with Artificial Intelligence (AI) and numeric sources of data in multimodal pipelines. We present several recent examples, where United States Department of Agriculture (USDA) Agricultural Research Service (ARS) researchers and collaborators are using AI methods with text and images to improve core scientific knowledge, the management of agricultural research, and agricultural practice. NLP enables automated indexing, clustering, and classification for agricultural research project management. We explore two case studies where combining techniques and data sources in new ways could accelerate progress in personalized nutrition and invasive pest detection. One challenge in applying these techniques is the difficulty in obtaining high-quality training data. Other challenges are a lack of machine learning (ML) techniques customized for use and ML skills or experience among researchers and other stakeholders. Initiatives are underway at USDA-ARS to address these challenges.

BackgroundBovine milk oligosaccharides (BMOs) have several demonstrated and hypothesized benefits including roles in cognitive development and antipathogenic activities, making them promising ingredients for infant formulas and nutraceutical applications. BMO extraction from bovine milk is challenged by low concentrations relative to nonbioactive simple sugars like lactose. BMO abundances are known to vary with a cow's lactation stage, breed, and parity, but these characteristics are difficult to modify in existing dairy herds. In contrast, diet modification is an accessible target, and is already known to influence milk yield, lipid content, protein levels, and monosaccharide compositions.ObjectivesTo determine the impact of a low starch high fiber versus a high starch low fiber diet on overall BMO profiles and individual BMO abundances in Holstein dairy cattle.MethodsMilk samples were collected from 59 midlactation Holsteins in a crossover study featuring dietary modification with either a low starch high fiber or high starch low fiber feed. BMO profiles were evaluated by nano-LC quadrupole time-of-flight tandem MS, and differences in BMO abundances between diets were evaluated using linear mixed effects modeling.ResultsA total of 19 BMOs were identified across the sample set, including 4 large fucosylated compounds. Seven BMOs were found to have significantly more positive percent changes in yield-adjusted abundance from the pre-experiment baseline period for milk samples collected during feeding with the low starch high fiber diet compared with the high starch low fiber diet.ConclusionsConsuming the low starch high fiber diet promoted greater overall BMO production than the high starch low fiber diet in a population of midlactation Holsteins. Additionally, this study afforded the opportunity to investigate the impact of other factors potentially influencing BMO abundances, furthering understanding of how dairy herd management practices can positively impact milk composition and support the potential use of BMOs as functional ingredients.

We provide an overview of the Special Issue on current advances, challenges, and opportunities for AI technologies in agriculture. We illustrate the potential of AI using four major components of the food system: production, distribution, consumption, and uncertainty. We recognize that the transformation of agriculture will require new tools to more precisely manage fields to increase production while minimizing the environmental risk to water and air quality. Combining AI with other technologies will be needed to provide effective production management strategies for a given combination of soil, climate, pest complexes, and vegetation. New methods will be needed to determine production limitations, and effective management options. The agricultural enterprise is prime for the use of AI and other technologies if they can be adapted for the unique characteristics of agroecosystems, including variability and directional changes in climate and other global change drivers as well as novel management and policy decisions, and economic market volatility.

BackgroundA more sustainable dairy cow diet was designed that minimizes use of feed components digestible by monogastric animals by increasing the quantity of forages.ObjectivesThis study determined if feeding lactating cows the more sustainable, low-starch and high-fiber (LSHF) diet was associated with changes in raw milk microbiota composition and somatic cell count (SCC).MethodsIn a crossover design, 76 lactating Holstein cows were assigned to an LSHF diet or a high-starch and low-fiber (HSLF) diet, similar to common dairy cow diets in the United States, for 10 wk then placed on the opposite diet for 10 wk. The LSHF diet contained greater quantities of forages, beet pulp, and corn distillers' grain, but contained less canola meal and no high-moisture corn compared with the HSLF diet. Raw milk samples were collected from each cow 4-5 d before intervention and 5 wk into each diet treatment. Within 4 d, additional milk samples were collected for measurement of SCC using Fossmatic 7. The microbial community was determined by sequencing the 16S rRNA gene V4-V5 region and analyzing sequences with QIIME2. After quality filtering, 53 cows remained.ResultsRaw milk microbial communities differed by diet and time. Taxa associated with fiber consumption, such as Lachnospiraceae, Lactobacillus, Bacteroides, and Methanobrevibacter, were enriched with the LSHF diet. Meanwhile, taxa associated with mastitis, such as Pseudomonas, Stenotrophomonas, and Enterobacteriaceae, were enriched with the HSLF diet. Relatedly, an interaction of diet and time was found to impact SCC.ConclusionsIn raw milk, consumption of an LSHF diet compared with an HSLF diet was associated with changes in abundance of microbes previously associated with fiber consumption, udder health, and milk spoilage. Further research is needed to determine if an LSHF diet indeed leads to lower rates of mastitis and milk spoilage, which could benefit the dairy industry.

Successful lactation and the risk for developing breast cancer depend on growth and differentiation of the mammary gland (MG) epithelium that is regulated by ovarian steroids (17β-estradiol [E] and progesterone [P]) and pituitary-derived prolactin (PRL). Given that the MG of pigs share histomorphogenic features present in the normal human breast, we sought to define the transcriptional responses within the MG of pigs following exposure to all combinations of these hormones. Hormone-ablated female pigs were administered combinations of E, medroxyprogesterone 17-acetate (source of P), and either haloperidol (to induce PRL) or 2-bromo-α-ergocryptine. We subsequently monitored phenotypic changes in the MG including mitosis, receptors for E and P (ESR1 and PGR), level of phosphorylated STAT5 (pSTAT5), and the frequency of terminal ductal lobular unit (TDLU) subtypes; these changes were then associated with all transcriptomic changes. Estrogen altered the expression of approximately 20% of all genes that were mostly associated with mitosis, whereas PRL stimulated elements of fatty acid metabolism and an inflammatory response. Several outcomes, including increased pSTAT5, highlighted the ability of E to enhance PRL action. Regression of transcriptomic changes against several MG phenotypes revealed 1669 genes correlated with proliferation, among which 29 were E inducible. Additional gene expression signatures were associated with TDLU formation and the frequency of ESR1 or PGR. These data provide a link between the hormone-regulated genome and phenome of the MG in a species having a complex histoarchitecture like that in the human breast, and highlight an underexplored synergy between the actions of E and PRL during MG development.

OBJECTIVES: A variety of modifiable and non-modifiable 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. The objective of this study was to use dietary, physiological, and lifestyle data to identify directly modifiable and non-modifiable 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 adults (n = 313) using non-modifiable anthropometric, physiological, and demographic variables, directly modifiable lifestyle (physical activity, tobacco use) and dietary (nutrient or food groups intake via food frequency questionnaire) variables, and variables approximating directly modifiable behavior (circulating vitamin D and stool pH). Model feature importances were used to identify variables useful for predicting BMC and BMD. RESULTS: Machine learning models using non-modifiable variables explained more variation in BMC and BMD (highest R(2) = 0.750) compared to when using only directly modifiable variables (highest R(2) = 0.107). Machine learning models had better performance compared to multivariate linear regression, which had lower predictive value (highest R(2 )= 0.063) when using directly modifiable variables only. BMI, body fat %, height, and menstruation history were predictors of BMC and BMD. For the 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: Machine learning models can be used to identify previously unforeseen variables that may contribute to bone health. Modifiable factors explained less variation in the data compared to other features. Low stool pH, which has been shown to be associated with fermentable fiber intake, short chain fatty acid production, and enhanced calcium absorption, was associated with higher BMC and BMD in a healthy US population. FUNDING SOURCES: USDA-ARS

Background A variety of modifiable and non-modifiable 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 non-modifiable variables predictive of bone mineral content (BMC) and bone mineral density (BMD) in healthy U.S. 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 U.S. men and women ages 18-66 y, BMI 18-44 kg/m2 (n = 313) using non-modifiable anthropometric, physiological, and demographic variables, directly modifiable lifestyle (physical activity, tobacco use) and dietary (via food frequency questionnaire) variables, and variables approximating directly-modifiable behavior (circulating 25-hydroxycholecalciferol and stool pH). Results Machine learning models using non-modifiable variables explained more variation in BMC and BMD (highest R2 = 0.75) compared to when using only directly modifiable variables (highest R2 = 0.11). Machine learning models had better performance compared to multivariate linear regression, which had lower predictive value (highest R2 = 0.06) when using directly modifiable variables only. BMI, body fat %, 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. Conclusion Modifiable factors, such as diet, explained less variation in the data compared to non-modifiable factors, such as age, sex, and ethnicity in healthy U.S. men and women. Low stool pH predicted higher BMC and BMD.Clinical trial: registered on clinicaltrials.gov (Identifier: NCT02367287).

Background It has been estimated that at least 3% of the USA population consumes unpasteurized (raw) milk from animal sources, and the demand to legalize raw milk sales continues to increase. However, consumption of raw milk can cause foodborne illness and be a source of bacteria containing transferrable antimicrobial resistance genes (ARGs). To obtain a comprehensive understanding of the microbiome and antibiotic resistome in both raw and processed milk, we systematically analyzed 2034 retail milk samples including unpasteurized milk and pasteurized milk via vat pasteurization, high-temperature-short-time pasteurization, and ultra-pasteurization from the United States using complementary culture-based, 16S rRNA gene, and metagenomic sequencing techniques. Results Raw milk samples had the highest prevalence of viable bacteria which were measured as all aerobic bacteria, coliform, and Escherichia coli counts, and their microbiota was distinct from other types of milk. 16S rRNA gene sequencing revealed that Pseudomonadaceae dominated raw milk with limited levels of lactic acid bacteria. Among all milk samples, the microbiota remained stable with constant bacterial populations when stored at 4 °C. In contrast, storage at room temperature dramatically enriched the bacterial populations present in raw milk samples and, in parallel, significantly increased the richness and abundance of ARGs. Metagenomic sequencing indicated raw milk possessed dramatically more ARGs than pasteurized milk, and a conjugation assay documented the active transfer of blaCMY-2, one ceftazidime resistance gene present in raw milk-borne E. coli, across bacterial species. The room temperature-enriched resistome differed in raw milk from distinct geographic locations, a difference likely associated with regionally distinct milk microbiota. Conclusion Despite advertised “probiotic” effects, our results indicate that raw milk microbiota has minimal lactic acid bacteria. In addition, retail raw milk serves as a reservoir of ARGs, populations of which are readily amplified by spontaneous fermentation. There is an increased need to understand potential food safety risks from improper transportation and storage of raw milk with regard to ARGs.

OBJECTIVES: Lactase persistence (LP), a heritable trait in which lactose can be digested throughout adulthood, is often used to predict dairy intake. However, it is currently unknown how LP relates to daily lactose consumption in healthy US adults. The objective of the study was to estimate lactose from foods reported in the Automated Self-Administered 24-Hour Dietary Assessment Tool (ASA24) and to compare lactose and total dairy consumption in relation to LP genotypes. METHODS: ASA24 dietary data were collected from healthy Caucasians and Hispanics (n = 215) genotyped for the lactase persistence SNP ID: rs4988235. ASA24 outputs servings of dairy but not the nutrient lactose. Lactose was estimated by matching ASA24-reported foods to foods in the Nutrition Coordinating Center Food and Nutrient Database, which outputs lactose. Analysis of covariance was used to identify whether genotype influenced lactose and total dairy consumption with total energy intake and weight as covariates. Pairwise testing was conducted on the adjusted means using the Tukey adjustment to correct for multiple testing. RESULTS: Median lactose consumption for subjects with the AA genotype (homozygous LP) was 12.08 g (min: 0.91 g, max: 81.85 g), 10.05 g (min: 0.46 g, max: 45.82) for subjects with the AG genotype (heterozygous LP), and 8.97 g (min: 0.47, max: 38.96 g) for subjects with the GG genotype (homozygous lactase non-persistent). AA subjects consumed more lactose than GG subjects (P = 0.024). When stratifying by sex, there were no significant differences among genotypes in women, although GG women consumed the least amount of lactose compared to AA and AG women. AA men consumed more lactose than AG (P = 0.028) and GG men (P = 0.032). Subjects with the AA genotype consumed the most, and GG subjects the least amount of dairy. However, there were no significant differences in total dairy consumption among genotypes. CONCLUSIONS: The median amount of lactose consumed daily by healthy adults is 9–12 g/day, with higher consumption by those with an LP genotype. Total dairy intake was not significantly influenced by genotype, highlighting the value in specifically estimating lactose consumption. Maximal lactose intake by lactase non-persisters exceeding 15 g/day suggests alternate routes of lactose catabolism. FUNDING SOURCES: The California Dairy Research Foundation and the USDA-ARS.

OBJECTIVES: Lactase persistence (LP) is a heritable trait in which lactose can be digested into adulthood. Lactase non-persisters (LNP) who consume lactose may experience microbial adaptations in response to the undigested lactose. The objective of this study was to determine the interaction between lactose consumption, LP genotype and gut microbiota in an observational cross-sectional study of healthy U.S. adults. METHODS: ASA24 dietary data and stool samples were collected from healthy U.S. adults genotyped for the lactase persistence SNP ID: rs4988235 (n = 280). Lactose was estimated by matching ASA24-reported foods to foods in the Nutrition Coordinating Center Food and Nutrient Database. The 16S rRNA V4/V5 region, amplified from bacterial DNA extracted from each frozen stool sample, was sequenced using Illumina MiSeq (300bp PE) and analyzed using Qiime 2 (v 2019.10). Bacterial sequence counts present at greater than 0.1% of the total data set were analyzed using DESeq2 and LEfSe. Taxa that were differentially abundant by both analyses at the family or genus level are reported here. RESULTS: On average 246 ng/uL (3.9 – 646.5ng/uL) DNA was obtained from each sample, yielding 21,470 sequences (10,122 – 56,837). LP genotypes were unevenly distributed by ethnicity and Clostridium (family Lachnospiraceae) was significantly enriched in Asian ethnicities. Therefore, only Caucasian and Hispanic participants were grouped as LP (AA or AG genotype) or LNP (GG genotype) for further analysis. The abundance of Roseburia and family Lachnospriaceae was higher in the upper (>12.4g), relative to lower (< 5.78g), tertile of lactose consumption in LNP adults, but not in LP. CONCLUSIONS: Increased abundance of Roseburia, a microbe capable of utilizing lactose, in LNP individuals consuming >12.4 g lactose/day suggests that this genus may metabolize lactose in LNP adults. FUNDING SOURCES: The California Dairy Research Foundation and the United States Department of Agriculture, Agricultural Research Service.

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.

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 (&ldquo, Nutrient-Only&rdquo, ) or the nutrient and food descriptions (&ldquo, Nutrient + Text&rdquo, ). 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.

OBJECTIVES: We examined associations between reported dairy intake and markers of gastrointestinal (GI) inflammation in a healthy human adult population. We hypothesized there would be a negative association of yogurt intake with GI inflammation, suggesting a protective effect, and no associations of total dairy, fluid milk, and cheese intake with GI inflammation. METHODS: Participants completed up to 3 unannounced 24-hour dietary recalls using ASA24 and a Block 2014 Food Frequency Questionnaire (FFQ) to assess recent and habitual intake, respectively. Those who also provided a stool sample were included in this analysis (n = 342). Stool samples were stored on ice immediately after collection and homogenized within 24 hours. Inflammatory markers from stool, including calprotectin, neopterin, and myeloperoxidase were measured by ELISA along with LPS-binding protein (LBP) from plasma. Regression models tested associations between dairy intake variables and inflammatory markers with and without covariates: sex, age, and body mass index (BMI). As yogurt is episodically consumed, t-tests were used to examine differences in inflammatory marker levels between consumers (>1 cup per month reported in FFQ) and non-consumers. Each dairy variable was also expressed as a percentage of mean energy intake from 24-hour recalls and regressed with inflammatory markers. RESULTS: Without covariates, we found no associations between total dairy, fluid milk, and yogurt from both dietary assessments with any inflammatory markers. Cheese intake reported in the FFQ was positively associated with plasma LBP (P = 0.02). However, this association was not significant after covariate adjustment. There were no significant differences in GI inflammatory marker levels between yogurt consumers and non-consumers (P > 0.05). When expressed as a percentage of mean energy intake, cheese from ASA24 was associated with increased LBP (P = 0.03), but this was not significant after adjustment for covariates. There were no other associations of dairy variables as a percentage of energy intake with GI inflammatory markers. CONCLUSIONS: We found no clinically relevant associations between dairy intake and markers of GI inflammation in a healthy human adult cohort. FUNDING SOURCES: California Dairy Research Foundation, USDA ARS 2032–51,530-026–00D.

Allometric growth of ducts in the mammary glands (MGs) is widely held to be estrogen dependent. We previously discovered that the dietary fatty acid trans-10, cis-12 conjugated linoleic acid (CLA) stimulates estrogen-independent allometric growth and terminal end bud formation in ovariectomized mice. Given the similar phenotype induced by estrogen and CLA, we investigated the shared and/or divergent mechanisms underlying these changes. We confirmed MG growth induced by CLA is temporally distinct from that elicited by estrogen. We then used RNA sequencing to compare the transcriptome of the MG during similar proliferative and morphological states. Both estrogen and CLA affected the genes involved in proliferation. The transcriptome for estrogen-treated mice included canonical estrogen-induced genes, including Pgr, Areg, and Foxa1. In contrast, their expression was unchanged by CLA. However, CLA, but not estrogen, altered expression of a unique set of inflammation-associated genes, consistent with stromal changes. This CLA-altered signature included increased expression of epidermal growth factor receptor (EGFR) pathway components, consistent with the demonstration that CLA-induced MG growth is EGFR dependent. Our findings highlight a unique role for diet-induced inflammation that underlies estrogen-independent MG development.

Background Idiopathic chronic diarrhea (ICD) is a common cause of morbidity and mortality among juvenile rhesus macaques. Characterized by chronic inflammation of the colon and repeated bouts of diarrhea, ICD is largely unresponsive to medical interventions, including corticosteroid, antiparasitic, and antibiotic treatments. Although ICD is accompanied by large disruptions in the composition of the commensal gut microbiome, no single pathogen has been concretely identified as responsible for the onset and continuation of the disease. Results Fecal samples were collected from 12 ICD-diagnosed macaques and 12 age- and sex-matched controls. RNA was extracted for metatranscriptomic analysis of organisms and functional annotations associated with the gut microbiome. Bacterial, fungal, archaeal, protozoan, and macaque (host) transcripts were simultaneously assessed. ICD-afflicted animals were characterized by increased expression of host-derived genes involved in inflammation and increased transcripts from bacterial pathogens such as Campylobacter and Helicobacter and the protozoan Trichomonas. Transcripts associated with known mucin-degrading organisms and mucin-degrading enzymes were elevated in the fecal microbiomes of ICD-afflicted animals. Assessment of colon sections using immunohistochemistry and of the host transcriptome suggests differential fucosylation of mucins between control and ICD-afflicted animals. Interrogation of the metatranscriptome for fucose utilization genes reveals possible mechanisms by which opportunists persist in ICD. Bacteroides sp. potentially cross-fed fucose to Haemophilus whereas Campylobacter expressed a mucosa-associated transcriptome with increased expression of adherence genes. Conclusions The simultaneous profiling of bacterial, fungal, archaeal, protozoan, and macaque transcripts from stool samples reveals that ICD of rhesus macaques is associated with increased gene expression by pathogens, increased mucin degradation, and altered fucose utilization. The data suggest that the ICD-afflicted host produces fucosylated mucins that are leveraged by potentially pathogenic microbes as a carbon source or as adhesion sites.

Over half of all children with autism spectrum disorders (ASD) have gastrointestinal (GI) co-morbidities including chronic constipation, diarrhea, and irritable bowel syndrome. The severity of these symptoms has been correlated with the degree of GI microbial dysbiosis. The study objective was to assess tolerability of a probiotic (Bifidobacterium infantis) in combination with a bovine colostrum product (BCP) as a source of prebiotic oligosaccharides and to evaluate GI, microbiome and immune factors in children with ASD and GI co-morbidities. This pilot study is a randomized, double blind, controlled trial of combination treatment (BCP + B. infantis) vs. BCP alone in a cross-over study in children ages 2-11 with ASD and GI co-morbidities (n = 8). This 12-week study included 5 weeks of probiotic-prebiotic supplementation, followed by a two-week washout period, and 5 weeks of prebiotic only supplementation. The primary outcome of tolerability was assessed using validated questionnaires of GI function and atypical behaviors, along with side effects. Results suggest that the combination treatment is well-tolerated in this cohort. The most common side effect was mild gassiness. Some participants on both treatments saw a reduction in the frequency of certain GI symptoms, as well as reduced occurrence of particular aberrant behaviors. Improvement may be explained by a reduction in IL-13 and TNF-α production in some participants. Although limited conclusions can be drawn from this small pilot study, the results support the need for further research into the efficacy of these treatments.

BACKGROUND: Automated dietary assessment tools such as ASA24(®) are useful for collecting 24-hour recall data in large-scale studies. Modifications made during manual data cleaning may affect nutrient intakes. OBJECTIVES: We evaluated the effects of modifications made during manual data cleaning on nutrient intakes of interest: energy, carbohydrate, total fat, protein, and fiber. METHODS: Differences in mean intake before and after data cleaning modifications for all recalls and average intakes per subject were analyzed by paired t-tests. The Chi-squared test was used to determine whether unsupervised recalls had more open-ended text responses that required modification than supervised recalls. We characterized food types of text response modifications. Correlations between predictive energy requirements, measured total energy expenditure (TEE), and mean energy intake from raw and modified data were examined. RESULTS: After excluding 11 recalls with invalidating technical errors, 1499 valid recalls completed by 393 subjects were included in this analysis. We found significant differences before and after modifications for energy, carbohydrate, total fat, and protein intakes for all recalls (P < 0.05). Limiting to modified recalls, there were significant differences for all nutrients of interest, including fiber (P < 0.02). There was not a significantly greater proportion of text responses requiring modification for home compared with supervised recalls (P = 0.271). Predicted energy requirements correlated highly with TEE. There was no significant difference in correlation of mean energy intake with TEE for modified compared with raw data. Mean intake for individual subjects was significantly different for energy, protein, and fat intakes following cleaning modifications (P < 0.001). CONCLUSIONS: Manual modifications can change mean nutrient intakes for an entire cohort and individuals. However, modifications did not significantly affect the correlation of energy intake with predictive requirements and measured expenditure. Investigators can consider their research question and nutrients of interest when deciding to make cleaning modifications.

OBJECTIVES: To characterize the effect of manual data cleaning on individual dietary intake assessment using Automated Self-Administered 24-hour recalls(®) (ASA24s). We aimed to determine whether there was a difference in measures of key nutrients (energy, carbohydrate, fat, protein, fiber) before and after manual correction of automated coding errors. METHODS: Dietary recalls up to 4 days were collected using ASA24. Data cleaning was completed by systematically reviewing and excluding recalls with technical errors identified by a quality control team. A registered dietitian reviewed open-ended text responses and corrected nutrient data for foods where automated coding did not produce an optimal match. Total energy expenditure (TEE) was calculated using measurements of physical activity, resting metabolic rate, and estimated thermic effect of food. The Wilcoxon signed rank test was used to determine differences in median nutrient measures. Mean energy intake was compared to TEE using Spearman correlation. RESULTS: After removing recalls that were incomplete or with technical errors, 1499 recalls were completed by 393 subjects. Recalls modified during the data cleaning process constitute 209 (14%) from 140 subjects with at least 1 recall modified (36%). Using all recalls, median intakes of energy, total fat, protein, and fiber were different after data cleaning compared to before (P 0.05). However, for the 140 subjects with at least one modified recall, significant differences were found for energy, fat, and protein of mean data (P

BackgroundComplex microbial communities are an area of rapid growth in biology. Metatranscriptomics allows one to investigate the gene activity in an environmental sample via high-throughput sequencing. Metatranscriptomic experiments are computationally intensive because the experiments generate a large volume of sequence data and the sequences must be compared with many references.ResultsHere we present SAMSA2, an upgrade to the original Simple Annotation of Metatranscriptomes by Sequence Analysis (SAMSA) pipeline that has been redesigned for 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.ConclusionsUsing publicly available example data, we demonstrate that SAMSA2 is a rapid and efficient metatranscriptome pipeline for analyzing large paired-end 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 specifics of any experiment.

Collaboration among scientists has a major influence on scientific progress. Such collaboration often results from scientific meetings, where scientists gather to present and discuss their research and to meet potential collaborators. However, most scientific meetings have inherent biases, such as the availability of research funding or the selection bias of professional societies that make it difficult to study the effect of the meeting per se on scientific productivity. To evaluate the effects of scientific meetings on collaboration and progress independent of these biases, we conducted a study of the annual symposia held by the International Milk Genomics Consortium (IMGC) over a 12-year period. In our study, we conducted permutation testing to analyze the effectiveness of the IMGC in facilitating collaboration and productivity in a community of milk scientists who were meeting attendees relative to non-attendees. Using the number of co-authorships on published papers as a measure of collaboration, our analysis revealed that scientists who attended the symposium were associated with more collaboration than were scientists who did not attend. Furthermore, we evaluated the scientific progress of consortium attendees by analyzing publication rate and article impact. We found that IMGC attendees, in addition to being more collaborative, were also more productive and influential than were non-attendees who published in the same field. The results of our study suggest that the annual symposium encouraged interactions among disparate scientists and increased research productivity, exemplifying the positive effect of scientific meetings on both collaboration and progress.

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.

Scientific, technological, and economic progress over the last 100years 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.

Multiple factors help shape the infant intestinal microbiota early in life. Environmental conditions such as the presence of bioactive molecules from breast milk dictate gut microbial growth and survival. Infants also receive distinct, personalized, bacterial exposures leading to differential colonization. Microbial exposures and gut environmental conditions differ between infants in different locations, as does the typical microbial community structure in an infant’s gut. Here we evaluate potential influences on the infant gut microbiota through a longitudinal study on cohorts of breast-fed infants from the neighboring countries of Armenia and Georgia, an area of the world for which the infant microbiome has not been previously investigated. Marker gene sequencing of 16S ribosomal genes revealed that the gut microbial communities of infants from these countries were dominated by bifidobacteria, were different from each other, and were marginally influenced by their mother’s secretor status. Species-level differences in the bifidobacterial communities of each country and birth method were also observed. These community differences suggest that environmental variation between individuals in different locations may influence the gut microbiota of infants.

Mechanistic research suggests a unique evolutionary relationship between complex milk oligosaccharides and cognate bifidobacteria enriched in breast-fed infants. Bovine milk oligosaccharides (BMO) were recently identified as structurally and functionally similar to human milk oligosaccharides. The present single-blind three-way crossover study is the first to determine the safety and tolerability of BMO consumption by healthy human participants (n 12) and its effects on faecal microbiota and microbial metabolism. Participants consumed each supplement (placebo-control; low- and high-BMO doses) for eleven consecutive days, followed by a 2-week washout period prior to initiating the next supplement arm. Low and high BMO doses were consumed as 25 and 35 % of each individual's daily fibre intake, respectively. Safety and tolerability were measured using standardised questionnaires on gut and stomach discomfort and stool consistency. Faecal extracts were profiled for bacterial populations by next-generation sequencing (NGS) and bifidobacteria presence was confirmed using quantitative PCR. Urine was analysed for changes in microbial metabolism using nuclear magnetic resonance spectroscopy (1H-NMR). Consumption of both the low and high BMO doses was well tolerated and did not change stool consistency from baseline. Multivariate analysis of the NGS results demonstrated no change in faecal microbiota phyla among the placebo-control and BMO supplement groups. In conclusion, BMO supplementation was well tolerated in healthy adults and has the potential to shift faecal microbiota toward beneficial strains as part of a synbiotic treatment with probiotic cultures that selectively metabolise oligosaccharides.

White blood cells are among the first responders to dietary components and their metabolites absorbed from the gut. The objective of this study was to determine the whole blood transcriptome response to high-fat challenge meals. A total of 45 fasting and postprandial (3-h and 6-h) whole blood transcriptomes from 5 subjects in a crossover intervention trial of a high-fat meal supplemented with placebo, blueberry powder or docosahexaenoic acid (DHA) were analyzed using RNA sequencing. Select target genes were validated by quantitative reverse-transcription polymerase chain reaction in 180 samples from 20 subjects. The largest contributor to variance was the subject (13,856 genes differentially expressed), followed by the subject on a specific day (2276 genes), followed by the subject's postprandial response (651 genes). After determining the nonsignificance of individual dietary treatments (blueberry, DHA, placebo), treatments were used as replicates to examine postprandial responses to a high-fat meal. The universal postprandial response (95 genes) was associated with lipid utilization, fatty acid beta-oxidation and circadian rhythms. Subject-specific postprandial responses were enriched for genes involved in the innate immune response, particularly those of pattern recognition receptors and their downstream signaling components. Genes involved in innate immune responses are differentially expressed in a subject-specific and time-dependent manner in response to the high-fat meals. These genes can serve as biomarkers to assess individual responsiveness to a high-fat diet in inducing postprandial inflammation. Furthermore, the dynamic temporal change in gene expression in postprandial blood suggests that monitoring these genes at multiple time points is necessary to reveal responders to dietary intervention.