Fragile X-associated tremor ataxia syndrome (FXTAS) results from a CGG repeat expansion in the 5′ UTR of FMR1. This repeat is thought to elicit toxicity as RNA, yet disease brains contain ...ubiquitin-positive neuronal inclusions, a pathologic hallmark of protein-mediated neurodegeneration. We explain this paradox by demonstrating that CGG repeats trigger repeat-associated non-AUG-initiated (RAN) translation of a cryptic polyglycine-containing protein, FMRpolyG. FMRpolyG accumulates in ubiquitin-positive inclusions in Drosophila, cell culture, mouse disease models, and FXTAS patient brains. CGG RAN translation occurs in at least two of three possible reading frames at repeat sizes ranging from normal (25) to pathogenic (90), but inclusion formation only occurs with expanded repeats. In Drosophila, CGG repeat toxicity is suppressed by eliminating RAN translation and enhanced by increased polyglycine protein production. These studies expand the growing list of nucleotide repeat disorders in which RAN translation occurs and provide evidence that RAN translation contributes to neurodegeneration.
•CGG repeats in the 5′ UTR of FMR1 elicit AUG-independent (RAN) translation•This produces an aggregation-prone polyglycine protein found in patients•CGG RAN translation explains pathologic differences in FXTAS mice•CGG RAN translation is critical for CGG repeat toxicity in fly disease models
CGG repeat expansions underlie the neurodegenerative disorder fragile X-associated tremor ataxia syndrome. Todd et al. describe how CGG repeats trigger non-AUG-initiated translation, producing a polyglycine protein that accumulates in FXTAS brains and contributes to toxicity in model systems.
The human gut is inhabited by trillions of microorganisms composing a dynamic ecosystem implicated in health and disease. The composition of the gut microbiota is unique to each individual and tends ...to remain relatively stable throughout life, yet daily transient fluctuations are observed. Diet is a key modifiable factor influencing the composition of the gut microbiota, indicating the potential for therapeutic dietary strategies to manipulate microbial diversity, composition, and stability. While diet can induce a shift in the gut microbiota, these changes appear to be temporary. Whether prolonged dietary changes can induce permanent alterations in the gut microbiota is unknown, mainly due to a lack of long-term human dietary interventions, or long-term follow-ups of short-term dietary interventions. It is possible that habitual diets have a greater influence on the gut microbiota than acute dietary strategies. This review presents the current knowledge around the response of the gut microbiota to short-term and long-term dietary interventions and identifies major factors that contribute to microbiota response to diet. Overall, further research on long-term diets that include health and microbiome measures is required before clinical recommendations can be made for dietary modulation of the gut microbiota for health.
Diet is a key determinant of human gut microbiome variation. However, the fine-scale relationships between daily food choices and human gut microbiome composition remain unexplored. Here, we used ...multivariate methods to integrate 24-h food records and fecal shotgun metagenomes from 34 healthy human subjects collected daily over 17 days. Microbiome composition depended on multiple days of dietary history and was more strongly associated with food choices than with conventional nutrient profiles, and daily microbial responses to diet were highly personalized. Data from two subjects consuming only meal replacement beverages suggest that a monotonous diet does not induce microbiome stability in humans, and instead, overall dietary diversity associates with microbiome stability. Our work provides key methodological insights for future diet-microbiome studies and suggests that food-based interventions seeking to modulate the gut microbiota may need to be tailored to the individual microbiome. Trial Registration: ClinicalTrials.gov: NCT03610477.
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•Daily microbiome variation is related to food choices, but not to conventional nutrients•Daily microbiome variation depends on at least two days of dietary history•Similar foods have different effects on different people’s microbiomes
Dietary intake is often considered to be a driver of microbiome variation. Johnson et al. use longitudinal sampling and daily dietary records to model microbiome changes in response to diet and find that microbiome responses to diet are personalized.
The gut microbiome has been implicated in multiple human chronic gastrointestinal (GI) disorders. Determining its mechanistic role in disease has been difficult due to apparent disconnects between ...animal and human studies and lack of an integrated multi-omics view of disease-specific physiological changes. We integrated longitudinal multi-omics data from the gut microbiome, metabolome, host epigenome, and transcriptome in the context of irritable bowel syndrome (IBS) host physiology. We identified IBS subtype-specific and symptom-related variation in microbial composition and function. A subset of identified changes in microbial metabolites correspond to host physiological mechanisms that are relevant to IBS. By integrating multiple data layers, we identified purine metabolism as a novel host-microbial metabolic pathway in IBS with translational potential. Our study highlights the importance of longitudinal sampling and integrating complementary multi-omics data to identify functional mechanisms that can serve as therapeutic targets in a comprehensive treatment strategy for chronic GI diseases.
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•Longitudinal sampling limits heterogeneity seen in cross-sectional microbiome studies•Alteration in the gut microbiome and microbial metabolites underlie IBS and symptom flares•Data integration reveals effect of microbial metabolites on gastrointestinal function•Purine starvation is identified as a possible therapeutic target in IBS
Integrated and longitudinal multiomic analyses of patients with irritable bowel syndrome reveals a role for the gut microbiota in modulating purine metabolism and influencing host gastrointestinal function.
There are few resources available for researchers aiming to conduct 24-h dietary record and recall analysis using R.
We aimed to develop DietDiveR, which is a toolkit of functions written in R for ...the analysis of recall or record data collected with the Automated Self-Administered 24-h Dietary Assessment Tool or 2-d 24-h dietary recall data from the National Health and Nutrition Examination Survey (NHANES). The R functions are intended for food and nutrition researchers who are not computational experts.
DietDiveR provides users with functions to 1) clean dietary data, 2) analyze 24-h dietary intakes in relation to other study-specific metadata variables, 3) visualize percentages of energy intake from macronutrients, 4) perform principal component analysis or k-means clustering to group participants by similar data-driven dietary patterns, 5) generate foodtrees based on the hierarchical food group information for food items consumed, 6) perform principal coordinate analysis taking food grouping information into account, and 7) calculate diversity metrics for overall diet and specific food groups. DietDiveR includes a self-paced tutorial on a website (https://computational-nutrition-lab.github.io/DietDiveR/). As a demonstration, we applied DietDiveR to a demonstration data set and data from NHANES 2015-2016 to derive a dietary diversity measure of nuts, seeds, and legumes consumption.
Adult participants in the NHANES 2015-2016 cycle were grouped depending on the diversity in their mean consumption of nuts, seeds, and legumes. The group with the highest diversity in nuts, seeds, and legumes consumption had 3.8 cm lower waist circumference (95% confidence interval: 1.0, 6.5) than those who did not consume nuts, seeds, and legumes.
DietDiveR enables users to visualize dietary data and conduct data-driven dietary pattern analyses using R to answer research questions regarding diet. As a demonstration of this toolkit, we explored the diversity of nuts, seeds, and legumes consumption to highlight some of the ways DietDiveR can be used for analyses of dietary diversity.
Regular monitoring is an important component of the successful management of pelagic animals of interest to commercial fisheries. Here we provide a biomass estimate for Antarctic krill (
Euphausia ...superba
) in the eastern sector of the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR) Division 58.4.2 (55°E to 80°E; area = 775,732 km
2
) using data collected during an acoustic-trawl survey carried out in February and March 2021. Using acoustic data collected in day-time and trawl data, areal biomass density was estimated as 8.3 gm
-2
giving a total areal krill biomass of 6.48 million tonnes, with a 28.9% coefficient of variation (CV). The inaccessibility of the East Antarctic makes fisheries-independent surveys of Antarctic krill expensive and time consuming, so we also assessed the efficacy of extrapolating smaller surveys to a wider area. During the large-scale survey a smaller scale survey (centre coordinates -66.28°S 63.35°E, area = 4,902 km
2
) was conducted. We examine how representative krill densities from the small-scale (Mawson box) survey were over a latitudinal range by comparing krill densities from the large-scale survey split into latitudinal bands. We found the small scale survey provided a good representation of the statistical distribution of krill densities within its latitudinal band (KS-test,
D
= 0.048,
p
-value = 0.98), as well as mean density (
t
-test
p
-value = 0.44), but not outside of the band. We recommend further
in situ
testing of this approach.
Consumption of plant-based milk alternatives is increasing. Current dietary guidance primarily relies on dairy milk as a source of key nutrients of public health concern including calcium and vitamin ...D.
To compare the nutritional content of plant-based milk alternatives between categories (eg, soy, almond, and oat) and with dairy milk.
This study presents an evaluation of the nutritional content of 219 plant-based milk alternatives from 21 brands available in the US marketplace using data from the University of Minnesota Nutrition Coordinating Center’s database.
Nutrients of focus include those identified as nutrients of public health concern in the Dietary Guidelines for Americans or used by the US Department of Agriculture as criteria for determining whether a plant-based milk is a suitable substitute for dairy milk.
Most data are presented as percent Daily Values. Nutrients and food components were compared using means, medians, IQRs, and ranges. Statistical tests for significance were not used to evaluate between category differences because the plant-based milk alternatives included are a full census of the products from 21 brands available in the marketplace. Because data are a census, differences can be understood to be true differences.
Fortified soy-based products most closely mimic the nutrient content of dairy milk. High variability was present in all the nutrients and food components. Plant-based milk alternatives were generally lower in protein and saturated fatty acids than dairy milk, with high variability in added sugars content. Approximately 70% were fortified with both calcium and vitamin D.
These results indicate that most plant-based milk alternative products are not nutritionally equivalent to dairy milk, and there is high nutritional variability between and within product types. These findings highlight the importance of communicating the nutritional differences between plant-based milk alternatives and dairy milk to consumers.
The human gut microbiome is linked to metabolic and cardiovascular disease risk. Dietary modulation of the human gut microbiome offers an attractive pathway to manipulate the microbiome to prevent ...microbiome-related disease. However, this promise has not been realized. The complex system of diet and microbiome interactions is poorly understood. Integrating observational human diet and microbiome data can help researchers and clinicians untangle the complex systems of interactions that predict how the microbiome will change in response to foods. The use of dietary patterns to assess diet–microbiome relations holds promise to identify interesting associations and result in findings that can directly translate into actionable dietary intake recommendations and eating plans. In this article, we first highlight the complexity inherent in both dietary and microbiome data and introduce the approaches generally used to explore diet and microbiome simultaneously in observational studies. Second, we review the food group and dietary pattern–microbiome literature focusing on dietary complexity—moving beyond nutrients. Our review identified a substantial and growing body of literature that explores links between the microbiome and dietary patterns. However, there was very little standardization of dietary collection and assessment methods across studies. The 54 studies identified in this review used ≥7 different methods to assess diet. Coupled with the variation in final dietary parameters calculated from dietary data (e.g., dietary indices, dietary patterns, food groups, etc.), few studies with shared methods and assessment techniques were available for comparison. Third, we highlight the similarities between dietary and microbiome data structures and present the possibility that multivariate and compositional methods, developed initially for microbiome data, could have utility when applied to dietary data. Finally, we summarize the current state of the art for diet–microbiome data integration and highlight ways dietary data could be paired with microbiome data in future studies to improve the detection of diet–microbiome signals.
Many US immigrant populations develop metabolic diseases post immigration, but the causes are not well understood. Although the microbiome plays a role in metabolic disease, there have been no ...studies measuring the effects of US immigration on the gut microbiome. We collected stool, dietary recalls, and anthropometrics from 514 Hmong and Karen individuals living in Thailand and the United States, including first- and second-generation immigrants and 19 Karen individuals sampled before and after immigration, as well as from 36 US-born European American individuals. Using 16S and deep shotgun metagenomic DNA sequencing, we found that migration from a non-Western country to the United States is associated with immediate loss of gut microbiome diversity and function in which US-associated strains and functions displace native strains and functions. These effects increase with duration of US residence and are compounded by obesity and across generations.
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•US immigration is associated with loss of gut microbiome diversity•US immigrants lose bacterial enzymes associated with plant fiber degradation•Bacteroides strains displace Prevotella strains according to time spent in the USA•Loss of diversity increases with obesity and is compounded across generations
Migration from a non-western nation to the United States is found to be associated with a loss in gut microbiome diversity and function in a manner that may predispose individuals to metabolic disease.
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