Biology subjects

Blakeley-Ruiz, J. A.

Publications and source records attributed to Blakeley-Ruiz, J. A..

5 recordsLinked to original sources

bioRxiv2026

Diet-derived peptides mediate the effects of dietary protein source on gastrointestinal health

Plant-based diets support gastrointestinal (GI) health while animal-based diets can disrupt gut homeostasis. Although multiple aspects of these diet types are believed to confer their respective effects, the role of their protein component is less well understood. Here, we conducted a randomized crossover-controlled feeding trial wherein healthy subjects consumed 70% of their daily protein intake in the form of pea protein (PP) or egg white protein (EWP) isolate (NCT05619939). Individuals who consumed EWP reported increased GI symptoms and exhibited elevated intestinal permeability. In contrast, these endpoints did not change following PP consumption. Fecal analysis showed increased diet-derived peptides only following EWP consumption, which was associated with resistance of EWP isolate to degradation by digestive enzymes in vitro. Metagenomic, metaproteomic and metabolomic analyses of stool after the EWP-based diet showed reduced abundance of multiple gut-protective bacterial species and increased bacterial amino acid utilization compared to samples following the PP-based diet. Dietary peptides in the gut luminal content of EWP-fed subjects reduced metabolic function of intestinal epithelial cell in culture. Providing an amino acid-based diet mimicking EWP composition to mice prevented colonic accumulation of diet-derived proteins and GI dysfunction associated with EWP diet consumption. Collectively, these findings demonstrate that dietary protein source is a key mediator of GI function, revealing a modifiable lifestyle factor that impacts human health.

physiology↗

bioRxiv2026

Metagenomics-enabled proteomics reveals how AMF and PSB co-inoculation reshapes tomato rhizosphere dynamics across growth stages

Urban agriculture increasingly relies on compost-based substrates for sustainable production, yet we lack a clear characterization of how these systems respond to biological amendments aimed at introducing beneficial microbiota. Here we investigated how developmental stage and co-inoculation with arbuscular mycorrhizal fungi (AMF) and phosphate-solubilizing bacteria (PSB) reshape rhizosphere microbial function in Solanum lycopersicum grown in compost-based urban farm substrate. Using plant physiology assays, 16S rRNA amplicon sequencing, and metagenome-informed metaproteomics, we characterized tomato physiological responses and rhizosphere microbial activity during flowering and fruiting across control, single AMF, single PSB, and AMF and PSB co-inoculation treatments. Co-inoculation synergistically enriched beneficial taxa, improved fruit nutrient accumulation, elevated nutrient transporter and quorum sensing protein production, and drove stress-driven dormancy in competitively excluded taxa, with responses varying between developmental stages. Our findings establish metagenome-informed metaproteomics as essential for resolving stage-specific rhizosphere microbiome functional responses to tomato development and AMF and PSB co-inoculation.

bioinformatics↗

bioRxiv2025

Evaluation of statistical approaches for differential metaproteomics

BackgroundMetaproteomics characterizes and compares molecular phenotypes of organisms in communities by comprehensively analyzing their protein expression profiles using statistical methods. However, not all statistical methods are suitable for determining differentially abundant protein groups in metaproteomic analyses. Statistical challenges in metaproteomics include: data sparsity, non-normality, compositionality, and large between-sample variability. These challenges can potentially be addressed with several data processing steps, including imputation, normalization, transformation, and selection of the appropriate statistical tests. The potential combinations of different processing methods create a complex matrix of analysis options and it is currently unclear how these combinations impact the results of statistical tests on metaproteomic data. ResultsTo determine what data processing methods and statistical tests are best for identifying differentially abundant proteins in metaproteomics datasets, we generated a set of thirteen metaproteomic samples with known compositions, known differences, and differing levels of complexity. These defined metaproteomes address the general challenges outlined above, using various scenarios in metaproteomic data analyses. We compared over 110 different statistical analysis combination options, including regression-based tools, general statistics inference, and machine learning techniques. We found that several combinations within the frameworks of limma, edgeR, MaAslin2, custom linear and Bayesian linear models, and random forests all offer suitable evaluation options. ConclusionsWe highlight key recommendations for differential expression analysis in metaproteomics. Our work enables improved assessment of statistical methods for metaproteomics by establishing a framework for testing statistical approaches, including comprehensive raw mass spectrometry data and reproducible benchmarking code.

molecular biology↗

bioRxiv2024

Stable isotope fingerprinting can directly link intestinal microorganisms with their carbon source and captures diet-induced substrate switching in vivo

Diet has strong impacts on the composition and function of the gut microbiota with implications for host health. Therefore, it is critical to identify the dietary components that support growth of specific microorganisms in vivo. We used protein-based stable isotope fingerprinting (Protein-SIF) to link microbial species in gut microbiota to their carbon sources by measuring each microbes natural 13C content ({delta}13C) and matching it to the 13C content of available substrates. We fed gnotobiotic mice, inoculated with a 13 member microbiota, diets in which the 13C content of all components was known. We varied the source of protein, fiber or fat to observe 13C signature changes in microbial consumers of these substrates. We observed significant changes in the {delta}13C values and abundances of specific microbiota species, as well as host proteins, in response to changes in 13C signature or type of protein, fiber, and fat sources. Using this approach we were able to show that upon switching dietary source of protein, fiber, or fat (1) some microbial species continued to obtain their carbon from the same dietary component (e.g., protein); (2) some species switched their main substrate type (e.g., from protein to carbohydrates); and (3) some species might derive their carbon through foraging on host compounds. Our results demonstrate that Protein-SIF can be used to identify the dietary-derived substrates assimilated into proteins by microbes in the intestinal tract; this approach holds promise for the analysis of microbiome substrate usage in humans without the need of substrate labeling. SignificanceThe gut microbiota plays a critical role in the health of animals including humans, influencing metabolism, the immune system, and even behavior. Diet is one of the most significant factors in determining the function and composition of the gut microbiota, but our understanding of how specific dietary components directly impact individual microbes remains limited. We present the application of an approach that measures the carbon isotope "fingerprint" of proteins in biological samples. This fingerprint is similar to the fingerprint of the substrate used to make the proteins. We describe how we used this approach in mice to determine which dietary components specific intestinal microbes use as carbon sources to make their proteins. This approach can directly identify components of an animals diet that are consumed by gut microbes.

microbiology↗

bioRxiv2024

Dietary protein source strongly alters gut microbiota composition and function

The source of protein in a persons diet affects their total life expectancy. However, the mechanisms by which dietary protein sources differentially impact human health and life expectancy are poorly understood. Dietary choices have major impacts on the composition and function of the intestinal microbiota that ultimately modulate host health. This raises the possibility that health outcomes based on dietary protein sources might be driven by interactions between dietary protein and the gut microbiota. In this study, we determined the effects of seven different sources of dietary protein on the gut microbiota of mice using an integrated metagenomics-metaproteomics approach. The protein abundances measured by metaproteomics can provide microbial species abundances, and evidence for the molecular phenotype of microbiota members because measured proteins indicate the metabolic and physiological processes used by a microbial community. We showed that dietary protein source significantly altered the species composition and overall function of the gut microbiota. Different dietary protein sources led to changes in the abundance of microbial proteins involved in the degradation of amino acids and the degradation of glycosylations conjugated to dietary protein. In particular, brown rice and egg white protein increased the abundance of amino acid degrading enzymes. Egg white protein increased the abundance of bacteria and proteins usually associated with the degradation of the intestinal mucus barrier. These results show that dietary protein sources can change the gut microbiotas metabolism, which could have major implications in the context of gut microbiota mediated diseases.

microbiology↗