Biology subjects

Prado, S.

Publications and source records attributed to Prado, S..

4 recordsLinked to original sources

bioRxiv2025

Effects of incrementally increased plant-based protein intake on gut microbiota and inflammatory-metabolic biomarkers in healthy adults

Shifting to a plant-based diet naturally alters protein source choices. In many countries, protein from yellow pea is widely used as main ingredient in meat alternatives. Still, its biological effects, especially regarding gastrointestinal health, remain incompletely understood. The aim of our study was to investigate how a weekly increase in the intake of a well-characterized pea protein isolate affects surrogate markers of health, fecal short-chain fatty acids and gut microbiota composition in healthy individuals. Male and female adults (N=29) participated in this exploratory intervention study. A 4-week pre-intervention period for questionnaires and fecal samples collection was followed by a 4-week supplementation. Participants consumed isolated pea protein in weekly increasing amounts, starting from 0.25 g/kg body mass/day in week 5 to 1.00 g/kg body mass/day in week 8. Questionnaire data, fecal samples as well as fasting blood and 24-h urine samples were collected weekly. Data from biological samples and questionnaires confirmed a healthy study population and compliance. Fecal calprotectin levels significantly increased only in a subset of participants, which was also accompanied by higher fecal water cytotoxicity in vitro. Short-chain fatty acids mainly rose in those subjects with stable calprotectin levels. Relative abundances of Limosilactobacillus frumenti, Odoribacter splanchnicus and Lactobacillus crispatus increased significantly in the total population during the intervention while the relative abundance of Bifidobacterium longum and Bifidobacterium catenulatum decreased. Our results indicate that an increased intake of pea protein isolate affects the growth of certain beneficial bacteria strains and differentially influences markers related to gut inflammation in healthy individuals.

biochemistry↗

bioRxiv2025

Interpretable Machine Learning on Soybean Multi Omics Data Reveals Drought-Driven Shifts of Plant-Microbe Interactions

BackgroundPlant-microbe interactions in the rhizosphere are central to plant growth, nutrient acquisition, and stress resilience. Although multi-omics approaches enable comprehensive profiling of different biological layers, integrating these data to understand the mechanisms underlying plant-microbe symbiosis, particularly under drought stress, remains a challenge. ResultsGenomic, metabolomic, and microbiome data from 198 soybean accessions grown under both control and drought conditions were integrated to identify environment-specific predictive features of the plant phenotypes. We compared best linear unbiased prediction (BLUP), genome-wide association study (GWAS), and a nonlinear machine learning model to evaluate their ability to detect informative features. The machine learning models provided flexible variable selection and outperformed linear models in capturing nonlinear dependencies. Model interpretation using SHapley Additive exPlanations (SHAP) indicated that the isoflavone derivative, daidzin, and the drought-tolerant Candidatus Nitrosocosmicus, were major contributors to phenotypic variation, specifically under drought stress. SHAP-based interaction networks indicated cross-omics links, including connections between daidzin, gamma-aminobutyric acid (GABA), and Paenibacillus. ConclusionThe proposed interpretable machine learning approach for plant phenotype prediction identified multi-omics biomarkers and interactions, providing insights into plant adaptation to drought stress through environment-dependent rhizosphere networks and symbiotic associations.

bioinformatics↗

bioRxiv2025

Amyloid pathology reduces dynamic range and disrupts neural coding in a mouse model of Alzheimer's Disease

Alzheimers disease (AD) disrupts neural circuits vital for memory and cognition. Using two-photon calcium imaging in behaving 5xFAD mice, we examined how amyloid pathology alters hippocampal CA1 activity and spatial coding. We found elevated baseline activity but reduced locomotion-driven firing, leading to a diminished neuronal dynamic range. To our knowledge, this is the first direct experimental evidence for reduced dynamic range in an AD model. These abnormalities were strongest near amyloid plaques and became more widespread with age. We also observed altered network synchrony, degraded spatial coding and increased neuronal response variability. Furthermore, place fields emerged more slowly in both familiar and novel environments, indicating impaired recall and learning. By showing a link between local plaque pathology and impaired flexible modulation of CA1 activity and progressive deficits in spatial memory coding, our study offers new insights into the circuit basis of cognitive decline in AD.

neuroscience↗

bioRxiv2020

Place cells in head-fixed mice navigating a floating real-world environment

The hippocampal place cell system in rodents has provided a major paradigm for the scientific investigation of memory function and dysfunction. Place cells have been observed in area CA1 of the hippocampus of both freely moving animals, and of head-fixed animals navigating in virtual reality environments. However, spatial coding in virtual reality preparations has been observed to be impaired. Here we show that the use of a real-world environment system for head-fixed mice, consisting of a track floating on air, provides some advantages over virtual reality systems for the study of spatial memory. We imaged the hippocampus of head-fixed mice injected with the genetically encoded calcium indicator GCaMP6s while they navigated circularly constrained or open environments on the floating platform. We observed consistent place tuning in a substantial fraction of cells with place fields remapping when animals entered a different environment. When animals re-entered the same environment, place fields typically remapped over a time period of multiple days, faster than in freely moving preparations, but comparable with virtual reality. Spatial information rates were within the range observed in freely moving mice. Manifold analysis indicated that spatial information could be extracted from a low-dimensional subspace of the neural population dynamics. This is the first demonstration of place cells in head-fixed mice navigating on an air-lifted real-world platform, validating its use for the study of brain circuits involved in memory and affected by neurodegenerative disorders.

neuroscience↗