Biology subjects

Lacruz-Pleguezuelos, B.

Publications and source records attributed to Lacruz-Pleguezuelos, B..

4 recordsLinked to original sources

bioRxiv2025

Gut microbial ecosystems differ across metabolic and obesity phenotypes

Obesity is a heterogeneous condition comprising a continuum of phenotypes with various metabolic and inflammatory profiles. Metabolically healthy obesity (MHO) identifies individuals with obesity but a relatively preserved metabolic state. However, the criteria defining MHO remain inconsistent, and little is known about the gut microbiome (GM) features underlying this intermediate phenotype. Here, we aim to describe microbial structures contributing to metabolic health and disease. To do so, we analyzed the GM of 959 individuals classified as metabolically healthy non-obese (MHNO), MHO, metabolically unhealthy non-obese (MUNO), and metabolically unhealthy obese (MUO), using stool shotgun metagenomics. MHO subjects display intermediate anthropometric and biochemical profiles, with a GM composition and diversity in an in-between state among MHNO and MUO individuals. Network science analyses reveal that metabolic health, rather than obesity, drives microbial connectivity: MHNO and MHO individuals harbor more robust and functionally cohesive microbial networks, whose most influential nodes are focused toward SCFA production. In contrast, MUO and MUNO communities exhibit a dysbiotic state with reduced connectivity and increased influence of low-abundance, ectopic and potentially pro-inflammatory species resulting in a damaged, unstable microbial community network. These findings suggest that metabolic disorders disrupt microbial ecology beyond compositional shifts, emphasizing the need for systems-level approaches. Our findings show differences in microbial connectivity and association patterns across metabolic and obesity phenotypes, shedding light on how distinct microbial structures may contribute to metabolic health and disease.

bioinformatics↗

bioRxiv2023

Non-responsive Celiac disease symptoms associated with microbiome network structure and function

Non-responsive celiac disease (NRCD) poses a challenge for clinicians due to the persistence of symptoms despite maintaining a gluten-free diet (GFD). This study investigated the gut microbiome, mucosal integrity, and metabolomic profiles of 39 NRCD patients to gain insights into the underlying mechanisms contributing to symptom persistence. Two distinct clusters of patients were identified based on clinical and demographic variables not influenced by gluten consumption. Cluster 1, labelled "Low-grade symptoms," displayed milder symptoms and lower inflammatory markers. In contrast, Cluster 2, named "High-grade symptoms," exhibited more severe gastrointestinal and extraintestinal symptoms, along with elevated inflammatory markers and increased intestinal permeability. Despite similar mucosal damage in both clusters, network analysis of the gut microbiome revealed specific microbial taxa with potential functional implications. Cluster 1 displayed a microbiome associated with immune homeostasis and gut barrier integrity, potentially lowering inflammation and symptom severity. In contrast, Cluster 2 had a distinct microbiome linked to lactate production, Th17 activation, possibly contributing to heightened inflammation and gastrointestinal symptoms. Metabolomic analysis revealed differential metabolites between clusters, particularly in amino acid metabolism pathways. Metabolites associated with specific symptoms were identified, implicating their potential role in symptom manifestation. Notably, vitamin D deficiency was observed in both clusters, suggesting its relevance in the context of NRCD. The study highlights the importance of the gut microbiome, mucosal integrity, and metabolic pathways in symptom persistence among NRCD patients. The associations between microbial-derived metabolites and symptom severity provide valuable insights into potential therapeutic targets. Further research is needed to validate these findings and develop targeted interventions for improving clinical outcomes in NRCD patients.

microbiology↗

bioRxiv2023

Uncovering the link between gut microbiome, highly processed food consumption and diet quality through bioinformatics methods

The consumption of highly processed foods, along with other dietary and lifestyle poor habits, has an impact on health by increasing the risk of several non-communicable pathologies, such as diabetes. Gut microbiome composition, in specific, can be modulated by nutrients, deriving in different metabolic outcomes that have an influence on this high disease susceptibility and making it a possible therapeutic target for these comorbidities. In this work, gut microbiome of 60 and 46 individuals from 2 different studies focused, among other aspects, on diet-microbiome interactions, was characterised. By means of differential abundance analyses and supervised machine learning techniques based on random forest, gradient boosting and support vector machines, a set of microbial genera that could be potential biomarkers for the differentiation of individuals with poorer dietary patterns was discovered, after comparing coincidences in these taxa among classifiers and testing them for significant differences. Among these, Dialister, Phocea and Pseudoflavonifractor were suggested to have a role in the way highly processed foods affect health negatively, along with Prevotellaceae NK3B31 group and an undetermined genus from Muribaculaceae in the opposite sense. Furthermore, all the identified genera in this study had already been linked to type 2 diabetes, among which Bacteroides and Pseudoflavonifractor proved to be differentially abundant in groups of individuals with different levels of biomarkers for this disease. Nevertheless, further research via longitudinal studies and experimental validation of these genera should be carried out to confirm the association of these taxa with diet and diabetes.

bioinformatics↗

bioRxiv2022

Bioinformatic methods for stratification of obese patients and identification of cancer susceptibility biomarkers based on the analysis of the gut microbiome

Obesity has an impact on health by increasing the risk of various diseases. However, these risks might also depend on the metabolic health status, as it seems that metabolically healthy obese subjects are under a reduced risk of suffering comorbidities such as colorectal cancer. The gut microbiome has an effect on obesity and metabolic disorders through several integration pathways, making it a potential therapeutic target for these diseases. In this study, we characterized the gut microbiota of 356 obese and non-obese European individuals with different comorbidities associated with obesity. Using approaches based on supervised machine learning and network biology, we found a set of biomarkers of interest for differentiating metabolically healthy from unhealthy subjects. Then, we performed a linear discriminant analysis of effect size on a population of 1593 colorectal cancer, adenoma and control subjects assembled by the COST Action ML4Microbiome to investigate their role in colorectal cancer risk. Four of our biomarkers appeared in both approaches, suggesting their possible role in colorectal cancer development, prognosis and follow up: Clostridium leptum, Gordonibacter pamelaeae, Eggerthella lenta and Collinsella intestinalis. Further research via longitudinal studies or experimental validation of these microbial species would be necessary to confirm this association.

bioinformatics↗