Biology subjects

Resendis-Antonio, O.

Publications and source records attributed to Resendis-Antonio, O..

8 recordsLinked to original sources

bioRxiv2025

MICOMWeb: a website for microbial community metabolic modeling of the human gut

SummaryMICOMWeb is a user-friendly website for easy microbial community metabolic modeling of the human gut. This website tackles three constraints when generating in silico metagenome-scale metabolic models: (i) the prior Python user knowledge for metabolic modeling using flux balance analysis with MICOM Python package, (ii) predefined and user-defined diets to generate ad hoc metabolic models, and (iii) the high- throughput computational infrastructure required to obtain the simulated growth and metabolic exchange fluxes, using real abundance from metagenomic shotgun or 16S amplicon sequencing; we present MICOMWebs features to easily run in silico experiments as a functional hypothesis generator for experimental validation on previously published data. Availability and ImplementationMICOMWeb is freely accessible for academic purposes at https://micomweb.c3.unam.mx.

systems biology↗

bioRxiv2024

Composition of saliva metabolome is significantly associated with SARS-CoV2 infection and with severity of COVID-19 disease

BackgroundThe metabolome of COVID-19 patients has been studied sparsely, with most research focusing on a limited number of plasma metabolites or small cohorts. This is the first study to test saliva metabolites in COVID-19 patients in a comprehensive way, revealing significant changes linked to disease severity and highlighting saliva is potential as a non-invasive diagnostic tool. MethodsWe included 30 asymptomatic subjects with no prior COVID-19 infection or vaccination, 102 patients with mild SARS-CoV-2 infection, and 61 hospitalized patients with confirmed SARS-CoV-2 status. Saliva samples were analyzed using hydrophilic interaction liquid chromatography-mass spectrometry (HILIC-MS/MS) in positive and negative ionization modes. ResultsSignificant changes in metabolites were identified in COVID-19 patients, with distinct patterns based on disease severity. Healthy individuals exhibited a well-regulated bacterial network, while severe cases showed disordered microbial networks. Elevated dipeptides such as Val-Glu and Met-Gln in moderate cases suggest specific protease activity related to SARS-CoV-2. Increased acetylated amino acids like N-Acetylserine and N-Acetylhistidine indicate potential biomarkers for stress and disease severity. Bacterial metabolites, including muramic acid and indole-3-carboxaldehyde, were higher in mild-moderate cases, indicating oral microbiota changes. In severe cases, polyamines and organ damage-related metabolites, such as N-acetylspermine and 3-methylcytidine, were significantly increased. Interestingly, metabolites reduced in moderate cases were elevated in severe cases. ConclusionsSaliva metabolomics offers insight into disease progression and potential biomarkers for COVID-19.

bioinformatics↗

bioRxiv2024

Exploring Metabolic Anomalies in COVID-19 and Post-COVID-19: A Machine Learning Approach with Explainable Artificial Intelligence

The COVID-19 pandemic, caused by SARS-CoV-2, has led to significant challenges worldwide, including diverse clinical outcomes and prolonged post-recovery symptoms known as Long COVID or Post-COVID-19 syndrome. Emerging evidence suggests a crucial role of metabolic reprogramming in the infections long-term consequences. This study employs a novel approach utilizing machine learning (ML) and explainable artificial intelligence (XAI) to analyze metabolic alterations in COVID-19 and Post-COVID-19 patients. By integrating ML with SHAP (SHapley Additive exPlanations) values, we aimed to uncover metabolomic signatures and identify potential biomarkers for these conditions. Our analysis included a cohort of 142 COVID-19, 48 Post-COVID-19 samples and 38 CONTROL patients, with 111 identified metabolites. Traditional analysis methods like PCA and PLS-DA were compared with advanced ML techniques to discern metabolic changes. Notably, XGBoost models, enhanced by SHAP for explainability, outperformed traditional methods, demonstrating superior predictive performance and providing different insights into the metabolic basis of the diseases progression and its aftermath, the analysis revealed several metabolomic subgroups within the COVID-19 and Post-COVID-19 conditions, suggesting heterogeneous metabolic responses to the infection and its long-term impacts. This study highlights the potential of integrating ML and XAI in metabolomics research.

molecular biology↗

bioRxiv2022

mb-PHENIX: Diffusion and Supervised Uniform Manifold Approximation for denoising microbiota data

MotivationMicrobiota data suffers from technical noise (reflected as excess of zeros in the count matrix) and the curse of dimensionality. This complicates downstream data analysis and compromises the scientific discoverys reliability. Data sparsity makes it difficult to obtain a well-cluster structure and distorts the abundance distributions. Currently, there is a rised need to develop new algorithms with improved capacities to reduce noise and recover missing information. ResultsWe present mb-PHENIX, an open-source algorithm developed in Python, that recovers taxa abundances from the noisy and sparse microbiota data. Our method deals with sparsity in the count matrix (in 16S microbiota and shotgun studies) by applying imputation via diffusion onto the supervised Uniform Manifold Approximation Projection (sUMAP) space. Our hybrid machine learning approach allows the user to denoise microbiota data. Thus, the differential abundance of microbes is more accurate among study groups, where abundance analysis fails. AvailabilityThe mb-PHENIX algorithm is available at https://github.com/resendislab/mb-PHENIX. An easy-to-use implementation is available on Google Colab (see GitHub) ContactOresendis@inmegen.gob.mx Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2022

Diffusion on PCA-UMAP manifold captures a well-balance of local, global, and continuum structures to denoise single-cell RNA sequencing data

Single-cell transcriptomics (scRNA-seq) is becoming a technology that is transforming biological discovery in many fields of medicine. Despite its impact in many areas, scRNASeq is technologically and experimentally limited by the inefficient transcript capture and the high rise of noise sources. For that reason, imputation methods were designed to denoise and recover missing values. Many imputation methods (e.g., neighbor averaging or graph diffusion) rely on k nearest neighbor graph construction derived from a mathematical space as a low-dimensional manifold. Nevertheless, the construction of mathematical spaces could be misleading the representation of densities of the distinct cell phenotypes due to the negative effects of the curse of dimensionality. In this work, we demonstrated that the imputation of data through diffusion approach on PCA space favor over-smoothing when increases the dimension of PCA and the diffusion parameters, such k-NN (k-nearest neighbors) and t (value of the exponentiation of the Markov matrix) parameters. In this case, the diffusion on PCA space distorts the cell neighborhood captured in the Markovian matrix creating an artifact by connecting densities of distinct cell phenotypes, even though these are not related phenotypically. In this situation, over-smoothing of data is due to the fact of shared information among spurious cell neighbors. Therefore, it can not account for more information on the variability (from principal components) or nearest neighbors for a well construction of a cell-neighborhood. To solve above mentioned issues, we propose a new approach called sc-PHENIX( single cell-PHEnotype recovery by Non-linear Imputation of gene eXpression) which uses PCA-UMAP initialization for revealing new insights into the recovered gene expression that are masked by diffusion on PCA space. sc-PHENIX is an open free algorithm whose code and some examples are shown at https://github.com/resendislab/sc-PHENIX.

bioinformatics↗

bioRxiv2019

Progressive shifts in the gut microbiome reflect prediabetes and diabetes development in a treatment-naive Mexican cohort

Type 2 diabetes (T2D) is a global epidemic that affects more than 8% of the worlds population and is a leading cause of death in Mexico. Diet and lifestyle are known to contribute to the onset of T2D. However, the role of the gut microbiome in T2D progression remains uncertain. Associations between microbiome composition and diabetes are confounded by medication use, diet, and obesity. Here we present data on a treatment-naive cohort of 405 Mexican individuals across varying stages of T2D severity. Associations between gut bacteria and more than 200 clinical variables revealed a defined set of bacterial genera that were consistent biomarkers of T2D prevalence and risk. Specifically, gradual increases in blood glucose levels, beta cell dysfunction, and the accumulation of measured T2D risk factors was correlated with the relative abundances of four bacterial genera. In a cohort of 25 individuals, T2D treatment - predominantly metformin - reliably returned the microbiome to the normoglycemic community state. Deep clinical characterization allowed us to broadly control for confounding variables, indicating that these microbiome patterns were independent of common T2D comorbidities, like obesity or cardiovascular disease. Thus, our work provides the first solid evidence for a direct link between the gut microbiome and T2D in a critically high-risk population. Whether or not these T2D-associated changes in the gut contribute to the etiology of T2D or its comorbidities remains to be seen.

systems biology↗

bioRxiv2018

Micom: metagenome-scale modeling to infer metabolicinteractions in the microbiota.

Compositional changes in the gut microbiota have been associated with a variety of medical conditions such as obesity, Crohns disease and diabetes. However, connecting microbial community composition to ecosystem function remains a challenge. Here, we introduce MICOM - a customizable metabolic model of the human gut microbiome. By using a heuristic optimization approach based on L2 regularization we were able to obtain a unique set of realistic growth rates that corresponded well with observed replication rates. We integrated adjustable dietary and taxon abundance constraints to generate personalized metabolic models for individual metagenomic samples. We applied MICOM to a balanced cohort of metagenomes from 186 people, including a metabolically healthy population and individuals with type 1 and type 2 diabetes. Model results showed that individual bacterial genera maintained conserved niche structures across humans, while the community-level production of short chain fatty acids (SCFAs) was heterogeneous and highly individual-specific. Model output revealed complex cross-feeding interactions that would be difficult to measure in vivo. Metabolic interaction networks differed somewhat consistently between healthy and diabetic subjects. In particular MICOM predicted reduced butyrate and propionate production in a diabetic cohort, with restoration of SCFA production profiles found in healthy subjects following metformin treatment. Overall, we found that changes in diet or taxon abundances have highly personalized effects. We believe MICOM can serve as a useful tool for generating mechanistic hypotheses for how diet and microbiome composition influence community function. All methods are implemented in the open source Python package, which is available at https://github.com/micom-dev/micom.

systems biology↗

bioRxiv2018

Distinct Microbes, Metabolites, and Ecologies Define the Microbiome in Deficient and Proficient Mismatch Repair Colorectal Cancers

Background\n\nThe link between colorectal cancer (CRC) and the gut microbiome has been established, but the specific microbial species and their role in carcinogenesis remain controversial. Our understanding would be enhanced by better accounting for tumor subtype, microbial community interactions, metabolism, and ecology.\n\nMethods\n\nWe collected paired colon tumor and normal-adjacent tissue and mucosa samples from 83 individuals who underwent partial or total colectomies for CRC. Mismatch repair (MMR) status was determined in each tumor sample and classified as either deficient MMR (dMMR) or proficient MMR (pMMR) tumor subtypes. Samples underwent 16S rRNA gene sequencing and a subset of samples from 50 individuals were submitted for targeted metabolomic analysis to quantify amino acids and short-chain fatty acids. A PERMANOVA was used to identify the biological variables that explained variance within the microbial communities. dMMR and pMMR microbial communities were then analyzed separately using a generalized linear mixed effects model that accounted for MMR status, sample location, intra-subject sample correlation, and read depth. Genome-scale metabolic models were then used to generate microbial interaction networks for dMMR and pMMR microbial communities. We assessed global network properties as well as the metabolic influence of each microbe within the dMMR and pMMR networks.\n\nResults\n\nWe demonstrate distinct roles for microbes in dMMR and pMMR CRC. Sulfidogenic Fusobacterium nucleatum and hydrogen sulfide production were significantly enriched in dMMR CRC, but not pMMR CRC. We also surveyed the butyrate-producing microbial species, but did not find a significant difference in predicted or actual butyrate production between dMMR and pMMR microbial communities. Finally, we observed that dMMR microbial communities were predicted to be less stable than pMMR microbial communities. Community stability may play an important role in CRC development, progression, or immune activation within the respective MMR subtypes.\n\nConclusions\n\nIntegrating tumor biology and microbial ecology highlighted distinct microbial, metabolic, and ecological properties unique to dMMR and pMMR CRC. This approach could critically improve our ability to define, predict, prevent, and treat colorectal cancers.

cancer biology↗