Biology subjects

Avina, M.

Publications and source records attributed to Avina, M..

5 recordsLinked to original sources

bioRxiv2024

Gut microbiome shifts in chronic systolic heart failure are associated with disease severity and clinical improvement

Chronic systolic heart failure (HF) is a prevalent and morbid disease with marked variability in its progression and response to therapies. The gut microbiome may play a role in pathophysiology and progression of chronic HF, but clinical studies investigating relationships between the two are lacking. We analyzed the gut microbiome in a cohort of adults with chronic systolic HF caused by non-ischemic cardiomyopathy (n=59) using multi-omics profiling and, in some cases, longitudinal sampling. We identified microbiome differences compared to healthy subjects (n=50) and associated these differences with host metabolites, inflammatory markers and physiology. We found depletion of the anti-inflammatory probiotic Bifidobacterium and the associated short chain fatty acid producing and formaldehyde detoxifying pathways in the chronic HF cohort. We also discovered HF-specific microbiome-host immunome interactions. In addition to identifying several taxa and microbial pathways broadly associated with HF disease severity, we found significant links between Bifidobacterium and clinical HF improvement over time. Gut microbiome-host multi-omic data integration revealed a close association between Bifidobacterium and circulating metabolites previously implicated in cardiovascular physiology (e.g., malonic acid), thus pointing to potential mechanisms through which Bifidobacterium may affect chronic HF physiology. Our results suggest that Bifidobacterium may serve as a biomarker for chronic HF trajectory as well as suggest potential novel therapeutic interventions strategies.

microbiology↗

bioRxiv2024

Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease

To understand dynamic interplay between the human microbiome and host during health and disease, we analyzed the microbial composition, temporal dynamics, and associations with host multi-omics, immune and clinical markers of microbiomes from four body sites in 86 participants over six years. We found that microbiome stability and individuality are body-site-specific and heavily influenced by the host. The stool and oral microbiome were more stable than the skin and nasal microbiomes, possibly due to their interaction with the host and environment. Also, we identified individual-specific and commonly shared bacterial taxa, with individualized taxa showing greater stability. Interestingly, microbiome dynamics correlated across body sites, suggesting systemic coordination influenced by host-microbial-environment interactions. Notably, insulin-resistant individuals showed altered microbial stability and associations between microbiome, molecular markers, and clinical features, suggesting their disrupted interaction in metabolic disease. Our study offers comprehensive views of multi-site microbial dynamics and their relationship with host health and disease. Study HighlightsO_LIThe stability of the human microbiome varies among individuals and body sites. C_LIO_LIHighly individualized microbial genera are more stable over time. C_LIO_LIAt each of the four body sites, systematic interactions between the environment, the host and bacteria can be detected. C_LIO_LIIndividuals with insulin resistance have lower microbiome stability, a more diversified skin microbiome, and significantly altered host-microbiome interactions. C_LI

microbiology↗

bioRxiv2022

Longitudinal Urine Metabolic Profiling and Gestational Age Prediction in Pregnancy

Pregnancy is a critical time that has long-term impacts on both maternal and fetal health. During pregnancy, the maternal metabolome undergoes dramatic systemic changes, although correlating longitudinal changes in maternal urine remain largely unexplored. We applied an LCMS-based untargeted metabolomics profiling approach to analyze 346 longitudinal maternal urine samples collected throughout pregnancy for 36 women from diverse ethnic backgrounds with differing clinical characteristics. We detected 20,314 metabolic peaks and annotated 875 metabolites. Altered metabolites include a broad panel of glucocorticoids, lipids, and amino acid derivatives, which revealed systematic pathway alterations during pregnancy. We also developed a machine-learning model to precisely predict gestational age (GA) at time of sampling using urine metabolites that provides a non-invasive method for pregnancy dating. This longitudinal maternal urine study demonstrates the clinical utility of using untargeted metabolomics in obstetric settings. One Sentence SummaryMachine-learning based gestational age and due date using longitudinal urine samples of pregnancy.

systems biology↗

bioRxiv2020

Molecular Choreography of Acute Exercise

Exercise testing is routinely used in clinical practice to assess fitness - a strong predictor of survival - as well as causes of exercise limitations. While these studies often focus on cardiopulmonary response and selected molecular pathways, the dynamic system-wide molecular response to exercise has not been fully characterized. We performed a longitudinal multi-omic profiling of plasma and peripheral blood mononuclear cells including transcriptome, immunome, proteome, metabolome and lipidome in 36 well-characterized volunteers before and after a controlled bout of acute exercise (2, 15, 30 min and 1 hour in recovery). Integrative analysis revealed an orchestrated choreography of biological processes across key tissues. Most of these processes were dampened in insulin resistant participants. Finally, we discovered biological pathways involved in exercise capacity and developed prediction models revealing potential resting blood-based biomarkers of fitness.

systems biology↗

bioRxiv2017

High Frequency Actionable Pathogenic Exome Mutations in an Average-Risk Cohort

Whole exome sequencing (WES) is increasingly utilized in both clinical and non-clinical settings, but little is known about the utility of WES in healthy individuals. In order to determine the frequency of both medically actionable and non-actionable but medically relevant exome findings in the general population we assessed the exomes of 70 participants who have been extensively characterized over the past several years as part of a longitudinal integrated multi-omics profiling study at Stanford University. We assessed exomes for rare likely pathogenic and pathogenic variants in genes associated with Mendelian disease in the Online Mendelian Inheritance in Man (OMIM) database. We used American College of Medical Genetics (ACMG) guidelines were used for the classification of rare sequence variants, and additionally we assessed pharmacogenetic variants. Twelve out of 70 (17%) participants had medically actionable findings in Mendelian disease genes, including 6 (9%) with mutations in genes not currently included in the ACMGs list of 59 actionable genes. This number is higher than that reported in previous studies and suggests added benefit from utilizing expanded gene lists and manual curation to assess actionable findings. A total of 60 participants (89%) had non-actionable findings identified including 57 who were found to be mutation carriers for recessive diseases and 21 who have increased Alzheimers disease risk due to heterozyg ous or homozygous APOE e4 alleles (18 participants had both). These results suggest that exome sequencing may have considerably more utility for health management in the general population than previously thought.

genomics↗