Biology subjects

Hamid, Z.

Publications and source records attributed to Hamid, Z..

3 recordsLinked to original sources

bioRxiv2023

Multi-omics Analysis of Aging Liver Reveals Changes in Endoplasmic Stress and Degradation Pathways in Female Nonhuman Primates

The liver is critical for functions that support metabolism, immunity, digestion, detoxification, and vitamin storage. Aging is associated with severity and poor prognosis of various liver diseases such as nonalcoholic fatty liver disease (NAFLD). Previous studies have used multi-omic approaches to study liver diseases or to examine the effects of aging on the liver. However, to date, no studies have used an integrated omics approach to investigate aging-associated molecular changes in the livers of healthy female nonhuman primates. The goal of this study was to identify molecular changes associated with healthy aging in the livers of female baboons (Papio sp., n=35) by integrating multiple omics data types (transcriptomics, proteomics, metabolomics) from samples across the adult age span. To integrate omics data, we performed unbiased weighted gene co-expression network analysis (WGCNA), and the results revealed 3 modules containing 3,149 genes and 33 proteins were positively correlated with age, and 2 modules containing 37 genes and 216 proteins were negatively correlated with age. Pathway enrichment analysis showed that unfolded protein response (UPR) and endoplasmic reticulum (ER) stress were positively associated with age, whereas xenobiotic metabolism and melatonin and serotonin degradation pathways were negatively associated with age. The findings of our study suggest that UPR and a reduction in reactive oxygen species generated from serotonin degradation could protect the liver from oxidative stress during the aging process in healthy female baboons.

molecular biology↗

bioRxiv2022

Integrated Multi-Omics Analysis of Brain Aging in Female Nonhuman Primates Reveals Altered Signaling Pathways Relevant to Age-Related Disorders

The prefrontal cortex (PFC) has been implicated as a key brain region responsible for age-related cognitive decline. Little is known about aging-related molecular changes in PFC that may mediate these effects. To date, no studies have used untargeted discovery methods with integrated analyses to determine PFC molecular changes in healthy female primates. We quantified PFC changes associated with healthy aging in female baboons by integrating multiple omics data types (transcriptomics, proteomics, metabolomics) from samples across the adult age span. Our integrated omics approach using unbiased weighted gene co-expression network analysis (WGCNA) to integrate data and treat age as a continuous variable, revealed highly interconnected known and novel pathways associated with PFC aging. We found GABA tissue content associated with these signaling pathways, providing one potential biomarker to assess PFC changes with age. These highly coordinated pathway changes during aging may represent early steps for aging-related decline in PFC functions, such as learning and memory, and provide potential biomarkers to assess cognitive status in humans.

systems biology↗

bioRxiv2021

Assessment of label-free quantification and missing value imputation for proteomics in non-human primates

IntroductionReliable and effective label-free quantification (LFQ) analyses are dependent not only on the method of data acquisition in the mass spectrometer, but also on the downstream data processing, including software tools, query database, data normalization and imputation. In non-human primates (NHP), LFQ is challenging because the query databases for NHP are limited since the genomes of these species are not comprehensively annotated. This invariably results in limited discovery of proteins and associated Post Translational Modifications (PTMs) and a higher fraction of missing data points. While identification of fewer proteins and PTMs due to database limitations can negatively impact uncovering important and meaningful biological information, missing data also limits downstream analyses (e.g., multivariate analyses), decreases statistical power, biases statistical inference, and makes biological interpretation of the data more challenging. In this study we attempted to address both issues: first, we used the MetaMorphues proteomics search engine to counter the limits of NHP query databases and maximize the discovery of proteins and associated PTMs, and second, we evaluated different imputation methods for accurate data inference. ResultsUsing the MetaMorpheus proteomics search engine we obtained quantitative data for 1,622 proteins and 10,634 peptides including 58 different PTMs (biological, metal and artifacts) across a diverse age range of NHP brain frontal cortex. However, among the 1,622 proteins identified, only 293 proteins were quantified across all samples with no missing values, emphasizing the importance of implementing an accurate and statiscaly valid imputation method to fill in missing data. In our imputation analysis we demonstrate that Single Imputation methods that borrow information from correlated proteins such as Generalized Ridge Regression (GRR), Random Forest (RF), local least squares (LLS), and a Bayesian Principal Component Analysis methods (BPCA), are able to estimate missing protein abundance values with great accuracy. ConclusionsOverall, this study offers a detailed comparative analysis of LFQ data generated in NHP and proposes strategies for improved LFQ in NHP proteomics data.

genomics↗