Biology subjects

Zare, H.

Publications and source records attributed to Zare, H..

8 recordsLinked to original sources

bioRxiv2026

Simultaneous Inhibition of ACLY and OGDH Has a Synergistic Effect on Hepatocellular Carcinoma Cell Lines

Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality worldwide. Current treatments offer limited efficacy and no definitive cure, underscoring the urgent need for more selective and effective therapeutic strategies. This study investigated the synthetic lethality caused by co-targeting two metabolic genes, ATP citrate lyase (ACLY) and oxoglutarate dehydrogenase (OGDH), in HCC cells. Using valproic acid (VPA) and bempedoic acid (BA) as pharmacological inhibitors of OGDH and ACLY, respectively, we observed a strong synergistic effect in inhibiting the proliferation of HCC cell lines (Hep3B and Huh7), compared to using these drugs individually. Importantly, this combination treatment exhibited little increased cytotoxicity in the non-cancerous liver cell line THLE-2, indicating a degree of selectivity. Our findings are consistent with previous reports implicating USP13 as a metabolic regulator of ACLY and OGDH in various cancers, suggesting that the inhibition of USP13 may prevent HCC cell proliferation primarily through its downstream effects on ACLY and OGDH. By directly co-targeting ACLY and OGDH, our approach may offer a more precise and safer alternative to USP13 inhibition. Additionally, while both VPA and BA have been individually associated with beneficial effects in liver disease, their combined application in the context of HCC has not been previously investigated. Limitations include the reliance on cell line models, highlighting the need for validation in more physiologically relevant systems such as human organoids and animal models. Overall, this study provides a compelling rationale for further investigation into ACLY and OGDH as a synthetic lethal pair and the therapeutic potential of the VPA-BA combination treatment in HCC.

cancer biology↗

bioRxiv2025

HypoAD: volumetric and single-cell analysis reveals changes in the human hypothalamus in aging and Alzheimer's disease

Alterations in metabolism, stress response, sleep, circadian rhythms, and neuroendocrine processes are key features of aging and neurodegeneration. These fundamental processes are regulated by the hypothalamus, yet how its functionally distinct subregions and cell types change during human aging and Alzheimers Disease (AD) remains largely unexplored. Here, we present HypoAD, a comprehensive atlas of the human hypothalamus in aging and AD, integrating high-resolution MRI from 202 individuals with single-nucleus RNA-seq (snRNA-seq) of 614,403 nuclei from young, AD, and age-matched non-dementia controls. Our analysis reveals that hypothalamic subregions governing metabolism, stress, and circadian rhythms are particularly vulnerable, exhibiting significant changes in both volumes and gene expression during aging and AD. At the molecular level, machine learning models identified the inflammatory response and regulators of circadian rhythms as key cellular predictors of AD. These signatures were reflected in specific cell types: microglia transitioned to a pro-inflammatory state, while inhibitory neurons within sleep-and circadian-regulating hypothalamic subregions showed the most profound transcriptional alterations, including disruptions in ligand-receptor interactions and G-protein-coupled receptor signaling. Together, HypoAD provides a high-resolution volumetric map and a comprehensive transcriptomic atlas of the human hypothalamus in aging and AD, linking lifestyle and behavioral changes to their underlying volumetric and molecular pathways. Additionally, HypoAD provides a framework to investigate hypothalamic dysfunction and establishes a roadmap for targeted interventions aimed at mitigating physiological disruptions to potentially slow disease progression.

neuroscience↗

bioRxiv2025

Higher order synthetic lethals are keys to minimize cancer treatment effects on non-tumor cells

Metabolic rewiring in cancer cells facilitates the provision of essential precursors for the unbridled growth of tumors. Exploring these cancer-specific metabolic changes offers potential selective therapeutic strategies. However, targeting a single essential gene in cancer treatment often faces challenges, including resistance, lack of targetable oncogenes, and potential harm to non-tumor cells. Attacking multiple targets is hypothesized as a solution to overcome these issues, e.g., a synthetic lethal set, defined as a minimal combination of non-lethal genetic mutations leading to cell death. This study examined the potential of synthetic lethal sets to identify selective drug targets for 13 cancers and the corresponding non-tumor tissues, utilizing context-specific genome-scale metabolic models. To ensure the minimization of therapeutic side effects, this work introduced the concept of strictly-selective drug targets (SSDTs) and the harmlessness of identified targets in all 13 different non-tumor tissues was meticulously verified. Accordingly, for 13 types of cancers, over 500 SSDTs were identified, predominantly including higher-order synthetic lethal sets with more than two targets in each set. Interestingly, for specific cancers where single essential or synthetic lethal genes could not provide acceptable solutions, SSDTs were provided by higher-order synthetic lethal sets. Therefore, for the first time, this study successfully showed that leveraging higher-order synthetic lethal sets holds the key to promising strictly-selective solutions. Furthermore, nine quadruple SSDTs were identified which commonly target five different cancers without harming any of the 13 non-tumor tissues. Further experimental validation of these findings is required to select the most promising treatments for clinical studies.

systems biology↗

bioRxiv2024

A deep neural network to de-noise single-cell RNAsequencing data

Single-cell RNA sequencing (scRNA-seq), a powerful technique for investigating the transcriptome of individual cells, enables the discovery of heterogeneous cell populations, rare cell types, and transcriptional dynamics in separate cells. Yet, scRNA-seq data analysis is limited by the problem of measurement dropouts, i.e., genes displaying zero expression levels. We introduce ZiPo, a deep artificial neural network for rate estimation and library size prediction in scRNA-seq data which incorporates adjustable zero inflation in the distribution to capture the dropouts. ZiPo builds upon established concepts, including using deep autoencoders and adopting the Poisson and negative binomial distributions, by taking advantage of novel strategies, including library size prediction and residual connections, to improve the overall performance. A significant innovation of ZiPo is the introduction of a scale-invariant loss term, making the weights sparse and, hence, the model biologically more interpretable. ZiPo quickly handles vast singular and mixed datasets, with the processing time directly proportional to the number of cells. In this paper, we demonstrate the power of ZiPo on three datasets and show its advantages over other current techniques. The code used to produce the results in this manuscript is available at https://bitbucket.org/habilzare/alzheimer/src/master/code/deep/ZiPo/.

bioinformatics↗

bioRxiv2024

Nanopore-based DNA long-read sequencing analysis of the aged human brain

Studies in laboratory models and postmortem human brain tissue from patients with Alzheimers disease have revealed disruption of basic cellular processes such as DNA repair and epigenetic control as drivers of neurodegeneration. While genomic alterations in regions of the genome that are rich in repetitive sequences, often termed "dark regions," are difficult to resolve using traditional sequencing approaches, long-read technologies offer promising new avenues to explore previously inaccessible regions of the genome. In the current study, we leverage nanopore-based long-read whole-genome sequencing of DNA extracted from postmortem human frontal cortex at early and late stages of Alzheimers disease, as well as age-matched controls, to analyze retrotransposon insertion events, non-allelic homologous recombination (NAHR), structural variants and DNA methylation within retrotransposon loci and other repetitive/dark regions of the human genome. Interestingly, we find that retrotransposon insertion events and repetitive element-associated NAHR are particularly enriched within centromeric and pericentromeric regions of DNA in the aged human brain, and that ribosomal DNA (rDNA) is subject to a high degree of NAHR compared to other regions of the genome. We detect a trending increase in potential somatic retrotransposition events of the small interfering nuclear element (SINE) AluY in late-stage Alzheimers disease, and differential changes in methylation within repetitive elements and retrotransposons according to disease stage. Taken together, our analysis provides the first long-read DNA sequencing-based analysis of retrotransposon sequences, NAHR, structural variants, and DNA methylation in the aged brain, and points toward transposable elements, centromeric/pericentromeric regions and rDNA as hotspots for genomic variation.

genomics↗

bioRxiv2023

Systematic analysis of microorganisms' metabolism for selective targeting

Selective drug targets (i.e., narrow-spectrum antibiotics) can minimize side effects of antibiotic treatments compared to broad-spectrum antibiotics due to their specific targeting of the organisms responsible for the infection. Furthermore, combating an infectious pathogen, especially a drug-resistant organism, is more efficient by attacking multiple targets. Here, we combined synthetic lethality with selective drug targeting to obtain multi-target and organism-specific potential drug candidates by systematically analyzing the genome-scale metabolic models of six different microorganisms. By considering microorganisms as targeted or conserved in one- to six-member groups, we designed 665 individual case studies. For each case, we identified single essential reactions as well as double, triple, and quadruple synthetic lethal reaction sets that are lethal for targeted microorganisms and neutral for conserved ones. As expected, the number of obtained solutions for each case depends on the genomic similarity between the studied microorganisms. Mapping the identified potential drug targets to their corresponding pathways showed the importance of key subsystems such as cell envelope biosynthesis, glycerophospholipid metabolism, membrane lipid metabolism, and the nucleotide salvage pathway. To assist validation and further investigation of our proposed potential drug targets, we introduced two sets of targets that can potentially address a substantial portion of the 665 cases. We expect that the obtained solutions provide helpful insights into designing narrow-spectrum drugs that selectively cause system-wide damage only to the target microorganisms.

systems biology↗

bioRxiv2020

Molecular Characterization of Depression Trait and State

Major Depressive disorder (MDD) is a chronic and recurrent brain disorder characterized by episode and remission phases, and poor therapeutic responses. The molecular correlates of MDD have been investigated in case-control settings, but the biological changes associated with trait (regardless of episode/remission) or state (illness phases) remains largely unknown, hence preventing therapeutic opportunities. To address this gap, we generated transcriptome profiles in the subgenual anterior cingulate cortex of MDD subjects who died during a single or recurrent episode or when in remission. We show that biological changes associated with MDD trait (inflammation, immune activation, reduced bioenergetics) are distinct from those associated with MDD phases or state (neuronal structure and function, neurotransmission). On the cell-type level, gene variability in subsets of GABAergic interneurons positive for corticotropin-releasing hormone, somatostatin or vasoactive-intestinal peptide was associated with MDD phases. Applying a probabilistic Bayesian network approach, we next show that gene modules enriched for immune system activation, cytokine response and oxidative stress, may exert causal roles across MDD phases. Finally, using a database of drug-induced transcriptome perturbations, we show that MDD-induced changes in putative causal pathways are antagonized by families of drugs associated with clinical response, including dopaminergic and monoaminergic ligands, and uncover potential novel therapeutic targets. Collectively, these integrative transcriptome analyses provide novel insight into cellular and molecular pathologies associated with trait and state MDD, and a method of drug discovery focused on disease-causing pathways. One Sentence SummaryIntegrating transcriptomic with various in-silico analyses identified cellular, molecular and putative biological causal pathways in trait and state depression

neuroscience↗

bioRxiv2018

Genetic Determinants of Cortical Structure (Thickness, Surface Area and Volumes) among Disease Free Adults in the CHARGE Consortium

Cortical thickness, surface area and volumes (MRI cortical measures) vary with age and cognitive function, and in neurological and psychiatric diseases. We examined heritability, genetic correlations and genome-wide associations of cortical measures across the whole cortex, and in 34 anatomically predefined regions. Our discovery sample comprised 22,822 individuals from 20 cohorts within the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the United Kingdom Biobank. Significant associations were replicated in the Enhancing Neuroimaging Genetics through Meta-analysis (ENIGMA) consortium, and their biological implications explored using bioinformatic annotation and pathway analyses. We identified genetic heterogeneity between cortical measures and brain regions, and 161 genome-wide significant associations pointing to wnt/{beta}-catenin, TGF-{beta} and sonic hedgehog pathways. There was enrichment for genes involved in anthropometric traits, hindbrain development, vascular and neurodegenerative disease and psychiatric conditions. These data are a rich resource for studies of the biological mechanisms behind cortical development and aging.

genetics↗