Biology subjects

Habibi, M.

Publications and source records attributed to Habibi, M..

13 recordsLinked to original sources

bioRxiv2026

Thyroid Dysfunction in Male Patients at Asia Med Laboratory, Herat, Afghanistan July 2021-Jan 2022

Objective: Hyperthyroidism and hypothyroidism related to iodine deficiency are major public health concerns in Afghanistan. This study aimed to assess the frequency of thyroid dysfunction among male patients referred for thyroid testing and its association with age, and to examine monthly trends in thyroid dysfunction at Asia Med Laboratory in Herat, Afghanistan, from July 2021 to January 2022. Methods: A retrospective analysis was conducted on 250 male patients aged 0-69 years. We measured Serum TSH, total T4, and total T3 levels, and thyroid status was classified using age specific reference ranges. In addition, the frequency of thyroid dysfunction was analyzed across age groups with monthly trends of thyroid state. Results: Overall, the euthyroid state consisted of 69.2% of participants, 24.8% with overt hypothyroidism, 3.2% with overt hyperthyroidism, and 2.8% with subclinical hyperthyroidism. Thyroid status differed significantly by age (p = 0.0135), with hypothyroidism increasing in older age groups and reaching its highest proportion among men aged 60-69 years (55.6%). Euthyroidism predominated in patients aged 10-39 years, while hyperthyroidism across age groups remained relatively infrequent. After September 2021, a threefold increase was observed in the total number of male patients referred for thyroid testing. During this period, the proportion of hyperthyroidism increased slightly, whereas hypothyroidism cases declined. Conclusion: In conclusion, hypothyroidism was more frequent with older age. The rise in absolute case numbers after September 2021 likely reflects increased patient referrals, underscoring the need for ongoing monitoring of thyroid function. The study may assist in the early management of thyroid disorders and in reducing their complications.

pathology↗

bioRxiv2026

Pharmacological rescue of mitochondrial dysfunction, neurite degeneration, and premature death of ALS and AD iPSC-derived neurons

Mitochondrial (MT) dysfunction is a key driver of ALS pathology. Without a healthy MT system, motor neurons (MN) function at sub-optimal levels and die. In addition, other effects of ALS, like axon/dendrite degeneration, may occur from a pathophysiological cascade spurred by MT dysfunction. A phenotypic screen identified Dipyridamole (DPM), an FDA-approved and safe drug, as having extraordinary effects on ALS patient induced pluripotent stem cell (iPSC)-derived MNs. The drug prevented MT fragmentation, loss of MT content, impaired MT bioenergetics, axon/dendrite degeneration, and premature MN death, extending neuronal survival by more than fivefold. Importantly, its efficacy extended across iPSC-derived neurons representing two different familial forms of ALS (C9orf72, TDP43) and Alzheimers disease (PSEN1), implying broad neuroprotection across ALS forms and other neurodegenerative diseases. DPM increased MT respiration and pyruvate uptake in a mechanism requiring the Mitochondrial Pyruvate Carrier (MPC), mechanistically explaining its biological activities. Thus, DPM is a promising drug to repurpose or refine for treating neurodegenerative diseases or other diseases that would benefit by augmenting pyruvate uptake into MT. TeaserDipyridamole, an FDA-approved drug, restores mitochondrial function and protects neurons in ALS and Alzheimers disease.

neuroscience↗

bioRxiv2025

Adipose Tissue Overexpression of Nicotinamide Phosphoribosyltransferase Prevents Metabolic Dysfunction in Obese Mice

Nicotinamide adenine dinucleotide (NAD+) is a vital coenzyme and a central factor in energy metabolism. Nicotinamide phosphoribosyltransferase (NAMPT) maintains the cellular NAD+ pool by synthesizing the NAD+ precursor, nicotinamide mononucleotide (NMN), and diminished adipocyte NAMPT activity has been implicated in aging- and obesity-related metabolic dysfunction. Herein, we examined the effects of overexpressing or knocking out NAMPT in adipocytes on metabolic dysfunction and interorgan communication in mice. We generated new adipocyte-specific NAMPT overexpressing(ANOV) mice model. Male ANOV mice are protected from diet-induced metabolic dysfunction including adipose tissue inflammation, glucose intolerance, and insulin resistance. In contrast female ANOV mice were less protected from metabolic dysfunction, possibly due to higher endogenous expression of NAMPT in WT female mice. Livers of ANOV mice showed improved insulin signaling, increased NAD content, and reduced steatosis, suggesting that NAMPT regulates interorgan communication between adipocytes and hepatocytes. Extracellular vesicles (EV) isolated from ANOV mice enhanced insulin signaling in HepG2 cells and improved glucose tolerance in WT obese mice. In contrast, EV from ANKO mice suppressed HepG2 insulin signaling and inhibition of EV release improved glucose tolerance in ANKO female mice. Collectively, these data highlight a novel mechanism by which adipocyte NAD+ metabolism regulates systemic metabolic dysfunction via EVs.

physiology↗

bioRxiv2023

Disruption of Hepatic Mitochondrial Pyruvate and Amino Acid Metabolism Impairs Gluconeogenesis and Endurance Exercise Capacity in Mice

Exercise robustly increases the glucose demands of skeletal muscle. This demand is met not only by muscle glycogenolysis, but also by accelerated liver glucose production from hepatic glycogenolysis and gluconeogenesis to fuel mechanical work and prevent hypoglycemia during exercise. Hepatic gluconeogenesis during exercise is dependent on highly coordinated responses within and between muscle and liver. Specifically, exercise increases the rate at which gluconeogenic precursors such as pyruvate/lactate or amino acids are delivered from muscle to the liver, extracted by the liver, and channeled into glucose. Herein, we examined the effects of interrupting gluconeogenic efficiency and capacity on exercise performance by deleting hepatic mitochondrial pyruvate carrier 2 (MPC2) and/or alanine transaminase 2 (ALT2) in mice. We found that deletion of MPC2 or ALT2 alone did not significantly affect time to exhaustion or post-exercise glucose concentrations in treadmill exercise tests, but mice lacking both MPC2 and ALT2 in liver (DKO) reached exhaustion faster and exhibited lower circulating glucose during and after exercise. Use of {superscript 2}H/{superscript 1}3C metabolic flux analyses demonstrated that DKO mice exhibited lower endogenous glucose production owing to decreased glycogenolysis and gluconeogenesis at rest and during exercise. The decreased gluconeogenesis was accompanied by lower anaplerotic, cataplerotic, and TCA cycle fluxes. Collectively, these findings demonstrate that the transition of the liver to the gluconeogenic mode is critical for preventing hypoglycemia and sustaining performance during exercise. The results also illustrate the need for interorgan crosstalk during exercise as described by the Cahill and Cori cycles.

physiology↗

bioRxiv2023

Small extracellular vesicles promote stiffness-mediated metastasis

Tissue stiffness is a critical prognostic factor in breast cancer and is associated with metastatic progression. Here we show an alternative and complementary hypothesis of tumor progression whereby physiological matrix stiffness affects the quantity and protein cargo of small EVs produced by cancer cells, which in turn drive their metastasis. Primary patient breast tissue produces significantly more EVs from stiff tumor tissue than soft tumor adjacent tissue. EVs released by cancer cells on matrices that model human breast tumors (25 kPa; stiff EVs) feature increased adhesion molecule presentation (ITG2{beta}1, ITG6{beta}4, ITG6{beta}1, CD44) compared to EVs from softer normal tissue (0.5 kPa; soft EVs), which facilitates their binding to extracellular matrix (ECM) protein collagen IV, and a 3-fold increase in homing ability to distant organs in mice. In a zebrafish xenograft model, stiff EVs aid cancer cell dissemination through enhanced chemotaxis. Moreover, normal, resident lung fibroblasts treated with stiff and soft EVs change their gene expression profiles to adopt a cancer associated fibroblast (CAF) phenotype. These findings show that EV quantity, cargo, and function depend heavily on the mechanical properties of the extracellular microenvironment. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/545937v3_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@1be1dbdorg.highwire.dtl.DTLVardef@928710org.highwire.dtl.DTLVardef@1e14e9borg.highwire.dtl.DTLVardef@efdb7f_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2023

Mitochondrial Pyruvate Carrier Inhibition in Hepatic Stellate Cells Attenuates Fibrosis in Nonalcoholic Steatohepatitis in Mice

Hepatic stellate cells (HSC) are non-parenchymal liver cells that produce extracellular matrix comprising fibrotic lesions in chronic liver diseases. Prior work demonstrated that mitochondrial pyruvate carrier (MPC) inhibitors suppress HSC activation and fibrosis in a mouse model of metabolic dysfunction-associated steatohepatitis (MASH). In the present study, pharmacologic or genetic inhibition of the MPC in HSC decreased expression of markers of activation in vitro. MPC knockdown also reduced the abundance of several intermediates of the TCA cycle, and diminished -ketoglutarate played a key role in attenuating HSC activation by suppressing hypoxia inducible factor-1 signaling. On high fat diets, mice with HSC-specific MPC deletion exhibited reduced circulating transaminases, numbers of HSC, and hepatic expression of markers of HSC activation and inflammation compared to wild-type mice. These data suggest that MPC inhibition modulates HSC metabolism to attenuate activation and illuminate mechanisms by which MPC inhibitors could prove therapeutically beneficial for treating MASH.

physiology↗

bioRxiv2022

A Novel Machine Learning Method for Mutational Analysis to Identifying Driver Genes in Breast Cancer

Breast cancer has emerged as a severe public health issue and one of the main reasons for cancer-related mortality in women worldwide. Although the definitive reason for breast cancer is unknown, many genes and mutations in these genes associated with breast cancer have been identified using developed methods. The recurrence of a mutation in patients is a highly used feature for finding driver mutations. However, for various reasons, some mutations are more likely to arise than others. Sequencing analysis has demonstrated that cancer-driver genes perform across complicated pathways and networks, with mutations often arising in a modular pattern. In this work, we proposed a novel machine-learning method to study the functionality of genes in the networks derived from mutation associations, gene-gene interactions, and graph clustering for breast cancer analysis. These networks have revealed essential biological elements in the vital pathways, notably those that undergo low-frequency mutations. The statistical power of the clinical study is considerably increased when evaluating the network rather than just the effects of a single gene. The proposed method discovered key driver genes with various mutation frequencies. We investigated the function of the potential driver genes and related pathways. By presenting lower-frequency genes, we recognized breast cancer-related pathways that are less studied. In addition, we suggested a novel Monte Carlo-based algorithm to identify driver modules in breast cancer. We demonstrated our proposed modules importance and role in critical signaling pathways in breast cancer, and this evaluation for breast cancer-related driver modules gave us an inclusive insight into breast cancer development.

cancer biology↗

bioRxiv2022

A new machine learning method for cancer mutation analysis

It is complicated to identify cancer-causing mutations. The recurrence of a mutation in patients remains one of the most reliable features of mutation driver status. However, some mutations are more likely to happen than others for various reasons. Different sequencing analysis has revealed that cancer driver genes operate across complex pathways and networks, with mutations often arising in a mutually exclusive pattern. Genes with low-frequency mutations are understudied as cancer-related genes, especially in the context of networks. Here we propose a machine learning method to study the functionality of mutually exclusive genes in the networks derived from mutation associations, gene-gene interactions, and graph clustering. These networks have indicated critical biological components in the essential pathways, especially those mutated at low frequency. Studying the network and not just the impact of a single gene significantly increases the statistical power of clinical analysis. The proposed method identified important driver genes with different frequencies. We studied the function and the associated pathways in which the candidate driver genes participate. By introducing lower-frequency genes, we recognized less studied cancer-related pathways. We also proposed a novel clustering method to specify driver modules in each type of cancer. We evaluated each cluster with different criteria, including the terms of biological processes and the number of simultaneous mutations in each cancer. Materials and implementations are available at: https://github.com/MahnazHabibi/Mutation_Analysis

cancer biology↗

bioRxiv2022

Comprehensive analysis of pathways in Coronavirus 2019 (COVID-19) using an unsupervised machine learning method

The World Health Organization (WHO) introduced "Coronavirus disease 19" or "COVID-19" as a novel coronavirus in March 2020. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requires the fast discovery of effective treatments to fight this worldwide crisis. Artificial intelligence and bioinformatics analysis pipelines can assist with finding biomarkers, explanations, and cures. Artificial intelligence and machine learning methods provide powerful infrastructures for interpreting and understanding the available data. On the other hand, pathway enrichment analysis, as a dominant tool, could help researchers discover potential key targets present in biological pathways of host cells that are targeted by SARS-CoV-2. In this work, we propose a two-stage machine learning approach for pathway analysis. During the first stage, four informative gene sets that can represent important COVID-19 related pathways are selected. These "representative genes" are associated with the COVID-19 pathology. Then, two distinctive networks were constructed for COVID-19 related signaling and disease pathways. In the second stage, the pathways of each network are ranked with respect to some unsupervised scorning method based on our defined informative features. Finally, we present a comprehensive analysis of the top important pathways in both networks. Materials and implementations are available at: https://github.com/MahnazHabibi/Pathway.

systems biology↗

bioRxiv2022

Using unsupervised learning algorithms to identify essential genes associated with SARS-CoV-2 as potential therapeutic targets for COVID-19

MotivationSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requires the fast discovery of effective treatments to fight this worldwide concern. Several genes associated with the SARS-CoV-2, which are essential for its functionality, pathogenesis, and survival, have been identified. These genes, which play crucial roles in SARS-CoV-2 infection, are considered potential therapeutic targets. Developing drugs against these essential genes to inhibit their regular functions could be a good approach for COVID-19 treatment. Artificial intelligence and machine learning methods provide powerful infrastructures for interpreting and understanding the available data and can assist in finding fast explanations and cures. ResultsWe propose a method to highlight the essential genes that play crucial roles in SARS-CoV-2 pathogenesis. For this purpose, we define eleven informative topological and biological features for the biological and PPI networks constructed on gene sets that correspond to COVID-19. Then, we use three different unsupervised learning algorithms with different approaches to rank the important genes with respect to our defined informative features. Finally, we present a set of 18 important genes related to COVID-19. AvailabilityMaterials and implementations are available at: https://github.com/MahnazHabibi/Gene_analysis. Contactm_habibi@qiau.ac.ir Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2020

Deep Learning Identification of Stiffness Markers in Breast Cancer

While essential to our understanding of solid tumor progression, the study of cell and tissue mechanics has yet to find traction in the clinic. Determining tissue stiffness, a mechanical property known to promote a malignant phenotype in vitro and in vivo, is not part of the standard algorithm for the diagnosis and treatment of breast cancer. Instead, clinicians routinely use mammograms to identify malignant lesions and radiographically dense breast tissue is associated with an increased risk of developing cancer. Whether breast density is related to tumor tissue stiffness, and what cellular and non-cellular components of the tumor contribute the most to its stiffness are not well understood. Through training of a deep learning network and mechanical measurements of fresh patient tissue, we create a bridge in understanding between clinical and mechanical markers. The automatic identification of cellular and extracellular features from hematoxylin and eosin (H&E)-stained slides reveals that global and local breast tissue stiffness best correlate with the percentage of straight collagen. Global breast tissue mechanics correlate weakly with the percentage of blood vessels and fibrotic tissue, and non-significantly with the percentage of fat, ducts, tumor cells, and wavy collagen in tissue. Importantly, the percentage of dense breast tissue does not directly correlate with tissue stiffness or straight collagen content.

cancer biology↗

bioRxiv2020

Multi-compartment tumor organoids

Organoid cultures are widely used because they preserve many features of cancer cells in vivo. Here, we developed high-throughput oil-in-water droplet microtechnology to generate highly uniform, small-volume, multi-compartment organoids. Each organoid culture features a microenvironmental architecture that mimics both the basement membrane and stromal barriers. This matrix architecture, which allows accessing both proliferative and invasive features of cancer cells in a single platform, has profound effect on observed drug responsiveness and tumor progression that correlate well with in vivo and clinical outcomes. The method was tested on multiple types of cancer cells including primary cells and immortalized cell lines, and we determined our platform is suitable even for cells of poor organoid-forming ability. These new organoids also allow for direct orthotopic mouse implantation of cancer cells with unprecedented success.

cancer biology↗

bioRxiv2018

In Silico Determined Properties of Designed Superoxide Dismutase-1 Mutants Predict ALS-like Phenotypes In Vitro and In Vivo

The underlying physical causes of SOD1-related ALS are still not well-understood. We address this problem here by computationally designing two de novo mutants, A89R and K128N, which were predicted theoretically to be either significantly destabilizing or stabilizing respectively. We subjected these in silico designed mutants to a series of experimental tests, including in vitro measures of thermodynamic stability, cell-based aggregation and toxicity assays, and an in vivo developmental model of zebrafish motor neuron axonopathy. The experimental tests validated the theoretical predictions: A89R is an unstable, highly-deleterious mutant, and K128N is a stable, non-toxic mutant. Moreover, K128N is predicted computationally to form an unusually stable heterodimer with the familial ALS mutant A4V. Consistent with this prediction, co-injection of K128N and A4V into zebrafish shows profound rescue of motor neuron pathology. The demonstrated success of these first principles calculations to predict the physical properties of SOD1 mutants holds promise for rationally designed therapies to counter the progression of ALS.\n\nSignificanceMutations in the protein superoxide dismutase cause ALS, and many of these mutants have decreased folding stability. We sought to pursue this thread using a synthetic biology approach, where we designed two de novo mutations, one stabilizing and one destabilizing, as predicted using computational molecular dynamics simulations. We then tested these mutants using in vitro, cell-based, and in vivo zebrafish models. We found that the unstable mutant was toxic, and induced a severe ALS phenotype in zebrafish; the predicted stable mutant, on the other hand, behaved even better than WT. In fact, it was able to rescue the ALS phenotype caused by mutant SOD1. We propose a mechanism for this rescue, which may provide an avenue for therapeutic intervention.

biophysics↗