Biology subjects

Yazdani, S.

Publications and source records attributed to Yazdani, S..

8 recordsLinked to original sources

bioRxiv2025

Deformed Probability Estimation in Goal-Directed reinforcement learning model explains anxious-depression dimensions of psychiatric disorders

Psychiatric disorders are complex, multi-dimensional pathologies rooted in diverse cognitive processes. Computational psychiatry aims to reveal distortions in these processes through behavior modeling, providing a deeper understanding of psychiatric disorders. Previous studies, using Daws two-stage task, had linked the imbalance between habitual/model-free and goal-directed/model-based behaviors to disorders with compulsive behaviors and intrusive thoughts. The model-based component relies on the estimation of environmental probabilities. Therefore, we added a well-known deformation in subjective probability estimation to the model and this improved the model fitting. More importantly, the fitted deformation explains some variance of the anxious-depression dimension of psychiatric symptoms. The deformation parameter is aligned with the description-experience gap in decision-making literature. Our results point to subjective possibly distortion as the probable underlying cognitive process of anxiety, apathy, and depression. This study also shows that the inclusion of cognitive biases in modeling can extract the hidden aspects of behavior possibility linked to disorders. Our approach enhances the precision of computational psychiatry and provides deeper insights into the cognitive processes underlying psychiatric symptoms, paving the way for more effective, personalized therapeutic strategies. Significance StatementThis study builds on a previously used model and Data that indicated a correlation between Model-Based preference and certain psychiatric disorders. By adding distortion to the probability estimation, we identified a new parameter correlated with depression and anxiety. The augmented model demonstrates an improved fit to behavior and aligns with Gillans previous findings. Our approach enhances the precision of computational psychiatry and provides deeper insights into the cognitive processes underlying psychiatric symptoms, paving the way for more effective, personalized therapeutic strategies.

neuroscience↗

bioRxiv2024

PIKfyve controls dendritic cell function and tumor immunity

The modern armamentarium for cancer treatment includes immunotherapy and targeted therapy, such as protein kinase inhibitors. However, the mechanisms that allow cancer-targeting drugs to effectively mobilize dendritic cells (DCs) and affect immunotherapy are poorly understood. Here, we report that among shared gene targets of clinically relevant protein kinase inhibitors, high PIKFYVE expression was least predictive of complete response in patients who received immune checkpoint blockade (ICB). In immune cells, high PIKFYVE expression in DCs was associated with worse response to ICB. Genetic and pharmacological studies demonstrated that PIKfyve ablation enhanced DC function via selectively altering the alternate/non-canonical NF-{kappa}B pathway. Both loss of Pikfyve in DCs and treatment with apilimod, a potent and specific PIKfyve inhibitor, restrained tumor growth, enhanced DC-dependent T cell immunity, and potentiated ICB efficacy in tumor-bearing mouse models. Furthermore, the combination of a vaccine adjuvant and apilimod reduced tumor progression in vivo. Thus, PIKfyve negatively controls DCs, and PIKfyve inhibition has promise for cancer immunotherapy and vaccine treatment strategies.

cancer biology↗

bioRxiv2021

The SARS-CoV-2 replication-transcription complex is a priority target for broad-spectrum pan-coronavirus drugs

In the absence of effective treatment, COVID-19 is likely to remain a global disease burden. Compounding this threat is the near certainty that novel coronaviruses with pandemic potential will emerge in years to come. Pan-coronavirus drugs - agents active against both SARS-CoV-2 and other coronaviruses - would address both threats. A strategy to develop such broad-spectrum inhibitors is to pharmacologically target binding sites on SARS-CoV-2 proteins that are highly conserved in other known coronaviruses, the assumption being that any selective pressure to keep a site conserved across past viruses will apply to future ones. Here, we systematically mapped druggable binding pockets on the experimental structure of fifteen SARS-CoV-2 proteins and analyzed their variation across twenty-seven - and {beta}-coronaviruses and across thousands of SARS-CoV-2 samples from COVID-19 patients. We find that the two most conserved druggable sites are a pocket overlapping the RNA binding site of the helicase nsp13, and the catalytic site of the RNA-dependent RNA polymerase nsp12, both components of the viral replication-transcription complex. We present the data on a public web portal (https://www.thesgc.org/SARSCoV2_pocketome/) where users can interactively navigate individual protein structures and view the genetic variability of drug binding pockets in 3D.

pharmacology and toxicology↗

bioRxiv2021

Structure, Mechanism and Crystallographic fragment screening of the SARS-CoV-2 NSP13 helicase

The global COVID-19 pandemic is caused by the SARS-CoV-2 virus and has infected over 100 million and caused over 2 million fatalities worldwide at the point of writing. There is currently a lack of effective drugs to treat people infected with SARS-CoV-2. The SARS-CoV-2 Non-structural protein 13 (NSP13) is a superfamily1B helicase that has been identified as a possible target for anti-viral drugs due to its high sequence conservation and essential role in viral replication. In this study we present crystal structures of SARS-CoV-2 NSP13 solved in the APO form and in the presence of both phosphate and the non-hydrolysable ATP analogue (AMP-PNP). Comparisons of these structures reveal details of global and local conformational changes that are induced by nucleotide binding and hydrolysis and provide insights into the helicase mechanism and possible modes of inhibition. Structural analysis reveals two pockets on NSP13 that are classified as "druggable" and include one of the most conserved sites in the entire SARS-CoV-2 proteome. To identify possible starting points for anti-viral drug development we have performed a crystallographic fragment screen against SARS-CoV-2 NSP13 helicase. The fragment screen reveals 65 fragment hits across 52 datasets, with hot spots in pockets predicted to be of functional importance, including the druggable nucleotide and nucleic acid binding sites, opening the way to structure guided development of novel antiviral agents.

biochemistry↗

bioRxiv2020

The Druggable Genome as Seen from the Protein Data Bank

Almost twenty years after the human genome was sequenced, the wealth of data produced by the international human genome project has not translated into a significantly improved drug discovery enterprise. This is in part because small molecule modulators that could be used to explore the cellular function of their target proteins and to discover new therapeutic opportunities are only available for a limited portion of the human proteome. International efforts are underway to develop such chemical tools for a few, specific protein families, and a "Target 2035" call to enable, expand and federate these efforts towards a comprehensive chemical coverage of the druggable genome was recently announced. But what is the druggable genome? Here, we systematically review structures of human proteins bound to drug-like ligands available from the protein databank (PDB) and use ligand desolvation upon binding as a druggability metric to draw a landscape of the human druggable genome. We show that the vast majority of druggable protein families, including some highly populated and deeply associated with cancer according to genomic screens, are almost orphan of small molecule ligands, and propose a list of 46 druggable domains representing 3440 human proteins that could be the focus of large chemical probe discovery efforts.

bioinformatics↗

bioRxiv2019

A lab-on-a-chip model of glaucoma

We developed a glaucoma-on-a-chip (GOC) model to evaluate the viability of retinal ganglion cells (RGCs) against high pressure and the potential effect of neuroprotection. A three-layered chip consisting of interconnecting microchannels and culture wells was designed based on simulation of physical parameters. The chip layers were fabricated from poly-methyl methacrylate sheets. Multiple inlet ports allow culture media and gas into the wells under elevated hydrostatic pressure (EHP). The bottom surface of the wells was modified by air plasma and coated with different membranes to model an extracellular microenvironment. The SH-SY5Y neuroblastoma cell line served as model cells to determine the best supporting membrane which was revealed to be PDL/laminin. Thereafter, the study experiments were performed using RGCs obtained from postnatal 5-7 Wistar rats purified by magnetic assisted cell sorting. Flow cytometry and immunocytochemistry assays demonstrated 70% purification for RGCs. The cultured RGCs were exposed to normal (15 mmHg) or elevated pressure (33 mmHg) for 6, 12, 24, 36 and 48 hours, with and without adding brain-derived neurotrophic factor (BDNF) or a novel BDNF mimetic (RNYK). RGC survival rates were 85, 78, 70, 67 and 61 percent under normal pressure versus 40, 22, 18, 12 and 10 percent under high pressure at 6, 12, 24, 36 and 48 hours, respectively (P <0.0001). BDNF and RNYK treatments induced separately an approximate two-fold decrease in the rate of RGC death under both normal and elevated pressures (p <0.01 to 0.0001). This GOC model recapitulated the effects of elevated pressure during relatively short time periods and demonstrated the neuroprotective effects of BDNF and RNYK.

neuroscience↗

bioRxiv2018

Better than maximum likelihood estimation of model-based and model-free learning style

Multiple decision making systems work together to shape the final choices in human behavior. Habitual and goal-directed systems are the two most important systems that are studied in the reinforcement learning (RL) literature by model-free and model-based learning methods. Human behavior resembles the weighted combination of these systems and such a combination is modeled by weighted summation of actions value from the model based and model free systems. Extraction of this weighted parameter, which is important for many applications and computational modeling, has been mostly based on the maximum likelihood or maximum a posteriori methods. We show these methods bring many challenges and their respective extracted values are less reliable especially in the proximity of extremes values. We propose that using a free format learning method (k-nearest neighbor) which uses more information besides the fitted values e.g. global information like stay probability instead of trial by trial information can ameliorate the estimation error. The proposed method is examined by simulation and results show the advantage of the proposed method. In addition, investigation of the human behavior data from previous researchers proved the proposed method to result in more statistically robust results in predicting other behavioral indices such as the number of gaze directions toward each target. In brief, the proposed method increases the reliability of the estimated parameters and enhances the applicability of reinforcement learning paradigms in clinical trials.

neuroscience↗

bioRxiv2017

Polygenic Link Between Blood Lipids And Amyotrophic Lateral Sclerosis

Dyslipidemia is common among patients with amyotrophic lateral sclerosis (ALS). We aimed to test the association and causality between blood lipids and ALS, using polygenic analyses on the summary results of genome-wide association studies. Polygenic risk scores (PRS) based on low-density lipoprotein cholesterol (LDL-C) and total cholesterol (TC) risk alleles were significantly associated with a higher risk of ALS. Using single nucleotide polymorphisms (SNPs) specifically associated with LDL-C and TC as the instrumental variables, statistically significant causal effects of LDL-C and TC on ALS risk were identified in Mendelian randomization analysis. No significant association was noted between PRS based on triglycerides or high-density lipoprotein cholesterol risk alleles and ALS, and the PRS based on ALS risk alleles were not associated with any studied lipids. This study supports that high levels of LDL-C and TC are risk factors for ALS, and it also suggests a causal relationship of LDL-C and TC to ALS.

genetics↗