Biology subjects

Nazari, A.

Publications and source records attributed to Nazari, A..

4 recordsLinked to original sources

bioRxiv2026

Lipid Defect Analyzer (LipiDeA): A Python Framework for Quantifying Lipid Packing Defects in Curved and Neutral Lipid-Containing Membranes

Lipid packing defects expose hydrophobic regions of membranes and influence protein-membrane recognition, membrane remodeling, and lipid sensing. Quantitative analysis of these defects remains challenging for heterogeneous membranes with strong curvature, mixed bilayer and monolayer organization, and substantial neutral-lipid content. Here, we present Lipid Defect Analyzer (LipiDeA), an open-source Python framework for quantitative analysis of lipid packing defects in complex membrane systems. LipiDeA reconstructs membrane surfaces using triangulated meshes that adapt to local membrane geometry and identifies packing defects from lipid coverage of the reconstructed surface. The framework further classifies bilayer and monolayer regions, resolves defect properties according to local membrane curvature, and distinguishes defects associated with phospholipid tails and neutral lipids. Validation using planar and buckled DPPC bilayers reproduced previously reported packing defect constants and showed that defects preferentially occur in positively curved membrane regions. Application to lipid-droplet-like membranes revealed composition- and topology-dependent defect formation, with the largest defects occurring during the transition from bilayer-dominated to monolayer-containing membranes and distinct defect properties for DAG- and TAG-containing systems. These results highlight the importance of local membrane geometry and composition in determining lipid packing defects.

biophysics↗

bioRxiv2026

Lipid composition-driven sorting of ABHD5 between monolayer and bilayer surfaces

Lipid droplets (LDs) are ubiquitous organelles that store neutral lipids and serve as central regulators of lipid homeostasis. Their structure includes a hydrophobic core of triacylglycerols and sterol esters surrounded by a phospholipid monolayer. This organization creates biophysical properties that guide selective protein recruitment. Among LD-associated proteins, /{beta}-hydrolase domain-containing protein 5 (ABHD5, also known as CGI-58) is a key regulator of lipolysis and broader lipid metabolism, yet the mechanisms guiding its distribution between endoplasmic reticulum (ER) bilayers and LD monolayers remain poorly understood. Because proper membrane association of ABHD5 is essential for activating PNPLA family lipases, identifying the determinants of its membrane selectivity is critical for understanding LD function in health and disease. In this study, we examined ABHD5 binding and sorting behavior using model membrane systems composed of giant unilamellar vesicles (GUVs) and droplet-embedded vesicles (DEVs) incorporating defined phospholipid and neutral lipid compositions. By integrating experimental assays with computational modeling, we quantified how ABHD5 partitions between bilayer membranes mimicking the ER and monolayer surfaces mimicking LDs. Systematic variation of membrane composition and physical properties allowed us to assess how packing defects and neutral lipid content shape ABHD5 localization. Our findings reveal the biophysical features that favor ABHD5 association with LD-like monolayers and provide new mechanistic insight into how cells target regulatory proteins to distinct membrane environments to control lipid metabolism.

biophysics↗

bioRxiv2025

Clinical development of gene edited tacrolimus-resistant Treg (FKBP12KO-Treg) to enable simultaneous immunosuppression and support of immune regulation

Background: Unwanted immune responses play a central role in the pathogenesis of solid organ allograft rejection. These are managed by life-long immunosuppression with considerable burden for the patient and society. Adoptive therapy with regulatory T-cells (Treg) is a promising approach to restore sustainable immune balance and avoid long-term adverse effects of immunosuppression. While Treg effectively inhibit activation of unwanted immune responses, they are less effective in controlling pre-existing/activated memory effector T-cells (Teff). Thus, co-administration of Treg with immunosuppressants is required to achieve a sustainable organ acceptance. Calcineurin inhibitors (CNI) are powerful in controlling de novo generated and preformed Teff. However, CNI also dampen Treg immunoregulatory function. Thus, we hypothesize improved results of adoptive Treg therapy in immunosuppressed patients applying tacrolimus-resistant Treg. Methods: While retaining CNI modulation of Teff with tacrolimus, we knocked-out FKBP12 in Treg (FKBP12KO-Treg) by gene-editing using ribonucleoprotein-based CRISPR/Cas9 technology to generate tacrolimus-resistant Treg and characterised them using flow cytometry, functional assays and in-depth phenotyping. Results: This detailed in vitro analysis showed FKBP12KO-Treg were comparable to non-gene edited Treg and impervious to tacrolimus while maintaining immunoregulatory function and sensitivity to alternative CNIs raising no safety concerns. Furthermore, we aligned our methodology to achieve GMP compliance laying the basis for a manufacturing license in preparation of a clinical trial. Conclusion: Based on the presented preclinical dataset implying safety and efficacy of FKBP12KO-Treg, we are now seeking to undertake a proof-of-concept clinical trial to evaluate the co-administrationof FKBP12KO-Treg and tacrolimus to enhance the management of living donor kidney transplant recipients.

immunology↗

bioRxiv2024

AI-Enhanced RAIN Protocol: A Systematic Approach to Optimize Drug Combinations for Rectal Neoplasm Treatment

BackgroundRectal cancers, or rectal neoplasms, are tumors that develop from the lining of the rectum, the concluding part of the large intestine ending at the anus. These tumors often start as benign polyps and may evolve into malignancies over several years. The causes of rectal cancer are diverse, with genetic mutations being a key factor. These mutations lead to uncontrolled cell growth, resulting in tumors that can spread and damage healthy tissue. Age, genetic predisposition, diet, and hereditary conditions are among the risk factors. Treating rectal cancer is critical to prevent severe health issues and death. Untreated, it can cause intestinal blockage, metastasis, and deteriorate the patients quality of life. Effective treatment hinges on finding the right drug combinations to improve therapeutic outcomes. Given the intricacies of cancer biology, treatments often combine surgery, chemotherapy, and radiation, with drugs chosen to target different tumor growth mechanisms, aiming to reduce the tumor and limit side effects. The continuous advancement in cancer treatments highlights the need for ongoing research to discover new drug combinations, offering patients improved recovery prospects and a better quality of life. This background encapsulates a detailed yet succinct understanding of rectal neoplasms, their origins, the urgency of treatment, and the quest for effective drug therapies, paving the way for discussions on treatment advancements and patient care impacts. MethodThis study employed the RAIN protocol, comprising three steps: firstly, utilizing the GraphSAGE model to propose drug combinations for rectal neoplasm treatment Each node in the graph model is a drug or a human gene/protein that acts as potential target for the disease, and the edges are P-values between them; secondly, conducting a systematic review across various databases including Web of Science, Google Scholar, Scopus, Science Direct, PubMed, and Embase, with NLP investigation; and thirdly, employing a meta-analysis network to assess the efficacy of drugs and genes in relation to each other. All implementations was conducted using Python software. ResultThe study evaluated the efficacy of Oxaliplatin, Leucovorin, and Capecitabine in treating Rectal Neoplasms, confirming their effectiveness through a review of 30 studies. The p-values for individual drugs were 0.019, 0.019, and 0.016 respectively, while the combined use of all three yielded a p-value of 0.016. ConclusionGiven the significance of rectal neoplasms, policymakers are urged to prioritize the healthcare needs of affected individuals. Utilizing artificial intelligence within the RAIN protocol can offer valuable insights for tailoring effective drug combinations to better address the treatment and management of rectal neoplasms patients. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=90 SRC="FIGDIR/small/596215v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@1967f95org.highwire.dtl.DTLVardef@1921c52org.highwire.dtl.DTLVardef@181809dorg.highwire.dtl.DTLVardef@122d8b4_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIRectal cancers, evolving from benign polyps to malignancies, underscore the critical need for timely and effective treatment to prevent severe health complications. C_LIO_LIGenetic mutations, a pivotal factor in rectal cancer, trigger uncontrolled cell growth and necessitate targeted drug therapies to combat tumor spread. C_LIO_LIThe RAIN protocol, leveraging the GraphSAGE model and systematic reviews, offers a novel approach to identify potent drug combinations for rectal neoplasm treatment. C_LIO_LIThe studys findings advocate for policy intervention to ensure that healthcare systems adequately support individuals battling rectal neoplasms, with AI-driven protocols enhancing patient care. C_LI

bioinformatics↗