Biology subjects

Abdollahi, M.

Publications and source records attributed to Abdollahi, M..

7 recordsLinked to original sources

bioRxiv2026

Evaluating codon optimization strategies for mammalian glycoprotein production with an open-source expression vector

Efficient production of human proteins for the development of tool compounds and biologics depends on a detailed understanding of the protein expression machinery in mammalian cells. Codon optimization is widely believed to enhance protein yield, yet its impact in homologous mammalian systems remains poorly defined. Here, we systematically compare five codon usage strategies reflecting common assumptions about rare codons, RNA stability, and synthesis efficiency. We developed pTipi, an efficient open-source mammalian expression vector, and evaluated its performance in antibody production. We generated plasmids for common epitope tag antibodies such as V5, anti-biotin and anti-His for distribution by Addgene. To compare codon usage schemes, we performed a bake-off of 18 human and murine Wnt pathway glycoproteins in mammalian cells. Small-scale expression screens revealed that codon optimization did not provide a general advantage over native coding sequences, while strategies prioritizing RNA stability consistently reduced expression. Interestingly, a skewed codon scheme using the most abundant codons produced yields comparable to native sequences and occasionally enhanced protein output. To enable flexible evaluation of codon strategies, we implemented a Golden Gate-compatible pTipi platform for efficient synthetic gene incorporation. We conclude that native codons are sufficient for robust homologous mammalian expression of glycoproteins, while selective codon skewing can be beneficial for some targets.

molecular biology↗

bioRxiv2025

Fatty Acid Pathways Regulate Thermal Nociception in Caenorhabditis elegans

Chronic pain remains a major unmet medical challenge, and lipid signaling pathways have emerged as key modulators of nociception. Using Caenorhabditis elegans as a genetically tractable model, we investigated how fatty acid composition influences thermal avoidance behavior. Mutant strains lacking functional desaturase enzymes (elo-1, fat-1, fat-2, fat-3, fat-4, fat-6/fat-7), and consequently depleted in polyunsaturated fatty acids (PUFAs) such as arachidonic acid, displayed significantly reduced sensitivity to noxious heat compared to wild-type animals. These findings indicate that intact PUFA biosynthesis is essential for normal thermal nociception in C. elegans. Given that arachidonic acid is a precursor of endocannabinoids (AEA and 2-AG) known to modulate TRPV1-dependent pain signaling, our results suggest that a conserved lipid-based mechanism regulates heat avoidance in nematodes. This study establishes a functional link between fatty acid metabolism and nociceptive behavior, providing a powerful platform to explore metabolic modulation of pain pathways.

neuroscience↗

bioRxiv2025

ComplexMatrixComb: Predicting Drug Combination IC50 Doses via Complex Numbers and Matrix Factorization

Determining precise drug concentrations to inhibit cancer cell growth is a critical but resource-intensive challenge, especially for combinations requiring many dose pairs. Existing computational methods often predict synergy or classify interactions but rarely estimate exact concentration pairs for a defined inhibitory effect. We introduce ComplexMatrixComb, a matrix factorization framework predicting drug combination dosages achieving IC50 for specific cancer cell lines. It encodes drug concentrations as real and imaginary components of complex numbers, modeling joint dose-response dynamics and enabling accurate IC50 estimation. Across ONeil, NCI-ALMANAC, and AZ-Dream datasets, ComplexMatrixComb outperformed traditional machine learning in regression and classification, showing robustness to drug order and adaptability to varied designs. Integration with models such as ComboFM and ComboLTR showed predicted concentrations closely matched ground-truth doses. Experimental validation of five drug pair-cell line predictions using MTT assays confirmed 50% growth inhibition. By reducing reliance on exhaustive screens, ComplexMatrixComb offers a scalable, data-driven tool to accelerate preclinical research and support personalized oncology strategies.

bioinformatics↗

bioRxiv2025

High-Throughput Machine Learning-Aided Antibody Discovery for Cell Surface Antigens

Machine learning (ML) has the potential to revolutionize antibody design and selection, but its success depends on access to extensive, well-curated datasets of antibody-antigen interactions. To address this need, we developed a synthetic Fab yeast display library optimized for seamless ML integration, focusing on sequence diversity within the CDRH3 loop. The library incorporates key sequence features derived from human B cell repertoires essential for efficient antibody generation captured in a compact antigen recognition module (ARM) format. Built using the VH1-69 heavy chain and four light chains, the library was evaluated against ten human and murine cell surface antigens, including PD-L1, TIGIT, and ROBO1. This approach yielded hundreds of antibodies with robust biophysical properties, validated for functional performance in flow cytometry and immunohistochemistry. Furthermore, ML analysis identified additional antibodies for ROBO2 and PD-L2 from the aggregate sequencing data, demonstrating utility for hybrid in silico and experimental workflows. We provide a publicly accessible dataset comprising more than 68,000 Fab sequences and 486 characterized antibodies. This study establishes an ML-compatible framework designed to accelerate and streamline antibody discovery and development.

biophysics↗

bioRxiv2025

Single-Cell Multimodal Profiling Highlights Persistent Aortic Smooth Muscle Cell Changes in Diabetic Mice Despite Glycemic Control

BackgroundType 2 diabetes (T2D) is associated with accelerated vascular complications like hypertension and atherosclerosis. "Phenotypic switching" of vascular smooth muscle cells (SMC), a major driver of these complications, is enhanced in diabetes. Despite adequate glycemic control, SMC dysfunction can persist due to "metabolic memory" of prior hyperglycemia. However, the mechanisms of hyperglycemic memory associated with persistent SMC dysfunction are unclear. Here, leveraging single-cell (sc) multi-omics, we examined the effect of glucose normalization on transcriptomic and epigenomic changes associated with SMC phenotypic transition in T2D mice. MethodsWe treated T2D db/db mice with the antidiabetic drug dapagliflozin (DAPA) (db/dbDAPA) or vehicle (db/db), and non-diabetic control db/+ mice with vehicle for 6 weeks. Dissected aortas were subjected to scRNA-seq, scATAC-seq, and spatial transcriptomics (Xenium) to determine single-cell changes in gene expression and chromatin accessibility. ResultsDAPA treatment conferred effective glycemic control in db/db mice, with significant reductions in blood glucose/hemoglobin A1c. scRNA and scATAC-seq analysis of aortas identified major cell populations, including SMC, fibroblasts, endothelial and immune cells. SMC were further clustered into 9 subtypes, including contractile and fibromyocyte-like cells. Cell composition analysis revealed decreases in contractile SMC and increases in vascular remodeling associated fibromyocyte-like SMC in db/db versus db/+ mice. Interestingly, DAPA did not reverse diabetes-induced decreases in contractile markers but reversed changes in several fibromyocyte markers in db/db mice. Pseudotime trajectory analysis revealed increased activities of fibromyocyte enriched transcription factors (TFs) during contractile to fibromyocyte transition. Furthermore, increased expression of TFs regulating fibromyocyte phenotype (e.g. Atf4, Bach1, Hand2, Fosl2) in db/db were partially reversed by DAPA, whereas reduced contractile TF (Mef2c) expression was unchanged. Spatial transcriptomics analysis further mapped aortic cell types within intact aortas and confirmed that DAPA reversed alterations in key fibromyocyte but not contractile genes in db/db mouse aortas. ConclusionsPersistent epigenetic changes may contribute to sustained vascular remodeling and dysfunction in T2D. T2D reduced contractile SMC gene expression and related chromatin accessibility and promoted phenotypic transition to fibromyocytes. These changes are only partially reversed by a widely used antidiabetic drug like DAPA, underscoring the need for more effective therapies that target hyperglycemic memory.

biochemistry↗

bioRxiv2019

Using a Motion Sensor to Categorize Low Back Pain Patients: A Machine Learning Approach

Low back pain (LBP) remains a critical health issue impacting literally millions of people worldwide. Currently, clinical practitioners rely on subjective measures such as the STarT Back Screening Tool to categorize LBP patients, which then informs specific treatment regimens. This study sought to develop a machine learning model to classify LBP patients into different groups according to kinematic data. Specifically, an inertial measurement unit (IMU) was attached to each patients chest while he performed trunk flexion/extension motions at a self-selected pace. Machine learning algorithms such as support vector machine (SVM) and multi-layer perceptron (MLP) were implemented to evaluate the efficiency of the models. The results showed that the kinematic data we obtained could be used to categorize the patients into two groups: high vs. low-medium risk. We achieved accuracy levels of ~75% and 60% for SVM and MLP, respectively. Additionally, among a range of variables detailed herein, we determined that time-scaled IMU signal resulted in the highest accuracy. Our findings support the use of body-motion measures in developing prognosis tools for healthcare applications. Our results could help overcome the need for objective clinic-based diagnosis approaches, which in turn would lead to assigning better treatment approaches and rehabilitation services for LBP sufferers.

bioengineering↗

bioRxiv2019

A Data-Driven Model to Identify Fatigue Level Based on the Motion Data from a Smartphone

The fatigue due to repetitive and physically challenging jobs may result in workers poor performance and Work-related Musculoskeletal Disorder (WMSD). Thus, it is imperative to frequently monitor fatigue and take necessary recovery actions. Our purpose was to develop a methodology to objectively classify subjects fatigue level in the workplace utilizing the motion sensors embedded in the smartphones. An experiment consisting of twenty-four participants (12 M, 12 F) with a smartphone attached to their right shank was conducted using a fatiguing exercise (squatting), targeted mainly the lower extremity musculature. After each set of an exercise (2-min squatting), participants were asked about their ratings of perceived exertion (RPE), then a reference gait data were collected during a straight walk of 20-32 steps. This process was continued until they reported strong fatigue ([≥]17). Using the RPE to label the gait data, we have developed machine learning algorithms (i.e., binary and multi-class SVM models) to classify the individuals gait into two (no-vs. strong-fatigue) and four levels (no-, low-, medium-, and strong-fatigue). The models reached the accuracies of 91% and 61% for two and four-level classification, respectively. The outcomes of this study may facilitate the implementation of a proactive approach in continuous monitoring of operators fatigue level, which may subsequently increase the workers performance and reduce the risk of WMSDs.

bioengineering↗