Biology subjects

Alsiyabi, A.

Publications and source records attributed to Alsiyabi, A..

8 recordsLinked to original sources

bioRxiv2025

A Minimal Plasma Proteome-Based Biomarker Panel for Accurate Prostate Cancer Diagnosis

Early and accurate diagnosis of prostate cancer (PRC) remains a major clinical challenge, particularly with existing biomarker panels relying on invasive sampling or large biomarker panels with limited interpretability. Here, we present a machine learning framework for discovering compact and biologically grounded plasma protein signatures for PRC classification using publicly available pan-cancer proteomic data. We coupled a genetic algorithm-based protein identification method with LASSO-regularized logistic regression to identify minimal protein subsets optimized for diagnostic performance. A 14-protein panel, recurrent across 1,000 genetic algorithm iterations, achieved a mean accuracy of 98.0%, an F1 score of 0.98, and an ROC AUC of 0.997 on a held-out test dataset. This performance exceeded models trained on high dimensionality data (>1,400 proteins) and surpassed published transcriptomic, methylomic, and cfDNA classifiers, many of which reported AUCs less than 0.91. Functional analysis revealed enrichment in protease binding and DNA repair pathways, with known markers such as beta-microseminoprotein (MSMB) and poly(ADP-ribose) polymerase 1 (PARP1) appearing alongside under-characterized proteins like IGSF3 and XG. Models trained only on previously reported PRC-associated proteins showed lower performance, highlighting the added diagnostic value of including novel, data-driven candidates. This study outlines a scalable proteomic workflow and demonstrates that high diagnostic performance can be achieved using small, interpretable panels derived from blood-based proteomics. The findings lay the foundation for the development of interpretable, clinically deployable assays for PRC detection and risk stratification. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=136 SRC="FIGDIR/small/686712v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@1901a55org.highwire.dtl.DTLVardef@1b0e206org.highwire.dtl.DTLVardef@1c71894org.highwire.dtl.DTLVardef@1ed8f4_HPS_FORMAT_FIGEXP M_FIG C_FIG Overview of the study workflow: blood-derived proteomic profiles from cancer and normal cohorts were analyzed using machine learning to identify a compact diagnostic panel, enabling clinical decision support for new patients.

cancer biology↗

bioRxiv2025

An integrative machine learning approach identifies the centrality of ferroptosis, cuproptosis, and immune pathway crosstalk for breast cancer stratification and therapy guidance.

Breast cancer (BRCA) is a leading cause of cancer-related mortality in women, characterized by marked heterogeneity in molecular subtypes, immune microenvironment, and therapeutic response. Current gene expression classifiers often lack mechanistic grounding, limiting their clinical utility. Using an integrated machine learning approach, we identified a four-gene panel, FOXO4, EGFR, FGF2, and CDKN2A, capturing convergent dysregulation across ferroptosis, cuproptosis, and immune pathways. The panel reflects not only redox and proliferative dysregulation but also distinct immune microenvironmental patterns, with FGF2 linked to stromal remodeling and CDKN2A correlated with adaptive immune responses, underscoring its biological integration into BRCA pathology. This panel is rooted in recurrent dysregulation of oxidative stress control (FOXO4), proliferative and angiogenic signalling (EGFR, FGF2), and cell-cycle-immune interfaces (CDKN2A), linking classification to central regulatory mechanisms. The model achieved 97-98% test accuracy (AUC 0.99) for tumour-healthy discrimination. Our findings reveal transcriptional convergence between redox-metabolic and immune-escape programs in BRCA. We propose a compact and interpretable panel with translational potential, offering a minimal, mechanism-informed diagnostic framework for clinical deployment in breast cancer for precision oncology.

cancer biology↗

bioRxiv2025

Minimal Gene Signatures Enable High-Accuracy Prediction of Antibiotic Resistance in Pseudomonas aeruginosa

Antimicrobial resistance (AMR) in Pseudomonas aeruginosa poses a critical global health challenge, with current diagnostics relying on slow, culture-based methods. Here, we present a ML framework leveraging transcriptomic data to predict antibiotic resistance with high accuracy. We applied a genetic algorithm to 414 clinical isolates to identify minimal, highly predictive gene sets ([~]35-40 genes) distinguishing resistant from susceptible strains for meropenem, ciprofloxacin, tobramycin, and ceftazidime. Automated ML classifiers trained on these sets achieved accuracies of 96-99% on test data (F1 scores: 0.93-0.99), surpassing clinical deployment thresholds. Multiple distinct, non-overlapping gene subsets exhibited comparable performance, indicating that resistance acquisition broadly impacts the expression of diverse regulatory and metabolic genes. Comparison with known resistance markers from CARD and operon annotations revealed a substantial number of previously unannotated clusters, highlighting significant knowledge gaps in current AMR understanding. Mapping these genes onto independently modulated gene sets (iModulons) revealed transcriptional adaptations across diverse genetic regions. Overall, this study presents a streamlined machine-learning workflow for transcriptomic data and offers a pathway toward rapid diagnostics and personalized treatment strategies against AMR.

systems biology↗

bioRxiv2024

A thermodynamic bottleneck in the TCA cycle contributes to acetate overflow in Staphylococcus aureus

During aerobic growth, S. aureus relies on acetate overflow metabolism, a process where glucose is incompletely oxidized to acetate, for its bioenergetic needs. Acetate is not immediately captured as a carbon source and is excreted as waste by cells. The underlying factors governing acetate overflow in S. aureus have not been identified. Here, we show that acetate overflow is favored due to a thermodynamic bottleneck in the TCA cycle, specifically involving the oxidation of succinate to fumarate by succinate dehydrogenase. This bottleneck reduces flux through the TCA cycle, making it more efficient for S. aureus to generate ATP via acetate overflow metabolism. Additionally, the protein allocation cost of maintaining ATP flux through the restricted TCA cycle is greater than that of acetate overflow metabolism. Finally, we show that the TCA cycle bottleneck provides S. aureus the flexibility to redirect carbon towards maintaining redox balance through lactate overflow when oxygen becomes limiting, albeit at the expense of ATP production through acetate overflow. Overall, our findings suggest that overflow metabolism offers S. aureus distinct bioenergetic advantages over a thermodynamically constrained TCA cycle, potentially supporting its commensal-pathogen lifestyle.

systems biology↗

bioRxiv2024

An Automated Machine Learning Framework for Antimicrobial Resistance Prediction Through Transcriptomics

The emergence of antimicrobial resistance (AMR) poses a global threat of growing concern to the healthcare system. To mitigate the spread of resistant pathogens, physicians must identify the susceptibility profile of every patients infection in order to prescribe the appropriate antibiotic. Furthermore, disease control centers need to be able to accurately track the patterns of resistance and susceptibility of pathogens to different antibiotics. To achieve this, high-throughput methods are required to accurately predict the resistance profile of a pathogenic microbe in an automated manner. In this work, a transcriptomics-based approach utilizing a machine learning framework is used to achieve this goal. The study highlights the potential of using gene expression as an indicator of resistance to different antibiotics. Results indicate the importance of starting with a high-quality training dataset containing high genetic diversity and a sufficient number of resistant samples. Furthermore, the performed analysis reveals the importance of developing new methods of feature reduction specific to transcriptomic data. Most importantly, this study serves as a proof-of-concept to the potential impact of deploying such models to reduce the mortality rate associated with AMR.

systems biology↗

bioRxiv2022

Characterizing the interplay of rubisco and nitrogenase enzymes in anaerobic-photoheterotrophically grown Rhodopseudomonas palustris CGA009 through a genome-scale metabolic and expression model

Rhodopseudomonas palustris CGA009 (R. palustris) is a gram negative purple non-sulfur bacteria that grows phototrophically or chemotrophically by fixing or catabolizing a wide array of substrates including lignin breakdown products (e.g., p-coumarate) for its carbon and nitrogen requirements. It can grow aerobically or anaerobically and can use light, inorganic, and organic compounds for energy production. Due to its ability to convert different carbon sources into useful products in anaerobic mode, this study, for the first time, reconstructed a metabolic and expression (ME-) model of R. palustris to investigate its anaerobic-photoheterotrophic growth. Unlike metabolic (M-) models, ME-models include transcription and translation reactions along with macromolecules synthesis and couple these reactions with growth rate. This unique feature of the ME-model led to nonlinear growth curve predictions which matched closely with experimental growth rate data. At the theoretical maximum growth rate, the ME-model suggested a diminishing rate of carbon fixation and predicted malate dehydrogenase and glycerol-3 phosphate dehydrogenase as alternate electron sinks. Moreover, the ME-model also identified ferredoxin as a key regulator in distributing electrons between major redox balancing pathways. Since ME-models include turnover rate for each metabolic reaction, it was used to successfully capture experimentally observed temperature regulation of different nitrogenases. Overall, these unique features of the ME-model demonstrated the influence of nitrogenases and rubiscos on R. palustris growth and predicted a key regulator in distributing electrons between major redox balancing pathways, thus establishing a platform for in silico investigation of R. palustris metabolism from a multi-omics perspective. IMPORTANCEIn this work, we reconstructed the first ME-model for a purple non-sulfur bacterium (PNSB). Using the ME-model, different aspects of R. palustris metabolism were examined. First, the ME-model was used to analyze how reducing power entering the R. palustris cell through organic carbon sources gets partitioned into biomass, carbon dioxide fixation, and nitrogen fixation. Furthermore, the ME-model predicted electron flux through ferredoxin as a major bottleneck in distributing electrons to nitrogenase enzymes. Next, the ME-model characterized different nitrogenase enzymes and successfully recapitulated experimentally observed temperature regulations of those enzymes. Identifying the bottleneck responsible for transferring electron to nitrogenase enzymes and recapitulating the temperature regulation of different nitrogenase enzymes can have profound implications in metabolic engineering, such as hydrogen production from R. palustris. Another interesting application of this ME-model can be to take advantage of its redox balancing strategy to gain understanding on regulatory mechanism of biodegradable plastic production precursors, such as polyhydroxybutyrate (PHB).

systems biology↗

bioRxiv2020

Dissecting the regulatory roles of ORM proteins in the sphingolipid pathway of plants

Sphingolipids are a vital component of plant cellular endomembranes and carry out multiple functional and regulatory roles. Different sphingolipid species confer rigidity to the membrane structure, facilitate trafficking of secretory proteins, and initiate programmed cell death. Although the regulation of the sphingolipid pathway is yet to be uncovered, increasing evidence has pointed to orosomucoid proteins (ORMs) playing a major regulatory role and potentially interacting with a number of components in the pathway, including both enzymes and sphingolipids. However, experimental exploration of new regulatory interactions is time consuming and often infeasible. In this work, a computational approach was taken to address this challenge. A metabolic network of the sphingolipid pathway in plants was reconstructed. The steady-state rates of reactions in the network were then determined through measurements of growth and cellular composition of the different sphingolipids in Arabidopsis seedlings. The Ensemble modeling framework was modified to accurately account for activation mechanisms and subsequently used to generate sets of kinetic parameters that converge to the measured steady-state fluxes in a thermodynamically consistent manner. In addition, the framework was appended with an additional module to automate screening the parameters and to output models consistent with previously reported network responses to different perturbations. By analyzing the networks response in the presence of different combinations of regulatory mechanisms, the model captured the experimentally observed repressive effect of ORMs on SPT. Furthermore, predictions point to a second regulatory role of ORM proteins, namely as an activator of class II (or LOH1 and LOH3) ceramide synthases. This activating role was found to be modulated by the concentration of free ceramides, where an accumulation of these sphingolipid species dampened the activating effect of ORMs on ceramide synthase. The predictions pave the way for future guided experiments and have implications in engineering crops with higher biotic stress tolerance. Author summaryDue to their vital functional and regulatory roles in plant cells, increasing interest has gone into obtaining a complete understanding of the regulatory behavior of the sphingolipid pathway. However, the process of identifying new regulatory interactions is time consuming and often infeasible. To address this issue, ensemble modeling was used as an in silico method to test the ability of different regulatory schemes to predict all known pathway responses in a thermodynamically consistent manner. The analysis resulted in a significant reduction in the number of possible regulatory interactions. Mainly, the model predicts regulatory interactions between ceramides, ORMs, and ceramide synthases (especially class II). This framework can pave the way for biochemists to systematically identify plausible regulatory networks in understudied metabolic networks where knowledge on the underlying regulatory mechanisms is often missing. As future experimental works explore these predictions, an iterative cycle can begin wherein model predictions allow for targeted experiments which in turn generate results that can be reincorporated into the model to further increase prediction accuracy. Such a model-driven approach will significantly reduce the solution space traversed by the experimentalist.

systems biology↗

bioRxiv2019

Modeling the Interplay between Photosynthesis, CO2 Fixation, and the Quinone Pool in a Purple Non-Sulfur Bacterium

Rhodopseudomonas palustris CGA009 is a purple non-sulfur bacterium (PNSB) that can fix CO2 and nitrogen or break down organic compounds for its carbon and nitrogen requirements. Light, inorganic, and organic compounds can all be used for its source of energy. Excess electrons produced during its metabolic processes can be exploited to produce hydrogen gas or biodegradable polyesters (polyhydroxybutyrate). A genome-scale metabolic model of the bacterium was reconstructed to study the interactions between photosynthesis, carbon dioxide fixation, and the redox state of the quinone pool. A comparison of model-predicted flux values with published in vivo MFA fluxes resulted in predicted errors of 5-19% across four different growth substrates. The model predicted the presence of an unidentified sink responsible for the oxidation of excess quinols generated by the TCA cycle. Furthermore, light-dependent energy production was found to be highly dependent on the rate of quinol oxidation. Finally, the extent of CO2 fixation was predicted to be dependent on the amount of ATP generated through the electron transport cycle, with excess ATP going toward the energy-demanding CBB pathway. Based on this analysis, it is hypothesized that the quinone redox state acts as a feed-forward controller of the CBB pathway, signaling the amount of ATP available.

systems biology↗