Biology subjects

Al-Harrasi, A.

Publications and source records attributed to Al-Harrasi, 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↗

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

Anticodon Table of the Chloroplast Genome and Identification of Putative Quadruplet Anticodons in Chloroplast tRNAs

The chloroplast genome of 5959 species was analyzed to construct the anticodon table of the chloroplast genome. Analysis of the chloroplast transfer ribonucleic acid (tRNA) revealed the presence of a putative quadruplet anticodon containing tRNAs in the chloroplast genome. The tRNAs with putative quadruplet anticodons were UAUG, UGGG, AUAA, GCUA, and GUUA, where the GUUA anticodon putatively encoded tRNAAsn. The study also revealed the complete absence of tRNA genes containing ACU, CUG, GCG, CUC, CCC, and CGG anticodons in the chloroplast genome from the species studied so far. The chloroplast genome was also found to encode tRNAs encoding N-formylmethionine (fMet), Ile2, selenocysteine, and pyrrolysine. The chloroplast genomes of mycoparasitic and heterotrophic plants have had heavy losses of tRNA genes. Furthermore, the chloroplast genome was also found to encode putative spacer tRNA, tRNA fragments (tRFs), tRNA-derived, stress-induced RNA (tiRNAs), and group I introns. An evolutionary analysis revealed that chloroplast tRNAs had evolved via multiple common ancestors and the GC% had more influence toward encoding the tRNA number in the chloroplast genome compared to the genome size.

genomics↗

bioRxiv2021

Evaluation of solubility and histological effect of 11-keto-β-Boswellic acid on the diabetic mice liver using FTIR-PCA analysis and microscopy

This study was designed to develop a rapid, sensitive, accurate, and inexpensive Fourier Transform Infrared Reflectance (FT-IR) Spectroscopy coupled with Principle Component Analysis (PCA) as a detection technique to evaluate the solubility of 11-Keto-{beta}-Boswellic acid (KBA), from the gum resin extracted from the Omani frankincense, (Boswellia sacra) in the liver of STZ induced diabetic mice. This study also investigated the effect of KBA on the histological changes of hepatocytes of diabetic mice. Liver tissue samples from three groups of mice included normal control group, diabetic control group and diabetic group treated IP with KBA were scanned with FT-IR spectrophotometer in the reflection mode. FT-IR Spectra were collected in the wavenumber range from 400 to 4000cm-1 using ATR accessorry. The results of FT-IR Spectra were analyzed by using multivariate method Principle Component Analysis. The PCA score plot is an exploratory multivariate method indicated that there was a complete segregation among the three groups of liver samples based on change in variation of position of wavenumber in FT-IR spectra, which revealed that there is a clear effect of KBA solubility on treatments. The histological features showed an improvement in the liver tissues with normal structures of hepatocytes with exhibiting mild vacuolations in their cytoplasm. In conclusion, reflectance FT-IR spectroscopy coupled with PCA could be deployed as a new detection method for rapid, low cost and non-destructive method for evaluating of treatment effects in diseased liver tissue based on the solubility of KBA. Histological findings demonstrated the protective effective of KBA on improving the morphology of liver tissue in diabetic mice which resulted in complete recovery to the damage observed in diabetic control group. Summary StatementReflectance FT-IR spectroscopy coupled with PCA has been deployed as a new rapid, inexpensive and non-destructive detection method to examine the solubility of 11-keto-{beta}-Boswellic acid (KBA) in streptozotocin (STZ) induced-diabetes mice liver tissue following intraperitoneal treatment. Moreover, microscopic study of liver tissue histopathology revealed that KBA has a protecting effect against STZ damage.

developmental biology↗

bioRxiv2019

Gene Loss and Evolution of the Plastome

Chloroplasts are unique organelles within the plant cells and are responsible for sustaining life forms on the earth due to their ability to conduct photosynthesis. Multiple functional genes within the chloroplast are responsible for a variety of metabolic processes that occur in the chloroplast. Considering its fundamental role in sustaining life on the earth, it is important to identify the level of diversity present in the chloroplast genome, what genes and genomic content have been lost, what genes have been transferred to the nuclear genome, duplication events, and the overall origin and evolution of the chloroplast genome. Our analysis of 2511 chloroplast genomes indicated that the genome size and number of coding DNA sequences (CDS) in the chloroplasts genome of algae are higher relative to other lineages. Approximately 10.31% of the examined species have lost the inverted repeats (IR) in the chloroplast genome that span across all the lineages. Genome-wide analyses revealed the loss of the Rbcl gene in parasitic and heterotrophic plants occurred approximately 56 Ma ago. PsaM, Psb30, ChlB, ChlL, ChlN, and Rpl21 were found to be characteristic signature genes of the chloroplast genome of algae, bryophytes, pteridophytes, and gymnosperms; however, none of these genes were found in the angiosperm or magnoliid lineage which appeared to have lost them approximately 203-156 Ma ago. A variety of chloroplast-encoded genes were lost across different species lineages throughout the evolutionary process. The Rpl20 gene, however, was found to be the most stable and intact gene in the chloroplast genome and was not lost in any of the analyzed species, suggesting that it is a signature gene of the plastome. Our evolutionary analysis indicated that chloroplast genomes evolved from multiple common ancestors ~1293 Ma ago and have undergone vivid recombination events across different taxonomic lineages.

plant biology↗

bioRxiv2019

Genomics, Molecular and Evolutionary Perspective of NAC Transcription Factors

NAC (NAM, ATAF1,2, and CUC2) transcription factors are one of the largest transcription factor families found in plants and are involved in diverse developmental and signalling events. Despite the availability of comprehensive genomic information from diverse plant species, the basic genomic, biochemical, and evolutionary details of NAC TFs have not been established. Therefore, NAC TFs family proteins from 160 plant species were analyzed in the current study. The analysis, among other things, identified the first algal NAC TF in the Charophyte, Klebsormidium flaccidum. Furthermore, our analysis revealed that NAC TFs are membrane bound and contain monopartite, bipartite, and multipartite nuclear localization signals. NAC TFs were also found to encode a novel chimeric protein domain and are part of a complex interactome network. Synonymous codon usage is absent in NAC TFs and it appears that they have evolved from orthologous ancestors and undergone significant duplication events to give rise to paralogous NAC TFs.

genomics↗

bioRxiv2019

The Molecular Mass and Isoelectric Point of Plant Proteomes

A proteomic analysis of proteomes from 145 plant species revealed a pI range of 1.99 (epsin) to 13.96 (hypothetical protein). The molecular mass of the plant proteins ranged from 0.54 to 2236.8 kDa. A putative Type-I polyketide synthase (22244 amino acids) in Volvox carteri was found to be the largest protein in the plant kingdom and was not found in higher plant species. Titin (806.46 kDa) and misin/midasin (730.02 kDa) were the largest proteins identified in higher plant species. The pI and molecular weight of the plant proteome exhibited a trimodal distribution. An acidic pI (56.44% of proteins) was found to be predominant over a basic pI (43.34% of proteins) and the abundance of acidic pI proteins was higher in unicellular algae species relative to multicellular higher plants. In contrast, the seaweed, Porphyra umbilicalis, possesses a higher proportion of basic pI proteins (70.09%). Plant proteomes were also found to contain the amino acid, selenocysteine (Sec), which is the first report of the presence of this amino acid in plants. Additionally, plant proteomes also possess ambiguous amino acids Xaa (unknown), Asx (asparagine or aspartic acid), Glx (glutamine or glutamic acid), and Xle (leucine or isoleucine) as well.

plant biology↗