Biology subjects

Bayati, M.

Publications and source records attributed to Bayati, M..

10 recordsLinked to original sources

bioRxiv2026

Spatial Recruitment Model for Motor Potentials Evoked By Transcranial Magnetic Stimulation (TMS) for In-Silico Testing and Tuning of Closed-Loop Procedures

The primary motor cortex has a prominent role in transcranial magnetic stimulation (TMS). It is one of few locations that provide directly observable responses, and its physiology serves as model or reference for almost all other TMS targets, e.g., through the motor threshold and spatial targeting relative to its position. It furthermore sets the safety limits for the entire brain. Its easily detectable responses have led to closed-loop methods for a range of aspects, e.g., for automated thresholding, amplitude tracking, and targeting. The high variability of brain stimulation methods would substantially benefit from fast unbiased closed-loop methods. However, the development of more potent methods would early on in the design phase require proper models that allowed tuning and testing with sufficient conditions but without a high number of experiments, which are time-consuming and expensive or even impossible at the needed scale. On the one hand, theoretical researchers without access to experiments miss realistic spatial response models of brain stimulation to develop better methods. On the other hand, subjects should potentially not be exposed to early closed-loop-methods without sufficient prior testing. After all, initial, poorly tuned feed-back as needed for closed-loop operation is known to demonstrate erratic behavior. To bridge this gap, we developed a digital-twin-style population model that generates motor evoked potentials in response to virtual stimuli and includes statistical information on spatial (coil position and orientation) as well as pulse-strength-dependent neural recruitment in the population to represent inter- and intra-individual variability. The population data were extracted from a combination of datasets of healthy and depressed subjects to reach large numbers and wide coverage. The model allows users to simulate different subjects and millions of runs for software-in-the loop testing. The model provides all code open-source to stimulate further development.

neuroscience↗

bioRxiv2026

Pairwise genomic alterations identify prognostic tumor states in multiple cancer types

Genomic models of cancer prognosis usually rely on individual genomic alterations, potentially overlooking clinically meaningful combinations of events. We analyzed genomic and clinical data from nearly 10,000 primary tumors across major cancer types to identify prognostic genomic interactions (PGIs), defined as pairs of genomic alterations whose joint status was associated with patient outcome beyond either alteration alone. By systematically integrating survival associations with pairwise combinations of recurrent copy-number alterations and frequently mutated driver genes, we identified 57 PGIs. These PGIs refined prognostic stratification and were linked to distinct transcriptomic programs representing immune-response, epithelial-mesenchymal transition, and proliferation-related themes. Gene-level mapping highlighted dosage-sensitive candidate genes within recurrent copy-number regions, and gene essentiality profiles supported subsets of PGI-derived gene pairs. Two PGIs were validated in independent datasets. Together, these results establish a framework for identifying prognostic combinations of genomic alterations and connecting them to pathway programs, candidate genes, and functional dependencies in human tumors.

cancer biology↗

bioRxiv2026

Mutational signatures in cancer genomes alter protein sequence motifs in cellular signaling networks

Somatic mutations in cancer genomes arise from distinct, context-specific mutational processes, yet their functional consequences at the protein and network level remain incompletely understood. Here, we show that mutational processes of single-nucleotide variants (SNVs) can systematically rewire signaling networks by inducing amino acid substitutions in short linear motifs (SLiMs) that mediate interactions with kinases and other signaling proteins. By analysing 11,000 cancer genomes and 144 classes of SLiMs, we identify motif-rewiring SNVs (rwSNVs) that create or disrupt SLiMs or remove phosphorylated residues. Mutational processes of methylcytosine deamination, APOBEC activity, and ultraviolet light exposure emerge as major contributors to motif rewiring. rwSNVs are enriched in cancer driver genes and pathways, linking mutation etiology to functional consequences. rwSNVs at the BRAF V600E hotspot associated with UV-related mutagenesis are predicted to generate a phosphorylation motif recognized by PLK1 kinase. Together, these findings reveal how mutational processes shape oncogenic signaling and tumor heterogeneity.

cancer biology↗

bioRxiv2024

Combinations of genomic alterations and immune microenvironmental features associate with patient survival in multiple cancer types

Oncogenesis and tumor progression are shaped by somatic alterations in the cancer genome and features of the tumor immune microenvironment (TME). How interactions of these two systems influence tumor development and clinical outcomes remains incompletely understood. To address this challenge, we developed the multi-omics analysis framework PACIFIC to systematically integrate genetic cancer drivers and infiltration profiles of immune cells with clinical information. In an analysis of 8500 cancer samples, we report 34 immunogenomic interactions (IGXs) in 13 cancer types in which context-specific combinations of genomic alterations and immune cell activities associate with disease outcomes. Risk associations of IGXs are potentially explained by tumor-intrinsic and microenvironmental metrics of immunogenicity and differential expression of therapeutic targets. In luminal-A breast cancer, MEN1 deletion combined with reduced neutrophils is associated with poor prognosis and deregulation of immune signalling pathways. These findings help elucidate how cancer drivers interact with TME to contribute to tumorigenesis.

cancer biology↗

bioRxiv2024

Exploring American Elderberry Compounds for Antioxidant, Antiviral, and Antibacterial Properties Through High-Throughput Screening Assays Combined with Untargeted Metabolomics

American elderberry (Sambucus nigra subsp. canadensis) is a rapidly emerging new perennial crop for Missouri, recognized for its high level of bioactive compounds with significant health benefits, including antibacterial, antiviral, and antioxidant properties. A high-throughput screening assay combined with untargeted metabolomics analysis was utilized on American elderberry juice from 21 genotypes to explore and characterize these bioactive compounds. Our metabolomics study has identified 32 putative bioactive compounds in the American Elderberry juices. An array of high-throughput screening bioassays was conducted to evaluate 1) total antioxidant capacity, 2) activation of antioxidant response elements (ARE), 3) antiviral activity, and 4) antibacterial activity of the putatively identified compounds. Our results revealed that 14 of the 32 American elderberry compounds exhibited strong antioxidant activity. Four compounds (isorhamnetin 3-O-glucoside, kaempferol, quercetin, and naringenin) activated ARE activity and were found to be non-cytotoxic to cells. Notably, six of the 32 compounds demonstrated significant antiviral activity in an in vitro TZM-bl assay against two strains of HIV-1 virus, CXCR4-dependent NL4-3 virus and CCR5-dependent BaL virus. Luteolin showed the most potent anti-HIV activity against the NL4-3 virus (IC50 = 1.49 {micro}M), followed by isorhamnetin (IC50 = 1.67 {micro}M). The most potent anti-HIV compound against the BaL virus was myricetin (IC50 = 1.14 {micro}M), followed by luteolin (IC50 = 4.38 {micro}M). Additionally, six compounds were found to have antibacterial activity against gram-positive bacteria S. aureus, with cyanidin 3-O-rutinoside having the most potent antibacterial activity in vitro (IC50 = 2.9 {micro}M), followed by cyanidin 3-O-glucoside (IC50 = 3.7 {micro}M). These findings support and validate the potential health benefits of compounds found in American elderberry juices and highlight their potential for use in dietary supplements as well as innovative applications in health and medicine.

biochemistry↗

bioRxiv2023

Directional integration and pathway enrichment analysis for multi-omics data

Omics techniques generate comprehensive profiles of biomolecules in cells and tissues. However, a holistic understanding of the data requires joint multi-omics analyses that are challenging. Here we present DPM, a data fusion method for combining multiple omics datasets using directionality and significance estimates of genes, transcripts, or proteins. DPM allows users to define how the input datasets are expected to interact directionally, reflecting the initial experimental design or regulatory relationships between the datasets. DPM statistically prioritises genes and pathways that change consistently across the datasets, while penalising those violating the constraints. Joint analyses of transcriptomic, proteomic, DNA methylation, and clinical datasets of cancer samples demonstrate how directional integration identifies genes and pathways modulated across omics datasets, highlights those with inconsistent evidence, and reveals candidate biomarkers with prognostic signals in multiple datasets. DPM is implemented in the ActivePathways method and provides a general framework for testing detailed hypotheses in multi-omics data.

bioinformatics↗

bioRxiv2023

Pan-cancer analysis of the ion permeome reveals functional regulators of glioblastoma aggression

Ion channels, transporters, and other ion-permeating proteins, collectively comprising the ion permeome (IP), are common drug targets. However, their roles in cancer are understudied. Our integrative pan-cancer analysis shows that IP genes display highly-elevated expression patterns in subsets of cancer samples significantly more often than expected transcriptome-wide. To enable target identification, we identified 410 survival-associated IP genes in 29 cancer types using a machine learning approach. Notably, GJB2 and SCN9A show prominent expression in neoplastic cells and associate with poor prognosis in glioblastoma (GBM), the most common and aggressive brain cancer. GJB2 or SCN9A knockdown in patient-derived GBM cells induces transcriptome-wide changes involving neural projection and proliferation pathways, impairs cell viability and tumor sphere formation, mitigates tunneling nanotube formation, and extends the survival of GBM-bearing mice. Thus, aberrant activation of IP genes appears as a pan-cancer feature of tumor heterogeneity that can be exploited for mechanistic insights and therapy development.

cancer biology↗

bioRxiv2021

Somatic point mutations are enriched in long non-coding RNAs with possible regulatory function in breast cancer

De novo somatic point mutations identified in breast cancer are predominantly non-coding and typically attributed to altered regulatory elements such as enhancers and promoters. However, while the non-coding RNAs (ncRNAs) form a large portion of the mammalian genome, their biological functions are mostly poorly characterized in cancers. In this study, using a newly developed tool, SomaGene, we reanalyze de novo somatic point mutations from the International Cancer Genome Consortium (ICGC) whole-genome sequencing data of 1,855 breast cancers. We identify 929 candidates of ncRNAs that are significantly and explicitly mutated in breast cancer samples. By integrating data from the ENCODE regulatory features and FANTOM5 expression atlas, we show that the candidate ncRNAs in breast cancer samples significantly enrich for active chromatin histone marks (1.9 times), CTCF binding sites (2.45 times), DNase accessibility (1.76 times), HMM predicted enhancers (2.26 times) and eQTL polymorphisms (1.77 times). Importantly, we show that the 929 ncRNAs contain a much higher level (3.64 times) of breast cancer-associated genome-wide association (GWAS) single nucleotide polymorphisms (SNPs) than genome-wide expectation. Such enrichment has not been seen with GWAS SNPs from other diseases. Using breast tissue related Hi-C data we then show that 82% of our candidate ncRNAs (1.9 times) significantly interact with the promoter of protein-coding genes, including previously known cancer-associated genes, suggesting the critical role for candidate ncRNA genes in activation of essential regulators of development and differentiation in breast cancer. We provide an extensive web-based resource (http://ncrna.ictic.sharif.edu), to communicate our results with the research community. Our list of breast cancer-specific ncRNA genes has the potential to provide a better understanding of the underlying genetic causes of breast cancer. Lastly, the tool developed in this study can be used in the analysis of somatic mutations in all cancers.

cancer biology↗

bioRxiv2020

Integrative analysis of mutated genes and mutational processes reveals seven colorectal cancer subtypes

Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths in the world. It has been reported that [~]10%-15% of individuals with colorectal cancer experience a causative mutation in the known susceptibility genes, highlighting the importance of identifying mutations for early detection in high risk individuals. Through extensive sequencing projects such as the International Cancer Genome Consortium (ICGC), a large number of somatic point mutations have been identified that can be used to identify cancer-associated genes, as well as the signature of mutational processes defined by the tri-nucleotide sequence context (motif) of mutated sites. Mutation is the hallmark of cancer genome, and many studies have reported cancer subtyping based on the type of frequently mutated genes, or the proportion of mutational processes, however, none of these cancer subtyping methods consider these features simultaneously. This highlights the need for a better and more inclusive subtype classification approach to enable biomarker discovery and thus inform drug development for CRC. In this study, we developed a statistical pipeline based on a novel concept gene-motif, which merges mutated gene information with tri-nucleotide motif of mutated sites, to identify cancer subtypes, in this case CRCs. Our analysis identified for the first time, 3,131 gene-motif combinations that were significantly mutated in 536 ICGC colorectal cancer samples compared to other cancer types, identifying seven CRC subtypes with distinguishable phenotypes and biomarkers. Interestingly, we identified several genes that were mutated in multiple subtypes but with unique sequence contexts. Taken together, our results highlight the importance of considering both the mutation type and mutated genes in identification of cancer subtypes and cancer biomarkers.

cancer biology↗

bioRxiv2018

CANCERSIGN: a user-friendly and robust tool for identification and classification of mutational signatures and patterns in cancer genomes

Analyses of large somatic mutation datasets, using advanced computational algorithms, have revealed at least 30 independent mutational signatures in tumor samples. These studies have been instrumental in identification and quantification of responsible endogenous and exogenous molecular processes against cancer. The quantitative approach used to deconvolute mutational signatures is becoming an integral part of cancer research. Therefore, development of a stand-alone tool with a user-friendly graphical interface for analysis of cancer mutational signatures is necessary. In this manuscript, we introduce CANCERSIGN as an open access1 bioinformatics tool that uses raw mutation data (BED files) as input, and generates 3-mer and 5-mer mutational signatures. Additionally, this tool enables users to perform clustering on tumor samples based on the raw mutation counts as well as using the proportion of mutational signatures in each sample. Using this tool, we analysed all the whole genome somatic mutation datasets of International Cancer Genome Consortium (ICGC) samples and identified a number of novel signatures.

bioinformatics↗