Biology subjects

Nikpour, P.

Publications and source records attributed to Nikpour, P..

4 recordsLinked to original sources

bioRxiv2025

Decoding calcium oscillation frequency in transcriptional regulation

Cells continuously experience fluctuating intracellular calcium (Ca{superscript 2}) signals that orchestrate diverse processes such as transcription, proliferation, and apoptosis. Temporal features of Ca{superscript 2} dynamics, including oscillation frequency, are hypothesized to encode information, allowing cells to discriminate between relevant and stochastic signals. However, the mechanisms of frequency decoding and their transcriptional consequences remain incompletely understood. To address this, we investigated how defined Ca{superscript 2} oscillation frequencies are translated into signaling cascades and gene expression programs in human non-excitable cells. Using optogenetic control of melanopsin-mediated Ca{superscript 2} influx, we induced slow (8 mHz) or fast (15 mHz) oscillations with identical single-pulse kinetics to isolate the effect of frequency. We found that TNF and IL8 transcription via NF-{kappa}B displayed sigmoidal frequency dependence, strictly requiring regular periodic stimulation, while random or low-frequency inputs with equal cumulative Ca{superscript 2} exposure were ineffective. Bulk RNA sequencing revealed a MYC-centered transcriptional response, with 116 of 215 differentially expressed genes predicted as MYC targets, despite unchanged MYC mRNA levels. Label-free phosphoproteomics identified PRKDC, CHEK2 and ATM as the top upstream kinases, forming a network linking Ca{superscript 2} oscillations to cell cycle and stress signaling. These findings demonstrate that cells can decode Ca{superscript 2} oscillation frequency through a multi-kinase network that tunes transcription via NF-{kappa}B and MYC, providing mechanistic insight into how temporal dynamics of second messengers shape cellular decision-making.

cell biology↗

bioRxiv2024

Investigating Combined Hypoxia and Stemness Indices for Prognostic Transcripts in Gastric Cancer: Machine Learning and Network Analysis Approaches

IntroductionGastric cancer (GC) is among the deadliest malignancies globally, characterized by hypoxia-driven pathways that promote cancer progression, including mechanisms associated with stemness facilitating invasion and metastasis. This study aimed to develop a prognostic decision tree using genes implicated in hypoxia and stemness pathways to predict outcomes in GC patients. Material and MethodsGC RNA-seq data from The Cancer Genome Atlas (TCGA) were utilized to compute hypoxia and stemness scores via Gene Set Variation Analysis (GSVA) and mRNA expression-based stemness index (mRNAsi). Hierarchical clustering based on these scores identified clusters with distinct survival outcomes, and differentially expressed genes (DEGs) between these clusters were identified. Weighted Gene Co-expression Network Analysis (WGCNA) was employed to identify modules and hub genes associated with clinical traits. Hub genes overlapping with DEGs were extracted, followed by functional enrichment, protein-protein interaction (PPI) network analysis, and survival analysis of shared genes. A prognostic decision tree was constructed using survival-associated genes. ResultsHierarchical clustering identified six clusters among 375 TCGA GC patients, showing significant differences in survival outcomes between cluster 1 (with low hypoxia and high stemness) and cluster 4 (high hypoxia and stemness). Validation in the GSE62254 dataset corroborated these findings. WGCNA revealed modules correlating with clinical traits and survival. Functional enrichment highlighted pathways such as cell adhesion and calcium signaling. The decision tree based on survival-related genes including AKAP6, GLRB, LINC00578, LINC00968, MIR145, NBEA, NEGR1 and RUNX1T1 and achieved an area under the curve (AUC) of 0.81 (training) and 0.67 (test), demonstrating the utility of combined scores in patient stratification. ConclusionThis study introduces a novel hypoxia-stemness-based prognostic decision tree for GC. The identified genes show promise as prognostic biomarkers for GC, warranting further validation in clinical settings.

cancer biology↗

bioRxiv2022

Construction of a three-component regulatory network of transcribed ultraconserved regions for the identification of prognostic biomarkers in gastric cancer

Although altered expression and functional roles of the transcribed ultraconserved regions (T-UCRs) in the pathophysiology of neoplasms has already been investigated, relevance of the functions for T-UCRs in gastric cancer (GC) is still the subject of inquiry. In the current study, The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) dataset was used as a resource for the RNA-sequencing data. Differential expression analysis was conducted using DESeq2. Interactions between T-UCRs, miRNAs, and mRNAs were combined into a three-component network. The Survival package was utilized to identify survival-related differentially-expressed T-UCRs (DET-UCRs). Using an in-house cohort of GC tissues, expression of two DET-UCRs was experimentally verified. Thirty-four T-UCRs were dysregulated in TCGA-STAD tumoral samples compared to non-tumoral counterparts. The network was composed of 34 DET-UCRs, 275 miRNAs and 796 mRNAs nodes. Five T-UCRs were significantly correlated with the overall survival. While no expression of uc.232 was observed in our in-house cohort of GC tissues, uc.343 showed an increased expression in gastric tumoral tissues. The constructed three-component regulatory network of T-UCRs in GC presents a comprehensive understanding of the underlying gene expression regulation processes involved in tumor development and can serve as a basis to investigate potential prognostic biomarkers and therapeutic targets. Simple summaryGastric cancer (GC) is one of the most common cancers in the world and is considered as a highly heterogeneous disease based on subtypes and genetic alterations. GC is mostly detected in the advanced stages, hence, identifying diagnostic and prognostic biomarkers is of urgent need. Transcribed ultraconserved regions (T-UCRs) are a type of long non-coding RNAs which are linked to human carcinogenesis. Their mechanisms of action and the factors regulating their expression in cancers are poorly understood. In the current study, by applying a systems biology approach and constructing a regulatory network, we have presented a T-UCR as a potential diagnostic biomarker. Additionally, five T-UCRs with significant correlation with patients overall survival were found, which can be potentially used as prognostic biomarkers in future.

cancer biology↗

bioRxiv2020

Inference and validation of an integrated regulatory network of autism

Autism is a complex neurodevelopmental disorder. Functional roles of several non-coding transcripts including long noncoding RNAs (lncRNAs) have been shown to influence the pathobiology of autism. We hypothesized that there are more autism-associated lncRNAs to be discovered. Here, we utilized a systems biology approach to identify novel lncRNAs that might play a role in the molecular pathogenesis of autism. Based on the data provided by the Simons Foundation Autism Research Initiative (SFARI), a three-component regulatory network comprising mRNAs, microRNAs (miRNAs) and lncRNAs was constructed. Functional enrichment analysis was performed to identify molecular pathways potentially mediated by components of the network. The potential association of four candidate lncRNAs with autism was investigated experimentally by developing and verifying a valproic acid (VPA)-exposed mouse model of autism. We composed a network of 33 mRNA, 25 miRNA and 4 lncRNA nodes associated with neurologically-relevant pathways and functions. We then verified the differential expression of four candidate lncRNAs: Gm10033, 1500011B03Rik, A930005H10Rik and Gas5 in the brain of VPA-exposed mice. We furthermore identified a novel splice variant of Gm10033, designated as Gm10033-{Delta}Ex2, which was expressed in various mouse tissues. The integrative approach, we utilized, combines the analysis of a three-component regulatory network with experimental validation of targets in an animal model of autism. As a result of the analysis, we prioritized a set of candidate autism-associated lncRNAs. These links add to the common understanding of the molecular and cellular mechanisms that are involved in disease etiology, specifically in the autism.

systems biology↗