Biology subjects

Kavousi, K.

Publications and source records attributed to Kavousi, K..

13 recordsLinked to original sources

bioRxiv2026

A genetic network coordinated by TCP16 and LHY integrates regulation of the vegetative-reproductive phase transition in Arabidopsis thaliana

The transition to flowering in Arabidopsis thaliana is a complex process governed by many biological and environmental stimuli. Although many of the genes which regulate this process have been identified over the past 30 years, it remains unclear how these networks are integrated. In this study, we used the transcriptional responses of Col-0, Ler-1, and three mutant lines, to build a genome wide regulatory network of Arabidopsis thaliana during the flowering transition. The expression profiles of 22,810 genes across five genotypes were collected from the GEO database Series GSE57 from which we assigned flowering-time genes to different interacting modules by an adapted form of Hierarchical Complete Linkage Clustering (HCLC) after reconstruction of regulatory networks according to the Position Weight Matrix (PWM)-based method. Within these modules, we identified 77 core genes and 31 controller or driver genes. We identify two genes, LHY and, less expectedly, the transcription factor TCP16, to be topographically positioned at the regulatory hubs a nine-gene transcriptional control unit, implying they have the capacity to integrate information from across the flowering time pathways which interpret different environmental or endogenous cues during the vegetative-reproductive transition. Interrogating their behaviour across transcriptional datasets, we show that both LHY and TCP16 show transcriptional oscillations during the flowering transition, with a wavelength that varies depending on environmental conditions. We suggest that the transcriptional responses of LHY and TCP16 allow them to regulate the flow of information through the genetic networks which integrates different floral transition cues, and that genetic modelling approaches can provide new insights into the regulation of well-studied biological processes such as the flowering transition. Author summaryHow plants decide when to flower is a critical stage for completing their life cycles. It is also of key agricultural importance, as crops need to flower at the right time of year to allow efficient pollination and harvesting. Many genes are known to affect flowering time control in plants. Here, we use computational approaches to estimate how different genes interact in flowering time control in Arabidopsis, a small plant in the mustard family which is widely used for molecular studies. We use large-scale studies of how gene expression changes in different plant lines which have disrupted or adjusted flowering time to group the many genes involved in flowering into different interacting pathway, which we visualise as sets of coloured nodes controlling one another in a network. We show that two genes may have new rols in integrating information from different pathways, and discuss how their behaviour might help them to function as intregrators of biological information - including the daily oscaillations in their expression.

genetics↗

bioRxiv2026

Relationship Between Gene Expression and Drug Response in Triple-Negative Breast Cancer: Leveraging Single-Cell RNA Sequencing and Machine Learning to Identify Biomarker Profiles

Triple-negative breast cancer (TNBC) is an aggressive subtype characterized by limited therapeutic options and poor prognosis. To address these challenges, we combined single-cell RNA sequencing (scRNA-seq) data with advanced machine learning techniques to find biomarkers that predict treatment response. Using tumor and blood samples from TNBC patients treated with either paclitaxel alone or in combination with atezolizumab, we constructed cellular maps, identified differentially expressed genes, and analyzed co-expression networks. Copy number variation (CNV) profiling and weighted gene co-expression network analysis (WGCNA) identified immune-related genes, including IL7R, CD6, and TNFAIP3, as candidate biomarkers linked to therapeutic response. A feature selection using Random Forest followed by bootstrap-enhanced K-nearest neighbors (K-NN) classification achieved high predictive accuracy across all subgroups (AUC > 0.93). Local Interpretable Model-Agnostic Explanations (LIME) based interpretability further identified important factors influencing treatment sensitivity and resistance, including EGR1, MKI67, C1QA/B/C, GZMB, and PRF1. Notably, blood-derived biomarkers showed strong predictive potential, highlighting the variability of liquid biopsy approaches for non-invasive monitoring. Our results indicate that integration of scRNA-seq with interpretable machine learning facilitates the identification of reliable biomarkers and yields clinically actionable insights for personalized therapeutic strategies in TNBC.

bioinformatics↗

bioRxiv2025

Musical Insights into Protein Sequences and Functions via NMR Data

This study explores an innovative technique for transforming rhythmic patterns found in nature into music, with a focus on protein sequences. For the past twenty years, various strategies have been developed for musical translation of biological sequences. This research presents a groundbreaking approach where music is derived from protein nano-scale structures using the atomic frequencies of amino acids extracted from NMR experiments. Each amino acid is assigned a unique musical note and duration, determined by its 13CNMR spectrum and relaxation period. Applying this method, music was composed from different protein sequences, including homologue {beta}-globin, where a set theory-based comparative analysis was conducted. This novel approach opens up a new auditory dimension for understanding protein functions.

bioinformatics↗

bioRxiv2025

Unlocking Precision Diagnostics: A Multimodal Framework Integrating Metabolomics with Advanced Machine Learning Techniques

Integrating multiple omics modalities is a crucial strategy in cancer research, particularly in metabolomics, enabling early detection and detailed exploration of cancer biomarker signatures. This study evaluates five strategies for integrating metabolomics data from liquid chromatography-mass spectrometry, gas chromatography-mass spectrometry, and nuclear magnetic resonance. Deep Transfer Learning and Multiple Kernel Learning demonstrated superior performance, significantly improving classification accuracy, sensitivity, and robustness compared to single-modality analyses. Deep Transfer Learning employed a custom autoencoder for feature extraction followed by artificial neural network classification, while Multiple Kernel Learning optimized kernel matrices across different modalities. Feature extraction in the Deep Transfer Learning approach, combined with the selection of important features and subsequent analysis, revealed elevated levels of monounsaturated phospholipids such as phosphatidylcholine 30:1, phosphatidylethanolamine 32:1, and sphingomyelin 32:1 in HER2-positive cases. Additionally, {beta}-alanine, gluconic acid, and N-acetylaspartic acid were increased, whereas 5-deoxy-5-methylthioadenosine and nicotinamide were decreased. These methods advance cancer detection, biomarker discovery, and the development of precise diagnostic and therapeutic tools while offering robust and adaptable strategies for multi-omics data integration across diverse biological datasets.

bioinformatics↗

bioRxiv2022

Sturgeons: An Evolutionary Insight to The α-globin Protein Speciation and Diversification

Sturgeons are living fossils and among the oldest surviving vertebrate species, and Caspian Sea is habitat of the vast majority of sturgeon species. It is tempting to assume that speciation of sturgeons occurred in response to the severe environmental changes during the formation of Caspian Sea from the ancient Paratethys Sea. Hemoglobin (Hb) reflects the evolutionary history of fishes. Changes in the Hb sequences will affect the protein structure and consequently the vital function of oxygen delivery. As complete sequence data for any sturgeon hemoglobin gene or protein was hitherto not available, we determined the complete gene and protein sequences of an -globin chain (as one of the constituents of Hb complex) from six Caspian Sea sturgeons of different species and compared these sequences mutually and with all available fish -globin sequences. The phylogenetic analyses based on alpha globin sequences can highlight the essentiality of proper changes in globin chains in the speciation process. Estimating the divergence time of -globin genes for different fish families discloses important facts about the rate of evolution and decodes. The results support the hypothesis of simultaneity between the time of morphological speciation of fish families and their -globin divergence. The correlation and concurrency between morphological and molecular changes can be interpreted as a sign of struggling to acquire more fitness to changing environment.

evolutionary biology↗

bioRxiv2022

Computational modeling of the evolutionary transition from C3 to C4 photosynthesis

C4 photosynthesis is an evolutionary adaptation that minimizes the adverse effects of the high photorespiration rate. Although it is widely accepted that the C4 plants are evolved from C3 ancestors, the knowledge about the details of this process is yet to be complete. One application of constraint-based metabolic network modeling is to simulate evolutionary trajectories that an organism endures under a certain selective pressure. However, this approach is barely used to predict the evolution of a complex trait in eukaryotes. Here, we utilized a genetic algorithm combined with a constraint-based metabolic network model of Arabidopsis thaliana to simulate the trajectories of C3 to C4 conversion under high photorespiration rate conditions combined with different environmental conditions. Our modeling predicted that the C3-C4 intermediates and C4 strategies are superior to C3 photosynthesis in these environmental conditions. Besides, resource scarcities drive different evolutionary trajectories toward the emergence of C4 photosynthesis. AUTHOR SUMMARYIt is estimated that high photorespiration reduces C3 crops productivity up to 50% [1]. Carbon concentrating mechanisms have evolved in many plants to overcome adverse effects of high photorespiration rate in especial environments. C4 photosynthesis is one of these adaptations that separate carbon assimilation reactions spatially between mesophyll and bundle sheath cells of the leaves. It is reported that C4 photosynthesis is evolved more than 65 times in 19 different families of angiosperms independently from C3 ancestors. Therefore, it is hypothesized that the potential modules, at the level of metabolism, anatomy, and gene regulation, which are needed to perform C4 photosynthesis are present in C3 plants. Since C4 plants grow better than C3 plants in hot and dry environments, scientists are working on converting C3 crops such as rice to C4 plants. However, several challenges, including a lack of understanding of the evolutionary paths toward manifestation of C4 trait, have hampered the C3-to-C4 engineering ([2]). Here, we used a metabolic network model of Arabidopsis thaliana as a C3 model plant and attempted to understand the possible evolutionary events leading to emerging of C4 photosynthesis in various scenarios and examined the results.

bioinformatics↗

bioRxiv2021

Global non-random abundance of short tandem repeats in rodents and primates.

BackgroundWhile of predominant abundance across vertebrate genomes and significant biological implications, the relevance of short tandem repeat (STR) abundance to speciation remains largely elusive and attributed to random coincidence for the most part. In a model study, here we collected whole-genome abundance of mono-, di-, and trinucleotide STRs in nine species, encompassing rodents and primates, including rat, mouse, olive baboon, gelada, macaque, gorilla, chimpanzee, bonobo, and human. The obtained unnormalized and normalized data were used to analyze hierarchical clustering of the STR abundances in the selected species. ResultsWe found massive differential abundances between the rodent and primate orders. In addition, while numerous STRs had random abundance across the nine selected species, the global abundance conformed to three consistent , as follows: , , , which coincided with the phylogenetic distances of the selected species (p< 4E-05). Exceptionally, in the trinucleotide STR compartment, human was significantly distant from all other species. ConclusionWe propose that the global abundance of STRs is non-random in rodents and primates, and probably had a determining impact on the speciation of the two orders. We also propose the STRs and STR lengths which specifically coincided with the phylogeny of the selected species.

evolutionary biology↗

bioRxiv2020

Distinct microbial communities along the chronic oil pollution continuum of the Persian Gulf converge with oil spill accidents

Persian Gulf hosting ca. 48% of the worlds oil reserves; has been chronically exposed to natural oil seepage. Oil spill events have been studied over the last decade; however, the influence of chronic oil exposure on the microbial community of the Persian Gulf has remained unknown. We performed genome-resolved comparative analyses of the water and sediments prokaryotic community along the Gulfs pollution continuum (Strait of Hormuz, Asalouyeh and Khark Island). The continuous exposure to trace amounts of pollution has shifted the microbial profile toward the dominance of Oceanospirillales, Flavobacteriales, Alteromonadales, and Rhodobacterales in Asalouyeh and Khark samples. Intrinsic oil-degrading microbes present in low abundances in marine habitats; experience a bloom in response to oil pollution. Comparative analysis of the Persian Gulf samples with 106 oil-polluted marine samples reveals the pollutants hydrocarbon content, exposure time and sediment depth as main determinants of microbial response to pollution. High aliphatic content enriches for Oceanospirillales, Alteromonadales and Pseudomonadales whereas, Alteromonadales, Cellvibrionales, Flavobacteriales and Rhodobacterales dominate polyaromatic polluted samples. In sediment samples, Deltaproteobacteria and Gammaproteobacteria had the highest abundance. In chronic exposure and oil spill events, the community composition converges towards higher dominance of oil-degrading constituents while promoting the division of labor for successful bioremediation. Originality-Significance StatementThe impact of anthropogenic oil pollution on the microbial community has been studied for oil spill events; while the influence of long-term chronic exposure to oil derivatives on The microbes has remained unknown. Persian Gulf hosts ca. 48% of the worlds oil reserves and has been chronically exposed to natural and accidental oil pollutions. Different pollutant profilesin different locations and the recurrent pollution events; make Persian Gulf an ideal model system to analyse the impact of oil hydrocarbon on the microbial community and the recovery potential of marine ecosystems after pollution. In this study we perform an extensive analysis of thhe Persian Gulfs water and sediment samples along the water circulation and pollution continuum for the first time. Our results show that these long-standing trace exposure to oil has imposed a consistent selection pressure on the Gulfs microbes; developing unique and distinct communities along the pollution continuum. Our extensive genome-resolved analysis of the metabolic capabilities of the reconstructed MAGs shows an intricate division of labor among different microbes for oil degradation and determine the major drivers of each degradation step. Intrinsic oil-degrading microbes (e.g., Immundisolibacter, Roseovarius and Lutimaribacter) bloom along the Persian Gulfs pollution continuum and function as the main oil degraders. Comparative study of PG datasets with 106 oil-polluted marine samples (water and sediment) reveals similar community compositions in the Persian Gulfs water and sediment samples to those of oil spill events and suggests hydrocarbon type and exposure time as the main determinants of the microbial response to oil pollution.

microbiology↗

bioRxiv2020

BNrich: A Bayesian network approach to the pathway enrichment analysis

MotivationOne of the most popular techniques in biological studies for analyzing high throughput data is pathway enrichment analysis (PEA). Many researchers apply the existing methods without considering the topology of pathways or at least they have overlooked a significant part of the structure, which may reduce the accuracy and generalizability of the results. Developing a new approach while considering gene expression data and topological features like causal relations regarding edge directions will help the investigators to achieve more accurate results. ResultsWe proposed a new pathway enrichment analysis based on Bayesian network (BNrich) as an approach in PEA. To this end, the cycles were eliminated in 187 KEGG human signaling pathways concerning intuitive biological rules and the Bayesian network structures were constructed. The constructed networks were simplified by the Least Absolute Shrinkage Selector Operator (LASSO), and their parameters were estimated using the gene expression data. We finally prioritize the impacted pathways by Fishers Exact Test on significant parameters. Our method integrates both edge and node related parameters to enrich modules in the affected signaling pathway network. In order to evaluate the proposed method, consistency, discrimination, false positive rate and empirical P-value criteria were calculated, and the results are compared to well-known enrichment methods such as signaling pathway impact analysis (SPIA), bi-level meta-analysis (BLMA) and topology-based pathway enrichment analysis (TPEA). AvailabilityThe R package is available on carn.

bioinformatics↗

bioRxiv2019

TAXyl: An in-silico method for predicting the thermal activity for xylanases from GH10 and GH11 families

Growing industrial utilization of enzymes, and the increasing availability of metagenomic data highlights the demand for effective methods of targeted identification and verification of novel enzymes from various environmental microbiota. Xylanases are a class of enzymes with numerous industrial applications and are involved in the degradation of xylose, a component of lignocellulose. Optimum temperature of enzymes are essential factors to be considered when choosing appropriate biocatalysts for a particular purpose. Therefore, in-silico prediction of this attribute is a significant cost and time-effective step in the effort to characterize novel enzymes. The objective of this study was to develop a computational method to predict the thermal dependence of xylanases. This tool was then implemented for targeted screening of putative xylanases with specific thermal dependencies from metagenomic data and resulted in identification of three novel xylanases from sheep and cow rumen microbiota. Here we present TAXyl (Thermal Activity Prediction for Xylanase), a new sequence-based machine learning method that has been trained using a selected combination of various protein features. This random forest classifier discriminates non-thermophilic, thermophilic, and hyper-thermophilic xylanases. Models performance was evaluated through multiple iterations of six-fold cross-validations, and it exhibited a mean accuracy of [~]0.79. TAXyl is freely accessible as a web-service.

bioinformatics↗

bioRxiv2019

A hierarchical approach to model decision making: a study in chemotactic behavior of Escherichia coli

Reducing the complex behavior of living entities to its underlying physical and chemical processes is a formidable task in biology. Complex behaviors can be characterized as decision making: the ability to process the incoming information via an intracellular network and act upon this information to choose appropriate strategies. Motility is one such behavior that has been the focus many modeling efforts in the past. Our aim is to reduce the chemotactic behavior in E. coli to its molecular constituents in order to paint a comprehensive and end-to-end picture of this intricate behavior. We utilize a hierarchical approach, consisting of three layers, to achieve this goal: at the first level, chemical reactions involved in chemotaxis are simulated. In the second level, the chemical reactions give rise to the mechanical movement of six independent flagella. At the last layer, the two lower layers are combined to allow a digital bacterium to receive information from its environment and swim through it with verve. Our results are in concert with the experimental studies concerning the motility of E. coli cells. In addition, we show that our detailed model of chemotaxis is reducible to a non-homogeneous Markov process.

systems biology↗

bioRxiv2018

A relative comparison between Hidden Markov- and Log-Linear- based models for differential expression analysis in a real time course RNA sequencing data

With the advent of the Next Generation Sequencing technologies, RNA-seq has become known as an optimal approach for studying gene expression profiling. Particularly, time course RNA-seq differential expression analysis has been used in many studies to identify candidate genes. However, applying a statistical method to efficiently identify differentially expressed genes (DEGs) in time course studies is challenging due to inherent characteristics of such data including correlation and dependencies over time. Here we aim to relatively compare EBSeq-HMM, a Hidden Markov-based model, with multiDE, a Log-Linear-based model, in a real time course RNA sequencing data. In order to conduct the comparison, common DEGs detected by edgeR, DESeq2 and Voom (referred to as Benchmark DEGs) were utilized as a measure. Each of the two models were compared using different normalization methods. The findings revealed that multiDE identified more Benchmark DEGs and showed a higher agreement with them than EBSeq-HMM. Furthermore, multiDE and EBSeq-HMM displayed their best performance using TMM and Upper-Quartile normalization methods, respectively.

bioinformatics↗

bioRxiv2018

Link Between Short tandem Repeats and Translation Initiation Site Selection.

Recent work in yeast and humans suggest that evolutionary divergence in cis-regulatory sequences impact translation initiation sites (TISs). Cis-elements can also affect the efficacy and amount of protein synthesis. Despite their vast biological implication, the landscape and relevance of short tandem repeats (STRs)/microsatellites to the human protein-coding gene TISs remain largely unknown. Here we characterized the STR distribution at the 120 bp cDNA sequence upstream of all annotated human protein-coding gene TISs based on the Ensembl database. Furthermore, we performed a comparative genomics study of all annotated orthologous TIS-flanking sequences across 47 vertebrate species (755,956 transcripts), aimed at identifying human-specific STRs in this interval. We also hypothesized that STRs may be used as genetic codes for the initiation of translation. The initial five amino acid sequences (excluding the initial methionine) that were flanked by STRs in human were BLASTed against the initial orthologous five amino acids in other vertebrate species (2,025,817 pair-wise TIS comparisons) in order to compare the number of events in which human-specific and non-specific STRs occurred with homologous and non-homologous TISs (i.e. [≥]50% and <50% similarity of the five amino acids). We characterized human-specific STRs and a bias of this compartment in comparison to the overall (human-specific and non-specific) distribution of STRs (Mann Whitney p=1.4 x 10-11). We also found significant enrichment of non-homologous TISs flanked by human-specific STRs (p<0.00001). In conclusion, our data indicate a link between STRs and TIS selection, which is supported by differential evolution of the human-specific STRs in the TIS upstream flanking sequence.\n\nAbbreviations

genomics↗