Biology subjects

R, S.

Publications and source records attributed to R, S..

15 recordsLinked to original sources

bioRxiv2026

Taxonomic classification cost tracks neither sequencing depth nor community richness at single-sample scale: a measured resource protocol for 16S rRNA amplicon pipelines

Marker-gene amplicon workflows are routinely run on shared compute, yet the cores, memory and wall time they are given are chosen by convention and not by measurement. We present a protocol for measuring them, applied to the two dominant stages of a QIIME 2 16S rRNA pipeline, DADA2 denoising and Naive Bayes taxonomic classification, across nine upper-respiratory samples from a paediatric otitis media cohort. The two stages do not consume the same input: denoising reads every sequence, classification only those surviving it. Subsampling one library across a 27-fold range of sequencing depth, denoising wall time rose 14.3-fold while classification changed by 1% and its peak memory not at all (3.11 GiB). Amplicon sequence variant (ASV) richness rose 2.8-fold over that range, so this is not richness saturating: the stage is dominated by a fixed per-invocation cost. Across a body-site gradient of 5 to 70 ASVs, denoising followed read count (exponent 0.75) while classification followed neither: a 5-ASV effusion and a 70-ASV adenoid community cost 40.81 s and 40.79 s. One ASV took 36.20 s and 218 took 37.27 s, 97% fixed cost. Thread-level parallelism offered little benefit. Denoising peaked at 1.18x near 8 threads and then declined; classification was slower at every setting above one job, consuming 10.5 times the CPU at 40. Representative sequences and their taxonomic assignments were identical at 1, 4 and 40 threads, so a reduced allocation changes what the analysis costs, not what it reports. Extending the query set to 10,000 sequences located two distinct boundaries: eight jobs first beat one at roughly 5,000 queries, and fitted fixed and per-query costs become equal at 15,248. Both lie roughly two orders of magnitude above the richest single sample measured. Practically: size denoising by read count, calibrate classification once against the reference in use, request one job for classification below a few thousand sequences, and take throughput from sample-level parallelism. Protocol, data and analysis code are released with the pipeline.

bioinformatics↗

bioRxiv2026

EnzyKAN: Protein Language Model Embeddings and Kolmogorov-Arnold Network Variants for Enzyme Commission Classification with a Proposed Electron-Transfer Physics Feature Framework

MotivationComputational enzyme classification has previously utilised sequence homology features and protein language model embeddings. The Kolmogorov-Arnold Network (KAN) paradigm, which uses learnable edge functions rather than fixed ones, has shown promising results in biological sequence tasks. ResultsA fully reproducible investigation of KAN variants for seven-class EC classification on up to 9,516 labelled sequences from the CLEAN benchmark [1] (9,386 for language model experiments). In the sequence only settings, fixed basis KAN variants outperformed an MLP baseline moderately (macro F1 = 0.17-0.29). Utilisation of ESM-2 650M embeddings [2] greatly improved results via 5-fold cross-validation: MLP macro F1 = 0.750 {+/-} 0.009, accuracy = 0.823 {+/-} 0.009; learnable SineKAN macro F1 = 0.716 {+/-} 0.023, accuracy = 0.788 {+/-} 0.019. MLP performed comparably but did not exceed conventional baselines. As an aside, we introduce but do not investigate an approach to EC oxidoreductase sub-classification through the use of a Marcus theory-based electron transfer feature framework. AvailabilityCode and result files are available at https://github.com/sanjuz-cas/ENZYKAN.

bioinformatics↗

bioRxiv2025

Interconnected axes of phenotypic plasticity drive coordinated cellular behaviour and worse clinical outcomes in breast cancer

Phenotypic plasticity plays a key role in cancer progression and metastasis, enabling cancer cells to adapt and evolve, but precisely how distinct axes governing phenotypic plasticity interact to shape tumour progression and patient outcomes remains unclear. We investigated five major interconnected axes of plasticity in ER-positive (ER+) breast cancer: Metabolic Reprogramming, Epithelial-to-Mesenchymal Plasticity (EMP), Luminal-Basal (Lineage) Switching, Stemness, and Drug-resistance using network dynamics simulations, integrative bulk and single-cell transcriptomic analyses and patient survival analyses. We show that these axes are not independent but drive one another, forming two mutually inhibiting teams of nodes enabling specified cellular behaviour. One team (favouring high glycolysis, stem-like, basal-like, mesenchymal/hybrid and tamoxifen-resistant phenotype) was found to be associated with aggressive progression and worse survival. On the other hand, the opposing team (favouring high oxidative phosphorylation, non-stem-like, luminal-like, epithelial and tamoxifen-sensitive phenotype) correlated with better outcomes. Importantly, altering one axis of plasticity often drove coordinated responses along other axes and vice versa. Our findings establish phenotypic plasticity in cancer as a coordinated, multi-axis dynamical process, thus suggesting novel strategies to disrupt systems-level reprogramming enabling metastasis and therapeutic resistance.

systems biology↗

bioRxiv2025

CAcnesDB: A database of Cutibacterium acnes with Integrated Functional Insights derived from Multi-modal Genome Annotation

Cutibacterium acnes is a predominant member of the human skin microbiome, generally as a beneficial or a benign inhabitant, which under specific conditions transforms into a pathogen, causing acne and associated inflammatory responses. While there is a wealth of data on the microbiology and genomics of the microbe, there is a lack of comprehensive annotation of the proteins coded by the genome, slowing progress in understanding its unique metabolic versatility and survival in host hypoxic sebaceous niches. We configured an integrated pipeline for functional annotation of the C. acnes genome, combining gene calling, domain and ontology mapping, KEGG pathways assignment, proteome-scale structural modeling, predicting Pockets and associating the small-molecule metabolites based on binding site similarity to facilitate structure-to-function annotation. Structural predictions using ColabFold covered nearly the full proteome, with 67.4% of models showing high confidence. To infer protein function, ligand-binding sites in the C. acnes modeled proteome were predicted using a consensus of three independent algorithms, PocketDepth, SiteHound, and FPocket. The resulting pockets were matched with known small-molecule binding sites in PDB to map potential ligands, resulting in the C. acnes predicted metabolome (CApM) comprising 1,954 ligands across 1,865 proteins. CAcnesDB is a comprehensive up-to-date database of the C. acnes genome, hosting functional annotation from multiple levels of input and analyses, covering 2297 protein-coding genes. Impact StatementCutibacterium acnes (C. acnes) is implicated in acne vulgaris, a common skin condition affecting millions of people worldwide, yet the absence of a unified genomic resource limits comprehensive understanding of the underlying biology. In this work, we introduce CAcnesDB, a genome-wide annotation resource built through multi-modal integration of sequence- and structure-based analyses, structural proteome models, predicted binding sites associated with small-molecule metabolites, functional classes, protein-protein interactions, and pathways. This resource consolidates enriched annotations to enable systematic exploration of C. acnes biology and pathogenic mechanisms. We showcase its utility through detailed analyses of membrane transporters and hypothetical proteins, highlighting novel functional insights into this skin microbe. Given the clinical relevance of C. acnes in skin health and its role in skincare product development, CAcnesDB represents a valuable platform for both basic research and translational applications.

bioinformatics↗

bioRxiv2025

Androgen receptor activation stabilizes a hybrid epithelial/mesenchymal phenotype in presence of Notch-Jagged signaling

Metastasis is a key cause of mortality in cancer. It is driven majorly by clusters of circulating tumor cells which are often comprised of cells in a hybrid epithelial/mesenchymal (E/M) phenotype. Hybrid E/M phenotype has also been shown to be more tumor-initiating and therapy-resistant, but how cells maintain the hybrid E/M phenotype(s) remains an active area of investigation. Here, we develop a mathematical model that couples the intracellular dynamics of key players of Epithelial-Mesenchymal Transition (EMT) with the Androgen Receptor (AR), and cell-cell communication through Notch-Delta-Jagged signaling, in the context of prostate cancer. Our simulations show that AR can stabilize a hybrid E/M phenotype predominantly in the presence of Notch-Jagged, but not Notch-Delta, signaling. Implementing this model on a multi-cellular lattice, we observed that AR can alter the fraction of cells exhibiting a hybrid E/M and a mesenchymal phenotype. Finally, through analysis of transcriptomic and patient survival data, we found that the co-expression of AR and Jagged correlated with worse out-comes. Together, these results highlight the outcome of emergent dynamics between the Notch and AR signaling axis in promoting prostate cancer aggressiveness.

systems biology↗

bioRxiv2025

Extensive Benchmarking of Community Detection Algorithms

The detection of clusters or community in networks is an important problem in network science. We systematically evaluate many widely used community detection algorithms and their variants to identify clusters in complex networks. As the ground truth for assessing accuracy, we use artificial networks modeled on power-law distributions and real-world social networks. In addition, we also performed gene enrichment analysis on human and yeast protein-protein interaction networks to evaluate algorithms on their ability to uncover enriched communities. We implement and adapt an extensive suite of classical algorithms and their modern variants, classified into five types: stochastic, kernel-based, modularity-based, hierarchical, and local search-based. The algorithms are benchmarked primarily using the Normalized Mutual Information metric, with additional analyses focused on granularity by examining cluster ratio and computational time complexity. We find that decreasing the modularity of networks leads to a consistent decline in performance that follows a sigmoidal trajectory as communities become less defined. Algorithms with greater granularity remain stable when community structures are less distinct, while computation time remains independent of network modularity. Additionally, algorithms tend to perform poorly on smaller networks, and higher accuracy often requires a time complexity trade-off for specific high-performing methods. However, as the analysis expands to more extensive networks, this trade-off becomes more pronounced, highlighting the need for efficient scalability. Based on our benchmark and gene enrichment analysis results, we also present recommendations to practitioners. Our robust Python package, complete with a user-friendly command-line interface, empowers users to easily apply these algorithms to their datasets. Author summaryIn the era of big data, clustering has become an essential tool for processing and analyzing vast amounts of information. By dividing large data sets into smaller, meaningful clusters, we can simplify complex data structures, parallelize tasks, and reveal hidden patterns. This has become an essential preprocessing step that is widely used in various computational domains. Although traditional clustering algorithms remain popular, our work implements range of hybrid techniques that combine classical methods and some novel approaches. We also benchmark the performance of these approaches, their efficiency and efficacy in handling different types of networks.

systems biology↗

bioRxiv2023

Characterizing heterogeneity along EMT and metabolic axes in colorectal cancer reveals underlying consensus molecular subtype-specific trends

Colorectal cancer (CRC) is highly heterogenous with variable survival outcomes and therapeutic vulnerabilities. A commonly used classification system in CRC is the Consensus Molecular Subtypes (CMS) based on gene expression patterns. However, how these CMS categories connect to axes of phenotypic plasticity and heterogeneity remains unclear. Here, we analyze 101 bulk transcriptomic datasets, along with patient tumor samples from TCGA and single-cell RNA sequencing data, to evaluate the extent of variation among CMS subtypes across metabolic plasticity and EMT axes. Our results show that CMS2 and CMS3 samples were relatively more epithelial as compared to CMS1 and CMS4. Single-cell RNA-seq analysis of CMS1 revealed two subpopulations: one close to CMS4 (more mesenchymal) and the other closer to CMS2 or CMS3 (more epithelial), indicating a partial EMT-like behavior. Further, in our meta-analysis and in TCGA data, epithelial phenotype score was positively correlated with scores of glycolysis, OXPHOS and FAO pathways, while mesenchymal scores showed CMS subtype-specific associations with metabolic axes. PD-L1 activity scores, however, consistently correlated positively with mesenchymal signature ones and negatively with epithelial signature ones, across the four CMS categories. Together, our results quantify the patterns of two interconnected axes of phenotypic heterogeneity - EMT and metabolic reprogramming - at a CMS subtype level in CRC.

cancer biology↗

bioRxiv2023

A systems-level analysis of the mutually antagonistic roles of RKIP and BACH1 in dynamics of cancer cell plasticity

Phenotypic plasticity is a hallmark of cancer metastasis. Epithelial-mesenchymal transition (EMT) is an important axis of phenotypic plasticity. Raf kinase-B inhibitor protein (RKIP) and BTB and CNC homology 1 (BACH1) are two proteins reported to influence EMT. In breast cancer, they act antagonistically, but the exact nature of their roles in mediating EMT and associated other axes of plasticity remains unclear. Here, analysing transcriptomic data, we reveal their antagonistic trends in a pan-cancer manner, in terms of association with EMT, metabolic reprogramming and immune evasion via PD-L1. Next, we developed and simulated a mechanism-based gene regulatory network that captures how RKIP and BACH1 engage in feedback loops with drivers of EMT and stemness. We found that RKIP and BACH1 belong to two separate "teams" of players - while BACH1 belonged to the one that drove pro-EMT, stem-like and therapy-resistant cell-states, RKIP is a member of a team that enables pro-epithelial, less stem-like and therapy-sensitive phenotypes. Finally, we observed that low RKIP levels and concomitant upregulated BACH1 levels associated with worse clinical outcomes in many cancer types. Together, our systems-level analysis indicates that the emergent dynamics of underlying regulatory network underlie the antagonistic patterns of RKIP and BACH1 with various axes of cancer cell plasticity, as well as with patient survival data.

systems biology↗

bioRxiv2023

GPX4-VIM equates a proliferating DTP state in TNBC subtypes with converged vulnerabilities to autophagy and glutathione inhibition.

Frequent metastatic relapses in Triple-Negative Breast Cancer (TNBC) patients with residual disease is a clinical challenge, largely due to tumor heterogeneity and absence of strategies that target proliferating chemo-tolerant cells. Here, we longitudinally modeled cellular state transitions from dormant drug-tolerant persister (DTP) into proliferating drug-tolerant persister (PDTP) in cells representing all TNBC subtypes. Combining subcellular imaging with phenotypic and biochemical assays, we identified distinct and converged spectrums of alterations in TNBC-PDTPs. We show that PDTPs retain acquired resistance with increased invasion potential. Moreover, Basal-Like DTPs enter into a non-reversible mesenchymal state while luminal androgen receptor-positive gain partial-Epithelial-to-Mesenchymal Transition (EMT) with vimentin upregulation. PDTP state dwells on high autophagy with reduced glutathione and GPX4 levels, rendering it vulnerable to autophagy suppression and ferroptosis. Interestingly, we find that GPX4 negatively regulates EMT and autophagy in TNBC, and an inverse correlation of GPX4-VIM expression along with autophagy genes predicts survival in TNBC patients undergoing chemotherapy.

cancer biology↗

bioRxiv2023

Evaluation Of Effect Of Bacteria On The Surface Of The Dental Implant

BackgroundDental Implantology is the most preferred treatment among patients who are opting for the replacement of their missing teeth. It is said that the success of the dental depends on the effectiveness of the dental implant bonding to the maxilla/mandible. This effectiveness depends on many factors. The microbial flora surrounding the dental implant also affects the integrity of the dental implant. AimThe study aims to analyze any effect bacteria on the surface of the implant. Materials and methodsIn the present study, 10 sterile dental implants were taken. Plaque samples were collected from patients with poor oral hygiene, who underwent dental implants and were under review for more than one month to six months. It was incubated with broth at 37 degrees Celcius. The surface changes in the dental implant were then observed in a scanning electron microscope. ResultsThe SEM results show a significant difference in the surface texture of the implant. There were pits, small craters, and cracks on the surface. The sharpness of the screw thread had also been noticeably blunt. ConclusionThus the study concludes that bacteria have a significant effect on the surface of the dental implant.

microbiology↗

bioRxiv2022

PD-L1 activity is associated with partial EMT and metabolic reprogramming in carcinomas

Immune evasion and metabolic reprogramming are hallmarks of cancer progression often associated with a poor prognosis and frequently present significant challenge for cancer therapies. Recent studies have emphasized on the dynamic interaction between immunosuppression and the dysregulation of energy metabolism in modulating the tumor microenvironment to promote cancer aggressiveness. However, a pan-cancer association among these two hallmarks, and a potent common driver for them - Epithelial-Mesenchymal Transition (EMT) - remains to be done. Here, our meta-analysis across 184 publicly available transcriptomic datasets as well as The Cancer Genome Atlas (TCGA) data reveals that an enhanced PD-L1 activity signature along with other immune checkpoint markers correlate positively with a partial EMT and elevated glycolysis signature but a reduced OXPHOS signature in many carcinomas. These trends were also recapitulated in single-cell RNA-seq time-course EMT induction data across cell lines. Furthermore, across multiple cancer types, concurrent enrichment of glycolysis and PD-L1 results in worse outcomes in terms of overall survival as compared to enrichment for only PD-L1 activity or expression. Our results highlight potential functional synergy among these interconnected axes of cellular plasticity in enabling metastasis and/or multi-drug resistance in cancer.

cancer biology↗

bioRxiv2022

The ELF3 transcription factor is associated with an epithelial phenotype and represses epithelial-mesenchymal transition

Epithelial-mesenchymal plasticity (EMP) involves bidirectional transitions between epithelial, mesenchymal and multiple intermediary hybrid epithelial/mesenchymal phenotypes. While the process of epithelial-mesenchymal transition (EMT) and its associated transcription factors are well-characterised, the transcription factors that promote mesenchymal-epithelial transition (MET) and stabilise hybrid E/M phenotypes are less well understood. Here, we analyse multiple publicly-available transcriptomic datasets at bulk and single-cell level and pinpoint ELF3 as a factor that is strongly associated with an epithelial phenotype and is inhibited during EMT. Using mechanism-based mathematical modelling, we also show that ELF3 inhibits the progression of EMT, suggesting ELF3 may be able to counteract EMT induction, including in the presence of EMT-inducing factors, such as WT1. Our model predicts that the MET induction capacity of ELF3 is stronger than that of KLF4, but weaker than that of GRHL2. Finally, we show that ELF3 levels correlates with worse patient survival in a subset of solid tumor types, suggesting cell-of-origin or lineage specificity in the prognostic capacity of ELF3.

cancer biology↗

bioRxiv2021

Water soluble polysaccharides from Spirulina platensis exhibited cyto/DNA protection and suppress the growth of gastric cancer cells via modulation of galectin-3

Polysaccharides from natural sources play a significant role in the management of different cancer types including gastric cancer. In this study, we reported the effect of spirulina polysaccharide (Sp) on galectin-3 modulatory activity on gastric cancer cells. The polysaccharide was isolated from the spirulina biomass, characterized, and the in silico, in vitro studies are carried out to assess the bioactivities. The isolated Sp possessed average molecular weight of 1457 kDa, and galactose (42%) as major sugar along with Rhamnose, Arabinose, Xylose, and Mannose. Further, characterization of Sp by FT-IR and NMR spectrum indicated the presence of ({beta}1-4D) galactose sugar with galactoarabinorhamnoglycan backbone. Among the monosaccharides, galactose showed highest binding affinity with galectin-3 protein as evidenced by the in silico interaction study. The obtained Sp, inhibited the proliferation of AGS gastric cancer cells by 48 % without affecting normal NIH/3T3 cells as opposed to doxorubicin, a known anticancer drug. Also, Sp exhibited galectin-3 mediated haemagglutination inhibition with MIC of 9.37 g/mL compared to galactose 6.25 g/mL, sugar specific to galectin-3. The Sp treatment significantly (p<0.05) lowered the expression of galectin-3 by 32 % compared to untreated control cells. In addition, Sp exhibited the potent cytoprotection in RBCs, Buccal cells, and DNA exposed to oxidants. Thus, the findings suggest that the polysaccharide from spirulina offer a promising therapeutic strategy in the management of gastric cancer in addition to its currently known nutritional and pharmaceutical applications.

cancer biology↗

bioRxiv2021

An innovative approach for determining composite wheat quality index to identify quality enriched genotypes - insights and implications

Ranking test entries or test sites on a quality basis is very difficult in wheat as value addition is perceived by several grain properties and end-products. Here, a novel approach has been developed and tested by deriving wheat quality index based on principal component analysis of 13 physico-chemical grain parameters and 3 end products of 45 wheat varieties. Depending upon the observed index range (0.15 to 0.71), the cultivars were assorted into 3 distinct classes i.e. elite, moderate and poor. The top group ascertained high quality standards of grain suited for bread and chapati whereas bottom group assured better cookies quality. This technique was also tested to differentiate quality enriched test sites within a zone or demarcate the most suited production environments to harness good quality wheat. The index will have an implication on farmers (premium price for varietal segregation), industry (product specific quality cultivars), and consumers (superior quality products).

biochemistry↗

bioRxiv2020

Genome Wide Identification, Characterization and Expression Analysis of Non-Arginine Aspartate Receptor like kinase gene family in Hot pepper under Colletotrichum truncatum stress conditions

Receptor Like kinases (RLKs) are conserved upstream signaling molecules that regulate several biological processes, including plant development and stress adaptation. Non arginine aspartate (non-RD) an important class of RLKs plays a vital role in disease resistance and apoptosis in plants. In present investigation, a comprehensive Insilco analysis for non-RD Kinase gene family including identification, sequence similarity, phylogeny, chromosomal localization, gene structures, gene duplication analysis, promoter analysis and transcript expression profiles were elucidated. In this study twenty six genes were observed on nine out of twelve chromosomes. All these genes were clustered into seven subfamilies under large monophyletic group termed as Interleukin-1 Receptor-Associated Kinase (IRAK) family. Structural diversity in genomic structure among non-RD kinase gene family were identified and presence of pathogen induced cis regulatory elements like STRE, MYC, MYB,W box were found. Expression profiles of genes involved in providing resistance to anthracnose pathogen Colletotrichum truncatum in hot pepper were analyzed at different infective stages in both resistant and susceptible genotypes. Among twenty six genes, CaRLK1 gene belonging to LRRXII subfamily was up regulated under severe stress after infection in resistant genotype PBC-80. This integrative approach has helped us to identify candidate genes involved in disease resistance which would be helpful in future crop improvement programs.

plant biology↗