BackgroundOral submucosal fibrosis (OSF) is a chronic and progressive disorder, caused by chewing areca nuts, affecting the oral cavity and oropharynx. OSF is characterized by severe symptoms like severe burning sensation, restricted mouth opening, etc. Given the multifactorial and poorly understood molecular basis of the disease, there is a need for novel biomarkers and therapeutic targets.MethodWe downloaded 3 RNA-seq, two microarray, one epigenomic, and one single-cell RNA-seq datasets from the gene expression omnibus database. Differentially expressed genes (DEGs) were characterized using DESeq2. Several analyses, including gene enrichment, immune cell infiltration, protein-protein interaction, and more, were performed. Machine learning models were developed using all DEGs and top5 selected features with leave one out cross validation technique. Independent validations were performed using two microarray datasets with appropriate statistical measures. Epigenetic analysis revealed hyper- and hypomethylated genes based on delta-beta values, and an integrative analysis of the transcriptome and methylome was performed to obtain key biomarkers. Single-cell analysis was performed to identify key cell types showing higher DEG expression.ResultDESeq2 analysis identified 29 upregulated and 15 downregulated DEGs. Upregulated DEGs show enrichment for the inflammatory, metabolic, and signaling processes, whereas downregulated DEGs were largely associated with metabolic processes. Immune cell enrichment analysis using CIBERSORTx shows higher enrichment of “T cells,” “mast cells,” and “macrophages” in OSF patients. We validated our findings in two independent microarray datasets and observed a similar gene expression pattern of DEGs. Machine learning performed using top5 features where Random Forest model achieved AUROC of 0.99 and AUPRC of 0.99. Further, ROC analysis and AUC plot show that DEGs can discriminate OSF patients from the normal population with high AUROC. Integrative analysis of methylation and transcriptomic data identified 11 genes as potential diagnostic biomarkers and therapeutic targets. Finally, single-cell analysis elucidates the higher expression of DEGs in “keratinocyte”, “epithelial cells” and “dendritic cells”.ConclusionIntegrative analysis identified 11 gene signatures as potential early diagnostic biomarkers and therapeutic targets for the OSF. Furthermore, the study hints towards mechanistic insight into potential mechanism leading to oral cancer. All the codes and ML models are provided at our GitHub repository https://github.com/agrawalpiyush-srm/OSF.

Allele Specific Expression data quantifies expression variation between the two haplotypes of a diploid individual distinguished by heterozygous sites. Current methodologies of genome-wide sequencing produce large amounts of missing data that may affect statistical inference and bias the outcome of experiments. Machine learning tools could be employed to explore the data and to estimate missing signatures. We present a two-phase procedure based on Self-Organizing Maps (SOMs), an unsupervised clustering technique, to recover missing allele specific expression data from RNA-seq experiments. Specifically, a SOM trained on a complete population is used to assign a so-called corrupted individual to its most fitting cluster ; then, a completion rule based on allele frequencies within the subpopulation of defined by is employed to reconstruct . To evaluate our approach, we first apply it to purely artificial datasets, in order to have full control over all experimental conditions. After that, we consider a real population of Vitis vinifera, which we also extend by applying a computational framework to generate synthetic individuals from allele expression data. We then introduce two local feature relevance indices in order to assess the influence of specific alleles on the topological placement of corrupted individuals in the SOM structure. Our results, showing promising accuracy in the prediction of missing alleles, suggest that the developed approach could be very useful for recovering incomplete samples in a dataset instead of discarding them, mainly in situations where experiments are challenging.

Modern virtual high-throughput screening (VHTS) pipelines can be suboptimally validated, with no rigorous studies conclusively demonstrating that every one of their steps reliably adds increasing enrichment atop the baseline random hit rate. Moreover, what little benchmarking studies are available primarily focus on the docking aspect of the pipelines, which is usually only the beginning or near the beginning, and even there, authors tend to use flawed data sets that artificially inflate performance metrics. Herein, we present an alternative method to pipeline validation and data set generation that requires no additional experimental work and expenditure, yet offers vast amounts of negative data that can be used in VHTS pipeline validation. By randomizing ligands across published experimental structures and generating structural isomers of known binders, practically unlimited amounts of negative data can be generated. Such sets of positive and negative data points match closely in key molecular properties and are well suited to pipeline validation. Once such sets are generated, they are to be run through any proposed pipeline, assessing performance at every step. We stress the importance of using negative data of adequate quality and quantity in validation studies to definitively and verifiably demonstrate the utility of a given tool or workflow. Our goal is to help distinguish tools and pipelines that truly accelerate hit discovery and lead optimization from ones that promise to do so but actually do not, whereupon academia and industry can begin to tackle the many unaddressed medical needs of the 21st century.

A novel Kolmogorov–Arnold Network (KAN) based machine learning model is proposed for the automatic sleep stage classification task. The redefined architecture of the Multilayer Perceptron (MLP) aims to build a more flexible model by using learnable activation functions. In this study, an effective KAN model named SimpleKANSleepNet is evaluated on two different datasets with temporal features and frequency features extracted from electroencephalography (EEG), electromyogram (EMG), electrooculogram (EOG), and electrocardiogram (ECG) signals through a dual-stream convolutional neural network (CNN). Compared with existing CNN-based methods and graph convolutional networks (GCNs), the proposed model achieves an overall classification accuracy, F1-score, and Cohen’s kappa on the ISRUC-S1 and the Sleep-EDF-153 datasets of 0.812, 0.793, 0.757, 0.928, 0.929, and 0.910, respectively, which demonstrates its competitive classification performance and generality. Moreover, several data balancing methods are tested on Sleep-EDF-153 to further evaluate the potential for achieving the best results. Finally, the factors that may affect the classification ability are tested on the ISRUC-S1 dataset.

Effective communication of scientific content can be challenging due to cognitive overload. This is experienced especially during conferences and poster presentations, where the presence of competing stimuli limits message retention. Scientific visuals offer a means to overcome this limitation by emphasizing the essential components of a narrative in a form that is rapidly and intuitively processed. Rather than serving primarily as demonstrations of complexity or markers of personal accomplishment, scientific visuals should function as tools for idea exchange, enabling broader comprehension and facilitating dialogue. The Gestalt principles are an important guide for the visual creation process. These perceptual principles exploit pre-attentive processing mechanisms that allow viewers to extract essential structure and meaning immediately with minimal conscious effort. Effective development of scientific visuals can be approached in three stages: an initial sketch phase, focused on defining the core content and refining the central message, followed by a design phase and refinement phase, in which form, layout and color are used according to perceptual principles. This structured process ensures that complex narratives can be communicated with clarity and precision. By prioritizing cognitive accessibility over ornamental design, visuals become a central and intrinsic component of scientific discourse, supporting insight generation, fostering dialogue, and contributing to collaborative learning and consecutive knowledge building.

Artificial intelligence (AI) has become a common tool for bioinformatics, with hundreds of methods published in recent years. Due to the training data demands of deep-learning algorithms, high-throughput single-cell and spatial transcriptomics is one of the most popular areas for these applications. Here we review how AI is being used for single-cell and spatial transcriptomics analysis, and how these approaches compare to alternative statistical or heuristic-based methods. We explored 10 common analysis tasks: dimensionality reduction, cross-dataset integration, data denoising, data augmentation, deconvolution, cell-cell interactions, transcriptional velocity, transcriptomic-chromatin accessibility integration, and integrating single-cell and spatial transcriptomics modalities. We highlight which algorithms are likely to be useful for discovery researchers, and which are not yet ready for general research use.

Nothing inside organisms is at rest; everything moves. Cells are transported through the vascular system, proteins move cargo away or toward the cell nucleus, and enzymes repair DNA, which is constantly modified by metabolic cell processes or external influences affecting the organism. Various processes occur simultaneously in the crowded spaces of cells and across tissues, carefully coordinated and orchestrated. In contemporary science, movement lies at the root of studying cellular and molecular processes-in short, of all the activities that occur within the organism. This article provides a historical perspective and methodological reflection on the study of cellular and subcellular motion in current biotechnology. It shows that, far from being evident, movement is not simply observed but actively made. I argue, first, that the conceptualization of motion in current biotechnology occurs through image-making and is thus shaped by a long pictorial history and the struggle to depict movement. Second, to make the invisible move under the conditions of visibility, metaphors and imaginaries drawn from our everyday experience of animal motion are transposed into the nanoscopic sphere, thereby setting the framework and limits of understanding motion at the molecular level.

IntroductionTNIK (Traf2- and Nck-interacting kinase) is a serine/threonine kinase that plays a crucial role in cytoskeletal organization, Wnt pathway activation, and cancer progression. Recent studies have implicated the role of TNIK in oncogenic signaling pathways and neuropsychiatric regulation. However, the phosphosignaling dynamics of TNIK remain largely unknown.MethodsTo explore TNIK phosphoregulation, we systematically assembled and integrated global human phosphoproteomic datasets. We identified the predominant phosphosites based on the frequency. Relative solvent accessibility (RSA) and Phosphosite accessibility index (PAI) were calculated to determine the solvent exposure and structural flexibility of TNIK predominant phosphosites. To assess the functional significance of TNIK, we examined proteins that were differentially co-regulated with its predominant phosphosite, along with the corresponding upstream kinases, downstream substrates, and interacting proteins.ResultsAnalysis of the global human cellular phosphoproteome datasets revealed phosphosites S640, S680, S707, and S769 of TNIK to be the most frequently perturbed phosphosites across diverse experimental conditions. The results of the RSA and PAI analysis revealed that the predominant sites are located within highly solvent-exposed and structurally flexible regions. Notably, we obtained a large number of co-regulated proteins that were associated with cell growth, carcinogenesis, and apoptosis. The interactors identified were primarily enriched towards carcinogenesis. Our analysis revealed PRKAA1 and RPS6KB2 as robust upstream kinases of TNIK_S640 and TNIK_S707. We also identified many proteins involved in RNA splicing, cytoskeletal organisation, and cell migration as potential downstream substrates of TNIK.DiscussionConsidering the challenges in targeted experimental analysis of these sites, a global co-regulation analysis approach was employed. Our results show that these phosphorylation sites in TNIK can influence carcinogenesis and related biological functions. It offers new insights into TNIK-mediated cellular functions, deepening our comprehension of its involvement in carcinogenesis and RNA splicing.

Quorum-sensing (QS) systems based on acyl-homoserine lactones (AHLs) regulate gene expression in response to cell density in many bacteria, including Rhizobium. These systems, typically composed of LuxI-like synthases and LuxR-like regulators, control processes such as plasmid conjugation, biofilm formation, and plant interactions. However, their evolutionary dynamics and genomic distribution in Rhizobium remain poorly understood. We analyzed 142 complete Rhizobium genomes using comparative genomics, phylogenetic reconstruction, and genomic context analysis. LuxI/LuxR homologs were identified based on sequence similarity and Pfam domain architecture, and their genomic contexts were examined. Phylogenetic relationships and coevolution between LuxI/LuxR pairs were assessed using cophylogenetic approaches. QS systems showed a highly heterogeneous distribution across Rhizobium genomes: some strains lacked canonical systems, whereas others encoded one or multiple systems in chromosomes and/or plasmids. Chromosomal QS systems were associated with multiple distinct genomic contexts, supporting at least seven independent acquisition events. In contrast, plasmid-encoded systems exhibited substantially greater diversity in both sequence and genomic organization. Phylogenetic and comparative analyses revealed dynamic gains and losses of QS systems, variable coevolution among LuxI/LuxR pairs, and evidence of partner recruitment. Notably, plasmids appear to act as major reservoirs of QS systems and likely sources of their transfer to chromosomes. These findings indicate that QS systems in Rhizobium evolve through a combination of horizontal gene transfer, genomic rearrangement, and differential retention across replicons. The higher diversity and mobility of plasmid-encoded systems highlight their central role in shaping QS evolution and functional innovation. Overall, this study provides a comprehensive framework for understanding the diversification and evolutionary trajectories of QS systems in complex multipartite bacterial genomes.

Eucalyptus grandis is a cornerstone of global forestry, yet its productivity is increasingly threatened by drought. Understanding the molecular mechanisms underlying drought response is essential for improving resilience. We conducted a meta-analysis of three independent RNA-Seq drought studies in E. grandis, applying a rigorous bioinformatic pipeline with leave-one-out Jackknife validation to ensure robustness and reduce single-study bias. We identified a high-confidence set of 472 differentially expressed genes (DEGs), including 274 upregulated and 198 downregulated genes. Functional analysis revealed a growth-defense tradeoff, with upregulated genes associated with stress response pathways such as protein folding, osmolyte biosynthesis, and ABA signaling, while genes involved in cell division, DNA replication, and cell wall biosynthesis were repressed. Protein-protein interaction analysis showed a coordinated network linking stress response activation to suppression of growth-related processes. These findings provide a robust catalog of candidate genes, including previously uncharacterized proteins, supporting future functional studies and molecular breeding strategies aimed at enhancing drought tolerance in eucalyptus under climate change.