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Transfer learning has revolutionized fields including natural language understanding and computer vision by leveraging large‐scale general datasets to pretrain models with foundational knowledge that can then be transferred to improve predictions in a vast range of downstream tasks. More recently, there has been a growth in the adoption of transfer learning approaches in biological fields, where models have been pretrained on massive amounts of biological data and employed to make predictions in a broad range of biological applications. However, unlike in natural language where humans are best suited to evaluate models given a clear understanding of the ground truth, biology presents the unique challenge of being in a setting where there are a plethora of unknowns while at the same time needing to abide by real‐world physical constraints. This perspective provides a discussion of some key points we should consider as a field in designing benchmarks for foundation models in network biology. [ABSTRACT FROM AUTHOR]

Lung cancer is a complex disease integrated with diverse histological and molecular types that have clinical relevance. Non-small cell lung cancer (NSCLC), comprised of adenocarcinoma (LUAD), squamous cell carcinoma (LUSC), and large cell carcinoma (LCC), accounts for the majority of lung cancers. This study harnesses the recent surge in biomarker identification, exploring the promising potential of the competing endogenous RNA (ceRNA) network in deciphering the intricate interplay among lncRNA-miRNA-mRNA components, providing insights into the tumorigenesis of NSCLC. Datasets encompassing differentially expressed mRNA, miRNAs, and lncRNAs were curated from the Gene Expression Omnibus (GEO), Differentially Expressed miRNAs in human Cancers (dbDEMC), and The Cancer Genome Atlas (TCGA) databases. miRNAs were selected with respect to their established roles in immune regulation, pathways, and the tumor microenvironment (TME), and an integrative analysis was performed, based on which a comprehensive ceRNA regulatory network was constructed using Cytoscape software with 26 lncRNAs, 13 miRNAs, and 143 target genes. Gene Ontology (GO)-based functional enrichment analysis focusing specifically on immune system processes uncovered genes in pathways associated with immune cell differentiation, cytokine signaling, the immune response to cancer, and so on, positioning them as potential key biomarkers within the tumor microenvironment. Furthermore, a novel lncRNA, ALAL1, supported by a lone study, was known to bind with the SART3 gene in NSCLC. Simultaneously, another study also established a connection between the SART3 gene and miR-34a. Leveraging these individual interactions, this study revealed a previously unexplored link between ALAL1, SART3, and miR-34a. This plausible ceRNA interaction is suggested to potentially exert a regulatory influence on the progression of NSCLC cells. These computationally inferred interactions, though promising, necessitate rigorous clinical validation. However, the identified biomarkers lay the groundwork for subsequent research, offering valuable insights that can guide future investigations in the realm of NSCLC. [ABSTRACT FROM AUTHOR]

Graph databases are becoming increasingly popular across scientific disciplines, being highly suitable for storing and connecting complex heterogeneous data. In systems biology, they are used as a backend solution for biological data repositories, ontologies, networks, pathways, and knowledge graph databases. In this review, we analyse all publications using or mentioning graph databases retrieved from PubMed and PubMed Central full-text search, focusing on the top 16 available graph databases, Publications are categorized according to their domain and application, focusing on pathway and network biology and relevant ontologies and tools. We detail different approaches and highlight the advantages of outstanding resources, such as UniProtKB, Disease Ontology, and Reactome, which provide graph-based solutions. We discuss ongoing efforts of the systems biology community to standardize and harmonize knowledge graph creation and the maintenance of integrated resources. Outlining prospects, including the use of graph databases as a way of communication between biological data repositories, we conclude that efficient design, querying, and maintenance of graph databases will be key for knowledge generation in systems biology and other research fields with heterogeneous data. [ABSTRACT FROM AUTHOR]

Climate change threatens global agriculture, impacting plant health and crop yield, while plant microbiomes offer potential solutions to enhance resilience. In this forum, we discuss the prospects of single cell multiome and network science in understanding intricate plant–microbe interactions, providing insights for sustainable agriculture and improved crop productivity for global food security. [ABSTRACT FROM AUTHOR]

Premature Aging (PA) diseases are rare genetic disorders that mimic some aspects of physiological aging at an early age. Various causative genes of PA diseases have been identified in recent years, providing insights into some dysfunctional cellular processes. However, the identification of PA genes also revealed significant genetic heterogeneity and highlighted the gaps in this understanding of PA‐associated molecular mechanisms. Furthermore, many patients remain undiagnosed. Overall, the current lack of knowledge about PA diseases hinders the development of effective diagnosis and therapies and poses significant challenges to improving patient care. Here, a network‐based approach to systematically unravel the cellular functions disrupted in PA diseases is presented. Leveraging a network community identification algorithm, it is delved into a vast multilayer network of biological interactions to extract the communities of 67 PA diseases from their 132 associated genes. It is found that these communities can be grouped into six distinct clusters, each reflecting specific cellular functions: DNA repair, cell cycle, transcription regulation, inflammation, cell communication, and vesicle‐mediated transport. That these clusters collectively represent the landscape of the molecular mechanisms that are perturbed in PA diseases, providing a framework for better understanding their pathogenesis is proposed. Intriguingly, most clusters also exhibited a significant enrichment in genes associated with physiological aging, suggesting a potential overlap between the molecular underpinnings of PA diseases and natural aging. [ABSTRACT FROM AUTHOR]

In the post-COVID-19 era, treatment options for potential SARS-CoV-2 outbreaks remain limited. An increased incidence of central nervous system (CNS) disorders has been observed in long-term COVID-19 patients. Understanding the shared molecular mechanisms between these conditions may provide new insights for developing effective therapies. This study developed an integrative drug-repurposing framework for COVID-19, leveraging comorbidity data with CNS disorders, network-based modular analysis, and dynamic perturbation analysis to identify potential drug targets and candidates against SARS-CoV-2. We constructed a comorbidity network based on the literature and data collection, including COVID-19-related proteins and genes associated with Alzheimer's disease, Parkinson's disease, multiple sclerosis, and autism spectrum disorder. Functional module detection and annotation identified a module primarily involved in protein synthesis as a key target module, utilizing connectivity map drug perturbation data. Through the construction of a weighted drug–target network and dynamic network-based drug-repurposing analysis, ubiquitin–carboxy-terminal hydrolase L1 emerged as a potential drug target. Molecular dynamics simulations suggested pregnenolone and BRD-K87426499 as two drug candidates for COVID-19. This study introduces a dynamic-perturbation-network-based drug-repurposing approach to identify COVID-19 drug targets and candidates by incorporating the comorbidity conditions of CNS disorders. [ABSTRACT FROM AUTHOR]

Head and neck cancer ranks as the sixth-most common malignancy worldwide, characterized by high mortality and recurrence rates. Research studies indicate that molecular diagnostics play a crucial role in the early detection and prognostic evaluation of these diseases. This study aimed to identify potential biomarkers for head and neck cancer and elucidate their interactions with miRNAs and possible therapeutic drugs. Four drivers, namely, FN1, IL1A, COL1A1, and MMP9, were identified using network biology and machine learning approaches. Gene set variation analysis (GSVA) showed that these genes were significantly involved in different biological processes and pathways, including coagulation, UV-response-down, apoptosis, NOTCH signaling, Wnt-beta catenin, and other signal pathways. The diagnostic value of these hub genes was validated using receiver operating characteristic (ROC) curves. The top interactive miRNAs, including miR-128-3p, miR-218-5p, miR-214-3p, miR-124-3p, miR-129-2-3p, and miR-1-3p, targeted the key genes. Furthermore, the interaction between the key genes and drugs was also identified. In summary, the key genes and miRNAs or drugs reported in this study might provide valuable information for potential biomarkers to increase the prognosis and diagnosis of head and neck cancer. [ABSTRACT FROM AUTHOR]

Simple Summary: Head and neck squamous cell carcinoma (HNSCC) remains a global health concern due to the lack of precise early diagnostic biomarkers and often-delayed diagnosis. This study employs machine learning, weighted gene co-expression network analysis, and network biology to identify transcriptomic markers for HNSCC detection. We identified nine genes with significantly differentially expressed activity in samples from HNSCC patients. These gene signatures could greatly improve early HNSCC identification and warrant further investigation to confirm their predictive and therapeutic significance. The transcriptional landscape of platelets in head and neck cancer patients revealed distinct gene expression profiles compared to healthy controls, underscoring the systemic impact of the tumor on blood platelets. Additionally, the study emphasizes the role of tumor-educated platelets (TEPs), which carry RNA signatures indicative of tumor-derived material, offering a non-invasive source for early-detection biomarkers. Although there has been a reduction in head and neck squamous cell carcinoma occurrence, it continues to be a serious global health concern. The lack of precise early diagnostic biomarkers and postponed diagnosis in the later stages are notable constraints that contribute to poor survival rates and emphasize the need for innovative diagnostic methods. In this study, we employed machine learning alongside weighted gene co-expression network analysis (WGCNA) and network biology to investigate the gene expression patterns of blood platelets, identifying transcriptomic markers for HNSCC diagnosis. Our comprehensive examination of publicly available gene expression datasets revealed nine genes with significantly elevated expression in samples from individuals diagnosed with HNSCC. These potential diagnostic markers were further assessed using TCGA and GTEx datasets, demonstrating high accuracy in distinguishing between HNSCC and non-cancerous samples. The findings indicate that these gene signatures could revolutionize early HNSCC identification. Additionally, the study highlights the significance of tumor-educated platelets (TEPs), which carry RNA signatures indicative of tumor-derived material, offering a non-invasive source for early-detection biomarkers. Despite using platelet and tumor samples from different individuals, our results suggest that TEPs reflect the transcriptomic and epigenetic landscape of tumors. Future research should aim to directly correlate tumor and platelet samples from the same patients to further elucidate this relationship. This study underscores the potential of these biomarkers in transforming early diagnosis and personalized treatment strategies for HNSCC, advocating for further research to validate their predictive and therapeutic potential. [ABSTRACT FROM AUTHOR]

Klebsiella pneumoniae (K. pneumoniae) is a pathogen that has been identified as the leading cause of pneumonia and septicemia worldwide, compounded by its multi-drug resistant nature. Computational and bioinformatics approaches are yet understudied in terms of K. pneumoniae, and only recently systems and network biology-based approaches have gained attention for examining antimicrobial resistance. In this review, we highlight the prevalent use of computational systems and network biology methods in understanding K. pneumoniae infection mechanisms. We summarized ranges from basic methods including differential equations, network science analysis, and statistical insights into large processes, to intricate condition-specific genome-wide networks. More specifically, the availability of large-scale systematic genome-wide data, and detailed cellular and molecular information have enabled the use of mathematical modeling to study K. pneumoniae infection mechanisms. Thus, these approaches have proven to be effective in supporting academic exploration, complementing experimental studies, and deepening overall understanding in terms of K. pneumoniae. This review is essential to advance our knowledge of K. pneumoniae host-pathogen interactions and infection mechanisms. Furthermore, it serves as a valuable resource for researchers seeking guidance in selecting optimal computational systems and network biology models for K. pneumoniae-related investigations.

Chromatin conformation capture technologies permit the study of chromatin spatial organization on a genome-wide scale at a variety of resolutions. Despite the increasing precision and resolution of high-throughput chromatin conformation capture (Hi-C) methods, it remains challenging to conclusively link transcriptional activity to spatial organizational phenomena. We have developed a clique-based approach for analyzing Hi-C data that helps identify chromosomal hotspots that feature considerable enrichment of chromatin annotations for transcriptional start sites and, building on previously published work, show that these chromosomal hotspots are not only significantly enriched in RNA polymerase II binding sites as identified by the ENCODE project, but also identify a noticeable increase in FANTOM5 and GTEx transcription within our identified cliques across a variety of tissue types. From the obtained data, we surmise that our cliques are a suitable method for identifying transcription factories in Hi-C data, and outline further extensions to the method that may make it useful for locating regions of increased transcriptional activity in datasets where in-depth expression or polymerase data may not be available. [ABSTRACT FROM AUTHOR]

In this white paper, we describe the founding of a new ELIXIR Community - the Systems Biology Community - and its proposed future contributions to both ELIXIR and the broader community of systems biologists in Europe and worldwide. The Community believes that the infrastructure aspects of systems biology - databases, (modelling) tools and standards development, as well as training and access to cloud infrastructure - are not only appropriate components of the ELIXIR infrastructure, but will prove key components of ELIXIR’s future support of advanced biological applications and personalised medicine. By way of a series of meetings, the Community identified seven key areas for its future activities, reflecting both future needs and previous and current activities within ELIXIR Platforms and Communities. These are: overcoming barriers to the wider uptake of systems biology; linking new and existing data to systems biology models; interoperability of systems biology resources; further development and embedding of systems medicine; provisioning of modelling as a service; building and coordinating capacity building and training resources; and supporting industrial embedding of systems biology. A set of objectives for the Community has been identified under four main headline areas: Standardisation and Interoperability, Technology, Capacity Building and Training, and Industrial Embedding. These are grouped into short-term (3-year), mid-term (6-year) and long-term (10-year) objectives.

Introduction: Enteric infections are a major cause of under-5 (age) mortality in low/middle-income countries. Although vaccines against these infections have already been licensed, unwavering efforts are required to boost suboptimalefficacy and effectiveness in regions that are highly endemic to enteric pathogens. The role of baseline immunological profiles in influencing vaccine-induced immune responses is increasingly becoming clearer for several vaccines. Hence, for the development of advanced and region-specific enteric vaccines, insights into differences in immune responses to perturbations in endemic and non-endemic settings become crucial. Materials and methods: For this reason, we employed a two-tiered system and computational pipeline (i) to study the variations in differentially expressed genes (DEGs) associated with immune responses to enteric infections in endemic and non-endemic study groups, and (ii) to derive features (genes) of importance that keenly distinguish between these two groups using unsupervised machine learning algorithms on an aggregated gene expression dataset. The derived genes were further curated using topological analysis of the constructed STRING networks. The findings from these two tiers are validated using multilayer perceptron classifier and were further explored using correlation and regression analysis for the retrieval of associated gene regulatory modules. Results: Our analysis reveals aggressive suppression of GRB-2, an adaptor molecule integral for TCR signaling, as a primary immunomodulatory response against S. typhi infection in endemic settings. Moreover, using retrieved correlation modules and multivariant regression models, we found a positive association between regulators of activated T cells and mediators of Hedgehog signaling in the endemic population, which indicates the initiation of an effector (involving differentiation and homing) rather than an inductive response upon infection. On further exploration, we found STAT3 to be instrumental in designating T-cell functions upon early responses to enteric infections in endemic settings. Conclusion: Overall, through a systems and computational biology approach, we characterized distinct molecular players involved in immune responses to enteric infections in endemic settings in the process, contributing to the mounting evidence of endemicity being a major determiner of pathogen/vaccine-induced immune responses. The gained insights will have important implications in the design and development of region/endemicity-specific vaccines. [ABSTRACT FROM AUTHOR]

In this white paper, we describe the founding of a new ELIXIR Community - the Systems Biology Community - and its proposed future contributions to both ELIXIR and the broader community of systems biologists in Europe and worldwide. The Community believes that the infrastructure aspects of systems biology - databases, (modelling) tools and standards development, as well as training and access to cloud infrastructure - are not only appropriate components of the ELIXIR infrastructure, but will prove key components of ELIXIR’s future support of advanced biological applications and personalised medicine. By way of a series of meetings, the Community identified seven key areas for its future activities, reflecting both future needs and previous and current activities within ELIXIR Platforms and Communities. These are: overcoming barriers to the wider uptake of systems biology; linking new and existing data to systems biology models; interoperability of systems biology resources; further development and embedding of systems medicine; provisioning of modelling as a service; building and coordinating capacity building and training resources; and supporting industrial embedding of systems biology. A set of objectives for the Community has been identified under four main headline areas: Standardisation and Interoperability, Technology, Capacity Building and Training, and Industrial Embedding. These are grouped into short-term (3-year), mid-term (6-year) and long-term (10-year) objectives.

Background: Understanding how cells and tissues respond to stress factors and perturbations during disease processes is crucial for developing effective prevention, diagnosis, and treatment strategies. Single-cell RNA sequencing (scRNA-seq) enables high-resolution identification of cells and exploration of cell heterogeneity, shedding light on cell differentiation/maturation and functional differences. Recent advancements in multimodal sequencing technologies have focused on improving access to cell-specific subgroups for functional genomics analysis. To facilitate the functional annotation of cell groups and characterization of molecular mechanisms underlying cell trajectories, we introduce the Pathways, Annotated Gene Lists, and Gene Signatures Electronic Repository for Single-Cell Functional Genomics Analysis (PAGER-scFGA).Results: We have developed PAGER-scFGA, which integrates cell functional annotations and gene-set enrichment analysis into popular single-cell analysis pipelines such as Scanpy. Using differentially expressed genes (DEGs) from pairwise cell clusters, PAGER-scFGA infers cell functions through the enrichment of potential cell-marker genesets. Moreover, PAGER-scFGA provides pathways, annotated gene lists, and gene signatures (PAGs) enriched in specific cell subsets with tissue compositions and continuous transitions along cell trajectories. Additionally, PAGER-scFGA enables the construction of a gene subcellular map based on DEGs and allows examination of the gene functional compartments (GFCs) underlying cell maturation/differentiation. In a real-world case study of mouse natural killer (mNK) cells, PAGER-scFGA revealed two major stages of natural killer (NK) cells and three trajectories from the precursor stage to NK T-like mature stage within blood, spleen, and bone marrow tissues. As the trajectories progress to later stages, the DEGs exhibit greater divergence and variability. However, the DEGs in different trajectories still interact within a network during NK cell maturation. Notably, PAGER-scFGA unveiled cell cytotoxicity, exocytosis, and the response to interleukin (IL) signaling pathways and associated network models during the progression from precursor NK cells to mature NK cells.Conclusion: PAGER-scFGA enables in-depth exploration of functional insights and presents a comprehensive knowledge map of gene networks and GFCs, which can be utilized for future studies and hypothesis generation. It is expected to become an indispensable tool for inferring cell functions and detecting molecular mechanisms within cell trajectories in single-cell studies. The web app (accessible at https://au-singlecell.streamlit.app/) is publicly available.

IntroductionEnteric infections are a major cause of under-5 (age) mortality in low/middle-income countries. Although vaccines against these infections have already been licensed, unwavering efforts are required to boost suboptimalefficacy and effectiveness in regions that are highly endemic to enteric pathogens. The role of baseline immunological profiles in influencing vaccine-induced immune responses is increasingly becoming clearer for several vaccines. Hence, for the development of advanced and region-specific enteric vaccines, insights into differences in immune responses to perturbations in endemic and non-endemic settings become crucial.Materials and methodsFor this reason, we employed a two-tiered system and computational pipeline (i) to study the variations in differentially expressed genes (DEGs) associated with immune responses to enteric infections in endemic and non-endemic study groups, and (ii) to derive features (genes) of importance that keenly distinguish between these two groups using unsupervised machine learning algorithms on an aggregated gene expression dataset. The derived genes were further curated using topological analysis of the constructed STRING networks. The findings from these two tiers are validated using multilayer perceptron classifier and were further explored using correlation and regression analysis for the retrieval of associated gene regulatory modules.ResultsOur analysis reveals aggressive suppression of GRB-2, an adaptor molecule integral for TCR signaling, as a primary immunomodulatory response against S. typhi infection in endemic settings. Moreover, using retrieved correlation modules and multivariant regression models, we found a positive association between regulators of activated T cells and mediators of Hedgehog signaling in the endemic population, which indicates the initiation of an effector (involving differentiation and homing) rather than an inductive response upon infection. On further exploration, we found STAT3 to be instrumental in designating T-cell functions upon early responses to enteric infections in endemic settings.ConclusionOverall, through a systems and computational biology approach, we characterized distinct molecular players involved in immune responses to enteric infections in endemic settings in the process, contributing to the mounting evidence of endemicity being a major determiner of pathogen/vaccine-induced immune responses. The gained insights will have important implications in the design and development of region/endemicity-specific vaccines.

Alzheimer's disease (AD) is a neurogenerative disease that affects millions worldwide with no effective treatment. Several studies have been conducted to decipher to genomic underpinnings of AD. Due to its complex nature, many genes have been found to be associated with AD. Despite these findings, the pathophysiology of the disease is still elusive. To discover new putative AD-associated genes, in this study, we integrated multimodal gene and phenotype datasets of AD using network biology methods to prioritize potential AD-related genes. We constructed a multiplex heterogeneous network composed of patient and gene similarity networks utilizing phenotypic and omics datasets of AD patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We applied PhenoGeneRanker to traverse this network to discover potential AD-associated genes. To assess the impact of each network layer and seed gene, we also run PhenoGeneRanker on different variants of the network and seed genes. Our results showed that top-ranked genes captured several known AD-related genes and were enriched in Gene Ontology (GO) terms related to AD. We also observed that several top-ranked genes that are not in AD-associated gene list had literature supporting their potential relevance to AD., Competing Interests: Conflicts None declared.

Tuberculosis (TB) is a serious public health issue in India. Numerous molecular mechanisms and immunological responses play significant roles in the pathogenesis of tuberculosis. This study aimed to identify host immune-related biomarkers that are significantly differentially expressed in active TB and that play a vital role in disease progression. The methodology employed in this study included data collection, pre-processing, analysis, and interpretation of the results. Six microarray datasets were used to identify differentially expressed genes (DEGs), and only the common DEGs were used for further downstream analysis, such as hub gene identification, gene ontology, pathway enrichment analysis, and drug-gene interaction analysis. The study identified 1728 DEGs, including 906 upregulated and 822 downregulated genes. Five hub genes were identified that were: STAT1, GBP5, GBP1, FCGR1A, and BATF2. Gene ontology and pathway enrichment revealed that most of the genes were involved in interferon-gamma signaling. In addition, through drug-gene interactions, known drugs have been identified for STAT1, FCGR1A and GBP1. The findings of this study may contribute to early detection and treatment of active TB. [ABSTRACT FROM AUTHOR]

In this study, we introduce the Mondrian Map, an innovative visualization tool inspired by Piet Mondrian's abstract art, to address the complexities inherent in visualizing biological networks. By converting intricate biological data into a structured and intuitive format, the Mondrian Map enables clear and meaningful representations of biological pathways, facilitating a deeper understanding of molecular dynamics. Each pathway is represented by a square whose size corresponds to fold change, with color indicating the direction of regulation (up or down) and statistical significance. The spatial arrangement of pathways is derived from language model embeddings, preserving neighborhood relationships and enabling the identification of clusters of related pathways. Additionally, colored lines highlight potential crosstalk between pathways, with distinctions between short- and long-range functional interactions. In a case study of glioblastoma multiforme (GBM), the Mondrian Map effectively revealed distinct pathway patterns across patient profiles at different stages of disease progression. These insights demonstrate the tool's potential to enhance downstream bioinformatics analysis by providing a more comprehensive and visually accessible overview of pathway interactions, offering new avenues for therapeutic exploration and personalized medicine.

Spinal Muscular Atrophy is caused by partial loss of survival of motoneuron (SMN) protein expression. The numerous interaction partners and mechanisms influenced by SMN loss result in a complex disease. Current treatments restore SMN protein levels to a certain extent, but do not cure all symptoms. The prolonged survival of patients creates an increasing need for a better understanding of SMA. Although many SMN-protein interactions, dysregulated pathways, and organ phenotypes are known, the connections among them remain largely unexplored. Monogenic diseases are ideal examples for the exploration of cause-and-effect relationships to create a network describing the disease-context. Machine learning tools can utilize such knowledge to analyze similarities between disease-relevant molecules and molecules not described in the disease so far. We used an artificial intelligence-based algorithm to predict new genes of interest. The transcriptional regulation of 8 out of 13 molecules selected from the predicted set were successfully validated in an SMA mouse model. This bioinformatic approach, using the given experimental knowledge for relevance predictions, enhances efficient targeted research in SMA and potentially in other disease settings., (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Space particle radiation is a major environmental factor in spaceflight, and it is known to cause body damage and even trigger cancer, but with unknown molecular etiologies. To examine these causes, we developed a systems biology approach by focusing on the co-expression network analysis of transcriptomics profiles obtained from single high-dose (SE) and multiple low-dose (ME) α -particle radiation exposures of BEAS-2B human bronchial epithelial cells. First, the differential network and pathway analysis based on the global network and the core modules showed that genes in the ME group had higher enrichment for the extracellular matrix (ECM)-receptor interaction pathway. Then, collagen gene COL1A1 was screened as an important gene in the ME group assessed by network parameters and an expression study of lung adenocarcinoma samples. COL1A1 was found to promote the emergence of the neoplastic characteristics of BEAS-2B cells by both in vitro experimental analyses and in vivo immunohistochemical staining. These findings suggested that the degree of malignant transformation of cells in the ME group was greater than that of the SE, which may be caused by the dysregulation of the ECM-receptor pathway., Competing Interests: The authors declare no competing interests., (© 2024 The Author(s).)

Pseudomonas putida KT2440 is an important bioplastic-producing industrial microorganism capable of synthesizing the polymeric carbon-rich storage material, polyhydroxyalkanoate (PHA). PHA is sequestered in discrete PHA granules, or carbonosomes, and accumulates under conditions of stress, for example, low levels of available nitrogen. The pha locus responsible for PHA metabolism encodes both anabolic and catabolic enzymes, a transcription factor, and carbonosome-localized proteins termed phasins. The functions of phasins are incompletely understood but genetic disruption of their function causes PHA-related phenotypes. To improve our understanding of these proteins, we investigated the PHA pathways of P.putida KT2440 using three types of experiments. First, we profiled cells grown in nitrogen-limited and nitrogen-excess media using global expression proteomics, identifying sets of proteins found to coordinately increase or decrease within clustered pathways. Next, we analyzed the protein composition of isolated carbonosomes, identifying two new putative components. We carried out physical interaction screens focused on PHA-related proteins, generating a protein-protein network comprising 434 connected proteins. Finally, we confirmed that the outer membrane protein OprL (the Pal component of the Pal-Tol system) localizes to the carbonosome and shows a PHA-related phenotype and therefore is a novel phasin. The combined datasets represent a valuable overview of the protein components of the PHA system in P.putida highlighting the complex nature of regulatory interactions responsive to nutrient stress., Competing Interests: Conflict of interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Understanding how cells and tissues respond to stress factors and perturbations during disease processes is crucial for developing effective prevention, diagnosis, and treatment strategies. Single-cell RNA sequencing (scRNA-seq) enables high-resolution identification of cells and exploration of cell heterogeneity, shedding light on cell differentiation/maturation and functional differences. Recent advancements in multimodal sequencing technologies have focused on improving access to cell-specific subgroups for functional genomics analysis. To facilitate the functional annotation of cell groups and characterization of molecular mechanisms underlying cell trajectories, we introduce the Pathways, Annotated Gene Lists, and Gene Signatures Electronic Repository for Single-Cell Functional Genomics Analysis (PAGER-scFGA). Results: We have developed PAGER-scFGA, which integrates cell functional annotations and gene-set enrichment analysis into popular single-cell analysis pipelines such as Scanpy. Using differentially expressed genes (DEGs) from pairwise cell clusters, PAGER-scFGA infers cell functions through the enrichment of potential cell-marker genesets. Moreover, PAGER-scFGA provides pathways, annotated gene lists, and gene signatures (PAGs) enriched in specific cell subsets with tissue compositions and continuous transitions along cell trajectories. Additionally, PAGER-scFGA enables the construction of a gene subcellular map based on DEGs and allows examination of the gene functional compartments (GFCs) underlying cell maturation/differentiation. In a real-world case study of mouse natural killer (mNK) cells, PAGER-scFGA revealed two major stages of natural killer (NK) cells and three trajectories from the precursor stage to NK T-like mature stage within blood, spleen, and bone marrow tissues. As the trajectories progress to later stages, the DEGs exhibit greater divergence and variability. However, the DEGs in different trajectories still interact within a network during NK cell maturation. Notably, PAGER-scFGA unveiled cell cytotoxicity, exocytosis, and the response to interleukin (IL) signaling pathways and associated network models during the progression from precursor NK cells to mature NK cells. Conclusion: PAGER-scFGA enables in-depth exploration of functional insights and presents a comprehensive knowledge map of gene networks and GFCs, which can be utilized for future studies and hypothesis generation. It is expected to become an indispensable tool for inferring cell functions and detecting molecular mechanisms within cell trajectories in single-cell studies. The web app (accessible at https://au-singlecell.streamlit.app/) is publicly available., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Huang, Welner, Chen and Yue.)

• Prediction of understudied protein kinase and pseudokinase functions using evolutionary inference. • Integration and mining of heterogeneous data sources for drug discovery. • Protein language models are powerful new tools for illuminating understudied proteomes. The understudied members of the druggable proteomes offer promising prospects for drug discovery efforts. While large-scale initiatives have generated valuable functional information on understudied members of the druggable gene families, translating this information into actionable knowledge for drug discovery requires specialized informatics tools and resources. Here, we review the unique informatics challenges and advances in annotating understudied members of the druggable proteome. We demonstrate the application of statistical evolutionary inference tools, knowledge graph mining approaches, and protein language models in illuminating understudied protein kinases, pseudokinases, and ion channels. [ABSTRACT FROM AUTHOR]

Background and introduction Periodontal disease is one of the most prevalent chronic conditions that affects the oral cavity. Identifying and predicting biomarkers is essential for the prevention of high-morbidity oral diseases. The genomic interaction network identifies common hub genes involved in crucial protein formation in periodontal inflammation. Diabetes mellitus is a metabolic disorder that has a double-edged sword relationship with periodontitis. Chloride intracellular channel 1 (CLIC1) was identified as a hub gene linking the pathogenesis of periodontitis and diabetes mellitus using a bioinformatic tool. Therefore, this current study aimed to assess the concentration of the pro-inflammatory biomarker CLIC1 in saliva among individuals with periodontal health and those with periodontal disease linked to diabetes mellitus. Materials and methods Differentially expressed genes (DEGs) in periodontitis were identified using datasets retrieved from the Gene Expression Omnibus (GEO) database. DEGs were combined to build the network, and GeneMANIA was used to find and rank the interconnecting genes. CLIC1 was identified as the hub gene, and clinical validation was done using patient samples. The study involved 30 participants. Based on clinical and radiographic periodontal findings, they were split into three groups: healthy (group 1, n=10), with periodontitis but no diabetes mellitus (group 2, n=10), and with periodontitis and diabetes mellitus (group 3, n=10). The collection of saliva samples, followed by quantifying these samples, was performed using an enzyme-linked immunosorbent assay (ELISA). Results From network graph analysis, it was discovered that CLIC1 functions as a hub gene in the majority of toll-like receptor pathways. The mean concentration of CLIC1 in saliva increased consistently as the disease was observed in periodontitis patients and periodontitis patients with diabetes mellitus.  Conclusion CLIC1 concentrations were positively correlated with periodontitis in individuals with diabetes. Therefore, CLIC1 could be a diagnostic biomarker for patients with periodontitis. However, large-scale studies are needed to confirm more positive associations., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2024, Chatterjee et al.)

Plants produce diverse specialized metabolites (SMs) that do not participate in plant growth and development but help them adapt to various environmental conditions. In addition to aiding in plant adaptation, different SMs serve as active ingredients for pharmaceutical and cosmetics products. However, despite their significant role in plant adaptation and industrial importance, the genes involved in the biosynthesis and regulation of many SMs remain largely unknown. This hinders deciphering the specific role of SMs in plant adaptation and limits their industrial utilization. Since many SMs pathway genes are expected to act in tight association with each other within a coexpression network, the network biology approach, such as weighted gene coexpression network analysis, could be used to identify the unknown genes. This chapter describes a workflow for constructing a gene coexpression network to identify genes that could be associated with the biosynthesis and regulation of SMs., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Drug repurposing, or drug repositioning, refers to the identification of alternative therapeutic applications for established medications that go beyond their initial indications. This strategy has becoming increasingly popular since it has the potential to significantly reduce the overall costs of drug development by around $300 million. System biology methodologies have been employed to facilitate medication repurposing, encompassing computational techniques such as signature matching and network-based strategies. These techniques utilize pre-existing drug-related data types and databases to find prospective repurposed medications that have minimal or acceptable harmful effects on patients. The primary benefit of medication repurposing in comparison to drug development lies in the fact that approved pharmaceuticals have already undergone multiple phases of clinical studies, thereby possessing well-established safety and pharmacokinetic properties. Utilizing system biology methodologies in medication repurposing offers the capacity to expedite the discovery of viable candidates for drug repurposing and offer novel perspectives for structure-based drug design., (Copyright © 2024. Published by Elsevier Inc.)

Growing evidence has shown that besides the protein coding genes, the non-coding elements of the genome are indispensable for maintaining the property of self-renewal in human embryonic stem cells and in cell fate determination. However, the regulatory mechanisms and the landscape of interactions between the coding and non-coding elements is poorly understood. In this work, we used weighted gene co-expression network analysis (WGCNA) on transcriptomic data retrieved from RNA-seq and small RNA-seq experiments and reconstructed the core human pluripotency network (called PluriMLMiNet) consisting of 375 mRNA, 57 lncRNA and 207 miRNAs. Furthermore, we derived networks specific to the naïve and primed states of human pluripotency (called NaiveMLMiNet and PrimedMLMiNet respectively) that revealed a set of molecular markers (RPS6KA1, ZYG11A, ZNF695, ZNF273, and NLRP2 for naive state, and RAB34, TMEM178B, PTPRZ1, USP44, KIF1A and LRRN1 for primed state) which can be used to distinguish the pluripotent state from the non-pluripotent state and also to identify the intra-pluripotency states (i.e., naïve and primed state). The lncRNA DANT1 was found to be a crucial as it formed a bridge between the naive and primed state-specific networks. Analysis of the genes neighbouring DANT1 suggested its possible role as a competing endogenous RNA (ceRNA) for the induction and maintenance of human pluripotency. This was computationally validated by predicting the missing DANT1-miRNA interactions to complete the ceRNA circuit. Here we first report that DANT1 might harbour binding sites for miRNAs hsa-miR-30c-2-3p, hsa-miR-210-3p and hsa-let-7b-5p which may influence pluripotency., Competing Interests: Declaration of competing interest The authors declare that they have no conflict of interest. The work was carried out by AG while working as a JRF at the University of Allahabad., (Copyright © 2023 International Society of Differentiation. Published by Elsevier B.V. All rights reserved.)