Potential protein kinase inhibitors that target G-quadruplex DNA structures in the human telomeric regions.

Telomeric regions contain Guanine-rich sequences arranged in a planar manner and connected by Hoogsteen hydrogen bonds that can fold into G-quadruplex (G4) DNA structures, and can be stabilized by monovalent metal cations. The presence of G4 DNA holds significance in cancer-related processes, especially due to their regulatory potential at transcriptional and translational levels of oncogene and tumor suppressor genes. The objective of this current research is to explore the evolving realm of FDA-approved protein kinase inhibitors, with a specific emphasis on their capacity to stabilize the G4 DNA structures formed at the human telomeric regions. This involves investigating the possibility of repurposing FDA-approved protein kinase inhibitors as a novel approach for targeting multiple cancer types. In this context, we have selected 16 telomeric G4 DNA structures as targets and 71 FDA-approved small-molecule protein kinase inhibitors as ligands. To investigate their binding affinities, molecular docking of human telomeric G4 DNA with nuclear protein kinase inhibitors and their corresponding co-crystalized ligands were performed. We found that Ponatinib and Lapatinib interact with all the selected G4 targets, the binding free energy calculations, and molecular dynamic simulations confirm their binding efficacy and stability. Thus, it is hypothesized that Ponatinib and Lapatinib may stabilize human telomeric G4 DNA in addition to their ability to inhibit BCR-ABL and the other members of the EGFR family. As a result, we also hypothesize that the stabilization of G4 DNA might represent an additional underlying mechanism contributing to their efficacy in exerting anti-cancer effects.

A modified Delphi exercise in physician-perceived risk factors for drug-induced pneumotoxicity in patients with rheumatological disease.

BACKGROUND: Drugs used to treat rheumatic disease are associated with pneumotoxicity (drug-induced lung disease), but little is known about associated risk factors. AIM: To determine expert physician-perceived risk factors for developing pneumotoxicity in patients with rheumatologic conditions. METHODS: A modified international 3-tier Delphi exercise was performed. Tier 1 determined patient and drug variables that physicians perceive to be risk factors. Tier 2 determined degree of risk associated with the Tier-1 derived variables. Tier 3 aimed to internally validate and stratify exemplar cases into risk categories. RESULTS: 134 pulmonologists and 49 rheumatologists responded to Tier 1;157 physicians completed all tiers. Perceived risk factors included: drug type; history of previous pneumotoxicity; age; smoking; underlying rheumatic disease type and activity; renal function; pulmonary hypertension; left ventricular failure;presence, nature, severity and progression of pre-existing interstitial lung disease. Tier 2 data stratified these variables into risk profiles e.g. never versus current smoking was perceived as low and high risk respectively. An example of perceived high risk resulting from Tier 3 is a 75-year-old current smoker with high-activity rheumatoid arthritis (RA) with severe, progressive ILD being started on methotrexate. A perceived low risk is a 75-year-old currentsmoker with moderate-activity RA and emphysema with no cardiac or renal disease and no pre-existing ILD being started on rituximab. A risk prediction scoring tool is being developed to be used in validation studies. CONCLUSION: This modified Delphi exercise defined and stratified the perceived risk factors for developing pneumotoxicity. Age, current smoking, high underlying rheumatological disease activity, HRCT definite UIP and honeycombing, severity and progression of pre-existing ILD were perceived to be the highest risk-factors.

Tyr352 as a Predominant Phosphosite in the Understudied Kinase and Molecular Target, HIPK1: Implications for Cancer Therapy.

Homeodomain-interacting protein kinase 1 (HIPK1) is majorly found in the nucleoplasm. HIPK1 is associated with cell proliferation, tumor necrosis factor-mediated cellular apoptosis, transcription regulation, and DNA damage response, and thought to play significant roles in health and common diseases such as cancer. Despite this, HIPK1 remains an understudied molecular target. In the present study, based on a systematic screening and mapping approach, we assembled 424 qualitative and 44 quantitative phosphoproteome datasets with 15 phosphosites in HIPK1 reported across multiple studies. These HIPK1 phosphosites were not currently attributed to any functions. Among them, Tyr352 within the kinase domain was identified as the predominant phosphosite modulated in 22 differential datasets. To analyze the functional association of HIPK1 Tyr352, we first employed a stringent criterion to derive its positively and negatively correlated protein phosphosites. Subsequently, we categorized the correlated phosphosites in known interactors, known/predicted kinases, and substrates of HIPK1, for their prioritized validation. Bioinformatics analysis identified their significant association with biological processes such as the regulation of RNA splicing, DNA-templated transcription, and cellular metabolic processes. HIPK1 Tyr352 was also identified to be upregulated in Her2+ cell lines and a subset of pancreatic and cholangiocarcinoma tissues. These data and the systems biology approach undertaken in the present study serve as a platform to explore the functional role of other phosphosites in HIPK1, and by extension, inform cancer drug discovery and oncotherapy innovation. In all, this study highlights the comprehensive phosphosite map of HIPK1 kinase and the first of its kind phosphosite-centric analysis of HIPK1 kinase based on global-level phosphoproteomics datasets derived from human cellular differential experiments across distinct experimental conditions.

Machine Learning Strategies in MicroRNA Research: Bridging Genome to Phenome.

MicroRNAs (miRNAs) have emerged as a prominent layer of regulation of gene expression. This article offers the salient and current aspects of machine learning (ML) tools and approaches from genome to phenome in miRNA research. First, we underline that the complexity in the analysis of miRNA function ranges from their modes of biogenesis to the target diversity in diverse biological conditions. Therefore, it is imperative to first ascertain the miRNA coding potential of genomes and understand the regulatory mechanisms of their expression. This knowledge enables the efficient classification of miRNA precursors and the identification of their mature forms and respective target genes. Second, and because one miRNA can target multiple mRNAs and vice versa, another challenge is the assessment of the miRNA-mRNA target interaction network. Furthermore, long-noncoding RNA (lncRNA)and circular RNAs (circRNAs) also contribute to this complexity. ML has been used to tackle these challenges at the high-dimensional data level. The present expert review covers more than 100 tools adopting various ML approaches pertaining to, for example, (1) miRNA promoter prediction, (2) precursor classification, (3) mature miRNA prediction, (4) miRNA target prediction, (5) miRNA- lncRNA and miRNA-circRNA interactions, (6) miRNA-mRNA expression profiling, (7) miRNA regulatory module detection, (8) miRNA-disease association, and (9) miRNA essentiality prediction. Taken together, we unpack, critically examine, and highlight the cutting-edge synergy of ML approaches and miRNA research so as to develop a dynamic and microlevel understanding of human health and diseases.

A global phosphosite-correlated network map of Thousand And One Kinase 1 (TAOK1).

Thousand and one amino acid kinase 1 (TAOK1) is a sterile 20 family Serine/Threonine kinase linked to microtubule dynamics, checkpoint signaling, DNA damage response, and neurological functions. Molecular-level alterations of TAOK1 have been associated with neurodevelopment disorders and cancers. Despite their known involvement in physiological and pathophysiological processes, and as a core member of the hippo signaling pathway, the phosphoregulatory network of TAOK1 has not been visualized. Aimed to explore this network, we first analyzed the predominantly detected and differentially regulated TAOK1 phosphosites in global phosphoproteome datasets across diverse experimental conditions. Based on 709 qualitative and 210 quantitative differential cellular phosphoproteome datasets that were systematically assembled, we identified that phosphorylation at Ser421, Ser9, Ser965, and Ser445 predominantly represented TAOK1 in almost 75% of these datasets. Surprisingly, the functional role of all these phosphosites in TAOK1 remains unexplored. Hence, we employed a robust strategy to extract the phosphosites in proteins that significantly correlated in expression with predominant TAOK1 phosphosites. This led to the first categorization of the phosphosites including those in the currently known and predicted interactors, kinases, and substrates, that positively/negatively correlated with the expression status of each predominant TAOK1 phosphosites. Subsequently, we also analyzed the phosphosites in core proteins of the hippo signaling pathway. Based on the TAOK1 phosphoregulatory network analysis, we inferred the potential role of the predominant TAOK1 phosphosites. Especially, we propose pSer9 as an autophosphorylation and TAOK1 kinase activity-associated phosphosite and pS421, the most frequently detected phosphosite in TAOK1, as a significant regulatory phosphosite involved in the maintenance of genome integrity. Considering that the impact of all phosphosites that predominantly represent each kinase is essential for the efficient interpretation of global phosphoproteome datasets, we believe that the approach undertaken in this study is suitable to be extended to other kinases for accelerated research.

VirhostlncR: A comprehensive database to explore lncRNAs and their targets in viral infections.

Long non-coding-RNAs (lncRNAs) are an expanding set of cis-/trans-regulatory RNA genes that outnumber the protein-coding genes. Although being increasingly discovered, the functional role of the majority of lncRNAs in diverse biological conditions is undefined. Increasing evidence supports the critical role of lncRNAs in the emergence, regulation, and progression of various viral infections including influenza, hepatitis, coronavirus, and human immunodeficiency virus. Hence, the identification of signature lncRNAs would facilitate focused analysis of their functional roles accounting for their targets and regulatory mechanisms associated with infections. Towards this, we compiled 2803 lncRNAs identified to be modulated by 33 viral strains in various mammalian cell types and are provided through the resource named VirhostlncR (http://ciods.in/VirhostlncR/). The information on each of the viral strains, their multiplicity of infection, duration of infection, host cell name and cell types, fold change of lncRNA expression, and their specific identification methods are integrated into VirhostlncR. Based on the current datasets, we report 150 lncRNAs including differentiation antagonizing non-protein coding RNA (DANCR), metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), maternally expressed gene 3 (MEG3), nuclear paraspeckle assembly transcript 1 (NEAT1), and plasmacytoma variant translocation 1 (PVT1) to be perturbed by two or more viruses. Analysis of viral protein interactions with human transcription factors (TFs) or TF-containing protein complexes identified that distinct viruses can transcriptionally regulate many of these lncRNAs through multiple protein complexes. Together, we believe that the current dataset will enable priority selection of lncRNAs for identification of their targets and serve as an effective platform for the analysis of noncoding RNA-mediated regulations in viral infections.