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Whatever the mucosa primary infected, HPV-positive cancers are traditionally associated with a favorable outcome, attributable to a high sensitivity to radiation therapy. However, the direct impact of viral E6/E7 oncoproteins on the intrinsic cellular radiosensitivity (and, globally, on host DNA repair) remains mostly speculative. Using several isogenic cell models expressing HPV16 E6 and/or E7, the effect of viral oncoproteins on global DNA damage response was first investigated by approaches. The binary interactome of each individual HPV oncoprotein with factors involved in the various host DNA damage/repair mechanisms was then precisely mapped by luciferase complementation assay (and validated by co-immunoprecipitation). The stability/half-life of protein targets for HPV E6 and/or E7 as well as their subcellular localizations were determined. At last, the host genome integrity following E6/E7 expression and the synergy between radiotherapy and compounds targeting DNA repair were analyzed. We first showed that the sole expression of one viral oncoprotein from HPV16 was able to significantly increase the sensitivity to irradiation of cells without affecting their basal viability parameters. In total, 10 novel targets (CHEK2, CLK2, CLK2/3, ERCC3, MNAT1, PER1, RMI1, RPA1, UVSSA and XRCC6) for E6 and 11 (ALKBH2, CHEK2, DNA2, DUT, ENDOV, ERCC3, PARP3, PMS1, PNKP, POLDIP2 and RBBP8) for E7 were identified. Importantly, not degraded following their interaction with E6 or E7, these proteins have been shown to be less linked to host DNA and to colocalize with HPV replication foci, denoting their crucial implication in viral life cycle. Finally, we found that E6/E7 oncoproteins globally jeopardize host genome integrity, increase the cellular sensitivity to DNA repair inhibitors and enhance their synergy with radiotherapy. Taken together, our findings provide a molecular insight into the direct hijacking of host DNA damage/repair responses by HPV oncoproteins, demonstrate the significant impact of this phenomenon on both intrinsic cellular radiosensitivity and host DNA integrity and suggest novel connected therapeutic vulnerabilities.

INTRODUCTION High microglial heterogeneities hinder the development of microglia‐targeted treatment for Alzheimer's disease (AD). METHODS We integrated 0.7 million single‐nuclei RNA‐sequencing transcriptomes from human brains using a variational autoencoder. We predicted AD‐relevant microglial subtype‐specific transition networks for disease‐associated microglia (DAM), tau microglia, and neuroinflammation‐like microglia (NIM). We prioritized drugs by specifically targeting microglia‐specific transition networks and validated drugs using two independent real‐world patient databases. RESULTS We identified putative AD molecular drivers (e.g., SYK, CTSB, and INPP5D) in transition networks of DAM and NIM. Via specifically targeting NIM, we identified that usage of ketorolac was associated with reduced AD incidence in both MarketScan (hazard ratio [HR] = 0.89) and INSIGHT (HR = 0.83) Clinical Research Network databases, mechanistically supported by ketorolac‐treated transcriptomic data from AD patient induced pluripotent stem cell–derived microglia. DISCUSSION This study offers insights into the pathobiology of AD‐relevant microglial subtypes and identifies ketorolac as a potential anti‐inflammatory treatment for AD. Highlights An integrative analysis of ≈ 0.7 million single‐nuclei RNA‐sequencing transcriptomes from human brains identified Alzheimer's disease (AD)–relevant microglia subtypes. Network‐based analysis identified putative molecular drivers (e.g., SYK, CTSB, INPP5D) of transition networks between disease‐associated microglia (DAM) and neuroinflammation‐like microglia (NIM). Via network‐based prediction and population‐based validation, we identified that usage of ketorolac (a US Food and Drug Administration–approved anti‐inflammatory medicine) was associated with reduced AD incidence in two independent patient databases. Mechanistic observation showed that ketorolac treatment downregulated the Type‐I interferon signaling in patient induced pluripotent stem cell–derived microglia, mechanistically supporting its protective effects in real‐world patient databases.

Viruses exploit host kinases to phosphorylate their proteins, enabling viral replication and interference with host-cell functions. Understanding phosphorylation in SARS-CoV-2 proteins necessitates identifying viral phosphoproteins, their phosphosites, and the host kinase-viral protein interactions critical for evading host antiviral responses. Employing the protein kinase substrate sequence-preference motifs derived by Poll B G. ., 2024, we performed kinase-substrate phosphomotif pattern analysis on the SARS-CoV-2 proteome. We identified major host kinases by analyzing SARS-CoV-2 perturbed phosphoproteomes from various studies and cell systems. These kinases were subjected to interactome analysis and literature-based validation for the impact of kinase inhibitors on infection. Further, conservation of viral phosphosites across SARS CoV-2 variants were also assessed. The human kinome-substrate phosphomotif analysis predicted 49 kinases capable of phosphorylating 639 phosphosites across 33 SARS-CoV-2 proteins. From these, 24 kinases were also perturbed in SARS-CoV-2-infected phosphoproteomes. Literature review identified seven kinases, including MAP2K1, whose inhibition may reduce viral replication. MAP2K1 was found to target key viral phosphosites, including N protein (S206, T198) and ORF9b (S50), conserved across SARS-CoV-2 variants. Docking analysis showed MAP2K1 forms stronger, closer interactions with N protein compared to SRPK1, highlighting MAP2K1 as a potential host kinase for therapeutic targeting in SARS-CoV-2 infection. This study presents a framework for predicting human kinases of specific SARS-CoV-2 protein phosphosites by integrating kinase specificity, virus-host interactions, and post-translational modifications. MAP2K1 was identified as a key host kinase, showing stronger interactions than SRPK1, and is proposed as an antiviral drug target for repurposing in SARS-CoV-2 infections.

Autism spectrum disorder (ASD) is characterized by high heritability and clinical heterogeneity. The main core symptoms are social communication deficits. There are no medications approved for the treatment of these symptoms, and medications used to treat non-specific symptoms have serious side effects. To identify potential drugs for repurposing to effectively treat ASD core symptoms, we studied ASD risk genes within networks of protein-protein interactions of gene products. We first defined an ASD network from network-based analyses, and identified approved drugs known to interact with proteins within this network. Thereafter, we evaluated if these drugs can change ASD-associated gene expression perturbations in genes in the ASD network. This was done by analyses of drug-induced versus ASD-associated gene expression, where opposite gene expression perturbations in drug versus ASD indicate that the drug could counteract ASD-associated perturbations. Four drugs showing significant ( p < 0.05) opposite gene expression perturbations in drug versus ASD were identified: Loperamide, bromocriptine, drospirenone, and progesterone. These drugs act on ASD-related biological systems, indicating that these drugs could effectively treat ASD core symptoms. Based on our bioinformatics analyses of ASD genetics, we shortlist potential drug repurposing candidates that warrant clinical translation to treat core symptoms in ASD.

Inflammatory response triggered by innate immunity can act to protect against microorganisms that behave as pathogens, with the aim to restore the homeostatic state between host and beneficial microbes. As a filter-feeder organism, the ascidian Ciona robusta is continuously exposed to external microbes that may be harmful under some conditions. In this work, we used transcriptional and proteomic approaches to investigate the inflammatory response induced by stimuli of bacterial (lipopolysaccharide -LPS- and diacylated lipopeptide - Pam2CSK4) and fungal (zymosan) origin, in Ciona juveniles at stage 4 of metamorphosis. We focused on receptors, co-interactors, transcription factors and cytokines belonging to the TLR and Dectin-1 pathways and on immune factors identified by homology approach ( i.e . immunoglobulin (Ig) or C-type lectin domain containing molecules). While LPS did not induce a significant response in juvenile ascidians, Pam2CSK4 and zymosan exposure triggered the activation of specific inflammatory mechanisms. In particular, Pam2CSK4-induced inflammation was characterized by modulation of TLR and Dectin-1 pathway molecules, including receptors, transcription factors, and cytokines, while immune response to zymosan primarily involved C-type lectin receptors, co-interactors, Ig-containing molecules, and cytokines. A targeted proteomic analysis enabled to confirm transcriptional data, also highlighting a temporal delay between transcriptional induction and protein level changes. Finally, a protein-protein interaction network of Ciona immune molecules was rendered to provide a wide visualization and analysis platform of innate immunity. The in vivo inflammatory model described here reveals interconnections of innate immune pathways in specific responses to selected microbial stimuli. It also represents the starting point for studying ontogeny and regulation of inflammatory disorders in different physiological conditions.

Establishing the protein–protein interaction network sheds light on functional genomics studies by providing insights from known counterparts. However, the rice interactome has barely been studied due to the lack of massive, reliable, and cost‐effective methodologies. Here, the development of a barcode‐indexed PCR coupled with HiFi long‐read sequencing pipeline (BIP‐seq) is reported for high throughput Protein Protein Interaction (PPI)identification. BIP‐seq is essentially built on the integration of library versus library Y2H mating strategy to facilitate the efficient acquisition of random PPI colonies, semi‐mechanized dual barcode‐indexed yeast colony PCR for the large‐scale indexed amplification of bait and prey cDNAs, and massive pac‐bio sequencing of PCR amplicon pools. It is demonstrated that BIP‐seq could map over 15 000 high‐confidence (≈62.5% could be verified by Bimolecular fluorescence Complementation (BiFC)) rice PPIs within 2 months, outperforming the other reported methods. In addition, the obtained 23 032 rice PPIs, including 22,665 newly identified PPIs, greatly expanded the current rice PPI dataset, provided a comprehensive overview of the rice PPIs networks, and could be a valuable asset in facilitating functional genomics research in rice. BIP‐seq is developed, a cost‐effective pipeline for high‐throughput protein–protein interaction (PPI) identification using yeast two‐hybrid system. BIP‐seq successfully map over 15 000 high‐confidence rice PPIs (≈62.5% could be verified by BiFC) within 2 months. A rice PPI database (RiPPID) containing 23032 PPIs is established, which greatly enhances the understanding of the interactome in rice.

Aging is an inevitable process that negatively affects all living organisms and their vital functions. The brain is one of the most important organs in living beings and is primarily impacted by aging. The molecular mechanisms of learning, memory and cognition are altered over time, and the impairment in these mechanisms can lead to neurodegenerative diseases. Transcriptomics can be used to study these impairments to acquire more detailed information on the affected molecular mechanisms. Here we analyzed learning- and memory-related transcriptome data by mapping it on the organism-specific protein–protein interactome network. Subnetwork discovery algorithms were applied to discover highly dysregulated subnetworks, which were complemented with co-expression-based interactions. The functional analysis shows that the identified subnetworks are enriched with genes having roles in synaptic plasticity, gliogenesis, neurogenesis and cognition, which are reported to be related to memory and learning. With a detailed analysis, we show that the results from different subnetwork discovery algorithms or from different transcriptomic datasets can be successfully reconciled, leading to a memory-learning network that sheds light on the molecular mechanisms behind aging and memory-related impairments.

The clinical characteristics and biological effects on the nervous system of infection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remain poorly understood. The aim of this study is to advance epidemiological and mechanistic understanding of the neurological manifestations of coronavirus disease 2019 (COVID-19) using stroke as a case study. In this study, we performed a meta-analysis of clinical studies reporting stroke history, intensive inflammatory response, and procoagulant state C-reactive protein (CRP), Procalcitonin (PCT), and coagulation indicator (D-dimer) in patients with COVID-19. Via network-based analysis of SARS-CoV-2 host genes and stroke-associated genes in the human protein-protein interactome, we inspected the underlying inflammatory mechanisms between COVID-19 and stroke. Finally, we further verified the network-based findings using three RNA-sequencing datasets generated from SARS-CoV-2 infected populations. We found that the overall pooled prevalence of stroke history was 2.98% (95% CI, 1.89–4.68; I 2 =69.2%) in the COVID-19 population. Notably, the severe group had a higher prevalence of stroke (6.06%; 95% CI 3.80–9.52; I 2 = 42.6%) compare to the non-severe group (1.1%, 95% CI 0.72–1.71; I 2 = 0.0%). There were increased levels of CRP, PCT, and D-dimer in severe illness, and the pooled mean difference was 40.7 mg/L (95% CI, 24.3–57.1), 0.07 μg/L (95% CI, 0.04–0.10) and 0.63 mg/L (95% CI, 0.28–0.97), respectively. Vascular cell adhesion molecule 1 (VCAM-1), one of the leukocyte adhesion molecules, is suspected to play a vital role of SARS-CoV-2 mediated inflammatory responses. RNA-sequencing data analyses of the SARS-CoV-2 infected patients further revealed the relative importance of inflammatory responses in COVID-19-associated neurological manifestations. In summary, we identified an elevated vulnerability of those with a history of stroke to severe COVID-19 underlying inflammatory responses (i.e., VCAM-1) and procoagulant pathways, suggesting monotonic relationships, thus implicating causality.

The protein folding capacity of the endoplasmic reticulum (ER) is tightly regulated by a network of signaling pathways, known as the unfolded protein response (UPR). UPR sensors monitor the ER folding status to adjust ER folding capacity according to need. To understand how the UPR sensor IRE1 maintains ER homeostasis, we identified zero-length crosslinks of RNA to IRE1 with single nucleotide precision in vivo. We found that IRE1 specifically crosslinks to a subset of ER-targeted mRNAs, SRP RNA, ribosomal and transfer RNAs. Crosslink sites cluster in a discrete region of the ribosome surface spanning from the A-site to the polypeptide exit tunnel. Moreover, IRE1 binds to purified 80S ribosomes with high affinity, indicating association with ER-bound ribosomes. Our results suggest that the ER protein translocation and targeting machineries work together with the UPR to tune the ER’s protein folding load.

RNA molecules in the cell are bound by a multitude of RNA-binding proteins (RBPs) with a variety of regulatory consequences. Often, interactions with these RNA-binding proteins are facilitated by the complex secondary and tertiary structures of RNA molecules. Viral RNAs especially are known to be heavily structured and interact with many RBPs, with roles including genome packaging, immune evasion, enhancing replication and transcription, and increasing translation efficiency. As such, the RNA–protein interactome represents a critical facet of the viral replication cycle. Characterization of these interactions is necessary for the development of novel therapeutics targeted at the disruption of essential replication cycle events. In this review, we aim to summarize the various roles of RNA structures in shaping the RNA–protein interactome, the regulatory roles of these interactions, as well as up-to-date methods developed for the characterization of the interactome and directions for novel, RNA-directed therapeutics.