The human immunodeficiency virus (HIV-1) is highly dependent on a variety of host factors. Beside proteins, host RNA molecules are reported to aid HIV-1 replication and latency maintenance. Here, we implement multiple workflows of native RNA immunoprecipitation and sequencing (nRIPseq) to determine direct host RNA interaction partners of all 18 HIV-1 (poly)proteins. We identify 1,727 HIV-1 protein - human RNA interactions in the Jurkat cell line and 1,558 interactions in SupT1 cells for a subset of proteins, and discover distinct cellular pathways that seem to be used or controlled by HIV-1 on the RNA level: Tat binds mRNAs of proteins involved in the super elongation complex (AFF1-4, Cyclin-T1). Correlation of the interaction scores (based on binding abundancy) allows identifying the highest confidence interactions, for which we perform a small-scale knockdown screen that leads to the identification of three HIV-1 protein binding RNA interactors involved in HIV-1 replication (AFF2, H4C9 and RPLP0).

The sensitivity of phosphorylation site identification by mass spectrometry (MS)-based phosphoproteomics has improved significantly. However, the lack of kinase-substrate relationship (KSR) data has hindered improvement of the range and accuracy of kinase activity prediction using phosphoproteome data. We herein describe the application of a systematic identification of KSR by integrated phosphoproteome and interactome analysis using doxycycline (Dox)-induced target kinase-overexpressing HEK-293 cells.

Hepatitis B virus (HBV) infects hepatocytes that are in the G0/G1 phase with intact nuclear membrane and organized chromosome architecture. In the nucleus of the infected cells, HBV covalently closed circular (ccc) DNA, an episomal minichromosome, serves as the template for all viral transcripts and the reservoir of persistent infection. Nuclear positioning of cccDNA can be assessed by the spatial distance between viral DNA and host chromosomal DNA through Circular Chromosome Conformation Capture (4C) combined with high-throughput sequencing (4C-seq). The 4C-seq analysis relies on proximity ligation and is commonly used for mapping genomic DNA regions that communicate within a host chromosome. The method has been tailored for studying nuclear localization of HBV episomal cccDNA in relation to the host chromosomes. In this study, we present a step-by-step protocol for 4C-seq analysis of HBV infection, including sample collection and fixation, 4C DNA library preparation, sequence library preparation, and data analysis. Although limited by proximity ligation of DNA fragments, 4C-seq analysis provides useful information of HBV localization in 3D genome, and aids the understanding of viral transcription in light of host chromatin conformation.

UNASSIGNED: Betel nut/areca nut/Areca catechu is one of the most commonly used psychoactive substance, and is also a major preventable cause of cancer. Unlike other psychoactive substances, such as nicotine, the mechanisms underlying addiction to areca nuts and related oncogenesis remain elusive. Recent reports suggest a possible overlap in the mechanisms of action of nicotine and areca nuts in the human body. Thus, this study aimed to investigate the interactome of human proteins associated with areca nut exposure and the intricate similarities and differences in the effects of the two psychoactive substances on humans.
UNASSIGNED: A list of proteins associated with areca nut use was obtained from the available literature using terms from Medical Subject Headings (MeSH). Protein-protein interaction (PPI) networks and functional enrichment were analyzed. The results obtained for both psychoactive substances were compared.
UNASSIGNED: Given the limited number of common proteins (36/226, 16%) in the two sets, a substantial overlap (612/1176 nodes, 52%) was observed in the PPI networks, as well as in Gene Ontology. Areca nuts mainly affect signaling pathways through three hub proteins (alpha serine/threonine-protein kinase, tumor protein 53, and interleukin-6), which are common to both psychoactive substances, as well as two unique hub proteins (epidermal growth factor receptor and master regulator of cell cycle entry and proliferative metabolism). Areca nut-related proteins are associated with unique pathways, such as extracellular matrix organization, lipid storage, and metabolism, which are not found in nicotine-associated proteins.
UNASSIGNED: Areca nuts affect regulatory mechanisms, leading to systemic toxicity and oncogenesis. Areca nuts also affect unique pathways that can be studied as potential markers of exposure, as well as targets for anticancer therapeutic agents.

Receptor-mediated endocytosis provides a mechanism for the selective uptake of specific molecules thereby controlling the composition of the extracellular environment and biological processes. The low-density lipoprotein receptor-related protein 1 (LRP1) is a widely expressed endocytic receptor that regulates cellular events by modulating the levels of numerous extracellular molecules via rapid endocytic removal. LRP1 also participates in signalling pathways through this modulation as well as in the interaction with membrane receptors and cytoplasmic adaptor proteins. LRP1 SNPs are associated with several diseases and conditions such as migraines, aortic aneurysms, cardiopulmonary dysfunction, corneal clouding, and bone dysmorphology and mineral density. Studies using Lrp1 KO mice revealed a critical, nonredundant and tissue-specific role of LRP1 in regulating various physiological events. However, exactly how LRP1 functions to regulate so many distinct and specific processes is still not fully clear. Our recent proteomics studies have identified more than 300 secreted proteins that either directly interact with LRP1 or are modulated by LRP1 in various tissues. This review will highlight the remarkable ability of this receptor to regulate secreted molecules in a tissue-specific manner and discuss potential mechanisms underpinning such specificity. Uncovering the depth of these \"hidden\" specific interactions modulated by LRP1 will provide novel insights into a dynamic and complex extracellular environment that is involved in diverse biological and pathological processes.

We describe a next-generation Drosophila protein interaction map-\"DPIM2\"-established from affinity purification-mass spectrometry of 5,805 baits, covering the largest fraction of the Drosophila proteome. The network contains 32,668 interactions among 3,644 proteins, organized into 632 clusters representing putative functional modules. Our analysis expands the pool of known protein interactions in Drosophila, provides annotation for poorly studied genes, and postulates previously undescribed protein interaction relationships. The predictive power and functional relevance of this network are probed through the lens of the Notch signaling pathway, and we find that newly identified members of complexes that include known Notch modifiers can also modulate Notch signaling. DPIM2 allows direct comparisons with a recently published human protein interaction network, defining the existence of functional interactions conserved across species. Thus, DPIM2 defines a valuable resource for predicting protein co-complex memberships and functional associations as well as generates functional hypotheses regarding specific protein interactions.

Responsible for COVID-19, SARS-CoV-2 is a coronavirus in which contagious variants continue to appear. Therefore, some population groups have demonstrated greater susceptibility to contagion and disease progression. For these reasons, several researchers have been studying the SARS-CoV-2/human interactome to understand the pathophysiology of COVID-19 and develop new pharmacological strategies. D. melanogaster is a versatile animal model with approximately 90 % human protein orthology related to SARS-CoV-2/human interactome and is widely used in metabolic studies. In this context, our work assessed the potential interaction between human proteins (ZNF10, NUP88, BCL2L1, UBC9, and RBX1) and their orthologous proteins in D. melanogaster (gl, Nup88, Buffy, ubc9, and Rbx1a) with proteins from SARS-CoV-2 (nsp3, nsp9, E, ORF7a, N, and ORF10) using computational approaches. Our results demonstrated that all the proteins have the potential to interact, and we compared the binding sites between humans and fruit flies. The stability and consistency in the structure of the gl_nsp3 complex, specifically, could be crucial for its specific biological functions. Lastly, to enhance the understanding of the influence of host factors on coronavirus infection, we also analyse the mRNA expression of the five genes (mbo, gl, lwr, Buffy, and Roc1a) responsible for encoding the fruit fly proteins. Briefly, we demonstrated that those genes were differentially regulated according to diets, sex, and age. Two groups showed higher positive gene regulation than others: females in the HSD group and males in the aging group, which could imply a higher virus-host susceptibility. Overall, while preliminary, our work contributes to the understanding of host defense mechanisms and potentially identifies candidate proteins and genes for in vivo viral studies against SARS-CoV-2.

Formin Homology Proteins (Formins) are a highly conserved family of cytoskeletal regulatory proteins that participate in a diverse range of cellular processes. FMNL2 is a member of the Diaphanous-Related Formin sub-group, and previous reports suggest FMNL2\'s role in filopodia assembly, force generation at lamellipodia, subcellular trafficking, cell-cell junction assembly, and focal adhesion formation. How FMNL2 is recruited to these sites of action is not well understood. To shed light on how FMNL2 activity is partitioned between subcellular locations, we used biotin proximity labeling and proteomic analysis to identify an FMNL2 interactome. The interactome identified known and new FMNL2 interacting proteins with functions related to previously described FMNL2 activities. In addition, our interactome predicts a novel connection between FMNL2 and extracellular vesicle assembly. We show directly that FMNL2 protein is present in exosomes.

The voltage-gated potassium ion channel KV11.1 plays a critical role in cardiac repolarization. Genetic variants that render Kv11.1 dysfunctional cause long QT syndrome (LQTS), which is associated with fatal arrhythmias. Approximately 90% of LQTS-associated variants cause intracellular protein transport (trafficking) dysfunction, which pharmacological chaperones like E-4031 can rescue. Protein folding and trafficking decisions are regulated by chaperones, protein quality control factors, and trafficking machinery comprising the cellular proteostasis network. Here, we test whether trafficking dysfunction is associated with alterations in the proteostasis network of pathogenic Kv11.1 variants and whether pharmacological chaperones can normalize the proteostasis network of responsive variants. We used affinity-purification coupled with tandem mass tag-based quantitative mass spectrometry to assess protein interaction changes of WT KV11.1 or trafficking-deficient channel variants in the presence or absence of E-4031. We identified 572 core KV11.1 protein interactors. Trafficking-deficient variants KV11.1-G601S and KV11.1-G601S-G965∗ had significantly increased interactions with proteins responsible for folding, trafficking, and degradation compared to WT. We confirmed previous findings that the proteasome is critical for KV11.1 degradation. Our report provides the first comprehensive characterization of protein quality control mechanisms of KV11.1. We find extensive interactome remodeling associated with trafficking-deficient KV11.1 variants and with pharmacological chaperone rescue of KV11.1 cell surface expression. The identified protein interactions could be targeted therapeutically to improve KV11.1 trafficking and treat LQTS.

Introduction Oral cancer is a significant global health issue that is mainly caused by factors, such as smoking, alcohol consumption, poor oral hygiene, age, and the human papillomavirus. Unfortunately, delayed diagnosis contributes to high rates of illness and mortality. However, saliva shows promise as a potential source for early detection, prognosis, and treatment. By analyzing the proteins and their interactions in saliva, we can gain insights that can assist in early detection and prediction. In this study, we aim to identify and predict the key genes, known as hub genes, in the salivary transcriptomics data of oral cancer patients and healthy individuals. Methods The data used for the analysis were obtained from salivaryproteome.org (https://salivaryproteome.org/) . The retrieved data consisted of individuals with oral cancer who were assigned unique identification numbers (IDs) 1025, 1030, 1027, and 1029, while the healthy individuals were assigned IDs 4256, 4257, 4255, and 4258, respectively. Differential gene expression analysis was used to identify genes that showed significant differences between the two groups. Uniformity and clustering were assessed through heatmaps and principal component analysis. Protein-protein interactions were investigated using the STRING database and Cytoscape. In addition, machine learning algorithms were employed to identify key genes involved in the interatomic interactions by analyzing transcriptomics data generated from the differential gene expression analysis. Results The accuracy and class accuracy of the extra tree classifier showed 98% and 97% in predicting interactomic hub genes, and HSPB1 was identified as a hub gene using Cytohubba from Cytoscape. Conclusion The predictive extra tree classifier, with its high accuracy in analysing interactomic hub genes in oral cancer, can potentially improve diagnosis and treatment strategies.