Protein Interaction Map Research Articles

The risk of pancreatic adenocarcinoma following SARS-CoV family infection

COVID 19 disease has become a global catastrophe over the past year that has claimed the lives of over two million people around the world. Despite the introduction of vaccines against the disease, there is still a long way to completely eradicate it. There are concerns about the complications following infection with SARS-CoV-2. This research aimed to evaluate the possible correlation between infection with SARS-CoV viruses and cancer in an in-silico study model. To do this, the relevent dataset was selected from GEO database. Identification of differentially expressed genes among defined groups including SARS-CoV, SARS-dORF6, SARS-BatSRBD, and H1N1 were screened where the |Log FC| ≥ 1and p < 0.05 were considered statistically significant. Later, the pathway enrichment analysis and gene ontology (GO) were used by Enrichr and Shiny GO databases. Evaluation with STRING online was applied to predict the functional interactions of proteins, followed by Cytoscape analysis to identify the master genes. Finally, analysis with GEPIA2 server was carried out to reveal the possible correlation between candidate genes and cancer development. The results showed that the main molecular function of up- and down-regulated genes was “double-stranded RNA binding” and actin-binding, respectively. STRING and Cytoscape analysis presented four genes, PTEN, CREB1, CASP3, and SMAD3 as the key genes involved in cancer development. According to TCGA database results, these four genes were up-regulated notably in pancreatic adenocarcinoma. Our findings suggest that pancreatic adenocarcinoma is the most probably malignancy happening after infection with SARS-CoV family.

The dual role of SrbA from Paracoccidioides lutzii: a hypoxic regulator.

The fungus Paracoccidioides lutzii is one of the species of the Paracoccidioides genus, responsible for a neglected human mycosis, endemic in Latin America, the paracoccidioidomycosis (PCM). In order to survive in the host, the fungus overcomes a hostile environment under low levels of oxygen (hypoxia) during the infectious process. The hypoxia adaptation mechanisms are variable among human pathogenic fungi and worthy to be investigated in Paracoccidoides spp. Previous proteomic results identified that P. lutzii responds to hypoxia and it has a functional homolog of the SrbA transcription factor, a well-described hypoxic regulator. However, the direct regulation of genes by SrbA and the biological processes it governs while performing protein interactions have not been revealed yet. The goal of this study was to demonstrate the potential of SrbA targets genes in P. lutzii. In addition, to show the SrbA three-dimensional aspects as well as a protein interaction map and important regions of interaction with predicted targets. The results show that SrbA-regulated genes were involved with several biological categories, such as metabolism, energy, basal processes for cell maintenance, fungal morphogenesis, defense, virulence, and signal transduction. Moreover, in order to investigate the SrbA's role as a protein, we performed a 3D simulation and also a protein-protein network linked to this hypoxic regulator. These in silico analyses revealed relevant aspects regarding the biology of this pathogen facing hypoxia and highlight the potential of SrbA as an antifungal target in the future.

A Molecular Signature Response Classifier to Predict Inadequate Response to Tumor Necrosis Factor-α Inhibitors: The NETWORK-004 Prospective Observational Study.

IntroductionTimely matching of patients to beneficial targeted therapy is an unmet need in rheumatoid arthritis (RA). A molecular signature response classifier (MSRC) that predicts which patients with RA are unlikely to respond to tumor necrosis factor-α inhibitor (TNFi) therapy would have wide clinical utility.MethodsThe protein–protein interaction map specific to the rheumatoid arthritis pathophysiology and gene expression data in blood patient samples was used to discover a molecular signature of non-response to TNFi therapy. Inadequate response predictions were validated in blood samples from the CERTAIN cohort and a multicenter blinded prospective observational clinical study (NETWORK-004) among 391 targeted therapy-naïve and 113 TNFi-exposed patient samples. The primary endpoint evaluated the ability of the MSRC to identify patients who inadequately responded to TNFi therapy at 6 months according to ACR50. Additional endpoints evaluated the prediction of inadequate response at 3 and 6 months by ACR70, DAS28-CRP, and CDAI.ResultsThe 23-feature molecular signature considers pathways upstream and downstream of TNFα involvement in RA pathophysiology. Predictive performance was consistent between the CERTAIN cohort and NETWORK-004 study. The NETWORK-004 study met primary and secondary endpoints. A molecular signature of non-response was detected in 45% of targeted therapy-naïve patients. The MSRC had an area under the curve (AUC) of 0.64 and patients were unlikely to adequately respond to TNFi therapy according to ACR50 at 6 months with an odds ratio of 4.1 (95% confidence interval 2.0–8.3, p value 0.0001). Odds ratios (3.4–8.8) were significant (p value < 0.01) for additional endpoints at 3 and 6 months, with AUC values up to 0.74. Among TNFi-exposed patients, the MSRC had an AUC of up to 0.83 and was associated with significant odds ratios of 3.3–26.6 by ACR, DAS28-CRP, and CDAI metrics.ConclusionThe MSRC stratifies patients according to likelihood of inadequate response to TNFi therapy and provides patient-specific data to guide therapy choice in RA for targeted therapy-naïve and TNFi-exposed patients.Supplementary InformationThe online version contains supplementary material available at 10.1007/s40744-021-00330-y.

Potential therapeutic targets and biological mechanisms of Centella asiatica on hepatic fibrosis: a study of network pharmacology.

BackgroundLiver fibrosis is a common result of the repair process of various chronic liver diseases. This study is a network pharmacology study on the potential therapeutic targets and biological mechanisms of Centella asiatica for liver fibrosis.MethodsThe chemical components and potential targets of Centella asiatica were screened through TCMSP, PubChem database, and Swiss Target Prediction database. The DisGeNET and GeneCards databases were used to obtain targets of HF. Venn diagrams were used to find key targets, and draw protein interaction maps. Cytoscape software was used to construct an interaction network map of drug-component-target-disease-pathway. The mechanisms of action were predicted through enrichment analysis and KEGG analysis.ResultsIn total, 6 main components, 297 drug targets, 337 HF targets, and 48 drug-disease targets were obtained in Centella asiatica. The key targets involved IL6, TNF, VEGFA, TP53, IL1β, MMP9, CXCL8, EGFR, JUN, SRC, MMP2, and TGF-β, among others. A total of 1293 entries were obtained by Gene Ontology (GO) enrichment analysis, which mainly involved the regulation of reactive oxygen species metabolic process, the regulation of smooth muscle cells, and the regulation of DNA-binding transcription factor activity. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment mainly screened 191 pathways, including the MAPK signaling pathway, the relaxin signaling pathway, and the Toll-like receptor signaling pathway, among others.ConclusionsCentella asiatica may have a therapeutic effect on HF through multiple targets and pathways. Its mechanism is mainly related to the MAPK signaling pathway and the relaxin signaling pathway.

Extracellular crosslinking mass spectrometry reveals HLA class I – HLA class II interactions on the cell surface

Human Leukocyte Antigen (HLA) complexes are critical cell-surface protein assemblies that facilitate T-cell surveillance of almost all cell types in the body. While T-cell receptor binding to HLA class I and class II complexes is well-described with detailed structural information, the nature of cis HLA interactions within the plasma membrane of the surveyed cells remains to be better characterized, as protein-protein interactions in the membrane environment are technically challenging to profile. Here we performed extracellular chemical crosslinking on intact antigen presenting cells to specifically elucidate protein-protein interactions present in the external plasma membrane. We found that the crosslink dataset was dominated by inter- and intra-protein crosslinks involving HLA molecules, which enabled not only the construction of an HLA-centric plasma membrane protein interaction map, but also revealed multiple modes of HLA class I – HLA class II interactions with further structural modeling based on crosslinker distance restraints. Collectively, our data demonstrate that HLA molecules colocalize and can be densely packed on the plasma membrane.

Deciphering the Methylation Landscape in Breast Cancer: Diagnostic and Prognostic Biosignatures through Automated Machine Learning.

Simple SummaryBreast cancer (BrCa) is characterized by aberrant DNA methylation. We leveraged high-throughput methylation data from BrCa and normal breast tissues and identified 11,176 to 27,786 differentially methylated genes (DMGs) against clinically relevant end-points. Innovative automated machine learning was employed to construct three highly performing signatures for (1) the discrimination of BrCa patients from healthy individuals, (2) the identification of BrCa metastatic disease and (3) the early diagnosis of BrCa. Furthermore, functional analysis revealed that most genes selected in the signatures showed associations to BrCa, with regulation of transcription being the main biological process, the nucleus being the main cellular component and transcription factor activity and sequence-specific DNA binding being the main molecular functions. Overall, revisiting methylome datasets led to three high-performance signatures that are readily available for improving BrCa precision management and significant knowledge mining related to disease pathophysiology.DNA methylation plays an important role in breast cancer (BrCa) pathogenesis and could contribute to driving its personalized management. We performed a complete bioinformatic analysis in BrCa whole methylome datasets, analyzed using the Illumina methylation 450 bead-chip array. Differential methylation analysis vs. clinical end-points resulted in 11,176 to 27,786 differentially methylated genes (DMGs). Innovative automated machine learning (AutoML) was employed to construct signatures with translational value. Three highly performing and low-feature-number signatures were built: (1) A 5-gene signature discriminating BrCa patients from healthy individuals (area under the curve (AUC): 0.994 (0.982–1.000)). (2) A 3-gene signature identifying BrCa metastatic disease (AUC: 0.986 (0.921–1.000)). (3) Six equivalent 5-gene signatures diagnosing early disease (AUC: 0.973 (0.920–1.000)). Validation in independent patient groups verified performance. Bioinformatic tools for functional analysis and protein interaction prediction were also employed. All protein encoding features included in the signatures were associated with BrCa-related pathways. Functional analysis of DMGs highlighted the regulation of transcription as the main biological process, the nucleus as the main cellular component and transcription factor activity and sequence-specific DNA binding as the main molecular functions. Overall, three high-performance diagnostic/prognostic signatures were built and are readily available for improving BrCa precision management upon prospective clinical validation. Revisiting archived methylomes through novel bioinformatic approaches revealed significant clarifying knowledge for the contribution of gene methylation events in breast carcinogenesis.

2D MXenes with antiviral and immunomodulatory properties: A pilot study against SARS-CoV-2.

Two-dimensional transition metal carbides/carbonitrides known as MXenes are rapidly growing as multimodal nanoplatforms in biomedicine. Here, taking SARS-CoV-2 as a model, we explored the antiviral properties and immune-profile of a large panel of four highly stable and well-characterized MXenes - Ti3C2Tx, Ta4C3T x , Mo2Ti2C3T x and Nb4C3T x . To start with antiviral assessment, we first selected and deeply analyzed four different SARS-CoV-2 genotypes, common in most countries and carrying the wild type or mutated spike protein. When inhibition of the viral infection was tested in vitro with four viral clades, Ti3C2T x in particular, was able to significantly reduce infection only in SARS-CoV-2/clade GR infected Vero E6 cells. This difference in the antiviral activity, among the four viral particles tested, highlights the importance of considering the viral genotypes and mutations while testing antiviral activity of potential drugs and nanomaterials. Among the other MXenes tested, Mo2Ti2C3T x also showed antiviral properties. Proteomic, functional annotation analysis and comparison to the already published SARS-CoV-2 protein interaction map revealed that MXene-treatment exerts specific inhibitory mechanisms. Envisaging future antiviral MXene-based drug nano-formulations and considering the central importance of the immune response to viral infections, the immune impact of MXenes was evaluated on human primary immune cells by flow cytometry and single-cell mass cytometry on 17 distinct immune subpopulations. Moreover, 40 secreted cytokines were analyzed by Luminex technology. MXene immune profiling revealed i) the excellent bio and immune compatibility of the material, as well as the ability of MXene ii) to inhibit monocytes and iii) to reduce the release of pro-inflammatory cytokines, suggesting an anti-inflammatory effect elicited by MXene. We here report a selection of MXenes and viral SARS-CoV-2 genotypes/mutations, a series of the computational, structural and molecular data depicting deeply the SARS-CoV-2 mechanism of inhibition, as well as high dimensional single-cell immune-MXene profiling. Taken together, our results provide a compendium of knowledge for new developments of MXene-based multi-functioning nanosystems as antivirals and immune-modulators.

Mechanism of Baihe Decoction in the treatment of coronary heart disease based on network pharmacology and molecular docking.

Network pharmacology is widely used in mechanistic studies of traditional Chinese medicines (TCMs). The present study aimed to predict the target and signaling pathway of Baihe Decoction in the intervention of coronary heart disease (CHD) based on a network pharmacology approach and molecular docking. The active ingredients of Baihe Decoction were screened by the Traditional Chinese Medicine Systems Pharmacology (TCMSP), and their potential target genes and proteins in CHD were predicted. The targets were screened out using Online Mendelian Inheritance in Man and the Genecards database. Venn soft was used to obtain the common targets of drugs and diseases. The compound-target-disease network of Baihe Decoction in CHD was constructed in Cytoscape, and the functional protein interaction network was obtained through the STRING database. ClusterProfiler and Pathview were used to perform Gene Ontology function analysis and KEGG pathway enrichment analysis of the effective targets of Baihe Decoction in CHD. Finally, we used MOE software for molecular docking of the compounds to their targets. Fifteen active components of Baihe Decoction in CHD were screened, which corresponded to 145 targets in CHD, including 30 targets with strong correlations. The key targets included Jun, Aktl, MAPK1, RELA, IL6, CXCL8, EGFR, MAPK14, ESR1, and FOS, which were found to play important roles in the treatment of CHD. The results of molecular docking further illustrated the roles that the compounds with quercetin and β-sitosterol play in the treatment of CHD through their interference with AKT1 and MAPK1 target proteins. This study has preliminarily revealed the mechanism of Baihe Decoction in the treatment of CHD. The components of TCM may intervene in the processes of CHD occurrence and development by regulating cardiomyocytes and antioxidative stress, and by participating in inflammation and immune response. Moreover, in the clinical syndrome differentiation of TCM, Baihe Decoction can be used as the main drug to treat CHD and angina pectoris due to qi stagnation and blood stasis caused by emotional discomfort.

FAWMine: An integrated database and analysis platform for fall armyworm genomics.

Fall armyworm (Spodoptera frugiperda), a native insect species in the Americas, is rapidly becoming a major agricultural pest worldwide and is causing great damage to corn, rice, soybeans, and other crops. To control this pest, scientists have accumulated a great deal of high-throughput data of fall armyworm, and nine versions of its genomes and transcriptomes have been published. However, easily accessing and performing integrated analysis of these omics data sets is challenging. Here, we developed the Fall Armyworm Genome Database (FAWMine, http://159.226.67.243:8080/fawmine/) to maintain genome sequences, structural and functional annotations, transcriptomes, co-expression, protein interactions, homologs, pathways, and single-nucleotide variations. FAWMine provides a powerful framework that helps users to perform flexible and customized searching, present integrated data sets using diverse visualization methods, output results tables in a range of file formats, analyze candidate gene lists using multiple widgets, and query data available in other InterMine systems. Additionally, stand-alone JBrowse and BLAST services are also established, allowing the users to visualize RNA-Seq data and search genome and annotated gene sequences. Altogether, FAWMine is a useful tool for querying, visualizing, and analyzing compiled data sets rapidly and efficiently. FAWMine will be continually updated to function as a community resource for fall armyworm genomics and pest control research.

Phosphoproteomic Analysis and Protein-Protein Interaction of Rat Aorta GJA1 and Rat Heart FKBP1A after Secoiridoid Consumption from Virgin Olive Oil: A Functional Proteomic Approach.

Protein functional interactions could explain the biological response of secoiridoids (SECs), main phenolic compounds in virgin olive oil (VOO). The aim was to assess protein-protein interactions (PPIs) of the aorta gap junction alpha-1 (GJA1) and the heart peptidyl-prolyl cis-trans isomerase (FKBP1A), plus the phosphorylated heart proteome, to describe new molecular pathways in the cardiovascular system in rats using nanoliquid chromatography coupled with mass spectrometry. PPIs modified by SECs and associated with GJA1 in aorta rat tissue were calpain, TUBA1A, and HSPB1. Those associated with FKBP1A in rat heart tissue included SUCLG1, HSPE1, and TNNI3. In the heart, SECs modulated the phosphoproteome through the main canonical pathways PI3K/mTOR signaling (AKT1S1 and GAB2) and gap junction signaling (GAB2 and GJA1). PPIs associated with GJA1 and with FKBP1A, the phosphorylation of GAB2, and the dephosphorylation of GJA1 and AKT1S1 in rat tissues are promising protein targets promoting cardiovascular protection to explain the health benefits of VOO.

Proteomic Approaches to Study SARS-CoV-2 Biology and COVID-19 Pathology.

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), was declared a pandemic infection in March 2020. As of December 2020, two COVID-19 vaccines have been authorized for emergency use by the U.S. Food and Drug Administration, but there are no effective drugs to treat COVID-19, and pandemic mitigation efforts like physical distancing have had acute social and economic consequences. In this perspective, we discuss how the proteomic research community can leverage technologies and expertise to address the pandemic by investigating four key areas of study in SARS-CoV-2 biology. Specifically, we discuss how (1) mass spectrometry-based structural techniques can overcome limitations and complement traditional structural approaches to inform the dynamic structure of SARS-CoV-2 proteins, complexes, and virions; (2) virus–host protein–protein interaction mapping can identify the cellular machinery required for SARS-CoV-2 replication; (3) global protein abundance and post-translational modification profiling can characterize signaling pathways that are rewired during infection; and (4) proteomic technologies can aid in biomarker identification, diagnostics, and drug development in order to monitor COVID-19 pathology and investigate treatment strategies. Systems-level high-throughput capabilities of proteomic technologies can yield important insights into SARS-CoV-2 biology that are urgently needed during the pandemic, and more broadly, can inform coronavirus virology and host biology.

MTFR2, A Potential Biomarker for Prognosis and Immune Infiltrates, Promotes Progression of Gastric Cancer Based on Bioinformatics Analysis and Experiments.

Background: Mitochondrial fission regulator 2 (MTFR2) which can promote mitochondrial fission, has recently been reported to be involved in tumorigenesis. However, little is known about its expression levels and function in gastric cancer (GC). This study aims to clarify the role of MTFR2 in GC.Methods:We firstly determined the expression level and prognostic value of MTFR2 in GC by integrated bioinformatics (Oncomine, GEPIA, Kaplan-Meier Plotter database) and experimental approaches (RT-qPCR, western blot, immunohistochemistry). After constructing stable down-regulated GC cells, the biological functions of MTFR2 in vitro and in vivo were studied through cell clone formation, wound healing, transwell and tumor formation experiments.To understand the reason for the high expression of MTFR2 in GC, copy number alternation, promoter methylation and mutation of MTFR2 were detected by UALCAN and cBioPortal. TargetScanHuman and PROMO databases were also used to explore the miRNAs and transcription factors of MTFR2, and the regulatory network was visualized by Cytoscape. LinkedOmics was used to detect the co-expression profile, and then these co-expressed genes were used for gene oncology function and pathway enrichment analysis to deepen the understanding of MTFR2 mechanism. The protein interaction network of MTFR2 was constructed by the GeneMANIA platform. Docking study of the binding mode was conducted by H DOCK webserver, and PYMOL is used for visualization, and analysis. TIMER database was used to explore the correlation between MTFR2 expression level and immune cells infiltration and gene markers of tumor infiltrating immune cells.Results: We demonstrated that MTFR2 was up-regulated in GC, and its overexpression led to poorer prognosis. MTFR2 downregulation inhibited the proliferation, migration, and invasion of GC cells in vitro and in vivo. By bioinformatics analysis, we identified the possible factors in MTFR2 overexpression. Moreover, function and pathway enrichment analyses found that MTFR2 was involved in chromosome segregation, catalytic activity, cell cycle, and ribonucleic acid transport. A MTFR2-protein interaction network revealed a potential direct protein interaction between MTFR2 and protein kinase adenosine-monophosphate-activated catalytic subunit alpha 1 (PRKAA1), and their potential binding site was predicted in a molecular docking model. In addition, we also found that MTFR2 may be correlated with immune infiltration in GC.Conclusions: Our study has effectively revealed the expression, prognostic value, potential functional networks, protein interactions and immune infiltration of MTFR2 in GC. Altogether, our data identify the possible underlying mechanisms of MTFR2 and suggest that MTFR2 may be a prognostic biomarker and therapeutic target in GC.

AnnoLnc: A One-Stop Portal to Systematically Annotate Novel Human Long Noncoding RNAs.

While more than a hundred thousand long noncoding RNAs (lncRNAs) have been identified in human genome, their biological functions and regulation are largely elusive. Here we present AnnoLnc, a one-stop online annotation portal for human lncRNAs ( http://annolnc1.gao-lab.org/ ). As the first (and the most comprehensive) Web server to provide on-the-fly annotation for novel human lncRNAs, AnnoLnc exploits more than 700 data sources to annotate inputted lncRNA systematically, spanning genomic location, secondary structure, expression patterns, coexpression-based functional annotation, transcriptional regulation, miRNA interaction, protein interaction, genetic association, and evolution. Moreover, in addition to a user-friendly Web interface, AnnoLnc can also be integrated into existing pipelines by either a set of JSON-based web service APIs or a stand-alone version for Linux server.

Activity-Based Protein Profiling Reveals Potential Dasatinib Targets in Gastric Cancer.

Dasatinib is a multi-target kinase inhibitor, whose targets include BCR-ABL, SRC family kinases, and various cancer kinases. The elevated SRC activity in gastric cancer (GC) has prompted the need for the therapeutic application of dasatinib in GC. We observed that the efficacy of dasatinib varied with the GC cell lines. The differential effect of dasatinib was not correlated with the basal SRC activity of each cell line. Moreover, the GC cell lines showing the strong antitumor effects of dasatinib were refractory to other SRC inhibitors, i.e., bosutinib and saracatinib, suggesting that unexpected dasatinib’s targets could exist. To profile the targets of dasatinib in GC, we performed activity-based protein profiling (ABPP) via mass spectrometry using a desthiobiotin-ATP probe. We identified 29 and 18 kinases as potential targets in dasatinib-sensitive (SNU-216, MKN-1) and -resistant (SNU-484, SNU-601) cell lines, respectively. The protein–protein interaction mapping of the differential drug targets in dasatinib-sensitive and -resistant GC using the STRING database suggested that dasatinib could target cellular energy homeostasis in the drug-sensitive GC. RNAi screening for identified targets indicated p90RSK could be a novel dasatinib target, which is important for maintaining the viability and motility of GC cells. Further functional validation of dasatinib off-target actions will provide more effective therapeutic options for GC.

Transcriptome Remodeling in Gradual Development of Inverse Resistance between Paclitaxel and Cisplatin in Ovarian Cancer Cells

Resistance to anti-cancer drugs is the main challenge in oncology. In pre-clinical studies, established cancer cell lines are primary tools in deciphering molecular mechanisms of this phenomenon. In this study, we proposed a new, transcriptome-focused approach, utilizing a model of isogenic cancer cell lines with gradually changing resistance. We analyzed trends in gene expression in the aim to find out a scaffold of resistance development process. The ovarian cancer cell line A2780 was treated with stepwise increased concentrations of paclitaxel (PTX) to generate a series of drug resistant sublines. To monitor transcriptome changes we submitted them to mRNA-sequencing, followed by the identification of differentially expressed genes (DEGs), principal component analysis (PCA), and hierarchical clustering. Functional interactions of proteins, encoded by DEGs, were analyzed by building protein-protein interaction (PPI) networks. We obtained human ovarian cancer cell lines with gradually developed resistance to PTX and collateral sensitivity to cisplatin (CDDP) (inverse resistance). In their transcriptomes, we identified two groups of DEGs: (1) With fluctuations in expression in the course of resistance acquiring; and (2) with a consistently changed expression at each stage of resistance development, constituting a scaffold of the process. In the scaffold PPI network, the cell cycle regulator—polo-like kinase 2 (PLK2); proteins belonging to the tumor necrosis factor (TNF) ligand and receptor family, as well as to the ephrin receptor family were found, and moreover, proteins linked to osteo- and chondrogenesis and the nervous system development. Our cellular model of drug resistance allowed for keeping track of trends in gene expression and studying this phenomenon as a process of evolution, reflected by global transcriptome remodeling. This approach enabled us to explore novel candidate genes and surmise that abrogation of the osteomimic phenotype in ovarian cancer cells might occur during the development of inverse resistance between PTX and CDDP.

RNA sequencing identifies transcriptional changes in the rabbit larynx in response to low humidity challenge

BackgroundVoice disorders are a worldwide problem impacting human health, particularly for occupational voice users. Avoidance of surface dehydration is commonly prescribed as a protective factor against the development of dysphonia. The available literature inconclusively supports this practice and a biological mechanism for how surface dehydration of the laryngeal tissue affects voice has not been described. In this study, we used an in vivo male New Zealand white rabbit model to elucidate biological changes based on gene expression within the vocal folds from surface dehydration. Surface dehydration was induced by exposure to low humidity air (18.6% + 4.3%) for 8 h. Exposure to moderate humidity (43.0% + 4.3%) served as the control condition. Ilumina-based RNA sequencing was performed and used for transcriptome analysis with validation by RT-qPCR.ResultsThere were 103 statistically significant differentially expressed genes identified through Cuffdiff with 61 genes meeting significance by both false discovery rate and fold change. Functional annotation enrichment and predicted protein interaction mapping showed enrichment of various loci, including cellular stress and inflammatory response, ciliary function, and keratinocyte development. Eight genes were selected for RT-qPCR validation. Matrix metalloproteinase 12 (MMP12) and macrophage cationic peptide 1 (MCP1) were significantly upregulated and an epithelial chloride channel protein (ECCP) was significantly downregulated after surface dehydration by RNA-Seq and RT-qPCR. Suprabasin (SPBN) and zinc activated cationic channel (ZACN) were marginally, but non-significantly down- and upregulated as evidenced by RT-qPCR, respectively.ConclusionsThe data together support the notion that surface dehydration induces physiological changes in the vocal folds and justifies targeted analysis to further explore the underlying biology of compensatory fluid/ion flux and inflammatory mediators in response to airway surface dehydration.

Antagonism of Type I Interferon by Severe Acute Respiratory Syndrome Coronavirus 2.

The coronavirus disease 2019 (COVID-19) pandemic is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), warranting urgent study of the molecular mechanisms of SARS-CoV-2 infection and host immune response. Type I interferon (IFN-I) is a key component of host innate immune system responsible for eliminating the virus at the early stage of infection. In contrast, SARS-CoV-2 has evolved multiple strategies to evade innate immune response to facilitate viral replication, transmission, and pathogenesis. This review summarizes the recent progresses on SARS-CoV-2 proteins that antagonize host IFN-I production and/or signaling. These progresses have provided knowledge for new vaccine and antiviral development to prevent and control COVID-19.

Combined Proteomic and Genetic Interaction Mapping Reveals New RAS Effector Pathways and Susceptibilities.

Activating mutations in RAS GTPases drive many cancers, but limited understanding of less-studied RAS interactors, and of the specific roles of different RAS interactor paralogs, continues to limit target discovery. We developed a multistage discovery and screening process to systematically identify genes conferring RAS-related susceptibilities in lung adenocarcinoma. Using affinity purification mass spectrometry, we generated a protein-protein interaction map of RAS interactors and pathway components containing hundreds of interactions. From this network, we constructed a CRISPR dual knockout library targeting 119 RAS-related genes that we screened for KRAS-dependent genetic interactions (GI). This approach identified new RAS effectors, including the adhesion controller RADIL and the endocytosis regulator RIN1, and >250 synthetic lethal GIs, including a potent KRAS-dependent interaction between RAP1GDS1 and RHOA. Many GIs link specific paralogs within and between gene families. These findings illustrate the power of multiomic approaches to uncover synthetic lethal combinations specific for hitherto untreatable cancer genotypes. SIGNIFICANCE: We establish a deep network of protein-protein and genetic interactions in the RAS pathway. Many interactions validated here demonstrate important specificities and redundancies among paralogous RAS regulators and effectors. By comparing synthetic lethal interactions across KRAS-dependent and KRAS-independent cell lines, we identify several new combination therapy targets for RAS-driven cancers.This article is highlighted in the In This Issue feature, p. 1775.

Single-Protein Tracking to Study Protein Interactions During Integrin-Based Migration.

Cell migration is a complex biophysical process which involves the coordination of molecular assemblies including integrin-dependent adhesions, signaling networks andforce-generating cytoskeletal structures incorporating bothactin polymerization and myosin activity. During the last decades, proteomic studies have generated impressive protein-protein interaction maps, although the subcellular location, duration, strength, sequence, and nature of these interactions are still concealed. In this chapter we describe how recent developments in superresolution microscopy (SRM) and single-protein tracking (SPT) start to unravel protein interactions and actions in subcellular molecular assemblies driving cell migration.

Expert Variant Curation Combined with in-Silico analysis for Clinical Interpretation of BCL2 variants in Resistance to BCL2 Inhibitors in Chronic Lymphocytic Leukemia/ Small Lymphocytic Lymphoma

Venetoclax is an oral, highly selective, BCL2 inhibitor approved by the FDA for use in chronic lymphocytic leukemia/small lymphocytic lymphoma and acute myeloid leukemia. Despite favorable responses, multiple biological mechanisms lead to treatment resistance. One such mechanism includes somatic mutations in the BCL2 gene. Multiple lines of evidence suggest that hot-spot mutations in BCL2 such as Gly101Val induce treatment resistance by disrupting the binding of BCL2 to the BCL2 inhibitors such as venetoclax. Further, widespread use of high-throughput NGS technologies has identified multiple BCL2 mutations and additional concurrent molecular alterations at various variant allele frequencies in patients with progression while undergoing venetoclax therapy. In order to understand and determine the clinical significance of each of these mutations, careful expert curation and integration into somatic variant annotation AMP/ASCO/CAP guidelines is needed. Further, curation of those somatic variants that may not have sufficient functional evidence in literature may benefit from additional tools such as in silico analysis. To address these issues, we have undertaken an effort to integrate the contributions of a multidisciplinary expert panel (clinical laboratory diagnosticians, oncologists, biomedical informaticians and lab-based researchers) for curation of BCL2 variants in hematological malignancies under the umbrella of ClinGen, an NIH/NHGRI funded consortium to establish standards and centralized resources for assessing the clinical significance of gene variants. Within the ClinGen Somatic Cancer Clinical Domain Working Group (CDWG) ((https://www.clinicalgenome.org/working-groups/somatic/), the somatic hematological malignancy taskforce has identified 56 peer-reviewed publications on BCL2 inhibitors (Jan 2014 to June 2020). The functional evidence contained within these publications was curated using CIViC (Clinical Interpretation of Variants in Cancer, civicdb.org), an open access, crowdsourced aggregation of expert curated evidence. Only a fraction of the somatic variants identified in BCL2 has established functional evidence on variant induced disruption to Venetoclax inhibition. Curation of these remaining variants of unknown significance (VUS) only have in silico functional assays to provide evidence on their potential resistance to Venetoclax. The current curation guidelines do not consider in silico prediction as a strong line of evidence for the interpretation of somatic sequence variants, however this recommendation is meant to interpret generalized in silico predictors and not robust computational models of specific protein function. The latter are more comparable to an experimental functional assay, and provide curators with more trustworthy computational assessments of disruption to protein specific functions. In order to assess their potential to integrate and supplement experimental evidence, the interaction of Ventoclax with several drug resistant BCL2 variants was simulated using AutoDock Vina (J Comput Chem. 2010;31(2):455-61). Facilitated by the SNP2SIM workflow (BMC Bioinformatics. 2019;20(1):171), the relative impact on binding energy was compared to the wildtype system. The in silico binding assay accurately predicted resistance (Fig 1), and demonstrates the utility of applying these methods to the large number of VUS in BCL2. In conclusion, the evidence-based expert curation of BCL2 variants provides a standardized approach for reporting and interpretation across all labs. For those variants (Tier 3) with limited published evidence, computational models that can predict specific changes to functional protein interactions can provide additional tools to the expert curators. Development and incorporation of these tools into curation guidelines requires the refinement of the predictive models through focused validation studies. Disclosures No relevant conflicts of interest to declare.