GPS-Prot: a web-based visualization platform for integrating host-pathogen interaction data

The TRIM4 E3 ubiquitin ligase degrades TPL2 and is modulated by oncogenic KRAS

Loss-of-function mutations in the C terminus of TPL2 kinase promote oncogenesis by impeding its proteasomal degradation, leading to sustained protein expression. However, the degradation mechanism for TPL2 has remained elusive. Through proximity-dependent biotin identification (BioID), we uncovered tripartite motif-containing 4 (TRIM4) as the E3 ligase that binds and degrades TPL2 by polyubiquitination of lysines 415 and 439. The naturally occurring TPL2 mutants R442H and E188K exhibit impaired TRIM4 binding, enhancing their stability. We further discovered that TRIM4 itself is stabilized by another E3 ligase, TRIM21, which in turn is regulated by KRAS. Mutant KRAS recruits RNF185 to degrade TRIM21 and subsequently TRIM4, thereby stabilizing TPL2. In the presence of mutant KRAS, TPL2 phosphorylates and degrades GSK3β, resulting in β-catenin stabilization and activation of the Wnt pathway. These findings elucidate the physiological mechanisms regulating TPL2 and its exploitation by mutant KRAS, underscoring the need to develop TPL2 inhibitors for KRAS-mutant cancers.

Binding Promiscuity of Therapeutic Factor VIII

The binding promiscuity of proteins defines their ability to indiscriminately bind multiple unrelated molecules. Binding promiscuity is implicated, at least in part, in the off-target reactivity, nonspecific biodistribution, immunogenicity, and/or short half-life of potentially efficacious protein drugs, thus affecting their clinical use. In this review, we discuss the current evidence for the binding promiscuity of factor VIII (FVIII), a protein used for the treatment of hemophilia A, which displays poor pharmacokinetics, and elevated immunogenicity. We summarize the different canonical and noncanonical interactions that FVIII may establish in the circulation and that could be responsible for its therapeutic liabilities. We also provide information suggesting that the FVIII light chain, and especially its C1 and C2 domains, could play an important role in the binding promiscuity. We believe that the knowledge accumulated over years of FVIII usage could be exploited for the development of strategies to predict protein binding promiscuity and therefore anticipate drug efficacy and toxicity. This would open a mutational space to reduce the binding promiscuity of emerging protein drugs while conserving their therapeutic potency.

THOC6 is a novel biomarker of glioma and a target of anti-glioma drugs: An analysis based on bioinformatics and molecular docking

Glioma is a typical malignant tumor of the nervous system. It is of great significance to identify new biomarkers for accurate diagnosis of glioma. In this context, THOC6 has been studied as a highly diagnostic prognostic biomarker, which contributes to improve the dilemma in diagnosing gliomas. We used online databases and a variety of statistical methods, such as Wilcoxon rank sum test, Dunn test and t test. We analyzed the mutation, location and expression profile of THOC6, revealing the network of THOC6 interaction with disease. Wilcoxon rank sum test showed that THOC6 is highly expressed in gliomas (P < 0.001). Dunn test, Wilcoxon rank sum test and t test showed that THOC6 expression was correlated with multiple clinical features. Logistic regression analysis further confirmed that THOC6 gene expression was a categorical dependent variable related to clinical features of poor prognosis. Kaplan-Meier survival analysis showed that the overall survival (OS) of glioma patients with high expression of THOC6 was poor (P < 0.001). Both univariate (P < 0.001) and multivariate (P = 0.04) Cox analysis confirmed that THOC6 gene expression was an independent risk factor for OS in patients with glioma. ROC curve analysis showed that THOC6 had a high diagnostic value in glioma (AUC = 0.915). Based on this, we constructed a nomogram to predict patient survival. Enrichment analysis showed that THOC6 expression was associated with multiple signal pathways. Immuno-infiltration analysis showed that the expression of THOC6 in glioma was closely related to the infiltration level of multiple immune cells. Molecular docking results showed that THOC6 might be the target of anti-glioma drugs. THOC6 is a novel diagnostic factor and prognostic biomarker of glioma.

Alternative polyadenylation quantitative trait methylation mapping in human cancers provides clues into the molecular mechanisms of APA

Genetic variants are involved in the orchestration of alternative polyadenylation (APA) events, while the role of DNA methylation in regulating APA remains unclear. We generated a comprehensive atlas of APA quantitative trait methylation sites (apaQTMs) across 21 different types of cancer (1,612 to 60,219 acting in cis and 4,448 to 142,349 in trans). Potential causal apaQTMs in non-cancer samples were also identified. Mechanistically, we observed a strong enrichment of cis-apaQTMs near polyadenylation sites (PASs) and both cis- and trans-apaQTMs in proximity to transcription factor (TF) binding regions. Through the integration of ChIP-signals and RNA-seq data from cell lines, we have identified several regulators of APA events, acting either directly or indirectly, implicating novel functions of some important genes, such as TCF7L2, which is known for its involvement in type 2 diabetes and cancers. Furthermore, we have identified a vast number of QTMs that share the same putative causal CpG sites with five different cancer types, underscoring the roles of QTMs, including apaQTMs, in the process of tumorigenesis. DNA methylation is extensively involved in the regulation of APA events in human cancers. In an attempt to elucidate the potential underlying molecular mechanisms of APA by DNA methylation, our study paves the way for subsequent experimental validations into the intricate biological functions of DNA methylation in APA regulation and the pathogenesis of human cancers. To present a comprehensive catalog of apaQTM patterns, we introduce the Pancan-apaQTM database, available at https://pancan-apaqtm-zju.shinyapps.io/pancanaQTM/.

Understanding functions of eEF1 translation elongation factors beyond translation. A proteomic approach

Mammalian translation elongation factors eEF1A1 and eEF1A2 are 92% homologous isoforms whose mutually exclusive tissue-specific expression is regulated during development. The isoforms have similar translation functionality, but show differences in spatial organization and participation in various processes, such as oncogenesis and virus reproduction. The differences may be due to their ability to interact with isoform-specific partner proteins. We used the identified sets of eEF1A1 or eEF1A2 partner proteins to identify cell complexes and/or processes specific to one particular isoform. As a result, we found isoform-specific interactions reflecting the involvement of different eEF1A isoforms in different cellular processes, including actin-related, chromatin-remodeling, ribonuclease H2, adenylyl cyclase, and Cul3-RING ubiquitin ligase complexes as well as initiation of mitochondrial transcription. An essential by-product of our analysis is the elucidation of a number of cellular processes beyond protein biosynthesis, where both isoforms appear to participate such as large ribosomal subunit biogenesis, mRNA splicing, DNA mismatch repair, 26S proteasome activity, P-body and exosomes formation, protein targeting to the membrane. This information suggests that a relatively high content of eEF1A in the cell may be necessary not only to maintain efficient translation, but also to ensure its participation in various cellular processes, where some roles of eEF1A have not yet been described. We believe that the data presented here will be useful for deciphering new auxiliary functions of eEF1A and its isoforms, and provide a new look at the known non-canonical functions of this main component of the human translation-elongation machinery.

Antibody-mediated neutralization of galectin-3 as a strategy for the treatment of systemic sclerosis

Systemic sclerosis (SSc) is an autoimmune, inflammatory and fibrotic disease with limited treatment options. Developing new therapies is therefore crucial to address patient needs. To this end, we focused on galectin-3 (Gal-3), a lectin known to be associated with several pathological processes seen in SSc. Using RNA sequencing of whole-blood samples in a cross-sectional cohort of 249 patients with SSc, Gal-3 and its interactants defined a strong transcriptomic fingerprint associated with disease severity, pulmonary and cardiac malfunctions, neutrophilia and lymphopenia. We developed new Gal-3 neutralizing monoclonal antibodies (mAb), which were then evaluated in a mouse model of hypochlorous acid (HOCl)-induced SSc. We show that two of these antibodies, D11 and E07, reduced pathological skin thickening, lung and skin collagen deposition, pulmonary macrophage content, and plasma interleukin-5 and -6 levels. Moreover, E07 changed the transcriptional profiles of HOCl-treated mice, resulting in a gene expression pattern that resembled that of control mice. Similarly, pathological pathways engaged in patients with SSc were counteracted by E07 in mice. Collectively, these findings demonstrate the translational potential of Gal-3 blockade as a therapeutic option for SSc.

Genome-wide association analysis and Mendelian randomization proteomics identify drug targets for heart failure

We conduct a large-scale meta-analysis of heart failure genome-wide association studies (GWAS) consisting of over 90,000 heart failure cases and more than 1 million control individuals of European ancestry to uncover novel genetic determinants for heart failure. Using the GWAS results and blood protein quantitative loci, we perform Mendelian randomization and colocalization analyses on human proteins to provide putative causal evidence for the role of druggable proteins in the genesis of heart failure. We identify 39 genome-wide significant heart failure risk variants, of which 18 are previously unreported. Using a combination of Mendelian randomization proteomics and genetic cis-only colocalization analyses, we identify 10 additional putatively causal genes for heart failure. Findings from GWAS and Mendelian randomization-proteomics identify seven (CAMK2D, PRKD1, PRKD3, MAPK3, TNFSF12, APOC3 and NAE1) proteins as potential targets for interventions to be used in primary prevention of heart failure.

mTORC2 interactome and localization determine aggressiveness of high-grade glioma cells through association with gelsolin

mTOR complex 2 (mTORC2) has been implicated as a key regulator of glioblastoma cell migration. However, the roles of mTORC2 in the migrational control process have not been entirely elucidated. Here, we elaborate that active mTORC2 is crucial for GBM cell motility. Inhibition of mTORC2 impaired cell movement and negatively affected microfilament and microtubule functions. We also aimed to characterize important players involved in the regulation of cell migration and other mTORC2-mediated cellular processes in GBM cells. Therefore, we quantitatively characterized the alteration of the mTORC2 interactome under selective conditions using affinity purification-mass spectrometry in glioblastoma. We demonstrated that changes in cell migration ability specifically altered mTORC2-associated proteins. GSN was identified as one of the most dynamic proteins. The mTORC2-GSN linkage was mostly highlighted in high-grade glioma cells, connecting functional mTORC2 to multiple proteins responsible for directional cell movement in GBM. Loss of GSN disconnected mTORC2 from numerous cytoskeletal proteins and affected the membrane localization of mTORC2. In addition, we reported 86 stable mTORC2-interacting proteins involved in diverse molecular functions, predominantly cytoskeletal remodeling, in GBM. Our findings might help expand future opportunities for predicting the highly migratory phenotype of brain cancers in clinical investigations.

LSM12 facilitates the progression of colorectal cancer by activating the WNT/CTNNB1 signaling pathway

Aberrant activation of the WNT signaling pathway is a joint event in colorectal cancer (CRC), but the molecular mechanism is still unclear. Recently, RNA-splicing factor LSM12 (like-Sm protein 12) is highly expressed in CRC tissues. This study aimed to verify whether LSM12 is involved in regulating CRC progression via regulating the WNT signaling pathway. Here, we found that LSM12 is highly expressed in CRC patient-derived tissues and cells. LSM12 is involved in the proliferation, invasion, and apoptosis of CRC cells, similar to the function of WNT signaling in CRC. Furthermore, protein interaction simulation and biochemical experiments proved that LSM12 directly binds to CTNNB1 (also known as β-Catenin) and regulates its protein stability to affect the CTTNB1-LEF1-TCF1 transcriptional complex formation and the associated WNT downstream signaling pathway. LSM12 depletion in CRC cells decreased the in vivo tumor growth through repression of cancer cell growth and acceleration of cancer cell apoptosis. Taken together, we suggest that the high expression of LSM12 is a novel factor leading to aberrant WNT signaling activation, and that strategies targeting this molecular mechanism may contribute to developing a new therapeutic method for CRC.

Multi-omics analyses of CD276 in pan-cancer reveals its clinical prognostic value in glioblastoma and other major cancer types

BACKGROUND

CD276 (also known as B7-H3) is one of the most important immune checkpoints of the CD28 and B7 superfamily, and its abnormal expression is closely associated with various types of cancer. It has been shown that CD276 is able to inhibit the function of T cells, and that this gene may potentially be a promising immunotherapy target for different types of cancer.

METHODS

Since few systematic studies have been published on the role of CD276 in cancer to date, the present study has employed single-cell sequencing and bioinformatics methods to analyze the expression patterns, clinical significance, prognostic value, epigenetic alterations, DNA methylation level, tumor immune cell infiltration and immune functions of CD276 in different types of cancer. In order to analyze the potential underlying mechanism of CD276 in glioblastoma (GBM) to assess its prognostic value, the LinkedOmics database was used to explore the biological function and co-expression pattern of CD276 in GBM, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed. In addition, a simple validation of the above analyses was performed using reverse transcription-quantitative (RT-q)PCR assay.

RESULTS

The results revealed that CD276 was highly expressed, and was often associated with poorer survival and prognosis, in the majority of different types of cancer. In addition, CD276 expression was found to be closely associated with T cell infiltration, immune checkpoint genes and immunoregulatory interactions between lymphoid and a non-lymphoid cell. It was also shown that the CD276 expression network exerts a wide influence on the immune activation of GBM. The expression of CD276 was found to be positively correlated with neutrophil-mediated immunity, although it was negatively correlated with the level of neurotransmitters, neurotransmitter transport and the regulation of neuropeptide signaling pathways in GBM. It is noteworthy that CD276 expression was found to be significantly higher in GBM compared with normal controls according to the RT-qPCR analysis, and the co-expression network, biological function and chemotherapeutic drug sensitivity of CD276 in GBM were further explored. In conclusion, the findings of the present study have revealed that CD276 is strongly expressed and associated with poor prognosis in most types of cancer, including GBM, and its expression is strongly associated with T-cell infiltration, immune checkpoint genes, and immunomodulatory interactions between lymphocytes and non-lymphoid cells.

CONCLUSIONS

Taken together, based on our systematic analysis, our findings have revealed important roles for CD276 in different types of cancers, especially GBM, and CD276 may potentially serve as a biomarker for cancer.

Multiomics characteristics and immunotherapeutic potential of EZH2 in pan-cancer

Enhancer of zeste homolog 2 (EZH2) is a significant epigenetic regulator that plays a critical role in the development and progression of cancer. However, the multiomics features and immunological effects of EZH2 in pan-cancer remain unclear. Transcriptome and clinical raw data of pan-cancer samples were acquired from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and subsequent data analyses were conducted by using R software (version 4.1.0). Furthermore, numerous bioinformatics analysis databases also reapplied to comprehensively explore and elucidate the oncogenic mechanism and therapeutic potential of EZH2 from pan-cancer insight. Finally, quantitative reverse transcription polymerase chain reaction and immunohistochemical assays were performed to verify the differential expression of EZH2 gene in various cancers at the mRNA and protein levels. EZH2 was widely expressed in multiple normal and tumor tissues, predominantly located in the nucleoplasm. Compared with matched normal tissues, EZH2 was aberrantly expressed in most cancers either at the mRNA or protein level, which might be caused by genetic mutations, DNA methylation, and protein phosphorylation. Additionally, EZH2 expression was correlated with clinical prognosis, and its up-regulation usually indicated poor survival outcomes in cancer patients. Subsequent analysis revealed that EZH2 could promote tumor immune evasion through T-cell dysfunction and T-cell exclusion. Furthermore, expression of EZH2 exhibited a strong correlation with several immunotherapy-associated responses (i.e., immune checkpoint molecules, tumor mutation burden (TMB), microsatellite instability (MSI), mismatch repair (MMR) status, and neoantigens), suggesting that EZH2 appeared to be a novel target for evaluating the therapeutic efficacy of immunotherapy.

CCDC86 is a novel Ki-67-interacting protein important for cell division

The chromosome periphery is a network of proteins and RNAs that coats the outer surface of mitotic chromosomes. Despite the identification of new components, the functions of this complex compartment are poorly characterised. In this study, we identified a novel chromosome periphery-associated protein, CCDC86 (also known as cyclon). Using a combination of RNA interference, microscopy and biochemistry, we studied the functions of CCDC86 in mitosis. CCDC86 depletion resulted in partial disorganisation of the chromosome periphery with alterations in the localisation of Ki-67 (also known as MKI67) and nucleolin (NCL), and the formation of abnormal cytoplasmic aggregates. Furthermore, CCDC86-depleted cells displayed errors in chromosome alignment, altered spindle length and increased apoptosis. These results suggest that, within the chromosome periphery, different subcomplexes that include CCDC86, nucleolin and B23 (nucleophosmin or NPM1) are required for mitotic spindle regulation and correct kinetochore-microtubule attachments, thus contributing to chromosome segregation in mitosis. Moreover, we identified CCDC86 as a MYCN-regulated gene, the expression levels of which represent a powerful marker for prognostic outcomes in neuroblastoma.

Bioinformatics-based analysis of the roles of sex hormone receptors in endometriosis development

Endometriosis is a hormone-dependent disease in women of reproductive age and seriously affects women's health. To analyze the involvement of sex hormone receptors in endometriosis development, we performed bioinformatics analysis using four datasets derived from the Gene Expression Omnibus (GEO) database, which may help us understand the mechanisms by which the sex hormones act in vivo in endometriosis patients. The enrichment analysis and protein-protein interaction (PPI) analysis of the differentially expressed genes (DEGs) revealed that there are different key genes and pathways involved in eutopic endometrium aberrations of endometriosis patients and endometriotic lesions, and sex hormone receptors, including androgen receptor (AR), progesterone receptor (PGR) and estrogen receptor 1 (ESR1), may play important roles in endometriosis development. Androgen receptor (AR), as the hub gene of endometrial aberrations in endometriotic patients, showed positive expression in the main cell types for endometriosis development, and its decreased expression in the endometrium of endometriotic patients was also confirmed by immunohistochemistry (IHC). The nomogram model established based on it displayed good predictive value.

NEIL3 promotes hepatoma epithelial-mesenchymal transition by activating the BRAF/MEK/ERK/TWIST signaling pathway

Hepatocellular carcinoma (HCC) is among the most prevalent visceral neoplasms. So far, reliable biomarkers for predicting HCC recurrence in patients undergoing surgery are far from adequate. In the aim of searching for genetic biomarkers involved in HCC development, we performed analyses of cDNA microarrays and found that the DNA repair gene NEIL3 was remarkably overexpressed in tumors. NEIL3 belongs to the Fpg/Nei protein superfamily, which contains DNA glycosylase activity required for the base excision repair for DNA lesions. Notably, the other Fpg/Nei family proteins NEIL1 and NEIL2, which have the same glycosylase activity as NEIL3, were not elevated in HCC; NEIL3 was specifically induced to participate in HCC development independently of its glycosylase activity. Using RNA-seq and invasion/migration assays, we found that NEIL3 elevated the expression of epithelial-mesenchymal transition (EMT) factors, including the E/N-cadherin switch and the transcription of MMP genes, and promoted the invasion, migration, and stemness phenotypes of HCC cells. Moreover, NEIL3 directly interacted with the key EMT player TWIST1 to enhance invasion and migration activities. In mouse orthotopic HCC studies, NEIL3 overexpression also caused a prominent E-cadherin decrease, tumor volume increase, and lung metastasis, indicating that NEIL3 led to EMT and tumor metastasis in mice. We further found that NEIL3 induced the transcription of MDR1 (ABCB1) and BRAF genes through the canonical E-box (CANNTG) promoter region, which the TWIST1 transcription factor recognizes and binds to, leading to the BRAF/MEK/ERK pathway-mediated cell proliferation as well as anti-cancer drug resistance, respectively. In the HCC cohort, the tumor NEIL3 level demonstrated a high positive correlation with disease-free and overall survival after surgery. In conclusion, NEIL3 activated the BRAF/MEK/ERK/TWIST pathway-mediated EMT and therapeutic resistances, leading to HCC progression. Targeted inhibition of NEIL3 in HCC individuals with NEIL3 induction is a promising therapeutic approach. © 2022 The Pathological Society of Great Britain and Ireland.

Origination of LTR Retroelement-Derived NYNRIN Coincides with Therian Placental Emergence

The emergence of the placenta is a revolutionary event in the evolution of therian mammals, to which some LTR retroelement-derived genes, such as PEG10, RTL1, and syncytin, are known to contribute. However, therian genomes contain many more LTR retroelement-derived genes that may also have contributed to placental evolution. We conducted large-scale evolutionary genomic and transcriptomic analyses to comprehensively search for LTR retroelement-derived genes whose origination coincided with therian placental emergence and that became consistently expressed in therian placentae. We identified NYNRIN as another Ty3/Gypsy LTR retroelement-derived gene likely to contribute to placental emergence in the therian stem lineage. NYNRIN knockdown inhibited the invasion of HTR8/SVneo invasive-type trophoblasts, whereas the knockdown of its nonretroelement-derived homolog KHNYN did not. Functional enrichment analyses suggested that NYNRIN modulates trophoblast invasion by regulating epithelial-mesenchymal transition and extracellular matrix remodeling and that the ubiquitin-proteasome system is responsible for the functional differences between NYNRIN and KHNYN. These findings extend our knowledge of the roles of LTR retroelement-derived genes in the evolution of therian mammals.

The epiphany derived from T-cell–inflamed profiles: Pan-cancer characterization of CD8A as a biomarker spanning clinical relevance, cancer prognosis, immunosuppressive environment, and treatment responses

CD8A encodes the CD8 alpha chain of αβT cells, which has been proposed as a quantifiable indicator for the assessment of CD8⁺ cytotoxic T lymphocytes (CTLs) recruitment or activity and a robust biomarker for anti-PD-1/PD-L1 therapy responses. Nonetheless, the lack of research into the role of CD8A in tumor microenvironment predisposes to limitations in its clinical utilization. In the presented study, multiple computational tools were used to investigate the roles of CD8A in the pan-cancer study, revealing its essential associations with tumor immune infiltration, immunosuppressive environment formation, cancer progression, and therapy responses. Based on the pan-cancer cohorts of the Cancer Genome Atlas (TCGA) database, our results demonstrated the distinctive CD8A expression patterns in cancer tissues and its close associations with the prognosis and disease stage of cancer. We then found that CD8A was correlated with six major immune cell types, and immunosuppressive cells in multiple cancer types. Besides, epigenetic modifications of CD8A were related to CTL levels and T cell dysfunctional states, thereby affecting survival outcomes of specific cancer types. After that, we explored the co-occurrence patterns of CD8A mutation, thus identifying RMND5A, RNF103-CHMP3, CHMP3, CD8B, MRPL35, MAT2A, RGPD1, RGPD2, REEP1, and ANAPC1P1 genes, which co-occurred mutations with CD8A, and are concomitantly implicated in the regulation of cancer-related pathways. Finally, we tested CD8A as a therapeutic biomarker for multiple antitumor agents’ or compounds’ responsiveness on various cancer cell lines and cancer cohorts. Our findings denoted the underlying mechanics of CD8A in reflecting the T-cell-inflamed profiles, which has potential as a biomarker in cancer diagnosis, prognosis, and therapeutic responses.

Pan-cancer analyses identify DCBLD2 as an oncogenic, immunological, and prognostic biomarker

Discoidin, CUB, and LCCL domain-containing protein 2 (DCBLD2) is a two-domain transmembrane protein-coding gene located on chromosome 3, the protein expressed by which acts as the membrane receptor of semaphorin and vascular endothelial growth factor during the development of axons and blood vessels. Although several research evidences at the cellular and clinical levels have associated DCBLD2 with tumorigenesis, nothing is known regarding this gene from a pan-cancer standpoint. In this study, we systematically analyzed the influence of DCBLD2 on prognosis, cancer staging, immune characteristics, and drug sensitivity in a variety of cancers based on a unified and standardized pan-cancer dataset. In addition, we performed GO enrichment analyses and KEGG analyses of DCBLD2-related genes and DCBLD2-binding proteins. Our results showed that DCBLD2 is a potential oncogenic, immunological as well as a prognostic biomarker in terms of pan-cancer, and is expected to contribute to the improvement of tumor prognosis and the development of targeted therapy.

Viral informatics: bioinformatics-based solution for managing viral infections

Several new viral infections have emerged in the human population and establishing as global pandemics. With advancements in translation research, the scientific community has developed potential therapeutics to eradicate or control certain viral infections, such as smallpox and polio, responsible for billions of disabilities and deaths in the past. Unfortunately, some viral infections, such as dengue virus (DENV) and human immunodeficiency virus-1 (HIV-1), are still prevailing due to a lack of specific therapeutics, while new pathogenic viral strains or variants are emerging because of high genetic recombination or cross-species transmission. Consequently, to combat the emerging viral infections, bioinformatics-based potential strategies have been developed for viral characterization and developing new effective therapeutics for their eradication or management. This review attempts to provide a single platform for the available wide range of bioinformatics-based approaches, including bioinformatics methods for the identification and management of emerging or evolved viral strains, genome analysis concerning the pathogenicity and epidemiological analysis, computational methods for designing the viral therapeutics, and consolidated information in the form of databases against the known pathogenic viruses. This enriched review of the generally applicable viral informatics approaches aims to provide an overview of available resources capable of carrying out the desired task and may be utilized to expand additional strategies to improve the quality of translation viral informatics research.

Web-Based Protein Interactions Calculator Identifies Likely Proteome Coevolution with Alzheimer’s Disease-Associated Proteins

Protein–protein functional interactions arise from either transitory or permanent biomolecular associations and often lead to the coevolution of the interacting residues. Although mutual information has traditionally been used to identify coevolving residues within the same protein, its application between coevolving proteins remains largely uncharacterized. Therefore, we developed the Protein Interactions Calculator (PIC) to efficiently identify coevolving residues between two protein sequences using mutual information. We verified the algorithm using 2102 known human protein interactions and 233 known bacterial protein interactions, with a respective 1975 and 252 non-interacting protein controls. The average PIC score for known human protein interactions was 4.5 times higher than non-interacting proteins (p = 1.03 × 10⁻¹⁰⁸) and 1.94 times higher in bacteria (p = 1.22 × 10⁻³⁵). We then used the PIC scores to determine the probability that two proteins interact. Using those probabilities, we paired 37 Alzheimer’s disease-associated proteins with 8608 other proteins and determined the likelihood that each pair interacts, which we report through a web interface. The PIC had significantly higher sensitivity and residue-specific resolution not available in other algorithms. Therefore, we propose that the PIC can be used to prioritize potential protein interactions, which can lead to a better understanding of biological processes and additional therapeutic targets belonging to protein interaction groups.

Molecular Modelling Hurdle in the Next-Generation Sequencing Era

There are challenges in the genetic diagnosis of rare diseases, and pursuing an optimal strategy to identify the cause of the disease is one of the main objectives of any clinical genomics unit. A range of techniques are currently used to characterize the genomic variability within the human genome to detect causative variants of specific disorders. With the introduction of next-generation sequencing (NGS) in the clinical setting, geneticists can study single-nucleotide variants (SNVs) throughout the entire exome/genome. In turn, the number of variants to be evaluated per patient has increased significantly, and more information has to be processed and analyzed to determine a proper diagnosis. Roughly 50% of patients with a Mendelian genetic disorder are diagnosed using NGS, but a fair number of patients still suffer a diagnostic odyssey. Due to the inherent diversity of the human population, as more exomes or genomes are sequenced, variants of uncertain significance (VUSs) will increase exponentially. Thus, assigning relevance to a VUS (non-synonymous as well as synonymous) in an undiagnosed patient becomes crucial to assess the proper diagnosis. Multiple algorithms have been used to predict how a specific mutation might affect the protein's function, but they are far from accurate enough to be conclusive. In this work, we highlight the difficulties of genomic variability determined by NGS that have arisen in diagnosing rare genetic diseases, and how molecular modelling has to be a key component to elucidate the relevance of a specific mutation in the protein's loss of function or malfunction. We suggest that the creation of a multi-omics data model should improve the classification of pathogenicity for a significant amount of the detected genomic variability. Moreover, we argue how it should be incorporated systematically in the process of variant evaluation to be useful in the clinical setting and the diagnostic pipeline.

Network assisted analysis of de novo variants using protein-protein interaction information identified 46 candidate genes for congenital heart disease

De novo variants (DNVs) with deleterious effects have proved informative in identifying risk genes for early-onset diseases such as congenital heart disease (CHD). A number of statistical methods have been proposed for family-based studies or case/control studies to identify risk genes by screening genes with more DNVs than expected by chance in Whole Exome Sequencing (WES) studies. However, the statistical power is still limited for cohorts with thousands of subjects. Under the hypothesis that connected genes in protein-protein interaction (PPI) networks are more likely to share similar disease association status, we developed a Markov Random Field model that can leverage information from publicly available PPI databases to increase power in identifying risk genes. We identified 46 candidate genes with at least 1 DNV in the CHD study cohort, including 18 known human CHD genes and 35 highly expressed genes in mouse developing heart. Our results may shed new insight on the shared protein functionality among risk genes for CHD.

The topologic information in a pathway may be informative to identify functionally interrelated genes and help improve statistical power in DNV studies. Under the hypothesis that connected genes in PPI networks are more likely to share similar disease association status, we developed a novel statistical model that can leverage information from publicly available PPI databases. Through simulation studies under multiple settings, we proved our method can increase statistical power in identifying additional risk genes compared to methods without using the PPI network information. We then applied our method to a real example for CHD DNV data, and then visualized the subnetwork of candidate genes to find potential functional gene clusters for CHD.

Unique SMYD5 Structure Revealed by AlphaFold Correlates with Its Functional Divergence

SMYD5 belongs to a special class of protein lysine methyltransferases with an MYND (Myeloid-Nervy-DEAF1) domain inserted into a SET (Suppressor of variegation, Enhancer of Zeste, Trithorax) domain. Despite recent advances in its functional characterization, the lack of the crystal structure has hindered our understanding of the structure-and-function relationships of this most unique member of the SMYD protein family. Here, we demonstrate the reliability of using AlphaFold structures for understanding the structure and function of SMYD5 by comparing the AlphaFold structures to the known crystal structures of SMYD proteins, using an inter-residue distance maps-based metric. We found that the AlphaFold confidence scores are inversely associated with the refined B-factors and can serve as a structural indicator of conformational flexibility. We also found that the N-terminal sequence of SMYD5, predicted to be a mitochondrial targeting signal, contains a novel non-classical nuclear localization signal. This sequence is structurally flexible and does not have a well-defined conformation, which might facilitate its recognition for SMYD5’s cytonuclear transport. The structure of SMYD5 is unique in many aspects. The “crab”-like structure with a large negatively charged cleft provides a potential binding site for basic molecules such as protamines. The less positively charged MYND domain is associated with the undetectable DNA-binding ability. The most surprising feature is an incomplete target lysine access channel that lacks the evolutionarily conserved tri-aromatic arrangement, being associated with the low H3/H4 catalytic activity. This study expands our understanding of the SMYD protein family from a classical two-lobed structure to a structure of its own kind, being as a fundamental determinant of its functional divergence.

SOCS3 Acts as an Onco-immunological Biomarker With Value in Assessing the Tumor Microenvironment, Pathological Staging, Histological Subtypes, Therapeutic Effect, and Prognoses of Several Types of Cancer

The suppressor of cytokine signaling (SOCS) family contains eight members, including SOCS1–7 and CIS, and SOCS3 has been shown to inhibit cytokine signal transduction in various signaling pathways. Although several studies have currently shown the correlations between SOCS3 and several types of cancer, no pan-cancer analysis is available to date. We used various computational tools to explore the expression and pathogenic roles of SOCS3 in several types of cancer, assessing its potential role in the pathogenesis of cancer, in tumor immune infiltration, tumor progression, immune evasion, therapeutic response, and prognostic. The results showed that SOCS3 was downregulated in most The Cancer Genome Atlas (TCGA) cancer datasets but was highly expressed in brain tumors, breast cancer, esophageal cancer, colorectal cancer, and lymphoma. High SOCS3 expression in glioblastoma multiforme (GBM) and brain lower-grade glioma (LGG) were verified through immunohistochemical experiments. GEPIA and Kaplan–Meier Plotter were used, and this bioinformatics analysis showed that high SOCS3 expression was associated with a poor prognosis in the majority of cancers, including LGG and GBM. Our analysis also indicated that SOCS3 may be involved in tumor immune evasion via immune cell infiltration or T-cell exclusion across different types of cancer. In addition, SOCS3 methylation was negatively correlated with mRNA expression levels, worse prognoses, and dysfunctional T-cell phenotypes in various types of cancer. Next, different analytical methods were used to select genes related to SOCS3 gene alterations and carcinogenic characteristics, such as STAT3, SNAI1, NFKBIA, BCL10, TK1, PGS1, BIRC5, TMC8, and AFMID, and several biological functions were identified between them. We found that SOCS3 was involved in cancer development primarily through the JAK/STAT signaling pathway and cytokine receptor activity. Furthermore, SOCS3 expression levels were associated with immunotherapy or chemotherapy for numerous types of cancer. In conclusion, this study showed that SOCS3 is an immune-oncogenic molecule that may possess value as a biomarker for diagnosis, treatment, and prognosis of several types of cancer in the future.

Bioinformatic prediction of putative metallothioneins in non-ciliate protists

Intracellular ligands that bind heavy metals (HMs) and thereby minimize their detrimental effects to cellular metabolism are attracting great interest for a number of applications including bioremediation and development of HM-biosensors. Metallothioneins (MTs) are short, cysteine-rich, genetically encoded proteins involved in intracellular metal-binding and play a key role in detoxification of HMs. We searched approximately 700 genomes and transcriptomes of non-ciliate protists for novel putative MTs by similarity and structural analyses and found 21 unique proteins playing a potential role as MTs. Most putative MTs derive from heterokonts and dinoflagellates and share common features such as (i) a putative metal-binding domain in proximity of the N-terminus, (ii) two putative MT-specific domains near the C-terminus and (iii) one to three CTCGXXCXCGXXCXCXXC patterns. Although the biological function of these proteins has not been experimentally proven, knowledge of their genetic sequences adds useful information on proteins that are potentially involved in HM-binding and can contribute to the design of future biomolecular assays on HM-microbe interactions and MT-based biosensors.

The CCN2/CTGF interactome: an approach to understanding the versatility of CCN2/CTGF molecular activities

Cellular communication network 2 (CCN2), also known as connective tissue growth factor (CTGF) regulates diverse cellular processes, some at odds with others, including adhesion, proliferation, apoptosis, and extracellular matrix (ECM) protein synthesis. Although a cause-and-effect relationship between CCN2/CTGF expression and local fibrotic reactions has initially been established, CCN2/CTGF manifests cell-, tissue-, and context-specific functions and differentially affects developmental and pathological processes ranging from progenitor cell fate decisions and angiogenesis to inflammation and tumorigenesis. CCN2/CTGF multimodular structure, binding to and activation or inhibition of multiple cell surface receptors, growth factors and ECM proteins, and susceptibility for proteolytic cleavage highlight the complexity to CCN2/CTGF biochemical attributes. CCN2/CTGF expression and dosage in the local environment affects a defined community of its interacting partners, and this results in sequestration of growth factors, interference with or potentiation of ligand-receptor binding, cellular internalization of CCN2/CTGF, inhibition or activation of proteases, and generation of CCN2/CTGF degradome products that add molecular diversity and expand the repertoire of functional modules in the cells and their microenvironment. Through these interactions, different intracellular signals and cellular responses are elicited culminating into physiological or pathological reactions. Thus, the CCN2/CTGF interactome is a defining factor of its tissue- and context-specific effects. Mapping of new CCN2/CTGF binding partners might shed light on yet unknown roles of CCN2/CTGF and provide a solid basis for tissue-specific targeting this molecule or its interacting partners in a therapeutic context.

MXD3 as an onco-immunological biomarker encompassing the tumor microenvironment, disease staging, prognoses, and therapeutic responses in multiple cancer types

MAX dimerization (MXD) protein 3 (MXD3) is a member of the MXD family of basic-helix-loop-helix-leucine-zipper (bHLHZ) transcription factors that plays pivotal roles in cell cycle progression and cell proliferation. However, there is insufficient scientific evidence on the pathogenic roles of MXD3 in various cancers and whether MXD3 plays a role in the immuno-oncology context of the tumor microenvironment, pathogenesis, prognosis, and therapeutic response of different tumors through certain common molecular mechanisms; thus, we saw a need to conduct the present in silico pan-cancer study. Using various computational tools, we interrogated the role of MXD3 in tumor immune infiltration, immune evasion, tumor progression, therapy response, and prognosis of cohorts from various cancer types. Our results indicated that MXD3 was aberrantly expressed in almost all The Cancer Genome Atlas (TCGA) cancer types and subtypes and was associated with the tumor stage, metastasis, and worse prognoses of various cohorts. Our results also suggested that MXD3 is associated with tumor immune evasion via different mechanisms involving T-cell exclusion in different cancer types and by tumor infiltration of immune cells in thymoma (THYM), liver hepatocellular carcinoma (LIHC), and head and neck squamous cell carcinoma (HNSC). Methylation of MXD3 was inversely associated with messenger (m)RNA expression levels and mediated dysfunctional T-cell phenotypes and worse prognoses of cohorts from different cancer types. Finally, we found that genetic alterations and oncogenic features of MXD3 were concomitantly associated with deregulation of the DBN1, RAB24, SLC34A1, PRELID1, LMAN2, F12, GRK6, RGS14, PRR7, and PFN3 genes and were connected to phospholipid transport and ion homeostasis. Our results also suggested that MXD3 expression is associated with immune or chemotherapeutic outcomes in various cancers. In addition, higher MXD3 expression levels were associated with decreased sensitivity of cancer cell lines to several mitogen-activated protein kinase kinase (MEK) inhibitors but led to increased activities of other kinase inhibitors, including Akt inhibitors. Interestingly, MXD3 exhibited higher predictive power for response outcomes and overall survival of immune checkpoint blockade sub-cohorts than three of seven standardized biomarkers. Altogether, our study strongly suggests that MXD3 is an immune-oncogenic molecule and could serve as a biomarker for cancer detection, prognosis, therapeutic design, and follow-up.

Regulation of ER Composition and Extent, and Putative Action in Protein Networks by ER/NE Protein TMEM147

Nuclear envelope (NE) and endoplasmic reticulum (ER) collaborate to control a multitude of nuclear and cytoplasmic actions. In this context, the transmembrane protein TMEM147 localizes to both NE and ER, and through direct and indirect interactions regulates processes as varied as production and transport of multipass membrane proteins, neuronal signaling, nuclear-shape, lamina and chromatin dynamics and cholesterol synthesis. Aiming to delineate the emerging multifunctionality of TMEM147 more comprehensively, we set as objectives, first, to assess potentially more fundamental effects of TMEM147 on the ER and, second, to identify significantly TMEM147-associated cell-wide protein networks and pathways. Quantifying curved and flat ER markers RTN4 and CLIMP63/CKAP4, respectively, we found that TMEM147 silencing causes area and intensity increases for both RTN4 and CLIMP63, and the ER in general, with a profound shift toward flat areas, concurrent with reduction in DNA condensation. Protein network and pathway analyses based on comprehensive compilation of TMEM147 interactors, targets and co-factors then served to manifest novel and established roles for TMEM147. Thus, algorithmically simplified significant pathways reflect TMEM147 function in ribosome binding, oxidoreductase activity, G protein-coupled receptor activity and transmembrane transport, while analysis of protein factors and networks identifies hub proteins and corresponding pathways as potential targets of TMEM147 action and of future functional studies.

Tau Protein Interaction Partners and Their Roles in Alzheimer's Disease and Other Tauopathies

Tau protein plays a critical role in the assembly, stabilization, and modulation of microtubules, which are important for the normal function of neurons and the brain. In diseased conditions, several pathological modifications of tau protein manifest. These changes lead to tau protein aggregation and the formation of paired helical filaments (PHF) and neurofibrillary tangles (NFT), which are common hallmarks of Alzheimer's disease and other tauopathies. The accumulation of PHFs and NFTs results in impairment of physiological functions, apoptosis, and neuronal loss, which is reflected as cognitive impairment, and in the late stages of the disease, leads to death. The causes of this pathological transformation of tau protein haven't been fully understood yet. In both physiological and pathological conditions, tau interacts with several proteins which maintain their proper function or can participate in their pathological modifications. Interaction partners of tau protein and associated molecular pathways can either initiate and drive the tau pathology or can act neuroprotective, by reducing pathological tau proteins or inflammation. In this review, we focus on the tau as a multifunctional protein and its known interacting partners active in regulations of different processes and the roles of these proteins in Alzheimer's disease and tauopathies.

Drug Discovery of Spinal Muscular Atrophy (SMA) from the Computational Perspective: A Comprehensive Review

Spinal muscular atrophy (SMA), one of the leading inherited causes of child mortality, is a rare neuromuscular disease arising from loss-of-function mutations of the survival motor neuron 1 (SMN1) gene, which encodes the SMN protein. When lacking the SMN protein in neurons, patients suffer from muscle weakness and atrophy, and in the severe cases, respiratory failure and death. Several therapeutic approaches show promise with human testing and three medications have been approved by the U.S. Food and Drug Administration (FDA) to date. Despite the shown promise of these approved therapies, there are some crucial limitations, one of the most important being the cost. The FDA-approved drugs are high-priced and are shortlisted among the most expensive treatments in the world. The price is still far beyond affordable and may serve as a burden for patients. The blooming of the biomedical data and advancement of computational approaches have opened new possibilities for SMA therapeutic development. This article highlights the present status of computationally aided approaches, including in silico drug repurposing, network driven drug discovery as well as artificial intelligence (AI)-assisted drug discovery, and discusses the future prospects.

Predicting Protein-Protein Interactions Using Symmetric Logistic Matrix Factorization

Accurate assessment of protein-protein interactions (PPIs) is critical to deciphering disease mechanisms and developing novel drugs, and with rapidly growing PPI data, the need for more efficient predictive methods is emerging. We propose here a symmetric logistic matrix factorization (symLMF)-based approach to predict PPIs, especially useful for large PPI networks. Benchmarked against two widely used datasets (Saccharomyces cerevisiae and Homo sapiens benchmarks) and their extended versions, the symLMF-based method proves to outperform most of the state-of-the-art data-driven methods applied to human PPIs, and it shows a performance comparable to those of deep learning methods despite its conceptual and technical simplicity and efficiency. Tests performed on humans, yeast, and tissue (brain and liver)- and disease (neurodegenerative and metabolic disorders)-specific datasets further demonstrate the high capability to capture the hidden interactions. Notably, many "de novo predictions" made by symLMF are verified to exist in PPI databases other than those used for training/testing the method, indicating that the method could be of broad utility as a simple, yet efficient and accurate, tool applicable to PPI datasets.

Exploration of databases and methods supporting drug repurposing: a comprehensive survey

Drug development involves a deep understanding of the mechanisms of action and possible side effects of each drug, and sometimes results in the identification of new and unexpected uses for drugs, termed as drug repurposing. Both in case of serendipitous observations and systematic mechanistic explorations, confirmation of new indications for a drug requires hypothesis building around relevant drug-related data, such as molecular targets involved, and patient and cellular responses. These datasets are available in public repositories, but apart from sifting through the sheer amount of data imposing computational bottleneck, a major challenge is the difficulty in selecting which databases to use from an increasingly large number of available databases. The database selection is made harder by the lack of an overview of the types of data offered in each database. In order to alleviate these problems and to guide the end user through the drug repurposing efforts, we provide here a survey of 102 of the most promising and drug-relevant databases reported to date. We summarize the target coverage and types of data available in each database and provide several examples of how multi-database exploration can facilitate drug repurposing.

Proximity-dependent Mapping of the Androgen Receptor Identifies Kruppel-like Factor 4 as a Functional Partner

Prostate cancer (PCa) is the most frequently diagnosed cancer in men and the third cause of cancer mortality. PCa initiation and growth are driven by the androgen receptor (AR). The AR is activated by androgens such as testosterone and controls prostatic cell proliferation and survival. Here, we report an AR signaling network generated using BioID proximity labeling proteomics in androgen-dependent LAPC4 cells. We identified 31 AR-associated proteins in nonstimulated cells. Strikingly, the AR signaling network increased to 182 and 200 proteins, upon 24 h or 72 h of androgenic stimulation, respectively, for a total of 267 nonredundant AR-associated candidates. Among the latter group, we identified 213 proteins that were not previously reported in databases. Many of these new AR-associated proteins are involved in DNA metabolism, RNA processing, and RNA polymerase II transcription. Moreover, we identified 44 transcription factors, including the Kru¨ppel-like factor 4 (KLF4), which were found interacting in androgen-stimulated cells. Interestingly, KLF4 repressed the well-characterized AR-dependent transcription of the KLK3 (PSA) gene; AR and KLF4 also colocalized genome-wide. Taken together, our data report an expanded high-confidence proximity network for AR, which will be instrumental to further dissect the molecular mechanisms underlying androgen signaling in PCa cells.

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BioID proteomics identifies 267 androgen receptor (AR)-associated candidates

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Krüppel-like factor 4 (KLF4) is a new AR interaction partner

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AR and KLF4 colocalize genome-wide on >4000 genes, including KLK3 (PSA)

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KLF4 acts as a repressor for the AR target gene KLK3 (PSA)

Proteomimetic surface fragments distinguish targets by function

The fragment-centric design promises a means to develop complex xenobiotic protein surface mimetics, but it is challenging to find locally biomimetic structures. To address this issue, foldameric local surface mimetic (LSM) libraries were constructed. Protein affinity patterns, ligand promiscuity and protein druggability were evaluated using pull-down data for targets with various interaction tendencies and levels of homology. LSM probes based on H14 helices exhibited sufficient binding affinities for the detection of both orthosteric and non-orthosteric spots, and overall binding tendencies correlated with the magnitude of the target interactome. Binding was driven by two proteinogenic side chains and LSM probes could distinguish structurally similar proteins with different functions, indicating limited promiscuity. Binding patterns displayed similar side chain enrichment values to those for native protein-protein interfaces implying locally biomimetic behavior. These analyses suggest that in a fragment-centric approach foldameric LSMs can serve as useful probes and building blocks for undruggable protein interfaces.

Investigating the Role of Telomere and Telomerase Associated Genes and Proteins in Endometrial Cancer

Endometrial cancer (EC) is the commonest gynaecological malignancy. Current prognostic markers are inadequate to accurately predict patient survival, necessitating novel prognostic markers, to improve treatment strategies. Telomerase has a unique role within the endometrium, whilst aberrant telomerase activity is a hallmark of many cancers. The aim of the current in silico study is to investigate the role of telomere and telomerase associated genes and proteins (TTAGPs) in EC to identify potential prognostic markers and therapeutic targets. Analysis of RNA-seq data from The Cancer Genome Atlas identified differentially expressed genes (DEGs) in EC (568 TTAGPs out of 3467) and ascertained DEGs associated with histological subtypes, higher grade endometrioid tumours and late stage EC. Functional analysis demonstrated that DEGs were predominantly involved in cell cycle regulation, while the survival analysis identified 69 DEGs associated with prognosis. The protein-protein interaction network constructed facilitated the identification of hub genes, enriched transcription factor binding sites and drugs that may target the network. Thus, our in silico methods distinguished many critical genes associated with telomere maintenance that were previously unknown to contribute to EC carcinogenesis and prognosis, including NOP56, WFS1, ANAPC4 and TUBB4A. Probing the prognostic and therapeutic utility of these novel TTAGP markers will form an exciting basis for future research.

Novel insights into genetics and clinics of the HNF1A-MODY

MODY (Maturity Onset Diabetes of the Young) is a type of diabetes resulting from a pathogenic effect of gene mutations. Up to date, 13 MODY genes are known. Gene HNF1A is one of the most common causes of MODY diabetes (HNF1A-MODY; MODY3). This gene is polymorphic and more than 1200 pathogenic and non-pathogenic HNF1A variants were described in its UTRs, exons and introns. For HNF1A-MODY, not just gene but also phenotype heterogeneity is typical. Although there are some clinical instructions, HNF1A-MODY patients often do not meet every diagnostic criteria or they are still misdiagnosed as type 1 and type 2 diabetics. There is a constant effort to find suitable biomarkers to help with in distinguishing of MODY3 from Type 1 Diabetes (T1D) and Type 2 Diabetes (T2D). DNA sequencing is still necessary for unambiguous confirmation of clinical suspicion of MODY. NGS (Next Generation Sequencing) methods brought discoveries of multiple new gene variants and new instructions for their pathogenicity classification were required. The most actual problem is classification of variants with uncertain significance (VUS) which is a stumbling-block for clinical interpretation. Since MODY is a hereditary disease, DNA analysis of family members is helpful or even crucial. This review is updated summary about HNF1A-MODY genetics, pathophysiology, clinics functional studies and variant classification.

Epigenetic therapy of novel tumour suppressor ZAR1 and its cancer biomarker function

Background

Cancer still is one of the leading causes of death and its death toll is predicted to rise further. We identified earlier the potential tumour suppressor zygote arrest 1 (ZAR1) to play a role in lung carcinogenesis through its epigenetic inactivation.

Results

We are the first to report that ZAR1 is epigenetically inactivated not only in lung cancer but also across cancer types, and ZAR1 methylation occurs across its complete CpG island. ZAR1 hypermethylation significantly correlates with its expression reduction in cancers. We are also the first to report that ZAR1 methylation and expression reduction are of clinical importance as a prognostic marker for lung cancer and kidney cancer. We further established that the carboxy (C)-terminally present zinc-finger of ZAR1 is relevant for its tumour suppression function and its protein partner binding associated with the mRNA/ribosomal network. Global gene expression profiling supported ZAR1's role in cell cycle arrest and p53 signalling pathway, and we could show that ZAR1 growth suppression was in part p53 dependent. Using the CRISPR-dCas9 tools, we were able to prove that epigenetic editing and reactivation of ZAR1 is possible in cancer cell lines.

Conclusion

ZAR1 is a novel cancer biomarker for lung and kidney, which is epigenetically silenced in various cancers by DNA hypermethylation. ZAR1 exerts its tumour suppressive function in part through p53 and through its zinc-finger domain. Epigenetic therapy can reactivate the ZAR1 tumour suppressor in cancer.

Is MYND Domain-Mediated Assembly of SMYD3 Complexes Involved in Calcium Dependent Signaling?

Macromolecular complexes are essential to intracellular signal transduction by creating signaling niches and enabling a chain of reactions that transmit external signals into various cellular responses. Analysis of SMYD3 interactome indicates this protein lysine methyltransferase might be involved in calcium dependent signaling pathways through forming complexes with the phospholipase PLCB3, calcium/calmodulin dependent kinase CAMK2B, or calcineurin inhibitor RCAN3. SMYD3 is well-known as a histone H3K4 methyltransferase involved in epigenetic transcriptional regulation; however, any roles SMYD3 may play in signaling transduction remain unknown. KEGG pathway enrichment analysis reveals the SMYD3 interacting proteins are overrepresented in several signaling pathways such as estrogen signaling pathway, NOD-like receptor signaling pathway, and WNT signaling pathway. Sequence motif scanning reveals a significant enrichment of PXLXP motif in SMYD3 interacting proteins. The MYND domain of SMYD3 is known to bind to the PXLXP motif. The enrichment of the PXLXP motif suggests that the MYND domain is likely to be a key interaction module that mediates formation of some SMYD3 complexes. The presence of the PXLXP motifs in PLCB3 and CAMK2B indicates the potential role of the MYND domain in mediating complex formation in signaling. The structural basis of SMYD3 MYND domain-mediated interactions is unknown. The only available MYND-peptide complex structure suggests the MYND domain-mediated interaction is likely transient and dynamic. The transient nature will make this domain well-suited to mediate signaling transduction processes where it may allow rapid responses to cellular perturbations and changes in environment.

Nrf2 induced cisplatin resistance in ovarian cancer by promoting CD99 expression

Cisplatin resistance is a vital obstacle for the prognosis of ovarian cancer. However, the mechanism of cisplatin resistance is still unknown. This research was performed to explore the role of Nrf2 (nuclear factor, erythroid 2 like 2) and CD99 (CD99 molecule) in cisplatin resistance in ovarian cancer. QRT-PCR and Western blot were used to detect the expression of CD99 in ovarian cancer cells and tissues with different cisplatin sensitivities. Cell viability was analyzed by the Cell Counting Kit-8 (CCK8). The relationship of Nrf2 and CD99 was assessed by dual-luciferase reporter gene assay and chromatin immunoprecipitation (ChIP). Bioinformatics analysis was performed to search for the downstream gene of CD99. In this study, it was revealed that CD99 was highly expressed in cisplatin-resistant ovarian cancer cells and tissues, while lower CD99 expression was found in cisplatin-sensitive ovarian cancer cells and tissues. In addition, the overexpression of CD99 resulted in cisplatin resistance; on the other hand, knockdown of CD99 sensitized ovarian cancer to cisplatin. Furthermore, survival analysis indicated that overall survival (OS) and progression-free survival (PFS) of patients with higher CD99 expression were shorter than those with lower CD99 expression. It was also found that when Nrf2 was upregulated in cisplatin-sensitive ovarian cells, CD99 expression and cell viability increased after cisplatin treatment. Knockdown of CD99 could reverse cisplatin resistance induced by Nrf2. Conversely, when Nrf2 was knocked down in cisplatin-resistant ovarian cancer cells, CD99 expression and cell viability with cisplatin treatment decreased, while simultaneously upregulating CD99 reactivated cisplatin resistance in ovarian cancer cells. The dual-luciferase reporter gene assay and ChIP analysis suggested CD99 was a downstream gene of Nrf2, and Nrf2 positively regulated the expression of CD99 at the transcriptional level. In conclusion, Nrf2 induced cisplatin resistance in ovarian cancer cells by promoting CD99 expression. Targeted CD99 might be an effective way to reverse cisplatin resistance in ovarian cancer.

Defining Pharmacological Targets by Analysis of Virus-Host Protein Interactions

Viruses are obligate parasites that depend on cellular factors for replication. Pharmacological inhibition of essential viral proteins, mostly enzymes, is an effective therapeutic alternative in the absence of effective vaccines. However, this strategy commonly encounters drug resistance mechanisms that allow these pathogens to evade control. Due to the dependency on host factors for viral replication, pharmacological disruption of the host-pathogen protein-protein interactions (PPIs) is an important therapeutic alternative to block viral replication. In this review we discuss salient aspects of PPIs implicated in viral replication and advances in the development of small molecules that inhibit viral replication through antagonism of these interactions.

Elucidating the in vivo interactome of HIV-1 RNA by hybridization capture and mass spectrometry

HIV-1 replication requires myriad interactions between cellular proteins and the viral unspliced RNA. These interactions are important in archetypal RNA processes such as transcription and translation as well as for more specialized functions including alternative splicing and packaging of unspliced genomic RNA into virions. We present here a hybridization capture strategy for purification of unspliced full-length HIV RNA-protein complexes preserved in vivo by formaldehyde crosslinking, and coupled with mass spectrometry to identify HIV RNA-protein interactors in HIV-1 infected cells. One hundred eighty-nine proteins were identified to interact with unspliced HIV RNA including Rev and Gag/Gag-Pol, 24 host proteins previously shown to bind segments of HIV RNA, and over 90 proteins previously shown to impact HIV replication. Further analysis using siRNA knockdown techniques against several of these proteins revealed significant changes to HIV expression. These results demonstrate the utility of the approach for the discovery of host proteins involved in HIV replication. Additionally, because this strategy only requires availability of 30 nucleotides of the HIV-RNA for hybridization with a capture oligonucleotide, it is readily applicable to any HIV system of interest regardless of cell type, HIV-1 virus strain, or experimental perturbation.

A review on host-pathogen interactions: classification and prediction

The research on host-pathogen interactions is an ever-emerging and evolving field. Every other day a new pathogen gets discovered, along with comes the challenge of its prevention and cure. As the intelligent human always vies for prevention, which is better than cure, understanding the mechanisms of host-pathogen interactions gets prior importance. There are many mechanisms involved from the pathogen as well as the host sides while an interaction happens. It is a vis-a-vis fight of the counter genes and proteins from both sides. Who wins depends on whether a host gets an infection or not. Moreover, a higher level of complexity arises when the pathogens evolve and become resistant to a host's defense mechanisms. Such pathogens pose serious challenges for treatment. The entire human population is in danger of such long-lasting persistent infections. Some of these infections even increase the rate of mortality. Hence there is an immediate emergency to understand how the pathogens interact with their host for successful invasion. It may lead to discovery of appropriate preventive measures, and the development of rational therapeutic measures and medication against such infections and diseases. This review, a state-of-the-art updated scenario of host-pathogen interaction research, has been done by keeping in mind this urgency. It covers the biological and computational aspects of host-pathogen interactions, classification of the methods by which the pathogens interact with their hosts, different machine learning techniques for prediction of host-pathogen interactions, and future scopes of this research field.

Decoding protein networks during virus entry by quantitative proteomics

Virus entry into host cells relies on interactions between viral and host structures including lipids, carbohydrates and proteins. Particularly, protein–protein interactions between viral surface proteins and host proteins as well as secondary host protein–protein interactions play a pivotal role in coordinating virus binding and uptake. These interactions are dynamic and frequently involve multiprotein complexes. In the past decade mass spectrometry based proteomics methods have reached sensitivities and high throughput compatibilities of genomics methods and now allow the reliable quantitation of proteins in complex samples from limited material. As proteomics provides essential information on the biologically active entity namely the protein, including its posttranslational modifications and its interactions with other proteins, it is an indispensable method in the virologist's toolbox. Here we review protein interactions during virus entry and compare classical biochemical methods to study entry with novel technically advanced quantitative proteomics techniques. We highlight the value of quantitative proteomics in mapping functional virus entry networks, discuss the benefits and limitations and illustrate how the methodology will help resolve unsettled questions in virus entry research in the future.

Manipulation of the host protein acetylation network by human immunodeficiency virus type 1

Over the past 15 years, protein acetylation has emerged as a globally important post-translational modification that fine-tunes major cellular processes in many life forms. This dynamic regulatory system is critical both for complex eukaryotic cells and for the viruses that infect them. HIV-1 accesses the host acetylation network by interacting with several key enzymes, thereby promoting infection at multiple steps during the viral life cycle. Inhibitors of host histone deacetylases and bromodomain-containing proteins are now being pursued as therapeutic strategies to enhance current antiretroviral treatment. As more acetylation-targeting compounds are reaching clinical trials, it is time to review the role of reversible protein acetylation in HIV-infected CD4(+) T cells.

Recent strategies and progress in identifying host factors involved in virus replication

Viruses are completely dependent on their host cells for the successful production of progeny viruses. At each stage of the viral life cycle an intricate interplay between virus and host takes place with the virus aiming to usurp the host cell for its purposes and the host cell trying to block the intruder from propagation. In recent years these interactions have been studied on a global level by systems biology approaches, such as RNA interference screens, transcriptomic or proteomic methodologies, and exciting new insights into the pathogen-host relationship have been revealed. In this review, we summarize the available data, give examples for important findings from such studies and point out current limitations and potential future directions.

Interactions of HIV-1 proteins as targets for developing anti-HIV-1 peptides

Protein–protein interactions (PPI) are essential in every step of the HIV replication cycle. Mapping the interactions between viral and host proteins is a fundamental target for the design and development of new therapeutics. In this review, we focus on rational development of anti-HIV-1 peptides based on mapping viral–host and viral–viral protein interactions all across the HIV-1 replication cycle. We also discuss the mechanism of action, specificity and stability of these peptides, which are designed to inhibit PPI. Some of these peptides are excellent tools to study the mechanisms of PPI in HIV-1 replication cycle and for the development of anti-HIV-1 drug leads that modulate PPI.

Network representations of immune system complexity

The mammalian immune system is a dynamic multiscale system composed of a hierarchically organized set of molecular, cellular, and organismal networks that act in concert to promote effective host defense. These networks range from those involving gene regulatory and protein–protein interactions underlying intracellular signaling pathways and single‐cell responses to increasingly complex networks of in vivo cellular interaction, positioning, and migration that determine the overall immune response of an organism. Immunity is thus not the product of simple signaling events but rather nonlinear behaviors arising from dynamic, feedback‐regulated interactions among many components. One of the major goals of systems immunology is to quantitatively measure these complex multiscale spatial and temporal interactions, permitting development of computational models that can be used to predict responses to perturbation. Recent technological advances permit collection of comprehensive datasets at multiple molecular and cellular levels, while advances in network biology support representation of the relationships of components at each level as physical or functional interaction networks. The latter facilitate effective visualization of patterns and recognition of emergent properties arising from the many interactions of genes, molecules, and cells of the immune system. We illustrate the power of integrating ‘omics’ and network modeling approaches for unbiased reconstruction of signaling and transcriptional networks with a focus on applications involving the innate immune system. We further discuss future possibilities for reconstruction of increasingly complex cellular‐ and organism‐level networks and development of sophisticated computational tools for prediction of emergent immune behavior arising from the concerted action of these networks. WIREs Syst Biol Med 2015, 7:13–38. doi: 10.1002/wsbm.1288

This article is categorized under: 1

Analytical and Computational Methods > Computational Methods

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Laboratory Methods and Technologies > Macromolecular Interactions, Methods

HIV-1, human interaction database: current status and new features

The 'Human Immunodeficiency Virus Type 1 (HIV-1), Human Interaction Database', available through the National Library of Medicine at http://www.ncbi.nlm.nih.gov/genome/viruses/retroviruses/hiv-1/interactions, serves the scientific community exploring the discovery of novel HIV vaccine candidates and therapeutic targets. Each HIV-1 human protein interaction can be retrieved without restriction by web-based downloads and ftp protocols and includes: Reference Sequence (RefSeq) protein accession numbers, National Center for Biotechnology Information Gene identification numbers, brief descriptions of the interactions, searchable keywords for interactions and PubMed identification numbers (PMIDs) of journal articles describing the interactions. In addition to specific HIV-1 protein-human protein interactions, included are interaction effects upon HIV-1 replication resulting when individual human gene expression is blocked using siRNA. A total of 3142 human genes are described participating in 12,786 protein-protein interactions, along with 1316 replication interactions described for each of 1250 human genes identified using small interfering RNA (siRNA). Together the data identifies 4006 human genes involved in 14,102 interactions. With the inclusion of siRNA interactions we introduce a redesigned web interface to enhance viewing, filtering and downloading of the combined data set.