The BioGRID interaction database: 2013 update

Drug repurposing for cancer therapy

Cancer, a complex and multifactorial disease, presents a significant challenge to global health. Despite significant advances in surgical, radiotherapeutic and immunological approaches, which have improved cancer treatment outcomes, drug therapy continues to serve as a key therapeutic strategy. However, the clinical efficacy of drug therapy is often constrained by drug resistance and severe toxic side effects, and thus there remains a critical need to develop novel cancer therapeutics. One promising strategy that has received widespread attention in recent years is drug repurposing: the identification of new applications for existing, clinically approved drugs. Drug repurposing possesses several inherent advantages in the context of cancer treatment since repurposed drugs are typically cost-effective, proven to be safe, and can significantly expedite the drug development process due to their already established safety profiles. In light of this, the present review offers a comprehensive overview of the various methods employed in drug repurposing, specifically focusing on the repurposing of drugs to treat cancer. We describe the antitumor properties of candidate drugs, and discuss in detail how they target both the hallmarks of cancer in tumor cells and the surrounding tumor microenvironment. In addition, we examine the innovative strategy of integrating drug repurposing with nanotechnology to enhance topical drug delivery. We also emphasize the critical role that repurposed drugs can play when used as part of a combination therapy regimen. To conclude, we outline the challenges associated with repurposing drugs and consider the future prospects of these repurposed drugs transitioning into clinical application.

Advances in computational methods for identifying cancer driver genes

Cancer driver genes (CDGs) are crucial in cancer prevention, diagnosis and treatment. This study employed computational methods for identifying CDGs, categorizing them into four groups. The major frameworks for each of these four categories were summarized. Additionally, we systematically gathered data from public databases and biological networks, and we elaborated on computational methods for identifying CDGs using the aforementioned databases. Further, we summarized the algorithms, mainly involving statistics and machine learning, used for identifying CDGs. Notably, the performances of nine typical identification methods for eight types of cancer were compared to analyze the applicability areas of these methods. Finally, we discussed the challenges and prospects associated with methods for identifying CDGs. The present study revealed that the network-based algorithms and machine learning-based methods demonstrated superior performance.

Disentangling archaic introgression and genomic signatures of selection at human immunity genes

Pathogens and infectious diseases have imposed exceptionally strong selective pressure on ancient and modern human genomes and contributed to the current variation in many genes. There is evidence that modern humans acquired immune variants through interbreeding with ancient hominins, but the impact of such variants on human traits is not fully understood. The main objectives of this research were to infer the genetic signatures of positive selection that may be involved in adaptation to infectious diseases and to investigate the function of Neanderthal alleles identified within a set of 50 Lithuanian genomes. Introgressed regions were identified using the machine learning tool ArchIE. Recent positive selection signatures were analysed using iHS. We detected high-scoring signals of positive selection at innate immunity genes (EMB, PARP8, HLAC, and CDSN) and evaluated their interactions with the structural proteins of pathogens. Interactions with human immunodeficiency virus (HIV) 1 and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were identified. Overall, genomic regions introgressed from Neanderthals were shown to be enriched in genes related to immunity, keratinocyte differentiation, and sensory perception.

Challenges and opportunities in sharing microbiome data and analyses

Microbiome data, metadata and analytical workflows have become 'big' in terms of volume and complexity. Although the infrastructure and technologies to share data have been established, the interdisciplinary and multi-omic nature of the field can make resources difficult to identify and use. Following best practices for data deposition requires substantial effort, with sometimes little obvious reward. Gaps remain where microbiome-specific resources for data sharing or reproducibility do not yet exist. We outline available best practices, challenges to their adoption and opportunities in data sharing in microbiome research. We showcase examples of best practices and advocate for their enforcement and incentivization for data sharing. This includes recognition of data curation and sharing endeavours by individuals, institutions, journals and funders. Opportunities for progress include enabling microbiome-specific databases to incorporate future methods for data analysis, integration and reuse.

Global analysis of aging-related protein structural changes uncovers enzyme-polymerization-based control of longevity

Aging is associated with progressive phenotypic changes. Virtually all cellular phenotypes are produced by proteins, and their structural alterations can lead to age-related diseases. However, we still lack comprehensive knowledge of proteins undergoing structural-functional changes during cellular aging and their contributions to age-related phenotypes. Here, we conducted proteome-wide analysis of early age-related protein structural changes in budding yeast using limited proteolysis-mass spectrometry (LiP-MS). The results, compiled in online ProtAge catalog, unraveled age-related functional changes in regulators of translation, protein folding, and amino acid metabolism. Mechanistically, we found that folded glutamate synthase Glt1 polymerizes into supramolecular self-assemblies during aging, causing breakdown of cellular amino acid homeostasis. Inhibiting Glt1 polymerization by mutating the polymerization interface restored amino acid levels in aged cells, attenuated mitochondrial dysfunction, and led to lifespan extension. Altogether, this comprehensive map of protein structural changes enables identifying mechanisms of age-related phenotypes and offers opportunities for their reversal.

Construction of a circRNA- lincRNA-lncRNA-miRNA-mRNA ceRNA regulatory network identifies genes and pathways linked to goat fertility

Background: There is growing interest in the genetic improvement of fertility traits in female goats. With high-throughput genotyping, single-cell RNA sequencing (scRNA-seq) is a powerful tool for measuring gene expression profiles. The primary objective was to investigate comparative transcriptome profiling of granulosa cells (GCs) of high- and low-fertility goats, using scRNA-seq. Methods: Thirty samples from Ji'ning Gray goats (n = 15 for high fertility and n = 15 for low fertility) were retrieved from publicly available scRNA-seq data. Functional enrichment analysis and a literature mining approach were applied to explore modules and hub genes related to fertility. Then, interactions between types of RNAs identified were predicted, and the ceRNA regulatory network was constructed by integrating these interactions with other gene regulatory networks (GRNs). Results and discussion: Comparative transcriptomics-related analyses identified 150 differentially expressed genes (DEGs) between high- and low-fertility groups, based on the fold change (≥5 and ≤-5) and false discovery rate (FDR <0.05). Among these genes, 80 were upregulated and 70 were downregulated. In addition, 81 mRNAs, 58 circRNAs, 8 lincRNAs, 19 lncRNAs, and 55 miRNAs were identified by literature mining. Furthermore, we identified 18 hub genes (SMAD1, SMAD2, SMAD3, SMAD4, TIMP1, ERBB2, BMP15, TGFB1, MAPK3, CTNNB1, BMPR2, AMHR2, TGFBR2, BMP4, ESR1, BMPR1B, AR, and TGFB2) involved in goat fertility. Identified biological networks and modules were mainly associated with ovary signature pathways. In addition, KEGG enrichment analysis identified regulating pluripotency of stem cells, cytokine-cytokine receptor interactions, ovarian steroidogenesis, oocyte meiosis, progesterone-mediated oocyte maturation, parathyroid and growth hormone synthesis, cortisol synthesis and secretion, and signaling pathways for prolactin, TGF-beta, Hippo, MAPK, PI3K-Akt, and FoxO. Functional annotation of identified DEGs implicated important biological pathways. These findings provided insights into the genetic basis of fertility in female goats and are an impetus to elucidate molecular ceRNA regulatory networks and functions of DEGs underlying ovarian follicular development.

A machine learning method for the identification and characterization of novel COVID-19 drug targets

In addition to vaccines, the World Health Organization sees novel medications as an urgent matter to fight the ongoing COVID-19 pandemic. One possible strategy is to identify target proteins, for which a perturbation by an existing compound is likely to benefit COVID-19 patients. In order to contribute to this effort, we present GuiltyTargets-COVID-19 ( https://guiltytargets-covid.eu/ ), a machine learning supported web tool to identify novel candidate drug targets. Using six bulk and three single cell RNA-Seq datasets, together with a lung tissue specific protein-protein interaction network, we demonstrate that GuiltyTargets-COVID-19 is capable of (i) prioritizing meaningful target candidates and assessing their druggability, (ii) unraveling their linkage to known disease mechanisms, (iii) mapping ligands from the ChEMBL database to the identified targets, and (iv) pointing out potential side effects in the case that the mapped ligands correspond to approved drugs. Our example analyses identified 4 potential drug targets from the datasets: AKT3 from both the bulk and single cell RNA-Seq data as well as AKT2, MLKL, and MAPK11 in the single cell experiments. Altogether, we believe that our web tool will facilitate future target identification and drug development for COVID-19, notably in a cell type and tissue specific manner.

Designing Effective Multi-Target Drugs and Identifying Biomarkers in Recurrent Pregnancy Loss (RPL) Using In Vivo, In Vitro, and In Silico Approaches

Recurrent pregnancy loss (RPL) occurs in approximately 5% of women. Despite an abundance of evidence, the molecular mechanism of RPL’s pathology remains unclear. Here, we report the protective role of polo-like kinase 1 (PLK1) during RPL. We aimed to construct an RPL network utilizing GEO datasets and identified hub high-traffic genes. We also investigated whether the expressions of PLK1 were altered in the chorionic villi collected from women with RPL compared to those from healthy early pregnant women. Gene expression differences were evaluated using both pathway and gene ontology (GO) analyses. The identified genes were validated using in vivo and in vitro models. Mice with PLK1-overexpression and PLK1-knockdown in vitro models were produced by transfecting certain plasmids and si-RNA, respectively. The apoptosis in the chorionic villi, mitochondrial function, and NF-κB signaling activity was evaluated. To suppress the activation of PLK1, the PLK1 inhibitor BI2536 was administered. The HTR-8/SVneo and JEG-3 cell lines were chosen to establish an RPL model in vitro. The NF-κB signaling, Foxo signaling, PI3K/AKT, and endometrial cancer signaling pathways were identified via the RPL regulatory network. The following genes were identified: PLK1 as hub high-traffic gene and MMP2, MMP9, BAX, MFN1, MFN2, FOXO1, OPA1, COX15, BCL2, DRP1, FIS1, TRAF2, and TOP2A. Clinical samples were examined, and the results demonstrated that RPL patients had tissues with decreased PLK1 expression in comparison to women with normal pregnancies (p < 0.01). In vitro, PLK1 knockdown induced the NF-κB signaling pathway and apoptosis activation while decreasing cell invasion, migration, and proliferation (p < 0.05). Furthermore, the in vivo model proved that cell mitochondrial function and chorionic villi development are both hampered by PLK1 suppression. Our findings revealed that the PLK1/TRAF2/NF-κB axis plays a crucial role in RPL-induced chorionic villi dysfunction by regulating mitochondrial dynamics and apoptosis and might be a potential therapeutic target in the clinic.

Systematic approach to identify therapeutic targets and functional pathways for the cervical cancer

BACKGROUND

In today's society, cancer has become a big concern. The most common cancers in women are breast cancer (BC), endometrial cancer (EC), ovarian cancer (OC), and cervical cancer (CC). CC is a type of cervix cancer that is the fourth most common cancer in women and the fourth major cause of death.

RESULTS

This research uses a network approach to discover genetic connections, functional enrichment, pathways analysis, microRNAs transcription factors (miRNA-TF) co-regulatory network, gene-disease associations, and therapeutic targets for CC. Three datasets from the NCBI's GEO collection were considered for this investigation. Then, using a comparison approach between the datasets, 315 common DEGs were discovered. The PPI network was built using a variety of combinatorial statistical approaches and bioinformatics tools, and the PPI network was then utilized to identify hub genes and critical modules.

CONCLUSION

Furthermore, we discovered that CC has specific similar links with the progression of different tumors using Gene Ontology terminology and pathway analysis. Transcription factors-gene linkages, gene-disease correlations, and the miRNA-TF co-regulatory network were revealed to have functional enrichments. We believe the candidate drugs identified in this study could be effective for advanced CC treatment.

Deciphering Macromolecular Interactions Involved in Abiotic Stress Signaling: A Review of Bioinformatics Analysis

Plant functioning and responses to abiotic stresses largely involve regulations at the transcriptomic level via complex interactions of signal molecules, signaling cascades, and regulators. Nevertheless, all the signaling networks involved in responses to abiotic stresses have not yet been fully established. The in-depth analysis of transcriptomes in stressed plants has become a relevant state-of-the-art methodology to study these regulations and signaling pathways that allow plants to cope with or attempt to survive abiotic stresses. The plant science and molecular biology community has developed databases about genes, proteins, protein-protein interactions, protein-DNA interactions and ontologies, which are valuable sources of knowledge for deciphering such regulatory and signaling networks. The use of these data and the development of bioinformatics tools help to make sense of transcriptomic data in specific contexts, such as that of abiotic stress signaling, using functional biological approaches. The aim of this chapter is to present and assess some of the essential online tools and resources that will allow novices in bioinformatics to decipher transcriptomic data in order to characterize the cellular processes and functions involved in abiotic stress responses and signaling. The analysis of case studies further describes how these tools can be used to conceive signaling networks on the basis of transcriptomic data. In these case studies, particular attention was paid to the characterization of abiotic stress responses and signaling related to chemical and xenobiotic stressors.

Identify Biomarkers and Design Effective Multi-Target Drugs in Ovarian Cancer: Hit Network-Target Sets Model Optimizing

Epithelial ovarian cancer (EOC) is highly aggressive with poor patient outcomes, and a deeper understanding of ovarian cancer tumorigenesis could help guide future treatment development. We proposed an optimized hit network-target sets model to systematically characterize the underlying pathological mechanisms and intra-tumoral heterogeneity in human ovarian cancer. Using TCGA data, we constructed an epithelial ovarian cancer regulatory network in this study. We use three distinct methods to produce different HNSs for identification of the driver genes/nodes, core modules, and core genes/nodes. Following the creation of the optimized HNS (OHNS) by the integration of DN (driver nodes), CM (core module), and CN (core nodes), the effectiveness of various HNSs was assessed based on the significance of the network topology, control potential, and clinical value. Immunohistochemical (IHC), qRT-PCR, and Western blotting were adopted to measure the expression of hub genes and proteins involved in epithelial ovarian cancer (EOC). We discovered that the OHNS has two key advantages: the network's central location and controllability. It also plays a significant role in the illness network due to its wide range of capabilities. The OHNS and clinical samples revealed the endometrial cancer signaling, and the PI3K/AKT, NER, and BMP pathways. MUC16, FOXA1, FBXL2, ARID1A, COX15, COX17, SCO1, SCO2, NDUFA4L2, NDUFA, and PTEN hub genes were predicted and may serve as potential candidates for new treatments and biomarkers for EOC. This research can aid in better capturing the disease progression, the creation of potent multi-target medications, and the direction of the therapeutic community in the optimization of effective treatment regimens by various research objectives in cancer treatment.

WINNER: A network biology tool for biomolecular characterization and prioritization

Background and contribution

In network biology, molecular functions can be characterized by network-based inference, or “guilt-by-associations.” PageRank-like tools have been applied in the study of biomolecular interaction networks to obtain further the relative significance of all molecules in the network. However, there is a great deal of inherent noise in widely accessible data sets for gene-to-gene associations or protein-protein interactions. How to develop robust tests to expand, filter, and rank molecular entities in disease-specific networks remains an ad hoc data analysis process.

Results

We describe a new biomolecular characterization and prioritization tool called Weighted In-Network Node Expansion and Ranking (WINNER). It takes the input of any molecular interaction network data and generates an optionally expanded network with all the nodes ranked according to their relevance to one another in the network. To help users assess the robustness of results, WINNER provides two different types of statistics. The first type is a node-expansion p-value, which helps evaluate the statistical significance of adding “non-seed” molecules to the original biomolecular interaction network consisting of “seed” molecules and molecular interactions. The second type is a node-ranking p-value, which helps evaluate the relative statistical significance of the contribution of each node to the overall network architecture. We validated the robustness of WINNER in ranking top molecules by spiking noises in several network permutation experiments. We have found that node degree–preservation randomization of the gene network produced normally distributed ranking scores, which outperform those made with other gene network randomization techniques. Furthermore, we validated that a more significant proportion of the WINNER-ranked genes was associated with disease biology than existing methods such as PageRank. We demonstrated the performance of WINNER with a few case studies, including Alzheimer's disease, breast cancer, myocardial infarctions, and Triple negative breast cancer (TNBC). In all these case studies, the expanded and top-ranked genes identified by WINNER reveal disease biology more significantly than those identified by other gene prioritizing software tools, including Ingenuity Pathway Analysis (IPA) and DiAMOND.

Conclusion

WINNER ranking strongly correlates to other ranking methods when the network covers sufficient node and edge information, indicating a high network quality. WINNER users can use this new tool to robustly evaluate a list of candidate genes, proteins, or metabolites produced from high-throughput biology experiments, as long as there is available gene/protein/metabolic network information.

In Vivo Prenylomic Profiling in the Brain of a Transgenic Mouse Model of Alzheimer's Disease Reveals Increased Prenylation of a Key Set of Proteins

Dysregulation of protein prenylation has been implicated in many diseases, including Alzheimer's disease (AD). Prenylomic analysis, the combination of metabolic incorporation of an isoprenoid analogue (C15AlkOPP) into prenylated proteins with a bottom-up proteomic analysis, has allowed the identification of prenylated proteins in various cellular models. Here, transgenic AD mice were administered with C15AlkOPP through intracerebroventricular (ICV) infusion over 13 days. Using prenylomic analysis, 36 prenylated proteins were enriched in the brains of AD mice. Importantly, the prenylated forms of 15 proteins were consistently upregulated in AD mice compared to nontransgenic wild-type controls. These results highlight the power of this in vivo metabolic labeling approach to identify multiple post-translationally modified proteins that may serve as potential therapeutic targets for a disease that has proved refractory to treatment thus far. Moreover, this method should be applicable to many other types of protein modifications, significantly broadening its scope.

The in vivo Interaction Landscape of Histones H3.1 and H3.3

Chromatin structure, transcription, DNA replication, and repair are regulated via locus-specific incorporation of histone variants and posttranslational modifications that guide effector chromatin-binding proteins. Here we report unbiased, quantitative interactomes for the replication-coupled (H3.1) and replication-independent (H3.3) histone H3 variants based on BioID proximity labeling, which allows interactions in intact, living cells to be detected. Along with a significant proportion of previously reported interactions detected by affinity purification followed by mass spectrometry, three quarters of the 608 histone-associated proteins that we identified are new, uncharacterized histone associations. The data reveal important biological nuances not captured by traditional biochemical means. For example, we found that the chromatin assembly factor-1 histone chaperone not only deposits the replication-coupled H3.1 histone variant during S-phase but also associates with H3.3 throughout the cell cycle in vivo. We also identified other variant-specific associations, such as with transcription factors, chromatin regulators, and with the mitotic machinery. Our proximity-based analysis is thus a rich resource that extends the H3 interactome and reveals new sets of variant-specific associations.

Exploring the Novel Computational Drug Target and Associated Key Pathways of Oral Cancer

Oral cancer (OC) is a serious health concern that has a high fatality rate. The oral cavity has seven kinds of OC, including the lip, tongue, and floor of the mouth, as well as the buccal, hard palate, alveolar, retromolar trigone, and soft palate. The goal of this study is to look into new biomarkers and important pathways that might be used as diagnostic biomarkers and therapeutic candidates in OC. The publicly available repository the Gene Expression Omnibus (GEO) was to the source for the collection of OC-related datasets. GSE74530, GSE23558, and GSE3524 microarray datasets were collected for analysis. Minimum cut-off criteria of |log fold-change (FC)| > 1 and adjusted p < 0.05 were applied to calculate the upregulated and downregulated differential expression genes (DEGs) from the three datasets. After that only common DEGs in all three datasets were collected to apply further analysis. Gene ontology (GO) and pathway analysis were implemented to explore the functional behaviors of DEGs. Then protein−protein interaction (PPI) networks were built to identify the most active genes, and a clustering algorithm was also implemented to identify complex parts of PPI. TF-miRNA networks were also constructed to study OC-associated DEGs in-depth. Finally, top gene performers from PPI networks were used to apply drug signature analysis. After applying filtration and cut-off criteria, 2508, 3377, and 670 DEGs were found for GSE74530, GSE23558, and GSE3524 respectively, and 166 common DEGs were found in every dataset. The GO annotation remarks that most of the DEGs were associated with the terms of type I interferon signaling pathway. The pathways of KEGG reported that the common DEGs are related to the cell cycle and influenza A. The PPI network holds 88 nodes and 492 edges, and CDC6 had the highest number of connections. Four clusters were identified from the PPI. Drug signatures doxorubicin and resveratrol showed high significance according to the hub genes. We anticipate that our bioinformatics research will aid in the definition of OC pathophysiology and the development of new therapies for OC.

lncRNA-miRNA-mRNA ceRNA Network Involved in Sheep Prolificacy: An Integrated Approach

Understanding the molecular pattern of fertility is considered as an important step in breeding of different species, and despite the high importance of the fertility, little success has been achieved in dissecting the interactome basis of sheep fertility. However, the complex mechanisms associated with prolificacy in sheep have not been fully understood. Therefore, this study aimed to use competitive endogenous RNA (ceRNA) networks to evaluate this trait to better understand the molecular mechanisms responsible for fertility. A competitive endogenous RNA (ceRNA) network of the corpus luteum was constructed between Romanov and Baluchi sheep breeds with either good or poor genetic merit for prolificacy using whole-transcriptome analysis. First, the main list of lncRNAs, miRNAs, and mRNA related to the corpus luteum that alter with the breed were extracted, then miRNA−mRNA and lncRNA−mRNA interactions were predicted, and the ceRNA network was constructed by integrating these interactions with the other gene regulatory networks and the protein−protein interaction (PPI). A total of 264 mRNAs, 14 lncRNAs, and 34 miRNAs were identified by combining the GO and KEGG enrichment analyses. In total, 44, 7, 7, and 6 mRNAs, lncRNAs, miRNAs, and crucial modules, respectively, were disclosed through clustering for the corpus luteum ceRNA network. All these RNAs involved in biological processes, namely proteolysis, actin cytoskeleton organization, immune system process, cell adhesion, cell differentiation, and lipid metabolic process, have an overexpression pattern (Padj < 0.01). This study increases our understanding of the contribution of different breed transcriptomes to phenotypic fertility differences and constructed a ceRNA network in sheep (Ovis aries) to provide insights into further research on the molecular mechanism and identify new biomarkers for genetic improvement.

Drug repurposing: An emerging strategy in alleviating skin cancer

Skin cancer is one of the most common forms of cancer. Several million people are estimated to have affected with this condition worldwide. Skin cancer generally includes melanoma and non-melanoma with the former being the most dangerous. Chemotherapy has been one of the key therapeutic strategies employed in the treatment of skin cancer, especially in advanced stages of the disease. It could be also used as an adjuvant with other treatment modalities depending on the type of skin cancer. However, there are several shortfalls associated with the use of chemotherapy such as non-selectivity, tumour resistance, life-threatening toxicities, and the exorbitant cost of medicines. Furthermore, new drug discovery is a lengthy and costly process with minimal likelihood of success. Thus, drug repurposing (DR) has emerged as a new avenue where the drug approved formerly for the treatment of one disease can be used for the treatment of another disease like cancer. This approach is greatly beneficial over the de novo approach in terms of time and cost. Moreover, there is minimal risk of failure of repurposed therapeutics in clinical trials. There are a considerable number of studies that have reported on drugs repurposed for the treatment of skin cancer. Thus, the present manuscript offers a comprehensive overview of drugs that have been investigated as repurposing candidates for the efficient treatment of skin cancers mainly melanoma and its oncogenic subtypes, and non-melanoma. The prospects of repurposing phytochemicals against skin cancer are also discussed. Furthermore, repurposed drug delivery via topical route and repurposed drugs in clinical trials are briefed. Based on the findings from the reported studies discussed in this manuscript, drug repurposing emerges to be a promising approach and thus is expected to offer efficient treatment at a reasonable cost in devitalizing skin cancer.

In vitro ve in silico analizi ile metforminin meme tümörü hücrelerinde protein profili üzerindeki etkinliği

Aim: This study aimed to uncover the varieties in protein profiles of Met in breast tumor (BT) cells by assessment of in vitro and in silico analysis. Materials and Methods: Here, the cells obtained from mastectomy patients were cultured, the effective Met-dose was determined as 25 mM through cell viability and BrdU tests. Protein identification in the breast tumor cells was implemented by employing LC-MS/MS technology. Results: The expression of SSR3, THAP3, FTH1, NEFM, ANP32A, ANP32B, KRT7 proteins was significantly decreased whereas the GARS protein increased in the 25 mM Met group compared to the Non-Met (0 mM) control group. In silico analysis, we analyzed the probable interactions of all these proteins with each other and other proteins, to evaluate the analysis of the larger protein network, and which metabolic pathway proteins are involved in. Conclusion: The stated proteomics analysis in our study proposes a better understanding of the prognosis of breast cancer and future studies to investigate the effect of metformin in this field on proteomic pathways in other sorts of cancer.

Computational and Experimental Analysis of Genetic Variants

Genomics has grown exponentially over the last decade. Common variants are associated with physiological changes through statistical strategies such as Genome-Wide Association Studies (GWAS) and quantitative trail loci (QTL). Rare variants are associated with diseases through extensive filtering tools, including population genomics and trio-based sequencing (parents and probands). However, the genomic associations require follow-up analyses to narrow causal variants, identify genes that are influenced, and to determine the physiological changes. Large quantities of data exist that can be used to connect variants to gene changes, cell types, protein pathways, clinical phenotypes, and animal models that establish physiological genomics. This data combined with bioinformatics including evolutionary analysis, structural insights, and gene regulation can yield testable hypotheses for mechanisms of genomic variants. Molecular biology, biochemistry, cell culture, CRISPR editing, and animal models can test the hypotheses to give molecular variant mechanisms. Variant characterizations can be a significant component of educating future professionals at the undergraduate, graduate, or medical training programs through teaching the basic concepts and terminology of genetics while learning independent research hypothesis design. This article goes through the computational and experimental analysis strategies of variant characterization and provides examples of these tools applied in publications. © 2022 American Physiological Society. Compr Physiol 12:3303-3336, 2022.

Gene Network of Susceptibility to Atypical Femoral Fractures Related to Bisphosphonate Treatment

Atypical femoral fractures (AFF) are rare fragility fractures in the subtrocantheric or diaphysis femoral region associated with long-term bisphosphonate (BP) treatment. The etiology of AFF is still unclear even though a genetic basis is suggested. We performed whole exome sequencing (WES) analysis of 12 patients receiving BPs for at least 5 years who sustained AFFs and 4 controls, also long-term treated with BPs but without any fracture. After filtration and prioritization of rare variants predicted to be damaging and present in genes shared among at least two patients, a total of 272 variants in 132 genes were identified. Twelve of these genes were known to be involved in bone metabolism and/or AFF, highlighting DAAM2 and LRP5, both involved in the Wnt pathway, as the most representative. Afterwards, we intersected all mutated genes with a list of 34 genes obtained from a previous study of three sisters with BP-related AFF, identifying nine genes. One of these (MEX3D) harbored damaging variants in two AFF patients from the present study and one shared among the three sisters. Gene interaction analysis using the AFFNET web suggested a complex network among bone-related genes as well as with other mutated genes. BinGO biological function analysis highlighted cytoskeleton and cilium organization. In conclusion, several genes and their interactions could provide genetic susceptibility to AFF, that along with BPs treatment and in some cases with glucocorticoids may trigger this so feared complication.

Protein Complex Organization Imposes Constraints on Proteome Dysregulation in Cancer

Protein assembly is a highly dynamic process and proteins can interact in different ways and stoichiometries within a complex. The importance of maintaining protein stoichiometry for complex function and avoiding aggregation of orphan subunits has been demonstrated. However, how exactly the organization of proteins into complexes constrains differential protein abundance in extreme cellular conditions like cancer, where a lot of protein abundance changes occur, has not been systematically investigated. To study this, we collected proteomic data made available by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) to quantify proteomic changes during carcinogenesis and systematically tested five interaction types in complexes to investigate which of these features impact on protein abundance correlation patterns in cancer. We found that higher than expected fraction of protein complex subunits does not show changes in their abundances compared to those in the normal samples. Furthermore, we found that the way proteins interact in complexes indeed constrains their co-abundance patterns. Our results highlight the role of the interactions between the proteins and the need of cancer cells to deal with aberrant changes in protein abundance.

Human placental cytotrophoblast epigenome dynamics over gestation and alterations in placental disease

The human placenta and its specialized cytotrophoblasts rapidly develop, have a compressed lifespan, govern pregnancy outcomes, and program the offspring's health. Understanding the molecular underpinnings of these behaviors informs development and disease. Profiling the extraembryonic epigenome and transcriptome during the 2nd and 3rd trimesters revealed H3K9 trimethylation overlapping deeply DNA hypomethylated domains with reduced gene expression and compartment-specific patterns that illuminated their functions. Cytotrophoblast DNA methylation increased, and several key histone modifications decreased across the genome as pregnancy advanced. Cytotrophoblasts from severe preeclampsia had substantially increased H3K27 acetylation globally and at genes that are normally downregulated at term but upregulated in this syndrome. In addition, some cases had an immature pattern of H3K27ac peaks, and others showed evidence of accelerated aging, suggesting subtype-specific alterations in severe preeclampsia. Thus, the cytotrophoblast epigenome dramatically reprograms during pregnancy, placental disease is associated with failures in this process, and H3K27 hyperacetylation is a feature of severe preeclampsia.

KinPred: A unified and sustainable approach for harnessing proteome-level human kinase-substrate predictions

Tyrosine and serine/threonine kinases are essential regulators of cell processes and are important targets for human therapies. Unfortunately, very little is known about specific kinase-substrate relationships, making it difficult to infer meaning from dysregulated phosphoproteomic datasets or for researchers to identify possible kinases that regulate specific or novel phosphorylation sites. The last two decades have seen an explosion in algorithms to extrapolate from what little is known into the larger unknown-predicting kinase relationships with site-specific substrates using a variety of approaches that include the sequence-specificity of kinase catalytic domains and various other factors, such as evolutionary relationships, co-expression, and protein-protein interaction networks. Unfortunately, a number of limitations prevent researchers from easily harnessing these resources, such as loss of resource accessibility, limited information in publishing that results in a poor mapping to a human reference, and not being updated to match the growth of the human phosphoproteome. Here, we propose a methodological framework for publishing predictions in a unified way, which entails ensuring predictions have been run on a current reference proteome, mapping the same substrates and kinases across resources to a common reference, filtering for the human phosphoproteome, and providing methods for updating the resource easily in the future. We applied this framework on three currently available resources, published in the last decade, which provide kinase-specific predictions in the human proteome. Using the unified datasets, we then explore the role of study bias, the emergent network properties of these predictive algorithms, and comparisons within and between predictive algorithms. The combination of the code for unification and analysis, as well as the unified predictions are available under the resource we named KinPred. We believe this resource will be useful for a wide range of applications and establishes best practices for long-term usability and sustainability for new and existing predictive algorithms.

Comparison of the central human and mouse platelet signaling cascade by systems biological analysis

Background

Understanding the molecular mechanisms of platelet activation and aggregation is of high interest for basic and clinical hemostasis and thrombosis research. The central platelet protein interaction network is involved in major responses to exogenous factors. This is defined by systemsbiological pathway analysis as the central regulating signaling cascade of platelets (CC).

Results

The CC is systematically compared here between mouse and human and major differences were found. Genetic differences were analysed comparing orthologous human and mouse genes. We next analyzed different expression levels of mRNAs. Considering 4 mouse and 7 human high-quality proteome data sets, we identified then those major mRNA expression differences (81%) which were supported by proteome data. CC is conserved regarding genetic completeness, but we observed major differences in mRNA and protein levels between both species. Looking at central interactors, human PLCB2, MMP9, BDNF, ITPR3 and SLC25A6 (always Entrez notation) show absence in all murine datasets. CC interactors GNG12, PRKCE and ADCY9 occur only in mice. Looking at the common proteins, TLN1, CALM3, PRKCB, APP, SOD2 and TIMP1 are higher abundant in human, whereas RASGRP2, ITGB2, MYL9, EIF4EBP1, ADAM17, ARRB2, CD9 and ZYX are higher abundant in mouse. Pivotal kinase SRC shows different regulation on mRNA and protein level as well as ADP receptor P2RY12.

Conclusions

Our results highlight species-specific differences in platelet signaling and points of specific fine-tuning in human platelets as well as murine-specific signaling differences.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-020-07215-4.

Navigating the Global Protein-Protein Interaction Landscape Using iRefWeb

iRefWeb is a resource that provides web interface to a large collection of protein-protein interactions aggregated from major primary databases. The underlying data-consolidation process, called iRefIndex, implements a rigorous methodology of identifying redundant protein sequences and integrating disparate data records that reference the same peptide sequences, despite many potential differences in data identifiers across various source databases. iRefWeb offers a unified user interface to all interaction records and associated information collected by iRefIndex, in addition to a number of data filters and visual features that present the supporting evidence. Users of iRefWeb can explore the consolidated landscape of protein-protein interactions, establish the provenance and reliability of each data record, and compare annotations performed by different data curator teams. The iRefWeb portal is freely available at http://wodaklab.org/iRefWeb .

Integrating Single-Step GWAS and Bipartite Networks Reconstruction Provides Novel Insights into Yearling Weight and Carcass Traits in Hanwoo Beef Cattle

Simple Summary

Hanwoo is an indigenous cattle breed in Korea and popular for meat production owing to its rapid growth and high-quality meat. Its yearling weight and carcass traits (backfat thickness, carcass weight, eye muscle area, and marbling score) are economically important for the selection of young and proven bulls. In recent decades, the advent of high throughput genotyping technologies has made it possible to perform genome-wide association studies (GWAS) for the detection of genomic regions associated with traits of economic interest in different species. In this study, we conducted a weighted single-step genome-wide association study which combines all genotypes, phenotypes and pedigree data in one step (ssGBLUP). It allows for the use of all SNPs simultaneously along with all phenotypes from genotyped and ungenotyped animals. Our results revealed 33 relevant genomic regions related to the traits of interest. Gene set enrichment analysis indicated that the identified candidate genes were related to biological processes and functional terms that were involved in growth and lipid metabolism. In conclusion, these results suggest that the incorporation of GWAS results and network analysis can help us to better understand the genetic bases underlying growth and carcass traits.

Abstract

In recent years, studies on the biological mechanisms underlying complex traits have been facilitated by innovations in high-throughput genotyping technology. We conducted a weighted single-step genome-wide association study (WssGWAS) to evaluate backfat thickness, carcass weight, eye muscle area, marbling score, and yearling weight in a cohort of 1540 Hanwoo beef cattle using BovineSNP50 BeadChip. The WssGWAS uncovered thirty-three genomic regions that explained more than 1% of the additive genetic variance, mostly located on chromosomes 6 and 14. Among the identified window regions, seven quantitative trait loci (QTL) had pleiotropic effects and twenty-six QTL were trait-specific. Significant pathways implicated in the measured traits through Gene Ontology (GO) term enrichment analysis included the following: lipid biosynthetic process, regulation of lipid metabolic process, transport or localization of lipid, regulation of growth, developmental growth, and multicellular organism growth. Integration of GWAS results of the studied traits with pathway and network analyses facilitated the exploration of the respective candidate genes involved in several biological functions, particularly lipid and growth metabolism. This study provides novel insight into the genetic bases underlying complex traits and could be useful in developing breeding schemes aimed at improving growth and carcass traits in Hanwoo beef cattle.

Statistical Methods in Genome-Wide Association Studies

Since the initial success of genome-wide association studies (GWAS) in 2005, tens of thousands of genetic variants have been identified for hundreds of human diseases and traits. In a GWAS, genotype information at up to millions of genetic markers is collected from up to hundreds of thousands of individuals, together with their phenotype information. Several scientific goals can be accomplished through the analysis of GWAS data, including the identification of variants, genes, and pathways associated with diseases and traits of interest; the inference of the genetic architecture of these traits; and the development of genetic risk prediction models. In this review, we provide an overview of the statistical challenges in achieving these goals and recent progress in statistical methodology to address these challenges.

Network Analyses of the Differential Expression of Heat Shock Proteins in Glioma

Heat shock protein (HSP) is a family of highly conserved protein, which exists widely in various organisms and has a variety of important physiological functions. Currently, there is no systematic analysis of HSPs in human glioma. The aim of this study was to investigate the characteristics of HSPs through constructing protein-protein interaction network (PPIN) considering the expression level of HSPs in glioma. After the identification of the differentially expressed HSPs in glioma tissues, a specific PPIN was constructed and found that there were many interactions between the differentially expressed HSPs in glioma. Subcellular localization analysis shows that HSPs and their interacting proteins distribute from the cell membrane to the nucleus in a multilayer structure. By functional enrichment analysis, gene ontology analysis, and Kyoto Encyclopedia of Genes and Genomes pathway analysis, the potential function of HSPs and two meaningful enrichment pathways was revealed. In addition, nine HSPs (DNAJA4, DNAJC6, DNAJC12, HSPA6, HSP90B1, DNAJB1, DNAJB6, DNAJC10, and SERPINH1) are prognostic markers for human brain glioma. These analyses provide a full view of HSPs about their expression, biological process, as well as clinical significance in glioma.

United Neighborhood Closeness Centrality and Orthology for Predicting Essential Proteins

Identifying essential proteins plays an important role in disease study, drug design, and understanding the minimal requirement for cellular life. Computational methods for essential proteins discovery overcome the disadvantages of biological experimental methods that are often time-consuming, expensive, and inefficient. The topological features of protein-protein interaction (PPI) networks are often used to design computational prediction methods, such as Degree Centrality (DC), Betweenness Centrality (BC), Closeness Centrality (CC), Subgraph Centrality (SC), Eigenvector Centrality (EC), Information Centrality (IC), and Neighborhood Centrality (NC). However, the prediction accuracies of these individual methods still have space to be improved. Studies show that additional information, such as orthologous relations, helps discover essential proteins. Many researchers have proposed different methods by combining multiple information sources to gain improvement of prediction accuracy. In this study, we find that essential proteins appear in triangular structure in PPI network significantly more often than nonessential ones. Based on this phenomenon, we propose a novel pure centrality measure, so-called Neighborhood Closeness Centrality (NCC). Accordingly, we develop a new combination model, Extended Pareto Optimality Consensus model, named EPOC, to fuse NCC and Orthology information and a novel essential proteins identification method, NCCO, is fully proposed. Compared with seven existing classic centrality methods (DC, BC, IC, CC, SC, EC, and NC) and three consensus methods (PeC, ION, and CSC), our results on S.cerevisiae and E.coli datasets show that NCCO has clear advantages. As a consensus method, EPOC also yields better performance than the random walk model.

Selective Neuronal Vulnerability in Alzheimer's Disease: A Network-Based Analysis

A major obstacle to treating Alzheimer's disease (AD) is our lack of understanding of the molecular mechanisms underlying selective neuronal vulnerability, a key characteristic of the disease. Here, we present a framework integrating high-quality neuron-type-specific molecular profiles across the lifetime of the healthy mouse, which we generated using bacTRAP, with postmortem human functional genomics and quantitative genetics data. We demonstrate human-mouse conservation of cellular taxonomy at the molecular level for neurons vulnerable and resistant in AD, identify specific genes and pathways associated with AD neuropathology, and pinpoint a specific functional gene module underlying selective vulnerability, enriched in processes associated with axonal remodeling, and affected by amyloid accumulation and aging. We have made all cell-type-specific profiles and functional networks available at http://alz.princeton.edu. Overall, our study provides a molecular framework for understanding the complex interplay between Aβ, aging, and neurodegeneration within the most vulnerable neurons in AD.

DPCT: A Dynamic Method for Detecting Protein Complexes From TAP-Aware Weighted PPI Network

Detecting protein complexes from the Protein-Protein interaction network (PPI) is the essence of discovering the rules of the cellular world. There is a large amount of PPI data available, generated from high throughput experimental data. The enormous size of the data persuaded us to use computational methods instead of experimental methods to detect protein complexes. In past years, many researchers presented their algorithms to detect protein complexes. Most of the presented algorithms use current static PPI networks. New researches proved the dynamicity of cellular systems, and so, the PPI is not static over time. In this paper, we introduce DPCT to detect protein complexes from dynamic PPI networks. In the proposed method, TAP and GO data are used to make a weighted PPI network and to reduce the noise of PPI. Gene expression data are also used to make dynamic subnetworks from PPI. A memetic algorithm is used to bicluster gene expression data and to create a dynamic subnetwork for each bicluster. Experimental results show that DPCT can detect protein complexes with better correctness than state-of-the-art detection algorithms. The source code and datasets of DPCT used can be found at https://github.com/alisn72/DPCT.

Identification of feature risk pathways of smoking-induced lung cancer based on SVM

Objective

The present study aims to explore the role of smoking factors in the risk of lung cancer and screen the feature risk pathways of smoking-induced lung cancer.

Methods

The expression profiles of the patient data from GEO database were standardized, and differentially expressed genes (DEGs) were analyzed by limma algorithm. Samples and genes were analyzed by Unsupervised hierarchical clustering method, while GO and KEGG enrichment analyses were performed on DEGs. The data of the protein-protein interaction (PPI) network were downloaded from the BioGrid and HPRD databases, and the DEGs were mapped into the PPI network to identify the interaction relationship. The enriched significant pathways were used to calculate the anomaly score and RFE method was used to optimize the feature sets. The model was trained using the support vector machine (SVM) and the predicted results were plotted into ROC curves. The AUC value was calculated to evaluate the predictive performance of the SVM model.

Results

A total of 1923 DEGs were obtained, of which 826 were down-regulated and 1097 were up-regulated. Unsupervised hierarchical clustering analysis showed that the diagnosis accuracy of lung cancer smokers was 74%, and that of non-lung cancer smokers was 75%. Five optimal feature pathway sets were obtained by screening, the clinical diagnostic ability of which was detected by SVM model with the accuracy improved to 84%. The diagnostic accuracy was 90% after combining clinical information.

Conclusion

We verified that five signaling pathways combined with clinical information could be used as a feature risk pathway for identifying lung cancer smokers and non-lung cancer smokers and increased the diagnostic accuracy.

Constructing knowledge graphs and their biomedical applications

Knowledge graphs can support many biomedical applications. These graphs represent biomedical concepts and relationships in the form of nodes and edges. In this review, we discuss how these graphs are constructed and applied with a particular focus on how machine learning approaches are changing these processes. Biomedical knowledge graphs have often been constructed by integrating databases that were populated by experts via manual curation, but we are now seeing a more robust use of automated systems. A number of techniques are used to represent knowledge graphs, but often machine learning methods are used to construct a low-dimensional representation that can support many different applications. This representation is designed to preserve a knowledge graph's local and/or global structure. Additional machine learning methods can be applied to this representation to make predictions within genomic, pharmaceutical, and clinical domains. We frame our discussion first around knowledge graph construction and then around unifying representational learning techniques and unifying applications. Advances in machine learning for biomedicine are creating new opportunities across many domains, and we note potential avenues for future work with knowledge graphs that appear particularly promising.

Aligning functional network constraint to evolutionary outcomes

BACKGROUND

Functional constraint through genomic architecture is suggested to be an important dimension of genome evolution, but quantitative evidence for this idea is rare. In this contribution, existing evidence and discussions on genomic architecture as constraint for convergent evolution, rapid adaptation, and genic adaptation are summarized into alternative, testable hypotheses. Network architecture statistics from protein-protein interaction networks are then used to calculate differences in evolutionary outcomes on the example of genomic evolution in yeast, and the results are used to evaluate statistical support for these longstanding hypotheses.

RESULTS

A discriminant function analysis lent statistical support to classifying the yeast interactome into hub, intermediate and peripheral nodes based on network neighborhood connectivity, betweenness centrality, and average shortest path length. Quantitative support for the existence of genomic architecture as a mechanistic basis for evolutionary constraint is then revealed through utilizing these statistical parameters of the protein-protein interaction network in combination with estimators of protein evolution.

CONCLUSIONS

As functional genetic networks are becoming increasingly available, it will now be possible to evaluate functional genetic network constraint against variables describing complex phenotypes and environments, for better understanding of commonly observed deterministic patterns of evolution in non-model organisms. The hypothesis framework and methodological approach outlined herein may help to quantify the extrinsic versus intrinsic dimensions of evolutionary constraint, and result in a better understanding of how fast, effectively, or deterministically organisms adapt.

Constructing Disease Similarity Networks Based on Disease Module Theory

Quantifying the associations between diseases is now playing an important role in modern biology and medicine. Actually discovering associations between diseases could help us gain deeper insights into pathogenic mechanisms of complex diseases, thus could lead to improvements in disease diagnosis, drug repositioning, and drug development. Due to the growing body of high-throughput biological data, a number of methods have been developed for computing similarity between diseases during the past decade. However, these methods rarely consider the interconnections of genes related to each disease in protein-protein interaction network (PPIN). Recently, the disease module theory has been proposed, which states that disease-related genes or proteins tend to interact with each other in the same neighborhood of a PPIN. In this study, we propose a new method called ModuleSim to measure associations between diseases by using disease-gene association data and PPIN data based on disease module theory. The experimental results show that by considering the interactions between disease modules and their modularity, the disease similarity calculated by ModuleSim has a significant correlation with disease classification of Disease Ontology (DO). Furthermore, ModuleSim outperforms other four popular methods which are all using disease-gene association data and PPIN data to measure disease-disease associations. In addition, the disease similarity network constructed by MoudleSim suggests that ModuleSim is capable of finding potential associations between diseases.

Lipid rafts and neurodegeneration: structural and functional roles in physiologic aging and neurodegenerative diseases

Lipid rafts are small, dynamic membrane areas characterized by the clustering of selected membrane lipids as the result of the spontaneous separation of glycolipids, sphingolipids, and cholesterol in a liquid-ordered phase. The exact dynamics underlying phase separation of membrane lipids in the complex biological membranes are still not fully understood. Nevertheless, alterations in the membrane lipid composition affect the lateral organization of molecules belonging to lipid rafts. Neural lipid rafts are found in brain cells, including neurons, astrocytes, and microglia, and are characterized by a high enrichment of specific lipids depending on the cell type. These lipid rafts seem to organize and determine the function of multiprotein complexes involved in several aspects of signal transduction, thus regulating the homeostasis of the brain. The progressive decline of brain performance along with physiological aging is at least in part associated with alterations in the composition and structure of neural lipid rafts. In addition, neurodegenerative conditions, such as lysosomal storage disorders, multiple sclerosis, and Parkinson's, Huntington's, and Alzheimer's diseases, are frequently characterized by dysregulated lipid metabolism, which in turn affects the structure of lipid rafts. Several events underlying the pathogenesis of these diseases appear to depend on the altered composition of lipid rafts. Thus, the structure and function of lipid rafts play a central role in the pathogenesis of many common neurodegenerative diseases.jlr;61/5/636/F1F1f1.

WeiBI (web-based platform): Enriching integrated interaction network with increased coverage and functional proteins from genome-wide experimental OMICS data

Many molecular system biology approaches recognize various interactions and functional associations of proteins that occur in cellular processing. Further understanding of the characterization technique reveals noteworthy information. These types of known and predicted interactions, gained through multiple resources, are thought to be important for experimental data to satisfy comprehensive and quality needs. The current work proposes the “WeiBI (WeiBiologicalInteractions)” database that clarifies direct and indirect partnerships associated with biological interactions. This database contains information concerning protein’s functional partnerships and interactions along with their integration into a statistical model that can be computationally predicted for humans. This novel approach in WeiBI version 1.0 collects information using an improved algorithm by transferring interactions between more than 115570 entries, allowing statistical analysis with the automated background for the given inputs for functional enrichment. This approach also allows the input of an entity’s list from a database along with the visualization of subsets as an interaction network and successful performance of the enrichment analysis for a gene set. This wisely improved algorithm is user-friendly, and its accessibility and higher accuracy make it the best database for exploring interactions among genomes’ network and reflects the importance of this study. The proposed server “WeiBI” is accessible at http://weislab.com/WeiDOCK/?page=PKPD.

SG-LSTM-FRAME: a computational frame using sequence and geometrical information via LSTM to predict miRNA-gene associations

MOTIVATION

MircroRNAs (miRNAs) regulate target genes and are responsible for lethal diseases such as cancers. Accurately recognizing and identifying miRNA and gene pairs could be helpful in deciphering the mechanism by which miRNA affects and regulates the development of cancers. Embedding methods and deep learning methods have shown their excellent performance in traditional classification tasks in many scenarios. But not so many attempts have adapted and merged these two methods into miRNA-gene relationship prediction. Hence, we proposed a novel computational framework. We first generated representational features for miRNAs and genes using both sequence and geometrical information and then leveraged a deep learning method for the associations' prediction.

RESULTS

We used long short-term memory (LSTM) to predict potential relationships and proved that our method outperformed other state-of-the-art methods. Results showed that our framework SG-LSTM got an area under curve of 0.94 and was superior to other methods. In the case study, we predicted the top 10 miRNA-gene relationships and recommended the top 10 potential genes for hsa-miR-335-5p for SG-LSTM-core. We also tested our model using a larger dataset, from which 14 668 698 miRNA-gene pairs were predicted. The top 10 unknown pairs were also listed.

AVAILABILITY

Our work can be download in https://github.com/Xshelton/SG_LSTM.

CONTACT

luojiawei@hnu.edu.cn.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Briefings in Bioinformatics online.

The Evolution of Gene Duplicates in Angiosperms and the Impact of Protein-Protein Interactions and the Mechanism of Duplication

Gene duplicates, generated through either whole genome duplication (WGD) or small-scale duplication (SSD), are prominent in angiosperms and are believed to play an important role in adaptation and in generating evolutionary novelty. Previous studies reported contrasting evolutionary and functional dynamics of duplicate genes depending on the mechanism of origin, a behavior that is hypothesized to stem from constraints to maintain the relative dosage balance between the genes concerned and their interaction context. However, the mechanisms ultimately influencing loss and retention of gene duplicates over evolutionary time are not yet fully elucidated. Here, by using a robust classification of gene duplicates in Arabidopsis thaliana, Solanum lycopersicum, and Zea mays, large RNAseq expression compendia and an extensive protein-protein interaction (PPI) network from Arabidopsis, we investigated the impact of PPIs on the differential evolutionary and functional fate of WGD and SSD duplicates. In all three species, retained WGD duplicates show stronger constraints to diverge at the sequence and expression level than SSD ones, a pattern that is also observed for shared PPI partners between Arabidopsis duplicates. PPIs are preferentially distributed among WGD duplicates and specific functional categories. Furthermore, duplicates with PPIs tend to be under stronger constraints to evolve than their counterparts without PPIs regardless of their mechanism of origin. Our results support dosage balance constraint as a specific property of genes involved in biological interactions, including physical PPIs, and suggest that additional factors may be differently influencing the evolution of genes following duplication, depending on the species, time, and mechanism of origin.

CHG: A Systematically Integrated Database of Cancer Hallmark Genes

Background

The analysis of cancer diversity based on a logical framework of hallmarks has greatly improved our understanding of the occurrence, development and metastasis of various cancers.

Methods

We designed Cancer Hallmark Genes (CHG) database which focuses on integrating hallmark genes in a systematic, standard way and annotates the potential roles of the hallmark genes in cancer processes. Following the conceptual criteria description of hallmark function the keywords for each hallmark were manually selected from the literature. Candidate hallmark genes collected were derived from 301 pathways of KEGG database by Lucene and manually corrected.

Results

Based on the variation data, we finally identified the hallmark genes of various types of cancer and constructed CHG. And we also analyzed the relationships among hallmarks and potential characteristics and relationships of hallmark genes based on the topological structures of their networks. We manually confirm the hallmark gene identified by CHG based on literature and database. We also predicted the prognosis of breast cancer, glioblastoma multiforme and kidney papillary cell carcinoma patients based on CHG data.

Conclusions

In summary, CHG, which was constructed based on a hallmark feature set, provides a new perspective for analyzing the diversity and development of cancers.

Human cytomegalovirus interactome analysis identifies degradation hubs, domain associations and viral protein functions

Human cytomegalovirus (HCMV) extensively modulates host cells, downregulating >900 human proteins during viral replication and degrading ≥133 proteins shortly after infection. The mechanism of degradation of most host proteins remains unresolved, and the functions of many viral proteins are incompletely characterised. We performed a mass spectrometry-based interactome analysis of 169 tagged, stably-expressed canonical strain Merlin HCMV proteins, and two non-canonical HCMV proteins, in infected cells. This identified a network of >3400 virus-host and >150 virus-virus protein interactions, providing insights into functions for multiple viral genes. Domain analysis predicted binding of the viral UL25 protein to SH3 domains of NCK Adaptor Protein-1. Viral interacting proteins were identified for 31/133 degraded host targets. Finally, the uncharacterised, non-canonical ORFL147C protein was found to interact with elements of the mRNA splicing machinery, and a mutational study suggested its importance in viral replication. The interactome data will be important for future studies of herpesvirus infection.

A novel extended Pareto Optimality Consensus model for predicting essential proteins

Essential proteins have vital functions, when they are destroyed in cells, the cells will die or stop reproducing. Therefore, it is very important to identify essential proteins from a large number of other proteins. Due to the time-consuming, expensive, and inefficient process in biological experimental methods, computational methods become more and more popular to recognize them. In the early stages, these methods mainly rely on protein-protein interaction (PPI) information, which limits their discovery capacities. Researchers find novel methods by fusing multi-information to improve prediction accuracy. According to these features, essential proteins are more important and conservative in the evolution process, their neighbors in PPI networks are usually likely to be essential, there are many false positives in PPI data, whether a protein is essential can be assessed by the importance of a protein itself, the relevance of neighbors and the reliability of PPIs. The importance of neighbors and the reliability of PPIs can be further integrated into neighborhood feature. In the study, orthologous information, edge-clustering coefficient and gene expression information are used to measure the importance of a protein itself, the importance of the neighbors and the reliability of PPIs, respectively. Then, a novel expanded POC model, E_POC, is proposed to fuse the above information to discover essential proteins, a weighted PPI network is constructed. The proteins ranked high according to their weights are treated as candidate essential proteins. This novel method is named as E_POC. E_POC outperforms the existing classical methods on S. cerevisiae and E. coli data.

Drug repurposing for breast cancer therapy: Old weapon for new battle

Despite tremendous resources being invested in prevention and treatment, breast cancer remains a leading cause of cancer deaths in women globally. The available treatment modalities are very costly and produces severe side effects. Drug repurposing that relate to new uses for old drugs has emerged as a novel approach for drug development. Repositioning of old, clinically approved, off patent non-cancer drugs with known targets, into newer indication is like using old weapons for new battle. The advances in genomics, proteomics and information computational biology has facilitated the process of drug repurposing. Repositioning approach not only fastens the process of drug development but also offers more effective, cheaper, safer drugs with lesser/known side effects. During the last decade, drugs such as alkylating agents, anthracyclins, antimetabolite, CDK4/6 inhibitor, aromatase inhibitor, mTOR inhibitor and mitotic inhibitors has been repositioned for breast cancer treatment. The repositioned drugs have been successfully used for the treatment of most aggressive triple negative breast cancer. The literature review suggest that serendipity plays a major role in the drug development. This article describes the comprehensive overview of the current scenario of drug repurposing for the breast cancer treatment. The strategies as well as several examples of repurposed drugs are provided. The challenges associated with drug repurposing are discussed.

Genome-wide effects of social status on DNA methylation in the brain of a cichlid fish, Astatotilapia burtoni

BACKGROUND

Successful social behavior requires real-time integration of information about the environment, internal physiology, and past experience. The molecular substrates of this integration are poorly understood, but likely modulate neural plasticity and gene regulation. In the cichlid fish species Astatotilapia burtoni, male social status can shift rapidly depending on the environment, causing fast behavioral modifications and a cascade of changes in gene transcription, the brain, and the reproductive system. These changes can be permanent but are also reversible, implying the involvement of a robust but flexible mechanism that regulates plasticity based on internal and external conditions. One candidate mechanism is DNA methylation, which has been linked to social behavior in many species, including A. burtoni. But, the extent of its effects after A. burtoni social change were previously unknown.

RESULTS

We performed the first genome-wide search for DNA methylation patterns associated with social status in the brains of male A. burtoni, identifying hundreds of Differentially Methylated genomic Regions (DMRs) in dominant versus non-dominant fish. Most DMRs were inside genes supporting neural development, synapse function, and other processes relevant to neural plasticity, and DMRs could affect gene expression in multiple ways. DMR genes were more likely to be transcription factors, have a duplicate elsewhere in the genome, have an anti-sense lncRNA, and have more splice variants than other genes. Dozens of genes had multiple DMRs that were often seemingly positioned to regulate specific splice variants.

CONCLUSIONS

Our results revealed genome-wide effects of A. burtoni social status on DNA methylation in the brain and strongly suggest a role for methylation in modulating plasticity across multiple biological levels. They also suggest many novel hypotheses to address in mechanistic follow-up studies, and will be a rich resource for identifying the relationships between behavioral, neural, and transcriptional plasticity in the context of social status.

Predicting links between tumor samples and genes using 2-Layered graph based diffusion approach

Background

Determining the association between tumor sample and the gene is demanding because it requires a high cost for conducting genetic experiments. Thus, the discovered association between tumor sample and gene further requires clinical verification and validation. This entire mechanism is time-consuming and expensive. Due to this issue, predicting the association between tumor samples and genes remain a challenge in biomedicine.

Results

Here we present, a computational model based on a heat diffusion algorithm which can predict the association between tumor samples and genes. We proposed a 2-layered graph. In the first layer, we constructed a graph of tumor samples and genes where these two types of nodes are connected by “hasGene” relationship. In the second layer, the gene nodes are connected by “interaction” relationship. We applied the heat diffusion algorithms in nine different variants of genetic interaction networks extracted from STRING and BioGRID database. The heat diffusion algorithm predicted the links between tumor samples and genes with mean AUC-ROC score of 0.84. This score is obtained by using weighted genetic interactions of fusion or co-occurrence channels from the STRING database. For the unweighted genetic interaction from the BioGRID database, the algorithms predict the links with an AUC-ROC score of 0.74.

Conclusions

We demonstrate that the gene-gene interaction scores could improve the predictive power of the heat diffusion model to predict the links between tumor samples and genes. We showed the efficient runtime of the heat diffusion algorithm in various genetic interaction network. We statistically validated our prediction quality of the links between tumor samples and genes.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-3056-2) contains supplementary material, which is available to authorized users.

The role of structural pleiotropy and regulatory evolution in the retention of heteromers of paralogs

Gene duplication is a driver of the evolution of new functions. The duplication of genes encoding homomeric proteins leads to the formation of homomers and heteromers of paralogs, creating new complexes after a single duplication event. The loss of these heteromers may be required for the two paralogs to evolve independent functions. Using yeast as a model, we find that heteromerization is frequent among duplicated homomers and correlates with functional similarity between paralogs. Using in silico evolution, we show that for homomers and heteromers sharing binding interfaces, mutations in one paralog can have structural pleiotropic effects on both interactions, resulting in highly correlated responses of the complexes to selection. Therefore, heteromerization could be preserved indirectly due to selection for the maintenance of homomers, thus slowing down functional divergence between paralogs. We suggest that paralogs can overcome the obstacle of structural pleiotropy by regulatory evolution at the transcriptional and post-translational levels.

A seed-extended algorithm for detecting protein complexes based on density and modularity with topological structure and GO annotations

Background

The detection of protein complexes is of great significance for researching mechanisms underlying complex diseases and developing new drugs. Thus, various computational algorithms have been proposed for protein complex detection. However, most of these methods are based on only topological information and are sensitive to the reliability of interactions. As a result, their performance is affected by false-positive interactions in PPINs. Moreover, these methods consider only density and modularity and ignore protein complexes with various densities and modularities.

Results

To address these challenges, we propose an algorithm to exploit protein complexes in PPINs by a Seed-Extended algorithm based on Density and Modularity with Topological structure and GO annotations, named SE-DMTG to improve the accuracy of protein complex detection. First, we use common neighbors and GO annotations to construct a weighted PPIN. Second, we define a new seed selection strategy to select seed nodes. Third, we design a new fitness function to detect protein complexes with various densities and modularities. We compare the performance of SE-DMTG with that of thirteen state-of-the-art algorithms on several real datasets.

Conclusion

The experimental results show that SE-DMTG not only outperforms some classical algorithms in yeast PPINs in terms of the F-measure and Jaccard but also achieves an ideal performance in terms of functional enrichment. Furthermore, we apply SE-DMTG to PPINs of several other species and demonstrate the outstanding accuracy and matching ratio in detecting protein complexes compared with other algorithms.

miRDRN-miRNA disease regulatory network: a tool for exploring disease and tissue-specific microRNA regulatory networks

BACKGROUND

MicroRNA (miRNA) regulates cellular processes by acting on specific target genes, and cellular processes proceed through multiple interactions often organized into pathways among genes and gene products. Hundreds of miRNAs and their target genes have been identified, as are many miRNA-disease associations. These, together with huge amounts of data on gene annotation, biological pathways, and protein-protein interactions are available in public databases. Here, using such data we built a database and web service platform, miRNA disease regulatory network (miRDRN), for users to construct disease and tissue-specific miRNA-protein regulatory networks, with which they may explore disease related molecular and pathway associations, or find new ones, and possibly discover new modes of drug action.

METHODS

Data on disease-miRNA association, miRNA-target association and validation, gene-tissue association, gene-tumor association, biological pathways, human protein interaction, gene ID, gene ontology, gene annotation, and product were collected from publicly available databases and integrated. A large set of miRNA target-specific regulatory sub-pathways (RSPs) having the form (T, G 1, G 2) was built from the integrated data and stored, where T is a miRNA-associated target gene, G 1 (G 2) is a gene/protein interacting with T (G 1). Each sequence (T, G 1, G 2) was assigned a p-value weighted by the participation of the three genes in molecular interactions and reaction pathways.

RESULTS

A web service platform, miRDRN (http://mirdrn.ncu.edu.tw/mirdrn/), was built. The database part of miRDRN currently stores 6,973,875 p-valued RSPs associated with 116 diseases in 78 tissue types built from 207 diseases-associated miRNA regulating 389 genes. miRDRN also provides facilities for the user to construct disease and tissue-specific miRNA regulatory networks from RSPs it stores, and to download and/or visualize parts or all of the product. User may use miRDRN to explore a single disease, or a disease-pair to gain insights on comorbidity. As demonstrations, miRDRN was applied: to explore the single disease colorectal cancer (CRC), in which 26 novel potential CRC target genes were identified; to study the comorbidity of the disease-pair Alzheimer's disease-Type 2 diabetes, in which 18 novel potential comorbid genes were identified; and, to explore possible causes that may shed light on recent failures of late-phase trials of anti-AD, BACE1 inhibitor drugs, in which genes downstream to BACE1 whose suppression may affect signal transduction were identified.