ProtFinder: finding subcellular locations of proteins using protein interaction networks

Protein subcellular localization prediction plays a crucial role in improving our understandings of different diseases and consequently assists in building drug targeting and drug development pipelines. Proteins are known to co-exist at multiple subcellular locations which make the task of prediction extremely challenging. A protein interaction network is a graph that captures interactions between different proteins. It is safe to assume that if two proteins are interacting, they must share some subcellular locations. With this regard, we propose ProtFinder - the first deep learning-based model that exclusively relies on protein interaction networks to predict the multiple subcellular locations of proteins. We also integrate biological priors like the cellular component of Gene Ontology to make ProtFinder a more biology-aware intelligent system. ProtFinder is trained and tested using the STRING and BioPlex databases whereas the annotations of proteins are obtained from the Human Protein Atlas. Our model gives an AUC-ROC score of 90.00% and an MCC score of 83.42% on a held-out set of proteins. We also apply ProtFinder to annotate proteins that currently do not have confident location annotations. We observe that ProtFinder is able to confirm some of these unreliable location annotations, while in some cases complementing the existing databases with novel location annotations.

Protein interaction networks define the genetic architecture of preterm birth

The likely genetic architecture of complex diseases is that subgroups of patients share variants in genes in specific networks sufficient to express a shared phenotype. We combined high throughput sequencing with advanced bioinformatic approaches to identify such subgroups of patients with variants in shared networks. We performed targeted sequencing of patients with 2 or 3 generations of preterm birth on genes, gene sets and haplotype blocks that were highly associated with preterm birth. We analyzed the data using a multi-sample, protein–protein interaction (PPI) tool to identify significant clusters of patients associated with preterm birth. We identified shared protein interaction networks among preterm cases in two statistically significant clusters, p < 0.001. We also found two small control-dominated clusters. We replicated these data on an independent, large birth cohort. Separation testing showed significant similarity scores between the clusters from the two independent cohorts of patients. Canonical pathway analysis of the unique genes defining these clusters demonstrated enrichment in inflammatory signaling pathways, the glucocorticoid receptor, the insulin receptor, EGF and B-cell signaling, These results support a genetic architecture defined by subgroups of patients that share variants in genes in specific networks and pathways which are sufficient to give rise to the disease phenotype.

Transcriptome Profiling Based on Larvae at Different Time Points After Hatching Provides a Core Set of Gene Resource for Understanding the Metabolic Mechanisms of the Brood-Care Behavior in Octopus ocellatus

The metabolic processes of organisms are very complex. Each process is crucial and affects the growth, development, and reproduction of organisms. Metabolism-related mechanisms in Octopus ocellatus behaviors have not been widely studied. Brood-care is a common behavior in most organisms, which can improve the survival rate and constitution of larvae. Octopus ocellatus carried out this behavior, but it was rarely noticed by researchers before. In our study, 3,486 differentially expressed genes (DEGs) were identified based on transcriptome analysis of O. ocellatus. We identify metabolism-related DEGs using GO and KEGG enrichment analyses. Then, we construct protein–protein interaction networks to search the functional relationships between metabolism-related DEGs. Finally, we identified 10 hub genes related to multiple gene functions or involved in multiple signal pathways and verified them using quantitative real-time polymerase chain reaction (qRT-PCR). Protein–protein interaction networks were first used to study the effects of brood-care behavior on metabolism in the process of growing of O. ocellatus larvae, and the results provide us valuable genetic resources for understanding the metabolic processes of invertebrate larvae. The data lay a foundation for further study the brood-care behavior and metabolic mechanisms of invertebrates.

Low BACH2 Expression Predicts Adverse Outcome in Chronic Lymphocytic Leukaemia

Chronic lymphocytic leukaemia (CLL) is a heterogeneous disease with a highly variable clinical outcome. There are well-established CLL prognostic biomarkers that have transformed treatment and improved the understanding of CLL biology. Here, we have studied the clinical significance of two crucial B cell regulators, BACH2 (BTB and CNC homology 1, basic leucine zipper transcription factor 2) and BCL6 (B-cell CLL/lymphoma 6), in a cohort of 102 CLL patients and determined the protein interaction networks that they participate in using MEC-1 CLL cells. We observed that CLL patients expressing low levels of BCL6 and BACH2 RNA had significantly shorter overall survival (OS) than high BCL6- and BACH2-expressing cases. Notably, their low expression specifically decreased the OS of immunoglobulin heavy chain variable region-mutated (IGHV-M) CLL patients, as well as those with 11q and 13q deletions. Similar to the RNA data, a low BACH2 protein expression was associated with a significantly shorter OS than a high expression. There was no direct interaction observed between BACH2 and BCL6 in MEC-1 CLL cells, but they shared protein networks that included fifty different proteins. Interestingly, a prognostic index (PI) model that we generated, using integrative risk score values of BACH2 RNA expression, age, and 17p deletion status, predicted patient outcomes in our cohort. Taken together, these data have shown for the first time a possible prognostic role for BACH2 in CLL and have revealed protein interaction networks shared by BCL6 and BACH2, indicating a significant role for BACH2 and BCL6 in key cellular processes, including ubiquitination mediated B-cell receptor functions, nucleic acid metabolism, protein degradation, and homeostasis in CLL biology.

A New Method for Recognizing Protein Complexes Based on Protein Interaction Networks and GO Terms

Motivation: A protein complex is the combination of proteins which interact with each other. Protein–protein interaction (PPI) networks are composed of multiple protein complexes. It is very difficult to recognize protein complexes from PPI data due to the noise of PPI.Results: We proposed a new method, called Topology and Semantic Similarity Network (TSSN), based on topological structure characteristics and biological characteristics to construct the PPI. Experiments show that the TSSN can filter the noise of PPI data. We proposed a new algorithm, called Neighbor Nodes of Proteins (NNP), for recognizing protein complexes by considering their topology information. Experiments show that the algorithm can identify more protein complexes and more accurately. The recognition of protein complexes is vital in research on evolution analysis.Availability and implementation: https://github.com/bioinformatical-code/NNP.

A Molecular View of Amyotrophic Lateral Sclerosis Through the Lens of Interaction Network Modules

Background: Despite the discovery of familial cases with mutations in Cu/Zn-superoxide dismutase (SOD1), Guanine nucleotide exchange C9orf72, TAR DNA-binding protein 43 (TARDBP) and RNA-binding protein FUS as well as a number of other genes linked to Amyotrophic Lateral Sclerosis (ALS), the etiology and molecular pathogenesis of this devastating disease is still not understood. As proteins do not act alone, conducting an analysis of ALS at the system level may provide new insights into the molecular biology of ALS and put it into relationship to other neurological diseases.Methods: A set of ALS-associated genes/proteins were collected from publicly available databases and text mining of scientific literature. We used these as seed proteins to build protein-protein interaction (PPI) networks serving as a scaffold for further analyses. From the collection of networks, a set of core modules enriched in seed proteins were identified. The molecular biology of the core modules was investigated, as were their associations to other diseases. To assess the core modules’ ability to describe unknown or less well-studied ALS biology, they were queried for proteins more recently associated to ALS and not involved in the primary analysis.Results: We describe a set of 26 ALS core modules enriched in ALS-associated proteins. We show that these ALS core modules not only capture most of the current knowledge about ALS, but they also allow us to suggest biological interdependencies. In addition, new associations of ALS networks with other neurodegenerative diseases, e.g. Alzheimer’s, Huntington’s and Parkinson’s disease were found. A follow-up analysis of 140 ALS-associated proteins identified since 2014 reveals a significant overrepresentation of new ALS proteins in these 26 disease modules.Conclusions: Using protein-protein interaction networks offers a relevant approach for broadening the understanding of the biological context of known ALS-associated genes. Using a bottom-up approach for the analysis of protein-protein interaction networks is a useful method to avoid bias caused by over-connected proteins. Our ALS-enriched modules cover most known biological functions associated with ALS. The presence of recently identified ALS-associated proteins in the core modules highlights the potential for using these as a scaffold for identification of novel ALS disease mechanisms.

Identification of key genes as predictive biomarkers for osteosarcoma metastasis using translational bioinformatics

Background Osteosarcoma (OS) metastasis is the most common cause of cancer-related mortality, however, no sufficient clinical biomarkers have been identified. In this study, we identified five genes to help predict metastasis at diagnosis. Methods We performed weighted gene co-expression network analysis (WGCNA) to identify the most relevant gene modules associated with OS metastasis. An important machine learning algorithm, the support vector machine (SVM), was employed to predict key genes for classifying the OS metastasis phenotype. Finally, we investigated the clinical significance of key genes and their enriched pathways. Results Eighteen modules were identified in WGCNA, among which the pink, red, brown, blue, and turquoise modules demonstrated good preservation. In the five modules, the brown and red modules were highly correlated with OS metastasis. Genes in the two modules closely interacted in protein–protein interaction networks and were therefore chosen for further analysis. Genes in the two modules were primarily enriched in the biological processes associated with tumorigenesis and development. Furthermore, 65 differentially expressed genes were identified as common hub genes in both WGCNA and protein–protein interaction networks. SVM classifiers with the maximum area under the curve were based on 30 and 15 genes in the brown and red modules, respectively. The clinical significance of the 45 hub genes was analyzed. Of the 45 genes, 17 were found to be significantly correlated with survival time. Finally, 5/17 genes, including ADAP2 (P = 0.0094), LCP2 (P = 0.013), ARHGAP25 (P = 0.0049), CD53 (P = 0.016), and TLR7 (P = 0.04) were significantly correlated with the metastatic phenotype. In vitro verification, western blotting, wound healing analyses, transwell invasion assays, proliferation assays, and colony formation assays indicated that ARHGAP25 promoted OS cell migration, invasion, proliferation, and epithelial–mesenchymal transition. Conclusion We identified five genes, namely ADAP2, LCP2, ARHGAP25, CD53, and TLR7, as candidate biomarkers for the prediction of OS metastasis; ARHGAP25 inhibits MG63 OS cell growth, migration, and invasion in vitro, indicating that ARHGAP25 can serve as a promising specific and prognostic biomarker for OS metastasis.

TMOD-27. IDENTIFYING ONCOGENIC C-MYC AND MYCN COMPLEXES IN HIGH-RISK PEDIATRIC CANCERS

The MYC family of proto-oncogenes is activated in a variety of cancers, including multiple high-risk pediatric malignancies. c-MYC (MYC) is ubiquitously expressed in human tissues, while MYCN (MYCN) has tissue and developmentally restricted expression patterns. In both neuroblastoma and medulloblastoma, enhanced activity of either MYCN or c-MYC drives high-risk disease. As transcription factors, MYC proteins exert oncogenic functions through protein-protein interaction networks that alter gene expression, but also mediate a growing list of target-gene independent nuclear functions (transcriptional elongation, chromatin changes throughout the cell cycle, etc…). While c-MYC and MYCN share many functions, they also regulate distinct cellular processes, and within medulloblastoma, they are activated in distinct molecular sub-groups (i.e. MYCN amplification is found in aggressive sonic hedge hog (SHH) subgroup tumors, while MYC amplification is found in aggressive group 3 and group 4 tumors). Here, we present an approach to identify oncogenic functions of c-MYC and MYCN in medulloblastoma and neuroblastoma using human induced pluripotent stem cell (iPSCO based orthotopic model systems. We hypothesize that the protein interaction networks and oncogenic functions of c-MYC and MYCN are impacted by cellular context, which are recapitulated in our orthotopic models (cell transcriptional and epigenetic landscape, tumor microenvironment). This premise is supported by recent single cell sequencing efforts in medulloblastoma and neuroblastoma, where primary human tumor cells are found to recapitulate specific transcriptional cell states found in normal hindbrain and sympathetic nervous system development, respectively. Through proximity labeling and quantitative mass spectrometry, we aim to identify tumor and oncogene specific protein interaction networks. This information will guide functional screening approaches to identify tumor-specific vulnerabilities. * Note MYC(N) refers to c-MYC and MYCN.