Deep learning approaches for noncoding variant prioritization in neurodegenerative diseases.

Determining how noncoding genetic variants contribute to neurodegenerative dementias is fundamental to understanding disease pathogenesis, improving patient prognostication, and developing new clinical treatments. Next generation sequencing technologies have produced vast amounts of genomic data on cell type-specific transcription factor binding, gene expression, and three-dimensional chromatin interactions, with the promise of providing key insights into the biological mechanisms underlying disease. However, this data is highly complex, making it challenging for researchers to interpret, assimilate, and dissect. To this end, deep learning has emerged as a powerful tool for genome analysis that can capture the intricate patterns and dependencies within these large datasets. In this review, we organize and discuss the many unique model architectures, development philosophies, and interpretation methods that have emerged in the last few years with a focus on using deep learning to predict the impact of genetic variants on disease pathogenesis. We highlight both broadly-applicable genomic deep learning methods that can be fine-tuned to disease-specific contexts as well as existing neurodegenerative disease research, with an emphasis on Alzheimer's-specific literature. We conclude with an overview of the future of the field at the intersection of neurodegeneration, genomics, and deep learning.

Lenvatinib Induces AKT/NF-κB Inactivation, Apoptosis Signal Transduction and Growth Inhibition of Non-small Cell Lung Cancer In Vivo.

BACKGROUND/AIM: Non-small cell lung cancer (NSCLC) is the most common type of lung cancer with poor prognosis. Lenvatinib is a multi-kinase inhibitor that has the potential to suppress tumor progression. Our previous study suggested that lenvatinib induces cytotoxicity and apoptosis in CL-1-5-F4 cells in vitro. However, whether lenvatinib suppresses NSCLC progression in vivo remains unclear. MATERIALS AND METHODS: Tumor growth inhibition and normal tissue toxicity evaluation following lenvatinib treatment were performed on CL-1-5-F4-bearing mice. RESULTS: Tumor growth calculated by caliper and living cell intensity decreased by lenvatinib treatment as analysed by bioluminescence imaging. Phosphorylation of AKT, NF-κB, and NF-κB downstream proteins involved in tumor progression were reduced by lenvatinib in the tumor tissue. No pathological changes were found in the liver, kidney, and spleen after lenvatinib treatment. CONCLUSION: Induction of apoptosis and suppression of AKT/NF-κB were associated with lenvatinib-induced inhibition of the progression of NSCLC in vivo.

Identification of key genes and pathways associated with topotecan treatment using multiple bioinformatics tools.

BACKGROUND: The goal of this study is to determine critical genes and pathways associated with topotecan using publicly accessible bioinformatics tools. METHODS: Topotecan signatures were downloaded from the Library of Integrated Network-Based Cellular Signatures (LINCS) database (http://www.ilincs.org/ilincs/). Differentially expressed genes (DEGs) were defined as genes that appeared at least three times with p values <0.05 and a fold change of ≥50% (|log2FC| ≥ 0.58). Hub genes were identified by evaluating the following parameters using a protein-protein interaction network: node degrees, betweenness, and eigenfactor scores. Hub genes and the top-40 DEGs by |log2FC| were used to generate a Venn diagram, and key genes were identified. Functional and pathway enrichment analysis was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Information on ovarian cancer patients derived from The Cancer Genome Atlas (TCGA) database was analyzed, and the effect of topotecan on the protein expression was examined by Western blotting. RESULTS: Eleven topotecan signatures were downloaded, and 65 upregulated and 87 downregulated DEGs were identified. Twenty-one hub genes were identified. We identified eight key genes as upregulated genes, including NFKBIA, IKBKB, GADD45A, CDKN1A, and HIST2H2BE, while EZH2, CDC20, and CDK7 were identified as downregulated genes, which play critical roles in the cell cycle and carcinogenesis in KEGG analysis. In the TCGA analysis, the CDKN1A+/EZH2- group had the longest median survival, while the CDKN1A-/EZH2+ group had the shortest median survival. Topotecan-treated murine ovarian (MOSEC), colorectal (CT26), and lung (LLC) cancer cell lines displayed upregulated CDKN1A encoding p21 and downregulated Ezh2. CONCLUSION: Using publicly accessible bioinformatics tools, we evaluated key genes and pathways related to topotecan and examined the key genes using the TCGA database and in vitro studies.

A context-sensitive framework for the analysis of human signalling pathways in molecular interaction networks.

MOTIVATION: A major challenge in systems biology is to reveal the cellular pathways that give rise to specific phenotypes and behaviours. Current techniques often rely on a network representation of molecular interactions, where each node represents a protein or a gene and each interaction is assigned a single static score. However, the use of single interaction scores fails to capture the tendency of proteins to favour different partners under distinct cellular conditions. RESULTS: Here, we propose a novel context-sensitive network model, in which genes and protein nodes are assigned multiple contexts based on their gene ontology annotations, and their interactions are associated with multiple context-sensitive scores. Using this model, we developed a new approach and a corresponding tool, ContextNet, based on a dynamic programming algorithm for identifying signalling paths linking proteins to their downstream target genes. ContextNet finds high-ranking context-sensitive paths in the interactome, thereby revealing the intermediate proteins in the path and their path-specific contexts. We validated the model using 18 348 manually curated cellular paths derived from the SPIKE database. We next applied our framework to elucidate the responses of human primary lung cells to influenza infection. Top-ranking paths were much more likely to contain infection-related proteins, and this likelihood was highly correlated with path score. Moreover, the contexts assigned by the algorithm pointed to putative, as well as previously known responses to viral infection. Thus, context sensitivity is an important extension to current network biology models and can be efficiently used to elucidate cellular response mechanisms. AVAILABILITY: ContextNet is publicly available at http://netbio.bgu.ac.il/ContextNet. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The TissueNet database of human tissue protein-protein interactions.

Knowledge of protein-protein interactions (PPIs) is important for identifying the functions of proteins and the processes they are involved in. Although data of human PPIs are easily accessible through several public databases, these databases do not specify the human tissues in which these PPIs take place. The TissueNet database of human tissue PPIs (http://netbio.bgu.ac.il/tissuenet/) associates each interaction with human tissues that express both pair mates. This was achieved by integrating current data of experimentally detected PPIs with extensive data of gene and protein expression across 16 main human tissues. Users can query TissueNet using a protein and retrieve its PPI partners per tissue, or using a PPI and retrieve the tissues expressing both pair mates. The graphical representation of the output highlights tissue-specific and tissue-wide PPIs. Thus, TissueNet provides a unique platform for assessing the roles of human proteins and their interactions across tissues.