A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports.

The rapidly increasing and vast quantities of biomedical reports, each containing numerous entities and rich information, represent a rich resource for biomedical text-mining applications. These tools enable investigators to integrate, conceptualize, and translate these discoveries to uncover new insights into disease pathology and therapeutics. In this protocol, we present CaseOLAP LIFT, a new computational pipeline to investigate cellular components and their disease associations by extracting user-selected information from text datasets (e.g., biomedical literature). The software identifies sub-cellular proteins and their functional partners within disease-relevant documents. Additional disease-relevant documents are identified via the software's label imputation method. To contextualize the resulting protein-disease associations and to integrate information from multiple relevant biomedical resources, a knowledge graph is automatically constructed for further analyses. We present one use case with a corpus of ~34 million text documents downloaded online to provide an example of elucidating the role of mitochondrial proteins in distinct cardiovascular disease phenotypes using this method. Furthermore, a deep learning model was applied to the resulting knowledge graph to predict previously unreported relationships between proteins and disease, resulting in 1,583 associations with predicted probabilities >0.90 and with an area under the receiver operating characteristic curve (AUROC) of 0.91 on the test set. This software features a highly customizable and automated workflow, with a broad scope of raw data available for analysis; therefore, using this method, protein-disease associations can be identified with enhanced reliability within a text corpus.

MIND-S is a deep-learning prediction model for elucidating protein post-translational modifications in human diseases.

We present a deep-learning-based platform, MIND-S, for protein post-translational modification (PTM) predictions. MIND-S employs a multi-head attention and graph neural network and assembles a 15-fold ensemble model in a multi-label strategy to enable simultaneous prediction of multiple PTMs with high performance and computation efficiency. MIND-S also features an interpretation module, which provides the relevance of each amino acid for making the predictions and is validated with known motifs. The interpretation module also captures PTM patterns without any supervision. Furthermore, MIND-S enables examination of mutation effects on PTMs. We document a workflow, its applications to 26 types of PTMs of two datasets consisting of ∼50,000 proteins, and an example of MIND-S identifying a PTM-interrupting SNP with validation from biological data. We also include use case analyses of targeted proteins. Taken together, we have demonstrated that MIND-S is accurate, interpretable, and efficient to elucidate PTM-relevant biological processes in health and diseases.

Identification of novel lipid droplet factors that regulate lipophagy and cholesterol efflux in macrophage foam cells.

Macrophage autophagy is a highly anti-atherogenic process that promotes the catabolism of cytosolic lipid droplets (LDs) to maintain cellular lipid homeostasis. Selective autophagy relies on tags such as ubiquitin and a set of selectivity factors including selective autophagy receptors (SARs) to label specific cargo for degradation. Originally described in yeast cells, "lipophagy" refers to the degradation of LDs by autophagy. Yet, how LDs are targeted for autophagy is poorly defined. Here, we employed mass spectrometry to identify lipophagy factors within the macrophage foam cell LD proteome. In addition to structural proteins (e.g., PLIN2), metabolic enzymes (e.g., ACSL) and neutral lipases (e.g., PNPLA2), we found the association of proteins related to the ubiquitination machinery (e.g., AUP1) and autophagy (e.g., HMGB, YWHA/14-3-3 proteins). The functional role of candidate lipophagy factors (a total of 91) was tested using a custom siRNA array combined with high-content cholesterol efflux assays. We observed that knocking down several of these genes, including Hmgb1, Hmgb2, Hspa5, and Scarb2, significantly reduced cholesterol efflux, and SARs SQSTM1/p62, NBR1 and OPTN localized to LDs, suggesting a role for these in lipophagy. Using yeast lipophagy assays, we established a genetic requirement for several candidate lipophagy factors in lipophagy, including HSPA5, UBE2G2 and AUP1. Our study is the first to systematically identify several LD-associated proteins of the lipophagy machinery, a finding with important biological and therapeutic implications. Targeting these to selectively enhance lipophagy to promote cholesterol efflux in foam cells may represent a novel strategy to treat atherosclerosis.Abbreviations: ADGRL3: adhesion G protein-coupled receptor L3; agLDL: aggregated low density lipoprotein; AMPK: AMP-activated protein kinase; APOA1: apolipoprotein A1; ATG: autophagy related; AUP1: AUP1 lipid droplet regulating VLDL assembly factor; BMDM: bone-marrow derived macrophages; BNIP3L: BCL2/adenovirus E1B interacting protein 3-like; BSA: bovine serum albumin; CALCOCO2: calcium binding and coiled-coil domain 2; CIRBP: cold inducible RNA binding protein; COLGALT1: collagen beta(1-O)galactosyltransferase 1; CORO1A: coronin 1A; DMA: deletion mutant array; Faa4: long chain fatty acyl-CoA synthetase; FBS: fetal bovine serum; FUS: fused in sarcoma; HMGB1: high mobility group box 1; HMGB2: high mobility group box 2: HSP90AA1: heat shock protein 90: alpha (cytosolic): class A member 1; HSPA5: heat shock protein family A (Hsp70) member 5; HSPA8: heat shock protein 8; HSPB1: heat shock protein 1; HSPH1: heat shock 105kDa/110kDa protein 1; LDAH: lipid droplet associated hydrolase; LIPA: lysosomal acid lipase A; LIR: LC3-interacting region; MACROH2A1: macroH2A.1 histone; MAP1LC3: microtubule-associated protein 1 light chain 3; MCOLN1: mucolipin 1; NBR1: NBR1, autophagy cargo receptor; NPC2: NPC intracellular cholesterol transporter 2; OPTN: optineurin; P/S: penicillin-streptomycin; PLIN2: perilipin 2; PLIN3: perilipin 3; PNPLA2: patatin like phospholipase domain containing 2; RAB: RAB, member RAS oncogene family; RBBP7, retinoblastoma binding protein 7, chromatin remodeling factor; SAR: selective autophagy receptor; SCARB2: scavenger receptor class B, member 2; SGA: synthetic genetic array; SQSTM1: sequestosome 1; TAX1BP1: Tax1 (human T cell leukemia virus type I) binding protein 1; TFEB: transcription factor EB; TOLLIP: toll interacting protein; UBE2G2: ubiquitin conjugating enzyme E2 G2; UVRAG: UV radiation resistance associated gene; VDAC2: voltage dependent anion channel 2; VIM: vimentin.

MealTime-MS: A Machine Learning-Guided Real-Time Mass Spectrometry Analysis for Protein Identification and Efficient Dynamic Exclusion.

Mass spectrometry-based proteomics technologies are prime methods for the high-throughput identification of proteins in complex biological samples. Nevertheless, there are still technical limitations that hinder the ability of mass spectrometry to identify low abundance proteins in complex samples. Characterizing such proteins is essential to provide a comprehensive understanding of the biological processes taking place in cells and tissues. Still today, most mass spectrometry-based proteomics approaches use a data-dependent acquisition strategy, which favors the collection of mass spectra from proteins of higher abundance. Since the computational identification of proteins from proteomics data is typically performed after mass spectrometry analysis, large numbers of mass spectra are typically redundantly acquired from the same abundant proteins, and little to no mass spectra are acquired for proteins of lower abundance. We therefore propose a novel supervised learning algorithm, MealTime-MS, that identifies proteins in real-time as mass spectrometry data are acquired and prevents further data collection from confidently identified proteins to ultimately free mass spectrometry resources to improve the identification sensitivity of low abundance proteins. We use real-time simulations of a previously performed mass spectrometry analysis of a HEK293 cell lysate to show that our approach can identify 92.1% of the proteins detected in the experiment using 66.2% of the MS2 spectra. We also demonstrate that our approach outperforms a previously proposed method, is sufficiently fast for real-time mass spectrometry analysis, and is flexible. Finally, MealTime-MS' efficient usage of mass spectrometry resources will provide a more comprehensive characterization of proteomes in complex samples.

Identifying temporal molecular signatures underlying cardiovascular diseases: A data science platform.

OBJECTIVE: During cardiovascular disease progression, molecular systems of myocardium (e.g., a proteome) undergo diverse and distinct changes. Dynamic, temporally-regulated alterations of individual molecules underlie the collective response of the heart to pathological drivers and the ultimate development of pathogenesis. Advances in high-throughput omics technologies have enabled cost-effective, temporal profiling of targeted systems in animal models of human diseases. However, computational analysis of temporal patterns from omics data remains challenging. In particular, bioinformatic pipelines involving unsupervised statistical approaches to support cardiovascular investigations are lacking, which hinders one's ability to extract biomedical insights from these complex datasets. APPROACH AND RESULTS: We developed a non-parametric data analysis platform to resolve computational challenges unique to temporal omics datasets. Our platform consists of three modules. Module I preprocesses the temporal data using either cubic splines or principal component analysis (PCA), and it simultaneously accomplishes the tasks on missing data imputation and denoising. Module II performs an unsupervised classification by K-means or hierarchical clustering. Module III evaluates and identifies biological entities (e.g., molecular events) that exhibit strong associations to specific temporal patterns. The jackstraw method for cluster membership has been applied to estimate p-values and posterior inclusion probabilities (PIPs), both of which guided feature selection. To demonstrate the utility of the analysis platform, we employed a temporal proteomics dataset that captured the proteome-wide dynamics of oxidative stress induced post-translational modifications (O-PTMs) in mouse hearts undergoing isoproterenol (ISO)-induced hypertrophy. CONCLUSION: We have created a platform, CV.Signature.TCP, to identify distinct temporal clusters in omics datasets. We presented a cardiovascular use case to demonstrate its utility in unveiling biological insights underlying O-PTM regulations in cardiac remodeling. This platform is implemented in an open source R package (https://github.com/UCLA-BD2K/CV.Signature.TCP).

Structural Analysis of Hippocampal Kinase Signal Transduction.

Kinases are a major clinical target for human diseases. Identifying the proteins that interact with kinases in vivo will provide information on unreported substrates and will potentially lead to more specific methods for therapeutic kinase regulation. Here, endogenous immunoprecipitations of evolutionally distinct kinases (i.e., Akt, ERK2, and CAMK2) from rodent hippocampi were analyzed by mass spectrometry to generate three highly confident kinase protein-protein interaction networks. Proteins of similar function were identified in the networks, suggesting a universal model for kinase signaling complexes. Protein interactions were observed between kinases with reported symbiotic relationships. The kinase networks were significantly enriched in genes associated with specific neurodevelopmental disorders providing novel structural connections between these disease-associated genes. To demonstrate a functional relationship between the kinases and the network, pharmacological manipulation of Akt in hippocampal slices was shown to regulate the activity of potassium/sodium hyperpolarization-activated cyclic nucleotide-gated channel(HCN1), which was identified in the Akt network. Overall, the kinase protein-protein interaction networks provide molecular insight of the spatial complexity of in vivo kinase signal transduction which is required to achieve the therapeutic potential of kinase manipulation in the brain.

Temporal and Longitudinal Mercury Trends in Burbot (Lota lota) in the Russian Arctic.

Current understanding of mercury (Hg) dynamics in the Arctic is hampered by a lack of data in the Russian Arctic region, which comprises about half of the entire Arctic watershed. This study quantified temporal and longitudinal trends in total mercury (THg) concentrations in burbot (Lota lota) in eight rivers of the Russian Arctic between 1980 and 2001, encompassing an expanse of 118 degrees of longitude. Burbot THg concentrations declined by an average of 2.6% annually across all eight rivers during the study period, decreasing by 39% from 0.171 µg g-1 wet weight (w.w.) in 1980 to 0.104 µg g-1 w.w. in 2001. THg concentrations in burbot also declined by an average of 1.8% per 10° of longitude from west to east across the study area between 1988 and 2001. These results, in combination with those of previous studies, suggest that Hg trends in Arctic freshwater fishes before 2001 were spatially and temporally heterogeneous, as those in the North American Arctic were mostly increasing while those in the Russian Arctic were mostly decreasing. It is suggested that Hg trends in Arctic animals may be influenced by both depositional and postdepositional processes.

Quality Control for RNA-Seq (QuaCRS): An Integrated Quality Control Pipeline.

QuaCRS (Quality Control for RNA-Seq) is an integrated, simplified quality control (QC) system for RNA-seq data that allows easy execution of several open-source QC tools, aggregation of their output, and the ability to quickly identify quality issues by performing meta-analyses on QC metrics across large numbers of samples in different studies. It comprises two main sections. First is the QC Pack wrapper, which executes three QC tools: FastQC, RNA-SeQC, and selected functions from RSeQC. Combining these three tools into one wrapper provides increased ease of use and provides a much more complete view of sample data quality than any individual tool. Second is the QC database, which displays the resulting metrics in a user-friendly web interface. It was designed to allow users with less computational experience to easily generate and view QC information for their data, to investigate individual samples and aggregate reports of sample groups, and to sort and search samples based on quality. The structure of the QuaCRS database is designed to enable expansion with additional tools and metrics in the future. The source code for not-for-profit use and a fully functional sample user interface with mock data are available at http://bioserv.mps.ohio-state.edu/QuaCRS/.