MBDBMetrics: an online metrics tool to measure the impact of biological data resources.

Motivation: There now exist thousands of molecular biology databases covering every aspect of biological data. This database infrastructure takes significant effort and funding to develop and maintain. The creators of these databases need to make strong justifications to funders to prove their impact or importance. There are many publication metrics and tools available such as Google Scholar to measure citation impact or AltMetrics covering multiple measures including social media coverage. Results: In this article, we describe a series of novel impact metrics that have been applied initially to the UniProt database, and now made available via a Google Colab to enable any molecular biology resource to gain several additional metrics. These metrics, powered by freely available APIs from Europe PubMedCentral and SureCHEMBL cover mentions of the resource in full text articles, including which section of the paper the mention occurs in, grant acknowledgements and mentions in patent applications. This tool, that we call MBDBMetrics, is a useful adjunct to existing tools. Availability and implementation: The MBDBMetrics tool is available at the following locations: https://colab.research.google.com/drive/1aEmSQR9DGQIZmHAIuQV9mLv7Mw9Ppkin and https://github.com/g-insana/MBDBMetrics.

Rhea, the reaction knowledgebase in 2022.

Rhea (https://www.rhea-db.org) is an expert-curated knowledgebase of biochemical reactions based on the chemical ontology ChEBI (Chemical Entities of Biological Interest) (https://www.ebi.ac.uk/chebi). In this paper, we describe a number of key developments in Rhea since our last report in the database issue of Nucleic Acids Research in 2019. These include improved reaction coverage in Rhea, the adoption of Rhea as the reference vocabulary for enzyme annotation in the UniProt knowledgebase UniProtKB (https://www.uniprot.org), the development of a new Rhea website, and the designation of Rhea as an ELIXIR Core Data Resource. We hope that these and other developments will enhance the utility of Rhea as a reference resource to study and engineer enzymes and the metabolic systems in which they function.

A GO catalogue of human DNA-binding transcription factors.

To control gene transcription, DNA-binding transcription factors recognise specific sequence motifs in gene regulatory regions. A complete and reliable GO annotation of all DNA-binding transcription factors is key to investigating the delicate balance of gene regulation in response to environmental and developmental stimuli. The need for such information is demonstrated by the many lists of transcription factors that have been produced over the past decade. The COST Action Gene Regulation Ensemble Effort for the Knowledge Commons (GREEKC) Consortium brought together experts in the field of transcription with the aim of providing high quality and interoperable gene regulatory data. The Gene Ontology (GO) Consortium provides strict definitions for gene product function, including factors that regulate transcription. The collaboration between the GREEKC and GO Consortia has enabled the application of those definitions to produce a new curated catalogue of over 1400 human DNA-binding transcription factors, that can be accessed at https://www.ebi.ac.uk/QuickGO/targetset/dbTF. This catalogue has facilitated an improvement in the GO annotation of human DNA-binding transcription factors and led to the GO annotation of almost sixty thousand DNA-binding transcription factors in over a hundred species. Thus, this work will aid researchers investigating the regulation of transcription in both biomedical and basic science.

Updates in Rhea: SPARQLing biochemical reaction data.

Rhea (http://www.rhea-db.org) is a comprehensive and non-redundant resource of over 11 000 expert-curated biochemical reactions that uses chemical entities from the ChEBI ontology to represent reaction participants. Originally designed as an annotation vocabulary for the UniProt Knowledgebase (UniProtKB), Rhea also provides reaction data for a range of other core knowledgebases and data repositories including ChEBI and MetaboLights. Here we describe recent developments in Rhea, focusing on a new resource description framework representation of Rhea reaction data and an SPARQL endpoint (https://sparql.rhea-db.org/sparql) that provides access to it. We demonstrate how federated queries that combine the Rhea SPARQL endpoint and other SPARQL endpoints such as that of UniProt can provide improved metabolite annotation and support integrative analyses that link the metabolome through the proteome to the transcriptome and genome. These developments will significantly boost the utility of Rhea as a means to link chemistry and biology for a more holistic understanding of biological systems and their function in health and disease.

Proteomic Profiling of Secreted Proteins, Exosomes, and Microvesicles in Cell Culture Conditioned Media.

Secreted proteins are of tremendous biological interest since they can act as ligands for receptors to activate downstream signalling cascades or be used as biomarkers if altered abundance is correlated with a specific pathological state. Proteins can be secreted either as soluble molecules or as part of extracellular vesicles (i.e., exosomes or microvesicles). The complete proteomic profiling of secretomes requires analysis of secreted proteins and extracellular vesicles. Hence, the method described here enriches for microvesicles, exosomes, and secreted proteins from conditioned media using differential centrifugation. The three fractions are then analyzed by mass spectrometry-based proteomics for in-depth characterization and comparison of the protein secretome of cell lines.

Integrated omic analysis of lung cancer reveals metabolism proteome signatures with prognostic impact.

Cancer results from processes prone to selective pressure and dysregulation acting along the sequence-to-phenotype continuum DNA → RNA → protein → disease. However, the extent to which cancer is a manifestation of the proteome is unknown. Here we present an integrated omic map representing non-small cell lung carcinoma. Dysregulated proteins not previously implicated as cancer drivers are encoded throughout the genome including, but not limited to, regions of recurrent DNA amplification/deletion. Clustering reveals signatures composed of metabolism proteins particularly highly recapitulated between patient-matched primary and xenograft tumours. Interrogation of The Cancer Genome Atlas reveals cohorts of patients with lung and other cancers that have DNA alterations in genes encoding the signatures, and this was accompanied by differences in survival. The recognition of genome and proteome alterations as related products of selective pressure driving the disease phenotype may be a general approach to uncover and group together cryptic, polygenic disease drivers.

Integrated omic analysis of oropharyngeal carcinomas reveals human papillomavirus (HPV)-dependent regulation of the activator protein 1 (AP-1) pathway.

HPV-positive oropharyngeal carcinoma (OPC) patients have superior outcomes relative to HPV-negative patients, but the underlying mechanisms remain poorly understood. We conducted a proteomic investigation of HPV-positive (n = 27) and HPV-negative (n = 26) formalin-fixed paraffin-embedded OPC biopsies to acquire insights into the biological pathways that correlate with clinical behavior. Among the 2,633 proteins identified, 174 were differentially abundant. These were enriched for proteins related to cell cycle, DNA replication, apoptosis, and immune response. The differential abundances of cortactin and methylthioadenosine phosphorylase were validated by immunohistochemistry in an independent cohort of 29 OPC samples (p = 0.023 and p = 0.009, respectively). An additional 1,124 proteins were independently corroborated through comparison to a published proteomic dataset of OPC. Furthermore, utilizing the Cancer Genome Atlas, we conducted an integrated investigation of OPC, attributing mechanisms underlying differential protein abundances to alterations in mutation, copy number, methylation, and mRNA profiles. A key finding of this integration was the identification of elevated cortactin oncoprotein levels in HPV-negative OPCs. These proteins might contribute to reduced survival in these patients via their established role in radiation resistance. Through interrogation of Cancer Genome Atlas data, we demonstrated that activation of the ß1-integrin/FAK/cortactin/JNK1 signaling axis and associated differential regulation of activator protein 1 transcription factor target genes are plausible consequences of elevated cortactin protein levels.

In-depth proteomic analyses of ovarian cancer cell line exosomes reveals differential enrichment of functional categories compared to the NCI 60 proteome.

Molecular communication between cancer cells and its stromal microenvironment is a key factor for cancer progression. Alongside classic secretory pathways, it has recently been proposed that small membranous vesicles are alternative mediators of intercellular communication. Exosomes carry an effector-rich proteome with the ability to modulate various functional properties of the recipient cell. In this study, exosomes isolated from four epithelial ovarian cancer cell lines (OVCAR3, OVCAR433, OVCAR5 and SKOV3) were characterized using mass spectrometry-based proteomics. Using an optimized workflow consisting of efficient exosome solubilization and the latest generation of proteomic instrumentation, we demonstrate improved detection depth. Systematic comparison of our cancer cell line exosome proteome against public data (Exocarta) and the recently published NCI 60 proteome revealed enrichment of functional categories related to signaling biology and biomarker discovery.

Proteomic profiling of the planarian Schmidtea mediterranea and its mucous reveals similarities with human secretions and those predicted for parasitic flatworms.

The freshwater planarian Schmidtea mediterranea has been used in research for over 100 years, and is an emerging stem cell model because of its capability of regenerating large portions of missing body parts. Exteriorly, planarians are covered in mucous secretions of unknown composition, implicated in locomotion, predation, innate immunity, and substrate adhesion. Although the planarian genome has been sequenced, it remains mostly unannotated, challenging both genomic and proteomic analyses. The goal of the current study was to annotate the proteome of the whole planarian and its mucous fraction. The S. mediterranea proteome was analyzed via mass spectrometry by using multidimensional protein identification technology with whole-worm tryptic digests. By using a proteogenomics approach, MS data were searched against an in silico translated planarian transcript database, and by using the Swiss-Prot BLAST algorithm to identify proteins similar to planarian queries. A total of 1604 proteins were identified. The mucous subproteome was defined through analysis of a mucous trail fraction and an extract obtained by treating whole worms with the mucolytic agent N-acetylcysteine. Gene Ontology analysis confirmed that the mucous fractions were enriched with secreted proteins. The S. mediterranea proteome is highly similar to that predicted for the trematode Schistosoma mansoni associated with intestinal schistosomiasis, with the mucous subproteome particularly highly conserved. Remarkably, orthologs of 119 planarian mucous proteins are present in human mucosal secretions and tear fluid. We suggest planarians have potential to be a model system for the characterization of mucous protein function and relevant to parasitic flatworm infections and diseases underlined by mucous aberrancies, such as cystic fibrosis, asthma, and other lung diseases.

Translational analysis of mouse and human placental protein and mRNA reveals distinct molecular pathologies in human preeclampsia.

Preeclampsia (PE) adversely impacts ~5% of pregnancies. Despite extensive research, no consistent biomarkers or cures have emerged, suggesting that different molecular mechanisms may cause clinically similar disease. To address this, we undertook a proteomics study with three main goals: (1) to identify a panel of cell surface markers that distinguish the trophoblast and endothelial cells of the placenta in the mouse; (2) to translate this marker set to human via the Human Protein Atlas database; and (3) to utilize the validated human trophoblast markers to identify subgroups of human preeclampsia. To achieve these goals, plasma membrane proteins at the blood tissue interfaces were extracted from placentas using intravascular silica-bead perfusion, and then identified using shotgun proteomics. We identified 1181 plasma membrane proteins, of which 171 were enriched at the maternal blood-trophoblast interface and 192 at the fetal endothelial interface with a 70% conservation of expression in humans. Three distinct molecular subgroups of human preeclampsia were identified in existing human microarray data by using expression patterns of trophoblast-enriched proteins. Analysis of all misexpressed genes revealed divergent dysfunctions including angiogenesis (subgroup 1), MAPK signaling (subgroup 2), and hormone biosynthesis and metabolism (subgroup 3). Subgroup 2 lacked expected changes in known preeclampsia markers (sFLT1, sENG) and uniquely overexpressed GNA12. In an independent set of 40 banked placental specimens, GNA12 was overexpressed during preeclampsia when co-incident with chronic hypertension. In the current study we used a novel translational analysis to integrate mouse and human trophoblast protein expression with human microarray data. This strategy identified distinct molecular pathologies in human preeclampsia. We conclude that clinically similar preeclampsia patients exhibit divergent placental gene expression profiles thus implicating divergent molecular mechanisms in the origins of this disease.

Identification of novel ryanodine receptor 1 (RyR1) protein interaction with calcium homeostasis endoplasmic reticulum protein (CHERP).

The ryanodine receptor type 1 (RyR1) is a homotetrameric Ca(2+) release channel located in the sarcoplasmic reticulum of skeletal muscle where it plays a role in the initiation of skeletal muscle contraction. A soluble, 6×-histidine affinity-tagged cytosolic fragment of RyR1 (amino acids 1-4243) was expressed in HEK-293 cells, and metal affinity chromatography under native conditions was used to purify the peptide together with interacting proteins. When analyzed by gel-free liquid chromatography mass spectrometry (LC-MS), 703 proteins were identified under all conditions. This group of proteins was filtered to identify putative RyR interacting proteins by removing those proteins found in only 1 RyR purification and proteins for which average spectral counts were enriched by less than 4-fold over control values. This resulted in 49 potential RyR1 interacting proteins, and 4 were selected for additional interaction studies: calcium homeostasis endoplasmic reticulum protein (CHERP), endoplasmic reticulum-Golgi intermediate compartment 53-kDa protein (LMAN1), T-complex protein, and phosphorylase kinase. Western blotting showed that only CHERP co-purified with affinity-tagged RyR1 and was eluted with imidazole. Immunofluorescence showed that endogenous CHERP co-localizes with endogenous RyR1 in the sarcoplasmic reticulum of rat soleus muscle. A combination of overexpression of RyR1 in HEK-293 cells with siRNA-mediated suppression of CHERP showed that CHERP affects Ca(2+) release from the ER via RyR1. Thus, we propose that CHERP is an RyR1 interacting protein that may be involved in the regulation of excitation-contraction coupling.

In-depth proteomic analyses of direct expressed prostatic secretions.

It is expected that clinically obtainable fluids that are proximal to organs contain a repertoire of secreted proteins and shed cells reflective of the physiological state of that tissue and thus represent potential sources for biomarker discovery, investigation of tissue-specific biology, and assay development. The prostate gland secretes many proteins into a prostatic fluid that combines with seminal vesicle fluids to promote sperm activation and function. Proximal fluids of the prostate that can be collected clinically are seminal plasma and expressed prostatic secretion (EPS) fluids. In the current study, MudPIT-based proteomics was applied to EPS obtained from nine men with prostate cancer and resulted in the confident identification of 916 unique proteins. Systematic bioinformatics analyses using publicly available microarray data of 21 human tissues (Human Gene Atlas), the Human Protein Atlas database, and other published proteomics data of shed/secreted proteins were performed to systematically analyze this comprehensive proteome. Therefore, we believe this data will be a valuable resource for the research community to study prostate biology and potentially assist in the identification of novel prostate cancer biomarkers. To further streamline this process, the entire data set was deposited to the Tranche repository for use by other researchers.

Comparative systems biology of human and mouse as a tool to guide the modeling of human placental pathology.

Placental abnormalities are associated with two of the most common and serious complications of human pregnancy, maternal preeclampsia (PE) and fetal intrauterine growth restriction (IUGR), each disorder affecting approximately 5% of all pregnancies. An important question for the use of the mouse as a model for studying human disease is the degree of functional conservation of genetic control pathways from human to mouse. The human and mouse placenta show structural similarities, but there have been no systematic attempts to assess their molecular similarities or differences. We collected protein and mRNA expression data through shot-gun proteomics and microarray expression analysis of the highly vascular exchange region, microdissected from the human and mouse near-term placenta. Over 7000 ortholog genes were detected with 70% co-expressed in both species. Close to 90% agreement was found between our human proteomic results and 1649 genes assayed by immunohistochemistry for expression in the human placenta in the Human Protein Atlas. Interestingly, over 80% of genes known to cause placental phenotypes in mouse are co-expressed in human. Several of these phenotype-associated proteins form a tight protein-protein interaction network involving 15 known and 34 novel candidate proteins also likely important in placental structure and/or function. The entire data are available as a web-accessible database to guide the informed development of mouse models to study human disease.

Multidimensional protein identification technology analysis highlights mitoxantrone-induced expression modulations in the primary prostate cancer cell proteome.

Chemotherapeutic agents as they are used today have limited effectiveness against prostate cancer, but may potentially be used in new combinations with more efficacious results. Mitoxantrone, used for palliation of prostate cancer, has recently been found by our group to improve the susceptibility of primary prostate cancer cells to killing through the Fas-mediated death pathway. Here we used a shotgun proteomics approach to first profile the entire prostate cancer proteome and then identify specific factors involved in this mitoxantrone response. Peptides derived from primary prostate cancer cells treated with or without 100 nM mitoxantrone were analyzed by multidimensional protein identification technology (MudPIT). Strict limits and data filtering hierarchies were applied to identify proteins with high confidence. We identified 1498 proteins belonging to the prostate cancer proteome, 83 of which were significantly upregulated and 27 of which were markedly downregulated following mitoxantrone treatment. These proteins perform diverse functions, including ceramide production, tumour suppression, and oxidative reduction. Detailed proteomic analyses of prostate cancer cells and their response to mitoxantrone will further our understanding of its mechanisms of action. Identification of proteins influenced by treatment with mitoxantrone or other compounds may lead to the development of more effective drug combinations against prostate cancer.

Identification of pathways associated with invasive behavior by ovarian cancer cells using multidimensional protein identification technology (MudPIT).

Proteomic profiling has emerged as a useful tool for identifying tissue alterations in disease states including malignant transformation. The aim of this study was to reveal expression profiles associated with the highly motile/invasive ovarian cancer cell phenotype. Six ovarian cancer cell lines were subjected to proteomic characterization using multidimensional protein identification technology (MudPIT), and evaluated for their motile/invasive behavior, so that these parameters could be compared. Within whole cell extracts of the ovarian cancer cells, MudPIT identified proteins that mapped to 2245 unique genes. Western blot analysis for selected proteins confirmed the expression profiles revealed by MudPIT, demonstrating the fidelity of this high-throughput analysis. Unsupervised cluster analysis partitioned the cell lines in a manner that reflected their motile/invasive capacity. A comparison of protein expression profiles between cell lines of high (group 1) versus low (group 2) motile/invasive capacity revealed 300 proteins that were differentially expressed, of which 196 proteins were significantly upregulated in group 1. Protein network and KEGG pathway analysis indicated a functional interplay between proteins up-regulated in group 1 cells, with increased expression of several key members of the actin cytoskeleton, extracellular matrix (ECM) and focal adhesion pathways. These proteomic expression profiles can be utilized to distinguish highly motile, aggressive ovarian cancer cells from lesser invasive ones, and could prove to be essential in the development of more effective strategies that target pivotal cell signaling pathways used by cancer cells during local invasion and distant metastasis.

A proteome resource of ovarian cancer ascites: integrated proteomic and bioinformatic analyses to identify putative biomarkers.

Epithelial ovarian cancer is the most lethal gynecological malignancy, and disease-specific biomarkers are urgently needed to improve diagnosis, prognosis, and to predict and monitor treatment efficiency. We present an in-depth proteomic analysis of selected biochemical fractions of human ovarian cancer ascites, resulting in the stringent and confident identification of over 2500 proteins. Rigorous filter schemes were applied to objectively minimize the number of false-positive identifications, and we only report proteins with substantial peptide evidence. Integrated computational analysis of the ascites proteome combined with several recently published proteomic data sets of human plasma, urine, 59 ovarian cancer related microarray data sets, and protein-protein interactions from the Interologous Interaction Database I (2)D ( http://ophid.utoronto.ca/i2d) resulted in a short-list of 80 putative biomarkers. The presented proteomics analysis provides a significant resource for ovarian cancer research, and a framework for biomarker discovery.