PathDIP 5: improving coverage and making enrichment analysis more biologically meaningful.

Pathway Data Integration Portal (PathDIP) is an integrated pathway database that was developed to increase functional gene annotation coverage and reduce bias in pathway enrichment analysis. PathDIP 5 provides multiple improvements to enable more interpretable analysis: users can perform enrichment analysis using all sources, separate sources or by combining specific pathway subsets; they can select the types of sources to use or the types of pathways for the analysis, reducing the number of resulting generic pathways or pathways not related to users' research question; users can use API. All pathways have been mapped to seven representative types. The results of pathway enrichment can be summarized through knowledge-based pathway consolidation. All curated pathways were mapped to 53 pathway ontology-based categories. In addition to genes, pathDIP 5 now includes metabolites. We updated existing databases, included two new sources, PathBank and MetabolicAtlas, and removed outdated databases. We enable users to analyse their results using Drugst.One, where a drug-gene network is created using only the user's genes in a specific pathway. Interpreting the results of any analysis is now improved by multiple charts on all the results pages. PathDIP 5 is freely available at https://ophid.utoronto.ca/pathDIP.

Drugst.One - a plug-and-play solution for online systems medicine and network-based drug repurposing.

In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these tools often require complex installation and lack intuitive visual network mining capabilities. To tackle these challenges, we introduce Drugst.One, a platform that assists specialized computational medicine tools in becoming user-friendly, web-based utilities for drug repurposing. With just three lines of code, Drugst.One turns any systems biology software into an interactive web tool for modeling and analyzing complex protein-drug-disease networks. Demonstrating its broad adaptability, Drugst.One has been successfully integrated with 21 computational systems medicine tools. Available at https://drugst.one, Drugst.One has significant potential for streamlining the drug discovery process, allowing researchers to focus on essential aspects of pharmaceutical treatment research.

A Global Analysis of the Receptor Tyrosine Kinase-Protein Phosphatase Interactome.

Receptor tyrosine kinases (RTKs) and protein phosphatases comprise protein families that play crucial roles in cell signaling. We used two protein-protein interaction (PPI) approaches, the membrane yeast two-hybrid (MYTH) and the mammalian membrane two-hybrid (MaMTH), to map the PPIs between human RTKs and phosphatases. The resulting RTK-phosphatase interactome reveals a considerable number of previously unidentified interactions and suggests specific roles for different phosphatase families. Additionally, the differential PPIs of some protein tyrosine phosphatases (PTPs) and their mutants suggest diverse mechanisms of these PTPs in the regulation of RTK signaling. We further found that PTPRH and PTPRB directly dephosphorylate EGFR and repress its downstream signaling. By contrast, PTPRA plays a dual role in EGFR signaling: besides facilitating EGFR dephosphorylation, it enhances downstream ERK signaling by activating SRC. This comprehensive RTK-phosphatase interactome study provides a broad and deep view of RTK signaling.

MirDIP 5.2: tissue context annotation and novel microRNA curation.

MirDIP is a well-established database that aggregates microRNA-gene human interactions from multiple databases to increase coverage, reduce bias, and improve usability by providing an integrated score proportional to the probability of the interaction occurring. In version 5.2, we removed eight outdated resources, added a new resource (miRNATIP), and ran five prediction algorithms for miRBase and mirGeneDB. In total, mirDIP 5.2 includes 46 364 047 predictions for 27 936 genes and 2734 microRNAs, making it the first database to provide interactions using data from mirGeneDB. Moreover, we curated and integrated 32 497 novel microRNAs from 14 publications to accelerate the use of these novel data. In this release, we also extend the content and functionality of mirDIP by associating contexts with microRNAs, genes, and microRNA-gene interactions. We collected and processed microRNA and gene expression data from 20 resources and acquired information on 330 tissue and disease contexts for 2657 microRNAs, 27 576 genes and 123 651 910 gene-microRNA-tissue interactions. Finally, we improved the usability of mirDIP by enabling the user to search the database using precursor IDs, and we integrated miRAnno, a network-based tool for identifying pathways linked to specific microRNAs. We also provide a mirDIP API to facilitate access to its integrated predictions. Updated mirDIP is available at https://ophid.utoronto.ca/mirDIP.

USNAP: fast unique dense region detection and its application to lung cancer.

MOTIVATION: Many real-world problems can be modeled as annotated graphs. Scalable graph algorithms that extract actionable information from such data are in demand since these graphs are large, varying in topology, and have diverse node/edge annotations. When these graphs change over time they create dynamic graphs, and open the possibility to find patterns across different time points. In this article, we introduce a scalable algorithm that finds unique dense regions across time points in dynamic graphs. Such algorithms have applications in many different areas, including the biological, financial, and social domains. RESULTS: There are three important contributions to this manuscript. First, we designed a scalable algorithm, USNAP, to effectively identify dense subgraphs that are unique to a time stamp given a dynamic graph. Importantly, USNAP provides a lower bound of the density measure in each step of the greedy algorithm. Second, insights and understanding obtained from validating USNAP on real data show its effectiveness. While USNAP is domain independent, we applied it to four non-small cell lung cancer gene expression datasets. Stages in non-small cell lung cancer were modeled as dynamic graphs, and input to USNAP. Pathway enrichment analyses and comprehensive interpretations from literature show that USNAP identified biologically relevant mechanisms for different stages of cancer progression. Third, USNAP is scalable, and has a time complexity of O(m+mc log nc+nc log nc), where m is the number of edges, and n is the number of vertices in the dynamic graph; mc is the number of edges, and nc is the number of vertices in the collapsed graph. AVAILABILITY AND IMPLEMENTATION: The code of USNAP is available at https://www.cs.utoronto.ca/~juris/data/USNAP22.

Classifying patients with psoriatic arthritis according to their disease activity status using serum metabolites and machine learning.

INTRODUCTION: Psoriatic arthritis (PsA) is a heterogeneous inflammatory arthritis, affecting approximately a quarter of patients with psoriasis. Accurate assessment of disease activity is difficult. There are currently no clinically validated biomarkers to stratify PsA patients based on their disease activity, which is important for improving clinical management. OBJECTIVES: To identify metabolites capable of classifying patients with PsA according to their disease activity. METHODS: An in-house solid-phase microextraction (SPME)-liquid chromatography-high resolution mass spectrometry (LC-HRMS) method for lipid analysis was used to analyze serum samples obtained from patients classified as having low (n = 134), moderate (n = 134) or high (n = 104) disease activity, based on psoriatic arthritis disease activity scores (PASDAS). Metabolite data were analyzed using eight machine learning methods to predict disease activity levels. Top performing methods were selected based on area under the curve (AUC) and significance. RESULTS: The best model for predicting high disease activity from low disease activity achieved AUC 0.818. The best model for predicting high disease activity from moderate disease activity achieved AUC 0.74. The best model for classifying low disease activity from moderate and high disease activity achieved AUC 0.765. Compounds confirmed by MS/MS validation included metabolites from diverse compound classes such as sphingolipids, phosphatidylcholines and carboxylic acids. CONCLUSION: Several lipids and other metabolites when combined in classifying models predict high disease activity from both low and moderate disease activity. Lipids of key interest included lysophosphatidylcholine and sphingomyelin. Quantitative MS assays based on selected reaction monitoring, are required to quantify the candidate biomarkers identified.

IID 2021: towards context-specific protein interaction analyses by increased coverage, enhanced annotation and enrichment analysis.

Improved bioassays have significantly increased the rate of identifying new protein-protein interactions (PPIs), and the number of detected human PPIs has greatly exceeded early estimates of human interactome size. These new PPIs provide a more complete view of disease mechanisms but precise understanding of how PPIs affect phenotype remains a challenge. It requires knowledge of PPI context (e.g. tissues, subcellular localizations), and functional roles, especially within pathways and protein complexes. The previous IID release focused on PPI context, providing networks with comprehensive tissue, disease, cellular localization, and druggability annotations. The current update adds developmental stages to the available contexts, and provides a way of assigning context to PPIs that could not be previously annotated due to insufficient data or incompatibility with available context categories (e.g. interactions between membrane and cytoplasmic proteins). This update also annotates PPIs with conservation across species, directionality in pathways, membership in large complexes, interaction stability (i.e. stable or transient), and mutation effects. Enrichment analysis is now available for all annotations, and includes multiple options; for example, context annotations can be analyzed with respect to PPIs or network proteins. In addition to tabular view or download, IID provides online network visualization. This update is available at http://ophid.utoronto.ca/iid.

CFTR interactome mapping using the mammalian membrane two-hybrid high-throughput screening system.

Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) is a chloride and bicarbonate channel in secretory epithelia with a critical role in maintaining fluid homeostasis. Mutations in CFTR are associated with Cystic Fibrosis (CF), the most common lethal autosomal recessive disorder in Caucasians. While remarkable treatment advances have been made recently in the form of modulator drugs directly rescuing CFTR dysfunction, there is still considerable scope for improvement of therapeutic effectiveness. Here, we report the application of a high-throughput screening variant of the Mammalian Membrane Two-Hybrid (MaMTH-HTS) to map the protein-protein interactions of wild-type (wt) and mutant CFTR (F508del), in an effort to better understand CF cellular effects and identify new drug targets for patient-specific treatments. Combined with functional validation in multiple disease models, we have uncovered candidate proteins with potential roles in CFTR function/CF pathophysiology, including Fibrinogen Like 2 (FGL2), which we demonstrate in patient-derived intestinal organoids has a significant effect on CFTR functional expression.

Identifying Serum Metabolomic Markers Associated with Skin Disease Activity in Patients with Psoriatic Arthritis.

Psoriatic arthritis (PsA) is a chronic, systemic, immune-mediated inflammatory disease causing cutaneous and musculoskeletal inflammation that affects 25% of patients with psoriasis. Current methods for evaluating PsA disease activity are not accurate enough for precision medicine. A metabolomics-based approach can elucidate psoriatic disease pathogenesis, providing potential objective biomarkers. With the hypothesis that serum metabolites are associated with skin disease activity, we aimed to identify serum metabolites associated with skin activity in PsA patients. We obtained serum samples from patients with PsA (n = 150) who were classified into mild, moderate and high disease activity groups based on the Psoriasis Area Severity Index. We used solid-phase microextraction (SPME) for sample preparation, followed by data acquisition via an untargeted liquid chromatography-mass spectrometry (LC-MS) approach. Disease activity levels were predicted using identified metabolites and machine learning algorithms. Some metabolites tentatively identified include eicosanoids with anti- or pro-inflammatory properties, like 12-Hydroxyeicosatetraenoic acid, which was previously implicated in joint disease activity in PsA. Other metabolites of interest were associated with dysregulation of fatty acid metabolism and belonged to classes such as bile acids, oxidized phospholipids, and long-chain fatty acids. We have identified potential metabolites associated with skin disease activity in PsA patients.

pathDIP 4: an extended pathway annotations and enrichment analysis resource for human, model organisms and domesticated species.

PathDIP was introduced to increase proteome coverage of literature-curated human pathway databases. PathDIP 4 now integrates 24 major databases. To further reduce the number of proteins with no curated pathway annotation, pathDIP integrates pathways with physical protein-protein interactions (PPIs) to predict significant physical associations between proteins and curated pathways. For human, it provides pathway annotations for 5366 pathway orphans. Integrated pathway annotation now includes six model organisms and ten domesticated animals. A total of 6401 core and ortholog pathways have been curated from the literature or by annotating orthologs of human proteins in the literature-curated pathways. Extended pathways are the result of combining these pathways with protein-pathway associations that are predicted using organism-specific PPIs. Extended pathways expand proteome coverage from 81 088 to 120 621 proteins, making pathDIP 4 the largest publicly available pathway database for these organisms and providing a necessary platform for comprehensive pathway-enrichment analysis. PathDIP 4 users can customize their search and analysis by selecting organism, identifier and subset of pathways. Enrichment results and detailed annotations for input list can be obtained in different formats and views. To support automated bioinformatics workflows, Java, R and Python APIs are available for batch pathway annotation and enrichment analysis. PathDIP 4 is publicly available at http://ophid.utoronto.ca/pathDIP.

Association between the renin-angiotensin system and chronic lung allograft dysfunction.

Chronic lung allograft dysfunction (CLAD) is the major cause of death after lung transplantation. Angiotensin II (AngII), the main effector of the renin-angiotensin system, elicits fibrosis in both kidney and lung. We identified six AngII-regulated proteins (Ras homolog family member B (RHOB), bone marrow stromal cell antigen 1 (BST1), lysophospholipase 1 (LYPA1), glutamine synthetase (GLNA), thrombospondin 1 (TSP1) and laminin subunit ß2 (LAMB2)) that were increased in urine of patients with kidney allograft fibrosis. We hypothesised that the renin-angiotensin system is active in CLAD and that AngII-regulated proteins are increased in bronchoalveolar lavage fluid (BAL) of CLAD patients.We performed immunostaining of AngII receptors (AGTR1 and AGTR2), TSP1 and GLNA in 10 CLAD lungs and five controls. Using mass spectrometry, we quantified peptides corresponding to AngII-regulated proteins in BAL of 40 lung transplant recipients (stable, acute lung allograft dysfunction (ALAD) and CLAD). Machine learning algorithms were developed to predict CLAD based on BAL peptide concentrations.Immunostaining demonstrated significantly more AGTR1+ cells in CLAD versus control lungs (p=0.02). TSP1 and GLNA immunostaining positively correlated with the degree of lung fibrosis (R2=0.42 and 0.57, respectively). In BAL, we noted a trend towards higher concentrations of AngII-regulated peptides in patients with CLAD at the time of bronchoscopy, and significantly higher concentrations of BST1, GLNA and RHOB peptides in patients that developed CLAD at follow-up (p<0.05). The support vector machine classifier discriminated CLAD from stable and ALAD patients at the time of bronchoscopy (area under the curve (AUC) 0.86) and accurately predicted subsequent CLAD development (AUC 0.97).Proteins involved in the renin-angiotensin system are increased in CLAD lungs and BAL. AngII-regulated peptides measured in BAL may accurately identify patients with CLAD and predict subsequent CLAD development.

IID 2018 update: context-specific physical protein-protein interactions in human, model organisms and domesticated species.

Knowing the set of physical protein-protein interactions (PPIs) that occur in a particular context-a tissue, disease, or other condition-can provide valuable insights into key research questions. However, while the number of identified human PPIs is expanding rapidly, context information remains limited, and for most non-human species context-specific networks are completely unavailable. The Integrated Interactions Database (IID) provides one of the most comprehensive sets of context-specific human PPI networks, including networks for 133 tissues, 91 disease conditions, and many other contexts. Importantly, it also provides context-specific networks for 17 non-human species including model organisms and domesticated animals. These species are vitally important for drug discovery and agriculture. IID integrates interactions from multiple databases and datasets. It comprises over 4.8 million PPIs annotated with several types of context: tissues, subcellular localizations, diseases, and druggability information (the latter three are new annotations not available in the previous version). This update increases the number of species from 6 to 18, the number of PPIs from ∼1.5 million to ∼4.8 million, and the number of tissues from 30 to 133. IID also now supports topology and enrichment analyses of returned networks. IID is available at http://ophid.utoronto.ca/iid.

Extracellular Matrix Injury of Kidney Allografts in Antibody-Mediated Rejection: A Proteomics Study.

BACKGROUND: Antibody-mediated rejection (AMR) accounts for >50% of kidney allograft loss. Donor-specific antibodies (DSA) against HLA and non-HLA antigens in the glomeruli and the tubulointerstitium cause AMR while inflammatory cytokines such as TNFα trigger graft injury. The mechanisms governing cell-specific injury in AMR remain unclear. METHODS: Unbiased proteomic analysis of laser-captured and microdissected glomeruli and tubulointerstitium was performed on 30 for-cause kidney biopsy specimens with early AMR, acute cellular rejection (ACR), or acute tubular necrosis (ATN). RESULTS: A total of 107 of 2026 glomerular and 112 of 2399 tubulointerstitial proteins was significantly differentially expressed in AMR versus ACR; 112 of 2026 glomerular and 181 of 2399 tubulointerstitial proteins were significantly dysregulated in AMR versus ATN (P<0.05). Basement membrane and extracellular matrix (ECM) proteins were significantly decreased in both AMR compartments. Glomerular and tubulointerstitial laminin subunit γ-1 (LAMC1) expression decreased in AMR, as did glomerular nephrin (NPHS1) and receptor-type tyrosine-phosphatase O (PTPRO). The proteomic analysis revealed upregulated galectin-1, which is an immunomodulatory protein linked to the ECM, in AMR glomeruli. Anti-HLA class I antibodies significantly increased cathepsin-V (CTSV) expression and galectin-1 expression and secretion in human glomerular endothelial cells. CTSV had been predicted to cleave ECM proteins in the AMR glomeruli. Glutathione S-transferase ω-1, an ECM-modifying enzyme, was significantly increased in the AMR tubulointerstitium and in TNFα-treated proximal tubular epithelial cells. CONCLUSIONS: Basement membranes are often remodeled in chronic AMR. Proteomic analysis performed on laser-captured and microdissected glomeruli and tubulointerstitium identified early ECM remodeling, which may represent a new therapeutic opportunity.

Physiological Tau Interactome in Brain and Its Link to Tauopathies.

Alzheimer's disease (AD) and most of the other tauopathies are incurable neurodegenerative diseases with unpleasant symptoms and consequences. The common hallmark of all of these diseases is tau pathology, but its connection with disease progress has not been completely understood so far. Therefore, uncovering novel tau-interacting partners and pathology affected molecular pathways can reveal the causes of diseases as well as potential targets for the development of AD treatment. Despite the large number of known tau-interacting partners, a limited number of studies focused on in vivo tau interactions in disease or healthy conditions are available. Here, we applied an in vivo cross-linking approach, capable of capturing weak and transient protein-protein interactions, to a unique transgenic rat model of progressive tau pathology similar to human AD. We have identified 175 potential novel and known tau-interacting proteins by MALDI-TOF mass spectrometry. Several of the most promising candidates for possible drug development were selected for validation by coimmunoprecipitation and colocalization experiments in animal and cellular models. Three proteins, Baiap2, Gpr37l1, and Nptx1, were confirmed as novel tau-interacting partners, and on the basis of their known functions and implications in neurodegenerative or psychiatric disorders, we proposed their potential role in tau pathology.

Modeling tumor progression via the comparison of stage-specific graphs.

Can we use graph mining algorithms to find patterns in tumor molecular mechanisms? Can we model disease progression with multiple time-specific graph comparison algorithms? In this paper, we will focus on this area. Our main contributions are 1) we proposed the Temporal-Omics (Temp-O) workflow to model tumor progression in non-small cell lung cancer (NSCLC) using graph comparisons between multiple stage-specific graphs, and 2) we showed that temporal structures are meaningful in the tumor progression of NSCLC. Other identified temporal structures that were not highlighted in this paper may also be used to gain insights to possible novel mechanisms. Importantly, the Temp-O workflow is generic; while we applied it on NSCLC, it can be applied in other cancers and diseases. We used gene expression data from tumor samples across disease stages to model lung cancer progression, creating stage-specific tumor graphs. Validating our findings in independent datasets showed that differences in temporal network structures capture diverse mechanisms in NSCLC. Furthermore, results showed that structures are consistent and potentially biologically important as we observed that genes with similar protein names were captured in the same cliques for all cliques in all datasets. Importantly, the identified temporal structures are meaningful in the tumor progression of NSCLC as they agree with the molecular mechanism in the tumor progression or carcinogenesis of NSCLC. In particular, the identified major histocompatibility complex of class II temporal structures capture mechanisms concerning carcinogenesis; the proteasome temporal structures capture mechanisms that are in early or late stages of lung cancer; the ribosomal cliques capture the role of ribosome biosynthesis in cancer development and sustainment. Further, on a large independent dataset we validated that temporal network structures identified proteins that are prognostic for overall survival in NSCLC adenocarcinoma.

In silico prediction of physical protein interactions and characterization of interactome orphans.

Protein-protein interactions (PPIs) are useful for understanding signaling cascades, predicting protein function, associating proteins with disease and fathoming drug mechanism of action. Currently, only ∼ 10% of human PPIs may be known, and about one-third of human proteins have no known interactions. We introduce FpClass, a data mining-based method for proteome-wide PPI prediction. At an estimated false discovery rate of 60%, we predicted 250,498 PPIs among 10,531 human proteins; 10,647 PPIs involved 1,089 proteins without known interactions. We experimentally tested 233 high- and medium-confidence predictions and validated 137 interactions, including seven novel putative interactors of the tumor suppressor p53. Compared to previous PPI prediction methods, FpClass achieved better agreement with experimentally detected PPIs. We provide an online database of annotated PPI predictions (http://ophid.utoronto.ca/fpclass/) and the prediction software (http://www.cs.utoronto.ca/~juris/data/fpclass/).

Systematic protein-protein interaction mapping for clinically relevant human GPCRs.

G-protein-coupled receptors (GPCRs) are the largest family of integral membrane receptors with key roles in regulating signaling pathways targeted by therapeutics, but are difficult to study using existing proteomics technologies due to their complex biochemical features. To obtain a global view of GPCR-mediated signaling and to identify novel components of their pathways, we used a modified membrane yeast two-hybrid (MYTH) approach and identified interacting partners for 48 selected full-length human ligand-unoccupied GPCRs in their native membrane environment. The resulting GPCR interactome connects 686 proteins by 987 unique interactions, including 299 membrane proteins involved in a diverse range of cellular functions. To demonstrate the biological relevance of the GPCR interactome, we validated novel interactions of the GPR37, serotonin 5-HT4d, and adenosine ADORA2A receptors. Our data represent the first large-scale interactome mapping for human GPCRs and provide a valuable resource for the analysis of signaling pathways involving this druggable family of integral membrane proteins.

Integrated interactions database: tissue-specific view of the human and model organism interactomes.

IID (Integrated Interactions Database) is the first database providing tissue-specific protein-protein interactions (PPIs) for model organisms and human. IID covers six species (S. cerevisiae (yeast), C. elegans (worm), D. melonogaster (fly), R. norvegicus (rat), M. musculus (mouse) and H. sapiens (human)) and up to 30 tissues per species. Users query IID by providing a set of proteins or PPIs from any of these organisms, and specifying species and tissues where IID should search for interactions. If query proteins are not from the selected species, IID enables searches across species and tissues automatically by using their orthologs; for example, retrieving interactions in a given tissue, conserved in human and mouse. Interaction data in IID comprises three types of PPI networks: experimentally detected PPIs from major databases, orthologous PPIs and high-confidence computationally predicted PPIs. Interactions are assigned to tissues where their proteins pairs or encoding genes are expressed. IID is a major replacement of the I2D interaction database, with larger PPI networks (a total of 1,566,043 PPIs among 68,831 proteins), tissue annotations for interactions, and new query, analysis and data visualization capabilities. IID is available at http://ophid.utoronto.ca/iid.

The mammalian-membrane two-hybrid assay (MaMTH) for probing membrane-protein interactions in human cells.

Cell signaling, one of key processes in both normal cellular function and disease, is coordinated by numerous interactions between membrane proteins that change in response to stimuli. We present a split ubiquitin-based method for detection of integral membrane protein-protein interactions (PPIs) in human cells, termed mammalian-membrane two-hybrid assay (MaMTH). We show that this technology detects stimulus (hormone or agonist)-dependent and phosphorylation-dependent PPIs. MaMTH can detect changes in PPIs conferred by mutations such as those in oncogenic ErbB receptor variants or by treatment with drugs such as the tyrosine kinase inhibitor erlotinib. Using MaMTH as a screening assay, we identified CRKII as an interactor of oncogenic EGFR(L858R) and showed that CRKII promotes persistent activation of aberrant signaling in non-small cell lung cancer cells. MaMTH is a powerful tool for investigating the dynamic interactomes of human integral membrane proteins.

Reduced BRCA1 transcript levels in freshly isolated blood leukocytes from BRCA1 mutation carriers is mutation specific.

BACKGROUND: BRCA1 mutation carriers face a high lifetime risk of developing both breast and ovarian cancer. Haploinsufficiency is thought to predispose these women to cancer by reducing the pool of available BRCA1 transcript and protein, thereby compromising BRCA1 function. Whether or not cancer-free BRCA1 mutation carriers have lower messenger (m)RNA transcript levels in peripheral blood leukocytes has not been evaluated. The primary aim of this study was to characterize an association between BRCA1 mutation status and BRCA1 mRNA leukocyte expression levels among healthy women with a BRCA1 mutation. METHOD: RNA was extracted from freshly isolated peripheral blood leukocytes of 58 cancer-free, female participants (22 BRCA1 mutation carriers and 36 non-carriers). The expression levels of 236 cancer-associated genes, including BRCA1, were quantified using the Human Cancer Reference gene panel from the Nanostring Technologies nCounter Analysis System. RESULTS: Multivariate modeling demonstrated that carrying a BRCA1 mutation was the most significant predictor of BRCA1 mRNA levels. BRCA1 mRNA levels were significantly lower in BRCA1 mutation carriers compared to non-carriers (146.7 counts vs. 175.1 counts; P = 0.002). Samples with BRCA1 mutations within exon 11 had lower BRCA1 mRNA levels than samples with mutations within the 5' and 3' regions of the BRCA1 gene (122.1 counts vs. 138.9 and 168.6 counts, respectively; P = 0.003). Unsupervised hierarchical clustering of gene expression profiles from freshly isolated blood leukocytes revealed that BRCA1 mutation carriers cluster more closely with other BRCA1 mutation carriers than with BRCA1 wild-type samples. Moreover, a set of 17 genes (including BRCA1) previously shown to be involved in carcinogenesis, were differentially expressed between BRCA1 mutation carriers and non-carriers. CONCLUSION: Overall, these findings support the concept of BRCA1 haploinsufficiency wherein a specific mutation results in dosage-dependent alteration of BRCA1 at the transcriptional level. This study is the first to show a decrease in BRCA1 mRNA expression in freshly isolated blood leukocytes from healthy, unaffected BRCA1 mutation carriers.