The Complex Subtype-Dependent Role of Connexin 43 (GJA1) in Breast Cancer.

Gap junction transmembrane channels allow the transfer of small molecules between the cytoplasm of adjacent cells. They are formed by proteins named connexins (Cxs) that have long been considered as a tumor suppressor. This widespread view has been challenged by recent studies suggesting that the role of Connexin 43 (Cx43) in cancer is tissue- and stage-specific and can even promote tumor progression. High throughput profiling of invasive breast cancer has allowed for the construction of subtyping schemes that partition patients into at least four distinct intrinsic subtypes. This study characterizes Cx43 expression during cancer progression with each of the tumor subtypes using a compendium of publicly available gene expression data. In particular, we show that Cx43 expression depends greatly on intrinsic subtype. Tumor grade also co-varies with patient subtype, resulting in Cx43 co-expression with grade in a subtype-dependent manner. Better survival was associated with a high expression of Cx43 in unstratified and luminal tumors but with a low expression in Her2e subtype. A better understanding of Cx43 regulation in a subtype-dependent manner is needed to clarify the context in which Cx43 is associated with tumor suppression or cancer progression.

Met synergizes with p53 loss to induce mammary tumors that possess features of claudin-low breast cancer.

Triple-negative breast cancer (TNBC) accounts for ∼20% of cases and contributes to basal and claudin-low molecular subclasses of the disease. TNBCs have poor prognosis, display frequent mutations in tumor suppressor gene p53 (TP53), and lack targeted therapies. The MET receptor tyrosine kinase is elevated in TNBC and transgenic Met models (Met(mt)) develop basal-like tumors. To investigate collaborating events in the genesis of TNBC, we generated Met(mt) mice with conditional loss of murine p53 (Trp53) in mammary epithelia. Somatic Trp53 loss, in combination with Met(mt), significantly increased tumor penetrance over Met(mt) or Trp53 loss alone. Unlike Met(mt) tumors, which are histologically diverse and enriched in a basal-like molecular signature, the majority of Met(mt) tumors with Trp53 loss displayed a spindloid pathology with a distinct molecular signature that resembles the human claudin-low subtype of TNBC, including diminished claudins, an epithelial-to-mesenchymal transition signature, and decreased expression of the microRNA-200 family. Moreover, although mammary specific loss of Trp53 promotes tumors with diverse pathologies, those with spindloid pathology and claudin-low signature display genomic Met amplification. In both models, MET activity is required for maintenance of the claudin-low morphological phenotype, in which MET inhibitors restore cell-cell junctions, rescue claudin 1 expression, and abrogate growth and dissemination of cells in vivo. Among human breast cancers, elevated levels of MET and stabilized TP53, indicative of mutation, correlate with highly proliferative TNBCs of poor outcome. This work shows synergy between MET and TP53 loss for claudin-low breast cancer, identifies a restricted claudin-low gene signature, and provides a rationale for anti-MET therapies in TNBC.

SOCIO-ETHICAL ISSUES IN PERSONALIZED MEDICINE: A SYSTEMATIC REVIEW OF ENGLISH LANGUAGE HEALTH TECHNOLOGY ASSESSMENTS OF GENE EXPRESSION PROFILING TESTS FOR BREAST CANCER PROGNOSIS.

OBJECTIVES: There have been multiple calls for explicit integration of ethical, legal, and social issues (ELSI) in health technology assessment (HTA) and addressing ELSI has been highlighted as key in optimizing benefits in the Omics/Personalized Medicine field. This study examines HTAs of an early clinical example of Personalized Medicine (gene expression profile tests [GEP] for breast cancer prognosis) aiming to: (i) identify ELSI; (ii) assess whether ELSIs are implicitly or explicitly addressed; and (iii) report methodology used for ELSI integration. METHODS: A systematic search for HTAs (January 2004 to September 2012), followed by descriptive and qualitative content analysis. RESULTS: Seventeen HTAs for GEP were retrieved. Only three (18%) explicitly presented ELSI, and only one reported methodology. However, all of the HTAs included implicit ELSI. Eight themes of implicit and explicit ELSI were identified. "Classical" ELSI including privacy, informed consent, and concerns about limited patient/clinician genetic literacy were always presented explicitly. Some ELSI, including the need to understand how individual patients' risk tolerances affect clinical decision-making after reception of GEP results, were presented both explicitly and implicitly in HTAs. Others, such as concern about evidentiary deficiencies for clinical utility of GEP tests, occurred only implicitly. CONCLUSIONS: Despite a wide variety of important ELSI raised, these were rarely explicitly addressed in HTAs. Explicit treatment would increase their accessibility to decision-makers, and may augment HTA efficiency maximizing their utility. This is particularly important where complex Personalized Medicine applications are rapidly expanding choices for patients, clinicians and healthcare systems.

Conserved epigenetic sensitivity to early life experience in the rat and human hippocampus.

Early life experience is associated with long-term effects on behavior and epigenetic programming of the NR3C1 (GLUCOCORTICOID RECEPTOR) gene in the hippocampus of both rats and humans. However, it is unlikely that such effects completely capture the evolutionarily conserved epigenetic mechanisms of early adaptation to environment. Here we present DNA methylation profiles spanning 6.5 million base pairs centered at the NR3C1 gene in the hippocampus of humans who experienced abuse as children and nonabused controls. We compare these profiles to corresponding DNA methylation profiles in rats that received differential levels of maternal care. The profiles of both species reveal hundreds of DNA methylation differences associated with early life experience distributed across the entire region in nonrandom patterns. For instance, methylation differences tend to cluster by genomic location, forming clusters covering as many as 1 million bases. Even more surprisingly, these differences seem to specifically target regulatory regions such as gene promoters, particularly those of the protocadherin α, ß, and γ gene families. Beyond these high-level similarities, more detailed analyses reveal methylation differences likely stemming from the significant biological and environmental differences between species. These results provide support for an analogous cross-species epigenetic regulatory response at the level of the genomic region to early life experience.

Cut homeobox 1 causes chromosomal instability by promoting bipolar division after cytokinesis failure.

Cell populations able to generate a large repertoire of genetic variants have increased potential to generate tumor cells that survive through the multiple selection steps involved in tumor progression. A mechanism for the generation of aneuploid cancer cells involves passage through a tetraploid stage. Supernumerary centrosomes, however, can lead to multipolar mitosis and cell death. Using tissue culture and transgenic mouse models of breast cancer, we report that Cut homeobox 1 (CUX1) causes chromosomal instability by activating a transcriptional program that prevents multipolar divisions and enables the survival of tetraploid cells that evolve to become genetically unstable and tumorigenic. Transcriptional targets of CUX1 involved in DNA replication and bipolar mitosis defined a gene expression signature that, across 12 breast cancer gene expression datasets, was associated with poor clinical outcome. The signature not only was higher in breast tumor subtypes of worse prognosis, like the basal-like and HER2(+) subtypes, but also identified poor outcome among estrogen receptor-positive/node-negative tumors, a subgroup considered to be at lower risk. The CUX1 signature therefore represents a unique criterion to stratify patients and provides insight into the molecular determinants of poor clinical outcome.

Stromal retinoic acid receptor beta promotes mammary gland tumorigenesis.

Retinoic acid is a potent differentiation and antiproliferative agent of breast cancer cells, and one of its receptors, retinoic acid receptor ß (RARß), has been proposed to act as a tumor suppressor. In contrast, we report herein that inactivation of Rarb in the mouse results in a protective effect against ErbB2-induced mammary gland tumorigenesis. Strikingly, tissue recombination experiments indicate that the presence of Rarb in the stromal compartment is essential for the growth of mammary carcinoma. Ablation of Rarb leads to a remodeling of the stroma during tumor progression that includes a decrease in angiogenesis, in the recruitment of inflammatory cells, and in the number myofibroblasts. In agreement with this finding, we observed that a markedly reduced expression of chemokine (C-X-C motif) ligand 12 (Cxcl12) in the stroma of Rarb-null mice is accompanied by a decrease in the CXCL12/chemokine C-X-C receptor 4 (CXCR4)/ErbB2 signaling axis in the tumors. Relevance to the human disease is underlined by the finding that gene-expression profiling of the Rarb-deficient mammary stromal compartment identified an ortholog RARß signature in human microdissected breast tissues that differentiates tumor from normal stroma. Our study thus implicates RARß in promoting tumorigenesis and suggests that retinoid-based approaches for the prevention and treatment of breast cancer should be redesigned.

Candida albicans exhibits heterogeneous and adaptive cytoprotective responses to antifungal compounds.

Candida albicans, an opportunistic human pathogen, poses a significant threat to human health and is associated with significant socio-economic burden. Current antifungal treatments fail, at least in part, because C. albicans can initiate a strong drug tolerance response that allows some cells to grow at drug concentrations above their minimal inhibitory concentration. To better characterize this cytoprotective tolerance program at the molecular single-cell level, we used a nanoliter droplet-based transcriptomics platform to profile thousands of individual fungal cells and establish their subpopulation characteristics in the absence and presence of antifungal drugs. Profiles of untreated cells exhibit heterogeneous expression that correlates with cell cycle stage with distinct metabolic and stress responses. At 2 days post-fluconazole exposure (a time when tolerance is measurable), surviving cells bifurcate into two major subpopulations: one characterized by the upregulation of genes encoding ribosomal proteins, rRNA processing machinery, and mitochondrial cellular respiration capacity, termed the Ribo-dominant (Rd) state; and the other enriched for genes encoding stress responses and related processes, termed the Stress-dominant (Sd) state. This bifurcation persists at 3 and 6 days post-treatment. We provide evidence that the ribosome assembly stress response (RASTR) is activated in these subpopulations and may facilitate cell survival.

Many drugs currently used to treat fungal diseases are becoming less effective. This is partly due to the rise of antifungal resistance, where certain fungal cells acquire mutations that enable them to thrive and proliferate despite the medication. Antifungal tolerance also contributes to this problem, wherein certain cells can continue to grow and multiply, while other ­ genetically identical ones ­ cannot. This variability is partly due to differences in gene expression within the cells. The specific nature of these differences has remained elusive, mainly because their study requires the use of expensive and challenging single-cell technologies. To address this challenge, Dumeaux et al. adapted an existing technique to perform single-cell transcriptomics in the pathogenic yeast Candida albicans. Their approach was cost effective and made it possible to examine the gene expression in thousands of individual cells within a population that had either been treated with antifungal drugs or were left untreated. After two to three days following exposure to the antifungal treatment, C. albicans cells commonly exhibited one of two states: one subgroup, the 'Ribo-dominant' cells, predominantly expressed genes for ribosomal proteins, while the other group, the 'Stress-dominant' cells, upregulated their expression of stress-response genes. This suggests that drug tolerance may be related to different gene expression patterns in growing cell subpopulations compared with non-growing subpopulations. The findings also indicate that the so-called 'ribosome assembly stress response' known to help baker's yeast cells to survive, might also aid C. albicans in surviving exposure to antifungal treatments. The innovative use of single-cell transcriptomics in this study could be applied to other species of fungi to study differences in cell communication under diverse growth conditions. Moreover, the unique gene expression patterns in C. albicans identified by Dumeaux et al. may help to design new antifungal treatments that target pathways linked to drug resistance.

Respiration supports intraphagosomal filamentation and escape of Candida albicans from macrophages.

IMPORTANCE: Candida albicans is a leading human fungal pathogen that often causes life-threatening infections in immunocompromised individuals. The ability of C. albicans to transition between yeast and filamentous forms is key to its virulence, and this occurs in response to many host-relevant cues, including engulfment by host macrophages. While previous efforts identified C. albicans genes required for filamentation in other conditions, the genes important for this morphological transition upon internalization by macrophages remained largely enigmatic. Here, we employed a functional genomic approach to identify genes that enable C. albicans filamentation within macrophages and uncovered a role for the mitochondrial ribosome, respiration, and the SNF1 AMP-activated kinase complex. Additionally, we showed that glucose uptake and glycolysis by macrophages support C. albicans filamentation. This work provides insights into the metabolic dueling that occurs during the interaction of C. albicans with macrophages and identifies vulnerabilities in C. albicans that could serve as promising therapeutic targets.

Gene-expression profiling of microdissected breast cancer microvasculature identifies distinct tumor vascular subtypes.

INTRODUCTION: Angiogenesis represents a potential therapeutic target in breast cancer. However, responses to targeted antiangiogenic therapies have been reported to vary among patients. This suggests that the tumor vasculature may be heterogeneous and that an appropriate choice of treatment would require an understanding of these differences. METHODS: To investigate whether and how the breast tumor vasculature varies between individuals, we isolated tumor-associated and matched normal vasculature from 17 breast carcinomas by laser-capture microdissection, and generated gene-expression profiles. Because microvessel density has previously been associated with disease course, tumors with low (n = 9) or high (n = 8) microvessel density were selected for analysis to maximize heterogeneity for this feature. RESULTS: We identified differences between tumor and normal vasculature, and we describe two subtypes present within tumor vasculature. These subtypes exhibit distinct gene-expression signatures that reflect features including hallmarks of vessel maturity. Potential therapeutic targets (MET, ITGAV, and PDGFRß) are differentially expressed between subtypes. Taking these subtypes into account has allowed us to derive a vascular signature associated with disease outcome. CONCLUSIONS: Our results further support a role for tumor microvasculature in determining disease progression. Overall, this study provides a deeper molecular understanding of the heterogeneity existing within the breast tumor vasculature and opens new avenues toward the improved design and targeting of antiangiogenic therapies.

A Deep Learning Approach to Capture the Essence of Candida albicans Morphologies.

We present deep learning-based approaches for exploring the complex array of morphologies exhibited by the opportunistic human pathogen Candida albicans. Our system, entitled Candescence, automatically detects C. albicans cells from differential image contrast microscopy and labels each detected cell with one of nine morphologies. This ranges from yeast white and opaque forms to hyphal and pseudohyphal filamentous morphologies. The software is based upon a fully convolutional one-stage (FCOS) object detector, a deep learning technique that uses an extensive set of images that we manually annotated with the location and morphology of each cell. We developed a novel cumulative curriculum-based learning strategy that stratifies our images by difficulty from simple yeast forms to complex filamentous architectures. Candescence achieves very good performance (~85% recall; 81% precision) on this difficult learning set, where some images contain hundreds of cells with substantial intermixing between the predicted classes. To capture the essence of each C. albicans morphology and how they intermix, we used a second technique from deep learning entitled generative adversarial networks. The resultant models allow us to identify and explore technical variables, developmental trajectories, and morphological switches. Importantly, the model allows us to quantitatively capture morphological plasticity observed with genetically modified strains or strains grown in different media and environments. We envision Candescence as a community meeting point for quantitative explorations of C. albicans morphology. IMPORTANCE The fungus Candida albicans can "shape shift" between 12 morphologies in response to environmental variables. The cytoprotective capacity provided by this polymorphism makes C. albicans a formidable pathogen to treat clinically. Microscopy images of C. albicans colonies can contain hundreds of cells in different morphological states. Manual annotation of images can be difficult, especially as a result of densely packed and filamentous colonies and of technical artifacts from the microscopy itself. Manual annotation is inherently subjective, depending on the experience and opinion of annotators. Here, we built a deep learning approach entitled Candescence to parse images in an automated, quantitative, and objective fashion: each cell in an image is located and labeled with its morphology. Candescence effectively replaces simple rules based on visual phenotypes (size, shape, and shading) with neural circuitry capable of capturing subtle but salient features in images that may be too complex for human annotators.

Identification of Interacting Stromal Axes in Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC) is a molecularly heterogeneous cancer that is difficult to treat. Despite the role it may play in tumor progression and response to therapy, microenvironmental (stromal) heterogeneity in TNBC has not been well characterized. To address this challenge, we investigated the transcriptome of tumor-associated stroma isolated from TNBC (n = 57). We identified four stromal axes enriched for T cells (T), B cells (B), epithelial markers (E), or desmoplasia (D). Our analysis method (STROMA4) assigns a score along each stromal axis for each patient and then combined the axis scores to subtype patients. Analysis of these subtypes revealed that prognostic capacity of the B, T, and E scores was governed by the D score. When compared with a previously published TNBC subtyping scheme, the STROMA4 method better captured tumor heterogeneity and predicted patient benefit from therapy with increased sensitivity. This approach produces a simple ontology that captures TNBC heterogeneity and informs how tumor-associated properties interact to affect prognosis. Cancer Res; 77(17); 4673-83. ©2017 AACR.

Strain-dependent pulmonary gene expression profiles of a cystic fibrosis mouse model.

Cystic fibrosis (CF) lung disease severity is influenced by unknown genetic factors apart from the disease causative gene, cystic fibrosis transmembrane conductance regulator (CFTR). Previous studies have shown the C57BL/6J congenic Cftr(-/-) (B6 CF) mouse to develop a fibrotic lung disease compared with both CF mice of the BALB/c background and wild-type animals. In this report, gene expression profiling with microarrays was used to identify genes differentially expressed in the lungs of B6 and BALB CF mice compared with non-CF littermates. Seven hundred two genes or expressed sequence tags (ESTs) were identified to be differentially expressed between the B6 CF and non-CF control lungs (P < 0.05), and, by Gene Ontology classification, the B6 CF response included the cell proliferation categories of DNA metabolism and mitosis. In the response of BALB mice to nonfunctional Cftr, 943 genes/ESTs were differentially expressed compared with controls. The biological processes of apoptosis and T and B cell proliferation were prominent in the gene list of the BALB CF strain. In support of this strain difference, increased T lymphocyte infiltration was evident in the lungs of BALB CF mice, through immunohistochemical staining, compared with the lungs from both B6 CF and non-CF control mice. Four hundred forty-four genes/ESTs were differentially expressed between B6 CF and BALB CF mice (P < 0.05, fold > 2), including 56 that map to previously identified linkage intervals. These results suggest that the variable severity of CF lung disease in this mouse model is controlled by multiple genetic factors, including those of an immune response.

Refining protein subcellular localization.

The study of protein subcellular localization is important to elucidate protein function. Even in well-studied organisms such as yeast, experimental methods have not been able to provide a full coverage of localization. The development of bioinformatic predictors of localization can bridge this gap. We have created a Bayesian network predictor called PSLT2 that considers diverse protein characteristics, including the combinatorial presence of InterPro motifs and protein interaction data. We compared the localization predictions of PSLT2 to high-throughput experimental localization datasets. Disagreements between these methods generally involve proteins that transit through or reside in the secretory pathway. We used our multi-compartmental predictions to refine the localization annotations of yeast proteins primarily by distinguishing between soluble lumenal proteins and soluble proteins peripherally associated with organelles. To our knowledge, this is the first tool to provide this functionality. We used these sub-compartmental predictions to characterize cellular processes on an organellar scale. The integration of diverse protein characteristics and protein interaction data in an appropriate setting can lead to high-quality detailed localization annotations for whole proteomes. This type of resource is instrumental in developing models of whole organelles that provide insight into the extent of interaction and communication between organelles and help define organellar functionality.

STAT3 Establishes an Immunosuppressive Microenvironment during the Early Stages of Breast Carcinogenesis to Promote Tumor Growth and Metastasis.

Immunosurveillance constitutes the first step of cancer immunoediting in which developing malignant lesions are eliminated by antitumorigenic immune cells. However, the mechanisms by which neoplastic cells induce an immunosuppressive state to evade the immune response are still unclear. The transcription factor STAT3 has been implicated in breast carcinogenesis and tumor immunosuppression in advanced disease, but its involvement in early disease development has not been established. Here, we genetically ablated Stat3 in the tumor epithelia of the inducible PyVmT mammary tumor model and found that Stat3-deficient mice recapitulated the three phases of immunoediting: elimination, equilibrium, and escape. Pathologic analyses revealed that Stat3-deficient mice initially formed hyperplastic and early adenoma-like lesions that later completely regressed, thereby preventing the emergence of mammary tumors in the majority of animals. Furthermore, tumor regression was correlated with massive immune infiltration into the Stat3-deficient lesions, leading to their elimination. In a minority of animals, focal, nonmetastatic Stat3-deficient mammary tumors escaped immune surveillance after a long latency or equilibrium period. Taken together, our findings suggest that tumor epithelial expression of Stat3 plays a critical role in promoting an immunosuppressive tumor microenvironment during breast tumor initiation and progression, and prompt further investigation of Stat3-inhibitory strategies that may reactivate the immunosurveillance program.

Absolute assignment of breast cancer intrinsic molecular subtype.

BACKGROUND: Massively parallel gene expression profiling has provided a more objective, molecular-level characterization of breast cancer subtypes. Several bioinformatics tools are available to infer patient subtype from a gene expression profile including the well-studied PAM50. The specific algorithmic methods used in these tools require access to a broad patient dataset. The choice of subtype for an individual is determined relative to all other patients across the panel, making subtypes heavily dependent on the composition of the dataset. Our aim was to develop a bioinformatics approach assigning absolute breast cancer subtypes, independent of dataset composition. METHODS: Using a dataset of 4924 breast cancer patients, we defined a new bioinformatics approach: Absolute Intrinsic Molecular Subtyping (AIMS) that assigns subtype from a gene expression profile for an individual sample without the need for a large, diverse, and normalized dataset. We evaluated the agreement of AIMS with PAM50 and compared subtype assignment and prognostic value of the subtypes. We assessed AIMS' robustness using a benchmark set of tests including subtype reproducibility between technologies, gene removal, and normal gene expression contamination, and compared it with PAM50. All statistical tests, except where noted, were two-sided. RESULTS: AIMS vastly agreed with PAM50, with 76% and 77% agreement for cross validation and the test set, respectively, and the prognostic capacity of the intrinsic subtypes was preserved. AIMS is fully stable, and its absolute nature enables its use on a wide range of datasets and technologies, including RNA-seq. CONCLUSIONS: The instability of a breast cancer subtyping scheme like PAM50 could have important consequences in clinical management of patients. AIMS is a fully stable and robust subtyping scheme that recapitulates PAM50.

A 12-gene signature to distinguish colon cancer patients with better clinical outcome following treatment with 5-fluorouracil or FOLFIRI.

Currently, there is no marker in use in the clinical management of colon cancer to predict which patients will respond efficiently to 5-fluorouracil (5-FU), a common component of all cytotoxic therapies. Our aim was to develop and validate a multigene signature associated with clinical outcome from 5-FU therapy and to determine if it could be used to identify patients who might respond better to alternate treatments. Using a panel of 5-FU resistant and sensitive colon cancer cell lines, we identified 103 differentially expressed genes providing us with a 5-FU response signature. We refined this signature using a clinically relevant DNA microarray-based dataset of 359 formalin-fixed and paraffin-embedded (FFPE) colon cancer samples. We then validated the final signature in an external independent DNA microarray-based dataset of 316 stage III FFPE samples from the PETACC-3 (Pan-European Trails in Alimentary Tract Cancers) clinical trial. Finally, using a drug sensitivity database of 658 cell lines, we generated a list of drugs that could sensitize 5-FU resistant patients using our signature. We confirmed using the PETACC-3 dataset that the overall survival of subjects responding well to 5-FU did not improve with the addition of irinotecan (FOLFIRI; two-sided log-rank test p = 0.795). Conversely, patients who responded poorly to 5-FU based on our 12-gene signature were associated with better survival on FOLFIRI therapy (one-sided log-rank test p = 0.039). This new multigene signature is readily applicable to FFPE samples and provides a new tool to help manage treatment in stage III colon cancer. It also provides the first evidence that a subgroup of colon cancer patients can respond better to FOLFIRI than 5-FU treatment alone.

A trade-off between sample complexity and computational complexity in learning Boolean networks from time-series data.

A key problem in molecular biology is to infer regulatory relationships between genes from expression data. This paper studies a simplified model of such inference problems in which one or more Boolean variables, modeling, for example, the expression levels of genes, each depend deterministically on a small but unknown subset of a large number of Boolean input variables. Our model assumes that the expression data comprises a time series, in which successive samples may be correlated. We provide bounds on the expected amount of data needed to infer the correct relationships between output and input variables. These bounds improve and generalize previous results for Boolean network inference and continuous-time switching network inference. Although the computational problem is intractable in general, we describe a fixed-parameter tractable algorithm that is guaranteed to provide at least a partial solution to the problem. Most interestingly, both the sample complexity and computational complexity of the problem depend on the strength of correlations between successive samples in the time series but in opposing ways. Uncorrelated samples minimize the total number of samples needed while maximizing computational complexity; a strong correlation between successive samples has the opposite effect. This observation has implications for the design of experiments for measuring gene expression.

Global survey of organ and organelle protein expression in mouse: combined proteomic and transcriptomic profiling.

Organs and organelles represent core biological systems in mammals, but the diversity in protein composition remains unclear. Here, we combine subcellular fractionation with exhaustive tandem mass spectrometry-based shotgun sequencing to examine the protein content of four major organellar compartments (cytosol, membranes [microsomes], mitochondria, and nuclei) in six organs (brain, heart, kidney, liver, lung, and placenta) of the laboratory mouse, Mus musculus. Using rigorous statistical filtering and machine-learning methods, the subcellular localization of 3274 of the 4768 proteins identified was determined with high confidence, including 1503 previously uncharacterized factors, while tissue selectivity was evaluated by comparison to previously reported mRNA expression patterns. This molecular compendium, fully accessible via a searchable web-browser interface, serves as a reliable reference of the expressed tissue and organelle proteomes of a leading model mammal.

Predicting subcellular localization via protein motif co-occurrence.

The prediction of subcellular localization of proteins from their primary sequence is a challenging problem in bioinformatics. We have created a Bayesian network localization predictor called PSLT that is based on the combinatorial presence of InterPro motifs and specific membrane domains in human proteins. This probabilistic framework generates a likelihood of localization to all organelles and allows to predict multicompartmental proteins. When used to predict on nine compartments, PSLT achieves an accuracy of 78% as estimated by using a 10-fold cross-validation test and a coverage of 74%. When used to predict the localization of proteins from other closely related species, it achieves a prediction accuracy and a coverage >80%. We compared the localization predictions of PSLT to those determined through GFP-tagging and microscopy for a group of human proteins. We found two general classes of proteins that are mislocalized by the GFP-tagging strategy but are correctly localized by PSLT. This suggests that PSLT can be used in combination with experimental approaches for localization to identify proteins for which additional experimental validation is required. We used our predictor to annotate all 9793 human proteins from SWISS-PROT release 41.25, 16% of which are predicted by PSLT to be present in more than one compartment.