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1.
Nat Methods ; 16(9): 843-852, 2019 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-31471613

RESUMO

Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of network remains poorly understood. We launched the 'Disease Module Identification DREAM Challenge', an open competition to comprehensively assess module identification methods across diverse protein-protein interaction, signaling, gene co-expression, homology and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies. Our robust assessment of 75 module identification methods reveals top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets. This community challenge establishes biologically interpretable benchmarks, tools and guidelines for molecular network analysis to study human disease biology.

2.
Genome Biol ; 20(1): 195, 2019 Sep 10.
Artigo em Inglês | MEDLINE | ID: mdl-31506093

RESUMO

Challenges are achieving broad acceptance for addressing many biomedical questions and enabling tool assessment. But ensuring that the methods evaluated are reproducible and reusable is complicated by the diversity of software architectures, input and output file formats, and computing environments. To mitigate these problems, some challenges have leveraged new virtualization and compute methods, requiring participants to submit cloud-ready software packages. We review recent data challenges with innovative approaches to model reproducibility and data sharing, and outline key lessons for improving quantitative biomedical data analysis through crowd-sourced benchmarking challenges.

3.
Mol Syst Biol ; 15(8): e8828, 2019 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-31464372

RESUMO

Endothelins (EDN) are peptide hormones that activate a GPCR signalling system and contribute to several diseases, including hypertension and cancer. Current knowledge about EDN signalling is fragmentary, and no systems level understanding is available. We investigated phosphoproteomic changes caused by endothelin B receptor (ENDRB) activation in the melanoma cell lines UACC257 and A2058 and built an integrated model of EDNRB signalling from the phosphoproteomics data. More than 5,000 unique phosphopeptides were quantified. EDN induced quantitative changes in more than 800 phosphopeptides, which were all strictly dependent on EDNRB. Activated kinases were identified based on high confidence EDN target sites and validated by Western blot. The data were combined with prior knowledge to construct the first comprehensive logic model of EDN signalling. Among the kinases predicted by the signalling model, AKT, JNK, PKC and AMP could be functionally linked to EDN-induced cell migration. The model contributes to the system-level understanding of the mechanisms underlying the pleiotropic effects of EDN signalling and supports the rational selection of kinase inhibitors for combination treatments with EDN receptor antagonists.

4.
J Mol Med (Berl) ; 2019 Aug 05.
Artigo em Inglês | MEDLINE | ID: mdl-31385015

RESUMO

Chronic kidney disease (CKD) may progress to end-stage renal disease (ESRD) at different pace. Early markers of disease progression could facilitate and improve patient management. However, conventional blood and urine chemistry have proven unable to predict the progression of disease at early stages. Therefore, we performed untargeted plasma peptidome analysis to select the peptides involved in progression, which are suitable for long prospective studies in future. The study consists of non-CKD (n = 66) and CKD (n = 106) patients with different stages. We performed plasma peptidomics on these subjects using chromatography and mass spectrometric approaches. Initially, we performed LC-ESI-MS and applied least absolute shrinkage and selection operator logistic regressions to select the peptides that are differentially expressed and we generated a peptidomic score for each subject. Later, we identified and sequenced the peptides with MALDI-MS/MS and also performed univariate and multivariate analyses with the clinical variables and peptidomic score to reveal their association with progression of renal disease. A logistic regression model selected 14 substances showing different concentrations according to renal function, of which seven substances were most likely occur in CKD patients. The peptidomic model had a global P value of < 0.01 with R2 of 0.466, and the area under the curve was 0.87 (95% CI, 0.8149-0.9186; P < 0.0001). The predicted score was significantly higher in CKD than in non-CKD patients (2.539 ± 0.2637 vs - 0.9382 ± 0.1691). The model was also able to predict stages of CKD: the Spearman correlation coefficient of the linear predictor with CKD stages was 0.83 with concordance indices of 0.899 (95% CI 0.863-0.927). In univariate analysis, the most consistent association of peptidomic score in CKD patients was with C-reactive protein, sodium level, and uric acid, which are unanticipated substances. Peptidomic analysis enabled to list some unanticipated substances that have not been extensively studied in the context of CKD but were associated with CKD progression, thus revealing interesting candidate markers or mediators of CKD of potential use in CKD progression management. KEY MESSAGES: • Conventional blood and urine chemistry have proven unable to predict the progression of disease at early stages of chronic kidney disease (CKD). • We performed untargeted plasma peptidome analysis to select the peptides involved in progression. • A logistic regression model selected 14 substances showing different concentrations according to renal function. • These peptides are unanticipated substances that have not been extensively studied in the context of CKD but were associated with CKD progression, thus revealing markers or mediators of CKD of potential use in CKD progression management.

5.
Genome Res ; 29(8): 1363-1375, 2019 08.
Artigo em Inglês | MEDLINE | ID: mdl-31340985

RESUMO

The prediction of transcription factor (TF) activities from the gene expression of their targets (i.e., TF regulon) is becoming a widely used approach to characterize the functional status of transcriptional regulatory circuits. Several strategies and data sets have been proposed to link the target genes likely regulated by a TF, each one providing a different level of evidence. The most established ones are (1) manually curated repositories, (2) interactions derived from ChIP-seq binding data, (3) in silico prediction of TF binding on gene promoters, and (4) reverse-engineered regulons from large gene expression data sets. However, it is not known how these different sources of regulons affect the TF activity estimations and, thereby, downstream analysis and interpretation. Here we compared the accuracy and biases of these strategies to define human TF regulons by means of their ability to predict changes in TF activities in three reference benchmark data sets. We assembled a collection of TF-target interactions for 1541 human TFs and evaluated how different molecular and regulatory properties of the TFs, such as the DNA-binding domain, specificities, or mode of interaction with the chromatin, affect the predictions of TF activity. We assessed their coverage and found little overlap on the regulons derived from each strategy and better performance by literature-curated information followed by ChIP-seq data. We provide an integrated resource of all TF-target interactions derived through these strategies, with confidence scores, as a resource for enhanced prediction of TF activities.

6.
NPJ Syst Biol Appl ; 5: 20, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31312514

RESUMO

Cancer cells with heterogeneous mutation landscapes and extensive functional redundancy easily develop resistance to monotherapies by emerging activation of compensating or bypassing pathways. To achieve more effective and sustained clinical responses, synergistic interactions of multiple druggable targets that inhibit redundant cancer survival pathways are often required. Here, we report a systematic polypharmacology strategy to predict, test, and understand the selective drug combinations for MDA-MB-231 triple-negative breast cancer cells. We started by applying our network pharmacology model to predict synergistic drug combinations. Next, by utilizing kinome-wide drug-target profiles and gene expression data, we pinpointed a synergistic target interaction between Aurora B and ZAK kinase inhibition that led to enhanced growth inhibition and cytotoxicity, as validated by combinatorial siRNA, CRISPR/Cas9, and drug combination experiments. The mechanism of such a context-specific target interaction was elucidated using a dynamic simulation of MDA-MB-231 signaling network, suggesting a cross-talk between p53 and p38 pathways. Our results demonstrate the potential of polypharmacological modeling to systematically interrogate target interactions that may lead to clinically actionable and personalized treatment options.

7.
Nat Commun ; 10(1): 2674, 2019 06 17.
Artigo em Inglês | MEDLINE | ID: mdl-31209238

RESUMO

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.


Assuntos
Protocolos de Quimioterapia Combinada Antineoplásica/farmacologia , Biologia Computacional/métodos , Neoplasias/tratamento farmacológico , Farmacogenética/métodos , Proteína ADAM17/antagonistas & inibidores , Protocolos de Quimioterapia Combinada Antineoplásica/uso terapêutico , Benchmarking , Biomarcadores Tumorais/genética , Linhagem Celular Tumoral , Biologia Computacional/normas , Conjuntos de Dados como Assunto , Antagonismo de Drogas , Resistencia a Medicamentos Antineoplásicos/efeitos dos fármacos , Resistencia a Medicamentos Antineoplásicos/genética , Sinergismo Farmacológico , Genômica/métodos , Humanos , Terapia de Alvo Molecular/métodos , Mutação , Neoplasias/genética , Farmacogenética/normas , Fosfatidilinositol 3-Quinases/antagonistas & inibidores , Fosfatidilinositol 3-Quinases/genética , Resultado do Tratamento
8.
Nat Commun ; 10(1): 2198, 2019 05 16.
Artigo em Inglês | MEDLINE | ID: mdl-31097696

RESUMO

Many gene fusions are reported in tumours and for most their role remains unknown. As fusions are used for diagnostic and prognostic purposes, and are targets for treatment, it is crucial to assess their function in cancer. To systematically investigate the role of fusions in tumour cell fitness, we utilized RNA-sequencing data from 1011 human cancer cell lines to functionally link 8354 fusion events with genomic data, sensitivity to >350 anti-cancer drugs and CRISPR-Cas9 loss-of-fitness effects. Established clinically-relevant fusions were identified. Overall, detection of functional fusions was rare, including those involving cancer driver genes, suggesting that many fusions are dispensable for tumour fitness. Therapeutically actionable fusions involving RAF1, BRD4 and ROS1 were verified in new histologies. In addition, recurrent YAP1-MAML2 fusions were identified as activators of Hippo-pathway signaling in multiple cancer types. Our approach discriminates functional fusions, identifying new drivers of carcinogenesis and fusions that could have clinical implications.


Assuntos
Biomarcadores Tumorais/genética , Sistemas CRISPR-Cas/genética , Fusão Gênica/genética , Neoplasias/genética , Antineoplásicos/farmacologia , Carcinogênese/genética , Linhagem Celular Tumoral , Conjuntos de Dados como Assunto , Resistencia a Medicamentos Antineoplásicos/genética , Detecção Precoce de Câncer/métodos , Perfilação da Expressão Gênica/métodos , Regulação Neoplásica da Expressão Gênica/efeitos dos fármacos , Genômica/métodos , Sequenciamento de Nucleotídeos em Larga Escala , Humanos , Neoplasias/diagnóstico , Análise de Sequência de RNA
9.
Sci Rep ; 9(1): 7106, 2019 May 03.
Artigo em Inglês | MEDLINE | ID: mdl-31053760

RESUMO

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

10.
Nat Commun ; 10(1): 2030, 2019 05 02.
Artigo em Inglês | MEDLINE | ID: mdl-31048689

RESUMO

Acquired resistance to MEK1/2 inhibitors (MEKi) arises through amplification of BRAFV600E or KRASG13D to reinstate ERK1/2 signalling. Here we show that BRAFV600E amplification and MEKi resistance are reversible following drug withdrawal. Cells with BRAFV600E amplification are addicted to MEKi to maintain a precise level of ERK1/2 signalling that is optimal for cell proliferation and survival, and tumour growth in vivo. Robust ERK1/2 activation following MEKi withdrawal drives a p57KIP2-dependent G1 cell cycle arrest and senescence or expression of NOXA and cell death, selecting against those cells with amplified BRAFV600E. p57KIP2 expression is required for loss of BRAFV600E amplification and reversal of MEKi resistance. Thus, BRAFV600E amplification confers a selective disadvantage during drug withdrawal, validating intermittent dosing to forestall resistance. In contrast, resistance driven by KRASG13D amplification is not reversible; rather ERK1/2 hyperactivation drives ZEB1-dependent epithelial-to-mesenchymal transition and chemoresistance, arguing strongly against the use of drug holidays in cases of KRASG13D amplification.


Assuntos
Antineoplásicos/farmacologia , Resistencia a Medicamentos Antineoplásicos/genética , Neoplasias/tratamento farmacológico , Inibidores de Proteínas Quinases/farmacologia , Proteínas Proto-Oncogênicas B-raf/genética , Proteínas Proto-Oncogênicas p21(ras)/genética , Antineoplásicos/uso terapêutico , Apoptose/efeitos dos fármacos , Apoptose/genética , Benzimidazóis/farmacologia , Benzimidazóis/uso terapêutico , Linhagem Celular Tumoral , Resistencia a Medicamentos Antineoplásicos/efeitos dos fármacos , Transição Epitelial-Mesenquimal/efeitos dos fármacos , Transição Epitelial-Mesenquimal/genética , Feminino , Amplificação de Genes/efeitos dos fármacos , Regulação Neoplásica da Expressão Gênica/efeitos dos fármacos , Humanos , MAP Quinase Quinase 1/antagonistas & inibidores , MAP Quinase Quinase 2/antagonistas & inibidores , Sistema de Sinalização das MAP Quinases/efeitos dos fármacos , Sistema de Sinalização das MAP Quinases/genética , Masculino , Proteína Quinase 1 Ativada por Mitógeno/metabolismo , Proteína Quinase 3 Ativada por Mitógeno/metabolismo , Neoplasias/genética , Inibidores de Proteínas Quinases/uso terapêutico , Suspensão de Tratamento , Homeobox 1 de Ligação a E-box em Dedo de Zinco/metabolismo
11.
Mol Cell ; 74(5): 1086-1102.e5, 2019 Jun 06.
Artigo em Inglês | MEDLINE | ID: mdl-31101498

RESUMO

Kinase and phosphatase overexpression drives tumorigenesis and drug resistance. We previously developed a mass-cytometry-based single-cell proteomics approach that enables quantitative assessment of overexpression effects on cell signaling. Here, we applied this approach in a human kinome- and phosphatome-wide study to assess how 649 individually overexpressed proteins modulated cancer-related signaling in HEK293T cells in an abundance-dependent manner. Based on these data, we expanded the functional classification of human kinases and phosphatases and showed that the overexpression effects include non-catalytic roles. We detected 208 previously unreported signaling relationships. The signaling dynamics analysis indicated that the overexpression of ERK-specific phosphatases sustains proliferative signaling. This suggests a phosphatase-driven mechanism of cancer progression. Moreover, our analysis revealed a drug-resistant mechanism through which overexpression of tyrosine kinases, including SRC, FES, YES1, and BLK, induced MEK-independent ERK activation in melanoma A375 cells. These proteins could predict drug sensitivity to BRAF-MEK concurrent inhibition in cells carrying BRAF mutations.

12.
Sci Transl Med ; 11(486)2019 Apr 03.
Artigo em Inglês | MEDLINE | ID: mdl-30944168

RESUMO

Fibrosis is the common endpoint and currently the best predictor of progression of chronic kidney diseases (CKDs). Despite several drawbacks, biopsies remain the only available means to specifically assess the extent of renal fibrosis. Here, we show that molecular imaging of the extracellular matrix protein elastin allows for noninvasive staging and longitudinal monitoring of renal fibrosis. Elastin was hardly expressed in healthy mouse, rat, and human kidneys, whereas it was highly up-regulated in cortical, medullar, and perivascular regions in progressive CKD. Compared to a clinically relevant control contrast agent, the elastin-specific magnetic resonance imaging agent ESMA specifically detected elastin expression in multiple mouse models of renal fibrosis and also in fibrotic human kidneys. Elastin imaging allowed for repetitive and reproducible assessment of renal fibrosis, and it enabled longitudinal monitoring of therapeutic interventions, accurately capturing anti-fibrotic therapy effects. Last, in a model of reversible renal injury, elastin imaging detected ensuing fibrosis not identifiable via routine assessment of kidney function. Elastin imaging thus has the potential to become a noninvasive, specific imaging method to assess renal fibrosis.

13.
Kidney Int ; 95(6): 1326-1337, 2019 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-30982672

RESUMO

There have been tremendous advances during the last decade in methods for large-scale, high-throughput data generation and in novel computational approaches to analyze these datasets. These advances have had a profound impact on biomedical research and clinical medicine. The field of genomics is rapidly developing toward single-cell analysis, and major advances in proteomics and metabolomics have been made in recent years. The developments on wearables and electronic health records are poised to change clinical trial design. This rise of 'big data' holds the promise to transform not only research progress, but also clinical decision making towards precision medicine. To have a true impact, it requires integrative and multi-disciplinary approaches that blend experimental, clinical and computational expertise across multiple institutions. Cancer research has been at the forefront of the progress in such large-scale initiatives, so-called 'big science,' with an emphasis on precision medicine, and various other areas are quickly catching up. Nephrology is arguably lagging behind, and hence these are exciting times to start (or redirect) a research career to leverage these developments in nephrology. In this review, we summarize advances in big data generation, computational analysis, and big science initiatives, with a special focus on applications to nephrology.

14.
Nature ; 568(7753): 511-516, 2019 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-30971826

RESUMO

Functional genomics approaches can overcome limitations-such as the lack of identification of robust targets and poor clinical efficacy-that hamper cancer drug development. Here we performed genome-scale CRISPR-Cas9 screens in 324 human cancer cell lines from 30 cancer types and developed a data-driven framework to prioritize candidates for cancer therapeutics. We integrated cell fitness effects with genomic biomarkers and target tractability for drug development to systematically prioritize new targets in defined tissues and genotypes. We verified one of our most promising dependencies, the Werner syndrome ATP-dependent helicase, as a synthetic lethal target in tumours from multiple cancer types with microsatellite instability. Our analysis provides a resource of cancer dependencies, generates a framework to prioritize cancer drug targets and suggests specific new targets. The principles described in this study can inform the initial stages of drug development by contributing to a new, diverse and more effective portfolio of cancer drug targets.

15.
Proc Natl Acad Sci U S A ; 116(19): 9671-9676, 2019 May 07.
Artigo em Inglês | MEDLINE | ID: mdl-31004050

RESUMO

Dysregulation of signaling pathways in multiple sclerosis (MS) can be analyzed by phosphoproteomics in peripheral blood mononuclear cells (PBMCs). We performed in vitro kinetic assays on PBMCs in 195 MS patients and 60 matched controls and quantified the phosphorylation of 17 kinases using xMAP assays. Phosphoprotein levels were tested for association with genetic susceptibility by typing 112 single-nucleotide polymorphisms (SNPs) associated with MS susceptibility. We found increased phosphorylation of MP2K1 in MS patients relative to the controls. Moreover, we identified one SNP located in the PHDGH gene and another on IRF8 gene that were associated with MP2K1 phosphorylation levels, providing a first clue on how this MS risk gene may act. The analyses in patients treated with disease-modifying drugs identified the phosphorylation of each receptor's downstream kinases. Finally, using flow cytometry, we detected in MS patients increased STAT1, STAT3, TF65, and HSPB1 phosphorylation in CD19+ cells. These findings indicate the activation of cell survival and proliferation (MAPK), and proinflammatory (STAT) pathways in the immune cells of MS patients, primarily in B cells. The changes in the activation of these kinases suggest that these pathways may represent therapeutic targets for modulation by kinase inhibitors.

16.
Nat Biotechnol ; 37(3): 314-322, 2019 03.
Artigo em Inglês | MEDLINE | ID: mdl-30778230

RESUMO

Reproducibility in research can be compromised by both biological and technical variation, but most of the focus is on removing the latter. Here we investigate the effects of biological variation in HeLa cell lines using a systems-wide approach. We determine the degree of molecular and phenotypic variability across 14 stock HeLa samples from 13 international laboratories. We cultured cells in uniform conditions and profiled genome-wide copy numbers, mRNAs, proteins and protein turnover rates in each cell line. We discovered substantial heterogeneity between HeLa variants, especially between lines of the CCL2 and Kyoto varieties, and observed progressive divergence within a specific cell line over 50 successive passages. Genomic variability has a complex, nonlinear effect on transcriptome, proteome and protein turnover profiles, and proteotype patterns explain the varying phenotypic response of different cell lines to Salmonella infection. These findings have implications for the interpretation and reproducibility of research results obtained from human cultured cells.


Assuntos
Variações do Número de Cópias de DNA/genética , Genoma Humano/genética , Células HeLa , Transcriptoma/genética , Genômica/normas , Humanos , Proteoma/genética , Reprodutibilidade dos Testes
17.
Front Genet ; 9: 527, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-30515189

RESUMO

In toxicogenomics, functional annotation is an important step to gain additional insights into genes with aberrant expression that drive pathophysiological mechanisms. Nevertheless, there exists a gap on annotation of these genes which often hampers the interpretation of results and limits their applicability in translational medicine. In this study, we evaluated the coverage of functional annotations of differentially expressed genes (DEGs) induced by 10 selected compounds from the TG-GATEs database identified as high- or no-risk in causing drug-induced liver injury (most-DILI or no-DILI, respectively) using in vitro human data. Functional roles of DEGs not present in the most common biological annotation databases - termed "dark genes" - were unveiled via literature mining and via the identification of shared regulatory transcription factors or signaling pathways. Our results demonstrated that there were approximately 13% of dark genes induced by these compounds in vitro and we were able to obtain additional relevant information for up to 76% of those. Using interactome data from several sources, we have uncovered genes such as LRBA, and WDR26 as highly connected in the protein network that play roles in drug response. Genes such as MALAT1, H19, and MIR29C - whose links to hepatotoxicity have been confirmed - were identified as markers for the most-DILI group and appeared as top hits across all literature-based mining methods. Furthermore, we investigated the potential impact of dark genes on liver toxicity by identifying their rat orthologs in combination with their correlation to drug-induced liver pathologies observed in vivo following chemical exposure. We identified a set of important regulatory transcription factors of dark genes for all most-DILI compounds including E2F1 and JUND with supporting evidences in literature and we found Magee1 correlated with chemically induced bile duct hyperplasia and adverse responses at 29 days in rats in vivo. In conclusion, in this study we show the potential role of these poorly annotated genes in mechanisms underlying hepatotoxicity and offer a number of computational approaches that may help to minimize current gaps in gene annotation and highlight their values as potential biomarkers in toxicological studies.

18.
Kidney Int ; 2018 Nov 29.
Artigo em Inglês | MEDLINE | ID: mdl-30522767

RESUMO

Acute kidney injury (AKI) leads to significant morbidity and mortality; unfortunately, strategies to prevent or treat AKI are lacking. In recent years, several preconditioning protocols have been shown to be effective in inducing organ protection in rodent models. Here, we characterized two of these interventions-caloric restriction and hypoxic preconditioning-in a mouse model of cisplatin-induced AKI and investigated the underlying mechanisms by acquisition of multi-layered omic data (transcriptome, proteome, N-degradome) and functional parameters in the same animals. Both preconditioning protocols markedly ameliorated cisplatin-induced loss of kidney function, and caloric restriction also induced lipid synthesis. Bioinformatic analysis revealed mRNA-independent proteome alterations affecting the extracellular space, mitochondria, and transporters. Interestingly, our analyses revealed a strong dissociation of protein and RNA expression after cisplatin treatment that showed a strong correlation with the degree of damage. N-degradomic analysis revealed that most posttranscriptional changes were determined by arginine-specific proteolytic processing. This included a characteristic cisplatin-activated complement signature that was prevented by preconditioning. Amyloid and acute-phase proteins within the cortical parenchyma showed a similar response. Extensive analysis of disease-associated molecular patterns suggested that transcription-independent deposition of amyloid P-component serum protein may be a key component in the microenvironmental contribution to kidney damage. This proof-of-principle study provides new insights into the pathogenesis of cisplatin-induced AKI and the molecular mechanisms underlying organ protection by correlating phenotypic and multi-layered omics data.

19.
Nat Commun ; 9(1): 4974, 2018 Nov 26.
Artigo em Inglês | MEDLINE | ID: mdl-30478315

RESUMO

Activation of brown adipose tissue-mediated thermogenesis is a strategy for tackling obesity and promoting metabolic health. BMP8b is secreted by brown/beige adipocytes and enhances energy dissipation. Here we show that adipocyte-secreted BMP8b contributes to adrenergic-induced remodeling of the neuro-vascular network in adipose tissue (AT). Overexpression of bmp8b in AT enhances browning of the subcutaneous depot and maximal thermogenic capacity. Moreover, BMP8b-induced browning, increased sympathetic innervation and vascularization of AT were maintained at 28 °C, a condition of low adrenergic output. This reinforces the local trophic effect of BMP8b. Innervation and vascular remodeling effects required BMP8b signaling through the adipocytes to 1) secrete neuregulin-4 (NRG4), which promotes sympathetic axon growth and branching in vitro, and 2) induce a pro-angiogenic transcriptional and secretory profile that promotes vascular sprouting. Thus, BMP8b and NRG4 can be considered as interconnected regulators of neuro-vascular remodeling in AT and are potential therapeutic targets in obesity.

20.
PLoS Comput Biol ; 14(10): e1006538, 2018 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-30372442

RESUMO

Protein signaling networks are static views of dynamic processes where proteins go through many biochemical modifications such as ubiquitination and phosphorylation to propagate signals that regulate cells and can act as feed-back systems. Understanding the precise mechanisms underlying protein interactions can elucidate how signaling and cell cycle progression occur within cells in different diseases such as cancer. Large-scale protein signaling networks contain an important number of experimentally verified protein relations but lack the capability to predict the outcomes of the system, and therefore to be trained with respect to experimental measurements. Boolean Networks (BNs) are a simple yet powerful framework to study and model the dynamics of the protein signaling networks. While many BN approaches exist to model biological systems, they focus mainly on system properties, and few exist to integrate experimental data in them. In this work, we show an application of a method conceived to integrate time series phosphoproteomic data into protein signaling networks. We use a large-scale real case study from the HPN-DREAM Breast Cancer challenge. Our efficient and parameter-free method combines logic programming and model-checking to infer a family of BNs from multiple perturbation time series data of four breast cancer cell lines given a prior protein signaling network. Because each predicted BN family is cell line specific, our method highlights commonalities and discrepancies between the four cell lines. Our models have a Root Mean Square Error (RMSE) of 0.31 with respect to the testing data, while the best performant method of this HPN-DREAM challenge had a RMSE of 0.47. To further validate our results, BNs are compared with the canonical mTOR pathway showing a comparable AUROC score (0.77) to the top performing HPN-DREAM teams. In addition, our approach can also be used as a complementary method to identify erroneous experiments. These results prove our methodology as an efficient dynamic model discovery method in multiple perturbation time course experimental data of large-scale signaling networks. The software and data are publicly available at https://github.com/misbahch6/caspo-ts.

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