COMPARTMENTS: unification and visualization of protein subcellular localization evidence

Binder, Janos X.; Pletscher-Frankild, Sune; Tsafou, Kalliopi; Stolte, Christian; O’Donoghue, Seán I.; Schneider, Reinhard; Jensen, Lars Juhl

doi:10.1093/database/bau012

Cited by 553 publications

(533 citation statements)

References 51 publications

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“…We visualized the top 50 genes in the consensus transcriptional profile of GABAergic neurons in developing human neocortex (plus 5 other known markers that ranked in the top 100), along with their protein-protein interactions (PPI) (Szklarczyk et al 2015), cellular localizations (Binder et al 2014), literature citations, and expression level ranks in neocortical GABAergic neurons purified from P0 Cxcr7-Gfp transgenic mice (Fig. 1D).…”

Section: Identification Of a Consensus Transcriptional Profile Of Gabmentioning

confidence: 99%

Secretagogin is Expressed by Developing Neocortical GABAergic Neurons in Humans but not Mice and Increases Neurite Arbor Size and Complexity

et al. 2017

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The neocortex of primates, including humans, contains more abundant and diverse inhibitory neurons compared with rodents, but the molecular foundations of these observations are unknown. Through integrative gene coexpression analysis, we determined a consensus transcriptional profile of GABAergic neurons in mid-gestation human neocortex. By comparing this profile to genes expressed in GABAergic neurons purified from neonatal mouse neocortex, we identified conserved and distinct aspects of gene expression in these cells between the species. We show here that the calcium-binding protein secretagogin (SCGN) is robustly expressed by neocortical GABAergic neurons derived from caudal ganglionic eminences (CGE) and lateral ganglionic eminences during human but not mouse brain development. Through electrophysiological and morphometric analyses, we examined the effects of SCGN expression on GABAergic neuron function and form. Forced expression of SCGN in CGE-derived mouse GABAergic neurons significantly increased total neurite length and arbor complexity following transplantation into mouse neocortex, revealing a molecular pathway that contributes to morphological differences in these cells between rodents and primates.

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Section: Identification Of a Consensus Transcriptional Profile Of Gabmentioning

confidence: 99%

Secretagogin is Expressed by Developing Neocortical GABAergic Neurons in Humans but not Mice and Increases Neurite Arbor Size and Complexity

et al. 2017

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“…It is a Web server that integrates evidence on protein localization based on manually curated literature, high-throughput screens, automatic text mining and sequence-based prediction methods. Confidence scores were assigned as color codes, ranging from light green for low confidence to dark green for high confidence, and presented on a schematic cell (34).…”

Section: In Silico Structural and Functional Network Analysis Of Genesmentioning

confidence: 99%

Stemness-Related Transcriptional Factors and Homing Gene Expression Profiles in Hepatic Differentiation and Cancer

Toraih

Fawzy

El-Falouji

et al. 2016

Mol Med

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and 6 Pulmonologist, Ministry of Health, EgyptStem cell transcriptional signature activation is an essential event in the development of cancer. This study aimed to investigate the differential expression profiles of three pluripotency-associated genes, OCT4, NANOG and SOX2, G-protein-coupled chemokine receptor 4 (CXCR4) and the ligand CXCL2, and alpha-fetoprotein (AFP) in hepatogenic differentiated stem cells and in sera of hepatitis C virus (HCV) and HCV-induced hepatocellular carcinoma (HCC) patients. Mesenchymal stem cells derived from umbilical cord blood were differentiated using hepatogenic differentiation media. Serum specimens were collected from 96 patients (32 cirrhotic HCV, 32 early HCC and 32 late HCC) and 96 controls. Real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR) was performed for relative quantification of the six target genes using the Livak method. In silico network analysis was also executed to explore the pluripotency and tumorigenetic regulatory circuits in liver cancer. The expression levels of all genes declined gradually during the stages of stem cell differentiation. On univariate and multivariate analyses, NANOG, CXCR4 and AFP were significantly upregulated in late clinical stage HCC patients. In contrast, SOX2 and CXCL2 were markedly overexpressed in cirrhotic patients and could be used for clear demarcation between cirrhotic and HCC patients in our cases. In conclusion, our data highlight the potential role of the SOX2 stem cell marker and CXCL2 chemokine in liver cell degeneration and fibrogenesis in HCV-induced hepatic cirrhosis in our sample of the Egyptian population. In addition, the significant association of NANOG and CXCR4 high expression with late HCC could contribute to the acquisition of stem celllike properties in hepatic cancer and dissemination in late stages, respectively. Taken together, our results could have potential application in HCC prognosis and treatment.

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“…A recently published high-quality human protein SCL benchmark set from the subcellular localization database Compartments [2] was used to evaluate the performance of our method. In total 9 SCLs including Cytosol, Endoplasmic Reticulum, Lysosome, Extracellular space, Golgi apparatus, Mitochondrion, Nucleus, Peroxisome and Plasma membrane are used for evaluation.…”

Section: Experimental Setup 31 Datasetmentioning

confidence: 99%

Bayesian Collective Markov Random Fields for Subcellular Localization Prediction of Human Proteins

Zhu

Ester

2017

Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics

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Advanced biotechnology makes it possible to access a multitude of heterogeneous proteomic, interactomic, genomic, and functional annotation data. One challenge in computational biology is to integrate these data to enable automated prediction of the Subcellular Localizations (SCL) of human proteins. For proteins that have multiple biological roles, their correct in silico assignment to di erent SCL can be considered as an imbalanced multi-label classi cation problem. In this study, we developed a Bayesian Collective Markov Random Fields (BCMRFs) model for multi-SCL prediction of human proteins. Given a set of unknown proteins and their corresponding protein-protein interaction (PPI) network, the SCLs of each protein can be inferred by the SCLs of its interacting partners. To do so, we integrate PPIs, the adjacency of SCLs and protein features, and perform transductive learning on the re-balanced dataset. Our experimental results show that the spatial adjacency of the SCLs improves multi-SCL prediction, especially for the SCLs with few annotated instances. Our approach outperforms the state-of-art PPIbased and feature-based multi-SCL prediction method for human proteins. KEYWORDSHuman protein subcellular localization; markov random eld; transductive learning; imbalanced multi-label classi cation.

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COMPARTMENTS: unification and visualization of protein subcellular localization evidence

Cited by 553 publications

References 51 publications

Secretagogin is Expressed by Developing Neocortical GABAergic Neurons in Humans but not Mice and Increases Neurite Arbor Size and Complexity

Secretagogin is Expressed by Developing Neocortical GABAergic Neurons in Humans but not Mice and Increases Neurite Arbor Size and Complexity

Stemness-Related Transcriptional Factors and Homing Gene Expression Profiles in Hepatic Differentiation and Cancer

Bayesian Collective Markov Random Fields for Subcellular Localization Prediction of Human Proteins

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