In silico approach to predict pancreatic β-cells classically secreted proteins

Research output: Contribution to journalJournal articleResearchpeer-review

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In silico approach to predict pancreatic β-cells classically secreted proteins. / Pinheiro-Machado, Erika; Sandberg, Tatiana Orli Milkewitz; Pihl, Celina; Hägglund, Per Mårten; Marzec, Michal Tomasz.

In: Bioscience Reports, Vol. 40, No. 2, BSR20193708, 01.01.2020.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Pinheiro-Machado, E, Sandberg, TOM, Pihl, C, Hägglund, PM & Marzec, MT 2020, 'In silico approach to predict pancreatic β-cells classically secreted proteins', Bioscience Reports, vol. 40, no. 2, BSR20193708. https://doi.org/10.1042/BSR20193708

APA

Pinheiro-Machado, E., Sandberg, T. O. M., Pihl, C., Hägglund, P. M., & Marzec, M. T. (2020). In silico approach to predict pancreatic β-cells classically secreted proteins. Bioscience Reports, 40(2), [BSR20193708]. https://doi.org/10.1042/BSR20193708

Vancouver

Pinheiro-Machado E, Sandberg TOM, Pihl C, Hägglund PM, Marzec MT. In silico approach to predict pancreatic β-cells classically secreted proteins. Bioscience Reports. 2020 Jan 1;40(2). BSR20193708. https://doi.org/10.1042/BSR20193708

Author

Pinheiro-Machado, Erika ; Sandberg, Tatiana Orli Milkewitz ; Pihl, Celina ; Hägglund, Per Mårten ; Marzec, Michal Tomasz. / In silico approach to predict pancreatic β-cells classically secreted proteins. In: Bioscience Reports. 2020 ; Vol. 40, No. 2.

Bibtex

@article{dfdc9aa5f89544a5b8264c1e756d8c19,
title = "In silico approach to predict pancreatic β-cells classically secreted proteins",
abstract = "Pancreatic β-cells, residents of the islets of Langerhans, are the unique insulin-producers in the body. Their physiology is a topic of intensive studies aiming to understand the biology of insulin production and its role in diabetes pathology. However, investigations about these cells' subset of secreted proteins, the secretome, are surprisingly scarce and a list describing islet/β-cell secretome upon glucose-stimulation is not yet available. In silico predictions of secretomes are an interesting approach that can be employed to forecast proteins likely to be secreted. In this context, using the rationale behind classical secretion of proteins through the secretory pathway, a Python tool capable of predicting classically secreted proteins was developed. This tool was applied to different available proteomic data (human and rodent islets, isolated β-cells, β-cell secretory granules, and β-cells supernatant), filtering them in order to selectively list only classically secreted proteins. The method presented here can retrieve, organize, search and filter proteomic lists using UniProtKB as a central database. It provides analysis by overlaying different sets of information, filtering out potential contaminants and clustering the identified proteins into functional groups. A range of 70-92% of the original proteomes analyzed was reduced generating predicted secretomes. Islet and β-cell signal peptide-containing proteins, and endoplasmic reticulum-resident proteins were identified and quantified. From the predicted secretomes, exemplary conservational patterns were inferred, as well as the signaling pathways enriched within them. Such a technique proves to be an effective approach to reduce the horizon of plausible targets for drug development or biomarkers identification.",
author = "Erika Pinheiro-Machado and Sandberg, {Tatiana Orli Milkewitz} and Celina Pihl and H{\"a}gglund, {Per M{\aa}rten} and Marzec, {Michal Tomasz}",
year = "2020",
month = jan,
day = "1",
doi = "10.1042/BSR20193708",
language = "English",
volume = "40",
journal = "Bioscience Reports",
issn = "0144-8463",
publisher = "Portland Press Ltd.",
number = "2",

}

RIS

TY - JOUR

T1 - In silico approach to predict pancreatic β-cells classically secreted proteins

AU - Pinheiro-Machado, Erika

AU - Sandberg, Tatiana Orli Milkewitz

AU - Pihl, Celina

AU - Hägglund, Per Mårten

AU - Marzec, Michal Tomasz

PY - 2020/1/1

Y1 - 2020/1/1

N2 - Pancreatic β-cells, residents of the islets of Langerhans, are the unique insulin-producers in the body. Their physiology is a topic of intensive studies aiming to understand the biology of insulin production and its role in diabetes pathology. However, investigations about these cells' subset of secreted proteins, the secretome, are surprisingly scarce and a list describing islet/β-cell secretome upon glucose-stimulation is not yet available. In silico predictions of secretomes are an interesting approach that can be employed to forecast proteins likely to be secreted. In this context, using the rationale behind classical secretion of proteins through the secretory pathway, a Python tool capable of predicting classically secreted proteins was developed. This tool was applied to different available proteomic data (human and rodent islets, isolated β-cells, β-cell secretory granules, and β-cells supernatant), filtering them in order to selectively list only classically secreted proteins. The method presented here can retrieve, organize, search and filter proteomic lists using UniProtKB as a central database. It provides analysis by overlaying different sets of information, filtering out potential contaminants and clustering the identified proteins into functional groups. A range of 70-92% of the original proteomes analyzed was reduced generating predicted secretomes. Islet and β-cell signal peptide-containing proteins, and endoplasmic reticulum-resident proteins were identified and quantified. From the predicted secretomes, exemplary conservational patterns were inferred, as well as the signaling pathways enriched within them. Such a technique proves to be an effective approach to reduce the horizon of plausible targets for drug development or biomarkers identification.

AB - Pancreatic β-cells, residents of the islets of Langerhans, are the unique insulin-producers in the body. Their physiology is a topic of intensive studies aiming to understand the biology of insulin production and its role in diabetes pathology. However, investigations about these cells' subset of secreted proteins, the secretome, are surprisingly scarce and a list describing islet/β-cell secretome upon glucose-stimulation is not yet available. In silico predictions of secretomes are an interesting approach that can be employed to forecast proteins likely to be secreted. In this context, using the rationale behind classical secretion of proteins through the secretory pathway, a Python tool capable of predicting classically secreted proteins was developed. This tool was applied to different available proteomic data (human and rodent islets, isolated β-cells, β-cell secretory granules, and β-cells supernatant), filtering them in order to selectively list only classically secreted proteins. The method presented here can retrieve, organize, search and filter proteomic lists using UniProtKB as a central database. It provides analysis by overlaying different sets of information, filtering out potential contaminants and clustering the identified proteins into functional groups. A range of 70-92% of the original proteomes analyzed was reduced generating predicted secretomes. Islet and β-cell signal peptide-containing proteins, and endoplasmic reticulum-resident proteins were identified and quantified. From the predicted secretomes, exemplary conservational patterns were inferred, as well as the signaling pathways enriched within them. Such a technique proves to be an effective approach to reduce the horizon of plausible targets for drug development or biomarkers identification.

UR - http://www.scopus.com/inward/record.url?scp=85081964277&partnerID=8YFLogxK

U2 - 10.1042/BSR20193708

DO - 10.1042/BSR20193708

M3 - Journal article

C2 - 32003782

AN - SCOPUS:85081964277

VL - 40

JO - Bioscience Reports

JF - Bioscience Reports

SN - 0144-8463

IS - 2

M1 - BSR20193708

ER -

ID: 240156671