Dataset Information

Multiclassifier combinatorial proteomics of organelle shadows at the example of mitochondria in chromatin data.

ABSTRACT: Subcellular localization is an important aspect of protein function, but the protein composition of many intracellular compartments is poorly characterized. For example, many nuclear bodies are challenging to isolate biochemically and thus remain inaccessible to proteomics. Here, we explore covariation in proteomics data as an alternative route to subcellular proteomes. Rather than targeting a structure of interest biochemically, we target it by machine learning. This becomes possible by taking data obtained for one organelle and searching it for traces of another organelle. As an extreme example and proof-of-concept we predict mitochondrial proteins based on their covariation in published interphase chromatin data. We detect about ? of the known mitochondrial proteins in our chromatin data, presumably most as contaminants. However, these proteins are not present at random. We show covariation of mitochondrial proteins in chromatin proteomics data. We then exploit this covariation by multiclassifier combinatorial proteomics to define a list of mitochondrial proteins. This list agrees well with different databases on mitochondrial composition. This benchmark test raises the possibility that, in principle, covariation proteomics may also be applicable to structures for which no biochemical isolation procedures are available.

SUBMITTER: Kustatscher G

PROVIDER: S-EPMC4862026 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature

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Multiclassifier combinatorial proteomics of organelle shadows at the example of mitochondria in chromatin data.

Kustatscher Georg G Grabowski Piotr P Rappsilber Juri J

Proteomics 20160125 3

Subcellular localization is an important aspect of protein function, but the protein composition of many intracellular compartments is poorly characterized. For example, many nuclear bodies are challenging to isolate biochemically and thus remain inaccessible to proteomics. Here, we explore covariation in proteomics data as an alternative route to subcellular proteomes. Rather than targeting a structure of interest biochemically, we target it by machine learning. This becomes possible by taking ...[more]

PMID: 26510496

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Project description:Congenital abnormalities cause serious fetal consequences. The term TORCH is used to designate the most common perinatal infections, where: (T) refers to toxoplasmosis, (O) means "others" and includes syphilis, varicella-zoster, parvovirus B19, zika virus (ZIKV), and malaria among others, (R) refers to rubella, (C) relates to cytomegalovirus infection, and (H) to herpes simplex virus infections. Among the main abnormalities identified in neonates exposed to congenital infections are central nervous system (CNS) damage, microcephaly, hearing loss, and ophthalmological impairment, all requiring regular follow-up to monitor its progression. Protein changes such as mutations, post-translational modifications, abundance, structure, and function may indicate a pathological condition before the onset of the first symptoms, allowing early diagnosis and understanding of a particular disease or infection. The term "proteomics" is defined as the science that studies the proteome, which consists of the total protein content of a cell, tissue or organism in a given space and time, including post-translational modifications (PTMs) and interactions between proteins. Currently, quantitative bottom-up proteomic strategies allow rapid and high throughput characterization of complex biological mixtures. Investigating proteome modulation during host-pathogen interaction helps in elucidating the mechanisms of infection and in predicting disease progression. This "molecular battle" between host and pathogen is a key to identify drug targets and diagnostic markers. Here, we conducted a survey on proteomic techniques applied to congenital diseases classified in the terminology "TORCH", including toxoplasmosis, ZIKV, malaria, syphilis, human immunodeficiency virus (HIV), herpes simplex virus (HSV) and human cytomegalovirus (HCVM). We have highlighted proteins and/or protein complexes actively involved in the infection. Most of the proteomic studies reported have been performed in cell line models, and the evaluation of tissues (brain, muscle, and placenta) and biofluids (plasma, serum and urine) in animal models is still underexplored. Moreover, there are a plethora of studies focusing on the pathogen or the host without considering the triad mother-fetus-pathogen as a dynamic and interconnected system.

Dataset Information

Multiclassifier combinatorial proteomics of organelle shadows at the example of mitochondria in chromatin data.

Publications

Multiclassifier combinatorial proteomics of organelle shadows at the example of mitochondria in chromatin data.

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