Co-expression networks between protein encoding mitochondrial genes and all the remaining genes in human tissues

• Type: Article in International Conference Proceedings Book
• Date: 2017

Authors

Almeida, J (Author) Other; Ferreira, J (Author) Other; Rui Camacho (Author) FEUP; Pereira, L (Author) Other

Conference proceedings International

Title: 2017 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM)

Pages: 70-73

IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM)

Kansas City, MI, NOV 13-16, 2017

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Authenticus ID: P-00N-DF5

DOI: 10.1109/BIBM.2017.8217626

Abstract (EN): Recent advances in sequencing allow the study of all identified human genes (22,000 protein encoding genes), which have differential expression between tissues. However, current knowledge on gene interactions lags behind, especially when one of the elements encodes a mitochondrial protein (1500). Mitochondrial proteins are encoded either by mitochondrial DNA (mtDNA; 13 proteins) or by nuclear DNA (nDNA; the remaining), which implies a coordinated communication between the two genomes. Since mitochondria coordinate several life-critical cellular activities, namely energy production and cell death, deregulation of this communication is implicated in many complex diseases such as neurodegenerative diseases, cancer and diabetes. Thus, this work aimed to identify high co-expression groups between mitochondrial genes-all genes, and associated protein networks in several human tissues (Genotype-Tissue Expression database). We developed a pipeline and a web tree viewer that is available at GitHub (https://github.com/Pereira-lab/CoExpression). Biologically, we confirmed the existence of highly correlated pairs of mitochondrial-all protein encoding genes, which act in pathways of functional importance such as energy production and metabolite synthesis, especially in brain tissues. The strongest correlation between mtDNA genes are with genes encoded by this genome, showing that correlation among genes encoded by the same genome is more efficient.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 4