Asmita Bharti

India

Identifying potential metabolic proteins and their cross-talk in neurodegenerative disorders via genomic and proteomic approach

Bharti A and Bubber P
Biochemistry Discipline, Indira Gandhi National Open University, New Delhi-110068, India

Abstract

Background

Neurodegeneration is a pathological condition involving progressive loss of neuronal cells in the brain. These conditions usually develop gradually and the symptoms tend to appear later in life. Neurodegenerative disorders affect millions of people worldwide. Globally, Alzheimer’s disease (AD) affects about 50 million people and Parkinson’s disease (PD) affects about 10 million people. By 2050, these numbers are projected to increase to 150 million and 12 million people, respectively. Several reasons such as genetics, medical conditions, toxins, chemicals, or viruses are considered to be the causative factors of these diseases. The role and mechanism of various proteins, such as APP, Tau, ??-synuclein, and TAR DNA-binding protein 43 are under surveillance as factors of dementia in older patients. Several studies have supported the idea of using in-silico approach like high-throughput RNA-Seq data for genome analysis and MS-MS data for proteome analysis. These approaches are the considered to be an effective way to comprehend differentially expressed genes, their cross-talk in revealing plausible molecular pathophysiological pathways at the molecular level that can aid in the early detection, protection or prevention of neurological disorders.

Methods

Secondary dataset from ENA data archive were retrieved for AD dataset (PRJNA232669) and PD dataset (PRJNA892925). In-silico approach was used for high-throughput RNA-Seq data analysis. STAR tool was used for alignment of RNA-Seq (fastq file format used) accessed from ENA and to study the Differentially Expressed Genes (DEG). DESeq2 package from Bioconductor was used followed by the identification of the molecular pathways together with gene ontology for commonly expressed genes in AD and PD. Proteome high-throughput Mass Spectrometry (MS-MS) data analysis was carried out using MaxQuant version 2.4.13.0 tool. MS-MS data was downloaded from a data archive named Pride, for AD (Pride ID PXD037133) and for PD (Pride ID PXD022092). MaxQuant was used for quantitative identification of proteins with their intensity for AD and PD. Proteins which are common in both AD and PD were considered for the study of molecular pathways as well as for the identification of top 10 hub proteins.

Results

RNA-Seq data analysis for AD and PD revealed list of Differentially Expressed Genes. Venny concept was applied and 40447 common genes (49%) were identified for AD and PD. Further up-regulated gene analysis identified 73 common DEGs. Top 10 hub identified genes are ALB, APOA1, APOC3, CLU, LIPC, CYP4A11, POLR1C, RRN3 and TTR. APOC3 gene is the highest interacting gene in the hub network interacting with 53 other genes. MS-MS data analysis identified 156 proteins common in AD (PXD037133) and PD (PXD022092). Protein-Protein interactions with 111 nodes with 892 edges were identified in the network using STRING database. Out of 892 PPI, only 58 were found to be co-expressed (p<0.5). 30 out of 73 KEGG pathways retrieved were determined as prominent pathways (strength value>1).

Conclusions

Top 10 hub identified genes common to AD and PD are associated with biological processes such as protein-lipid complex, protein containing complex remodelling, plasma lipoprotein particle remodelling. The findings seem to imply common convergence points at the molecular level influencing several cellular processes related to the disorders. Out proteomics approach suggest that proteins involved in the metabolic precursors and energy such as TCA cycle, Pyruvate metabolism, NADH metabolic process along with cellular respiration are the main affected pathways common to both neurodegenerative conditions.