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A bioinformatics insight to rhizobial globins: gene identification and mapping, polypeptide sequence and phenetic analysis, and protein modeling.

ABSTRACT: Globins (Glbs) are proteins widely distributed in organisms. Three evolutionary families have been identified in Glbs: the M, S and T Glb families. The M Glbs include flavohemoglobins (fHbs) and single-domain Glbs (SDgbs); the S Glbs include globin-coupled sensors (GCSs), protoglobins and sensor single domain globins, and the T Glbs include truncated Glbs (tHbs). Structurally, the M and S Glbs exhibit 3/3-folding whereas the T Glbs exhibit 2/2-folding. Glbs are widespread in bacteria, including several rhizobial genomes. However, only few rhizobial Glbs have been characterized. Hence, we characterized Glbs from 62 rhizobial genomes using bioinformatics methods such as data mining in databases, sequence alignment, phenogram construction and protein modeling. Also, we analyzed soluble extracts from Bradyrhizobium japonicum USDA38 and USDA58 by (reduced + carbon monoxide (CO) minus reduced) differential spectroscopy. Database searching showed that only fhb, sdgb, gcs and thb genes exist in the rhizobia analyzed in this work. Promoter analysis revealed that apparently several rhizobial glb genes are not regulated by a -10 promoter but might be regulated by -35 and Fnr (fumarate-nitrate reduction regulator)-like promoters. Mapping analysis revealed that rhizobial fhbs and thbs are flanked by a variety of genes whereas several rhizobial sdgbs and gcss are flanked by genes coding for proteins involved in the metabolism of nitrates and nitrites and chemotaxis, respectively. Phenetic analysis showed that rhizobial Glbs segregate into the M, S and T Glb families, while structural analysis showed that predicted rhizobial SDgbs and fHbs and GCSs globin domain and tHbs fold into the 3/3- and 2/2-folding, respectively. Spectra from B. japonicum USDA38 and USDA58 soluble extracts exhibited peaks and troughs characteristic of bacterial and vertebrate Glbs thus indicating that putative Glbs are synthesized in B. japonicum USDA38 and USDA58.

SUBMITTER: Gesto-Borroto R

PROVIDER: S-EPMC4648194 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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A bioinformatics insight to rhizobial globins: gene identification and mapping, polypeptide sequence and phenetic analysis, and protein modeling.

Gesto-Borroto Reinier R Sánchez-Sánchez Miriam M Arredondo-Peter Raúl R

F1000Research 20150513

Globins (Glbs) are proteins widely distributed in organisms. Three evolutionary families have been identified in Glbs: the M, S and T Glb families. The M Glbs include flavohemoglobins (fHbs) and single-domain Glbs (SDgbs); the S Glbs include globin-coupled sensors (GCSs), protoglobins and sensor single domain globins, and the T Glbs include truncated Glbs (tHbs). Structurally, the M and S Glbs exhibit 3/3-folding whereas the T Glbs exhibit 2/2-folding. Glbs are widespread in bacteria, including ...[more]

PMID: 26594329

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Project description:Since the decoding of the Human Genome, techniques from bioinformatics, statistics, and machine learning have been instrumental in uncovering patterns in increasing amounts and types of different data produced by technical profiling technologies applied to clinical samples, animal models, and cellular systems. Yet, progress on unravelling biological mechanisms, causally driving diseases, has been limited, in part due to the inherent complexity of biological systems. Whereas we have witnessed progress in the areas of cancer, cardiovascular and metabolic diseases, the area of neurodegenerative diseases has proved to be very challenging. This is in part because the aetiology of neurodegenerative diseases such as Alzheimer´s disease or Parkinson´s disease is unknown, rendering it very difficult to discern early causal events. Here we describe a panel of bioinformatics and modeling approaches that have recently been developed to identify candidate mechanisms of neurodegenerative diseases based on publicly available data and knowledge. We identify two complementary strategies-data mining techniques using genetic data as a starting point to be further enriched using other data-types, or alternatively to encode prior knowledge about disease mechanisms in a model based framework supporting reasoning and enrichment analysis. Our review illustrates the challenges entailed in integrating heterogeneous, multiscale and multimodal information in the area of neurology in general and neurodegeneration in particular. We conclude, that progress would be accelerated by increasing efforts on performing systematic collection of multiple data-types over time from each individual suffering from neurodegenerative disease. The work presented here has been driven by project AETIONOMY; a project funded in the course of the Innovative Medicines Initiative (IMI); which is a public-private partnership of the European Federation of Pharmaceutical Industry Associations (EFPIA) and the European Commission (EC).

Dataset Information

A bioinformatics insight to rhizobial globins: gene identification and mapping, polypeptide sequence and phenetic analysis, and protein modeling.

Publications

A bioinformatics insight to rhizobial globins: gene identification and mapping, polypeptide sequence and phenetic analysis, and protein modeling.

Similar Datasets

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