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Comparative analysis of autosomal and X-linked genes involved in nonspecific cognitive impairment.

Byline: Muzammil Ahmad Khan, Naureen Aslam and Muhammad Ansar

Abstract

The focus of research on candidate gene identification of recessive non-syndromic cognitive impairment is increasing and to date 19 genes for X-linked and 10 autosomal non-syndromic mental retardation have been reported. The X chromosome has higher proportion of cognitive genes as compared to autosomes; but the presence of 22 autosomes and the origination of X chromosome from autosome during the course of evolution is putting a mark of question on this fact and leads to the hypothesis that the number of autosomal cognitive genes should be higher in number than the genes on X chromosome. The comparative analysis of both sets (autosomal v/s X -linked) of genes revealed significant similarities with respect to their evolutionary conservation, cellular localization, molecular and biological functions, protein domain sharing, sub- cellular expression profiling in nervous tissues, etc.

The results and observation conclude that the knowledge of X -linked cognitive genes can be utilized in a variety of ays to explore more autosomal cognitive genes comp utationally.

Key words: Autosomal and X -linked cognitive genes, cognitive impairment, evolutionary conservation, molecular and biological data, subcellular localization.

Introduction

Nognitive disability is a major unsolved neuro- developmental disorder affecting 2-3% of general population. 1 Defining feature of mental retardation include sub average general intellectual functioning (IQ less than 70), limitation in at least two of the adaptive skill and onset before 18 year of age.2 According to ICD-10 classification, mental retardation is subcategories to Mild (50-55 to approximately 70), Moderate (35 -40 to 50-55), Severe (20-25 to 35- 40) and Profound (IQ below 20-25) on the basis of IQ score.3 The etiologies of mental retardation include both genetic and environmental factors. Genetic mental retardation is caused by chromosomal defects, genetic imprinting phenomenon, repeat extension, inborn error of metabolism and single gene defect.

Monogenetic mental retardation is subcategorized as either X-linked or autosomal (on the basis of responsible gene bearing chromosome) or recessive and dominant (on the basis of mode ofinheritance). Clinical presentation categorizes mental retardation into either syndromic (accompanied with additional clinical dysfunction) or nonsyndromic mental retardation (only learning impairment).4

Great extent of X chromosome has been explored in context of MR and so far more than 19 genes have been identified in case of nonspecific X -linked intellectual disability5 while in case of autosomal nonsyndromic cognitive impairment merely 10 genes have been reported till to-date (a literature survey). Most of the nonsyndromic mental retardation genes are involved in synaptic functioning; and their limitation causes cognitive dysfunction.6 Genome databases are the best resources for functional, expression and cytogenetic, evolutionary, gene ontology data. Utilizing these resources, the current analysis is done to overview the similarities among nonspecific (nonsyndromic) X-linked and autosomal cognitive genes and a concept is genera ted for future research. localization, molecular and biological functions).

For Tissue specific gene expression data, UCSC genome browser (http://genome.ucsc.edu/)9, GeneCards {version 3 (http://www.genecards.org/)}10 and BioGS can also be explored because of reliability. HomoloGene tool of NCBI (http://www.ncbi.nlm.nih.gov/homologene/) 11 and pfam database are trustworthy sources of gene conservation and protein domain data respectively.

STRING (a web based tool)12 and HPRD (Human Protein Reference Database)13 are best sites for finding protein interactor.

Table 1: Expression data was obtained from UCSC genome browser (GNF Expression Atlas 2 Data from U133A and GNF1H Chips), evolutionary conservation and protein domain data is retrieved by using HomoloGene tool of NCBI.

Gene name###Expression profile###Gene###Protein domain or motjf

(OMJM)###Conservation

###data

###Autosomal genes of cognition

PRSS12###Superior cervical ganglian###Euteleostomi###1 Kringle, 3 SRCR and 1 Trypsin like serine

(606709)###protease domain

CRBN###Amygdala, prefrontal cortex,###Eukaryota###ATP dependent LON domain

(609262)###hypothalamus

CC2D1A###Fetal brain, prefrontal cortex, cingulate###Eutheria###Contains 1 C2 domain and

(610055)###cortex###4 DM14 domain

TUSC3###Temporal lobe###Bilateria###1 Thioredoxine like domain

(601385)

GRIK2###High expression in whole brain###Eukaryota###3 Periplasmic binding protein type 1 (PBPb)

(138244)

TRAPPC9###Cerebellum###Bilateria###Trs 120 domain

(611966)

ST3GAL3###Superior cervical ganglian and fetal brain###Amniota###Glycotransferase domain

(606494)

MANIB1###Fetal brain, prefrontal cortex, cingulate###Eukaryota###Glycosyl Hydrolase domain 47 and seven-

(604346)###cortex###hairpin glycosidases

SWIP###Fetal brain, prefrontal cortex, amygdale,###Euteleostomi###WD4O domain, SOCS domain

(610091)###olfactoty bulbspinal cord

TECR###Cerebellum peduncles, hypothalamus,###Eukaryota###3 -oxo-5-alpha-steroid 4-dehydrogenase

(610057)###prefrontal cortex

###X chromosome genes of cognition

AGTR2###Medulla oblongata, pons, cingulate###Euteleostomi###Serpentine type 7TM GPCR

(300034)###cortex, temporal lobe, trigeminal

###ganglian

TM4 SF2###High expression in whole brain###Euteleostomi###Tetraspanin, extracellular domain or large

(300096)###extracellular loop(LEL)

GDIJ###High expression in whole brain###Eukaryota###GDP dissociation inhibitor

(300104)

PAK3###High expression in whole brain###Amniota###Protein kinase catalytic like domain and CRIB

(300142)###domain

FACL4###Hypothalamus, fetal brain, superior###Eukaryota###2 Lux E domain

(A CSL4)###cervical ganglian

(300157)

DLG3###Fetal brain###Euteleostomi###PDZ domain, Src homology 3, Guanylate kinase-

(300189)###like domain, PDZ-associated domain of NMDA

###receptors, MAGUK N PEST

IL1RAPL###superior cervical ganglian, dorsal root###Euteleostomi###Belongs to the interleukin- 1 receptor family

(300206)###ganglian###Contains 3 Ig-like C2-type (immunoglobulin-

###like) domains Contains 1 TIR domain

ARHGEF6###Thalamus, hypothalamus, spinal cord###Euteleostomi###Calponin homology domain, SH3, RhoGEF

(300267)###domain, PH DOMAIN

KLF8/ZNF74J###cingulate cortex, trigeminal ganglian###Euteleostomi###C0G5048, but never assign to any domain

(300286)###family

Functional Homology

Most of the autosomal genes have their functional homolog on X chromosome and more or less share the common functional aspect like post translational modification, signal transduction, regulation of synaptic transmission etc. For instance, CC2D1A, TRAPPC9 (on autosome) and AGTR2, GDI1 ( on X chromosome) are involved in signal transduction and in some case they share the common pathway (Table -2).

Discussion

The comparative analytical discussion is mainly concerned with the future direction of nonsyndromic cognitive dysfunction and identification of their genetic players. When we analyzed the functional data it was observed that almost every functional aspect of autosomal genes were present in X linked genes but still there are varieties of functional characteristics of X linked genes which need to be explored in autosomal genes (Table-2). Analyzing the paralogous genes of cognitive impairment, some autosomal genes showed their paralogs on other autosomes and was involved in MR, e.g. CC2D1A and CC2D2A (both on autosomes). So extending the assumption of X chromosome evolution from autosome we can assume that paralog of X-linked genes must be on autosome and should be involved in the same disorder; like ARHGEF6 (X chromosome) has its paralog ARHGEF3 and ARHGEF4 on autosome; similarly NLGN3, NLGN4 on X -chromosome has paralog NLGN1 and NLGN2 on autosome.

This paralogous relationship might be helpful in exploring more autosomal genes by using X-linked gene data. Protein- Protein interaction data has revealed some X linked gene showing interaction with autosomal gene e.g. DLG3 with GRIK2. Thus by pooling up the protein interactor idea and paralogous gene idea, disease gene identification can be done with high significance. Moreover, the functional data can be utilized for establishment of bioinformatics based software for candidate genes identification in which developer can utilize both set of genes as training set because of sharing expression profile, evolutionary conservation data, molecular pathway, biological processes, protein domain sharing and thus ultimately will help in exploring new genetic entities in molecular biology of nervous system.

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Publication:Pakistan Journal of Medical Research
Article Type:Report
Date:Mar 31, 2012
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