Amir Feisal Merican

Large-scale comparison of intron positions among animal, plant, and fungal genes

We purge large databases of animal, plant, and fungal intron-containing genes to a 20% similarity level and then identify the most similar animal–plant, animal–fungal, and plant–fungal protein pairs. We identify the introns in each BLAST 2.0 alignment and score matched intron positions and slid (near-matched, within six nucleotides) intron positions automatically. Overall we find that 10% of the animal introns match plant positions, and a further 7% are “slides.” Fifteen percent of fungal introns match animal positions, and 13% match plant positions. Furthermore, the number of alignments with high numbers of matches deviates greatly from the Poisson expectation. The 30 animal–plant alignments with the highest matches (for which 44% of animal introns match plant positions) when aligned with fungal genes are also highly enriched for triple matches: 39% of the fungal introns match both animal and plant positions. This is strong evidence for ancestral introns predating the animal–plant–fungal divergence, and in complete opposition to any expectations based on random insertion. In examining the slid introns, we show that at least half are caused by imperfections in the alignments, and are most likely to be actual matches at common positions. Thus, our final estimates are that ≈14% of animal introns match plant positions, and that ≈17–18% of fungal introns match animal or plant positions, all of these being likely to be ancestral in the eukaryotes.

Mutant Escherichia coli arginine repressor proteins that fail to bind L-arginine, yet retain the ability to bind their normal DNA binding sites

The Escherichia coli arginine repressor (ArgR) is an l‐arginine‐dependent DNA‐binding protein that controls expression of the arginine biosynthetic genes and is required as an accessory protein in Xer site‐specific recombination at cer and related recombination sites in plasmids. Site‐directed mutagenesis was used to isolate two mutants of E. coli ArgR that were defective in arginine binding. Results from in vivo and in vitro experiments demonstrate that these mutants still act as repressors and bind their specific DNA sequences in an arginine‐independent manner. Both mutants support Xer site‐specific recombination at cer. One of the mutant proteins was purified and shown to bind to its DNA target sequences in vitro with different affinity and as a different molecular species to wild‐type ArgR.

DNA binding of Escherichia coli arginine repressor mutants altered in oligomeric state

The Escherichia coli arginine repressor (ArgR) controls expression of the arginine biosynthetic genes and acts as an accessory protein in Xer site‐specific recombination at cer and related plasmid recombination sites. The hexameric wild‐type protein shows L‐arginine‐dependent DNA binding. In this work, ArgR mutants that are defective in trimer–trimer interactions and bind DNA as trimers in an L‐arginine‐independent manner are isolated and characterized. Whereas the wild‐type ArgR hexamer exhibits high‐affinity binding to two repeated ARG boxes separated by 3 bp (each ARG box containing two identical dyad symmetrical 9 bp half‐sites), the trimeric mutants bind to and footprint three adjacent half‐sites of this ‘idealized’ substrate. Trimeric ArgR is impaired in its ability to repress the arginine biosynthetic genes and in Xer site‐specific recombination. In the absence of L‐arginine, residual wild‐type ArgR‐binding occurs as trimers. The binding of an N‐terminal 77‐amino‐acid DNA‐binding domain to idealized ARG boxes is also characterized.

Oral cancer prognosis based on clinicopathologic and genomic markers using a hybrid of feature selection and machine learning methods

BackgroundMachine learning techniques are becoming useful as an alternative approach to conventional medical diagnosis or prognosis as they are good for handling noisy and incomplete data, and significant results can be attained despite a small sample size. Traditionally, clinicians make prognostic decisions based on clinicopathologic markers. However, it is not easy for the most skilful clinician to come out with an accurate prognosis by using these markers alone. Thus, there is a need to use genomic markers to improve the accuracy of prognosis. The main aim of this research is to apply a hybrid of feature selection and machine learning methods in oral cancer prognosis based on the parameters of the correlation of clinicopathologic and genomic markers.ResultsIn the first stage of this research, five feature selection methods have been proposed and experimented on the oral cancer prognosis dataset. In the second stage, the model with the features selected from each feature selection methods are tested on the proposed classifiers. Four types of classifiers are chosen; these are namely, ANFIS, artificial neural network, support vector machine and logistic regression. A k-fold cross-validation is implemented on all types of classifiers due to the small sample size. The hybrid model of ReliefF-GA-ANFIS with 3-input features of drink, invasion and p63 achieved the best accuracy (accuracy = 93.81%; AUC = 0.90) for the oral cancer prognosis.ConclusionsThe results revealed that the prognosis is superior with the presence of both clinicopathologic and genomic markers. The selected features can be investigated further to validate the potential of becoming as significant prognostic signature in the oral cancer studies.

A Comparative Analysis of Synonymous Codon Usage Bias Pattern in Human Albumin Superfamily

Synonymous codon usage bias is an inevitable phenomenon in organismic taxa across the three domains of life. Though the frequency of codon usage is not equal across species and within genome in the same species, the phenomenon is non random and is tissue-specific. Several factors such as GC content, nucleotide distribution, protein hydropathy, protein secondary structure, and translational selection are reported to contribute to codon usage preference. The synonymous codon usage patterns can be helpful in revealing the expression pattern of genes as well as the evolutionary relationship between the sequences. In this study, synonymous codon usage bias patterns were determined for the evolutionarily close proteins of albumin superfamily, namely, albumin, α-fetoprotein, afamin, and vitamin D-binding protein. Our study demonstrated that the genes of the four albumin superfamily members have low GC content and high values of effective number of codons (ENC) suggesting high expressivity of these genes and less bias in codon usage preferences. This study also provided evidence that the albumin superfamily members are not subjected to mutational selection pressure.

First comprehensive in silico analysis of the functional and structural consequences of SNPs in human GalNAc-T1 gene.

GalNAc-T1, a key candidate of GalNac-transferases genes family that is involved in mucin-type O-linked glycosylation pathway, is expressed in most biological tissues and cell types. Despite the reported association of GalNAc-T1 gene mutations with human disease susceptibility, the comprehensive computational analysis of coding, noncoding and regulatory SNPs, and their functional impacts on protein level, still remains unknown. Therefore, sequence- and structure-based computational tools were employed to screen the entire listed coding SNPs of GalNAc-T1 gene in order to identify and characterize them. Our concordant in silico analysis by SIFT, PolyPhen-2, PANTHER-cSNP, and SNPeffect tools, identified the potential nsSNPs (S143P, G258V, and Y414D variants) from 18 nsSNPs of GalNAc-T1. Additionally, 2 regulatory SNPs (rs72964406 and #x26; rs34304568) were also identified in GalNAc-T1 by using FastSNP tool. Using multiple computational approaches, we have systematically classified the functional mutations in regulatory and coding regions that can modify expression and function of GalNAc-T1 enzyme. These genetic variants can further assist in better understanding the wide range of disease susceptibility associated with the mucin-based cell signalling and pathogenic binding, and may help to develop novel therapeutic elements for associated diseases.

The first Malay database toward the ethnic-specific target molecular variation

BackgroundThe Malaysian Node of the Human Variome Project (MyHVP) is one of the eighteen official Human Variome Project (HVP) country-specific nodes. Since its inception in 9th October 2010, MyHVP has attracted the significant number of Malaysian clinicians and researchers to participate and contribute their data to this project. MyHVP also act as the center of coordination for genotypic and phenotypic variation studies of the Malaysian population. A specialized database was developed to store and manage the data based on genetic variations which also associated with health and disease of Malaysian ethnic groups. This ethnic-specific database is called the Malaysian Node of the Human Variome Project database (MyHVPDb).FindingsCurrently, MyHVPDb provides only information about the genetic variations and mutations found in the Malays. In the near future, it will expand for the other Malaysian ethnics as well. The data sets are specified based on diseases or genetic mutation types which have three main subcategories: Single Nucleotide Polymorphism (SNP), Copy Number Variation (CNV) followed by the mutations which code for the common diseases among Malaysians. MyHVPDb has been open to the local researchers, academicians and students through the registration at the portal of MyHVP (http://hvpmalaysia.kk.usm.my/mhgvc/index.php?id=register).ConclusionsThis database would be useful for clinicians and researchers who are interested in doing a study on genomics population and genetic diseases in order to obtain up-to-date and accurate information regarding the population-specific variations and also useful for those in countries with similar ethnic background.

Bioinformatics in Malaysia: Hope, Initiative, Effort, Reality, and Challenges

The published articles in PLoS Computational Biology on the development of computational biology research in Mexico, Brazil, Cuba, Costa Rica, and Thailand have inspired us to report on the development of bioinformatics activities in Malaysia. Rapid progress in molecular biology research and biotechnology in Malaysia has created sufficient demand for bioinformatics in Malaysia. Although bioinformatics in Malaysia started in the early 1990s, the initial focus on the development of the biotechnology industry has curtailed the early gains and overshadowed the systematic development of bioinformatics in Malaysia, which currently lacks in human capital development, research, and commercialization. However, government initiatives have been devised to develop the necessary national bioinformatics network and human resource development programs and to provide the necessary infrastructure, connectivity, and resources for bioinformatics. Stakeholders are experiencing reorientation and consolidating existing strengths to align with the global trends in bioinformatics. This exercise is expected to reinvigorate the bioinformatics industry in Malaysia. Tapping into niche expertise and resources such as biodiversity and coupling it with the existing biotechnology infrastructure will help to create sustainable development momentum for the future. An initiative arose from several senior scientists across local universities in Malaysia to promote this new scientific discipline in the country.

Long non-coding RNA expression in primary human monocytes.

Long non-coding RNAs (lncRNAs) have been shown to possess a wide range of functions in both cellular and developmental processes including cancers. Although some of the lncRNAs have been implicated in the regulation of the immune response, the exact function of the large majority of lncRNAs still remains unknown. In this study, we characterized the lncRNAs in human primary monocytes, an essential component of the innate immune system. We performed RNA sequencing of monocytes from four individuals and combined our data with eleven other publicly available datasets. Our analysis led to identification of ~8000 lncRNAs of which >1000 have not been previously reported in monocytes. PCR-based validation of a subset of the identified novel long intergenic noncoding RNAs (lincRNAs) revealed distinct expression patterns. Our study provides a landscape of lncRNAs in monocytes, which could facilitate future experimental studies to characterize the functions of these molecules in the innate immune system.

Amino Acid Sequence and Structural Comparison of BACE1 and BACE2 Using Evolutionary Trace Method

Beta-amyloid precursor protein cleavage enzyme 1 (BACE1) and beta-amyloid precursor protein cleavage enzyme 2 (BACE2), members of aspartyl protease family, are close homologues and have high similarity in their protein crystal structures. However, their enzymatic properties differ leading to disparate clinical consequences. In order to identify the residues that are responsible for such differences, we used evolutionary trace (ET) method to compare the amino acid conservation patterns of BACE1 and BACE2 in several mammalian species. We found that, in BACE1 and BACE2 structures, most of the ligand binding sites are conserved which indicate their enzymatic property of aspartyl protease family members. The other conserved residues are more or less randomly localized in other parts of the structures. Four group-specific residues were identified at the ligand binding site of BACE1 and BACE2. We postulated that these residues would be essential for selectivity of BACE1 and BACE2 biological functions and could be sites of interest for the design of selective inhibitors targeting either BACE1 or BACE2.