Central resources of variant discovery and annotation and its role in precision medicine

Rapid technological advancement in high-throughput genomics, microarray, and deep sequencing technologies has accelerated the possibility of more complex precision medicine research using large amounts of heterogeneous health-related data from patients, including genomic variants. Genomic variants can be identified and annotated based on the reference human genome either within the sequence as a whole or in a putative functional genomic element. The American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) mutually created standards and guidelines for the appraisal of proof to expand consistency and straightforwardness in clinical variation interpretations. Various efforts toward precision medicine have been facilitated by many national and international public databases that classify and annotate genomic variation. In the present study, several resources are highlighted with recognition and data spreading of clinically important genetic variations.

Mining Gene Expression Profile with Missing Values: An Integration of Kernel PCA and Robust Singular Values Decomposition

Background:

Gene expression profiling and transcriptomics provide valuable information about the role of genes that are differentially expressed between two or more samples. It is always important and challenging to analyse High-throughput DNA microarray data with a number of missing values under various experimental conditions. </P><P> Objectives: Graphical data visualizations of the expression of all genes in a particular cell provide holistic views of gene expression patterns, which improve our understanding of cellular systems under normal and pathological conditions. However, current visualization methods are sensitive to missing values, which are frequently observed in microarray-based gene expression profiling, potentially affecting the subsequent statistical analyses.

Methods:

We addressed in this study the problem of missing values with respect to different imputation methods using gene expression biplot (GE biplot), one of the most popular gene visualization techniques. The effects of missing values for mining differentially expressed genes in gene expression data were evaluated using four well-known imputation methods: Robust Singular Value Decomposition (Robust SVD), Column Average (CA), Column Median (CM), and K-nearest Neighbors (KNN). Frobenius norm and absolute distances were used to measure the accuracy of the methods.

Results:

Three numerical experiments were performed using simulated data (i) and publicly available colon cancer (ii) and leukemia data (iii) to analyze the performance of each method. The results showed that CM and KNN performed better than Robust SVD and CA for identifying the index gene profile in the biplot visualization in both the simulation study and the colon cancer and leukemia microarray datasets.

Conclusion:

The impact of missing values on the GE biplot was smaller when the data matrix was imputed by KNN than by CM. This study concluded that KNN performed satisfactorily in generating a GE biplot in the presence of missing values in microarray data.

Transcriptogenomics identification and characterization of RNA editing sites in human primary monocytes using high-depth next generation sequencing data

High-depth next generation sequencing data provide valuable insights into the number and distribution of RNA editing events. Here, we report the RNA editing events at cellular level of human primary monocyte using high-depth whole genomic and transcriptomic sequencing data. We identified over a ten thousand putative RNA editing sites and 69% of the sites were A-to-I editing sites. The sites enriched in repetitive sequences and intronic regions. High-depth sequencing datasets revealed that 90% of the canonical sites were edited at lower frequencies (<0.7). Single and multiple human monocytes and brain tissues samples were analyzed through genome sequence independent approach. The later approach was observed to identify more editing sites. Monocytes was observed to contain more C-to-U editing sites compared to brain tissues. Our results establish comparable pipeline that can address current limitations as well as demonstrate the potential for highly sensitive detection of RNA editing events in single cell type.

Enzymatic inhibitory activity of Ficus deltoidea leaf extract on matrix metalloproteinase-2, 8 and 9

Dysregulation of matrix metalloproteinases (MMPs) activity is known in many pathological conditions with which most of the conditions are related to elevate MMPs activities. Ficus deltoidea (FD) is a plant known for its therapeutic properties. In order to evaluate the therapeutic potential of FD leaf extract, we study the enzymatic inhibition properties of FD leaf extract and its major bioactive compounds (vitexin and isovitexin) on a panel of MMPs (MMP-2, MMP-8 and MMP-9) using experimental and computational approaches. FD leaf extract and its major bioactive compounds showed pronounced inhibition activity towards the MMPs tested. Computational docking analysis revealed that vitexin and isovitexin bind to the active site of the three tested MMPs. We also evaluated the cytotoxicity and cell migration inhibition activity of FD leaf extract in the endothelial EA.hy 926 cell line. Conclusively, this study provided additional information on the potential of FD leaf extract for therapeutical application.

Draft Genome Sequence of a Biosurfactant-Producing Bacillus subtilis UMX-103 Isolated from Hydrocarbon-Contaminated Soil in Terengganu, Malaysia

The draft genome here presents the sequence of Bacillus subtilis UMX-103. The bacterial strain was isolated from hydrocarbon-contaminated soil from Terengganu, Malaysia. The whole genome of the bacterium was sequenced using Illumina HiSeq 2000 sequencing platform. The genome was assembled using de novo approach. The genome size of UMX-103 is 4,234,627 bp with 4399 genes comprising 4301 protein-coding genes and 98 RNA genes. The analysis of assembled genes revealed the presence of 25 genes involved in biosurfactant production, where 14 of the genes are related to biosynthesis and 11 of the genes are in the regulation of biosurfactant productions. This draft genome will provide insights into the genetic bases of its biosurfactant-producing capabilities.

Experimental and computational approaches to reveal the potential of Ficus deltoidea leaves extract as α-amylase inhibitor

Ficus deltoidea leaves extract are known to have good therapeutic properties such as antioxidant, anti-inflammatory and anti-diabetic. We showed that 50% ethanol-water extract of F. deltoidea leaves and its pungent compounds vitexin and isovitexin exhibited significant (p < 0.05) α-amylase inhibition with IC₅₀ (vitexin: 4.6 μM [0.02 μg/mL]; isovitexin: 0.06 μg/mL [13.8 μM] and DPPH scavenging with IC₅₀ (vitexin: 92.5 μM [0.4 μg/mL]; isovitexin: 0.5 μg/mL [115.4 μM]). Additionally, molecular docking analysis confirmed that vitexin has a higher binding affinity (-7.54 kcal/mol) towards α-amylase compared to isovitexin (-5.61 kcal/mol). On the other hand, the molecular dynamics findings showed that vitexin-α-amylase complex is more stable during the simulation of 20 ns when compared to the isovitexin-α-amylase complex. Our results suggest that vitexin is more potent and stable against α-amylase enzyme, thus it could develop as a therapeutic drug for the treatment of diabetes.

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.

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

BACKGROUND

The 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 9(th) 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).

FINDINGS

Currently, 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 ).

CONCLUSIONS

This 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.

A whole genome analyses of genetic variants in two Kelantan Malay individuals

Background

The sequencing of two members of the Royal Kelantan Malay family genomes will provide insights on the Kelantan Malay whole genome sequences. The two Kelantan Malay genomes were analyzed for the SNP markers associated with thalassemia and Helicobacter pylori infection. Helicobacter pylori infection was reported to be low prevalence in the north-east as compared to the west coast of the Peninsular Malaysia and beta-thalassemia was known to be one of the most common inherited and genetic disorder in Malaysia.

Result

By combining SNP information from literatures, GWAS study and NCBI ClinVar, 18 unique SNPs were selected for further analysis. From these 18 SNPs, 10 SNPs came from previous study of Helicobacter pylori infection among Malay patients, 6 SNPs were from NCBI ClinVar and 2 SNPs from GWAS studies. The analysis reveals that both Royal Kelantan Malay genomes shared all the 10 SNPs identified by Maran (Single Nucleotide Polymorphims (SNPs) genotypic profiling of Malay patients with and without Helicobacter pylori infection in Kelantan, 2011) and one SNP from GWAS study. In addition, the analysis also reveals that both Royal Kelantan Malay genomes shared 3 SNP markers; HBG1 (rs1061234), HBB (rs1609812) and BCL11A (rs766432) where all three markers were associated with beta-thalassemia.

Conclusions

Our findings suggest that the Royal Kelantan Malays carry the SNPs which are associated with protection to Helicobacter pylori infection. In addition they also carry SNPs which are associated with beta-thalassemia. These findings are in line with the findings by other researchers who conducted studies on thalassemia and Helicobacter pylori infection in the non-royal Malay population.

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.

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.

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

Background

Machine 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.

Results

In 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.

Conclusions

The 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.

Growth curves for school children from Kuching, Sarawak: a methodological development

In this article, the authors propose reference curves for height and weight for school children in the Kuching area, Sarawak. The school children were from primary to secondary schools (aged 6.5 to 17 years old) and comprised both genders. Anthropometric measurements and demographic information for 3081 school-aged children were collected (1440 boys and 1641 girls). Fitted line plots and percentiles for height and weight (3rd, 10th, 25th, 50th, 75th, 90th, and 97th percentiles) were obtained. The height of school boys and school girls were almost similar at the start of their school-going age. For school girls, height and weight values stabilized when they reached 16 or 17 years old but kept increasing for school boys. School boys were taller than school girls as they entered adolescence. Height differences between school boys and school girls became significantly wider as they grew older. Chinese school children were taller and heavier than those of other ethnic groups.

Reference charts for height and weight of school children from west malaysia in comparison with the United States centers for disease control and prevention

BACKGROUND

Reference charts are widely used in healthcare as a screening tool. This study aimed to produce reference growth charts for school children from West Malaysia in comparison with the United States Centers for Disease Control and Prevention (CDC) chart.

METHODS

A total of 14,360 school children ranging from 7 to 17 years old from six states in West Malaysia were collected. A two-stage stratified random sampling technique was used to recruit the subjects. Curves were adjusted using Cole's LMS method. The LOWESS method was used to smooth the data.

RESULTS

The means and standard deviations for height and weight for both genders are presented. The results showed good agreement with growth patterns in other countries, i.e., males tend to be taller and heavier than females for most age groups. Height and weight of females reached a plateau at 17 years of age; however, males were still growing at this age. The growth charts for West Malaysian school children were compared with the CDC 2000 growth charts for school children in the United States.

CONCLUSION

The height and weight for males and females at the start of school-going ages were almost similar. The comparison between the growth charts from this study and the CDC 2000 growth charts indicated that the growth patterns of West Malaysian school children have improved, although the height and weight of American school children were higher than those for West Malaysian school children.

Application of the linear interaction energy method (LIE) to estimate the binding free energy values of Escherichia coli wild-type and mutant arginine repressor C-terminal domain (ArgRc)–l-arginine and ArgRc–l-citrulline protein–ligand complexes

Protein-ligand binding free energy values of wild-type and mutant C-terminal domain of Escherichia coli arginine repressor (ArgRc) protein systems bound to L-arginine or L-citrulline molecules were calculated using the linear interaction energy (LIE) method by molecular dynamics (MD) simulation. The binding behaviour predicted by the dissociation constant (K(d)) calculations from the binding free energy values showed preferences for binding of L-arginine to the wild-type ArgRc but not to the mutant ArgRc(D128N). On the other hand, L-citrulline do not favour binding to wild-type ArgRc but prefer binding to mutant ArgRc(D128N). The dissociation constant for the wild-type ArgRc-L-arginine complex obtained in this study is in agreement with reported experimental results. Our results also support the experimental data for the binding of L-citrulline to the mutant ArgRc(D128N). These showed that LIE method for protein-ligand binding free energy calculation could be applied to the wild-type and the mutant E. coli ArgRc-L-arginine and ArgRc-L-citrulline protein-ligand complexes and possibly to other transcriptional repressor-co-repressor systems as well.

Computational docking of L-arginine and its structural analogues to C-terminal domain of Escherichia coli arginine repressor protein (ArgRc)

The arginine repressor (ArgR) of Escherichia coli binds to six L-arginine molecules that act as its co-repressor in order to bind to DNA. The binding of L-arginine molecules as well as its structural analogues is compared by means of computational docking. A grid-based energy evaluation method combined with a Monte Carlo simulated annealing process was used in the automated docking. For all ligands, the docking procedure proposed more than one binding site in the C-terminal domain of ArgR (ArgRc). Interaction patterns of ArgRc with L-arginine were also observed for L-canavanine and L-citrulline. L-lysine and L-homoarginine, on the other hand, were shown to bind poorly at the binding site. Figure A general overview of the sites found from docking the various ligands into ArgRc ( grey ribbons). Red coloured sticks: residues in binding site H that was selected for docking

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 approximately equal 14% of animal introns match plant positions, and that approximately equal 17-18% of fungal introns match animal or plant positions, all of these being likely to be ancestral in the eukaryotes.

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.

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.