Project description:Immense phenotypic variability exists among healthy individuals which is a consequence of both genetic and environmental effects. Genome wide expression variability is a powerful tool for capturing these expression variations and hence can give insights into genotype phenotype correlations. In the ancient Indian system of medicine, of Ayurveda, the basic tenet for treatment relies on phenotypic assessment In this system, assessment of predisposition, susceptibility or prognosis for disease is based on the phenotypic assessment of the individual. Each individuals has a basic constitution which is termed as Prakriti, majorly decided by the relative concentration of three constituents called doshas - Vata (kinetic) Pitta (metabolic) and Kapha (structural) at the time of fertilization. Phenotypically, depending on the concentration the normal individuals can be classified onto 7 basic types of which 3 are more distinct and are called as Pure Prakriti and the rest of the 4 groups are of mixed types. Pure Prakriti individuals have one of the doshas in more prominence than the other. In this study individuals of Pure Prakrities have been selected for elucidation of the transcriptomic variation in peripheral whole blood cells. Keywords: Gene expression variability within healthy individuals, subgrouped on the basis of Prakriti (basic constitution of an individual based on Ayurveda) Healthy individuals of pure prakriti (as described in Ayurveda) of age between 18-40 years were identified, using a questionnaire (for objective assessment) developed, by qualified Ayurvedic practitioners. The individuals have been primarily assessed for phenotypic difference based on morphological characteristics / physiology along with some subjective assessment of physical capabilities and aptitudes. The individuals have been classified as â??Vataâ?? (V), â??Pittaâ?? (P) and â??Kaphaâ??(K) prakriti. The individuals were provided a similar diet 3 hours prior to sample collection with no interim intake of food or smoking in between. In order to find gene expression differences between V, P and K cDNA microarray experiment was performed in pooled samples wherein males and females experiments were carried out separately. We performed loop design experiments , where each prakriti was compared with the other two. Technical replicates involving dye swap for each prakriti was used in the same experiment. We present results of 4 biological replicates and 4 technical replicates of each prakriti in females, and 3 biological and 3 technical replicates of each prakriti in males. Total RNA was isolated from whole blood cells of volunteers using EZ-RNA isolation kit (Biological Industries) as per manufacturers protocol. Double stranded cDNA was synthesized from 15 Âµg (5 Âµg each sample) of total RNA using Microarray cDNA Synthesis Kit (Roche). The cDNA was purified using Micorarray Target Purification Kit (Roche), according to the manufacturerâ??s protocol. Each pool of Vata, Pitta and Kapha purified cDNA was divided into two halves, one half labeled with Cy5 and other with Cy3 (Amersham Biosciences) using Microarray RNA Target Synthesis Kit T7 (Roche) and labeled products were purified by Microarray Target Purification Kit (Roche). The two cRNA samples of each biological replicate, one labeled with Cy3 and another with Cy5, were pooled together, precipitated, washed and air-dried. The dried pellet was dissolved in RNAase free water (Sigma). Hybridization solution was prepared by mixing hybridization buffer (DIG Easy Hyb; Roche), 10mg/ml salmon testis DNA (0.05 mg/ml final concentration, Sigma) and 10mg/ml yeast tRNA (0.05 mg/ml final concentration, Sigma) and added to the labeled product. This mixture was denatured at 65ÂºC and applied onto cDNA microarray slides. Inter prakriti cohybridization was carried out at 37ÂºC for 16 hrs. The 48 Ã? 20 Ã? 20 (19,200 spots) human single spot 19K cDNA microarray was procured from University Health Network Microarray Centre, Toronto, Canada. Coverslips were removed by submerging the slides in a solution containing 1X SSC and 0.1% SDS at 50ÂºC. Slides were washed in 1X SSC and 0.1% SDS (three times for 15 minutes each) in a coplin jar at 50ÂºC with occasional swirling and then transferred to 1X SSC and washed with gentle swirling at room temperature (twice for 15 minutes each). Finally, slides were washed in 0.1X SSC for 15 minutes and then liquid was quickly removed from the slide surface by spinning at 600 rpm for 5 minutes. Microarray slides were scanned at 10Âµm resolution in GenePix 4000A Microarray Scanner (Molecular Devices), using both green and red lasers. The 16 bit TIFF images were preprocessed and quantified using Gene Pix Pro 6.0 software (Molecular Devices). Analysis was carried out taking two experiments at a time, separately for male and female data. Background subtracted mean values were used for the analysis. All the negative values were removed and only those genes having positive values for all the samples were considered for further analysis. Values were log2 transformed and lowess normalization was applied to remove dye bias. Across array normalization was carried out using quantile normalization method to correct for systematic variation between arrays. One-way analysis of variance was adopted to identify differentially expressed genes in all three categories. F-test was carried out in order to reject the hypothesis of equality of three group means. in order to investigate further the nature of differentially expressed genes; we have drawn strip charts and carried out pair wise comparisons using t-test. p-values obtained from ANOVA have been adjusted for multiplicity using Bonferroni method for controlling family wise error rate. Genes at pï?£0.1 level of significance were considered to be significantly different in expression between all three groups.

Project description:BackgroundDNA copy number variants (CNVs) are found in 15% of subjects with ID but their association with phenotypic abnormalities has been predominantly studied in smaller cohorts of subjects with detailed yet non-systematically categorized phenotypes, or larger cohorts (thousands of cases) with smaller number of generalized phenotypes.MethodsWe evaluated the association of de novo, familial and common CNVs detected in 78 ID subjects with phenotypic abnormalities classified using the Winter-Baraitser Dysmorphology Database (WBDD) (formerly the London Dysmorphology Database). Terminology for 34 primary (coarse) and 169 secondary (fine) phenotype features were used to categorize the abnormal phenotypes and determine the prevalence of each phenotype in patients grouped by the type of CNV they had.ResultsIn our cohort more than 50% of cases had abnormalities in primary categories related to head (cranium, forehead, ears, eye globes, eye associated structures, nose) as well as hands and feet. The median number of primary and secondary abnormalities was 12 and 18 per subject, respectively, indicating that the cohort consisted of subjects with a high number of phenotypic abnormalities (median De Vries score for the cohort was 5). The prevalence of each phenotypic abnormality was comparable in patients with de novo or familial CNVs in comparison to those with only common CNVs, although a trend for increased frequency of cranial and forehead abnormalities was noted in subjects with rare de novo and familial CNVs. Two clusters of subjects were identified based on the prevalence of each fine phenotypic feature, with an average of 28.3 and 13.5 abnormal phenotypes/subject in the two clusters respectively (P?<?0.05).ConclusionsOur study is a rare example of using standardized, deep morphologic phenotype clustering with phenotype/CNV correlation in a cohort of subjects with ID. The composition of the cohort inevitably influences the phenotype/genotype association, and our studies show that the influence of the de novo CNVs on the phenotype is less obvious in cohorts consisting of subjects with a high number of phenotypic abnormalities. The outcome of phenotype/genotype analysis also depends on the choice of phenotypes assessed and standardized phenotyping is required to minimize variability.

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Gene expression profile of healthy individuals phenotyped based on Prakriti types (Vadu cohort)

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