Genome scale meta analysis of microarrays for biological inferences

Title from PDF of title page (University of Missouri--Columbia, viewed on April 5, 2010).The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.Dissertation advisor: Dr. Dong Xu.Vita.Ph. D. University of Missouri--Columbia 2009.In this present era of high-throughput technologies, meta-analysis is being widely used to integrate multiple similar high throughput studies. Here we propose a novel framework for applying meta-analysis techniques on expression data for gene function characterizations and biological networks construction like the gene regulatory network. In particular, we developed a prototype for gene function annotation using multiple microarray datasets and tested the performance of our model using yeast and human microarray datasets. Our results show significant improvement in functional annotation in general. We further applied the same metaanalysis method on the Arabidopsis plant in a collaborative project with Monsanto Company to construct regulatory network for Arabidopsis. Our method shows significant improvement than any other existing methods for inferring gene regulatory network. Beside meta-analysis, I have invested a great deal of efforts in developing PRIMEGENS, an open source software, which could be used for large-scale primers and probe design for PCR, DNA synthesis, qRT-PCR (gene expression), and targeted next-generation sequencing (454, Solexa, Agilent sure-select technology etc.) for normal or bisulfite-treated genome. We recently extended its functionality including microarray probe design to cover genome-wide CpG islands in human, Taqman probes and discriminating transcripts from its multiple homologs or splice variants based on gene-specific unique fragment in soybean genome.Includes bibliographical reference

ArrayD: A general purpose software for Microarray design

BACKGROUND: Microarray is a high-throughput technology to study expression of thousands of genes in parallel. A critical aspect of microarray production is the design aimed at space optimization while maximizing the number of gene probes and their replicates to be spotted. RESULTS: We have developed a software called 'ArrayD' that offers various alternative design solutions for an array given a set of user requirements. The user feeds the following inputs: type of source plates to be used, number of gene probes to be printed, number of replicates and number of pins to be used for printing. The solutions are stored in a text file. The choice of a design solution to be used will be governed by the spotting chemistry to be used and the accuracy of the robot. CONCLUSIONS: ArrayD is a software for standard cartesian robots. The software aids users in preparing a judicious and elegant design. ArrayD is universally applicable and is available at

Indian railway track analysis for displacement and vibration pattern estimation

This paper presents the dynamic response of the Indian Railway track. Two track models are considered for the dynamic response in terms of vertical displacement and acceleration at different wheel speeds, keeping the moving point load at constant magnitude. The rail is treated as a beam either on viscoelastic foundation or on the discrete elastic support system. The governing equation is implemented in finite element analysis using ANSYS 14.0. For the validation of result from system equation are compared with those available in published literature and the maximum deviation for displacement at the midpoint of rail is found to be within 5 %. Different wheel speed generates variation in displacement and acceleration of the rail track. The study can be viewed as the foundation for the comparison of FEA based simulation of rail track to specify its dynamic response useful to provide better safety and comfort to commuters

Trials of Improved Practices (TIPs) to Enhance the Dietary and Iron-Folate Intake during Pregnancy- A Quasi Experimental Study among Rural Pregnant Women of Varanasi, India.

BACKGROUND: Behavior Change Communications (BCC) play a decisive role in modifying socio-cultural norms affecting the perception and nutritional practices during pregnancy. OBJECTIVE: To examine the effectiveness of 'Trials of Improved Practices' (TIPs) on dietary and iron-folate intake during pregnancy. DESIGN: Community based quasi experimental study with a control group. SETTING: Four villages of Chiraigaon Community Development Block of Varanasi, India from May 2010 and recruited from August 2010. End line assessment, after 12 weeks of intervention, was completed in April 2011. PARTICIPANTS: Pregnant women in 13-28 weeks of gestation. INTERVENTION: TIPs was implemented in addition to ongoing essential obstetric care services in two villages through 3 home (assessment, negotiation and evaluation) visits and only assessment and evaluation visits in the other two control villages. Interpersonal communication, endorsing the active participation of family members and home based reminder materials were the TIPs based strategies. The effect of TIPs was assessed by comparing key outcome variables at baseline and after 12 weeks of intervention. OUTCOME MEASURES: Hemoglobin%, anemia prevalence, weight gain, compliance for iron-folate supplementation and dietary intake of calorie, protein, calcium and iron. RESULTS: A total of 86 participants completed the study. At the end, mean hemoglobin levels were 11.5±1.24 g/dl and 10.37±1.38 g/dl in the TIPs and control groups, respectively. The prevalence of anemia reduced by half in TIPs group and increased by 2.4% in the control group. Weight gain (grams/week) was significantly (p<0.01) higher in TIPs group (326.9±91.8 vs. 244.6±97.4). More than 85% of the PW in TIPs group were compliant for Iron-folate and only 38% were compliant among controls. The mean intake of protein increased by 1.78gm in intervention group and decreased by 1.81 gm in controls (p<0.05). More than two thirds of PW in TIPs group were taking one extra meal and only one third of controls were doing the same. CONCLUSION: TIPs found to be an effective approach to improve the nutritional status of pregnant women in the study area. TIPs strategy could be further explored on larger sample representing different socio-cultural and geographical areas. TRIAL REGISTRATION: Clinical Trial Registry of India CTRI/2015/02/005517

24-Hour Rhythms of DNA Methylation and Their Relation with Rhythms of RNA Expression in the Human Dorsolateral Prefrontal Cortex

Circadian rhythms modulate the biology of many human tissues, including brain tissues, and are driven by a near 24-hour transcriptional feedback loop. These rhythms are paralleled by 24-hour rhythms of large portions of the transcriptome. The role of dynamic DNA methylation in influencing these rhythms is uncertain. While recent work in Neurospora suggests that dynamic site-specific circadian rhythms of DNA methylation may play a role in modulating the fungal molecular clock, such rhythms and their relationship to RNA expression have not, to our knowledge, been elucidated in mammalian tissues, including human brain tissues. We hypothesized that 24-hour rhythms of DNA methylation exist in the human brain, and play a role in driving 24-hour rhythms of RNA expression. We analyzed DNA methylation levels in post-mortem human dorsolateral prefrontal cortex samples from 738 subjects. We assessed for 24-hour rhythmicity of 420,132 DNA methylation sites throughout the genome by considering methylation levels as a function of clock time of death and parameterizing these data using cosine functions. We determined global statistical significance by permutation. We then related rhythms of DNA methylation with rhythms of RNA expression determined by RNA sequencing. We found evidence of significant 24-hour rhythmicity of DNA methylation. Regions near transcription start sites were enriched for high-amplitude rhythmic DNA methylation sites, which were in turn time locked to 24-hour rhythms of RNA expression of nearby genes, with the nadir of methylation preceding peak transcript expression by 1–3 hours. Weak ante-mortem rest-activity rhythms were associated with lower amplitude DNA methylation rhythms as were older age and the presence of Alzheimer's disease. These findings support the hypothesis that 24-hour rhythms of DNA methylation, particularly near transcription start sites, may play a role in driving 24-hour rhythms of gene expression in the human dorsolateral prefrontal cortex, and may be affected by age and Alzheimer's disease

Targeted bisulfite sequencing by solution hybrid selection and massively parallel sequencing

We applied a solution hybrid selection approach to the enrichment of CpG islands (CGIs) and promoter sequences from the human genome for targeted high-throughput bisulfite sequencing. A single lane of Illumina sequences allowed accurate and quantitative analysis of ~1 million CpGs in more than 21 408 CGIs and more than 15 946 transcriptional regulatory regions. Of the CpGs analyzed, 77–84% fell on or near capture probe sequences; 69–75% fell within CGIs. More than 85% of capture probes successfully yielded quantitative DNA methylation information of targeted regions. Differentially methylated regions (DMRs) were identified in the 5′-end regulatory regions, as well as the intra- and intergenic regions, particularly in the X-chromosome among the three breast cancer cell lines analyzed. We chose 46 candidate loci (762 CpGs) for confirmation with PCR-based bisulfite sequencing and demonstrated excellent correlation between two data sets. Targeted bisulfite sequencing of three DNA methyltransferase (DNMT) knockout cell lines and the wild-type HCT116 colon cancer cell line revealed a significant decrease in CpG methylation for the DNMT1 knockout and DNMT1, 3B double knockout cell lines, but not in DNMT3B knockout cell line. We demonstrated the targeted bisulfite sequencing approach to be a powerful method to uncover novel aberrant methylation in the cancer epigenome. Since all targets were captured and sequenced as a pool through a series of single-tube reactions, this method can be easily scaled up to deal with a large number of samples

Alzheimer’s loci: epigenetic associations and interaction with genetic factors

Objective: We explore the role of DNA methylation in Alzheimer’s disease (AD). To elucidate where DNA methylation falls along the causal pathway linking risk factors to disease, we examine causal models to assess its role in the pathology of AD. Methods: DNA methylation profiles were generated in 740 brain samples using the Illumina HumanMet450K beadset. We focused our analysis on CpG sites from 11 AD susceptibility gene regions. The primary outcome was a quantitative measure of neuritic amyloid plaque (NP), a key early element of AD pathology. We tested four causal models: (1) independent associations, (2) CpG mediating the association of a variant, (3) reverse causality, and (4) genetic variant by CpG interaction. Results: Six genes regions (17 CpGs) showed evidence of CpG associations with NP, independent of genetic variation – BIN1 (5), CLU (5), MS4A6A (3), ABCA7 (2), CD2AP (1), and APOE (1). Together they explained 16.8% of the variability in NP. An interaction effect was seen in the CR1 region for two CpGs, cg10021878 (P = 0.01) and cg05922028 (P = 0.001), in relation to NP. In both cases, subjects with the risk allele rs6656401AT/AA display more methylation being associated with more NP burden, whereas subjects with the rs6656401TT protective genotype have an inverse association with more methylation being associated with less NP. Interpretation These observations suggest that, within known AD susceptibility loci, methylation is related to pathologic processes of AD and may play a largely independent role by influencing gene expression in AD susceptibility loci

Integration of cardiovascular risk assessment with COVID-19 using artificial intelligence

Artificial Intelligence (AI), in general, refers to the machines (or computers) that mimic "cognitive" functions that we associate with our mind, such as "learning" and "solving problem". New biomarkers derived from medical imaging are being discovered and are then fused with non-imaging biomarkers (such as office, laboratory, physiological, genetic, epidemiological, and clinical-based biomarkers) in a big data framework, to develop AI systems. These systems can support risk prediction and monitoring. This perspective narrative shows the powerful methods of AI for tracking cardiovascular risks. We conclude that AI could potentially become an integral part of the COVID-19 disease management system. Countries, large and small, should join hands with the WHO in building biobanks for scientists around the world to build AI-based platforms for tracking the cardiovascular risk assessment during COVID-19 times and long-term follow-up of the survivors