Integrating lipidomics and genomics : emerging tools to understand cardiovascular diseases

Cardiovascular diseases (CVDs) are the leading cause of mortality and morbidity worldwide leading to 31% of all global deaths. Early prediction and prevention could greatly reduce the enormous socio-economic burden posed by CVDs. Plasma lipids have been at the center stage of the prediction and prevention strategies for CVDs that have mostly relied on traditional lipids (total cholesterol, total triglycerides, HDL-C and LDL-C). The tremendous advancement in the field of lipidomics in last two decades has facilitated the research efforts to unravel the metabolic dysregulation in CVDs and their genetic determinants, enabling the understanding of pathophysiological mechanisms and identification of predictive biomarkers, beyond traditional lipids. This review presents an overview of the application of lipidomics in epidemiological and genetic studies and their contributions to the current understanding of the field. We review findings of these studies and discuss examples that demonstrates the potential of lipidomics in revealing new biology not captured by traditional lipids and lipoprotein measurements. The promising findings from these studies have raised new opportunities in the fields of personalized and predictive medicine for CVDs. The review further discusses prospects of integrating emerging genomics tools with the high-dimensional lipidome to move forward from the statistical associations towards biological understanding, therapeutic target development and risk prediction. We believe that integrating genomics with lipidome holds a great potential but further advancements in statistical and computational tools are needed to handle the high-dimensional and correlated lipidome.Peer reviewe

MetaPhat : Detecting and Decomposing Multivariate Associations From Univariate Genome-Wide Association Statistics

Background: Multivariate testing tools that integrate multiple genome-wide association studies (GWAS) have become important as the number of phenotypes gathered from study cohorts and biobanks has increased. While these tools have been shown to boost statistical power considerably over univariate tests, an important remaining challenge is to interpret which traits are driving the multivariate association and which traits are just passengers with minor contributions to the genotype-phenotypes association statistic. Results: We introduce MetaPhat, a novel bioinformatics tool to conduct GWAS of multiple correlated traits using univariate GWAS results and to decompose multivariate associations into sets of central traits based on intuitive trace plots that visualize Bayesian Information Criterion (BIC) andP-value statistics of multivariate association models. We validate MetaPhat with Global Lipids Genetics Consortium GWAS results, and we apply MetaPhat to univariate GWAS results for 21 heritable and correlated polyunsaturated lipid species from 2,045 Finnish samples, detecting seven independent loci associated with a cluster of lipid species. In most cases, we are able to decompose these multivariate associations to only three to five central traits out of all 21 traits included in the analyses. We release MetaPhat as an open source tool written in Python with built-in support for multi-processing, quality control, clumping and intuitive visualizations using the R software. Conclusion: MetaPhat efficiently decomposes associations between multivariate phenotypes and genetic variants into smaller sets of central traits and improves the interpretation and specificity of genome-phenome associations. MetaPhat is freely available under the MIT license at:.Peer reviewe

Oligonucleotide properties determination and primer designing: a critical examination of predictions

Motivation: Precise prediction of melting temperature (Tm), secondary structures and design of oligonucleotides determine the efficiency and success of experimentation in molecular biology. Availability of a plethora of software and the users unawareness about their limitations compromises the accuracy and reliability of the predictions. Results: Comparative analysis of 56 modules was done for Tm prediction using a large set of oligonucleotide sequences spanning the whole range of GC-content and length. Allawi module of the calculator ‘MELTING’, Nearest Neighbor (NN) of oligo calculator (McLab), NN of Tm Calculation for Oligos (Biomath Calculator, Promega) and HYTHER provided the most precise Tm predictions. A model has also been proposed to calculate the optimum annealing temperature integrating the already reported formulations. Secondary structure predictions of oligonucleotides reveal a large number of structures in contrast to the experimental observations. Of the 11 primer designing tools evaluated, Primer 3 and WebPrimer performed the best for the AT-rich templates, Exon Primer for AT = GC templates, and Primer Design Assistant, Primer3 and Primer Quest for GC-rich templates. This study provides optimal choice for application to the user, increasing the success of a variety of experimentations, especially those that have high-throughput and complex assay designs

Gene prioritization in Type 2 Diabetes using domain interactions and network analysis

<p>Abstract</p> <p>Background</p> <p>Identification of disease genes for Type 2 Diabetes (T2D) by traditional methods has yielded limited success. Based on our previous observation that T2D may result from disturbed protein-protein interactions affected through disrupting modular domain interactions, here we have designed an approach to rank the candidates in the T2D linked genomic regions as plausible disease genes.</p> <p>Results</p> <p>Our approach integrates Weight value (Wv) method followed by prioritization using clustering coefficients derived from domain interaction network. Wv for each candidate is calculated based on the assumption that disease genes might be functionally related, mainly facilitated by interactions among domains of the interacting proteins. The benchmarking using a test dataset comprising of both known T2D genes and non-T2D genes revealed that Wv method had a sensitivity and specificity of 0.74 and 0.96 respectively with 9 fold enrichment. The candidate genes having a Wv > 0.5 were called High Weight Elements (HWEs). Further, we ranked HWEs by using the network property-the clustering coefficient (C_i). Each HWE with a C_i< 0.015 was prioritized as plausible disease candidates (HWEc) as previous studies indicate that disease genes tend to avoid dense clustering (with an average C_iof 0.015). This method further prioritized the identified disease genes with a sensitivity of 0.32 and a specificity of 0.98 and enriched the candidate list by 6.8 fold. Thus, from the dataset of 4052 positional candidates the method ranked 435 to be most likely disease candidates. The gene ontology sharing for the candidates showed higher representation of metabolic and signaling processes. The approach also captured genes with unknown functions which were characterized by network motif analysis.</p> <p>Conclusions</p> <p>Prioritization of positional candidates is essential for cost-effective and an expedited discovery of disease genes. Here, we demonstrate a novel approach for disease candidate prioritization from numerous loci linked to T2D.</p

Efficacy of Uterovaginal Packing Versus Uterine Balloon Tamponade to Control Postpartum Hemorrhage Due to Uterine Atony

OBJECTIVES To determine and compare the efficacy of Uterovaginal packing versus uterine balloon tamponade to control postpartum haemorrhage due to uterine atony unresponsive to medical treatment. METHODOLOGY This comparative prospective cross-sectional study was conducted in Hayatabad Medical Complex, OBG department. A total of 140 patients were categorised into two groups, group A underwent Uterovaginal packing and group B underwent uterine balloon tamponade. All women of 18 to 40 years with a history of delivery after 28 weeks of gestation, who developed primary postpartum haemorrhage due to uterine atony, unresponsive to medical treatment were included in the study. Women with a history of delivery before 28 weeks of gestation, secondary postpartum haemorrhage, genital tract trauma, retained placental tissue and membranes, placenta previa, morbidly adherent placenta, febrile illness and uterine structural lesion were excluded from the study. Efficacy was labelled if there was no ongoing blood loss after the procedure with concomitant hemodynamic stability. All information was recorded in a predesigned proforma, and data were analysed using SPSS version 22.RESULTS Our study included 140 women; 113 had a normal vaginal delivery, and 27 underwent cesarean section. Among cases with normal vaginal delivery, 45 women had Uterovaginal packing, and 68 had uterine balloon tamponade, while among cases of cesarean sections, 25 women had uterovaginal packing and 2 had uterine balloon tamponade. The efficacy of Uterovaginal packing was 90%, and that of uterine balloon tamponade was 87.1%, with no significant difference statistically (p- 0.51). Overall efficacy of both procedures was 88.6%.CONCLUSION All orthodontic and non-orthodontic treatment group participants required oral hygiene instructions and had periodontal treatment needs (TN1). The patients requiring scaling and prophylaxis and Oral hygiene instructions (TN 2) were more in the orthodontic treatment group than the non-orthodontic treatment group. A higher percentage of patients requiring complex treatment (deep scaling, root planning and complex surgical procedures), scaling and prophylaxis and Oral hygiene instructions (TN3) belonged to the non-orthodontic treatment group

Characterization of Mutations Linked with Second Line Anti-TB Drug Resistance in Pakistan

Background: The incidence of multiple drug resistance tuberculosis is on the rise worldwide and Pakistan is one of 30 high TB burden countries. Resistance to second line drugs especially fluoroquinolones is being reported by many laboratories. This is increasing the gravity of the situation resulting in extensively drug resistant cases, which is difficult to treat, and has more side effects.Methods: One hundred and thirty-three (133) clinical isolates of M. tuberculosis, collected by convenience sampling, were characterized for mutations in eth-A, gyrA, msh-A, rrs genes, and the promoter region of inh-A gene that confer resistance to second line anti-TB drugs. The mutations were detected by allele-specific-PCR and PCR amplification followed by SSCP and DNA sequencing.Results: Mutations in gyrA gene at codon 91, 94 and 95 were found in 4 (3.0%) M. tuberculosis isolates. Mutations in rrs gene were found in 17 (12.8%) isolates, ten (7.5%) isolates had mutation at A1401G position, 5 (3.76%) isolates at C1402T position and 3 (2.25%) isolates had G1484T mutation. For resistance to ethionamide, none of the isolates showed mutation in eth-A gene. In promoter region of inh-A gene, mutations were detected at -C15T, -A112G, -C110T in two samples. Two mutations, A312T and A332G, were found in msh-A gene in one sample. Collectively, 24 (18%) isolates were found to harbor mutations associated with second line anti TB drug resistance.Conclusion: Our work revealed high frequency of mutations (18%) associated with resistance against second line anti-TB drugs. This situation can lead to increase in XDR-TB cases. We, therefore, recommend improved diagnostic and drug sensitivity testing, better prescription, and development of superior drugs to control tuberculosis.   Keywords: Antibiotic resistance; Mycobacterium tuberculosis; Second line anti-TB drug

Common Variants of Homocysteine Metabolism Pathway Genes and Risk of Type 2 Diabetes and Related Traits in Indians

Hyperhomocysteinemia, a risk factor for cardiovascular disorder, obesity, and type 2 diabetes, is prevalent among Indians who are at high risk of these metabolic disorders. We evaluated association of common variants of genes involved in homocysteine metabolism or its levels with type 2 diabetes, obesity, and related traits in North Indians. We genotyped 90 variants in initial phase (2.115 subjects) and replicated top signals in an independent sample set (2.085 subjects). The variant MTHFR-rs1801133 was the top signal for association with type 2 diabetes (OR = 0.78 (95%  CI = 0.67–0.92), P = 0.003) and was also associated with 2 h postload plasma glucose (P = 0.04), high-density lipoprotein cholesterol (P = 0.004), and total cholesterol (P = 0.01) in control subjects. These associations were neither replicated nor significant after meta-analysis. Studies involving a larger study population and different ethnic groups are required before ruling out the role of these important candidate genes in type 2 diabetes, obesity, and related traits

Quantitative online survey of self-perceived knowledge and knowledge gaps of medicines research and development among Finnish general public

Objectives This study explored self-reported knowledge and interest to learn more about medicines research, development and health technology assessment among Finnish general public. It also aimed to define possible knowledge gaps and needs for public education regarding these topics. Design Online survey with 503 participants. The questionnaire was originally developed as a part of the Needs Assessment Work Package of the European Patients' Academy on Therapeutic Innovation Project. The survey was carried out in Finland in 2019. Methods The survey was conducted as an online survey by Kantar TNS Gallup Forum online panel. The data were analysed by using the freely available programming language R. Relationships between the demographic characteristics (such as age, gender and education level) of respondents and their knowledge or interest in medicines research and development were determined using Pearson's chi(2) tests. Statistically significant responses of demographic characteristics in the respondents' knowledge or interest in medicines research were determined by logistic regression. Results Of the 503 respondents (age 16-64) only 12% reported having good or very good knowledge of medicines research and development in general. Regarding health technology assessment, pharmacoeconomics and regulation, the percentage of respondents reporting good or very good knowledge was as low as 8%. Respondents were most interested in learning more about predictive and personalised medicine (47%) and least interested in medicines regulation (30%) and pharmacoeconomics (31%). Conclusions Self-reported knowledge about medicines research and development and health technology assessment appears to be very low in Finland. Patient and public participation is recognised as an important and essential element in up-to-date medical research and assessment of new treatments. In order to participate as an active and equal partner in these processes, the public needs more information and education in these topics.Peer reviewe

Prosthetic Rehabilitation of a Patient with Xerostomia Following Radiotherapy: A Case Report

Xerostomia, characterized by a subjective feeling of dryness in the mouth has profound consequences on oral health including dental caries, oral discomfort, and a diminished overall quality of life. The absence or alteration of saliva can have profound consequences, manifesting as dental caries, oral discomfort, and a diminished overall quality of life. The present case report details a case of a reservoir denture, showcasing successful outcomes in the innovative management of xerostomia. By describing an alternative technique for denture fabrication, the present case report focuses on the construction of a mandibular reservoir dentur

Omic personality : implications of stable transcript and methylation profiles for personalized medicine

Abstract Background Personalized medicine is predicated on the notion that individual biochemical and genomic profiles are relatively constant in times of good health and to some extent predictive of disease or therapeutic response. We report a pilot study quantifying gene expression and methylation profile consistency over time, addressing the reasons for individual uniqueness, and its relation to N = 1 phenotypes. Methods Whole blood samples from four African American women, four Caucasian women, and four Caucasian men drawn from the Atlanta Center for Health Discovery and Well Being study at three successive 6-month intervals were profiled by RNA-Seq, miRNA-Seq, and Illumina Methylation 450 K arrays. Standard regression approaches were used to evaluate the proportion of variance for each type of omic measure among individuals, and to quantify correlations among measures and with clinical attributes related to wellness. Results Longitudinal omic profiles were in general highly consistent over time, with an average of 67 % variance in transcript abundance, 42 % in CpG methylation level (but 88 % for the most differentiated CpG per gene), and 50 % in miRNA abundance among individuals, which are all comparable to 74 % variance among individuals for 74 clinical traits. One third of the variance could be attributed to differential blood cell type abundance, which was also fairly stable over time, and a lesser amount to expression quantitative trait loci (eQTL) effects. Seven conserved axes of covariance that capture diverse aspects of immune function explained over half of the variance. These axes also explained a considerable proportion of individually extreme transcript abundance, namely approximately 100 genes that were significantly up-regulated or down-regulated in each person and were in some cases enriched for relevant gene activities that plausibly associate with clinical attributes. A similar fraction of genes had individually divergent methylation levels, but these did not overlap with the transcripts, and fewer than 20 % of genes had significantly correlated methylation and gene expression. Conclusions People express an “omic personality” consisting of peripheral blood transcriptional and epigenetic profiles that are constant over the course of a year and reflect various types of immune activity. Baseline genomic profiles can provide a window into the molecular basis of traits that might be useful for explaining medical conditions or guiding personalized health decisions