Breaking the paradigm: Dr Insight empowers signature-free, enhanced drug repurposing

Motivation: Transcriptome-based computational drug repurposing has attracted considerable interest by bringing about faster and more cost-effective drug discovery. Nevertheless, key limitations of the current drug connectivity-mapping paradigm have been long overlooked, including the lack of effective means to determine optimal query gene signatures. Results: The novel approach Dr Insight implements a frame-breaking statistical model for the ‘hand-shake’ between disease and drug data. The genome-wide screening of concordantly expressed genes (CEGs) eliminates the need for subjective selection of query signatures, added to eliciting better proxy for potential disease-specific drug targets. Extensive comparisons on simulated and real cancer datasets have validated the superior performance of Dr Insight over several popular drug-repurposing methods to detect known cancer drugs and drug–target interactions. A proof-of-concept trial using the TCGA breast cancer dataset demonstrates the application of Dr Insight for a comprehensive analysis, from redirection of drug therapies, to a systematic construction of disease-specific drug-target networks

Fried food consumption, genetic risk, and body mass index: gene-diet interaction analysis in three US cohort studies

Objective: To examine the interactions between genetic predisposition and consumption of fried food in relation to body mass index (BMI) and obesity. Design: Prospective cohort study. Setting: Health professionals in the United States. Participants: 9623 women from the Nurses’ Health Study, 6379 men from the Health Professionals Follow-up Study, and a replication cohort of 21 421 women from the Women’s Genome Health Study. Main outcome measure Repeated measurement of BMI over follow-up. Results: There was an interaction between fried food consumption and a genetic risk score based on 32 BMI-associated variants on BMI in both the Nurses’ Health Study and Health Professionals Follow-up Study (P≤0.001 for interaction). Among participants in the highest third of the genetic risk score, the differences in BMI between individuals who consumed fried foods four or more times a week and those who consumed fried foods less than once a week amounted to 1.0 (SE 0.2) in women and 0.7 (SE 0.2) in men, whereas the corresponding differences were 0.5 (SE 0.2) and 0.4 (SE 0.2) in the lowest third of the genetic risk score. The gene-diet interaction was replicated in the Women’s Genome Health Study (P<0.001 for interaction). Viewed differently, the genetic association with adiposity was strengthened with higher consumption of fried foods. In the combined three cohorts, the differences in BMI per 10 risk alleles were 1.1 (SE 0.2), 1.6 (SE 0.3), and 2.2 (SE 0.6) for fried food consumption less than once, one to three times, and four or more times a week (P<0.001 for interaction); and the odds ratios (95% confidence intervals) for obesity per 10 risk alleles were 1.61 (1.40 to 1.87), 2.12 (1.73 to 2.59), and 2.72 (2.12 to 3.48) across the three categories of consumption (P=0.002 for interaction). In addition, the variants in or near genes highly expressed or known to act in the central nervous system showed significant interactions with fried food consumption, with the FTO (fat mass and obesity associated) variant showing the strongest result (P<0.001 for interaction). Conclusion: Our findings suggest that consumption of fried food could interact with genetic background in relation to obesity, highlighting the particular importance of reducing fried food consumption in individuals genetically predisposed to obesity

Формирование эмоциональной культуры как компонента инновационной культуры студентов

Homozygosity has long been associated with rare, often devastating, Mendelian disorders1 and Darwin was one of the first to recognise that inbreeding reduces evolutionary fitness2. However, the effect of the more distant parental relatedness common in modern human populations is less well understood. Genomic data now allow us to investigate the effects of homozygosity on traits of public health importance by observing contiguous homozygous segments (runs of homozygosity, ROH), which are inferred to be homozygous along their complete length. Given the low levels of genome-wide homozygosity prevalent in most human populations, information is required on very large numbers of people to provide sufficient power3,4. Here we use ROH to study 16 health-related quantitative traits in 354,224 individuals from 102 cohorts and find statistically significant associations between summed runs of homozygosity (SROH) and four complex traits: height, forced expiratory lung volume in 1 second (FEV1), general cognitive ability (g) and educational attainment (nominal p<1 × 10−300, 2.1 × 10−6, 2.5 × 10−10, 1.8 × 10−10). In each case increased homozygosity was associated with decreased trait value, equivalent to the offspring of first cousins being 1.2 cm shorter and having 10 months less education. Similar effect sizes were found across four continental groups and populations with different degrees of genome-wide homozygosity, providing convincing evidence for the first time that homozygosity, rather than confounding, directly contributes to phenotypic variance. Contrary to earlier reports in substantially smaller samples5,6, no evidence was seen of an influence of genome-wide homozygosity on blood pressure and low density lipoprotein (LDL) cholesterol, or ten other cardio-metabolic traits. Since directional dominance is predicted for traits under directional evolutionary selection7, this study provides evidence that increased stature and cognitive function have been positively selected in human evolution, whereas many important risk factors for late-onset complex diseases may not have been

The genetic architecture of type 2 diabetes

The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of heritability. To test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole genome sequencing in 2,657 Europeans with and without diabetes, and exome sequencing in a total of 12,940 subjects from five ancestral groups. To increase statistical power, we expanded sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support a major role for lower-frequency variants in predisposition to type 2 diabetes

Statistical methods for high-throughput data integration : methodologies in disease research and drug discovery.

The wide application of high-throughput technologies in biomedical research calls for integrative approaches for data mining and knowledge discovery. Consequently, methodologies that deliver robust and systems integrations are in unprecedented demand. Two important sub-disciplines in biomedical research, namely disease research and drug discovery, have become ever-evolving frontiers for integration of “big data”. In disease research, p-value combination has been broadly employed to integrate statistical evidences from multiple studies. Common assumptions of conventional p-value combination methods include independence and homogeneity of the combined tests, which are constantly challenged by the complex nature of high-throughput biomedical datasets. In this dissertation, we propose a novel and robust p-value combination algorithm based on the Pareto Dominance principle from multi-objective optimization, which accounts for dependency and heterogeneity in data. Compared to existing methods, the Pareto method attains adaptive rejection regions from “learning” the multivariate null distribution estimated by permutations, therefore achieves superior performance when combining heterogeneous effects from multiple datasets, meanwhile remains appropriate error control for correlated tests. The Pareto meta-gene-set-analysis tool, PEACH, was developed and tested on a 16-cancer pan-cancer dataset from The Cancer Genome Atlas (TCGA). Significantly improved statistical power of the PEACH algorithm and its ability to detect important pathways related to sub-groups of cancers were demonstrated. On the other hand, computational drug repurposing based on gene expression data has gained increasing popularity in the field of drug discovery. The Connectivity Map (CMap) is a major database to repurpose new drugs from gene expression data. However, key limitations of the current signature-based drug-repurposing paradigm have prohibited accurate and unbiased repurposing. In the second part of this dissertation, we developed a frame-breaking statistical approach, namely Dr. Insight, to remove the requirement of subjective selection of a gene signature to query CMap database. We performed comprehensive studies using simulation data and disease datasets and validated the superior performance of Dr. Insight compared to previous methods. A TCGA breast cancer case study was also performed to showcase the application of Dr. Insight to breast cancer drug repurposing, from drug redirection to systematic construction of disease-specific drug-target networks

Paeoniflorin inhibits pulmonary artery smooth muscle cells proliferation via upregulating A2B adenosine receptor in rat.

Paeoniflorin (PF), which is the main active ingredient in the root of Paeonia Radix, has many pharmacological effects. Here, we investigated the effect of PF on rat pulmonary artery smooth muscle cells (PASMCs) under hypoxic conditions and explored the mechanisms of the effects. The anti-proliferative effect of PF increased in a dose dependent manner. At the highest dose (20 μmol/L), the anti-proliferative effect of PF peaked at 24 h after administration. However, the selective A2B adenosine receptor (A2BAR) antagonist MRS1754 abolished it. PF increased A2BAR mRNA levels from 0.0763±0.0067 of β-actin mRNA levels (hypoxia group) to 0.1190±0.0139 (P<0.05) measured by Real Time Reverse Transcription-Polymerase Chain Reaction. A2BAR protein expression measured by Western Blot was also increased. PF inhibited the proliferation of PASMCs by blocking cell cycle progression in the S phase. These data indicated that activation of A2BAR might be involved in the anti-proliferative effect of PF on PASMCs under hypoxic conditions. The results suggested that a new mechanism of PF could be relevant to the management of clinical hypoxic pulmonary hypertension

Density Functional Theory Investigation on Thiophene Hydrodesulfurization Mechanism Catalyzed by ReS₂ (001) Surface

We present density functional theory calculations on the reaction mechanism of thiophene hydrodesulfurization (HDS) over ReS₂ (001) surface under typical HDS reaction conditions. It is found that thiophene adopts an “upright” adsorption configuration with the binding energy of 1.26 eV. Considering the factors such as Bader charge, two reaction mechanisms, named direct desulfurization (DDS) to the product of butadiene and hydrogenation (HYD) to 2-butene, 1-butene, and butane, are systematically investigated. Results show that H prefers to attack thiophenic C before the first C–S bond rupture but begins to hydrogenate S_T (S atom of thiophene) after ring-opening. Prehydrogenation has different effect on the activity of C–S bond breaking. When the ring is intact, it has nominal effect; but when the ring is open, appropriate prehydrogenation can dramatically decrease the energy barrier while complete hydrogenation makes the barrier rise again due to stereohindrance effect. The DDS mechanism is proved to be kinetically unfavorable while 2-butene is suggested to be a predominated product for HYD mechanism. The role of S_a (preadsorbed S) is a “ladder” which helps H approach the thiophenic molecule while S_T acts as an “intermediary” for H exchange. Changing reaction conditions through partial pressure of H₂ can only alter the rate-determining step but has nothing to do with the catalytic selectivity