Biological Mechanism(s) Underpinning the Association between Antipsychotic Drugs and Weight Gain

Weight gain and consequent metabolic alterations are common side-effects of many antipsychotic drugs. Interestingly, several studies have suggested that improvement in symptoms and adverse metabolic effects are correlated. We used next generation sequencing data from NT-2 (human neuronal) cells treated with aripiprazole, amisulpride, risperidone, quetiapine, clozapine, or vehicle control, and compared with the Pillinger P-score (ranked from 0 to 1, indicating greater increase in weight gain and related metabolic parameters) to identify the genes most associated with the drugs’ propensity to cause weight gain. The top 500 genes ranked for their correlation with the drugs’ propensity to cause weight gain were subjected to pathway analysis using DAVID (NIH). We further investigated transcription factors (TFs) that are more likely to regulate the genes involved in these processes using the prediction tool of key TFs from TRRUST. The results suggest an enrichment for genes involved in lipid biosynthesis and metabolism, which are of interest for mechanisms underpinning weight-gain. The list of genes involved in the lipid pathways that correlated with weight gain was enriched for genes transcriptionally regulated by SREBF1 and SREBF2. Furthermore, quetiapine significantly increased the expression of SREBF1 and SREBF2 in NT-2 cells. Our results suggest that the effects of these antipsychotic drugs on lipid metabolism may be mediated, at least in part, via regulation of SREBF1/SREBF2 expression, with evidence of a direct effect of quetiapine on the expression of SREBF1/2. The effects of antipsychotic drugs on lipid metabolism may influence white matter structure (therapeutic effect) and the risk of weight gain, lipid disturbances, and, consequently, metabolic syndrome (adverse effects). Understanding the different molecular effects of these drugs could inform a personalized medicine approach in treating patients with schizophrenia.

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Faculty Opinions recommendation of VarWalker: personalized mutation network analysis of putative cancer genes from next-generation sequencing data.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.718272765.793499663 ◽

2014 ◽

Author(s):

Gary Bader ◽

Mohamed Helmy

Keyword(s):

Next Generation Sequencing ◽

Network Analysis ◽

Next Generation Sequencing Data ◽

Cancer Genes ◽

Next Generation ◽

Sequencing Data ◽

Generation Sequencing

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Faculty Opinions recommendation of Bioinformatory-assisted analysis of next-generation sequencing data for precision medicine in pancreatic cancer.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.727775566.793536095 ◽

2017 ◽

Author(s):

Steve Pereira

Keyword(s):

Pancreatic Cancer ◽

Next Generation Sequencing ◽

Precision Medicine ◽

Next Generation Sequencing Data ◽

Next Generation ◽

Sequencing Data ◽

Assisted Analysis ◽

Generation Sequencing

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NGSremix: A software tool for estimating pairwise relatedness between admixed individuals from next-generation sequencing data

G3 Genes|Genome|Genetics ◽

10.1093/g3journal/jkab174 ◽

2021 ◽

Author(s):

Anne Krogh Nøhr ◽

Kristian Hanghøj ◽

Genis Garcia Erill ◽

Zilong Li ◽

Ida Moltke ◽

...

Keyword(s):

Next Generation Sequencing ◽

Genetic Research ◽

Likelihood Estimation ◽

Software Tool ◽

Estimation Methods ◽

Next Generation Sequencing Data ◽

Next Generation ◽

Sequencing Data ◽

Ngs Data ◽

Generation Sequencing

Abstract Estimation of relatedness between pairs of individuals is important in many genetic research areas. When estimating relatedness, it is important to account for admixture if this is present. However, the methods that can account for admixture are all based on genotype data as input, which is a problem for low-depth next-generation sequencing (NGS) data from which genotypes are called with high uncertainty. Here we present a software tool, NGSremix, for maximum likelihood estimation of relatedness between pairs of admixed individuals from low-depth NGS data, which takes the uncertainty of the genotypes into account via genotype likelihoods. Using both simulated and real NGS data for admixed individuals with an average depth of 4x or below we show that our method works well and clearly outperforms all the commonly used state-of-the-art relatedness estimation methods PLINK, KING, relateAdmix, and ngsRelate that all perform quite poorly. Hence, NGSremix is a useful new tool for estimating relatedness in admixed populations from low-depth NGS data. NGSremix is implemented in C/C ++ in a multi-threaded software and is freely available on Github https://github.com/KHanghoj/NGSremix.

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recoup: flexible and versatile signal visualization from next generation sequencing

BMC Bioinformatics ◽

10.1186/s12859-020-03902-x ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Panagiotis Moulos

Keyword(s):

Next Generation Sequencing ◽

Next Generation Sequencing Data ◽

Special Focus ◽

Next Generation ◽

Sequencing Data ◽

User Friendliness ◽

Computational Environment ◽

Level Data ◽

Data Signal ◽

Generation Sequencing

Abstract Background The relentless continuing emergence of new genomic sequencing protocols and the resulting generation of ever larger datasets continue to challenge the meaningful summarization and visualization of the underlying signal generated to answer important qualitative and quantitative biological questions. As a result, the need for novel software able to reliably produce quick, comprehensive, and easily repeatable genomic signal visualizations in a user-friendly manner is rapidly re-emerging. Results recoup is a Bioconductor package for quick, flexible, versatile, and accurate visualization of genomic coverage profiles generated from Next Generation Sequencing data. Coupled with a database of precalculated genomic regions for multiple organisms, recoup offers processing mechanisms for quick, efficient, and multi-level data interrogation with minimal effort, while at the same time creating publication-quality visualizations. Special focus is given on plot reusability, reproducibility, and real-time exploration and formatting options, operations rarely supported in similar visualization tools in a profound way. recoup was assessed using several qualitative user metrics and found to balance the tradeoff between important package features, including speed, visualization quality, overall friendliness, and the reusability of the results with minimal additional calculations. Conclusion While some existing solutions for the comprehensive visualization of NGS data signal offer satisfying results, they are often compromised regarding issues such as effortless tracking of processing and preparation steps under a common computational environment, visualization quality and user friendliness. recoup is a unique package presenting a balanced tradeoff for a combination of assessment criteria while remaining fast and friendly.

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