Comprehensive and Systematic Analysis of Gene Expression Patterns Associated with Body Mass Index

The Therapeutic Effectiveness of Laparoscopic Sleeve Gastrectomy Among Individuals with Low BMI Obesity (30-35 Kg/m2) and the Relationship of BMI to Weight Loss

Purpose

Investigating the therapeutic efficacy of Laparoscopic Sleeve Gastrectomy (LSG) in low BMI (30-35 kg/m2) patients with obesity, and exploring the correlation between patients' preoperative BMI and postoperative weight loss.

Methods

Comparing the weight loss, remission of comorbidities, occurrence of complications, and quality of life among the different BMI patients who underwent LSG. Analyzing the relationship between BMI and percentage of excess weight loss (%EWL) by using Spearman correlation analysis and linear regression analysis.

Results

The %EWL at 12 months after the surgical procedure was (104.26±16.41)%, (90.36±9.98)%, and (78.30±14.64)% for patients with Class I, II, and III obesity, respectively, P<0.05. Spearman correlation coefficients between %EWL and BMI at 1, 3, 6, and 12 months after surgery were R=-0.334 (P<0.001), R=-0.389 (P<0.001), and R=-0.442 (P<0.001), R=-0.641 (P<0.001), respectively. The remission of hypertension, diabetes and dyslipidaemia did not differ significantly between groups (P>0.05).

Conclusion

Individuals with obesity for varying BMI can experience favorable outcomes following LSG surgery. It is advisable to consider LSG treatment for patients with Class I obesity.

LipGene: Lipschitz Continuity Guided Adaptive Learning Rates for Fast Convergence on Microarray Expression Data Sets

Hyperparameter tuning, specifically tuning of learning rate, can often be a time-consuming process, especially when dealing with large data sets. A mathematical foundation in the choice of learning rate can minimize tuning efforts. We propose the application of a novel adaptive learning rate paradigm, guided by Lipschitz continuity of the loss functions (LipGene), to the task of Gene Expression Inference using shallow neural networks. We utilize Mean Absolute Error and Quantile loss separately for training. Our adaptive learning rate, which is dynamically computed for each epoch, is based on the principle of Lipschitz constant and requires no tuning. Experimentally, we prove that our proposed approach greatly surpasses conventional choices of learning rates in terms of both speed of convergence and generalizability. Advocating the principle of Parsimonious Computing, our method can reduce compute infrastructure required for training by using smaller networks with a minimal compromise on the prediction error.

Integrative global co-expression analysis identifies Key MicroRNA-target gene networks as key blood biomarkers for obesity

Obesity is associated with the quantitative changes in miRNAs and their target genes. However, the molecular basis of their dysregulation and expression status correlations is incompletely understood. Therefore, this study aims to examine the shared differentially expressed miRNAs and their target genes between blood and adipose tissues of obese individuals to identify potential blood-based biomarkers. In this study, 3 gene expression datasets (two mRNA and one miRNA), generated from blood and adipose tissues of 68 obese and 39 lean individuals, were analyzed by a series of robust computational concepts, like protein interactome mapping, functional enrichment of biological pathways and construction of miRNA-mRNA and transcription factor gene networks. The comparison of blood versus tissue datasets has revealed the shared differential expression of 210 genes (59.5% upregulated) involved in lipid metabolism and inflammatory reactions. The blood miRNA (GSE25470) analysis has identified 79 differentially expressed miRNAs (71% downregulated). The miRNA-target gene scan identified regulation of 30 shared genes by 22miRNAs. The gene network analysis has identified the inverse expression correlation between 8 target genes (TP53, DYSF, GAB2, GFRA2, NACC2, FAM53C, JNK and GAB2) and 3 key miRNAs (hsa-mir-940, hsa-mir-765, hsa-mir-612), which are further regulated by 24 key transcription factors. This study identifies potential obesity related blood biomarkers from largescale gene expression data by computational miRNA-target gene interactome and transcription factor network construction methods.

How β cells can smell insulin fragments

In this issue of Cell Metabolism, Cheng et al. identify olfactory receptor Olfr109 in β cells with increased expression in islets from mouse models of obesity and type 1 and type 2 diabetes. Binding of a small insulin fragment to Olfr109 fosters islet inflammation, β cell failure, and diabetes progression.

Placental Gene Co-expression Network for Maternal Plasma Lipids Revealed Enrichment of Inflammatory Response Pathways

Maternal dyslipidemia during pregnancy has been associated with suboptimal fetal growth and increased cardiometabolic diseasse risk in offspring. Altered placental function driven by placental gene expression is a hypothesized mechanism underlying these associations. We tested the relationship between maternal plasma lipid concentrations and placental gene expression. Among 64 pregnant women from the NICHD Fetal Growth Studies–Singleton cohort with maternal first trimester plasma lipids we extracted RNA-Seq on placental samples obtained at birth. Placental gene co-expression networks were validated by regulatory network analysis that integrated transcription factors and gene expression, and genome-wide transcriptome analysis. Network analysis detected 24 gene co-expression modules in placenta, of which one module was correlated with total cholesterol (r = 0.27, P-value = 0.03) and LDL-C (r = 0.31, P-value = 0.01). Genes in the module (n = 39 genes) were enriched in inflammatory response pathways. Out of the 39 genes in the module, three known lipid-related genes (MPO, PGLYRP1 and LTF) and MAGEC2 were validated by the regulatory network analysis, and one known lipid-related gene (ALX4) and two germ-cell development-related genes (MAGEC2 and LUZP4) were validated by genome-wide transcriptome analysis. Placental gene expression signatures associated with unfavorable maternal lipid concentrations may be potential pathways underlying later life offspring cardiometabolic traits.

Clinical Trial Registration:ClinicalTrials.gov, identifier NCT00912132.

Conserved immunomodulatory transcriptional networks underlie antipsychotic-induced weight gain

Although antipsychotics, such as olanzapine, are effective in the management of psychiatric conditions, some patients experience excessive antipsychotic-induced weight gain (AIWG). To illuminate pathways underlying AIWG, we compared baseline blood gene expression profiles in two cohorts of mice that were either prone (AIWG-P) or resistant (AIWG-R) to weight gain in response to olanzapine treatment for two weeks. We found that transcripts elevated in AIWG-P mice relative to AIWG-R are enriched for high-confidence transcriptional targets of numerous inflammatory and immunomodulatory signaling nodes. Moreover, these nodes are themselves enriched for genes whose disruption in mice is associated with reduced body fat mass and slow postnatal weight gain. In addition, we identified gene expression profiles in common between our mouse AIWG-P gene set and an existing human AIWG-P gene set whose regulation by immunomodulatory transcription factors is highly conserved between species. Finally, we identified striking convergence between mouse AIWG-P transcriptional regulatory networks and those associated with body weight and body mass index in humans. We propose that immunomodulatory transcriptional networks drive AIWG, and that these networks have broader conserved roles in whole body-metabolism.

New Insights in RBM20 Cardiomyopathy

PURPOSE OF REVIEW

This review aims to give an update on recent findings related to the cardiac splicing factor RNA-binding motif protein 20 (RBM20) and RBM20 cardiomyopathy, a form of dilated cardiomyopathy caused by mutations in RBM20.

RECENT FINDINGS

While most research on RBM20 splicing targets has focused on titin (TTN), multiple studies over the last years have shown that other splicing targets of RBM20 including Ca²⁺/calmodulin-dependent kinase IIδ (CAMK2D) might be critically involved in the development of RBM20 cardiomyopathy. In this regard, loss of RBM20 causes an abnormal intracellular calcium handling, which may relate to the arrhythmogenic presentation of RBM20 cardiomyopathy. In addition, RBM20 presents clinically in a highly gender-specific manner, with male patients suffering from an earlier disease onset and a more severe disease progression. Further research on RBM20, and treatment of RBM20 cardiomyopathy, will need to consider both the multitude and relative contribution of the different splicing targets and related pathways, as well as gender differences.

The State of Data Science in Genomic Nursing

Nurse scientists are generating, acquiring, distributing, processing, storing, and analyzing greater volumes of complex omics data than ever before. To take full advantage of big omics data, to address core biological questions, and to enhance patient care, however, genomic nurse scientists must embrace data science. Intended for readership with limited but expanding data science knowledge and skills, this article aims to provide a brief overview of the state of data science in genomic nursing. Our goal is to introduce key data science concepts to genomic nurses who participate at any stage of the data science lifecycle, from research patient recruitment to data wrangling, preprocessing, and analysis to implementation in clinical practice to policy creation. We address three major components in this review: (1) fundamental terminology for the field of genomic nursing data science, (2) current genomic nursing data science research exemplars, and (3) the spectrum of genomic nursing data science roles as well as education pathways and training opportunities. Links to helpful resources are included throughout the article.

DNA Methylation Profiles in a Group of Workers Occupationally Exposed to Nanoparticles

The risk of exposure to nanoparticles (NPs) has rapidly increased during the last decade due to the vast use of nanomaterials (NMs) in many areas of human life. Despite this fact, human biomonitoring studies focused on the effect of NP exposure on DNA alterations are still rare. Furthermore, there are virtually no epigenetic data available. In this study, we investigated global and gene-specific DNA methylation profiles in a group of 20 long-term (mean 14.5 years) exposed, nanocomposite, research workers and in 20 controls. Both groups were sampled twice/day (pre-shift and post-shift) in September 2018. We applied Infinium Methylation Assay, using the Infinium MethylationEPIC BeadChips with more than 850,000 CpG loci, for identification of the DNA methylation pattern in the studied groups. Aerosol exposure monitoring, including two nanosized fractions, was also performed as proof of acute NP exposure. The obtained array data showed significant differences in methylation between the exposed and control groups related to long-term exposure, specifically 341 CpG loci were hypomethylated and 364 hypermethylated. The most significant CpG differences were mainly detected in genes involved in lipid metabolism, the immune system, lung functions, signaling pathways, cancer development and xenobiotic detoxification. In contrast, short-term acute NP exposure was not accompanied by DNA methylation changes. In summary, long-term (years) exposure to NP is associated with DNA epigenetic alterations.

Gene co-expression networks are associated with obesity-related traits in kidney transplant recipients

Background

Obesity is common among kidney transplant recipients; However biological mediators of obesity are not well understood in this population. Because subcutaneous adipose tissue can be easily obtained during kidney transplant surgery, it provides a unique avenue for studying the mechanisms of obesity for this group. Although differential gene expression patterns were previously profiled for kidney transplant patients, gene co-expression patterns can shed light on gene modules not yet explored on the coordinative behaviors of gene transcription in biological and disease processes from a systems perspective.

Methods

In this study, we collected 29 demographic and clinical variables and matching microarray expression data for 26 kidney transplant patients. We conducted Weighted Gene Correlation Network Analysis (WGCNA) for 5758 genes with the highest average expression levels and related gene co-expression to clinical traits.

Results

A total of 35 co-expression modules were detected, two of which showed associations with obesity-related traits, mainly at baseline. Gene Ontology (GO) enrichment was found for these two clinical trait-associated modules. One module consisting of 129 genes was enriched for a variety of processes, including cellular homeostasis and immune responses. The other module consisting of 36 genes was enriched for tissue development processes.

Conclusions

Our study generated gene co-expression modules associated with obesity-related traits in kidney transplant patients and provided new insights regarding the cellular biological processes underlying obesity in this population.