Amphipathic helices target perilipins 1-3 to lipid droplets

Perilipins (PLINs) play a key role in energy storage by orchestrating the activity of lipases on the surface of lipid droplets. Failure of this activity results in severe metabolic disease in humans. Unlike all other lipid droplet-associated proteins, PLINs localize almost exclusively to the phospholipid monolayer surrounding the droplet. To understand how they sense and associate with the unique topology of the droplet surface, we studied the localization of human PLINs inSaccharomyces cerevisiae,demonstrating that the targeting mechanism is highly conserved and that 11-mer repeat regions are sufficient for droplet targeting. Mutations designed to disrupt folding of this region into amphipathic helices (AHs) significantly decreased lipid droplet targetingin vivoandin vitro Finally, we demonstrated a substantial increase in the helicity of this region in the presence of detergent micelles, which was prevented by an AH-disrupting missense mutation. We conclude that highly conserved 11-mer repeat regions of PLINs target lipid droplets by folding into AHs on the droplet surface, thus enabling PLINs to regulate the interface between the hydrophobic lipid core and its surrounding hydrophilic environment.This work was supported by grants from The Wellcome Trust (091551 and 107064 to DBS), the U.K. NIHR Cambridge Biomedical Research Centre, the Medical Research Council (G0701446 to SS and a Doctoral training grant awarded to the University of Cambridge for ERR), core facilities at the MRC Metabolic Diseases Unit (MC_UU_12012/5) and by the Innovative Medicines Initiative Joint Undertaking, EMIF-Metabolism award.This is the final version of the article. It first appeared from ASBMB via https://doi.org/10.1074/jbc.M115.69104

Effects of typical and atypical antipsychotic drugs on gene expression profiles in the liver of schizophrenia subjects

<p>Abstract</p> <p>Background</p> <p>Although much progress has been made on antipsychotic drug development, precise mechanisms behind the action of typical and atypical antipsychotics are poorly understood.</p> <p>Methods</p> <p>We performed genome-wide expression profiling to study effects of typical antipsychotics and atypical antipsychotics in the postmortem liver of schizophrenia patients using microarrays (Affymetrix U133 plus2.0). We classified the subjects into typical antipsychotics (n = 24) or atypical antipsychotics (n = 26) based on their medication history, and compared gene expression profiles with unaffected controls (n = 34). We further analyzed individual antipsychotic effects on gene expression by sub-classifying the subjects into four major antipsychotic groups including haloperidol, phenothiazines, olanzapine and risperidone.</p> <p>Results</p> <p>Typical antipsychotics affected genes associated with nuclear protein, stress responses and phosphorylation, whereas atypical antipsychotics affected genes associated with golgi/endoplasmic reticulum and cytoplasm transport. Comparison between typical antipsychotics and atypical antipsychotics further identified genes associated with lipid metabolism and mitochondrial function. Analyses on individual antipsychotics revealed a set of genes (151 transcripts, FDR adjusted p < 0.05) that are differentially regulated by four antipsychotics, particularly by phenothiazines, in the liver of schizophrenia patients.</p> <p>Conclusion</p> <p>Typical antipsychotics and atypical antipsychotics affect different genes and biological function in the liver. Typical antipsychotic phenothiazines exert robust effects on gene expression in the liver that may lead to liver toxicity. The genes found in the current study may benefit antipsychotic drug development with better therapeutic and side effect profiles.</p

Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data

Abstract: Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers

Transcriptome Sequencing Revealed Significant Alteration of Cortical Promoter Usage and Splicing in Schizophrenia

While hybridization based analysis of the cortical transcriptome has provided important insight into the neuropathology of schizophrenia, it represents a restricted view of disease-associated gene activity based on predetermined probes. By contrast, sequencing technology can provide un-biased analysis of transcription at nucleotide resolution. Here we use this approach to investigate schizophrenia-associated cortical gene expression.The data was generated from 76 bp reads of RNA-Seq, aligned to the reference genome and assembled into transcripts for quantification of exons, splice variants and alternative promoters in postmortem superior temporal gyrus (STG/BA22) from 9 male subjects with schizophrenia and 9 matched non-psychiatric controls. Differentially expressed genes were then subjected to further sequence and functional group analysis. The output, amounting to more than 38 Gb of sequence, revealed significant alteration of gene expression including many previously shown to be associated with schizophrenia. Gene ontology enrichment analysis followed by functional map construction identified three functional clusters highly relevant to schizophrenia including neurotransmission related functions, synaptic vesicle trafficking, and neural development. Significantly, more than 2000 genes displayed schizophrenia-associated alternative promoter usage and more than 1000 genes showed differential splicing (FDR<0.05). Both types of transcriptional isoforms were exemplified by reads aligned to the neurodevelopmentally significant doublecortin-like kinase 1 (DCLK1) gene.This study provided the first deep and un-biased analysis of schizophrenia-associated transcriptional diversity within the STG, and revealed variants with important implications for the complex pathophysiology of schizophrenia

Inter-domain Communication Mechanisms in an ABC Importer: A Molecular Dynamics Study of the MalFGK2E Complex

ATP-Binding Cassette transporters are ubiquitous membrane proteins that convert the energy from ATP-binding and hydrolysis into conformational changes of the transmembrane region to allow the translocation of substrates against their concentration gradient. Despite the large amount of structural and biochemical data available for this family, it is still not clear how the energy obtained from ATP hydrolysis in the ATPase domains is “transmitted” to the transmembrane domains. In this work, we focus our attention on the consequences of hydrolysis and inorganic phosphate exit in the maltose uptake system (MalFGK2E) from Escherichia coli. The prime goal is to identify and map the structural changes occurring during an ATP-hydrolytic cycle. For that, we use extensive molecular dynamics simulations to study three potential intermediate states (with 10 replicates each): an ATP-bound, an ADP plus inorganic phosphate-bound and an ADP-bound state. Our results show that the residues presenting major rearrangements are located in the A-loop, in the helical sub-domain, and in the “EAA motif” (especially in the “coupling helices” region). Additionally, in one of the simulations with ADP we were able to observe the opening of the NBD dimer accompanied by the dissociation of ADP from the ABC signature motif, but not from its corresponding P-loop motif. This work, together with several other MD studies, suggests a common communication mechanism both for importers and exporters, in which ATP-hydrolysis induces conformational changes in the helical sub-domain region, in turn transferred to the transmembrane domains via the “coupling helices”

Epigenomic profiling of preterm infants reveals DNA methylation differences at sites associated with neural function

DNA methylation (DNAm) plays a determining role in neural cell fate and provides a molecular link between early-life stress and neuropsychiatric disease. Preterm birth is a profound environmental stressor that is closely associated with alterations in connectivity of neural systems and long-term neuropsychiatric impairment. The aims of this study were to examine the relationship between preterm birth and DNAm, and to investigate factors that contribute to variance in DNAm. DNA was collected from preterm infants (birth<33 weeks gestation) and healthy controls (birth>37 weeks), and a genome-wide analysis of DNAm was performed; diffusion magnetic resonance imaging (dMRI) data were acquired from the preterm group. The major fasciculi were segmented, and fractional anisotropy, mean diffusivity and tract shape were calculated. Principal components (PC) analysis was used to investigate the contribution of MRI features and clinical variables to variance in DNAm. Differential methylation was found within 25 gene bodies and 58 promoters of protein-coding genes in preterm infants compared with controls; 10 of these have neural functions. Differences detected in the array were validated with pyrosequencing. Ninety-five percent of the variance in DNAm in preterm infants was explained by 23 PCs; corticospinal tract shape associated with 6th PC, and gender and early nutritional exposure associated with the 7th PC. Preterm birth is associated with alterations in the methylome at sites that influence neural development and function. Differential methylation analysis has identified several promising candidate genes for understanding the genetic/epigenetic basis of preterm brain injury