Impact of de novo tachyarrhythmias in patients with prior acute coronary syndrome.

Although the incidence of acute coronary syndrome (ACS) has increased over the decades, the overall prognosis has improved with newer stents, tailored medication, and better intervention techniques. Atrial fibrillation (AF) and ventricular arrhythmia at the time of ACS diagnosis are known indicators of a poor acute prognosis. However, there is a lack of data regarding the long-term arrhythmic impact of ventricular tachyarrhythmia (VA) on mortality in ACS patients. This study sought to elucidate the impact of tachyarrhythmia on mortality during long-term follow-up in patients with a history of ACS. This retrospective study was conducted in a single university hospital, and it evaluated the clinical outcomes, especially regarding cardiovascular mortality and readmission. The enrolled patients underwent percutaneous coronary intervention (PCI) for ACS between February 2004 and March 2018. Clinical information was attained by a thorough chart review. We retrospectively analyzed 560 ACS patients. We reviewed all electrocardiograms (ECGs) before and immediately after PCI, during hospitalization, and within 3 months of the index PCI. Three months after the index PCI procedure, any Holter monitoring or ECG was also reviewed for arrhythmia diagnosis. During follow-up, 91 patients were diagnosed with AF and 36 patients were diagnosed with VA. Overall mortality was related to the presence of anemia, low body mass index, low left ventricular ejection fraction after PCI, late-diagnosed AF, and any VA during follow-up. Readmission was higher in patients with chronic kidney disease and newly diagnosed AF during the follow-up. Diagnosis of late tachyarrhythmia during follow-up was associated with increased mortality in post-ACS patients.

Functional fine-mapping of noncoding risk variants in amyotrophic lateral sclerosis utilizing convolutional neural network.

Recent large-scale genome-wide association studies have identified common genetic variations that may contribute to the risk of amyotrophic lateral sclerosis (ALS). However, pinpointing the risk variants in noncoding regions and underlying biological mechanisms remains a major challenge. Here, we constructed a convolutional neural network model with a large-scale GWAS meta-analysis dataset to unravel functional noncoding variants associated with ALS based on their epigenetic features. After filtering and prioritizing of candidates, we fine-mapped two new risk variants, rs2370964 and rs3093720, on chromosome 3 and 17, respectively. Further analysis revealed that these polymorphisms are associated with the expression level of CX3CR1 and TNFAIP1, and affect the transcription factor binding sites for CTCF, NFATc1 and NR3C1. Our results may provide new insights for ALS pathogenesis, and the proposed research methodology can be applied for other complex diseases as well.

Computational inference of cancer-specific vulnerabilities in clinical samples.

BACKGROUND: Systematic in vitro loss-of-function screens provide valuable resources that can facilitate the discovery of drugs targeting cancer vulnerabilities. RESULTS: We develop a deep learning-based method to predict tumor-specific vulnerabilities in patient samples by leveraging a wealth of in vitro screening data. Acquired dependencies of tumors are inferred in cases in which one allele is disrupted by inactivating mutations or in association with oncogenic mutations. Nucleocytoplasmic transport by Ran GTPase is identified as a common vulnerability in Her2-positive breast cancers. Vulnerability to loss of Ku70/80 is predicted for tumors that are defective in homologous recombination and rely on nonhomologous end joining for DNA repair. Our experimental validation for Ran, Ku70/80, and a proteasome subunit using patient-derived cells shows that they can be targeted specifically in particular tumors that are predicted to be dependent on them. CONCLUSION: This approach can be applied to facilitate the development of precision therapeutic targets for different tumors.

Convolutional neural network model to predict causal risk factors that share complex regulatory features.

Major progress in disease genetics has been made through genome-wide association studies (GWASs). One of the key tasks for post-GWAS analyses is to identify causal noncoding variants with regulatory function. Here, on the basis of >2000 functional features, we developed a convolutional neural network framework for combinatorial, nonlinear modeling of complex patterns shared by risk variants scattered among multiple associated loci. When applied for major psychiatric disorders and autoimmune diseases, neural and immune features, respectively, exhibited high explanatory power while reflecting the pathophysiology of the relevant disease. The predicted causal variants were concentrated in active regulatory regions of relevant cell types and tended to be in physical contact with transcription factors while residing in evolutionarily conserved regions and resulting in expression changes of genes related to the given disease. We demonstrate some examples of novel candidate causal variants and associated genes. Our method is expected to contribute to the identification and functional interpretation of potential causal noncoding variants in post-GWAS analyses.

Impact of platelet reactivity on long-term prognosis in Korean patients receiving percutaneous coronary intervention.

Both high and low platelet responses to clopidogrel are highly associated with mortality. A therapeutic window for platelet reactivity was recently determined to be an important factor for improving clinical outcomes after percutaneous coronary intervention (PCI). We evaluated the impact of the antiplatelet activity of clopidogrel on long-term clinical outcomes in Korean patients receiving PCI. We analyzed the clinical outcomes of 814 Korean patients undergoing PCI for a median of 48 months. Platelet reactivity on clopidogrel was measured with the VerifyNow P2Y12 assay. The primary endpoint was all-cause death at 4 years. Patients were classified into three groups according to the P2Y12 reaction unit (PRU): low platelet reactivity (LPR; PRU < 85), normal platelet reactivity (NPR; 85 ≤ PRU < 208), and high platelet reactivity (HPR; PRU ≥ 208). The incidence of all-cause death was 7.0% in the LPR group, 1.5% in the NPR group, and 6.2% in the HPR group (log-rank p = 0.002). Based on multivariate analyses, all-cause death was significantly higher in both the LPR and HPR groups than in the NPR group (LPR, hazard ratio [HR]: 5.095; 95% confidence interval [95% CI]: 1.360-19.080, p = 0.016; HPR, HR: 3.315; 95% CI: 1.145-9.593, p = 0.027). Both LPR and HPR were significantly associated with long-term mortality in Korean patients receiving PCI, which suggests that the therapeutic concept of PRU may be an important prognostic factor.

Serine metabolism in the brain regulates starvation-induced sleep suppression in Drosophila melanogaster.

Sleep and metabolism are physiologically and behaviorally intertwined; however, the molecular basis for their interaction remains poorly understood. Here, we identified a serine metabolic pathway as a key mediator for starvation-induced sleep suppression. Transcriptome analyses revealed that enzymes involved in serine biosynthesis were induced upon starvation in Drosophila melanogaster brains. Genetic mutants of astray (aay), a fly homolog of the rate-limiting phosphoserine phosphatase in serine biosynthesis, displayed reduced starvation-induced sleep suppression. In contrast, a hypomorphic mutation in a serine/threonine-metabolizing enzyme, serine/threonine dehydratase (stdh), exaggerated starvation-induced sleep suppression. Analyses of double mutants indicated that aay and stdh act on the same genetic pathway to titrate serine levels in the head as well as to adjust starvation-induced sleep behaviors. RNA interference-mediated depletion of aay expression in neurons, using cholinergic Gal4 drivers, phenocopied aay mutants, while a nicotinic acetylcholine receptor antagonist selectively rescued the exaggerated starvation-induced sleep suppression in stdh mutants. Taken together, these data demonstrate that neural serine metabolism controls sleep during starvation, possibly via cholinergic signaling. We propose that animals have evolved a sleep-regulatory mechanism that reprograms amino acid metabolism for adaptive sleep behaviors in response to metabolic needs.

Selection on the regulation of sympathetic nervous activity in humans and chimpanzees.

Adrenergic α2C receptor (ADRA2C) is an inhibitory modulator of the sympathetic nervous system. Knockout mice for this gene show physiological and behavioural alterations that are associated with the fight-or-flight response. There is evidence of positive selection on the regulation of this gene during chicken domestication. Here, we find that the neuronal expression of ADRA2C is lower in human and chimpanzee than in other primates. On the basis of three-dimensional chromatin structure, we identified a cis-regulatory region whose DNA sequences have been significantly accelerated in human and chimpanzee. Active histone modification marks this region in rhesus macaque but not in human and chimpanzee; instead, repressive marks are enriched in various human brain samples. This region contains two neuron-restrictive silencer factor (NRSF) binding motifs, each of which harbours a polymorphism. Our genotyping and analysis of population genome data indicate that at both polymorphic sites, the derived allele has reached fixation in humans and chimpanzees but not in bonobos, whereas only the ancestral allele is present among macaques. Our CRISPR/Cas9 genome editing and reporter assays show that both derived nucleotides repress ADRA2C, most likely by increasing NRSF binding. In addition, we detected signatures of recent positive selection for lower neuronal ADRA2C expression in humans. Our findings indicate that there has been selective pressure for enhanced sympathetic nervous activity in the evolution of humans and chimpanzees.

Predicting the recurrence of noncoding regulatory mutations in cancer.

BACKGROUND: One of the greatest challenges in cancer genomics is to distinguish driver mutations from passenger mutations. Whereas recurrence is a hallmark of driver mutations, it is difficult to observe recurring noncoding mutations owing to a limited amount of whole-genome sequenced samples. Hence, it is required to develop a method to predict potentially recurrent mutations. RESULTS: In this work, we developed a random forest classifier that predicts regulatory mutations that may recur based on the features of the mutations repeatedly appearing in a given cohort. With breast cancer as a model, we profiled 35 quantitative features describing genetic and epigenetic signals at the mutation site, transcription factors whose binding motif was disrupted by the mutation, and genes targeted by long-range chromatin interactions. A true set of mutations for machine learning was generated by interrogating publicly available pan-cancer genomes based on our statistical model of mutation recurrence. The performance of our random forest classifier was evaluated by cross validations. The variable importance of each feature in the classification of mutations was investigated. Our statistical recurrence model for the random forest classifier showed an area under the curve (AUC) of ~0.78 in predicting recurrent mutations. Chromatin accessibility at the mutation sites, the distance from the mutations to known cancer risk loci, and the role of the target genes in the regulatory or protein interaction network were among the most important variables. CONCLUSIONS: Our methods enable to characterize recurrent regulatory mutations using a limited number of whole-genome samples, and based on the characterization, to predict potential driver mutations whose recurrence is not found in the given samples but likely to be observed with additional samples.

A conserved quality-control pathway that mediates degradation of unassembled ribosomal proteins.

Overproduced yeast ribosomal protein (RP) Rpl26 fails to assemble into ribosomes and is degraded in the nucleus/nucleolus by a ubiquitin-proteasome system quality control pathway comprising the E2 enzymes Ubc4/Ubc5 and the ubiquitin ligase Tom1. tom1 cells show reduced ubiquitination of multiple RPs, exceptional accumulation of detergent-insoluble proteins including multiple RPs, and hypersensitivity to imbalances in production of RPs and rRNA, indicative of a profound perturbation to proteostasis. Tom1 directly ubiquitinates unassembled RPs primarily via residues that are concealed in mature ribosomes. Together, these data point to an important role for Tom1 in normal physiology and prompt us to refer to this pathway as ERISQ, for excess ribosomal protein quality control. A similar pathway, mediated by the Tom1 homolog Huwe1, restricts accumulation of overexpressed hRpl26 in human cells. We propose that ERISQ is a key element of the quality control machinery that sustains protein homeostasis and cellular fitness in eukaryotes.

Ribosomal proteins produced in excess are degraded by the ubiquitin-proteasome system.

Ribosome assembly is an essential process that consumes prodigious quantities of cellular resources. Ribosomal proteins cannot be overproduced in Saccharomyces cerevisiae because the excess proteins are rapidly degraded. However, the responsible quality control (QC) mechanisms remain poorly characterized. Here we demonstrate that overexpression of multiple proteins of the small and large yeast ribosomal subunits is suppressed. Rpl26 overexpressed from a plasmid can be detected in the nucleolus and nucleoplasm, but it largely fails to assemble into ribosomes and is rapidly degraded. However, if the endogenous RPL26 loci are deleted, plasmid-encoded Rpl26 assembles into ribosomes and localizes to the cytosol. Chemical and genetic perturbation studies indicate that overexpressed ribosomal proteins are degraded by the ubiquitin-proteasome system and not by autophagy. Inhibition of the proteasome led to accumulation of multiple endogenous ribosomal proteins in insoluble aggregates, consistent with the operation of this QC mechanism in the absence of ribosomal protein overexpression. Our studies reveal that ribosomal proteins that fail to assemble into ribosomes are rapidly distinguished from their assembled counterparts and ubiquitinated and degraded within the nuclear compartment.

Selected heterozygosity at cis-regulatory sequences increases the expression homogeneity of a cell population in humans.

BACKGROUND: Examples of heterozygote advantage in humans are scarce and limited to protein-coding sequences. Here, we attempt a genome-wide functional inference of advantageous heterozygosity at cis-regulatory regions. RESULTS: The single-nucleotide polymorphisms bearing the signatures of balancing selection are enriched in active cis-regulatory regions of immune cells and epithelial cells, the latter of which provide barrier function and innate immunity. Examples associated with ancient trans-specific balancing selection are also discovered. Allelic imbalance in chromatin accessibility and divergence in transcription factor motif sequences indicate that these balanced polymorphisms cause distinct regulatory variation. However, a majority of these variants show no association with the expression level of the target gene. Instead, single-cell experimental data for gene expression and chromatin accessibility demonstrate that heterozygous sequences can lower cell-to-cell variability in proportion to selection strengths. This negative correlation is more pronounced for highly expressed genes and consistently observed when using different data and methods. Based on mathematical modeling, we hypothesize that extrinsic noise from fluctuations in transcription factor activity may be amplified in homozygotes, whereas it is buffered in heterozygotes. While high expression levels are coupled with intrinsic noise reduction, regulatory heterozygosity can contribute to the suppression of extrinsic noise. CONCLUSIONS: This mechanism may confer a selective advantage by increasing cell population homogeneity and thereby enhancing the collective action of the cells, especially of those involved in the defense systems in humans.

Proteome-wide remodeling of protein location and function by stress.

Protein location and function can change dynamically depending on many factors, including environmental stress, disease state, age, developmental stage, and cell type. Here, we describe an integrative computational framework, called the conditional function predictor (CoFP; http://nbm.ajou.ac.kr/cofp/), for predicting changes in subcellular location and function on a proteome-wide scale. The essence of the CoFP approach is to cross-reference general knowledge about a protein and its known network of physical interactions, which typically pool measurements from diverse environments, against gene expression profiles that have been measured under specific conditions of interest. Using CoFP, we predict condition-specific subcellular locations, biological processes, and molecular functions of the yeast proteome under 18 specified conditions. In addition to highly accurate retrieval of previously known gold standard protein locations and functions, CoFP predicts previously unidentified condition-dependent locations and functions for nearly all yeast proteins. Many of these predictions can be confirmed using high-resolution cellular imaging. We show that, under DNA-damaging conditions, Tsr1, Caf120, Dip5, Skg6, Lte1, and Nnf2 change subcellular location and RNA polymerase I subunit A43, Ino2, and Ids2 show changes in DNA binding. Beyond specific predictions, this work reveals a global landscape of changing protein location and function, highlighting a surprising number of proteins that translocate from the mitochondria to the nucleus or from endoplasmic reticulum to Golgi apparatus under stress.

Patterns of gene expression associated with Pten deficiency in the developing inner ear.

In inner ear development, phosphatase and tensin homolog (PTEN) is necessary for neuronal maintenance, such as neuronal survival and accurate nerve innervations of hair cells. We previously reported that Pten conditional knockout (cKO) mice exhibited disorganized fasciculus with neuronal apoptosis in spiral ganglion neurons (SGNs). To better understand the genes and signaling networks related to auditory neuron maintenance, we compared the profiles of differentially expressed genes (DEGs) using microarray analysis of the inner ear in E14.5 Pten cKO and wild-type mice. We identified 46 statistically significant transcripts using significance analysis of microarrays, with the false-discovery rate set at 0%. Among the DEGs, expression levels of candidate genes and expression domains were validated by quantitative real-time RT-PCR and in situ hybridization, respectively. Ingenuity pathway analysis using DEGs identified significant signaling networks associated with apoptosis, cellular movement, and axon guidance (i.e., secreted phosphoprotein 1 (Spp1)-mediated cellular movement and regulator of G-protein signaling 4 (Rgs4)-mediated axon guidance). This result was consistent with the phenotypic defects of SGNs in Pten cKO mice (e.g., neuronal apoptosis, abnormal migration, and irregular nerve fiber patterns of SGNs). From this study, we suggest two key regulatory signaling networks mediated by Spp1 and Rgs4, which may play potential roles in neuronal differentiation of developing auditory neurons.

A recurrent inactivating mutation in RHOA GTPase in angioimmunoblastic T cell lymphoma.

The molecular mechanisms underlying angioimmunoblastic T cell lymphoma (AITL), a common type of mature T cell lymphoma of poor prognosis, are largely unknown. Here we report a frequent somatic mutation in RHOA (encoding p.Gly17Val) using exome and transcriptome sequencing of samples from individuals with AITL. Further examination of the RHOA mutation encoding p.Gly17Val in 239 lymphoma samples showed that the mutation was specific to T cell lymphoma and was absent from B cell lymphoma. We demonstrate that the RHOA mutation encoding p.Gly17Val, which was found in 53.3% (24 of 45) of the AITL cases examined, is oncogenic in nature using multiple molecular assays. Molecular modeling and docking simulations provided a structural basis for the loss of GTPase activity in the RHOA Gly17Val mutant. Our experimental data and modeling results suggest that the RHOA mutation encoding p.Gly17Val is a driver mutation in AITL. On the basis of these data and through integrated pathway analysis, we build a comprehensive signaling network for AITL oncogenesis.

Understanding Epistatic Interactions between Genes Targeted by Non-coding Regulatory Elements in Complex Diseases.

Genome-wide association studies have proven the highly polygenic architecture of complex diseases or traits; therefore, single-locus-based methods are usually unable to detect all involved loci, especially when individual loci exert small effects. Moreover, the majority of associated single-nucleotide polymorphisms resides in non-coding regions, making it difficult to understand their phenotypic contribution. In this work, we studied epistatic interactions associated with three common diseases using Korea Association Resource (KARE) data: type 2 diabetes mellitus (DM), hypertension (HT), and coronary artery disease (CAD). We showed that epistatic single-nucleotide polymorphisms (SNPs) were enriched in enhancers, as well as in DNase I footprints (the Encyclopedia of DNA Elements [ENCODE] Project Consortium 2012), which suggested that the disruption of the regulatory regions where transcription factors bind may be involved in the disease mechanism. Accordingly, to identify the genes affected by the SNPs, we employed whole-genome multiple-cell-type enhancer data which discovered using DNase I profiles and Cap Analysis Gene Expression (CAGE). Assigned genes were significantly enriched in known disease associated gene sets, which were explored based on the literature, suggesting that this approach is useful for detecting relevant affected genes. In our knowledge-based epistatic network, the three diseases share many associated genes and are also closely related with each other through many epistatic interactions. These findings elucidate the genetic basis of the close relationship between DM, HT, and CAD.

Genome-wide bimolecular fluorescence complementation analysis of SUMO interactome in yeast.

The definition of protein-protein interactions (PPIs) in the natural cellular context is essential for properly understanding various biological processes. So far, however, most large-scale PPI analyses have not been performed in the natural cellular context. Here, we describe the construction of a Saccharomyces cerevisiae fusion library in which each endogenous gene is C-terminally tagged with the N-terminal fragment of Venus (VN) for a genome-wide bimolecular fluorescence complementation assay, a powerful technique for identifying PPIs in living cells. We illustrate the utility of the VN fusion library by systematically analyzing the interactome of the small ubiquitin-related modifier (SUMO) and provide previously unavailable information on the subcellular localization, types, and protease dependence of SUMO interactions. Our data set is highly complementary to the existing data sets and represents a useful resource for expanding the understanding of the physiological roles of SUMO. In addition, the VN fusion library provides a useful research tool that makes it feasible to systematically analyze PPIs in the natural cellular context.

Nsi1 plays a significant role in the silencing of ribosomal DNA in Saccharomyces cerevisiae.

In eukaryotic cells, ribosomal DNA (rDNA) forms the basis of the nucleolus. In Saccharomyces cerevisiae, 100-200 copies of a 9.1-kb rDNA repeat exist as a tandem array on chromosome XII. The stability of this highly repetitive array is maintained through silencing. However, the precise mechanisms that regulate rDNA silencing are poorly understood. Here, we report that S. cerevisiae Ydr026c, which we name NTS1 silencing protein 1 (Nsi1), plays a significant role in rDNA silencing. By studying the subcellular localization of 159 nucleolar proteins, we identified 11 proteins whose localization pattern is similar to that of Net1, a well-established rDNA silencing factor. Among these proteins is Nsi1, which is associated with the NTS1 region of rDNA and is required for rDNA silencing at NTS1. In addition, Nsi1 physically interacts with the known rDNA silencing factors Net1, Sir2 and Fob1. The loss of Nsi1 decreases the association of Sir2 with NTS1 and increases histone acetylation at NTS1. Furthermore, Nsi1 contributes to the longevity of yeast cells. Taken together, our findings suggest that Nsi1 is a new rDNA silencing factor that contributes to rDNA stability and lifespan extension in S. cerevisiae.

Rewiring of genetic networks in response to DNA damage.

Although cellular behaviors are dynamic, the networks that govern these behaviors have been mapped primarily as static snapshots. Using an approach called differential epistasis mapping, we have discovered widespread changes in genetic interaction among yeast kinases, phosphatases, and transcription factors as the cell responds to DNA damage. Differential interactions uncover many gene functions that go undetected in static conditions. They are very effective at identifying DNA repair pathways, highlighting new damage-dependent roles for the Slt2 kinase, Pph3 phosphatase, and histone variant Htz1. The data also reveal that protein complexes are generally stable in response to perturbation, but the functional relations between these complexes are substantially reorganized. Differential networks chart a new type of genetic landscape that is invaluable for mapping cellular responses to stimuli.

In vivo quantification of protein-protein interactions in Saccharomyces cerevisiae using bimolecular fluorescence complementation assay.

Most of the biological processes are carried out and regulated by dynamic networks of protein-protein interactions. In this study, we demonstrate the feasibility of the bimolecular fluorescence complementation (BiFC) assay for in vivo quantitative analysis of protein-protein interactions in Saccharomyces cerevisiae. We show that the BiFC assay can be used to quantify not only the amount but also the cell-to-cell variation of protein-protein interactions in S. cerevisiae. In addition, we show that protein sumoylation and condition-specific protein-protein interactions can be quantitatively analyzed by using the BiFC assay. Taken together, our results validate that the BiFC assay is a very effective method for quantitative analysis of protein-protein interactions in living yeast cells and has a great potential as a versatile tool for the study of protein function.

Potent modulation of P-glycoprotein activity by naturally occurring phenylbutenoids from Zingiber cassumunar.

Five phenylbutenoid derivatives from the rhizomes of Zingiber cassumunar Roxb. (Zingiberaceae) were evaluated for their P-glycoprotein (P-gp) inhibitory effects in a P-gp over-expressing multidrug resistant (MDR) human breast cancer cell line, MCF-7/ADR. As a result, a phenylbutenoid dimer, (+/-)-trans-3-(3,4-dimethoxyphenyl)-4-[(E)-3,4-dimethoxystyryl]cyclohex-1-ene (1), exhibited highly potent P-gp inhibitory activity, decreasing the IC(50) value of daunomycin (DNM) to 4.31 +/- 0.40 microm in the cells (DNM IC(50) = 37.1 +/- 0.59 microm). The positive control, verapamil decreased the IC(50) value of DNM to 6.94 +/- 0.40 microm. Three phenylbutenoid monomers, 2-4 from this plant, also resulted in a significant decrease in the IC(50) values of DNM compared with the control. In particular, compound 1 markedly enhanced [(3)H]-DNM accumulation and significantly reduced [(3)H]-DNM efflux compared with the control, and this effect was more potent than that of verapamil, a well-known P-gp inhibitor. These results suggest that compound 1 of Z. cassumunar can be developed as a potent chemo-sensitizing agent that reverses P-gp-mediated MDR in human cancer chemotherapy.