Is it time to reassess variant annotation?

Variant annotation is one of the most essential steps in selecting candidates for further investigation. With the advancement in functional genomics, new variant annotation tools focus on annotation based on empirically generated data instead of theoretically based predictions. This is a direct result of the large national and international consortia that generated enormous experiment-based or validated data at multiple omics levels. Here, we highlight the recent empirically based annotation methods and discuss their strengths and weaknesses.

How T118M peripheral myelin protein 22 predisposes humans to Charcot-Marie-Tooth disease.

Data from gnomAD indicate that a missense mutation encoding the T118M variation in human peripheral myelin protein 22 (PMP22) is found in roughly one of every 75 genomes of western European lineage (1:120 in the overall human population). It is unusual among PMP22 variants that cause Charcot-Marie-Tooth (CMT) disease in that it is not 100% penetrant. Here, we conducted cellular and biophysical studies to determine why T118M PMP22 predisposes humans to CMT, but with only incomplete penetrance. We found that T118M PMP22 is prone to mistraffic but differs even from the WT protein in that increased expression levels do not result in a reduction in trafficking efficiency. Moreover, the T118M mutant exhibits a reduced tendency to form large intracellular aggregates relative to other disease mutants and even WT PMP22. NMR spectroscopy revealed that the structure and dynamics of T118M PMP22 resembled those of WT. These results show that the main consequence of T118M PMP22 in WT/T118M heterozygous individuals is a reduction in surface-trafficked PMP22, unaccompanied by formation of toxic intracellular aggregates. This explains the incomplete disease penetrance and the mild neuropathy observed for WT/T118M CMT cases. We also analyzed BioVU, a biobank linked to deidentified electronic medical records, and found a statistically robust association of the T118M mutation with the occurrence of long and/or repeated episodes of carpal tunnel syndrome. Collectively, our results illuminate the cellular effects of the T118M PMP22 variation leading to CMT disease and indicate a second disorder for which it is a risk factor.

Pharmacogenetics of weight gain following switch from efavirenz- to integrase inhibitor-containing regimens.

BACKGROUND: Excessive weight gain affects some persons with HIV after switching to integrase strand transfer inhibitor (INSTI)-containing antiretroviral therapy (ART). We studied associations between CYP2B6 genotype and weight gain after ART switch among ACTG A5001 and A5322 participants. METHODS: Eligible participants switched from efavirenz- to INSTI-containing ART, had genotype data, and had weight data at least once from 4 weeks to 2 years post-switch. Multivariable linear mixed effects models adjusted for race/ethnicity, CD4, age, BMI and INSTI type assessed relationships between CYP2B6 genotype and estimated differences in weight change. RESULTS: A total of 159 eligible participants switched ART from 2007 to 2019, of whom 138 had plasma HIV-1 RNA < 200 copies/mL (65 CYP2B6 normal, 56 intermediate, 17 poor metabolizers). Among participants with switch HIV-1 RNA < 200 copies/mL, weight increased in all 3 CYP2B6 groups. The rate of weight gain was greater in CYP2B6 poor than in CYP2B6 normal metabolizers overall, and within 9 subgroups (male, female, White, Black, Hispanic, dolutegravir, elvitegravir, raltegravir, and TDF in the pre-switch regimen); only in Hispanic and elvitegravir subgroups were these associations statistically significant ( P  < 0.05). Compared to normal metabolizers, CYP2B6 intermediate status was not consistently associated with weight gain. CONCLUSION: CYP2B6 poor metabolizer genotype was associated with greater weight gain after switch from efavirenz- to INSTI-containing ART, but results were inconsistent. Weight gain in this setting is likely complex and multifactorial.

Fate or coincidence: do COPD and major depression share genetic risk factors?

Major depressive disorder (MDD) is a common comorbidity in chronic obstructive pulmonary disease (COPD), affecting up to 57% of patients with COPD. Although the comorbidity of COPD and MDD is well established, the causal relationship between these two diseases is unclear. A large-scale electronic health record clinical biobank and genome-wide association study summary statistics for MDD and lung function traits were used to investigate potential shared underlying genetic susceptibility between COPD and MDD. Linkage disequilibrium score regression was used to estimate genetic correlation between phenotypes. Polygenic risk scores (PRS) for MDD and lung function traits were developed and used to perform a phenome-wide association study (PheWAS). Multi-trait-based conditional and joint analysis identified single-nucleotide polymorphisms (SNPs) influencing both lung function and MDD. We found genetic correlations between MDD and all lung function traits were small and not statistically significant. A PRS-MDD was significantly associated with an increased risk of COPD in a PheWAS [odds ratio (OR) = 1.12, 95% confidence interval (CI): 1.09-1.16] when adjusting for age, sex and genetic ancestry, but this relationship became attenuated when controlling for smoking history (OR = 1.08, 95% CI: 1.04-1.13). No significant associations were found between the lung function PRS and MDD. Multi-trait-based conditional and joint analysis identified three SNPs that may contribute to both traits, two of which were previously associated with mood disorders and COPD. Our findings suggest that the observed relationship between COPD and MDD may not be driven by a strong shared genetic architecture.

Alternative Applications of Genotyping Array Data Using Multivariant Methods.

One of the forerunners that pioneered the revolution of high-throughput genomic technologies is the genotyping microarray technology, which can genotype millions of single-nucleotide variants simultaneously. Owing to apparent benefits, such as high speed, low cost, and high throughput, the genotyping array has gained lasting applications in genome-wide association studies (GWAS) and thus accumulated an enormous amount of data. Empowered by continuous manufactural upgrades and analytical innovation, unconventional applications of genotyping array data have emerged to address more diverse genetic problems, holding promise of boosting genetic research into human diseases through the re-mining of the rich accumulated data. Here, we review several unconventional genotyping array analysis techniques that have been built on the idea of large-scale multivariant analysis and provide empirical application examples. These unconventional outcomes of genotyping arrays include polygenic score, runs of homozygosity (ROH)/heterozygosity ratio, distant pedigree computation, and mitochondrial DNA (mtDNA) copy number inference.

Cancer-specific expression quantitative loci are affected by expression dysregulation.

Expression quantitative trait loci (eQTLs) have been touted as the missing piece that can bridge the gap between genetic variants and phenotypes. Over the past decade, we have witnessed a sharp rise of effort in the identification and application of eQTLs. The successful application of eQTLs relies heavily on their reproducibility. The current eQTL databases such as Genotype-Tissue Expression (GTEx) were populated primarily with eQTLs deriving from germline single nucleotide polymorphisms and normal tissue gene expression. The novel scenarios that employ eQTL models for prediction purposes often involve disease phenotypes characterized by altered gene expressions. To evaluate eQTL reproducibility across diverse data sources and the effect of disease-specific gene expression alteration on eQTL identification, we conducted an eQTL study using 5178 samples from The Cancer Genome Atlas (TCGA). We found that the reproducibility of eQTLs between normal and tumor tissues was low in terms of the number of shared eQTLs. However, among the shared eQTLs, the effect directions were generally concordant. This suggests that the source of the gene expression (normal or tumor tissue) has a strong effect on the detectable eQTLs and the effect direction of the eQTLs. Additional analyses demonstrated good directional concordance of eQTLs between GTEx and TCGA. Furthermore, we found that multi-tissue eQTLs may exert opposite effects across multiple tissue types. In summary, our results suggest that eQTL prediction models need to carefully address tissue and disease dependency of eQTLs. Tissue-disease-specific eQTL databases can afford more accurate prediction models for future studies.

MutEx: a multifaceted gateway for exploring integrative pan-cancer genomic data.

Somatic mutation and gene expression dysregulation are considered two major tumorigenesis factors. While independent investigations of either factor pervade, studies of associations between somatic mutations and gene expression changes have been sporadic and nonsystematic. Utilizing genomic data collected from 11 315 subjects of 33 distinct cancer types, we constructed MutEx, a pan-cancer integrative genomic database. This database records the relationships among gene expression, somatic mutation and survival data for cancer patients. MutEx can be used to swiftly explore the relationship between these genomic/clinic features within and across cancer types and, more importantly, search for corroborating evidence for hypothesis inception. Our database also incorporated Gene Ontology and several pathway databases to enhance functional annotation, and elastic net and a gene expression composite score to aid in survival analysis. To demonstrate the usability of MutEx, we provide several application examples, including top somatic mutations associated with the most extensive expression dysregulation in breast cancer, differential mutational burden downstream of DNA mismatch repair gene mutations and composite gene expression score-based survival difference in breast cancer. MutEx can be accessed at http://www.innovebioinfo.com/Databases/Mutationdb_About.php.

EditPredict: Prediction of RNA editable sites with convolutional neural network.

RNA editing exerts critical impacts on numerous biological processes. While millions of RNA editings have been identified in humans, much more are expected to be discovered. In this work, we constructed Convolutional Neural Network (CNN) models to predict human RNA editing events in both Alu regions and non-Alu regions. With a validation dataset resulting from CRISPR/Cas9 knockout of the ADAR1 enzyme, the validation accuracies reached 99.5% and 93.6% for Alu and non-Alu regions, respectively. We ported our CNN models in a web service named EditPredict. EditPredict not only works on reference genome sequences but can also take into consideration single nucleotide variants in personal genomes. In addition to the human genome, EditPredict tackles other model organisms including bumblebee, fruitfly, mouse, and squid genomes. EditPredict can be used stand-alone to predict novel RNA editing and it can be used to assist in filtering for candidate RNA editing detected from RNA-Seq data.

Mitochondrial DNA haplogroups and domain-specific neurocognitive performance in adults with HIV.

Neurocognitive (NC) impairment (NCI) is an important cause of morbidity in persons with HIV (PWH). In the high-energy environment of the central nervous system, mitochondria contribute to neuroinflammation and aging, which may ultimately drive the pathogenesis of neurodegenerative diseases. Mitochondrial DNA (mtDNA) haplogroups are associated with health outcomes in PWH. For example, we previously observed less global NCI in Hispanic ancestry PWH having mtDNA haplogroup B. Another study reported increased NCI among PWH having African subhaplogroup L2a. We therefore analyzed NC domains in relation to these haplogroups in CNS HIV Antiretroviral Therapy Effects Research (CHARTER), a multi-site observational neuro-HIV study. Haplogroups were assigned using mtDNA sequence in 1016 PWH. Outcomes were NCI, defined by domain deficit score and mean T-scores (TS) for seven NC domains. Ancestry-stratified analyses of NC performance included Wilcoxon rank sum, χ2, and Fisher's exact tests. Multivariable regression adjusted for NC comorbidity, antiretroviral therapy use, and nadir CD4+ T cells. Among 98 Hispanic ancestry PWH, executive function, learning, and recall performance were better with haplogroup B (N = 17) than other haplogroups. With adjustment for covariates, haplogroup B remained associated with better executive function (p = 0.04) and recall TS (p = 0.03). PWH with haplogroup B had fewer impaired domains than other haplogroups (p < 0.01). Subhaplogroup L2a (N = 89) was associated with greater NCI in learning, recall, and working memory among 478 PWH of African ancestry, and had more impaired domains than other subhaplogroups (p < 0.01). These findings may inform risk stratification for NCI and studies to define mechanisms by which mtDNA variation may influence NCI in PWH.

Non-canonical RNA-DNA differences and other human genomic features are enriched within very short tandem repeats.

Very short tandem repeats bear substantial genetic, evolutional, and pathological significance in genome analyses. Here, we compiled a census of tandem mono-nucleotide/di-nucleotide/tri-nucleotide repeats (MNRs/DNRs/TNRs) in GRCh38, which we term "polytracts" in general. Of the human genome, 144.4 million nucleotides (4.7%) are occupied by polytracts, and 0.47 million single nucleotides are identified as polytract hinges, i.e., break-points of tandem polytracts. Preliminary exploration of the census suggested polytract hinge sites and boundaries of AAC polytracts may bear a higher mapping error rate than other polytract regions. Further, we revealed landscapes of polytract enrichment with respect to nearly a hundred genomic features. We found MNRs, DNRs, and TNRs displayed noticeable difference in terms of locational enrichment for miscellaneous genomic features, especially RNA editing events. Non-canonical and C-to-U RNA-editing events are enriched inside and/or adjacent to MNRs, while all categories of RNA-editing events are under-represented in DNRs. A-to-I RNA-editing events are generally under-represented in polytracts. The selective enrichment of non-canonical RNA-editing events within MNR adjacency provides a negative evidence against their authenticity. To enable similar locational enrichment analyses in relation to polytracts, we developed a software Polytrap which can handle 11 reference genomes. Additionally, we compiled polytracts of four model organisms into a Track Hub which can be integrated into USCS Genome Browser as an official track for convenient visualization of polytracts.

African Mitochondrial DNA Haplogroup L2 Is Associated With Slower Decline of ß-cell Function and Lower Incidence of Diabetes Mellitus in Non-Hispanic, Black Women Living With Human Immunodeficiency Virus.

BACKGROUND: Susceptibility to metabolic diseases may be influenced by mitochondrial genetic variability among people living with human immunodeficiency virus (HIV; PLWH), but remains unexplored in populations with African ancestry. We investigated the association between mitochondrial DNA (mtDNA) haplogroups and the homeostatic model assessments of ß-cell function (HOMA-B) and insulin resistance (HOMA-IR), as well as incident diabetes mellitus (DM), among Black women living with or at risk for HIV. METHODS: Women without DM who had fasting glucose (FG) and insulin (FI) data for ≥2 visits were included. Haplogroups were inferred from genotyping data using HaploGrep. HOMA-B and HOMA-IR were calculated using FG and FI data. Incident DM was defined by a combination of FG ≥ 126 mg/dL, the use of DM medication, a DM diagnosis, or hemoglobin A1c ≥ 6.5%. We compared HOMA-B, HOMA-IR, and incident DM by haplogroups and assessed the associations between HOMA-B and HOMA-IR and DM by haplogroup. RESULTS: Of 1288 women (933 living with HIV and 355 living without HIV), PLWH had higher initial HOMA-B and HOMA-IR than people living without HIV. PLWH with haplogroup L2 had a slower decline in HOMA-B per year (Pinteraction = .02) and a lower risk of incident DM (hazard ratio [HR], 0.51; 95% confidence interval [CI], .32-.82) than PLWH with other haplogroups after adjustments for age, body mass index, combination antiretroviral therapy use, CD4 cell counts, and HIV RNA. The impact of HOMA-IR on incident DM was less significant in those with haplogroup L2, compared to non-L2 (HR, 1.28 [95% CI, .70-2.38] vs 4.13 [95% CI, 3.28-5.22], respectively; Pinteraction < .01), among PLWH. CONCLUSIONS: Mitochondrial genetic variation is associated with ß-cell functions and incident DM in non-Hispanic, Black women with HIV and alters the relationship between insulin resistance and DM.

Strategies for processing and quality control of Illumina genotyping arrays.

Illumina genotyping arrays have powered thousands of large-scale genome-wide association studies over the past decade. Yet, because of the tremendous volume and complicated genetic assumptions of Illumina genotyping data, processing and quality control (QC) of these data remain a challenge. Thorough QC ensures the accurate identification of single-nucleotide polymorphisms and is required for the correct interpretation of genetic association results. By processing genotyping data on > 100 000 subjects from >10 major Illumina genotyping arrays, we have accumulated extensive experience in handling some of the most peculiar scenarios related to the processing and QC of Illumina genotyping data. Here, we describe strategies for processing Illumina genotyping data from the raw data to an analysis ready format, and we elaborate on the necessary QC procedures required at each processing step. High-quality Illumina genotyping data sets can be obtained by following our detailed QC strategies.

Power and sample size calculations for high-throughput sequencing-based experiments.

Power/sample size (power) analysis estimates the likelihood of successfully finding the statistical significance in a data set. There has been a growing recognition of the importance of power analysis in the proper design of experiments. Power analysis is complex, yet necessary for the success of large studies. It is important to design a study that produces statistically accurate and reliable results. Power computation methods have been well established for both microarray-based gene expression studies and genotyping microarray-based genome-wide association studies. High-throughput sequencing (HTS) has greatly enhanced our ability to conduct biomedical studies at the highest possible resolution (per nucleotide). However, the complexity of power computations is much greater for sequencing data than for the simpler genotyping array data. Research on methods of power computations for HTS-based studies has been recently conducted but is not yet well known or widely used. In this article, we describe the power computation methods that are currently available for a range of HTS-based studies, including DNA sequencing, RNA-sequencing, microbiome sequencing and chromatin immunoprecipitation sequencing. Most importantly, we review the methods of power analysis for several types of sequencing data and guide the reader to the relevant methods for each data type.

Postoperative Critical Events Associated With Obstructive Sleep Apnea: Results From the Society of Anesthesia and Sleep Medicine Obstructive Sleep Apnea Registry.

BACKGROUND: Obstructive sleep apnea (OSA) patients are at increased risk for pulmonary and cardiovascular complications; perioperative mortality risk is unclear. This report analyzes cases submitted to the OSA Death and Near Miss Registry, focusing on factors associated with poor outcomes after an OSA-related event. We hypothesized that more severe outcomes would be associated with OSA severity, less intense monitoring, and higher cumulative opioid doses. METHODS: Inclusion criteria were age ≥18 years, OSA diagnosed or suspected, event related to OSA, and event occurrence 1992 or later and <30 days postoperatively. Factors associated with death or brain damage versus other critical events were analyzed by tests of association and odds ratios (OR; 95% confidence intervals [CIs]). RESULTS: Sixty-six cases met inclusion criteria with known OSA diagnosed in 55 (83%). Patients were middle aged (mean = 53, standard deviation [SD] = 15 years), American Society of Anesthesiologists (ASA) III (59%, n = 38), and obese (mean body mass index [BMI] = 38, SD = 9 kg/m); most had inpatient (80%, n = 51) and elective (90%, n = 56) procedures with general anesthesia (88%, n = 58). Most events occurred on the ward (56%, n = 37), and 14 (21%) occurred at home. Most events (76%, n = 50) occurred within 24 hours of anesthesia end. Ninety-seven percent (n = 64) received opioids within the 24 hours before the event, and two-thirds (41 of 62) also received sedatives. Positive airway pressure devices and/or supplemental oxygen were in use at the time of critical events in 7.5% and 52% of cases, respectively. Sixty-five percent (n = 43) of patients died or had brain damage; 35% (n = 23) experienced other critical events. Continuous central respiratory monitoring was in use for 3 of 43 (7%) of cases where death or brain damage resulted. Death or brain damage was (1) less common when the event was witnessed than unwitnessed (OR = 0.036; 95% CI, 0.007-0.181; P < .001); (2) less common with supplemental oxygen in place (OR = 0.227; 95% CI, 0.070-0.740; P = .011); (3) less common with respiratory monitoring versus no monitoring (OR = 0.109; 95% CI, 0.031-0.384; P < .001); and (4) more common in patients who received both opioids and sedatives than opioids alone (OR = 4.133; 95% CI, 1.348-12.672; P = .011). No evidence for an association was observed between outcomes and OSA severity or cumulative opioid dose. CONCLUSIONS: Death and brain damage were more likely to occur with unwitnessed events, no supplemental oxygen, lack of respiratory monitoring, and coadministration of opioids and sedatives. It is important that efforts be directed at providing more effective monitoring for OSA patients following surgery, and clinicians consider the potentially dangerous effects of opioids and sedatives-especially when combined-when managing OSA patients postoperatively.

Quality and concordance of genotyping array data of 12,064 samples from 5840 cancer patients.

Genotyping arrays characterize genome-wide SNPs for a study cohort and were the primary technology behind genome wide association studies over the last decade. The Cancer Genome Atlas (TCGA) is one of the largest cancer consortium studies, and it collected genotyping data for all of its participants. Using TCGA SNP data genotyped using the Affymetrix 6.0 SNP array from 12,064 samples, we conducted a comprehensive comparisons across DNA sources (tumor tissue, normal tissue, and blood) and sample storage protocols (formalin-fixed paraffin-embedded (FFPE) vs. freshly frozen (FF)), examining genotypes, transition/transversion ratios, and mutation catalogues. During the analysis, we made important observations in relevance to the data quality issues. SNP concordance was excellent between blood and normal tissues, and slightly lower between blood and tumor tissue due to potential somatic mutations in the tumors. The observed poor SNP concordance between FFPE and FF samples suggested a batch effect. The transition/transversion ratio, a metric commonly used for quality control purpose in exome sequencing projects, appeared less applicable for genotyping array data due to the whole-genome coverage built into the array design. Moreover, there were substantially more loss of heterozygosity events than gain of heterozygosity when comparing tumors relative to normal tissues and blood. This might be a consequence of extensive copy number deletions in tumors. In summary, our thorough evaluation calls for more adequate quality control practices and provides guidelines for improved application of TCGA genotyping data.

Architectures and accuracy of artificial neural network for disease classification from omics data.

BACKGROUND: Deep learning has made tremendous successes in numerous artificial intelligence applications and is unsurprisingly penetrating into various biomedical domains. High-throughput omics data in the form of molecular profile matrices, such as transcriptomes and metabolomes, have long existed as a valuable resource for facilitating diagnosis of patient statuses/stages. It is timely imperative to compare deep learning neural networks against classical machine learning methods in the setting of matrix-formed omics data in terms of classification accuracy and robustness. RESULTS: Using 37 high throughput omics datasets, covering transcriptomes and metabolomes, we evaluated the classification power of deep learning compared to traditional machine learning methods. Representative deep learning methods, Multi-Layer Perceptrons (MLP) and Convolutional Neural Networks (CNN), were deployed and explored in seeking optimal architectures for the best classification performance. Together with five classical supervised classification methods (Linear Discriminant Analysis, Multinomial Logistic Regression, Naïve Bayes, Random Forest, Support Vector Machine), MLP and CNN were comparatively tested on the 37 datasets to predict disease stages or to discriminate diseased samples from normal samples. MLPs achieved the highest overall accuracy among all methods tested. More thorough analyses revealed that single hidden layer MLPs with ample hidden units outperformed deeper MLPs. Furthermore, MLP was one of the most robust methods against imbalanced class composition and inaccurate class labels. CONCLUSION: Our results concluded that shallow MLPs (of one or two hidden layers) with ample hidden neurons are sufficient to achieve superior and robust classification performance in exploiting numerical matrix-formed omics data for diagnosis purpose. Specific observations regarding optimal network width, class imbalance tolerance, and inaccurate labeling tolerance will inform future improvement of neural network applications on functional genomics data.

Mitochondrial DNA Haplogroups and Delirium During Sepsis.

OBJECTIVES: Studies suggest that mitochondrial dysfunction underlies some forms of sepsis-induced organ failure. We sought to test the hypothesis that variations in mitochondrial DNA haplogroup affect susceptibility to sepsis-associated delirium, a common manifestation of acute brain dysfunction during sepsis. DESIGN: Retrospective cohort study. SETTING: Medical and surgical ICUs at a large tertiary care center. PATIENTS: Caucasian and African American adults with sepsis. MEASUREMENTS AND MAIN RESULTS: We determined each patient's mitochondrial DNA haplogroup using single-nucleotide polymorphisms genotyping data in a DNA databank and extracted outcomes from linked electronic medical records. We then used zero-inflated negative binomial regression to analyze age-adjusted associations between mitochondrial DNA haplogroups and duration of delirium, identified using the Confusion Assessment Method for the ICU. Eight-hundred ten patients accounted for 958 sepsis admissions, with 802 (84%) by Caucasians and 156 (16%) by African Americans. In total, 795 patient admissions (83%) involved one or more days of delirium. The 7% of Caucasians belonging to mitochondrial DNA haplogroup clade IWX experienced more delirium than the 49% in haplogroup H, the most common Caucasian haplogroup (age-adjusted rate ratio for delirium 1.36; 95% CI, 1.13-1.64; p = 0.001). Alternatively, among African Americans the 24% in haplogroup L2 experienced less delirium than those in haplogroup L3, the most common African haplogroup (adjusted rate ratio for delirium 0.60; 95% CI, 0.38-0.94; p = 0.03). CONCLUSIONS: Variations in mitochondrial DNA are associated with development of and protection from delirium in Caucasians and African Americans during sepsis. Future studies are now required to determine whether mitochondrial DNA and mitochondrial dysfunction contribute to the pathogenesis of delirium during sepsis so that targeted treatments can be developed.

OSBPL10, RXRA and lipid metabolism confer African-ancestry protection against dengue haemorrhagic fever in admixed Cubans.

Ethnic groups can display differential genetic susceptibility to infectious diseases. The arthropod-born viral dengue disease is one such disease, with empirical and limited genetic evidence showing that African ancestry may be protective against the haemorrhagic phenotype. Global ancestry analysis based on high-throughput genotyping in admixed populations can be used to test this hypothesis, while admixture mapping can map candidate protective genes. A Cuban dengue fever cohort was genotyped using a 2.5 million SNP chip. Global ancestry was ascertained through ADMIXTURE and used in a fine-matched corrected association study, while local ancestry was inferred by the RFMix algorithm. The expression of candidate genes was evaluated by RT-PCR in a Cuban dengue patient cohort and gene set enrichment analysis was performed in a Thai dengue transcriptome. OSBPL10 and RXRA candidate genes were identified, with most significant SNPs placed in inferred weak enhancers, promoters and lncRNAs. OSBPL10 had significantly lower expression in Africans than Europeans, while for RXRA several SNPs may differentially regulate its transcription between Africans and Europeans. Their expression was confirmed to change through dengue disease progression in Cuban patients and to vary with disease severity in a Thai transcriptome dataset. These genes interact in the LXR/RXR activation pathway that integrates lipid metabolism and immune functions, being a key player in dengue virus entrance into cells, its replication therein and in cytokine production. Knockdown of OSBPL10 expression in THP-1 cells by two shRNAs followed by DENV2 infection tests led to a significant reduction in DENV replication, being a direct functional proof that the lower OSBPL10 expression profile in Africans protects this ancestry against dengue disease.

Genotoxic, Metabolic, and Oxidative Stresses Regulate the RNA Repair Operon of Salmonella enterica Serovar Typhimurium.

The σ54 regulon in Salmonella enterica serovar Typhimurium includes a predicted RNA repair operon encoding homologs of the metazoan Ro60 protein (Rsr), Y RNAs (YrlBA), RNA ligase (RtcB), and RNA 3'-phosphate cyclase (RtcA). Transcription from σ54-dependent promoters requires that a cognate bacterial enhancer binding protein (bEBP) be activated by a specific environmental or cellular signal; the cognate bEBP for the σ54-dependent promoter of the rsr-yrlBA-rtcBA operon is RtcR. To identify conditions that generate the signal for RtcR activation in S Typhimurium, transcription of the RNA repair operon was assayed under multiple stress conditions that result in nucleic acid damage. RtcR-dependent transcription was highly induced by the nucleic acid cross-linking agents mitomycin C (MMC) and cisplatin, and this activation was dependent on RecA. Deletion of rtcR or rtcB resulted in decreased cell viability relative to that of the wild type following treatment with MMC. Oxidative stress from peroxide exposure also induced RtcR-dependent transcription of the operon. Nitrogen limitation resulted in RtcR-independent increased expression of the operon; the effect of nitrogen limitation required NtrC. The adjacent toxin-antitoxin module, dinJ-yafQ, was cotranscribed with the RNA repair operon but was not required for RtcR activation, although YafQ endoribonuclease activated RtcR-dependent transcription. Stress conditions shown to induce expression the RNA repair operon of Escherichia coli (rtcBA) did not stimulate expression of the S Typhimurium RNA repair operon. Similarly, MMC did not induce expression of the E. colirtcBA operon, although when expressed in S Typhimurium, E. coli RtcR responds effectively to the unknown signal(s) generated there by MMC exposure.IMPORTANCE Homologs of the metazoan RNA repair enzymes RtcB and RtcA occur widely in eubacteria, suggesting a selective advantage. Although the enzymatic activities of the eubacterial RtcB and RtcA have been well characterized, the physiological roles remain largely unresolved. Here we report stress responses that activate expression of the σ54-dependent RNA repair operon (rsr-yrlBA-rtcBA) of S Typhimurium and demonstrate that expression of the operon impacts cell survival under MMC-induced stress. Characterization of the requirements for activation of this tightly regulated operon provides clues to the possible functions of operon components in vivo, enhancing our understanding of how this human pathogen copes with environmental stressors.