MagicalRsq-X: A cross-cohort transferable genotype imputation quality metric.

Since genotype imputation was introduced, researchers have been relying on the estimated imputation quality from imputation software to perform post-imputation quality control (QC). However, this quality estimate (denoted as Rsq) performs less well for lower-frequency variants. We recently published MagicalRsq, a machine-learning-based imputation quality calibration, which leverages additional typed markers from the same cohort and outperforms Rsq as a QC metric. In this work, we extended the original MagicalRsq to allow cross-cohort model training and named the new model MagicalRsq-X. We removed the cohort-specific estimated minor allele frequency and included linkage disequilibrium scores and recombination rates as additional features. Leveraging whole-genome sequencing data from TOPMed, specifically participants in the BioMe, JHS, WHI, and MESA studies, we performed comprehensive cross-cohort evaluations for predominantly European and African ancestral individuals based on their inferred global ancestry with the 1000 Genomes and Human Genome Diversity Project data as reference. Our results suggest MagicalRsq-X outperforms Rsq in almost every setting, with 7.3%-14.4% improvement in squared Pearson correlation with true R2, corresponding to 85-218 K variant gains. We further developed a metric to quantify the genetic distances of a target cohort relative to a reference cohort and showed that such metric largely explained the performance of MagicalRsq-X models. Finally, we found MagicalRsq-X saved up to 53 known genome-wide significant variants in one of the largest blood cell trait GWASs that would be missed using the original Rsq for QC. In conclusion, MagicalRsq-X shows superiority for post-imputation QC and benefits genetic studies by distinguishing well and poorly imputed lower-frequency variants.

Cell-Type Composition Affects Adipose Gene Expression Associations With Cardiometabolic Traits.

Understanding differences in adipose gene expression between individuals with different levels of clinical traits may reveal the genes and mechanisms leading to cardiometabolic diseases. However, adipose is a heterogeneous tissue. To account for cell-type heterogeneity, we estimated cell-type proportions in 859 subcutaneous adipose tissue samples with bulk RNA sequencing (RNA-seq) using a reference single-nuclear RNA-seq data set. Cell-type proportions were associated with cardiometabolic traits; for example, higher macrophage and adipocyte proportions were associated with higher and lower BMI, respectively. We evaluated cell-type proportions and BMI as covariates in tests of association between >25,000 gene expression levels and 22 cardiometabolic traits. For >95% of genes, the optimal, or best-fit, models included BMI as a covariate, and for 79% of associations, the optimal models also included cell type. After adjusting for the optimal covariates, we identified 2,664 significant associations (P ≤ 2e-6) for 1,252 genes and 14 traits. Among genes proposed to affect cardiometabolic traits based on colocalized genome-wide association study and adipose expression quantitative trait locus signals, 25 showed a corresponding association between trait and gene expression levels. Overall, these results suggest the importance of modeling cell-type proportion when identifying gene expression associations with cardiometabolic traits.

Gangs, violence, and fear: punitive Darwinism in El Salvador.

This article evaluates the factors impacting support for tough on crime policies in El Salvador. Examining theoretical and empirical scholarly work, we look at how fear, together with social and political contexts drive public appetite for punitive policies towards criminals. We show that President Nayib Bukele is responding to public opinion and has implemented tough on crime policies at the expense of human rights violations and democratic institutions. Society favors candidates who are the "toughest" against criminal actors. Political candidates from all sides of the ideological spectrum tap into the fear of the populace to win votes, leading to punitive Darwinism. We provide an empirical assessment of which theoretically relevant factors are statistically associated with punitivism in the Salvadoran context, using multiple regression analysis of high-quality public opinion survey data from LAPOP.

MagicalRsq: Machine-learning-based genotype imputation quality calibration.

Whole-genome sequencing (WGS) is the gold standard for fully characterizing genetic variation but is still prohibitively expensive for large samples. To reduce costs, many studies sequence only a subset of individuals or genomic regions, and genotype imputation is used to infer genotypes for the remaining individuals or regions without sequencing data. However, not all variants can be well imputed, and the current state-of-the-art imputation quality metric, denoted as standard Rsq, is poorly calibrated for lower-frequency variants. Here, we propose MagicalRsq, a machine-learning-based method that integrates variant-level imputation and population genetics statistics, to provide a better calibrated imputation quality metric. Leveraging WGS data from the Cystic Fibrosis Genome Project (CFGP), and whole-exome sequence data from UK BioBank (UKB), we performed comprehensive experiments to evaluate the performance of MagicalRsq compared to standard Rsq for partially sequenced studies. We found that MagicalRsq aligns better with true R2 than standard Rsq in almost every situation evaluated, for both European and African ancestry samples. For example, when applying models trained from 1,992 CFGP sequenced samples to an independent 3,103 samples with no sequencing but TOPMed imputation from array genotypes, MagicalRsq, compared to standard Rsq, achieved net gains of 1.4 million rare, 117k low-frequency, and 18k common variants, where net gains were gained numbers of correctly distinguished variants by MagicalRsq over standard Rsq. MagicalRsq can serve as an improved post-imputation quality metric and will benefit downstream analysis by better distinguishing well-imputed variants from those poorly imputed. MagicalRsq is freely available on GitHub.

TOP-LD: A tool to explore linkage disequilibrium with TOPMed whole-genome sequence data.

Current publicly available tools that allow rapid exploration of linkage disequilibrium (LD) between markers (e.g., HaploReg and LDlink) are based on whole-genome sequence (WGS) data from 2,504 individuals in the 1000 Genomes Project. Here, we present TOP-LD, an online tool to explore LD inferred with high-coverage (∼30×) WGS data from 15,578 individuals in the NHLBI Trans-Omics for Precision Medicine (TOPMed) program. TOP-LD provides a significant upgrade compared to current LD tools, as the TOPMed WGS data provide a more comprehensive representation of genetic variation than the 1000 Genomes data, particularly for rare variants and in the specific populations that we analyzed. For example, TOP-LD encompasses LD information for 150.3, 62.2, and 36.7 million variants for European, African, and East Asian ancestral samples, respectively, offering 2.6- to 9.1-fold increase in variant coverage compared to HaploReg 4.0 or LDlink. In addition, TOP-LD includes tens of thousands of structural variants (SVs). We demonstrate the value of TOP-LD in fine-mapping at the GGT1 locus associated with gamma glutamyltransferase in the African ancestry participants in UK Biobank. Beyond fine-mapping, TOP-LD can facilitate a wide range of applications that are based on summary statistics and estimates of LD. TOP-LD is freely available online.

A systematic evaluation of Hi-C data enhancement methods for enhancing PLAC-seq and HiChIP data.

The three-dimensional organization of chromatin plays a critical role in gene regulation. Recently developed technologies, such as HiChIP and proximity ligation-assisted ChIP-Seq (PLAC-seq) (hereafter referred to as HP for brevity), can measure chromosome spatial organization by interrogating chromatin interactions mediated by a protein of interest. While offering cost-efficiency over genome-wide unbiased high-throughput chromosome conformation capture (Hi-C) data, HP data remain sparse at kilobase (Kb) resolution with the current sequencing depth in the order of 108 reads per sample. Deep learning models, including HiCPlus, HiCNN, HiCNN2, DeepHiC and Variationally Encoded Hi-C Loss Enhancer (VEHiCLE), have been developed to enhance the sequencing depth of Hi-C data, but their performance on HP data has not been benchmarked. Here, we performed a comprehensive evaluation of HP data sequencing depth enhancement using models developed for Hi-C data. Specifically, we analyzed various HP data, including Smc1a HiChIP data of the human lymphoblastoid cell line GM12878, H3K4me3 PLAC-seq data of four human neural cell types as well as of mouse embryonic stem cells (mESC), and mESC CCCTC-binding factor (CTCF) PLAC-seq data. Our evaluations lead to the following three findings: (i) most models developed for Hi-C data achieve reasonable performance when applied to HP data (e.g. with Pearson correlation ranging 0.76-0.95 for pairs of loci within 300 Kb), and the enhanced datasets lead to improved statistical power for detecting long-range chromatin interactions, (ii) models trained on HP data outperform those trained on Hi-C data and (iii) most models are transferable across cell types. Our results provide a general guideline for HP data enhancement using existing methods designed for Hi-C data.

Leveraging TOPMed imputation server and constructing a cohort-specific imputation reference panel to enhance genotype imputation among cystic fibrosis patients.

Cystic fibrosis (CF) is a severe genetic disorder that can cause multiple comorbidities affecting the lungs, the pancreas, the luminal digestive system and beyond. In our previous genome-wide association studies (GWAS), we genotyped approximately 8,000 CF samples using a mixture of different genotyping platforms. More recently, the Cystic Fibrosis Genome Project (CFGP) performed deep (approximately 30×) whole genome sequencing (WGS) of 5,095 samples to better understand the genetic mechanisms underlying clinical heterogeneity among patients with CF. For mixtures of GWAS array and WGS data, genotype imputation has proven effective in increasing effective sample size. Therefore, we first performed imputation for the approximately 8,000 CF samples with GWAS array genotype using the Trans-Omics for Precision Medicine (TOPMed) freeze 8 reference panel. Our results demonstrate that TOPMed can provide high-quality imputation for patients with CF, boosting genomic coverage from approximately 0.3-4.2 million genotyped markers to approximately 11-43 million well-imputed markers, and significantly improving polygenic risk score (PRS) prediction accuracy. Furthermore, we built a CF-specific CFGP reference panel based on WGS data of patients with CF. We demonstrate that despite having approximately 3% the sample size of TOPMed, our CFGP reference panel can still outperform TOPMed when imputing some CF disease-causing variants, likely owing to allele and haplotype differences between patients with CF and general populations. We anticipate our imputed data for 4,656 samples without WGS data will benefit our subsequent genetic association studies, and the CFGP reference panel built from CF WGS samples will benefit other investigators studying CF.

Transcriptome-wide association study in UK Biobank Europeans identifies associations with blood cell traits.

Previous genome-wide association studies (GWAS) of hematological traits have identified over 10 000 distinct trait-specific risk loci. However, at these loci, the underlying causal mechanisms remain incompletely characterized. To elucidate novel biology and better understand causal mechanisms at known loci, we performed a transcriptome-wide association study (TWAS) of 29 hematological traits in 399 835 UK Biobank (UKB) participants of European ancestry using gene expression prediction models trained from whole blood RNA-seq data in 922 individuals. We discovered 557 gene-trait associations for hematological traits distinct from previously reported GWAS variants in European populations. Among the 557 associations, 301 were available for replication in a cohort of 141 286 participants of European ancestry from the Million Veteran Program. Of these 301 associations, 108 replicated at a strict Bonferroni adjusted threshold ($\alpha$= 0.05/301). Using our TWAS results, we systematically assigned 4261 out of 16 900 previously identified hematological trait GWAS variants to putative target genes. Compared to coloc, our TWAS results show reduced specificity and increased sensitivity in external datasets to assign variants to target genes.

A large-scale transcriptome-wide association study (TWAS) of 10 blood cell phenotypes reveals complexities of TWAS fine-mapping.

Hematological measures are important intermediate clinical phenotypes for many acute and chronic diseases and are highly heritable. Although genome-wide association studies (GWAS) have identified thousands of loci containing trait-associated variants, the causal genes underlying these associations are often uncertain. To better understand the underlying genetic regulatory mechanisms, we performed a transcriptome-wide association study (TWAS) to systematically investigate the association between genetically predicted gene expression and hematological measures in 54,542 Europeans from the Genetic Epidemiology Research on Aging cohort. We found 239 significant gene-trait associations with hematological measures; we replicated 71 associations at p < 0.05 in a TWAS meta-analysis consisting of up to 35,900 Europeans from the Women's Health Initiative, Atherosclerosis Risk in Communities Study, and BioMe Biobank. Additionally, we attempted to refine this list of candidate genes by performing conditional analyses, adjusting for individual variants previously associated with hematological measures, and performed further fine-mapping of TWAS loci. To facilitate interpretation of our findings, we designed an R Shiny application to interactively visualize our TWAS results by integrating them with additional genetic data sources (GWAS, TWAS from multiple reference panels, conditional analyses, known GWAS variants, etc.). Our results and application highlight frequently overlooked TWAS challenges and illustrate the complexity of TWAS fine-mapping.

HPRep: Quantifying Reproducibility in HiChIP and PLAC-Seq Datasets.

HiChIP and PLAC-Seq are emerging technologies for studying genome-wide long-range chromatin interactions mediated by the protein of interest, enabling more sensitive and cost-efficient interrogation of protein-centric chromatin conformation. However, due to the unbalanced read distribution introduced by protein immunoprecipitation, existing reproducibility measures developed for Hi-C data are not appropriate for the analysis of HiChIP and PLAC-Seq data. Here, we present HPRep, a stratified and weighted correlation metric derived from normalized contact counts, to quantify reproducibility in HiChIP and PLAC-Seq data. We applied HPRep to multiple real datasets and demonstrate that HPRep outperforms existing reproducibility measures developed for Hi-C data. Specifically, we applied HPRep to H3K4me3 PLAC-Seq data from mouse embryonic stem cells and mouse brain tissues as well as H3K27ac HiChIP data from human lymphoblastoid cell line GM12878 and leukemia cell line K562, showing that HPRep can more clearly separate among pseudo-replicates, real replicates, and non-replicates. Furthermore, in an H3K4me3 PLAC-Seq dataset consisting of 11 samples from four human brain cell types, HPRep demonstrated the expected clustering of data that could not be achieved by existing methods developed for Hi-C data, highlighting the need for a reproducibility metric tailored to HiChIP and PLAC-Seq data.

Transcriptome-Wide Association Study of Blood Cell Traits in African Ancestry and Hispanic/Latino Populations.

BACKGROUND: Thousands of genetic variants have been associated with hematological traits, though target genes remain unknown at most loci. Moreover, limited analyses have been conducted in African ancestry and Hispanic/Latino populations; hematological trait associated variants more common in these populations have likely been missed. METHODS: To derive gene expression prediction models, we used ancestry-stratified datasets from the Multi-Ethnic Study of Atherosclerosis (MESA, including n = 229 African American and n = 381 Hispanic/Latino participants, monocytes) and the Depression Genes and Networks study (DGN, n = 922 European ancestry participants, whole blood). We then performed a transcriptome-wide association study (TWAS) for platelet count, hemoglobin, hematocrit, and white blood cell count in African (n = 27,955) and Hispanic/Latino (n = 28,324) ancestry participants. RESULTS: Our results revealed 24 suggestive signals (p < 1 × 10-4) that were conditionally distinct from known GWAS identified variants and successfully replicated these signals in European ancestry subjects from UK Biobank. We found modestly improved correlation of predicted and measured gene expression in an independent African American cohort (the Genetic Epidemiology Network of Arteriopathy (GENOA) study (n = 802), lymphoblastoid cell lines) using the larger DGN reference panel; however, some genes were well predicted using MESA but not DGN. CONCLUSIONS: These analyses demonstrate the importance of performing TWAS and other genetic analyses across diverse populations and of balancing sample size and ancestry background matching when selecting a TWAS reference panel.

It Is Time to Implement Primary Prevention in the Workplace to Ameliorate the Ongoing U.S. Opioid Epidemic.

The United States' opioid public health crisis continues having disastrous consequences on communities, including workers and employers. From May 2019 to May 2020, the largest number of drug overdose deaths was recorded over a twelve-month period. The "twindemics" of COVID-19 and opioids underscore the urgent need to address workers' physical and mental health. Although much has been written about the negative impacts of the opioid epidemic on the workplace, few initiatives have focused on primary prevention, addressing work-related root causes of opioid use disorders (e.g., injury, stress) that may lead to prescription or illicit opioid use. We suggest primary prevention efforts to address the connection between workplace hazards and opioid misuse, dependence, and addiction such as examining patterns of work injury and stress with records of opioid prescription. Government funding should be expanded to support primary prevention and research efforts to strengthen the evidence-base to support workplace primary prevention endeavors.

Cell-type-specific 3D epigenomes in the developing human cortex.

Lineage-specific epigenomic changes during human corticogenesis have been difficult to study owing to challenges with sample availability and tissue heterogeneity. For example, previous studies using single-cell RNA sequencing identified at least 9 major cell types and up to 26 distinct subtypes in the dorsal cortex alone1,2. Here we characterize cell-type-specific cis-regulatory chromatin interactions, open chromatin peaks, and transcriptomes for radial glia, intermediate progenitor cells, excitatory neurons, and interneurons isolated from mid-gestational samples of the human cortex. We show that chromatin interactions underlie several aspects of gene regulation, with transposable elements and disease-associated variants enriched at distal interacting regions in a cell-type-specific manner. In addition, promoters with increased levels of chromatin interactivity-termed super-interactive promoters-are enriched for lineage-specific genes, suggesting that interactions at these loci contribute to the fine-tuning of transcription. Finally, we develop CRISPRview, a technique that integrates immunostaining, CRISPR interference, RNAscope, and image analysis to validate cell-type-specific cis-regulatory elements in heterogeneous populations of primary cells. Our findings provide insights into cell-type-specific gene expression patterns in the developing human cortex and advance our understanding of gene regulation and lineage specification during this crucial developmental window.

Clinical Characteristics of Pain Among Five Chronic Overlapping Pain Conditions.

AIMS: To describe the pain characteristics of five index chronic overlapping pain conditions (COPCs) and to assess each COPC separately in order to determine whether the presence of comorbid COPCs is associated with bodily pain distribution, pain intensity, pain interference, and high-impact pain of the index COPC. METHODS: Data were from a convenience sample of 655 US adults, of whom 388 had one or more of the five COPCs: painful temporomandibular disorders, headache, low back pain, irritable bowel syndrome, and/or fibromyalgia. Data were collected using pain location checklists and self-report questions regarding pain attributes. The contributions of the COPCs to reported pain intensity and interference were assessed using multivariable regression models. RESULTS/CONCLUSION: Heat maps from a pain body manikin illustrated that very little of the body was pain free within these COPCs. All pain attributes were the most severe for fibromyalgia and the least severe for irritable bowel syndrome. Within each index COPC, pain intensity, pain interference, and the proportion of participants with high-impact pain increased with each additional comorbid COPC up to four or more COPCs (including the index COPC) (P < .01). High-impact pain associated with an index COPC was influenced by type and number of comorbid COPCs, largely in a gradient-specific manner.

Robust Hi-C Maps of Enhancer-Promoter Interactions Reveal the Function of Non-coding Genome in Neural Development and Diseases.

Genome-wide mapping of chromatin interactions at high resolution remains experimentally and computationally challenging. Here we used a low-input "easy Hi-C" protocol to map the 3D genome architecture in human neurogenesis and brain tissues and also demonstrated that a rigorous Hi-C bias-correction pipeline (HiCorr) can significantly improve the sensitivity and robustness of Hi-C loop identification at sub-TAD level, especially the enhancer-promoter (E-P) interactions. We used HiCorr to compare the high-resolution maps of chromatin interactions from 10 tissue or cell types with a focus on neurogenesis and brain tissues. We found that dynamic chromatin loops are better hallmarks for cellular differentiation than compartment switching. HiCorr allowed direct observation of cell-type- and differentiation-specific E-P aggregates spanning large neighborhoods, suggesting a mechanism that stabilizes enhancer contacts during development. Interestingly, we concluded that Hi-C loop outperforms eQTL in explaining neurological GWAS results, revealing a unique value of high-resolution 3D genome maps in elucidating the disease etiology.

Use of >100,000 NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium whole genome sequences improves imputation quality and detection of rare variant associations in admixed African and Hispanic/Latino populations.

Most genome-wide association and fine-mapping studies to date have been conducted in individuals of European descent, and genetic studies of populations of Hispanic/Latino and African ancestry are limited. In addition, these populations have more complex linkage disequilibrium structure. In order to better define the genetic architecture of these understudied populations, we leveraged >100,000 phased sequences available from deep-coverage whole genome sequencing through the multi-ethnic NHLBI Trans-Omics for Precision Medicine (TOPMed) program to impute genotypes into admixed African and Hispanic/Latino samples with genome-wide genotyping array data. We demonstrated that using TOPMed sequencing data as the imputation reference panel improves genotype imputation quality in these populations, which subsequently enhanced gene-mapping power for complex traits. For rare variants with minor allele frequency (MAF) < 0.5%, we observed a 2.3- to 6.1-fold increase in the number of well-imputed variants, with 11-34% improvement in average imputation quality, compared to the state-of-the-art 1000 Genomes Project Phase 3 and Haplotype Reference Consortium reference panels. Impressively, even for extremely rare variants with minor allele count <10 (including singletons) in the imputation target samples, average information content rescued was >86%. Subsequent association analyses of TOPMed reference panel-imputed genotype data with hematological traits (hemoglobin (HGB), hematocrit (HCT), and white blood cell count (WBC)) in ~21,600 African-ancestry and ~21,700 Hispanic/Latino individuals identified associations with two rare variants in the HBB gene (rs33930165 with higher WBC [p = 8.8x10-15] in African populations, rs11549407 with lower HGB [p = 1.5x10-12] and HCT [p = 8.8x10-10] in Hispanics/Latinos). By comparison, neither variant would have been genome-wide significant if either 1000 Genomes Project Phase 3 or Haplotype Reference Consortium reference panels had been used for imputation. Our findings highlight the utility of the TOPMed imputation reference panel for identification of novel rare variant associations not previously detected in similarly sized genome-wide studies of under-represented African and Hispanic/Latino populations.

Anatomical selectivity in overlap of chronic facial and bodily pain.

BACKGROUND: Chronic facial pain often overlaps with pain experienced elsewhere in the body, although previous studies have focused on a few, selected pain conditions when assessing the degree of overlap. AIM: To quantify the degree of overlap between facial pain and pain reported at multiple locations throughout the body. METHODS: Data were from a case-control study of US adults participating in the Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA) project. They were interviewed to determine the presence of chronic facial pain (n = 424 cases) or its absence (n = 912 controls). A mailed questionnaire with a body drawing asked about pain at other locations. Odds ratios (ORs) and 95% confidence limits (95% CLs) quantified the degree of overlap between facial pain and pain at other locations. For replication, cross-sectional data were analyzed from the UK Biobank study (n = 459,604 participants) and the US National Health Interview Survey (n = 27,731 participants). RESULTS: In univariate analysis, facial pain had greatest overlap with headache (OR = 14.2, 95% CL = 9.7-20.8) followed by neck pain (OR = 8.5, 95% CL = 6.5-11.0), whereas overlap decreased substantially (ORs of 4.4 or less) for pain at successively remote locations below the neck. The same anatomically based ranking of ORs persisted in multivariable analysis that adjusted for demographics and risk factors for facial pain. Findings were replicated in the UK Biobank study and the US National Health Interview Survey. The observed anatomical selectivity in the degree of overlap could be a consequence of neurosensory and/or affective processes that differentially amplify pain according to its location.

Overcoming Stigma and Discrimination: Challenges for Reinsertion of Gang Members in Developing Countries.

This article is an effort to better understand the discrimination mechanisms that ex-gang members perceive upon leaving the gang and seeking to reinsert themselves into a society marked by high levels of violence and inequality, as in Central America. Based on 24 in-depth interviews with former members of MS-13, the 18th Street gang, and other street gangs in El Salvador, this article analyzes the different mechanisms of discrimination perceived by respondents as a result of the stigma of past gang membership. This article also documents how these perceptions of discrimination can affect individuals who are searching for employment opportunities and seeking to reinsert themselves into society.

Ebola Virus Training: A Needs Assessment and Gap Analysis.

In response to the 2014 Ebola virus disease outbreak, the Worker Training Program embarked on an assessment of existing training for those at risk for exposure to the virus. Searches of the recent peer-reviewed literature were conducted for descriptions of relevant training. Federal guidance issued during 2015 was also reviewed. Four stakeholder meetings were conducted with representatives from health care, academia, private industry, and public health to discuss issues associated with ongoing training. Our results revealed few articles about training that provided sufficient detail to serve as models. Training programs struggled to adjust to frequently updated federal guidance. Stakeholders commented that most healthcare training focused solely on infection control, and there was an absence of employee health-related training for non-healthcare providers. Challenges to ongoing training included funding and organizational complacency. Best practices were noted where management and employees planned training cooperatively and where infection control, employee health, and hospital emergency managers worked together on the development of protective guidance. We conclude that sustainable training for infectious disease outbreaks requires annual funding, full support from organizational management, input from all stakeholders, and integration of infection control, emergency management, and employee health when implementing guidance and training.

Comparison of in vitro activities of fluoroquinolone-like 2,4- and 1,3-diones.

Bacterial resistance presents a difficult issue for fluoroquinolone treatment of bacterial infections. In previous work, we reported that 8-methoxy-quinazoline-2,4-diones are active against quinolone-resistant mutants of Escherichia coli. Here, we demonstrate the activity of a representative 8-methoxy-quinazoline-2,4-dione against quinolone-resistant gyrases. Furthermore, 8-methoxy-quinazoline-2,4-dione and other diones are shown to inhibit Staphylococcus aureus gyrase and topoisomerase IV with similar degrees of efficacy, suggesting that the diones might act as dual-targeting agents against S. aureus.