High-resolution analyses of associations between medications, microbiome, and mortality in cancer patients.

Discerning the effect of pharmacological exposures on intestinal bacterial communities in cancer patients is challenging. Here, we deconvoluted the relationship between drug exposures and changes in microbial composition by developing and applying a new computational method, PARADIGM (parameters associated with dynamics of gut microbiota), to a large set of longitudinal fecal microbiome profiles with detailed medication-administration records from patients undergoing allogeneic hematopoietic cell transplantation. We observed that several non-antibiotic drugs, including laxatives, antiemetics, and opioids, are associated with increased Enterococcus relative abundance and decreased alpha diversity. Shotgun metagenomic sequencing further demonstrated subspecies competition, leading to increased dominant-strain genetic convergence during allo-HCT that is significantly associated with antibiotic exposures. We integrated drug-microbiome associations to predict clinical outcomes in two validation cohorts on the basis of drug exposures alone, suggesting that this approach can generate biologically and clinically relevant insights into how pharmacological exposures can perturb or preserve microbiota composition. The application of a computational method called PARADIGM to a large dataset of cancer patients' longitudinal fecal specimens and detailed daily medication records reveals associations between drug exposures and the intestinal microbiota that recapitulate in vitro findings and are also predictive of clinical outcomes.

Widespread and Functional RNA Circularization in Localized Prostate Cancer.

The cancer transcriptome is remarkably complex, including low-abundance transcripts, many not polyadenylated. To fully characterize the transcriptome of localized prostate cancer, we performed ultra-deep total RNA-seq on 144 tumors with rich clinical annotation. This revealed a linear transcriptomic subtype associated with the aggressive intraductal carcinoma sub-histology and a fusion profile that differentiates localized from metastatic disease. Analysis of back-splicing events showed widespread RNA circularization, with the average tumor expressing 7,232 circular RNAs (circRNAs). The degree of circRNA production was correlated to disease progression in multiple patient cohorts. Loss-of-function screening identified 11.3% of highly abundant circRNAs as essential for cell proliferation; for ∼90% of these, their parental linear transcripts were not essential. Individual circRNAs can have distinct functions, with circCSNK1G3 promoting cell growth by interacting with miR-181. These data advocate for adoption of ultra-deep RNA-seq without poly-A selection to interrogate both linear and circular transcriptomes.

Inhibition of EZH2 transactivation function sensitizes solid tumors to genotoxic stress.

Drugs that block the activity of the methyltransferase EZH2 are in clinical development for the treatment of non-Hodgkin lymphomas harboring EZH2 gain-of-function mutations that enhance its polycomb repressive function. We have previously reported that EZH2 can act as a transcriptional activator in castration-resistant prostate cancer (CRPC). Now we show that EZH2 inhibitors can also block the transactivation activity of EZH2 and inhibit the growth of CRPC cells. Gene expression and epigenomics profiling of cells treated with EZH2 inhibitors demonstrated that in addition to derepressing gene expression, these compounds also robustly down-regulate a set of DNA damage repair (DDR) genes, especially those involved in the base excision repair (BER) pathway. Methylation of the pioneer factor FOXA1 by EZH2 contributes to the activation of these genes, and interaction with the transcriptional coactivator P300 via the transactivation domain on EZH2 directly turns on the transcription. In addition, CRISPR-Cas9-mediated knockout screens in the presence of EZH2 inhibitors identified these BER genes as the determinants that underlie the growth-inhibitory effect of EZH2 inhibitors. Interrogation of public data from diverse types of solid tumors expressing wild-type EZH2 demonstrated that expression of DDR genes is significantly correlated with EZH2 dependency and cellular sensitivity to EZH2 inhibitors. Consistent with these findings, treatment of CRPC cells with EZH2 inhibitors dramatically enhances their sensitivity to genotoxic stress. These studies reveal a previously unappreciated mechanism of action of EZH2 inhibitors and provide a mechanistic basis for potential combination cancer therapies.

Feasibility and Efficacy of Sentinel Lymph Node Mapping in Gastric Cancer.

BACKGROUND: Lymph node metastasis is a critical prognostic factor for patients with gastric carcinoma (GC). Sentinel lymph node (SLN) mapping has the potential to identify the initial site of draining lymph node metastasis and reduce the extent of surgical lymphadenectomy. This study aimed to evaluate the diagnostic accuracy of SLN mapping in GC. METHODS: The study enrolled 129 GC patients undergoing total or partial gastrectomy with D2 lymphadenectomy and indocyanine green fluorescence-guided SLN mapping. The primary outcomes were the negative predictive value (NPV) and sensitivity of SLN mapping. The secondary outcomes were clinicopathologic factors associated with SLN mapping accuracy and successful SLN mapping. RESULTS: The SLN detection rate in this study was 86.8 %. The study had an overall NPV of 83.1 % and an overall sensitivity of 65.8 %. The NPV was found to be significantly higher in the patients with no lymphovascular invasion (LVI) than in those with LVI (96.0 % vs 59.3 %; p < 0.001) and in the patients whose pathologic T (pT) stage lower than 3 than in those whose T stage was 3 or higher (92.0 % vs 66.7 %; p = 0.009). The sensitivity of SLN mapping was 50 % in the patients with no LVI and 33 % in the patients with a pT stage lower than 3. CONCLUSION: The study results showed that for patients with early-stage GC with no LVI, negative SLN findings may represent a potential additive predictor indicating the absence of regional LN metastasis. However, given the low sensitivity rates noted, further research is needed to identify specific patient populations that may benefit from SLN mapping in GC.

Preservation of the fecal microbiome is associated with reduced severity of graft-versus-host disease.

Following allogeneic hematopoietic cell transplantation (allo-HCT), the gastrointestinal (GI) tract is frequently affected by acute graft-versus-host disease (aGVHD), the pathophysiology of which is associated with a dysbiotic microbiome. Since microbial composition varies along the length of the GI tract, the authors hypothesized that microbiome features correlate with the pattern of organ involvement after allo-HCT. We evaluated 266 allo-HCT recipients from whom 1303 stool samples were profiled by 16S ribosomal gene sequencing. Patients were classified according to which organs were affected by aGVHD. In the 20 days prior to disease onset, GVHD patients had lower abundances of members of the class Clostridia, lower counts of butyrate producers, and lower ratios of strict-to-facultative (S/F) anaerobic bacteria compared with allograft recipients who were free of GVHD. GI GVHD patients showed significant reduction in microbial diversity preonset. Patients with lower GI aGVHD had lower S/F anaerobe ratios compared with those with isolated upper GI aGVHD. In the 20 days after disease onset, dysbiosis was observed only in GVHD patients with GI involvement, particularly those with lower-tract disease. Importantly, Clostridial and butyrate-producer abundance as well as S/F anaerobe ratio were predictors of longer overall survival; higher abundance of butyrate producers and higher S/F anaerobe ratio were associated with decreased risk of GVHD-related death. These findings suggest that the intestinal microbiome can serve as a biomarker for outcomes of allo-HCT patients with GVHD.

Design Strategies towards Advanced Hydrogen Evolution Reaction Electrocatalysts at Large Current Densities.

Hydrogen (H2), produced by water electrolysis with the electricity from renewable sources, is an ideal energy carrier for achieving a carbon-neutral and sustainable society. Hydrogen evolution reaction (HER) is the cathodic half-reaction of water electrolysis, which requires active and robust electrocatalysts to reduce the energy consumption for H2 generation. Despite numerous electrocatalysts have been reported by the academia for HER, most of them were only tested under relatively small current densities for a short period, which cannot meet the requirements for industrial water electrolysis. To bridge the gap between academia and industry, it is crucial to develop highly active HER electrocatalysts which can operate at large current densities for a long time. In this review, the mechanisms of HER in acidic and alkaline electrolytes are firstly introduced. Then, design strategies towards high-performance large-current-density HER electrocatalysts from five aspects including number of active sites, intrinsic activity of each site, charge transfer, mass transfer, and stability are discussed via featured examples. Finally, our own insights about the challenges and future opportunities in this emerging field are presented.

Differential oral and gut microbial structure related to systemic metabolism in kidney stone patients.

OBJECTIVES: To investigate the role of the oral and gut microbiome related to systemic metabolism and clinical parameters in various types of kidney stone disease. PATIENTS AND METHODS: We conducted a case-control study by analyzing 16S rRNA and untargeted metabolomics profiling of 76 fecal, 68 saliva, 73 urine, and 43 serum samples from 76 participants aged 18-75 years old. The participants included 15 patients with uric acid stones, 41 patients with calcium oxalate stones, and 20 healthy controls. Correlations among microbiome, metabolism, and clinical parameters were identified through Spearman's correlation analysis. (Clinical trial No. ChiCTR2200055316). RESULTS: Patients with uric acid stones exhibited reduced richness and diversity in their microbiome, as well as altered composition in both oral and gut microbiome. Furthermore, their fecal samples showed lower relative abundances of Bacteroides and Lachnospiraceae, while their saliva samples showed higher relative abundances of Porphyromonas and Neisseria. Predicted KEGG metabolism pathways, including amino acid and fatty acid metabolisms, were significantly altered in subjects with uric acid stones. Oral, gut microbiota, and metabolism were also associated with low water intake and urine pH. The area under the curve (AUC) of the specific microbiota and metabolite prediction models was over 0.85. CONCLUSION: The structure and composition of the oral and gut microbiome in different types of kidney stone disease, the correlations between oral and gut microbiome, and the associations among oral and gut microbiota, systemic metabolism and clinical parameters imply an important role that the oral and gut microbiome may play in kidney stone disease.

Unique Genomic Alterations and Microbial Profiles Identified in Patients With Gastric Cancer of African, European, and Asian Ancestry: A Novel Path for Precision Oncology.

OBJECTIVE: Here, we characterize differences in the genetic and microbial profiles of GC in patients of African (AFR), European, and Asian ancestry. BACKGROUND: Gastric cancer (GC) is a heterogeneous disease with clinicopathologic variations due to a complex interplay of environmental and biological factors, which may affect disparities in oncologic outcomes.. METHODS: We identified 1042 patients with GC with next-generation sequencing data from an institutional Integrated Mutation Profiling of Actionable Cancer Targets assay and the Cancer Genomic Atlas group. Genetic ancestry was inferred from markers captured by the Integrated Mutation Profiling of Actionable Cancer Targets and the Cancer Genomic Atlas whole exome sequencing panels. Tumor microbial profiles were inferred from sequencing data using a validated microbiome bioinformatics pipeline. Genomic alterations and microbial profiles were compared among patients with GC of different ancestries. RESULTS: We assessed 8023 genomic alterations. The most frequently altered genes were TP53 , ARID1A , KRAS , ERBB2 , and CDH1 . Patients of AFR ancestry had a significantly higher rate of CCNE1 alterations and a lower rate of KRAS alterations ( P < 0.05), and patients of East Asian ancestry had a significantly lower rate of PI3K pathway alterations ( P < 0.05) compared with other ancestries. Microbial diversity and enrichment did not differ significantly across ancestry groups ( P > 0.05). CONCLUSIONS: Distinct patterns of genomic alterations and variations in microbial profiles were identified in patients with GC of AFR, European, and Asian ancestry. Our findings of variation in the prevalence of clinically actionable tumor alterations among ancestry groups suggest that precision medicine can mitigate oncologic disparities.

Outcomes of Combination Platinum-Doublet Chemotherapy and Anti-PD(L)-1 Blockade in KRASG12C-Mutant Non-Small Cell Lung Cancer.

BACKGROUND: Direct KRASG12C inhibitors are approved for patients with non-small cell lung cancers (NSCLC) in the second-line setting. The standard-of-care for initial treatment remains immune checkpoint inhibitors, commonly in combination with platinum-doublet chemotherapy (chemo-immunotherapy). Outcomes to chemo-immunotherapy in this subgroup have not been well described. Our goal was to define the clinical outcomes to chemo-immunotherapy in patients with NSCLC with KRASG12C mutations. PATIENTS AND METHODS: Through next-generation sequencing, we identified patients with advanced NSCLC with KRAS mutations treated with chemo-immunotherapy at 2 institutions. The primary objective was to determine outcomes and determinants of response to first-line chemo-immunotherapy among patients with KRASG12C by evaluating objective response rate (ORR), progression-free survival (PFS), and overall survival (OS). We assessed the impact of coalterations in STK11/KEAP1 on outcomes. As an exploratory objective, we compared the outcomes to chemo-immunotherapy in KRASG12C versus non-G12C groups. RESULTS: One hundred and thirty eight patients with KRASG12C treated with first-line chemo-immunotherapy were included. ORR was 41% (95% confidence interval (CI), 32-41), median PFS was 6.8 months (95%CI, 5.5-10), and median OS was 15 months (95%CI, 11-28). In a multivariable model for PFS, older age (P = .042), squamous cell histology (P = .008), poor ECOG performance status (PS) (P < .001), and comutations in KEAP1 and STK11 (KEAP1MUT/STK11MUT) (P = .015) were associated with worse PFS. In a multivariable model for OS, poor ECOG PS (P = .004) and KEAP1MUT/STK11MUT (P = .009) were associated with worse OS. Patients with KRASG12C (N = 138) experienced similar outcomes to chemo-immunotherapy compared to patients with non-KRASG12C (N = 185) for both PFS (P = .2) and OS (P = .053). CONCLUSIONS: We define the outcomes to first-line chemo-immunotherapy in patients with KRASG12C, which provides a real-world benchmark for clinical trial design involving patients with KRASG12C mutations. Outcomes are poor in patients with specific molecular coalterations, highlighting the need to develop more effective frontline therapies.

Rapid and Accurate Identification of Cell Phenotypes of Different Drug Mechanisms by Using Single-Cell Fluorescence Images Via Deep Learning.

Identification of a drug mechanism is vital for drug development. However, it often resorts to the expensive and cumbersome omics methods along with complex data analysis. Herein, we developed a methodology to analyze organelle staining images of single cells using a deep learning algorithm (TL-ResNet50) for rapid and accurate identification of different drug mechanisms. Based on the organelle-related cell morphological changes caused by drug action, the constructed deep learning model can fast predict the drug mechanism with a high accuracy of 92%. Further analysis reveals that drug combination at different ratios can enhance a certain mechanism or generate a new mechanism. This work would highly facilitate clinical medication and drug screening.

A deep learning-based drug repurposing screening and validation for anti-SARS-CoV-2 compounds by targeting the cell entry mechanism.

The recent outbreak of Corona Virus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been a severe threat to the global public health and economy, however, effective drugs to treat COVID-19 are still lacking. Here, we employ a deep learning-based drug repositioning strategy to systematically screen potential anti-SARS-CoV-2 drug candidates that target the cell entry mechanism of SARS-CoV-2 virus from 2635 FDA-approved drugs and 1062 active ingredients from Traditional Chinese Medicine herbs. In silico molecular docking analysis validates the interactions between the top compounds and host receptors or viral spike proteins. Using a SARS-CoV-2 pseudovirus system, we further identify several drug candidates including Fostamatinib, Linagliptin, Lysergol and Sophoridine that can effectively block the cell entry of SARS-CoV-2 variants into human lung cells even at a nanomolar scale. These efforts not only illuminate the feasibility of applying deep learning-based drug repositioning for antiviral agents by targeting a specified mechanism, but also provide a valuable resource of promising drug candidates or lead compounds to treat COVID-19.

Construction of Dual-Channel Water Transport in Mesoporous Silica Low Humidity Sensors to Achieve High Sensitivity.

In this paper, strong hydrophilic poly(ionic liquid)s (PILs) are selectively grafted on different positions (mesoporous channels and outer surface) of mesoporous silica via thiol-ene click chemical reaction. The purposes of selective grafting are on the one hand, to explore the differences of adsorption and transportation of water molecules in mesoporous channels and on the outer surface, and on the other hand, to combine the two approaches (intra-pore grafting and external surface grafting) to reasonably design SiO2 @PILs low humidity sensing film with synergetic function to achieve high sensitivity. The results of low relativehumidity （RH） sensing test show that the sensing performance of humidity sensor based on mesoporous silica grafted with PILs in the channels is better than that of humidity sensor based on mesoporous silica grafted with PILs on the outer surface. Compared with water molecules transport single channel, the construction of dual-channel water transport significantly improves the sensitivity of the low humidity sensor, and the response of the sensor is up to 4112% in the range of 7-33% RH. Moreover, the existence of micropores and the formation of dual-channel water transport affect the adsorption/desorption behaviors of the sensor under different humidity ranges, especially below 11% RH.

Synergy of Two Intermolecular Hydrogen Bonds Promotes Highly Sensitive and Selective Room-Temperature Dimethyl Methylphosphonate Sensing: A Case of rGO-Based Gas Sensors.

The development of room-temperature chemiresistive gas sensors with low limit of detection, high sensitivity, and selectivity for dimethyl methylphosphonate (DMMP) detection remains a challenge. Herein, a synergy of the two intermolecular hydrogen bond-promoted approach was proposed to fabricate a room-temperature DMMP sensor with enhanced performances. As a proof of concept, ternary p-hexafluoroisopropanol phenyl (HFIP) functionalized polypyrrole-reduced graphene oxide hybrids (HFIP-PPy-rGO) were rationally designed. During the sensing process, rGO serves as a conductive carrier, ensuring that the sensors operate at room temperature, and both HFIP and PPy act as adsorption sites for DMMP through hydrogen bonding interactions. As expected, the HFIP-PPy-rGO sensor exhibits high selectivity and sensitivity to DMMP. Besides, the HFIP-PPy-rGO sensor also possesses excellent linear response to DMMP and long-term stability. Experimental results and quartz crystal microbalance measurements prove that the specific recognition of DMMP is realized by forming two intermolecular hydrogen bonds between HFIP and DMMP, as well as PPy and DMMP. Additionally, the introduction of HFIP groups also contributes to adjusting device conductivity, enhancing signal conversion function. To put the DMMP sensor into potential practical application, the obvious sensing response to different DMMP concentrations in soil was confirmed, and a wireless detection system was built to realize real-time monitoring of DMMP concentrations in the surroundings. Overall, this study provides a facile and practical solution for improving the sensing performance of room-temperature sensors based on the hydrogen bond theory.

Evaluating the association between latent classes and competing risks outcomes with multiphenotype data.

Latent class analysis is an intuitive tool to characterize disease phenotype heterogeneity. With data more frequently collected on multiple phenotypes in chronic disease studies, it is of rising interest to investigate how the latent classes embedded in one phenotype are related to another phenotype. Motivated by a cohort with mild cognitive impairment (MCI) from the Uniform Data Set (UDS), we propose and study a time-dependent structural model to evaluate the association between latent classes and competing risk outcomes that are subject to missing failure types. We develop a two-step estimation procedure which circumvents latent class membership assignment and is rigorously justified in terms of accounting for the uncertainty in classifying latent classes. The new method also properly addresses the realistic complications for competing risks outcomes, including random censoring and missing failure types. The asymptotic properties of the resulting estimator are established. Given that the standard bootstrapping inference is not feasible in the current problem setting, we develop analytical inference procedures, which are easy to implement. Our simulation studies demonstrate the advantages of the proposed method over benchmark approaches. We present an application to the MCI data from UDS, which uncovers a detailed picture of the neuropathological relevance of the baseline MCI subgroups.

Utilization of diverse oligosaccharides for growth by Bifidobacterium and Lactobacillus species and their in vitro co-cultivation characteristics.

Various approaches have been used to study the relationship between prebiotics and probiotics. The utilization of different carbohydrates by probiotics depends on the biochemical properties of the enzymes and substrates required by the microbial strain. However, few studies have systematically analyzed the ability of probiotics to utilize different prebiotics. Here, we investigated the effects of prebiotics from different manufacturers on the proliferation of 13 strains of the Lactobacillus group and the genus Bifidobacterium co-cultured in vitro. Inulin, fructose-oligosaccharide (FOS), and galactose-oligosaccharide (GOS) had broad growth-promoting effects. FOS significantly promoted the proliferation of B. longum. When strains from Lactobacillus group and Bifidobacterium were co-cultured, FOS caused each strain to proliferate cooperatively. GOS was effectively used by L. rhamnosus and L. reuteri for energy and growth promotion. L. casei and L. paracasei fully metabolized inulin; these strains performed better than other strains from Lactobacillus group and Bifidobacterium. Media containing a mixture of oligosaccharides had stronger effects on the growth of B. animalis subsp. lactis, L. acidophilus, and L. rhamnosus than media containing single oligosaccharides. Thus, different oligosaccharides had different effects on the growth of probiotics, providing a scientific basis for the use of synbiotics in health and related fields.

CRISP-view: a database of functional genetic screens spanning multiple phenotypes.

High-throughput genetic screening based on CRISPR/Cas9 or RNA-interference (RNAi) enables the exploration of genes associated with the phenotype of interest on a large scale. The rapid accumulation of public available genetic screening data provides a wealth of knowledge about genotype-to-phenotype relationships and a valuable resource for the systematic analysis of gene functions. Here we present CRISP-view, a comprehensive database of CRISPR/Cas9 and RNAi screening datasets that span multiple phenotypes, including in vitro and in vivo cell proliferation and viability, response to cancer immunotherapy, virus response, protein expression, etc. By 22 September 2020, CRISP-view has collected 10 321 human samples and 825 mouse samples from 167 papers. All the datasets have been curated, annotated, and processed by a standard MAGeCK-VISPR analysis pipeline with quality control (QC) metrics. We also developed a user-friendly webserver to visualize, explore, and search these datasets. The webserver is freely available at http://crispview.weililab.org.

Novel prognostic model predicts overall survival in colon cancer based on RNA splicing regulation gene expression.

Colon cancer is the third most common cancer and the second leading cause of cancer-related death worldwide. Dysregulated RNA splicing factors have been reported to be associated with tumorigenesis and development in colon cancer. In this study, we interrogated clinical and RNA expression data of colon cancer patients from The Cancer Genome Atlas (TCGA) dataset and the Gene Expression Omnibus (GEO) database. Genes regulating RNA splicing correlated with survival in colon cancer were identified and a risk score model was constructed using Cox regression analyses. In the risk model, RNA splicing factor peroxisome proliferator-activated receptor-γ coactivator-1α (PPARGC1) is correlated with a good survival outcome, whereas Cdc2-like kinase 1(CLK1), CLK2, and A-kinase anchor protein 8-like (AKAP8L) with a bad survival outcome. The risk model has a good performance for clinical prognostic prediction both in the TCGA cohort and the other two validation cohorts. In the tumor microenvironment (TME) analysis, the immune score was higher in the low-risk group, and TME-related pathway gene expression was also higher in low-risk group. We further verified the mRNA and protein expression levels of these four genes in the adjacent nontumor, tumor, and liver metastasis tissues of colon cancer patients, which were consistent with bioinformatics analysis. In addition, knockdown of AKAP8L can suppress the proliferation and migration of colon cancer cells. Animal studies have also shown that AKAP8L knockdown can inhibit tumor growth in colon cancer in vivo. We established a prognostic risk model for colon cancer based on genes related to RNA splicing regulation and uncovered the role of AKAP8L in promoting colon cancer progression.

High-performance quantum cascade lasers at λ ∼ 9†µm grown by MOCVD.

We demonstrate a high power InP-based quantum cascade laser (QCL) (λ ∼ 9 µm) with high characteristic temperature grown by metalorganic chemical vapor deposition (MOCVD) in this article. A 4-mm-long cavity length, 10.5-µm-wide ridge QCL with high-reflection (HR) coating demonstrates a maximum pulsed peak power of 1.55 W and continuous-wave (CW) output power of 1.02W at 293 K. The pulsed threshold current density of the device is as low as 1.52 kA/cm2. The active region adopted a dual-upper-state (DAU) and multiple-lower-state (MS) design and it shows a wide electroluminescence (EL) spectrum with 466 cm-1 wide full-width at half maximum (FWHM). In addition, the device performance is insensitive to the temperature change since the threshold-current characteristic temperature coefficient, T0, is as high as 228 K, and slope-efficiency characteristic temperature coefficient, T1, is as high as 680 K, over the heatsink-temperature range of 293 K to 353 K.

Associations and pathways between residential greenness and hyperuricemia among adults in rural and urban China.

BACKGROUND: Residential greenness may decrease the risk for hyperuricemia in rural areas, but the urban-rural disparities in this association and underlying pathways have not been studied. OBJECTIVES: To investigate the associations and potential pathways between residential greenness and hyperuricemia in urban and rural areas. METHODS: The baseline survey of the China Multi-Ethnic Cohort (CMEC) was used. Hyperuricemia was defined as serum uric acid (SUA) > 417 µmol/L for men and >357 µmol/L for women. The satellite-based normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were used to capture residential greenness. A propensity score inverse-probability weighting method was used to assess urban-rural differences in the associations between residential greenness and hyperuricemia, with possible mediation effects of physical activity (PA), body mass index (BMI), PM2.5, and NO2 examined by causal mediation analyses. RESULTS: A total of 72,372 participants were included. The increases in the EVI500m and NDVI500m residential greenness were associated with a decreased risk for hyperuricemia and the SUA level in both urban and rural areas. For example, each 0.1-unit increase in EVI500m was associated with a decreased hyperuricemia risk of 7% (OR = 0.93 [0.91, 0.96]) and a decreased SUA level of -1.77 µmol/L [-2.60, -0.93], respectively; such associations were stronger in urban areas for both the risk for hyperuricemia (OR = 0.84 [0.83, 0.86]) and SUA level (-7.18 µmol/L [-7.91, -6.46]). The subgroup analysis showed that the greenness-hyperuricemia/SUA association varied by age, sex, and annual household income. The percentage of the joint mediation effect of PA, BMI, PM2.5, and NO2 on the association between EVI500m and the risk for hyperuricemia was higher in urban (34.92%) than rural areas (15.40%). BMI, PM2.5, and PA showed significantly independently mediation effects for the greenness-hyperuricemia association in both rural and urban areas. CONCLUSIONS: Exposure to residential greenness was associated with a decreased risk for hyperuricemia, partially through the pathways of PA, BMI, PM2.5, and NO2, which varied in urban and rural areas.

Deciphering essential cistromes using genome-wide CRISPR screens.

Although millions of transcription factor binding sites, or cistromes, have been identified across the human genome, defining which of these sites is functional in a given condition remains challenging. Using CRISPR/Cas9 knockout screens and gene essentiality or fitness as the readout, we systematically investigated the essentiality of over 10,000 FOXA1 and CTCF binding sites in breast and prostate cancer cells. We found that essential FOXA1 binding sites act as enhancers to orchestrate the expression of nearby essential genes through the binding of lineage-specific transcription factors. In contrast, CRISPR screens of the CTCF cistrome revealed 2 classes of essential binding sites. The first class of essential CTCF binding sites act like FOXA1 sites as enhancers to regulate the expression of nearby essential genes, while a second class of essential CTCF binding sites was identified at topologically associated domain (TAD) boundaries and display distinct characteristics. Using regression methods trained on our screening data and public epigenetic profiles, we developed a model to predict essential cis-elements with high accuracy. The model for FOXA1 essentiality correctly predicts noncoding variants associated with cancer risk and progression. Taken together, CRISPR screens of cis-regulatory elements can define the essential cistrome of a given factor and can inform the development of predictive models of cistrome function.