Addressing dispersion in mis-measured multivariate binomial outcomes: A novel statistical approach for detecting differentially methylated regions in bisulfite sequencing data.

Motivated by a DNA methylation application, this article addresses the problem of fitting and inferring a multivariate binomial regression model for outcomes that are contaminated by errors and exhibit extra-parametric variations, also known as dispersion. While dispersion in univariate binomial regression has been extensively studied, addressing dispersion in the context of multivariate outcomes remains a complex and relatively unexplored task. The complexity arises from a noteworthy data characteristic observed in our motivating dataset: non-constant yet correlated dispersion across outcomes. To address this challenge and account for possible measurement error, we propose a novel hierarchical quasi-binomial varying coefficient mixed model, which enables flexible dispersion patterns through a combination of additive and multiplicative dispersion components. To maximize the Laplace-approximated quasi-likelihood of our model, we further develop a specialized two-stage expectation-maximization (EM) algorithm, where a plug-in estimate for the multiplicative scale parameter enhances the speed and stability of the EM iterations. Simulations demonstrated that our approach yields accurate inference for smooth covariate effects and exhibits excellent power in detecting non-zero effects. Additionally, we applied our proposed method to investigate the association between DNA methylation, measured across the genome through targeted custom capture sequencing of whole blood, and levels of anti-citrullinated protein antibodies (ACPA), a preclinical marker for rheumatoid arthritis (RA) risk. Our analysis revealed 23 significant genes that potentially contribute to ACPA-related differential methylation, highlighting the relevance of cell signaling and collagen metabolism in RA. We implemented our method in the R Bioconductor package called "SOMNiBUS."

Single base-pair resolution analysis of DNA binding motif with MoMotif reveals an oncogenic function of CTCF zinc-finger 1 mutation.

Defining the impact of missense mutations on the recognition of DNA motifs is highly dependent on bioinformatic tools that define DNA binding elements. However, classical motif analysis tools remain limited in their capacity to identify subtle changes in complex binding motifs between distinct conditions. To overcome this limitation, we developed a new tool, MoMotif, that facilitates a sensitive identification, at the single base-pair resolution, of complex, or subtle, alterations to core binding motifs, discerned from ChIP-seq data. We employed MoMotif to define the previously uncharacterized recognition motif of CTCF zinc-finger 1 (ZF1), and to further define the impact of CTCF ZF1 mutation on its association with chromatin. Mutations of CTCF ZF1 are exclusive to breast cancer and are associated with metastasis and therapeutic resistance, but the underlying mechanisms are unclear. Using MoMotif, we identified an extension of the CTCF core binding motif, necessitating a functional ZF1 to bind appropriately. Using a combination of ChIP-Seq and RNA-Seq, we discover that the inability to bind this extended motif drives an altered transcriptional program associated with the oncogenic phenotypes observed clinically. Our study demonstrates that MoMotif is a powerful new tool for comparative ChIP-seq analysis and characterising DNA-protein contacts.

A novel statistical method for modeling covariate effects in bisulfite sequencing derived measures of DNA methylation.

Identifying disease-associated changes in DNA methylation can help us gain a better understanding of disease etiology. Bisulfite sequencing allows the generation of high-throughput methylation profiles at single-base resolution of DNA. However, optimally modeling and analyzing these sparse and discrete sequencing data is still very challenging due to variable read depth, missing data patterns, long-range correlations, data errors, and confounding from cell type mixtures. We propose a regression-based hierarchical model that allows covariate effects to vary smoothly along genomic positions and we have built a specialized EM algorithm, which explicitly allows for experimental errors and cell type mixtures, to make inference about smooth covariate effects in the model. Simulations show that the proposed method provides accurate estimates of covariate effects and captures the major underlying methylation patterns with excellent power. We also apply our method to analyze data from rheumatoid arthritis patients and controls. The method has been implemented in R package SOMNiBUS.

Optimizing discharge decision-making in colorectal surgery: a prospective cohort study of discharge practices in a recently implemented enhanced recovery pathway.

AIM: The objectives of this project were (1) to compare time to readiness for discharge by set criteria and actual length of stay (LOS) in a newly implemented colorectal enhanced recovery pathway and (2) to identify reasons for delayed hospital discharge. METHOD: We conducted a prospective cohort study of 73 adult patients (age 67 ± 14 years, 56% men, 51% laparoscopic, 13% stoma creation) undergoing elective colorectal surgery in a university hospital with a recently implemented recovery pathway (<2 years). Time to readiness for discharge (oral intake, flatus, pain control, ability to walk, and no complications) was compared to actual LOS using a correlation-adjusted log-rank test. The treating team was interviewed, and thematic analysis was used to identify reasons for patients remaining in hospital after discharge criteria (DC) were achieved. RESULTS: Median LOS was 6 (4-8) days and median time to readiness for discharge was 5 (3-8) days (P < 0.001). Twenty-eight patients (37%) remained in hospital after DC were achieved. Although some delayed discharges were medically justified (e.g., workup [13%] or treatment of complications not captured by DC [2.6%]), unnecessary hospital stays were common (e.g., perceived need for observation [16%], or patients not willing to be discharged [11%]). CONCLUSIONS: Unnecessary hospital stays were common within a recently implemented enhanced recovery pathway and represent a target for quality improvement. Efforts should be directed at optimizing patient education regarding discharge expectations, early consultation of the discharge planning team and improving discharge decision-making using standardized DC.

Interactive online informational and peer support application for patients with low anterior resection syndrome: patient survey and protocol for a multicentre randomized controlled trial.

AIM: Low anterior resection syndrome (LARS) refers to a constellation of bowel symptoms that affect the majority of patients following restorative proctectomy. LARS is associated with poorer quality of life (QoL), and can lead to distress, anxiety and isolation. Peer support could be an important resource for people living with LARS, helping them normalize and validate their experience. The aim of this work is to describe the development of an interactive online informational and peer support app for LARS and the protocol for a randomized controlled trial. METHOD: A multicentre, randomized, assessor-blind, parallel-groups pragmatic trial will involve patients from five large colorectal surgery practices across Canada. The trial will evaluate the impact of an interactive online informational and peer support app for LARS, consisting of LARS informational modules and a closed forum for peers and trained peer support mentors, on patient-reported outcomes of people living with LARS. The primary outcome will be global QoL at 6 months following app exposure. The treatment effect on global QoL will be modelled using generalized estimating equations. Secondary outcomes will include patient activation and bowel function as measured by LARS scores. RESULTS: In order to better understand patients' interest and preferences for an online peer support intervention for LARS, we conducted a single institution cross-sectional survey study of rectal cancer survivors. In total, 35/69 (51%) participants reported interest in online peer support for LARS. Age <65 years (OR 9.1; 95% CI 2.3-50) and minor/major LARS (OR 20; 95% CI 4.2-100) were significant predictors of interest in LARS online peer support. CONCLUSION: There is significant interest in the use of online peer support for LARS among younger patients and those with significant bowel dysfunction. Based on results of the needs assessment study, the app content and features were modified reflect patients' needs and preferences. We are now in an optimal position to rigorously test the potential effects of this initiative on patient-centered outcomes using a randomized controlled trial.

Readmission for Treatment Failure After Nonoperative Management of Acute Diverticulitis: A Nationwide Readmissions Database Analysis.

BACKGROUND: The true incidence of, and risk factors for, readmission for treatment failure after nonoperative management of acute diverticulitis remain poorly understood. OBJECTIVE: The purpose of this study was to describe the incidence and risk factors for readmission for treatment failure after nonoperative management of acute diverticulitis using a large national database. DESIGN: This was a retrospective cohort study. SETTINGS: A representative sample of admissions and discharges from hospitals in the United States captured in the National Readmissions Database were included. PATIENTS: Adult patients (age ≥18 y) admitted with a primary diagnostic of colonic diverticulitis between 2010 and 2015 and who were managed nonoperatively and discharged from hospital alive were included. INTERVENTIONS: Study intervention included nonoperative management, consisting of medical therapy with or without percutaneous drainage. MAIN OUTCOME MEASURES: Readmission for treatment failure (defined as a nonelective readmission for diverticulitis within 90 d of discharge), complicated treatment failure (defined as a treatment failure with complicated diverticulitis), and time-to-treatment failure were measured. RESULTS: In total, 201,384 patients were included. The overall incidence of readmission for treatment failure was 6.6%. Treatment failure was significantly higher among patients with an index episode of acute complicated diverticulitis compared with acute uncomplicated diverticulitis (12.5% vs 5.7%; p < 0.001). The median time-to-readmission for treatment failure was 21.0 days (range, 20.4-21.6 d), and 85% of all readmissions occurred within 60 days of discharge. On multiple logistic regression, factors independently associated with readmission for treatment failure were an index admission of complicated diverticulitis (OR = 2.06 (95% CI, 1.97-2.16)), disposition on discharge (against medical advice: OR = 1.92 (95% CI, 1.66-2.20); home health care arrangements: OR = 1.24 (95% CI, 1.16-1.33)), and immunosuppression (OR = 1.42 (95% CI, 1.28-1.57)), among others. Risk factors for a complicated treatment failure were also described, after an index episode of complicated and uncomplicated diverticulitis. LIMITATIONS: The study was limited by residual confounding from missing covariates and its observational study design. CONCLUSIONS: The incidence of readmission for treatment failure after an episode of diverticulitis managed nonoperatively is 6.6%, and an index episode of complicated diverticulitis is the strongest risk factor for treatment failure. See Video Abstract at http://links.lww.com/DCR/B92. REINGRESO POR FRACASO DEL TRATAMIENTO DESPUÉS DEL TRATAMIENTO NO QUIRÚRGICO DE LA DIVERTICULITIS AGUDA: UN ANÁLISIS DE LA BASE DE DATOS DE REINGRESOS A NIVEL NACIONAL: La verdadera incidencia y los factores de riesgo para el reingreso por fracaso del tratamiento después de manejo no quirúrgico de la diverticulitis aguda siguen siendo mal definidos.Definir la incidencia y los factores de riesgo de reingreso por fracaso del tratamiento no quirúrgico de la diverticulitis aguda utilizando una base de datos nacional.Estudio de cohorte retrospectivo.Una muestra representativa de ingresos y egresos de hospitales en los Estados Unidos capturados en la base de datos nacional de reingresos hospitalarios.Pacientes adultos (≥18 años) ingresados con un diagnóstico primario de diverticulitis colónica entre 2010-2015, y que fueron tratados de forma no operativa y dados de alta del hospital vivos.Manejo no quirúrgico, que consiste en terapia médica con o sin drenaje percutáneo.Reingreso por fracaso del tratamiento (definido como un reingreso no electivo por diverticulitis dentro de los 90 días despues de ser dados de alta), fracaso del tratamiento complicado (definido como un fracaso del tratamiento con diverticulitis complicada) y el tiempo hasta el tratamiento en casos fracasaados.201.384 pacientes incluidos en total. La incidencia global de reingreso por fracaso del tratamiento fue del 6,6%. El fracaso del tratamiento fue significativamente mayor entre los pacientes con un episodio índice de diverticulitis aguda complicada en comparación con la diverticulitis aguda no complicada (12.5% vs. 5.7%, p <0.001). La mediana del tiempo hasta el reingreso por fracaso del tratamiento fue de 21.0 (20.4 - 21.6) días, y el 85% de todos los reingresos ocurrieron dentro de los 60 días posteriores a ser dados de alta. En la regresión logística múltiple, los factores asociados independientemente con el reingreso por fracaso del tratamiento fueron un índice de admisión de diverticulitis complicada (OR 2.06, IC 95% 1.97-2.16), disposición (de alta en contra del consejo médico: OR 1.92, IC 95% 1.66-2.2; atención médica domiciliaria: OR 1.24, IC 95% 1.16-1.33) e inmunosupresión (OR 1.42, IC 95% 1.28-1.57), entre otros. Los factores de riesgo para un fracaso del tratamiento complicado también se describieron, respectivamente, después de un episodio índice de diverticulitis complicada y no complicada.Covariables faltantes y diseño de estudio observacional.La incidencia de reingreso por fracaso del tratamiento después de un episodio de diverticulitis manejado de forma no operativa es del 6,6%, y un episodio índice de diverticulitis complicada es el factor de riesgo más fuerte para el fracaso del tratamiento. Consulte Video Resumen en http://links.lww.com/DCR/B92. (Traducción-Dr. Adrian E. Ortega).

Can we better predict readmission for dehydration following creation of a diverting loop ileostomy: development and validation of a prediction model and web-based risk calculator.

BACKGROUND: Dehydration is the most common morbidity following creation of a diverting loop ileostomy (DLI). We aimed to develop and validate a prediction model and web-based risk calculator for readmission for dehydration following DLI creation. METHODS: After institutional review board approval, we retrospectively reviewed the American College of Surgeons-National Surgical Quality Improvement Program (ACS-NSQIP) database between 2012 and 2017. Adult patients (> 18 years) who underwent DLI with a resection for colorectal cancer, inflammatory bowel disease, or diverticulitis were identified. Patient demographics, operative and postoperative data were collected. The final prediction model, developed in 60% of the cohort (training set) and which modeled the 30-day cumulative incidence of readmission for dehydration, was selected using highest area under the receiver operating curve (AUC) criterion. Model calibration was assessed with the Hosmer-Lemeshow goodness-of-fit test. The model was then assessed in validation and test sets, using 20% of the cohort for each. RESULTS: Of 25,638 patients in the ACS-NSQIP database who met inclusion criteria, 15,222 patients were randomly selected for the training set. The incidence of readmission for dehydration in this cohort was 2.1%. The final model with the highest AUC retained 12 candidate variables: age, sex, smoking status, diabetes, hypertension, American Society of Anesthesiologists score, type of admission, underlying diagnosis, procedure performed, operative time, index admission length of stay, and major morbidity. The model demonstrated good discrimination (AUC 0.76, 95% CI 0.74-0.79) and the Hosmer-Lemeshow goodness-of-fit test confirmed good calibration (p = 0.50). Five-thousand and seventy-three patients were available for the validation and test sets, respectively, and the model remained strong in both the validation and test sets (AUCs of 0.73 and 0.73, respectively). The prediction model was then converted into a web-based risk calculator. CONCLUSIONS: A prediction model and web-based risk calculator for readmission for dehydration after DLI creation was developed and validated, demonstrating good predictive capabilities.

A smoothed EM-algorithm for DNA methylation profiles from sequencing-based methods in cell lines or for a single cell type.

We consider the assessment of DNA methylation profiles for sequencing-derived data from a single cell type or from cell lines. We derive a kernel smoothed EM-algorithm, capable of analyzing an entire chromosome at once, and to simultaneously correct for experimental errors arising from either the pre-treatment steps or from the sequencing stage and to take into account spatial correlations between DNA methylation profiles at neighbouring CpG sites. The outcomes of our algorithm are then used to (i) call the true methylation status at each CpG site, (ii) provide accurate smoothed estimates of DNA methylation levels, and (iii) detect differentially methylated regions. Simulations show that the proposed methodology outperforms existing analysis methods that either ignore the correlation between DNA methylation profiles at neighbouring CpG sites or do not correct for errors. The use of the proposed inference procedure is illustrated through the analysis of a publicly available data set from a cell line of induced pluripotent H9 human embryonic stem cells and also a data set where methylation measures were obtained for a small genomic region in three different immune cell types separated from whole blood.

Novel insights into systemic sclerosis using a sensitive computational method to analyze whole-genome bisulfite sequencing data.

BACKGROUND: Abnormal DNA methylation is thought to contribute to the onset and progression of systemic sclerosis. Currently, the most comprehensive assay for profiling DNA methylation is whole-genome bisulfite sequencing (WGBS), but its precision depends on read depth and it may be subject to sequencing errors. SOMNiBUS, a method for regional analysis, attempts to overcome some of these limitations. Using SOMNiBUS, we re-analyzed WGBS data previously analyzed using bumphunter, an approach that initially fits single CpG associations, to contrast DNA methylation estimates by both methods. METHODS: Purified CD4+ T lymphocytes of 9 SSc and 4 control females were sequenced using WGBS. We separated the resulting sequencing data into regions with dense CpG data, and differentially methylated regions (DMRs) were inferred with the SOMNiBUS region-level test, adjusted for age. Pathway enrichment analysis was performed with ingenuity pathway analysis (IPA). We compared the results obtained by SOMNiBUS and bumphunter. RESULTS: Of 8268 CpG regions of ≥ 60 CpGs eligible for analysis with SOMNiBUS, we identified 131 DMRs and 125 differentially methylated genes (DMGs; p-values less than Bonferroni-corrected threshold of 6.05-06 controlling family-wise error rate at 0.05; 1.6% of the regions). In comparison, bumphunter identified 821,929 CpG regions, 599 DMRs (of which none had ≥ 60 CpGs) and 340 DMGs (q-value of 0.05; 0.04% of all regions). The top ranked gene identified by SOMNiBUS was FLT4, a lymphangiogenic orchestrator, and the top ranked gene on chromosome X was CHST7, known to catalyze the sulfation of glycosaminoglycans in the extracellular matrix. The top networks identified by IPA included connective tissue disorders. CONCLUSIONS: SOMNiBUS is a complementary method of analyzing WGBS data that enhances biological insights into SSc and provides novel avenues of investigation into its pathogenesis.

High Systolic Blood Pressure at Hospital Admission Is an Important Risk Factor in Models Predicting Outcome of COVID-19 Patients.

BACKGROUND: The risk that coronavirus disease 2019 (COVID-19) patients develop critical illness that can be fatal depends on their age and immune status and may also be affected by comorbidities like hypertension. The goal of this study was to develop models that predict outcome using parameters collected at admission to the hospital. METHODS AND RESULTS: This is a retrospective single-center cohort study of COVID-19 patients at the Seventh Hospital of Wuhan City, China. Forty-three demographic, clinical, and laboratory parameters collected at admission plus discharge/death status, days from COVID-19 symptoms onset, and days of hospitalization were analyzed. From 157 patients, 120 were discharged and 37 died. Pearson correlations showed that hypertension and systolic blood pressure (SBP) were associated with death and respiratory distress parameters. A penalized logistic regression model efficiently predicts the probability of death with 13 of 43 variables. A regularized Cox regression model predicts the probability of survival with 7 of above 13 variables. SBP but not hypertension was a covariate in both mortality and survival prediction models. SBP was elevated in deceased compared with discharged COVID-19 patients. CONCLUSIONS: Using an unbiased approach, we developed models predicting outcome of COVID-19 patients based on data available at hospital admission. This can contribute to evidence-based risk prediction and appropriate decision-making at hospital triage to provide the most appropriate care and ensure the best patient outcome. High SBP, a cause of end-organ damage and an important comorbid factor, was identified as a covariate in both mortality and survival prediction models.

Thousands of CpGs Show DNA Methylation Differences in ACPA-Positive Individuals.

High levels of anti-citrullinated protein antibodies (ACPA) are often observed prior to a diagnosis of rheumatoid arthritis (RA). We undertook a replication study to confirm CpG sites showing evidence of differential methylation in subjects positive vs. negative for ACPA, in a new subset of 112 individuals sampled from the population cohort and biobank CARTaGENE in Quebec, Canada. Targeted custom capture bisulfite sequencing was conducted at approximately 5.3 million CpGs located in regulatory or hypomethylated regions from whole blood; library and protocol improvements had been instituted between the original and this replication study, enabling better coverage and additional identification of differentially methylated regions (DMRs). Using binomial regression models, we identified 19,472 ACPA-associated differentially methylated cytosines (DMCs), of which 430 overlapped with the 1909 DMCs reported by the original study; 814 DMRs of relevance were clustered by grouping adjacent DMCs into regions. Furthermore, we performed an additional integrative analysis by looking at the DMRs that overlap with RA related loci published in the GWAS Catalog, and protein-coding genes associated with these DMRs were enriched in the biological process of cell adhesion and involved in immune-related pathways.

Resistance to different anthracycline chemotherapeutics elicits distinct and actionable primary metabolic dependencies in breast cancer.

Chemotherapy resistance is a critical barrier in cancer treatment. Metabolic adaptations have been shown to fuel therapy resistance; however, little is known regarding the generality of these changes and whether specific therapies elicit unique metabolic alterations. Using a combination of metabolomics, transcriptomics, and functional genomics, we show that two anthracyclines, doxorubicin and epirubicin, elicit distinct primary metabolic vulnerabilities in human breast cancer cells. Doxorubicin-resistant cells rely on glutamine to drive oxidative phosphorylation and de novo glutathione synthesis, while epirubicin-resistant cells display markedly increased bioenergetic capacity and mitochondrial ATP production. The dependence on these distinct metabolic adaptations is revealed by the increased sensitivity of doxorubicin-resistant cells and tumor xenografts to buthionine sulfoximine (BSO), a drug that interferes with glutathione synthesis, compared with epirubicin-resistant counterparts that are more sensitive to the biguanide phenformin. Overall, our work reveals that metabolic adaptations can vary with therapeutics and that these metabolic dependencies can be exploited as a targeted approach to treat chemotherapy-resistant breast cancer.

A Neanderthal OAS1 isoform protects individuals of European ancestry against COVID-19 susceptibility and severity.

To identify circulating proteins influencing Coronavirus Disease 2019 (COVID-19) susceptibility and severity, we undertook a two-sample Mendelian randomization (MR) study, rapidly scanning hundreds of circulating proteins while reducing bias due to reverse causation and confounding. In up to 14,134 cases and 1.2 million controls, we found that an s.d. increase in OAS1 levels was associated with reduced COVID-19 death or ventilation (odds ratio (OR) = 0.54, P = 7 × 10-8), hospitalization (OR = 0.61, P = 8 × 10-8) and susceptibility (OR = 0.78, P = 8 × 10-6). Measuring OAS1 levels in 504 individuals, we found that higher plasma OAS1 levels in a non-infectious state were associated with reduced COVID-19 susceptibility and severity. Further analyses suggested that a Neanderthal isoform of OAS1 in individuals of European ancestry affords this protection. Thus, evidence from MR and a case-control study support a protective role for OAS1 in COVID-19 adverse outcomes. Available pharmacological agents that increase OAS1 levels could be prioritized for drug development.

A Polygenic Risk Score as a Risk Factor for Medication-Associated Fractures.

Some commonly prescribed drugs are associated with increased risk of osteoporotic fractures. However, fracture risk stratification using skeletal measures is not often performed to identify those at risk before these medications are prescribed. We tested whether a genomically predicted skeletal measure, speed of sound (gSOS) from heel ultrasound, which was developed in 341,449 individuals from UK Biobank and tested in a separate subset consisting of 80,027 individuals, is an independent risk factor for fracture in users of fracture-related drugs (FRDs). To do this, we first assessed 80,014 UK Biobank participants (including 12,678 FRD users) for incident major osteoporotic fracture (MOF, n = 1189) and incident hip fracture (n = 209). Effects of gSOS on incident fracture were adjusted for baseline clinical fracture risk factors. We found that each standard deviation decrease in gSOS increased the adjusted odds of MOF by 42% (95% confidence interval [CI] 1.34-1.51, p < 2 × 10-16 ) and of hip fracture by 31% (95% CI 1.15-1.50, p = 9 × 10-5 ). gSOS below versus above the mean increased the adjusted odds of MOF by 79% (95% CI 1.58-2.01, p < 2 × 10-16 ) and of hip fracture by 42% (95% CI 1.08-1.88, p = 1.3 × 10-2 ). Among FRD users, each standard deviation decrease in gSOS increased the adjusted odds of MOF by 29% (nMOF = 256, 95% CI 1.14-1.46, p = 7 × 10-5 ) and of hip fracture by 30% (nhip fracture = 68, 95% CI 1.02-1.65, p = 0.0335). FRD users with gSOS below versus above the mean had a 54% increased adjusted odds of MOF (95% 1.19-1.99, p = 8.95 × 10-4 ) and a twofold increased adjusted odds of hip fracture (95% 1.19-3.31, p = 8.5 × 10-3 ). We therefore showed that genomically predicted heel SOS is independently associated with incident fracture among FRD users. © 2020 American Society for Bone and Mineral Research.

CpG-set association assessment of lipid concentration changes and DNA methylation.

Epigenome association studies that test a large number of methylation sites suffer from stringent multiple-testing corrections. This study's goals were to investigate region-based associations between DNA methylation sites and lipid-level changes in response to the treatment with fenofibrate in the GAW20 data and to investigate whether improvements in power could be obtained by taking into account correlations between DNA methylation at neighboring cytosine-phosphate-guanine (CpG) sites. To this end, we applied both a recently developed block-based data-dimension-reduction approach and a region-based variance-component (VC) linear mixed model to GAW20 data. We compared analyses of unrelated individuals with familial data. The region-based VC approach using unrelated (independent) individuals identified the gene LGALS9C as significantly associated with changes in triglycerides. However, univariate tests of individual CpG sites yielded no valid statistically significant results.

Investigating potential causal relationships between SNPs, DNA methylation and HDL.

Using data on 680 patients from the GAW20 real data set, we conducted Mendelian randomization (MR) studies to explore the causal relationships between methylation levels at selected probes (cytosine-phosphate-guanine sites [CpGs]) and high-density lipoprotein (HDL) changes (ΔHDL) using single-nucleotide polymorphisms (SNPs) as instrumental variables. Several methods were used to estimate the causal effects at CpGs of interest on ΔHDL, including a newly developed method that we call constrained instrumental variables (CIV). CIV performs automatic SNP selection while providing estimates of causal effects adjusted for possible pleiotropy, when the potentially-pleiotropic phenotypes are measured. For CpGs in or near the 10 genes identified as associated with ΔHDL using a family-based VC-score test, we compared CIV to Egger regression and the two-stage least squares (TSLS) method. All 3 approaches selected at least 1CpG in 2 genes-RNMT;C18orf19 and C6orf141-as showing a causal relationship with ΔHDL.

Associations between genetic variants in immunoregulatory genes and risk of non-Hodgkin lymphoma in a Chinese population.

We undertook a hospital-based case-control study to examine the associations between single nucleotide polymorphisms (SNPs) in selected immunoregulatory genes and non-Hodgkin lymphoma (NHL) risk in a Chinese population. One hundred and sixty-nine NHL patients diagnosed according to the World Health Organization (WHO) 2001 standard and 421 controls were recruited. Nine SNPs in three genes (IL-10, IL-1RN, and TNF-α) were selected based on predicted functions and previous study findings. Genetic association analysis was performed using the Cochran-Armitage trend test and multiple logistic regression. Four SNPs were associated with an increased risk of overall NHL: odds ratio per minor allele [ORper-minor-allele] and 95% confidence interval [CI] were 2.64 (1.75-3.98) for IL-10 rs1800893, 2.67 (1.72-4.16) for IL-1RN rs4251961, 1.80 (1.24-2.63) for TNF- α rs1800630, and 1.55 (1.02-2.37) for TNF- α rs2229094. These SNPs were also associated with an increased risk of diffuse large B-cell lymphoma (DLBCL). In addition, another SNP (TNF- α rs1041981) was associated with an increased risk of DLBCL (ORper-minor-allele=1.73, 95% CI 1.14-2.61). The findings provide evidence on the role of these immunoregulatory gene variants in NHL etiology.

Chlamydial Protease-Like Activity Factor and Type III Secreted Effectors Cooperate in Inhibition of p65 Nuclear Translocation.

The chlamydial protease-like activity factor (CPAF) is hypothesized to be an important secreted virulence factor; however, challenges in denaturing its proteolytic activity have hampered attempts to identify its legitimate targets. Here, we use a genetic and proteomic approach to identify authentic CPAF targets. Human epithelial cells infected with CPAF-sufficient and CPAF-deficient chlamydiae were lysed using known CPAF-denaturing conditions. Their protein profiles were analyzed using isobaric mass tags and liquid chromatography-tandem mass spectrometry. Comparative analysis of CPAF-sufficient and CPAF-deficient infections identified a limited number of CPAF host and chlamydial protein targets. Host targets were primarily interferon-stimulated gene products, whereas chlamydial targets were type III secreted proteins. We provide evidence supporting a cooperative role for CPAF and type III secreted effectors in blocking NF-κB p65 nuclear translocation, resulting in decreased beta interferon and proinflammatory cytokine synthesis. Genetic complementation of null organisms with CPAF restored p65 nuclear translocation inhibition and proteolysis of chlamydial type III secreted effector proteins (T3SEs). We propose that CPAF and T3SEs cooperate in the inhibition of host innate immunity. IMPORTANCE: Chlamydia trachomatis is an important human pathogen responsible for over 100 million infections each year worldwide. Its success as an intracellular pathogen revolves around its ability to evade host immunity. The chlamydial protease-like activity factor (CPAF) is a conserved serine protease secreted into the host cytosol of infected cells that is thought to play an important role in immune evasion. Currently, CPAF's authentic in situ target(s) and mechanism of action in immune evasion are poorly characterized. Using a CPAF-deficient strain and high-throughput proteomics, we report novel CPAF host and chlamydial targets. Host targets were primarily interferon-stimulated genes, whereas chlamydial targets were exclusively type III secreted proteins. We propose a novel mechanism for CPAF and type III secreted proteins in the evasion of host innate immune responses. These findings provide new insights into CPAF's function as a virulence factor and a better understanding of how chlamydiae evade host immunity.