PharmGWAS: a GWAS-based knowledgebase for drug repurposing.

Leveraging genetics insights to promote drug repurposing has become a promising and active strategy in pharmacology. Indeed, among the 50 drugs approved by FDA in 2021, two-thirds have genetically supported evidence. In this regard, the increasing amount of widely available genome-wide association studies (GWAS) datasets have provided substantial opportunities for drug repurposing based on genetics discoveries. Here, we developed PharmGWAS, a comprehensive knowledgebase designed to identify candidate drugs through the integration of GWAS data. PharmGWAS focuses on novel connections between diseases and small-molecule compounds derived using a reverse relationship between the genetically-regulated expression signature and the drug-induced signature. Specifically, we collected and processed 1929 GWAS datasets across a diverse spectrum of diseases and 724 485 perturbation signatures pertaining to a substantial 33609 molecular compounds. To obtain reliable and robust predictions for the reverse connections, we implemented six distinct connectivity methods. In the current version, PharmGWAS deposits a total of 740 227 genetically-informed disease-drug pairs derived from drug-perturbation signatures, presenting a valuable and comprehensive catalog. Further equipped with its user-friendly web design, PharmGWAS is expected to greatly aid the discovery of novel drugs, the exploration of drug combination therapies and the identification of drug resistance or side effects. PharmGWAS is available at https://ngdc.cncb.ac.cn/pharmgwas.

TargetGene: a comprehensive database of cell-type-specific target genes for genetic variants.

Annotating genetic variants to their target genes is of great importance in unraveling the causal variants and genetic mechanisms that underlie complex diseases. However, disease-associated genetic variants are often located in non-coding regions and manifest context-specific effects, making it challenging to accurately identify the target genes and regulatory mechanisms. Here, we present TargetGene (https://ngdc.cncb.ac.cn/targetgene/), a comprehensive database reporting target genes for human genetic variants from various aspects. Specifically, we collected a comprehensive catalog of multi-omics data at the single-cell and bulk levels and from various human tissues, cell types and developmental stages. To facilitate the identification of Single Nucleotide Polymorphism (SNP)-to-gene connections, we have implemented multiple analytical tools based on chromatin co-accessibility, 3D interaction, enhancer activities and quantitative trait loci, among others. We applied the pipeline to evaluate variants from nearly 1300 Genome-wide association studies (GWAS) and assembled a comprehensive atlas of multiscale regulation of genetic variants. TargetGene is equipped with user-friendly web interfaces that enable intuitive searching, navigation and browsing through the results. Overall, TargetGene provides a unique resource to empower researchers to study the regulatory mechanisms of genetic variants in complex human traits.

COLOCdb: a comprehensive resource for multi-model colocalization of complex traits.

Large-scale genome-wide association studies (GWAS) have provided profound insights into complex traits and diseases. Yet, deciphering the fine-scale molecular mechanisms of how genetic variants manifest to cause the phenotypes remains a daunting task. Here, we present COLOCdb (https://ngdc.cncb.ac.cn/colocdb), a comprehensive genetic colocalization database by integrating more than 3000 GWAS summary statistics and 13 types of xQTL to date. By employing two representative approaches for the colocalization analysis, COLOCdb deposits results from three key components: (i) GWAS-xQTL, pair-wise colocalization between GWAS loci and different types of xQTL, (ii) GWAS-GWAS, pair-wise colocalization between the trait-associated genetic loci from GWASs and (iii) xQTL-xQTL, pair-wise colocalization between the genetic loci associated with molecular phenotypes in xQTLs. These results together represent the most comprehensive colocalization analysis, which also greatly expands the list of shared variants with genetic pleiotropy. We expect that COLOCdb can serve as a unique and useful resource in advancing the discovery of new biological mechanisms and benefit future functional studies.

Brain Catalog: a comprehensive resource for the genetic landscape of brain-related traits.

A broad range of complex phenotypes are related to dysfunctions in brain (hereafter referred to as brain-related traits), including various mental and behavioral disorders and diseases of the nervous system. These traits in general share overlapping symptoms, pathogenesis, and genetic components. Here, we present Brain Catalog (https://ngdc.cncb.ac.cn/braincatalog), a comprehensive database aiming to delineate the genetic components of more than 500 GWAS summary statistics datasets for brain-related traits from multiple aspects. First, Brain Catalog provides results of candidate causal variants, causal genes, and functional tissues and cell types for each trait identified by multiple methods using comprehensive annotation datasets (58 QTL datasets spanning 6 types of QTLs). Second, Brain Catalog estimates the SNP-based heritability, the partitioning heritability based on functional annotations, and genetic correlations among traits. Finally, through bidirectional Mendelian randomization analyses, Brain Catalog presents inference of risk factors that are likely causal to each trait. In conclusion, Brain Catalog presents a one-stop shop for the genetic components of brain-related traits, potentially serving as a valuable resource for worldwide researchers to advance the understanding of how GWAS signals may contribute to the biological etiology of brain-related traits.

The association of metabolic syndrome score trajectory patterns with risk of all cancer types.

BACKGROUND: Metabolic syndrome (MetS) elevates cancer risk. However, a single MetS assessment does not fully reveal the long-term association with cancer. Inflammation, alongside MetS, could synergistically expedite both the onset and advancement of cancer. This study aims to investigate MetS score trajectories and cancer risk in a large, prospective cohort study. METHODS: The authors prospectively examined the relationship between MetS score trajectory patterns and new-onset cancer in 44,115 participants. Latent mixture modeling was used to identify the MetS score trajectories. Cox proportional hazards regression models were used to evaluate the association between MetS score trajectory patterns and the risk of overall and site-specific cancers. RESULTS: Four MetS score trajectory patterns were identified: low-stable (n = 4657), moderate-low (n = 18,018), moderate-high (n = 18,288), and elevated-increasing (n = 3152). Compared to participants with a low-stable trajectory pattern, the elevated-increasing trajectory pattern was associated with an elevated risk of overall (hazard ratio [HR], 1.27; 95% confidence interval [CI], 1.04-1.55), breast (HR, 2.11; 95% CI, 1.04-4.34), endometrial (HR, 3.33; 95% CI, 1.16-6.77), kidney (HR, 4.52; 95% CI, 1.17-10.48), colorectal (HR, 2.54; 95% CI, 1.27-5.09), and liver (HR, 1.61; 95% CI, 1.09-4.57) cancers. Among participants with chronic inflammation (C-reactive protein levels ≥3 mg/L), the elevated-increasing trajectory pattern was significantly associated with subsequent breast, endometrial, colorectal, and liver cancers. CONCLUSIONS: Trajectories of MetS scores are associated with the occurrence of cancers, especially breast, endometrial, kidney, colorectal, and liver cancers, emphasizing the importance of long-term monitoring and evaluation of MetS. PLAIN LANGUAGE SUMMARY: The association between long-term elevated metabolic syndrome (MetS) scores and a heightened risk of various cancers is a pivotal finding of our study. Our research further indicates that individuals with MetS, particularly when coupled with chronic inflammation, are at an increased risk of cancer. We propose that sustained monitoring and management of MetS could be beneficial in reducing cancer risk.

Phospholipid Type Regulates Protein Corona Composition and In Vivo Performance of Lipid Nanodiscs.

Over the years, there has been significant interest in PEGylated lipid-based nanocarriers within the drug delivery field. The inevitable interplay between the nanocarriers and plasma protein plays a pivotal role in their in vivo biological fate. Understanding the factors influencing lipid-based nanocarrier and protein corona interactions is of paramount importance in the design and clinical translation of these nanocarriers. Herein, discoid-shaped lipid nanodiscs (sNDs) composed of different phospholipids with varied lipid tails and head groups were fabricated. We investigated the impact of phospholipid components on the interaction between sNDs and serum proteins, particle stability, and biodistribution. The results showed that all of these lipid nanodiscs remained stable over a 15 day storage period, while their stability in the blood serum demonstrated significant differences. The sND composed of POPG exhibited the least stability due to its potent complement activation capability, resulting in rapid blood clearance. Furthermore, a negative correlation between the complement activation capability and serum stability was identified. Pharmacokinetic and biodistribution experiments indicated that phospholipid composition did not influence the capability of sNDs to evade the accelerated blood clearance phenomenon. Complement deposition on the sND was inversely associated with the area under the curve. Additionally, all lipid nanodiscs exhibited dominant adsorption of apolipoprotein. Remarkably, the POPC-based lipid nanodisc displayed a significantly higher deposition of apolipoprotein E, contributing to an obvious brain distribution, which provides a promising tool for brain-targeted drug delivery.

The Prognostic Utility of the Triceps Skinfold Thickness Albumin Index in Colorectal Cancer Patients with Cachexia.

PURPOSE: To develop a simple and convenient inflammation-nutrition-adiposity biomarker to complement the TNM staging system, further assess the prognosis of patients with colorectal cancer cachexia. METHODS: This study was a multi-centre cohort study. The triceps skinfold thickness-albumin index (TA) was calculated by combining the triceps skinfold thickness (TSF) and serum albumin levels. Kaplan-Meier analysis and Cox proportional risk regression models were used to assess the relationship between the TA and all-cause mortality. Internal validation was carried out. RESULTS: We included 1025 patients with colorectal cancer cachexia, 61.2% of whom were male, with a mean age of 58.91 (12.45) years. As the TA increased, overall mortality decreased in female patients (hazard ratio [HR], 0.95) but not in male patients (HR, 0.99). Multivariate Cox analysis showed that patients in the normal TA group had a significantly lower risk of death than those in the low TA group (HR, 0.53, 95% CI, 0.40-0.72). Patients with a normal TA had a lower risk of malnutrition, poor quality of life, and poor short-term prognosis than those with a low TA. CONCLUSIONS: TA index enables clinicians to assess the prognosis of patients as early as possible to improve the survival of patients with colorectal cancer cachexia.

Geriatric Nutritional Risk Index Combined with Calf Circumference Can be a Good Predictor of Prognosis in Patients Undergoing Surgery for Gastric or Colorectal Cancer.

OBJECTIVES: To explore the effect of combined hematological and physical measurement indicators on the prognosis of patients undergoing surgery for gastric or colorectal cancer and to screen for the best prognostic indicators. INTRODUCTION: Gastric and colorectal cancer is a widespread health concern worldwide and one of the major contributors to cancer-related death. The hematological and physical measurement indicators have been shown to associate with the prognosis of patients undergoing surgery for gastric or colorectal cancer, respectively, but it is still unclear whether the combination of the two can reflect the prognosis more effectively. METHODS: Thirteen hematological indicators and 5 physical measurement indicators were selected in this study, and the most promising ones were screened using LASSO regression. Then, the best prognostic indicators were selected by time-ROC curves. Survival curves were constructed using the Kaplan-Meier method, and the effects of hematological and physical measurement indicators on the prognosis of patients undergoing surgery for gastric or colorectal cancers were evaluated by Cox proportional risk regression analysis. In addition, the relationship between hematological and physical measurement indicators on secondary outcomes, including length of stay, hospitalization costs, intensive care unit (ICU) admission, and patients' subjective global assessment scores (PGSGA), was explored. RESULTS: After initial screening, among the hematological indicators, the geriatric nutritional risk index (GNRI) showed the highest mean area under the curve (AUC) values. Among body measures, calf circumference (CC) showed the highest mean AUC value. Further analyses showed that the combination of combined nutritional prognostic index (GNRI) and calf circumference (CC) (GNRI-CC) had the best performance in predicting the prognosis of patients undergoing surgery for gastric or colorectal cancers. Low GNRI, low CC, and low GNRI-low CC increased the risk of death by 44%, 48%, and 104%, respectively. Sensitivity analyses showed the same trend. In addition, low GNRI-low CC increased the risk of malnutrition by 17%. CONCLUSION: This study emphasizes that a combination of blood measures and body measures is essential to accurately assess the prognosis of patients undergoing surgery for gastric or colorectal cancers. The GNRI-CC is a good prognostic indicator and can also assess the risk of possible malnutrition.

Association of systemic inflammation with the obesity paradox in cancer: results from multi-cohort studies.

AIMS: This study aimed to explore whether the obesity paradox exists in overall and specific cancers and to investigate the role of systemic inflammation in the obesity paradox. METHODS: The Cox proportional hazard model was used to explore the relationship between body mass index (BMI) and all-cause mortality. The mediated effect was used to investigate the proportion of systemic inflammation mediating the relationship between BMI and cancer survival risk. RESULTS: The survival probability showed a step-like increase with an increase in BMI regardless of pathological stage. Approximately 10.8%-24.0% of the overall association between BMI and all-cause mortality in cancer was mediated by inflammation. In the internal validation, we found evidence of the obesity paradox in all body composition obtained using BIA, with inflammation remaining an important mediating factor. Furthermore, we also validated the existence of the obesity paradox of cancer in NHANES. Systemic inflammation remains an important factor in mediating the association between BMI and prognosis in cancer patients. CONCLUSIONS: The obesity paradox is prevalent in most cancers, except for hepatic biliary cancer and breast cancer. Inflammation may be one of the true features of the obesity paradox in cancer.

Reexamining the effects of drug loading on the in vivo performance of PEGylated liposomal doxorubicin.

Higher drug loading employed in nanoscale delivery platforms is a goal that researchers have long sought after. But such viewpoint remains controversial because the impacts that nanocarriers bring about on bodies have been seriously overlooked. In the present study we investigated the effects of drug loading on the in vivo performance of PEGylated liposomal doxorubicin (PLD). We prepared PLDs with two different drug loading rates: high drug loading rate, H-Dox, 12.9% w/w Dox/HSPC; low drug loading rate, L-Dox, 2.4% w/w Dox/HSPC (L-Dox had about 5 folds drug carriers of H-Dox at the same Dox dose). The pharmaceutical properties and biological effects of H-Dox and L-Dox were compared in mice, rats or 4T1 subcutaneous tumor-bearing mice. We showed that the lowering of doxorubicin loading did not cause substantial shifts to the pharmaceutical properties of PLDs such as in vitro and in vivo stability (stable), anti-tumor effect (equivalent effective), as well as tissue and cellular distribution. Moreover, it was even more beneficial for mitigating the undesired biological effects caused by PLDs, through prolonging blood circulation and alleviating cutaneous accumulation in the presence of pre-existing anti-PEG Abs due to less opsonins (e.g. IgM and C3) deposition on per particle. Our results warn that the effects of drug loading would be much more convoluted than expected due to the complex intermediation between nanocarriers and bodies, urging independent investigation for each individual delivery platform to facilitate clinical translation and application.

Machine learning to identify precachexia and cachexia: a multicenter, retrospective cohort study.

BACKGROUND: Detection of precachexia is important for the prevention and treatment of cachexia. However, how to identify precachexia is still a challenge. OBJECTIVE: This study aimed to detect cancer precachexia using a simple method and distinguish the different characteristics of precachexia and cachexia. METHODS: We included 3896 participants in this study. We used all baseline characteristics as input variables and trained machine learning (ML) models to calculate the importance of the variables. After filtering the variables based on their importance, the models were retrained. The best model was selected based on the receiver operating characteristic value. Subsequently, we used the same method and process to identify patients with precachexia in a noncachexia population using the same method and process. RESULTS: Participants in this study included 2228 men (57.2%) and 1668 women (42.8%), of whom 471 were diagnosed with precachexia, 1178 with cachexia, and the remainder with noncachexia. The most important characteristics of cachexia were eating changes, arm circumference, high-density lipoprotein (HDL) level, and C-reactive protein albumin ratio (CAR). The most important features distinguishing precachexia were eating changes, serum creatinine, HDL, handgrip strength, and CAR. The two logistic regression models for screening for cachexia and diagnosing precachexia had the highest area under the curve values of 0.830 and 0.701, respectively. Calibration and decision curves showed that the models had good accuracy. CONCLUSION: We developed two models for identifying precachexia and cachexia, which will help clinicians detect and diagnose precachexia.

Remnant cholesterol is an effective biomarker for predicting survival in patients with breast cancer.

BACKGROUND: Breast cancer is the most common malignancy in women worldwide. The relationship between remnant cholesterol (RC) and the prognosis of patients with breast cancer has not been clearly reported. This study investigated the prognostic value of RC in predicting mortality in patients with breast cancer. METHODS: This study prospectively analysed 709 women patients with breast cancer from the Investigation on Nutrition Status and Clinical Outcome of Common Cancers (INSCOC) project. Restricted cubic splines were used to analyse the dose-response relationship between RC and breast cancer mortality. The Kaplan-Meier method was used to evaluate the overall survival of patients with breast cancer. A Cox regression analyses was performed to assess the independent association between RC and breast cancer mortality. Inverse probability of treatment weighting (IPTW) using the propensity score was used to reduce confounding. Sensitivity analysis was performed after excluding patients with underlying diseases and survival times shorter than one year. RESULTS: A linear dose-response relationship was identified between RC and the risk of all-cause mortality in patients with breast cancer (p = 0.036). Kaplan-Meier survival analysis and log-rank test showed that patients with high RC levels had poorer survival than those with low RC levels (p = 0.007). Univariate and multivariate Cox regression analyses showed that RC was an independent risk factor for mortality in women patients with breast cancer. IPTW-adjusted analyses and sensitivity analyses showed that CR remained a prognostic factor. CONCLUSIONS: RC is an independent risk factor for the prognosis of patients with breast cancer, and patients with higher RC levels have poorer survival.

Exploring the impact of women-specific reproductive factors on phenotypic aging and the role of life's essential 8.

BACKGROUND: Aging is an inevitable biological process. Accelerated aging renders adults more susceptible to chronic diseases and increases their mortality rates. Previous studies have reported the relationship between lifestyle factors and phenotypic aging. However, the relationship between intrinsic factors, such as reproductive factors, and phenotypic aging remains unclear. METHODS: This study utilized data from the National Health and Nutrition Examination Survey (NHANES), spanning from 1999 to 2010 and 2015-2018, with 14,736 adult women. Random forest imputation was used to handle missing covariate values in the final cohort. Weighted linear regression was utilized to analyze the relationship between women-specific reproductive factors and PhenoAgeAccel. Considering the potential impact of menopausal status on the results, additional analyses were conducted on premenopausal and postmenopausal participants. Additionally, the Life's Essential 8 (LE8) was used to investigate the impact of healthy lifestyle and other factors on the relationship between women-specific reproductive factors and PhenoAgeAccel. Stratified analyses were conducted based on significant interaction p-values. RESULTS: In the fully adjusted models, delayed menarche and gynecological surgery were associated with increased PhenoAgeAccel, whereas pregnancy history were associated with a decrease. Additionally, early or late ages of menopause, first live birth, and last live birth can all negatively impact PhenoAgeAccel. The relationship between women-specific reproductive factors and PhenoAgeAccel differs between premenopausal and postmenopausal women. High LE8 scores positively impacted the relationship between certain reproductive factors (age at menarche, age at menopause, age at first live birth, and age at last live birth) and phenotypic age acceleration. Stratified analysis showed significant interactions for the following variables: BMI with age at menarche, pregnancy history, and age at menopause; ethnicity with age at menopause, age at first live birth, and parity; smoking status with use of contraceptive pills and gynecologic surgery; hypertension with use of contraceptive pills, pregnancy history, and age at menopause. CONCLUSION: Delayed menarche, gynecological surgery, and early or late ages of menopause, first live birth, and last live birth are associated with accelerated phenotypic aging. High LE8 score may alleviate the adverse effects of reproductive factors on phenotypic aging.

Cholesterol-modified prognostic nutritional index (CPNI) as an effective tool for assessing the nutrition status and predicting survival in patients with breast cancer.

BACKGROUND: Malnutrition is associated with poor overall survival (OS) in breast cancer patients; however, the most predictive nutritional indicators for the prognosis of patients with breast cancer are not well-established. This study aimed to compare the predictive effects of common nutritional indicators on OS and to refine existing nutritional indicators, thereby identifying a more effective nutritional evaluation indicator for predicting the prognosis in breast cancer patients. METHODS: This prospective study analyzed data from 776 breast cancer patients enrolled in the "Investigation on Nutritional Status and its Clinical Outcome of Common Cancers" (INSCOC) project, which was conducted in 40 hospitals in China. We used the time-dependent receiver operating characteristic curve (ROC), Kaplan-Meier survival curve, and Cox regression analysis to evaluate the predictive effects of several nutritional assessments. These assessments included the patient-generated subjective nutrition assessment (PGSGA), the global leadership initiative on malnutrition (GLIM), the controlling nutritional status (CONUT), the nutritional risk index (NRI), and the prognostic nutritional index (PNI). Utilizing machine learning, these nutritional indicators were screened through single-factor analysis, and relatively important variables were selected to modify the PNI. The modified PNI, termed the cholesterol-modified prognostic nutritional index (CPNI), was evaluated for its predictive effect on the prognosis of patients. RESULTS: Among the nutritional assessments (including PGSGA, GLIM, CONUT, NRI, and PNI), PNI showed the highest predictive ability for patient prognosis (time-dependent ROC = 0.58). CPNI, which evolved from PNI, emerged as the superior nutritional index for OS in breast cancer patients, with the time-dependent ROC of 0.65. It also acted as an independent risk factor for mortality (p < 0.05). Moreover, the risk of malnutrition and mortality was observed to increase gradually among both premenopausal and postmenopausal age women, as well as among women categorized as non-overweight, overweight, and obese. CONCLUSIONS: The CPNI proves to be an effective nutritional assessment tool for predicting the prognosis of patients with breast cancer.

Muscle distribution in relation to all-cause and cause-specific mortality in young and middle-aged adults.

BACKGROUND: The relationship between muscle and prognosis, especially that between muscle distribution across different body parts, and the related prognosis is not well established. OBJECTIVE: To investigate the relationship between muscle distribution and all-cause and cause-specific mortality and their potential modifiers. DESIGN: Longitudinal cohort study. C-index, IDI, and NRI were used to determine the best indicator of prognosis. COX regression analysis was performed to explore the relationship between variables and outcomes. Interaction and subgroup analyses were applied to identify the potential modifiers. PARTICIPANTS: A total of 5052 participants (weighted: 124,841,420) extracted from the NHANES 2003-2006 of median age 45 years and constituting 50.3% men were assessed. For validation, we included 3040 patients from the INSCOC cohort in China. MAIN MEASURES: Muscle mass and distribution. KEY RESULTS: COX regression analysis revealed that upper limbs (HR = 0.41, 95% CI 0.33-0.51), lower limbs (HR = 0.54, 95% CI 0.47-0.64), trunk (HR = 0.71, 95% CI, 0.59-0.85), gynoid (HR = 0.47, 95% CI 0.38-0.58), and total lean mass (HR = 0.55, 95% CI 0.45-0.66) were all associated with the better survival of participants (P trend < 0.001). The changes in the lean mass ratio of the upper and lower limbs and the lean mass ratio of the android and gynoid attenuated the protective effect of lean mass. Age and sex acted as potential modifiers, and the relationship between lean mass and the prognosis was more significant in men and middle-aged participants when compared to that in other age groups. Sensitive analyses depicted that despite lean mass having a long-term impact on prognosis (15 years), it has a more substantial effect on near-term survival (5 years). CONCLUSION: Muscle mass and its distribution affect the prognosis with a more significant impact on the near-term than that on the long-term prognosis. Age and sex acted as vital modifiers.

Machine learning model for anti-cancer drug combinations: Analysis, prediction, and validation.

Drug combination therapy is a highly effective approach for enhancing the therapeutic efficacy of anti-cancer drugs and overcoming drug resistance. However, the innumerable possible drug combinations make it impractical to screen all synergistic drug pairs. Moreover, biological insights into synergistic drug pairs are still lacking. To address this challenge, we systematically analyzed drug combination datasets curated from multiple databases to identify drug pairs more likely to show synergy. We classified drug pairs based on their MoA and discovered that 110 MoA pairs were significantly enriched in synergy in at least one type of cancer. To improve the accuracy of predicting synergistic effects of drug pairs, we developed a suite of machine learning models that achieve better predictive performance. Unlike most previous methods that were rarely validated by wet-lab experiments, our models were validated using two-dimensional cell lines and three-dimensional tumor slice culture (3D-TSC) models, implying their practical utility. Our prediction and validation results indicated that the combination of the RTK inhibitors Lapatinib and Pazopanib exhibited a strong therapeutic effect in breast cancer by blocking the downstream PI3K/AKT/mTOR signaling pathway. Furthermore, we incorporated molecular features to identify potential biomarkers for synergistic drug pairs, and almost all potential biomarkers found connections between drug targets and corresponding molecular features using protein-protein interaction network. Overall, this study provides valuable insights to complement and guide rational efforts to develop drug combination treatments.

Genome-wide epistasis study highlights genetic interactions influencing severity of COVID-19.

Coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may lead to life-threatening respiratory symptoms. Understanding the genetic basis of the prognosis of COVID-19 is important for risk profiling of potentially severe symptoms. Here, we conducted a genome-wide epistasis study of COVID-19 severity in 2243 patients with severe symptoms and 12,612 patients with no or mild symptoms from the UK Biobank, followed by a replication study in an independent Spanish cohort (1416 cases, 4382 controls). Our study highlighted 3 interactions with genome-wide significance in the discovery phase, nominally significant in the replication phase, and enhanced significance in the meta-analysis. For example, the lead interaction was found between rs9792388 upstream of PDGFRL and rs3025892 downstream of SNAP25, where the composite genotype of rs3025892 CT and rs9792388 CA/AA showed higher risk of severe disease than any other genotypes (P = 2.77 × 10-12, proportion of severe cases = 0.24 ~ 0.29 vs. 0.09 ~ 0.18, genotypic OR = 1.96 ~ 2.70). This interaction was replicated in the Spanish cohort (P = 0.002, proportion of severe cases = 0.30 ~ 0.36 vs. 0.14 ~ 0.25, genotypic OR = 1.45 ~ 2.37) and showed enhanced significance in the meta-analysis (P = 4.97 × 10-14). Notably, these interactions indicated a possible molecular mechanism by which SARS-CoV-2 affects the nervous system. The first exhaustive genome-wide screening for epistasis improved our understanding of genetic basis underlying the severity of COVID-19.

Association between the Dietary Inflammatory Index and All-Cause Mortality in Adults with Obesity.

INTRODUCTION: The dietary inflammatory index (DII) is associated with numerous chronic noncommunicable diseases. Previous studies have shown that the pro-inflammatory DII categories are associated with abdominal and simple obesity. However, the association between DII and mortality in patients with abdominal obesity and simple overweight or obesity remains unclear. METHODS: We used data from the US National Health and Nutrition Examination Survey (NHANES) from 2007 to 2018. A DII >0 (positive DII) was defined as a pro-inflammatory diet. A restricted cubic spline curve was used to describe the trend between DII and all-cause mortality. We then examined the association between DII and all-cause mortality in different body types using a Cox regression analysis and investigated the differences between sexes. Finally, the mediating effects of systemic inflammation were explored. RESULTS: A pro-inflammatory diet increased all-cause mortality in adults with abdominal obesity (aHR: 1.31, 95% confidence interval [CI]: 1.11-1.54; p < 0.001) and with simple overweight or obesity (aHR: 1.30, 95% CI: 1.11-1.53; p < 0.001). In addition, the most pro-inflammatory DII increased the risk of mortality by 43% (hazard ratio [HR]: Q4 vs. Q1 = 1.43, 95% CI = 1.14-1.79; p = 0.002; p for trend = 0.003) and 39% (HR: Q4 vs. Q1 = 1.39, 95% CI = 1.13-1.74; p = 0.003; p for trend = 0.009) in participants with abdominal obesity and with simple overweight or obesity, respectively. However, this association was not present in normal-sized participants. Compared with men, women resisted the effects of a pro-inflammatory diet. Mediation analysis showed that white blood cell and neutrophil were mediators of the association between DII and all-cause mortality (p < 0.001). CONCLUSION: A pro-inflammatory diet is associated with all-cause mortality in adults with abdominal obesity and simple overweight or obesity, and this effect differs between men and women. Systemic inflammation may mediate the association between DII and all-cause mortality.

Folic Acid Enables Targeting Delivery of Lipodiscs by Circumventing IgM-Mediated Opsonization.

Folic acid (FA) is one of the most widely utilized small-molecule ligands for cancer targeted drug delivery. Natural IgM was recently found to avidly absorb on the surface of FA-functionalized liposomes (FA-sLip), negatively regulating the in vivo performance by efficiently activating complement. Herein, FA-functionalized lipodiscs (FA-Disc) were constructed to successfully circumvent IgM-mediated opsonization and retained binding activity with folate receptors in vivo. The FA moiety along with the bound IgM was restricted to the highly curved rim of lipodiscs, leading to IgM incapability of presenting the membrane-bound conformation to trigger complement activation. The C1q docking, C3 binding, and C5a release were blocked and accelerated blood clearance phenomenon was mitigated of FA-Disc. FA-Disc retained folate binding activity and could effectively target folate receptor positive tumors in vivo. The present study provides a useful solution to avoid the negative regulation by IgM and achieve FA-enabled targeting by exploring disc-shaped nanocarriers.

Combating Cancer Stem-Like Cell-Derived Resistance to Anticancer Protein by Liposome-Mediated Acclimatization Strategy.

Antibody-based therapeutics, which induce apoptosis of malignant cells by selectively binding to their receptors, hold tremendous promise for clinical cancer therapy. Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) has received considerable interest due to its favorable capability of activating apoptosis in cancer cells by interacting with death receptors (DRs). However, cancer stem-like cells (CSCs) show deficient or lower DR and are highly resistant to TRAIL-mediated apoptosis limiting the therapeutic efficacy. Here, we report a liposome-mediated acclimatization strategy to overcome the CSC-emanated TRAIL resistance. The liposomal assemblies coencapsulating plasmid DNA encoding TRAIL and salinomycin enable cancer cells as protein generators to express TRAIL, and more importantly, can acclimatize resistant CSCs to be sensitized to the TRAIL-triggered apoptosis by salinomycin-induced upregulation of DR expression on CSCs. This programmable liposome-based drug codelivery system shows the potential to efficiently eliminate CSCs and inhibit CSC-enriched tumor growth in the orthotopic colon tumor mouse model.