An Antibody-CRISPR/Cas Conjugate Platform for Target-Specific Delivery and Gene Editing in Cancer.

The CRISPR/Cas system has been introduced as an innovative tool for therapy, however achieving specific delivery to the target has been a major challenge. Here, an antibody-CRISPR/Cas conjugate platform that enables specific delivery and target gene editing in HER2-positive cancer is introduced. The CRISPR/Cas system by replacing specific residues of Cas9 with an unnatural amino acid is engineered, that can be complexed with a nanocarrier and bioorthogonally functionalized with a monoclonal antibody targeting HER2. The resultant antibody-conjugated CRISPR/Cas nanocomplexes can be specifically delivered and induce gene editing in HER2-positive cancer cells in vitro. It is demonstrated that the in vivo delivery of the antibody-CRISPR/Cas nanocomplexes can effectively disrupt the plk1 gene in HER2-positive ovarian cancer, resulting in substantial suppression of tumor growth. The current study presents a useful therapeutic platform for antibody-mediated delivery of CRISPR/Cas for the treatment of various cancers and genetic diseases.

Gene-environment interaction explains a part of missing heritability in human body mass index.

Gene-environment (G×E) interaction could partially explain missing heritability in traits; however, the magnitudes of G×E interaction effects remain unclear. Here, we estimate the heritability of G×E interaction for body mass index (BMI) by subjecting genome-wide interaction study data of 331,282 participants in the UK Biobank to linkage disequilibrium score regression (LDSC) and linkage disequilibrium adjusted kinships-software for estimating SNP heritability from summary statistics (LDAK-SumHer) analyses. Among 14 obesity-related lifestyle factors, MET score, pack years of smoking, and alcohol intake frequency significantly interact with genetic factors in both analyses, accounting for the partial variance of BMI. The G×E interaction heritability (%) and standard error of these factors by LDSC and LDAK-SumHer are as follows: MET score, 0.45% (0.12) and 0.65% (0.24); pack years of smoking, 0.52% (0.13) and 0.93% (0.26); and alcohol intake frequency, 0.32% (0.10) and 0.80% (0.17), respectively. Moreover, these three factors are partially validated for their interactions with genetic factors in other obesity-related traits, including waist circumference, hip circumference, waist-to-hip ratio adjusted with BMI, and body fat percentage. Our results suggest that G×E interaction may partly explain the missing heritability in BMI, and two G×E interaction loci identified could help in understanding the genetic architecture of obesity.

Smoking-Interaction Loci Affect Obesity Traits: A Gene-Smoking Stratified Meta-Analysis of 545,131 Europeans.

INTRODUCTION: Although many studies have investigated the association between smoking and obesity, very few have analyzed how obesity traits are affected by interactions between genetic factors and smoking. Here, we aimed to identify the loci that affect obesity traits via smoking status-related interactions in European samples. METHODS: We performed stratified analysis based on the smoking status using both the UK Biobank (UKB) data (N = 334,808) and the Genetic Investigation of ANthropometric Traits (GIANT) data (N = 210,323) to identify gene-smoking interaction for obesity traits. We divided the UKB subjects into two groups, current smokers and nonsmokers, based on the smoking status, and performed genome-wide association study (GWAS) for body mass index (BMI), waist circumference adjusted for BMI (WCadjBMI), and waist-hip ratio adjusted for BMI (WHRadjBMI) in each group. And then we carried out the meta-analysis using both GWAS summary statistics of UKB and GIANT for BMI, WCadjBMI, and WHRadjBMI and computed the stratified p values (pstratified) based on the differences between meta-analyzed estimated beta coefficients with standard errors in each group. RESULTS: We identified four genome-wide significant loci in interactions with the smoking status (pstratified < 5 × 10-8): rs336396 (INPP4B) and rs12899135 (near CHRNB4) for BMI, and rs998584 (near VEGFA) and rs6916318 (near RSPO3) for WHRadjBMI. Moreover, we annotated the biological functions of the SNPs using expression quantitative trait loci (eQTL) and GWAS databases, along with publications, which revealed possible mechanisms underlying the association between the smoking status-related genetic variants and obesity. CONCLUSIONS: Our findings suggest that obesity traits can be modified by the smoking status via interactions with genetic variants through various biological pathways.