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What makes a successful drug target? A target molecule with an appropriate (druggable) tertiary structure is a necessary but not the sufficient condition for success. Here we analyzed specific properties of human genes and proteins targeted by 919 FDA-approved drugs and identified several quantitative measures that distinguish them from other genes and proteins at a highly significant level. Compared to an average gene and its encoded protein(s), successful drug targets are more highly connected (but far from being the most highly connected), have higher betweenness values, lower entropies of tissue expression, and lower ratios of nonsynonymous to synonymous single-nucleotide polymorphisms. Furthermore, we have identified human tissues that are significantly over-or undertargeted relative to the full spectrum of genes that are active in each tissue. Our study provides quantitative guidelines that could aid in the computational screening of new drug targets in human cells.

The coronavirus disease 2019 (COVID-19) pandemic has significantly affected utilization of preventative health care, including vaccines. We aimed to assess HPV vaccination rates during the pandemic, and conduct a simulation model-based analysis to estimate the impact of current coverage and future pandemic recovery scenarios on disease outcomes. The model population included females and males of all ages in the US. The model compares pre-COVID vaccine uptake to 3 reduced coverage scenarios with varying recovery speed. Vaccine coverage was obtained from Truven Marketscan™. Substantially reduced coverage between March-August 2020 was observed compared to 2018–2019. The model predicted that 130,853 to 213,926 additional cases of genital warts; 22,503 to 48,157 cases of CIN1; 48,682 to 110,192 cases of CIN2/3; and 2,882 to 6,487 cases of cervical cancer will occur over the next 100 years, compared to status quo. Providers should plan efforts to recover HPV vaccination and minimize potential long-term consequences.

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High-quality label-free proteome quantification (LFQ) is valuable for clinical and pharmaceutical studies yet remains extremely challenging despite technical advances. Particularly, fluctuating precision, limited robustness, and compromised accuracy are known issues. Here, we described and validated a new strategy enabling the discovery of the LFQs of simultaneously enhanced precision, robustness, and accuracy from thousands of LFQ manipulation chains. In the proof-of-concept study, this strategy showed superior ability in identifying well-performing LFQs. An online tool incorporating this novel strategy was also developed.

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Rationale: GWAS (Genome-Wide Association Studies) have identified hundreds of genetic loci associated with atrial fibrillation (AF). However, these loci explain only a small proportion of AF heritability. Objective: To develop an approach to identify additional AF-related genes by integrating multiple omics data. Methods and Results: Three types of omics data were integrated: (1) summary statistics from the AFGen 2017 GWAS; (2) a whole blood EWAS (Epigenome-Wide Association Study) of AF; and (3) a whole blood TWAS (Transcriptome-Wide Association Study) of AF. The variant-level GWAS results were collapsed into gene-level associations using fast set-based association analysis. The CpG-level EWAS results were also collapsed into gene-level associations by an adapted SNP-set Kernel Association Test approach. Both GWAS and EWAS gene-based associations were then meta-analyzed with TWAS using a fixed-effects model weighted by the sample size of each data set. A tissue-specific network was subsequently constructed using the NetWAS (Network-Wide Association Study). The identified genes were then compared with the AFGen 2018 GWAS that contained more than triple the number of AF cases compared with AFGen 2017 GWAS. We observed that the multiomics approach identified many more relevant AF-related genes than using AFGen 2018 GWAS alone (1931 versus 206 genes). Many of these genes are involved in the development and regulation of heart- and muscle-related biological processes. Moreover, the gene set identified by multiomics approach explained much more AF variance than those identified by GWAS alone (10.4% versus 3.5%). Conclusions: We developed a strategy to integrate multiple omics data to identify AF-related genes. Our integrative approach may be useful to improve the power of traditional GWAS, which might be particularly useful for rare traits and diseases with limited sample size.

This study proposes to aggregately measure energy security performance with the principal component analysis. In its application of the methodology to four resource-poor yet economically advanced island economies in East Asia-Singapore, South Korea, Japan, and Taiwan, this study establishes a novel framework to conceptualize energy security. The framework incorporates three dimensions: vulnerability, efficiency, and sustainability, three indicators being allocated to each dimension. The study finds that all the three dimensions are critical for the resource-poor economies but have different weights in each of them. An urgent task for these four economies is to implement energy efficiency and conservation measures. Liberalization of electricity sector can be a helpful tool to reduce energy consumption and increase efficiency. All of them have been committed to promoting renewable energy development, which shall be further expanded in these economies.