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Most complex diseases involve genetic and environmental risk factors, engage multiple cells and tissues, and follow a polygenic or omnigenic model depicting numerous genes contributing to pathophysiology. These multidimensional complexities pose challenges to traditional approaches that examine individual factors. In turn, multitissue multiomics systems biology has emerged to comprehensively elucidate within- and cross-tissue molecular networks underlying gene-by-environment interactions and contributing to complex diseases. The power of systems biology in retrieving novel insights and formulating new hypotheses has been well documented. However, the field faces various challenges that call for debate and action. In this opinion article, I discuss the concepts, benefits, current state, and challenges of the field and point to the next steps toward network-based systems medicine. Recent advances in omics technologies have empowered multitissue multiomics systems biology, a discipline that aims to dissect the multidimensional complexities of human diseases. Studies integrating large-scale genetic associations with other omics have resolved tissue-specific molecular networks and pathways perturbed by genetic risks of diseases. Systematic investigation of multiomic domains across tissues in response to environmental exposure, such as diet, chemicals, and pathogens, has revealed fresh insights into the target tissues, genes, and molecular networks underlying environmental risks of diseases. The holistic within- and between-tissue networks of diseases, unraveled by systems biology, offer comprehensive mechanistic insights and help formulate data-driven hypotheses to guide network-based medicine targeting specific tissues, genes, and pathways tailored to specific risks. [ABSTRACT FROM AUTHOR]

Approximately 140 million people worldwide are exposed to inorganic arsenic through contaminated drinking water. Chronic exposure increases risk for cancers as well as cardiovascular, respiratory, and neurologic diseases. Arsenic metabolism involves the AS3MT (arsenic methyltransferase) gene, and arsenic metabolism efficiency (AME, measured as relative concentrations of arsenic metabolites in urine) varies among individuals. Inherited genetic variation in the 10q24.32 region, containing AS3MT , influences AME, but the mechanisms remain unclear. To better understand these mechanisms, we use tissue-specific expression data from GTEx (Genotype-tissue Expression project) to identify cis -eQTLs (expression quantitative trait loci) for AS3MT and other nearby genes. We combined these data with results from a genome-wide association study of AME using "colocalization analysis," to determine if 10q24.32 SNPs (single nucleotide polymorphisms) that affect AME also affect expression of AS3MT or nearby genes. These analyses identified cis -eQTLs for AS3MT in 38 tissue types. Colocalization results suggest that the casual variant represented by AME lead SNP rs4919690 impacts expression of AS3MT in 13 tissue types (> 80% probability). Our results suggest this causal SNP also regulates/coregulates expression of nearby genes: BORCS7 (43 tissues), NT5C2 (2 tissues), CYP17A1-AS1 (1 tissue), and RP11-724N1.1 (1 tissue). The rs4919690 allele associated with decreased AME is associated with decreased expression of AS3MT (and other coregulated genes). Our study provides a potential biological mechanism for the association between 10q24.32 variation and AME and suggests that the causal variant, represented by rs4919690, may impact AME (as measured in urine) through its effects on arsenic metabolism occurring in multiple tissue types. [ABSTRACT FROM AUTHOR]

We present a novel algorithm for tetrahedral image-to-mesh conversion which allows for guaranteed bounds on the smallest dihedral angle and on the distance between the boundaries of the mesh and the boundaries of the tissues. The algorithm produces a small number of mesh elements that comply with these bounds. We also describe and evaluate our implementation of the proposed algorithm that is compatible in performance with a state-of-the art Delaunay code, but in addition solves the small dihedral angle problem. [ABSTRACT FROM AUTHOR]