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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.

Tibetan pigs are native mammalian species on the Tibetan Plateau that have evolved distinct physiological traits that allow them to tolerate high-altitude hypoxic environments. However, the genetic mechanism underlying this adaptation remains elusive. Here, based on multitissue transcriptional data from high-altitude Tibetan pigs and low-altitude Rongchang pigs, we performed a weighted correlation network analysis (WGCNA) and identified key modules related to these tissues. Complex network analysis and bioinformatics analysis were integrated to identify key genes and three-node network motifs. We found that among the six tissues (muscle, liver, heart, spleen, kidneys, and lungs), lung tissue may be the key organs for Tibetan pigs to adapt to hypoxic environment. In the lung tissue of Tibetan pigs, we identified KLF4, BCL6B, EGR1, EPAS1, SMAD6, SMAD7, KDR, ATOH8, and CCN1 genes as potential regulators of hypoxia adaption. We found that KLF4 and EGR1 genes might simultaneously regulate the BCL6B gene, forming a KLF4–EGR1–BCL6B complex. This complex, dominated by KLF4, may enhance the hypoxia tolerance of Tibetan pigs by mediating the TGF-β signaling pathway. The complex may also affect the PI3K-Akt signaling pathway, which plays an important role in angiogenesis caused by hypoxia. Therefore, we postulate that the KLF4–EGR1–BCL6B complex may be beneficial for Tibetan pigs to survive better in the hypoxia environments. Although further molecular experiments and independent large-scale studies are needed to verify our findings, these findings may provide new details of the regulatory architecture of hypoxia-adaptive genes and are valuable for understanding the genetic mechanism of hypoxic adaptation in mammals.

Tibetan pigs are native mammalian species on the Tibetan Plateau that have evolved distinct physiological traits that allow them to tolerate high-altitude hypoxic environments. However, the genetic mechanism underlying this adaptation remains elusive. Here, based on multitissue transcriptional data from high-altitude Tibetan pigs and low-altitude Rongchang pigs, we performed a weighted correlation network analysis (WGCNA) and identified key modules related to these tissues. Complex network analysis and bioinformatics analysis were integrated to identify key genes and three-node network motifs. We found that among the six tissues (muscle, liver, heart, spleen, kidneys, and lungs), lung tissue may be the key organs for Tibetan pigs to adapt to hypoxic environment. In the lung tissue of Tibetan pigs, we identified KLF4 , BCL6B , EGR1 , EPAS1 , SMAD6 , SMAD7 , KDR , ATOH8 , and CCN1 genes as potential regulators of hypoxia adaption. We found that KLF4 and EGR1 genes might simultaneously regulate the BCL6B gene, forming a KLF4–EGR1–BCL6B complex. This complex, dominated by KLF4 , may enhance the hypoxia tolerance of Tibetan pigs by mediating the TGF-β signaling pathway. The complex may also affect the PI3K-Akt signaling pathway, which plays an important role in angiogenesis caused by hypoxia. Therefore, we postulate that the KLF4–EGR1–BCL6B complex may be beneficial for Tibetan pigs to survive better in the hypoxia environments. Although further molecular experiments and independent large-scale studies are needed to verify our findings, these findings may provide new details of the regulatory architecture of hypoxia-adaptive genes and are valuable for understanding the genetic mechanism of hypoxic adaptation in mammals. [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]

Maintenance of protein homeostasis degrades with age, contributing to aging-related decline and disease. Previous studies have primarily surveyed transcriptional aging changes. To define the effects of age directly at the protein level, we perform discovery-based proteomics in 10 tissues from 20 C57BL/6J mice, representing both sexes at adult and late midlife ages (8 and 18 months). Consistent with previous studies, age-related changes in protein abundance often have no corresponding transcriptional change. Aging results in increases in immune proteins across all tissues, consistent with a global pattern of immune infiltration with age. Our protein-centric data reveal tissue-specific aging changes with functional consequences, including altered endoplasmic reticulum and protein trafficking in the spleen. We further observe changes in the stoichiometry of protein complexes with important roles in protein homeostasis, including the CCT/TriC complex and large ribosomal subunit. These data provide a foundation for understanding how proteins contribute to systemic aging across tissues. [Display omitted] • Quantified 10,250 total proteins across 20 mice ages 8 and 18 months • Quantified 200 anatomical proteomes across 10 tissues and at two ages • Immune, proteostatic, and metabolic protein abundances change with age • Altered protein complex stoichiometry and substrates correlate with age Organismal aging drives pleiotropic changes in tissue and cellular homeostasis. By quantifying the proteomes of 10 tissues, including three brain sections, in aging mice, Keele et al. explore the regulation of proteins and protein complexes with age. They observe cross-tissue and tissue-specific age- and age-by-sex-based remodeling of proteomes. [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]