Adenosine triphosphate (ATP) may be the most important biological small molecule. Since it was discovered in 1929, ATP has been regarded as life’s energy reservoir. However, this compound means more to life. Its legend starts at the dawn of life and lasts to this day. ATP must be the basic component of ancient ribozymes and may facilitate the origin of structured proteins. In the existing organisms, ATP continues to construct ribonucleic acid (RNA) and work as a protein cofactor. ATP also functions as a biological hydrotrope, which may keep macromolecules soluble in the primitive environment and can regulate phase separation in modern cells. These functions are involved in the pathogenesis of aging-related diseases and breast cancer, providing clues to discovering anti-aging agents and precision medicine tactics for breast cancer.

Oxidative damage can lead to a wide range of diseases. Nrf2 is an important transcription factor that regulates many of the cytoprotective enzymes involved in the oxidative stress response. Therefore, targeting the regulation of Nrf2 activation is one logical and effective strategy to prevent or lower the risk of oxidative stress-related diseases. Until now, most research has focused on electrophilic indirect Nrf2 activators, but the risk of ‘off-target’ effects may be associated with these activators. To find novel small non-electrophilic modulators of Nrf2, we started from chemical agents derived from a connectivity map (cMap) and identified 22 non-electrophilic potential Nrf2-activating drugs through a drug repositioning tactic. By determining the expression changes of antioxidant genes in MCF7 cells that were treated with the potential Nrf2 activators using quantitative real-time polymerase chain reaction RT-PCR (real-time polymerase chain reaction) (qRT-PCR), astemizole was found to have a greater scale of upregulating antioxidant genes NQO1, HO-1, and GCLM than the positive control d,l-sulforaphane, although the testing concentration was lower than that of the control. Astemizole is a good potential redox regulator and deserves more pharmacodynamic experimentation to test and verify its feasibility for use as an Nrf2 activator.

Phosphates are essential for modern metabolisms. A recent study reported a phosphate-free metabolic network and suggested that thioesters, rather than phosphates, could alleviate thermodynamic bottlenecks of network expansion. As a result, it was considered that a phosphorus-independent metabolism could exist before the phosphate-based genetic coding system. To explore the origin of phosphorus-dependent metabolism, the present study constructs a protometabolic network that contains phosphates prebiotically available using computational systems biology approaches. It is found that some primitive phosphorylated intermediates could greatly alleviate thermodynamic bottlenecks of network expansion. Moreover, the phosphorus-dependent metabolic network exhibits several ancient features. Taken together, it is concluded that phosphates played a role as important as that of thioesters during the origin and evolution of metabolism. Both phosphorus and sulfur are speculated to be critical to the origin of life.

The cancer atavistic theory suggests that carcinogenesis is a reverse evolution process. It is thus of great interest to explore the evolutionary origins of cancer driver genes and the relevant mechanisms underlying the carcinogenesis. Moreover, the evolutionary features of cancer driver genes could be helpful in selecting cancer biomarkers from high-throughput data. In this study, through analyzing the cancer endogenous molecular networks, we revealed that the subnetwork originating from eukaryota could control the unlimited proliferation of cancer cells, and the subnetwork originating from eumetazoa could recapitulate the other hallmarks of cancer. In addition, investigations based on multiple datasets revealed that cancer driver genes were enriched in genes originating from eukaryota, opisthokonta, and eumetazoa. These results have important implications for enhancing the robustness of cancer prognosis models through selecting the gene signatures by the gene age information.