The CpxAR two-component signal transduction system in Escherichia coli and other pathogens senses diverse envelope stresses and promotes the transcription of a variety of genes that remedy these stresses. An important member of the CpxAR regulon is cpxP. The CpxA-dependent transcription of cpxP has been linked to stresses such as misfolded proteins and alkaline pH. It also has been proposed that acetyl phosphate, the intermediate of the phosphotransacetylase (Pta)-acetate kinase (AckA) pathway, can activate the transcription of cpxP in a CpxA-independent manner by donating its phosphoryl group to CpxR. We tested this hypothesis by measuring the transcription of cpxP using mutants with mutations in the CpxAR pathway, mutants with mutations in the Pta-AckA pathway, and mutants with a combination of both types of mutations. From this epistasis analysis, we learned that CpxR integrates diverse stimuli. The stimuli that originate in the envelope depend on CpxA, while those associated with growth and central metabolism depend on the Pta-AckA pathway. While CpxR could receive a phosphoryl group from acetyl phosphate, this global signal was not the primary trigger for CpxR activation associated with the Pta-AckA pathway. On the strength of these results, we contend that the interactions between central metabolism and signal transduction can be quite complex and that successful investigations of such interactions must include a complete epistatic analysis.

Cancer has no borders: Generation and analysis of molecular data across multiple centers worldwide is necessary to gain statistically significant clinical insights for the benefit of patients. Here we conceived and standardized a proteotype data generation and analysis workflow enabling distributed data generation and evaluated the quantitative data generated across laboratories of the international Cancer Moonshot consortium. Using harmonized mass spectrometry (MS) instrument platforms and standardized data acquisition procedures, we demonstrate robust, sensitive, and reproducible data generation across eleven international sites on seven consecutive days in a 24/7 operation mode. The data presented from the high-resolution MS1-based quantitative data-independent acquisition (HRMS1-DIA) workflow shows that coordinated proteotype data acquisition is feasible from clinical specimens using such standardized strategies. This work paves the way for the distributed multi-omic digitization of large clinical specimen cohorts across multiple sites as a prerequisite for turning molecular precision medicine into reality.

Abbreviations Quality Control (QC) Mass spectrometry (MS) High-resolution MS1-based quantitative data-independent acquisition (HRMS1-DIA) Data-independent acquisition (DIA) Formalin-fixed, paraffin-embedded (FFPE) Data-dependent acquisition (DDA) High-grade serous ovarian carcinoma (HGSOC) Ovarian clear-cell carcinoma (OCCC) AbstractCancer has no borders: Generation and analysis of molecular data across multiple centers worldwide is necessary to gain statistically significant clinical insights for the benefit of patients.Here we conceived and standardized a proteotype data generation and analysis workflow enabling distributed data generation and evaluated the quantitative data generated across laboratories of the international Cancer Moonshot consortium. Using harmonized mass spectrometry (MS) instrument platforms and standardized data acquisition procedures, we demonstrated robust, sensitive, and reproducible data generation across eleven sites in nine countries on seven consecutive days in a 24/7 operation mode. The data presented from the high-resolution MS1-based quantitative data-independent acquisition (HRMS1-DIA) workflow shows that coordinated proteotype data acquisition is feasible from clinical specimens using such standardized strategies. This work paves the way for the distributed multi-omic digitization of large clinical specimen cohorts across multiple sites as a prerequisite for turning molecular precision medicine into reality.