# Phosphorelay signaling and regulation in bacteria

> **NIH NIH R35** · UNIVERSITY OF PENNSYLVANIA · 2021 · $437,107

## Abstract

Escherichia coli and related bacteria employ multi-step phosphorelays to sense intra- and extra-cellular
environmental signals and to modulate diverse cellular processes. However, the significance of the multiple
phosphoryl transfer steps that characterize these systems remains poorly understood. As part of our long-
term goal to understand the interplay between the various signal transduction systems that bacteria employ
to adapt to diverse environmental conditions, this proposal will explore several of these systems that are
particularly tractable, due to well-established inputs and/or outputs, and that play critically important roles in
E. coli physiology. The primary focus will be on two phosphorelays that are important for the transition to
anaerobiosis, Arc and Tor, as well as a phosphohistidine phosphatase that modulates the nitrogen-related
phosphotransferase system. Progress in understanding these networks will provide new insights into both
the mechanisms enabling infections by pathogens and the maintenance of a healthy microbiota in host
niches. The results may ultimately lead to the development of novel antibiotics or treatment regimens, as
well as strategies for manipulating the microbiomes to maintain the health of the host.

## Key facts

- **NIH application ID:** 10086191
- **Project number:** 1R35GM139541-01
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Mark D Goulian
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $437,107
- **Award type:** 1
- **Project period:** 2021-03-10 → 2026-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10086191

## Citation

> US National Institutes of Health, RePORTER application 10086191, Phosphorelay signaling and regulation in bacteria (1R35GM139541-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10086191. Licensed CC0.

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