# Functional and Structural Analysis of the Dot/Icm Type IVB Secretion Machine

> **NIH NIH R01** · YALE UNIVERSITY · 2024 · $586,250

## Abstract

PROJECT SUMMARY/ABSTRACT
Legionella pneumophila and Coxiella burnetii are intracellular bacterial pathogens capable of causing human
disease. A notable feature of these two pathogens is that they retain a common virulence determinant that is
essential for their ability to replicate intracellularly, which is the specialized type IV secretion system (T4SS)
called Dot/Icm. The Dot/Icm is an incredibly versatile secretion apparatus that has the capacity to translocate
into host cells a repertoire of over 300 different proteins with different biochemical functions and diverse structural
properties. The goal of this project is to determine the structure and assembly of the Dot/Icm machine and
elucidate how the individual Dot and Icm proteins contribute to machine function at the molecular level. We will
combine advanced cryo-electron tomography (cryo-ET) with genetic and biochemical approaches to determine
the pathway of Dot/Icm machine assembly (Aim 1) and to determine the mechanism by which cytosolic ATPases
recruit effectors and mediate changes in the Dot/Icm structure (Aim 2). Furthermore, we aim to characterize the
translocation pore in the host cell membrane that serves as the protein-conducting channel for Dot/Icm effectors
(Aim 3).

## Key facts

- **NIH application ID:** 10891500
- **Project number:** 5R01AI152421-05
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Jun Liu
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $586,250
- **Award type:** 5
- **Project period:** 2020-09-16 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10891500

## Citation

> US National Institutes of Health, RePORTER application 10891500, Functional and Structural Analysis of the Dot/Icm Type IVB Secretion Machine (5R01AI152421-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10891500. Licensed CC0.

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