UC Davis

Indole-3-acetic acid (IAA) is a naturally occurring heterocyclic compound that is best known for its role as a growth hormone (auxin) in plants but is produced by many other organisms from across the tree of life. This dissertation presents insights that I uncovered from the genomic analysis of the indole-3-acetic acid catabolic (iac) gene cluster in bacteria. This gene cluster confers the ability to enzymatically convert IAA to catechol, which if further degraded and channeled into central metabolism means that IAA can serve as a sole source of carbon, nitrogen, and energy. First identified and characterized by Leveau and Gerards in 2008 in a bacterial isolate from the pear phyllosphere, strain Pseudomonas putida 1290, the iac gene cluster was subsequently described and studied in other bacteria, including Enterobacter soli LF7, Acinetobacter baumannii ATCC 19606, Paraburkholderia phytofirmans PsJN, Caballeronia glathei DSM50014, and Pseudomonas composti LY1. I collectively refer to these strains and P. putida 1290 as the iac model strains.

Chapter 1, and its addendum, summarize the published literature in regards to the iac cluster in P. putida 1290 and other iac model strains. I discuss the contribution of individual iac-encoded proteins and enzymes to the multi-step conversion of IAA to catechol. I examine the IAA-degrading model strains in the context of the environments from which they were originally isolated. I elaborate on scenarios in which iac genes may benefit bacteria, such as the exploitation of IAA as a source of carbon, nitrogen, and energy and the interference with IAA-dependent processes and functions in plants and other organisms. I also compare and contrast the iac clusters with each other in terms of gene synteny and with two other bacterial gene clusters (designated iaa and iad) as alternative bacterial solutions to the catabolism of IAA.

Chapter 2 is an in-depth comparative genomics analysis aimed at cataloging the diversity of iac-harboring genomes in the bacterial tree of life. I identified iac gene clusters in 4% (4,620 out of 115,091) of the bacterial RefSeq genomes within the Genome Taxonomy Database (GTDB). These genomes belonged to Alphaproteobacteria, Gammaproteobacteria, and Actinobacteria. I also screened metagenomes in the IMG database, which revealed the presence of iac genes clusters in a diverse array of environments including not only plants, but also soils/sediments, surface water, humans, and animals. By analyzing the distribution and prevalence of iac gene clusters across iv the bacterial tree of life along with non-metric multidimensional scaling analysis of IacD protein sequences, I deduced patterns that suggest both vertical inheritance and horizontal transfer of iac genes. Certain functions, such as the catabolism of catechol, benzoic acid, phenylacetic acid, and malonic acid were found to be enriched in genomes with iac gene clusters, hinting at co-selection of these functions with the iac-encoded conversion of IAA into catechol.

Chapter 3 builds on findings from Chapter 2 and further explores the evolutionary origins of the iac genes by using a large and diverse subset of 94 iac-harboring genomes identified in Chapter 2. By applying a pangenomics approach to the iac gene neighborhoods in these genomes, I was able to identify a core set of iac genes (iacA, iacB, iacC, iacD, iacE, and iacI). A concatenated phylogeny of the proteins encoded by these core genes revealed four distinct clades of iac genes that corresponded well with the iac gene cluster synteny in members from these clades. I also compared individual iac proteins with their broader protein families, revealing possible shared ancestors between iac- and iad-encoded proteins, IacA and an ethionamide sulfoxygenating enzyme (Rv3094c) from Mycobacterium tuberculosis, as well as IacH and the indole-3-acetamide hydrolase (AMI1) proteins in plants. I also used structural phylogenetics to discover that the IacB protein belongs to the Dimeric alpha-beta barrel (Dabb) protein superfamily. Furthermore, using ancestral state reconstruction in conjunction with the creation of time trees, I was able to infer that the iac gene cluster most likely diverged from its closest ancestral relative around the time that plants moved to the land.

Chapter 4 uncovers the genome of P. putida 1290, which was the first bacterium for which the genetic underpinnings of IAA catabolism were discovered. Most notably, the iac gene neighborhood of P. putida 1290 exists as part of a genomic island, alongside many other genes that enable the catabolism of plant-derived compounds including methylamine, phenylacetaldehyde, and opines. I initially predicted that a gene encoding a methyl-accepting chemotaxis protein (MCP) located directly upstream of the iac cluster might be responsible for the previously observed chemotaxis of P. putida 1290 towards IAA. However, recent research from another lab proposed a different gene (deemed pcpI) for this function. An addendum to Chapter 4 details my bioinformatic analysis of the MCPs (including PcpI) encoded by P. putida 1290.

Chapter 5 delves into a potential application of our biological understanding of the iac gene v cluster in pathogenic Acinetobacter species. I explore the use of the IAA-analog 5-chloroindoleacetic acid (5-Cl-IAA), in combination with IAA, to act as an antibiotic against Acinetobacter lactucae NRRL B-41902. I show that growth inhibition by 5-Cl-IAA is conditional on iac gene expression, dose-dependent, and bacteriostatic, not bactericidal. This foundational research paves the way for further studies and potential treatments, which may help in combating hospital-acquired infections caused by iac-harboring bacteria such as A. baumannii.

Finally, Chapter 6 presents a Python package based on scripts originally used in Chapter 2 to identify the iac gene cluster in bacterial genomes from the RefSeq database. An example workflow is presented which outlines the use of this software to identify the phenylacetic acid (paa) gene cluster in Pseudomonas putida genomes. Using known proteins encoded by a particular gene cluster of interest, one can screen the vast array of bacterial genomes in the RefSeq database, setting the stage for performing downstream comparative and phylogenetic analyses as I have done in Chapters 2 and 3.