Show me your secret(ed) weapons: a multifaceted approach reveals a wide arsenal of type III-secreted effectors in the cucurbit pathogenic bacterium Acidovorax citrulli and novel effectors in the Acidovorax genus.

The cucurbit pathogenic bacterium Acidovorax citrulli requires a functional type III secretion system (T3SS) for pathogenicity. In this bacterium, as with Xanthomonas and Ralstonia spp., an AraC-type transcriptional regulator, HrpX, regulates expression of genes encoding T3SS components and type III-secreted effectors (T3Es). The annotation of a sequenced A. citrulli strain revealed 11 T3E genes. Assuming that this could be an underestimation, we aimed to uncover the T3E arsenal of the A. citrulli model strain, M6. Thorough sequence analysis revealed 51 M6 genes whose products are similar to known T3Es. Furthermore, we combined machine learning and transcriptomics to identify novel T3Es. The machine-learning approach ranked all A. citrulli M6 genes according to their propensity to encode T3Es. RNA-Seq revealed differential gene expression between wild-type M6 and a mutant defective in HrpX: 159 and 28 genes showed significantly reduced and increased expression in the mutant relative to wild-type M6, respectively. Data combined from these approaches led to the identification of seven novel T3E candidates that were further validated using a T3SS-dependent translocation assay. These T3E genes encode hypothetical proteins that seem to be restricted to plant pathogenic Acidovorax species. Transient expression in Nicotiana benthamiana revealed that two of these T3Es localize to the cell nucleus and one interacts with the endoplasmic reticulum. This study places A. citrulli among the 'richest' bacterial pathogens in terms of T3E cargo. It also revealed novel T3Es that appear to be involved in the pathoadaptive evolution of plant pathogenic Acidovorax species.

Inferring Rates and Length-Distributions of Indels Using Approximate Bayesian Computation.

The most common evolutionary events at the molecular level are single-base substitutions, as well as insertions and deletions (indels) of short DNA segments. A large body of research has been devoted to develop probabilistic substitution models and to infer their parameters using likelihood and Bayesian approaches. In contrast, relatively little has been done to model indel dynamics, probably due to the difficulty in writing explicit likelihood functions. Here, we contribute to the effort of modeling indel dynamics by presenting SpartaABC, an approximate Bayesian computation (ABC) approach to infer indel parameters from sequence data (either aligned or unaligned). SpartaABC circumvents the need to use an explicit likelihood function by extracting summary statistics from simulated sequences. First, summary statistics are extracted from the input sequence data. Second, SpartaABC samples indel parameters from a prior distribution and uses them to simulate sequences. Third, it computes summary statistics from the simulated sets of sequences. By computing a distance between the summary statistics extracted from the input and each simulation, SpartaABC can provide an approximation to the posterior distribution of indel parameters as well as point estimates. We study the performance of our methodology and show that it provides accurate estimates of indel parameters in simulations. We next demonstrate the utility of SpartaABC by studying the impact of alignment errors on the inference of positive selection. A C ++ program implementing SpartaABC is freely available in http://spartaabc.tau.ac.il.

Insights from the Genome Sequence of Acidovorax citrulli M6, a Group I Strain of the Causal Agent of Bacterial Fruit Blotch of Cucurbits.

Acidovorax citrulli is a seedborne bacterium that causes bacterial fruit blotch of cucurbit plants including watermelon and melon. A. citrulli strains can be divided into two major groups based on DNA fingerprint analyses and biochemical properties. Group I strains have been generally isolated from non-watermelon cucurbits, while group II strains are closely associated with watermelon. In the present study, we report the genome sequence of M6, a group I model A. citrulli strain, isolated from melon. We used comparative genome analysis to investigate differences between the genome of strain M6 and the genome of the group II model strain AAC00-1. The draft genome sequence of A. citrulli M6 harbors 139 contigs, with an overall approximate size of 4.85 Mb. The genome of M6 is ∼500 Kb shorter than that of strain AAC00-1. Comparative analysis revealed that this size difference is mainly explained by eight fragments, ranging from ∼35-120 Kb and distributed throughout the AAC00-1 genome, which are absent in the M6 genome. In agreement with this finding, while AAC00-1 was found to possess 532 open reading frames (ORFs) that are absent in strain M6, only 123 ORFs in M6 were absent in AAC00-1. Most of these M6 ORFs are hypothetical proteins and most of them were also detected in two group I strains that were recently sequenced, tw6 and pslb65. Further analyses by PCR assays and coverage analyses with other A. citrulli strains support the notion that some of these fragments or significant portions of them are discriminative between groups I and II strains of A. citrulli. Moreover, GC content, effective number of codon values and cluster of orthologs' analyses indicate that these fragments were introduced into group II strains by horizontal gene transfer events. Our study reports the genome sequence of a model group I strain of A. citrulli, one of the most important pathogens of cucurbits. It also provides the first comprehensive comparison at the genomic level between the two major groups of strains of this pathogen.

Inferring Indel Parameters using a Simulation-based Approach.

In this study, we present a novel methodology to infer indel parameters from multiple sequence alignments (MSAs) based on simulations. Our algorithm searches for the set of evolutionary parameters describing indel dynamics which best fits a given input MSA. In each step of the search, we use parametric bootstraps and the Mahalanobis distance to estimate how well a proposed set of parameters fits input data. Using simulations, we demonstrate that our methodology can accurately infer the indel parameters for a large variety of plausible settings. Moreover, using our methodology, we show that indel parameters substantially vary between three genomic data sets: Mammals, bacteria, and retroviruses. Finally, we demonstrate how our methodology can be used to simulate MSAs based on indel parameters inferred from real data sets.