Quantitative PCR is a molecular technique for DNA quantification that depends on reaction efficiency and the Ct value (“cycle threshold”). However, the results are dependent on laboratory conditions and mathematical approaches. Thus, the data of 16 genes from Pseudomonas aeruginosa strain AG1 were generated using qPCR to assess the effect of DNA concentration and three mathematical methods (a standard curve and two individual-curve-based approaches called exponential and sigmoidal models) on efficiency and DNA quantification. Differences in efficiency were revealed depending on the mathematical method used; the values were 100% in three out of the four standard curves, but estimations of the expected fold change in DNA serial dilutions were not achieved, indicating the possible overestimation of efficiency. Moreover, when efficiency was compared to DNA concentration, a decreasing trend in efficiency as DNA concentration increased in the reaction was observed in most cases, which is probably related to PCR inhibitors. For all 16 genes at a single DNA concentration, the efficiencies for the exponential model were found in the range of 1.5–2.79 (50–79%), and for the sigmoidal approach, the range was 1.52–1.75 (52–75%), with similar impact on normalized expression values, as indicated by the genes for standard curves. Jointly, DNA concentration and mathematical model choice were demonstrated to impact the estimation of reaction efficiency and, subsequently, DNA quantification when using qPCR.

Tolerance to stress conditions is vital for organismal survival, including bacteria under specific environmental conditions, antibiotics, and other perturbations. Some studies have described common modulation and shared genes during stress response to different types of disturbances (termed as perturbome), leading to the idea of central control at the molecular level. We implemented a robust machine learning approach to identify and describe genes associated with multiple perturbations or perturbome in a Pseudomonas aeruginosa PAO1 model. Using microarray datasets from the Gene Expression Omnibus (GEO), we evaluated six approaches to rank and select genes: using two methodologies, data single partition (SP method) or multiple partitions (MP method) for training and testing datasets, we evaluated three classification algorithms (SVM Support Vector Machine, KNN K-Nearest neighbor and RF Random Forest). Gene expression patterns and topological features at the systems level were included to describe the perturbome elements. We were able to select and describe 46 core response genes associated with multiple perturbations in P. aeruginosa PAO1 and it can be considered a first report of the P. aeruginosa perturbome. Molecular annotations, patterns in expression levels, and topological features in molecular networks revealed biological functions of biosynthesis, binding, and metabolism, many of them related to DNA damage repair and aerobic respiration in the context of tolerance to stress. We also discuss different issues related to implemented and assessed algorithms, including data partitioning, classification approaches, and metrics. Altogether, this work offers a different and robust framework to select genes using a machine learning approach.

Tomato is one of the most important vegetable crops in Costa Rica, where favorable environmental conditions enabled year-round production but also promote bacterial diseases. In recent years, pith necrosis has been frequently observed; nevertheless, the causal agents remain unidentified in the country. This study evaluated bacteria associated with symptomatic plants collected in the Central Valley of Costa Rica. From 32 plants, 61 bacterial isolates were obtained, described morphologically, and characterized through basic biochemical tests. Partial sequencing of the 16S rRNA gene revealed diverse bacterial taxa, predominantly belonging to the genus Pseudomonas. Thirteen isolates were selected for pathogenicity assays, which confirmed variable virulence levels. Multilocus sequence analysis based on concatenated sequences of the 16S rRNA, gyrB, rpoD, and rpoB genes identified Pseudomonas alliivorans LTM 13.1.2, P. flavescens LTM 14.2.2, and P. capsici LTM 78.3.2 as causal agents of pith necrosis. Additionally, P. straminea LTM 78.2.1 and Cedecea sp. LTM 72.2.1 caused tissue discoloration. Whole-genome sequencing of the two most virulent isolates (LTM 13.1.2 and LTM 78.3.2) supported their taxonomic classification and revealed virulence-associated genes and biosynthetic clusters. This study represents the first report of these Pseudomonas species as tomato pathogens in Costa Rica and expands their known distribution and host ranges.