Combined Transcriptome Sequencing of Mycoplasma hyopneumoniae and Infected Pig Lung Tissue Reveals Up-Regulation of Bacterial F1-Like ATPase and Down-Regulation of the P102 Cilium Adhesin in vivo

Effect of vaccination against Mycoplasma hyopneumoniae on divergent pig genetic groups

The bacterium Mycoplasma hyopneumoniae (Mhp) causes a chronic infectious respiratory disease in pigs, leading to important economic losses. This study aimed to compare the immune response of the local Piau breed and a commercial line to Mhp vaccination. For this, two phases were carried out. In the first, gene expression of toll-like receptors (TLR2, TLR4, TLR6, and TLR10) and cytokines (IL2, IL6, IL8, IL10, IL12, IL13, TNFα, and TGFβ) was assessed in porcine blood mononuclear cells (PBMC) from the two genetic groups before and after vaccination. In the second experiment, nitric oxide production, specific antibodies, and gene expression of toll-like receptors and cytokines were evaluated in bronchoalveolar lavage fluid (BALF) cells of vaccinated and unvaccinated pigs. After vaccination against Mhp, TLR2, TLR4, TLR6, TLR10, IL6, TNFα, and TGFβ expression levels were elevated in PBMC from commercial animals, and TLR6, TLR10, and TGFβ expression levels were elevated in PBMC from the Piau group. Vaccination also increased the production of Mhp-specific IgG antibodies in BALF cells in the Piau breed. Comparison of the two genetic groups revealed differences in TNFα and IL10 expression in BALF cells. These results show that Piau pigs have different immune responses to vaccination compared with commercial animals. It is worth noting that these genetic differences between both genetic groups may be related to phenotypic differences in Mhp resistance or susceptibility.

An improved bacterial mRNA enrichment strategy in dual RNA sequencing to unveil the dynamics of plant-bacterial interactions

BACKGROUND

Dual RNA sequencing is a powerful tool that enables a comprehensive understanding of the molecular dynamics underlying plant-microbe interactions. RNA sequencing (RNA-seq) poses technical hurdles in the transcriptional analysis of plant-bacterial interactions, especially in bacterial transcriptomics, owing to the presence of abundant ribosomal RNA (rRNA), which potentially limits the coverage of essential transcripts. Therefore, to achieve cost-effective and comprehensive sequencing of the bacterial transcriptome, it is imperative to devise efficient methods for eliminating rRNA and enhancing the proportion of bacterial mRNA. In this study, we modified a strand-specific dual RNA-seq method with the goal of enriching the proportion of bacterial mRNA in the bacteria-infected plant samples. The enriched method involved the sequential separation of plant mRNA by poly A selection and rRNA removal for bacterial mRNA enrichment followed by strand specific RNA-seq library preparation steps. We assessed the efficiency of the enriched method in comparison to the conventional method by employing various plant-bacterial interactions, including both host and non-host resistance interactions with pathogenic bacteria, as well as an interaction with a beneficial rhizosphere associated bacteria using pepper and tomato plants respectively.

RESULTS

In all cases of plant-bacterial interactions examined, an increase in mapping efficiency was observed with the enriched method although it produced a lower read count. Especially in the compatible interaction with Xanthmonas campestris pv. Vesicatoria race 3 (Xcv3), the enriched method enhanced the mapping ratio of Xcv3-infected pepper samples to its own genome (15.09%; 1.45-fold increase) and the CDS (8.92%; 1.49-fold increase). The enriched method consistently displayed a greater number of differentially expressed genes (DEGs) than the conventional RNA-seq method at all fold change threshold levels investigated, notably during the early stages of Xcv3 infection in peppers. The Gene Ontology (GO) enrichment analysis revealed that the DEGs were predominantly enriched in proteolysis, kinase, serine type endopeptidase and heme binding activities.

CONCLUSION

The enriched method demonstrated in this study will serve as a suitable alternative to the existing RNA-seq method to enrich bacterial mRNA and provide novel insights into the intricate transcriptomic alterations within the plant-bacterial interplay.

Advances in experimental and computational methodologies for the study of microbial-surface interactions at different omics levels

The study of the biological response of microbial cells interacting with natural and synthetic interfaces has acquired a new dimension with the development and constant progress of advanced omics technologies. New methods allow the isolation and analysis of nucleic acids, proteins and metabolites from complex samples, of interest in diverse research areas, such as materials sciences, biomedical sciences, forensic sciences, biotechnology and archeology, among others. The study of the bacterial recognition and response to surface contact or the diagnosis and evolution of ancient pathogens contained in archeological tissues require, in many cases, the availability of specialized methods and tools. The current review describes advances in in vitro and in silico approaches to tackle existing challenges (e.g., low-quality sample, low amount, presence of inhibitors, chelators, etc.) in the isolation of high-quality samples and in the analysis of microbial cells at genomic, transcriptomic, proteomic and metabolomic levels, when present in complex interfaces. From the experimental point of view, tailored manual and automatized methodologies, commercial and in-house developed protocols, are described. The computational level focuses on the discussion of novel tools and approaches designed to solve associated issues, such as sample contamination, low quality reads, low coverage, etc. Finally, approaches to obtain a systems level understanding of these complex interactions by integrating multi omics datasets are presented.

The influence of regulatory elements on Mycoplasma hyopneumoniae 7448 transcriptional response during oxidative stress and heat shock

BACKGROUND

The comprehension of genome organization and gene modulation is essential for understanding pathogens' infection mechanisms. Mycoplasma hyopneumoniae 7448 genome is organized in transcriptional units (TUs), which are flanked by regulatory elements such as putative promoters, terminators and repetitive sequences. Yet the relationship between the presence of these elements and bacterial responses during stress conditions remains unclear. Thus, in this study, in silico and RT-qPCR analyses were associated to determine the effect of regulatory elements in gene expression regulation upon heat shock and oxidative stress conditions.

METHODS AND RESULTS

Thirteen TU's organizational profiles were found based on promoters and terminators distribution. Differential expression in genes sharing the same TUs was observed, suggesting the activity of internal regulatory elements. Moreover, 88.8% of tested genes were differentially expressed under oxidative stress in comparison to the control condition, being 81.3% of them surrounded by their own regulatory elements. Similarly, under heat shock, 44.4% of the genes showed regulation when compared to control condition, being 75.0% of them surrounded by their own regulatory elements.

CONCLUSIONS

Altogether, this data suggests the activity of internal regulatory elements in gene modulation of M. hyopneumoniae 7448 transcription.

Integrating microbiome, transcriptome and metabolome data to investigate gastric disease pathogenesis: a concise review

Microbiome, the study of microbial communities in specific environments, has developed significantly since the Human Microbiome Project began. Microbiomes have been associated with changes within environmental niches and the development of various diseases. The development of high-throughput technology such as next-generation sequencing has also allowed us to perform transcriptome studies, which provide accurate functional profiling data. Metabolome studies, which analyse the metabolites found in the environment, are the most direct environmental condition indicator. Although each dataset provides valuable information on its own, the integration of multiple datasets provides a deeper understanding of the relationship between the host, agent and environment. Therefore, network analysis using multiple datasets might give a clearer understanding of disease pathogenesis.