Is targeting dysregulation in apoptosis splice variants in Mycobacterium tuberculosis (MTB) host interactions and splicing factors resulting in immune evasion by MTB strategies a possibility?

Alternative Splicing, An Overlooked Defense Frontier of Plants with Respect to Bacterial Infection

Disease represents a major problem in sustainable agricultural development. Plants interact closely with various microorganisms during their development and in response to the prevailing environment. In particular, pathogenic microorganisms can cause plant diseases, affecting the fertility, yield, and longevity of plants. During the long coevolution of plants and their pathogens, plants have evolved both molecular pattern-triggered immunity (PTI) and effector-triggered immunity (ETI) signaling networks in order to regulate host cells in response to pathogen infestation. Additionally, in the postgenomic era, alternative splicing (AS) has become uncovered as one of the major drivers of proteome diversity, and abnormal RNA splicing is closely associated with bacterial infections. Currently, the complexity of host-bacteria interactions is a much studied area of research that has shown steady progress over the past decade. Although the development of high-throughput sequencing technologies and their application in transcriptomes have revolutionized our understanding of AS, many mechanisms related to host-bacteria interactions remain still unclear. To this end, this review summarizes the changes observed in AS during host-bacteria interactions and outlines potential therapeutics for bacterial diseases based on existing studies. In doing so, we hope to provide guidelines for plant disease management in agriculture.

Epidemiological and Cytokine Profile of Patients with Pulmonary and Extrapulmonary Tuberculosis in a Population of the Brazilian Amazon

Several factors are associated with the development of different clinical forms of tuberculosis (TB). The present study evaluated epidemiological variables and cytokine levels in samples from 89 patients with TB (75 with pulmonary TB and 14 with extrapulmonary TB) and 45 controls. Cytokines were measured by flow cytometry (Human Th1/Th2/Th17 Cytometric Bead Array kit). The TB group had a higher frequency of individuals who were 39 years of age or older, married, with primary education or illiterate and had a lower family income (p < 0.05). All individuals with extrapulmonary TB reported that they were not working, and the main reasons were related to disease symptoms or treatment. The levels of IFN-γ (OR = 4.06) and IL-4 (OR = 2.62) were more likely to be elevated in the TB group (p = 0.05), and IFN-γ levels were lower in patients with extrapulmonary TB compared to those with pulmonary TB (OR = 0.11; p = 0.0050). The ROC curve was applied to investigate the diagnostic accuracy of IFN-γ levels between the different clinical forms of tuberculosis, resulting in high AUC (0.8661; p < 0.0001), sensitivity (93.85%) and specificity median (65.90%), suggesting that IFN-γ levels are useful to differentiate pulmonary TB from extrapulmonary TB. The dysregulation of pro- and anti-inflammatory cytokine levels represent a risk for the development of TB and contribute to the pathogenesis of the disease, especially variation in IFN-γ levels, which may determine protection or risk for extrapulmonary TB.

Structural modeling and phylogenetic analysis for infectious disease transmission pattern based on maximum likelihood tree approach

The contagious disease transmission pattern outbreak caused a massive human casualty and became a pandemic, as confirmed by the World Health Organization (WHO). The present research aims to understand the infectious disease transmission pattern outbreak due to molecular epidemiology. Hence, infected patients over time can spread infectious disease. The virus may develop further mutations, and that there might be a more toxic virulent strain, which leads to several environmental risk factors. Therefore, it is essential to monitor and characterize patient profiles, variants, symptoms, geographic locations, and treatment responses to analyze and evaluate infectious disease patterns among humans. This research proposes the Evolutionary tree analysis (ETA) for the molecular evolutionary genetic analysis to reduce medical risk factors. Furthermore, The Maximum likelihood tree method (MLTM) has been used to analyze the selective pressure, which is examined to identify a mutation that may influence the infectious disease transmission pattern's clinical progress. This study also utilizes ETA with Markov Chain Bayesian Statistics (MCBS) approach to reconstruct transmission trees with sequence information. The experimental shows that the proposed ETA-MCBS method achieves a 97.55% accuracy, prediction of 99.56%, and 98.55% performance compared to other existing methods.