Review of combinations of experimental and computational techniques to identify and understand genes involved in innate immunity and effector-triggered defence.

The innate immune system includes a first layer of defence that recognises conserved pathogen-associated molecular patterns that are essential for microbial fitness. Resistance (R) gene-based recognition of pathogen effectors, which function in modulation or avoidance of host immunity, activates a second layer of plant defence. In this review, experimental and computational techniques are considered to improve understanding of the plant immune system. Biocomputation contributes to discovery of the molecular genetic basis of host resistance against pathogens. Sequenced genomes have been used to identify R genes in plants. Resistance gene enrichment sequencing based on conserved protein domains has increased the number of R genes with nucleotide-binding site and leucine-rich repeat domains. Network analysis will contribute to an improved understanding of the innate immune system and identify novel genes for partial disease resistance. Machine learning algorithms are expected to become important in defining aspects of the immune system that are less well characterised, including identification of R genes that lack conserved protein domains.

Unmet needs in the management of psoriasis in Latin America: a systematic review.

BACKGROUND: Psoriasis is a chronic, systemic inflammatory disease with a worldwide prevalence of approximately 2%. Currently, despite the difficulties faced every day by patients and physicians in low-resource countries, literature describing the exact needs of psoriasis treatment in Latin America remains scarce. OBJECTIVE: To investigate the unmet needs in psoriasis treatment in Latin America. METHODS: The authors conducted a systematic review following PRISMA statements in PubMed, Embase, and LILACS of studies published from January 2011 to March 2021 addressing challenges in psoriasis treatment in Latin America. RESULTS: The search strategy identified 3,837 articles, of which 19 were included in the final analysis. Most were from Brazil (58%; n=11), all were observational, and most were cross-sectional (84%; n=16). Difficulties faced by psoriasis patients in Latin America included the high prevalence of opportunistic and endemic infections (42% of the studies addressed this matter; n=8), delay in diagnosis (5%; n=1), work productivity impairment (16%; n=3), limited access to medication/medical care (37%; n=7), poor adherence to treatment (5%; n=1) and poor adherence to guidelines (11%; n=2). STUDY LIMITATIONS: Number and quality of studies currently available on this subject. CONCLUSIONS: Current psoriasis guidelines do not always account for epidemiological, financial, and cultural characteristics. Most studies available are from Brazil, which might not accurately represent Latin America as a whole. In a region where neglected diseases and scarce resources remain a reality, it is imperative that dermatological training be offered to primary care providers, allowing for standardized conduct and earlier diagnosis.

Predicting metabolic pathways of plant enzymes without using sequence similarity: Models from machine learning.

Most of the bioinformatics tools for enzyme annotation focus on enzymatic function assignments. Sequence similarity to well-characterized enzymes is often used for functional annotation and to assign metabolic pathways. However, these approaches are not feasible for all sequences leading to inaccurate annotations or lack of metabolic pathway information. Here we present the mApLe (metabolic pathway predictor of plant enzymes), a high-performance machine learning-based tool with models to label the metabolic pathway of enzymes rather than specifying enzymes' reactions. The mApLe uses molecular descriptors of the enzyme sequences to perform predictions without considering sequence similarities with reference sequences. Hence, mApLe can classify a diversity of enzymes, even the ones without any homolog or with incomplete EC numbers. This tool can be used to improve the quality of genomic annotation of plants or to narrow down the number of candidate genes for metabolic engineering researches. The mApLe tool is available online, and the GUI can be locally installed.

HIV Reverse Transcriptase and Protease Genes Variability Can Be a Biomarker Associated with HIV and Hepatitis B or C Coinfection.

Variability of the HIV reverse transcriptase (RT) and protease (PR) genes has been used as indicators of drug resistance and as a mean to evaluate phylogenetic relationships among circulating virus. However, these studies have been carried in HIV mono-infected populations. The goal of this study was to evaluate, for the first time, the HIV PR and RT sequences from HIV/HBV and HIV/HCV co-infected patients. HIV PR and RT genes were amplificated and sequenced to resistance analysis. The bioinformatics analysis was performed to infer about sequences clustering and molecular evolution. The results showed that the most frequent amino acid substitutions in RT were L214F (67.6%), I135T (55.9%), and in PR was V15I (41.2%). The molecular clock analysis showed that the HIV circulating in co-infected patients were separated in two clusters in the years 1999-2000. Some patients included as HIV mono-infected according patients' medical records and inside the co-infected cluster were, in fact, co-infected by PCR analysis. Analysis of the decision trees showed susceptibility to lamivudine and emtricitabine were important attribute to characterize co-infected patients. In conclusion, the results obtained in this study suggest, for the first time, that HIV RT and PR genes variability could be a genetic biomarker to coinfection.