Non-specific lipid transfer proteins (nsLTPs) stand out among plant-specific peptide superfamilies due to their multifaceted roles in plant molecular physiology and development, including their protective functions against pathogens. These antimicrobial agents have demonstrated remarkable efficacy against bacterial and fungal pathogens. The discovery of plant-originated, cysteine-rich antimicrobial peptides such as nsLTPs has paved the way for exploring the mentioned organisms as potential biofactories for synthesizing antimicrobial compounds. Recently, nsLTPs have been the focus of a plethora of research and reviews, providing a functional overview of their potential activity. The present work compiles relevant information on nsLTP omics and evolution, and it adds meta-analysis of nsLTPs, including: (1) genome-wide mining in 12 plant genomes not studied before; (2) latest common ancestor analysis (LCA) and expansion mechanisms; (3) structural proteomics, scrutinizing nsLTPs’ three-dimensional structure/physicochemical characteristics in the context of nsLTP classification; and (4) broad nsLTP spatiotemporal transcriptional analysis using soybean as a study case. Combining a critical review with original results, we aim to integrate high-quality information in a single source to clarify unexplored aspects of this important gene/peptide family.

Plants have the ability to recognize and respond to a multitude of pathogens, resulting in a massive reprogramming of the plant to activate defense responses including Resistance (R) and Pathogenesis-Related (PR) genes. Abiotic stresses can also activate PR genes and enhance pathogen resistance, representing valuable genes for breeding purposes. The present work offers an overview of soybean R and PR genes present in the GENOSOJA (Brazilian Soybean Genome Consortium) platform, regarding their structure, abundance, evolution and role in the plant-pathogen metabolic pathway, as compared with Medicago and Arabidopsis. Searches revealed 3,065 R candidates (756 in Soybean, 1,142 in Medicago and 1,167 in Arabidopsis), and PR candidates matching to 1,261 sequences (310, 585 and 366 for the three species, respectively). The identified transcripts were also evaluated regarding their expression pattern in 65 libraries, showing prevalence in seeds and developing tissues. Upon consulting the SuperSAGE libraries, 1,072 R and 481 PR tags were identified in association with the different libraries. Multiple alignments were generated for Xa21 and PR-2 genes, allowing inferences about their evolution. The results revealed interesting insights regarding the variability and complexity of defense genes in soybean, as compared with Medicago and Arabidopsis.

A doença de Huntington (DH) é uma doença neurodegenerativa caracterizada por declínio motor e cognitivo progressivo, na qual a insuficiência metabólica e a disfunção mitocondrial desempenham um papel central na sua patogênese. Este estudo teve como objetivo identificar padrões transcricionais aberrantes em genes associados ao metabolismo energético e A função mitocondrial em pacientes em HD usando uma abordagem in silico. Dados diferenciais de expressão gênica foram obtidos de repositórios públicos e analisados com foco em vias metabólicas e enriquecimento funcional. Os resultados revelaram uma regulação negativa significativa dos principais genes mitocondriais, particularmente aqueles envolvidos na fosforilação oxidativa e na síntese de ATP, incluindo MT-ND4 e MT-ATP6. A Análise de Enriquecimento de Conjunto de Genes (GSEA) demonstrou uma perturbação nas vias do metabolismo energético no núcleo caudado cerebral. Esses achados sugerem que a desregulação transcricional dos componentes mitocondriais é uma característica associada à progressão da DH e pode representar um alvo potencial para intervenção terapêutica. Em conclusão, a presente análise in silico apoia a hipótese de que a falha metabólica sistêmica na DH é impulsionada por um padrão coordenado de repressão da atividade transcricional mitocondrial, fornecendo novas percepções sobre os mecanismos moleculares subjacentes à doença.

HTLV is a neglected retrovirus affecting 5-10 million people worldwide, with Brazil having the highest number of cases. About 0.3-3.8% of people living with HTLV (PLwHTLV) develop HTLV-associated myelopathy/tropical spastic paraparesis (HAM/TSP), a neurological disorder mainly driven by an exacerbated host immune response. Currently, there are no validated biomarkers available to assess the risk of progression to HAM/TSP. Therefore, this study aimed to evaluate the performance of machine learning models (MLMs) in predicting disease progression among PLwHTLV based on clinical data and ENTPD1 gene polymorphisms, which may play a role in HAM/TSP pathogenesis. Variables with higher predictive impact included clinical features such as motor function, urinary impairment and limb dysfunction, while ENTPD1 variants (rs10748643 and rs11188513) showed minimal contribution. Among the evaluated models, Naive Bayes exhibited the best performance, achieving good sensitivity (Recall: 0.78) and class discrimination (ROC AUC: 0.85; G-Mean: 0.75). However, a low precision (0.45), led to a higher incidence of type I errors. Given that this study focuses on applying MLMs as a screening tool rather than a diagnostic one, this limitation has a minor impact on the intended purpose. Overall, our findings suggest low prognostic potential for rs10748643 and rs11188513 but support the applicability of MLMs based on clinical features to identify individuals at higher risk of disease progression. This strategy could enhance clinical monitoring, enable earlier interventions, and potentially delay neurological decline in PLwHTLV.

Plant diseases impose major constraints on global crop productivity and pose a major threat to food security. Here, we review transcription factors (TFs) as central orchestrators of plant defense, consolidating recent advances in how these regulators connect pathogen perception to immune signaling, transcriptional reprogramming, and durable defense responses. Initially, we combined a literature-based synthesis with a natural language processing (NLP) analysis of 1647 PubMed abstracts published between 2021 and 2026 to map dominant and underexplored TF families associated with plant immunity. WRKY, MYB, AP2/ERF, bHLH/MYC, and NAC dominated the recent literature, whereas families such as NF-Y, Trihelix, PLATZ, TCP, and GRAS represent emerging regulatory actors. Across these and other families, TFs integrate pattern- and effector-triggered immunity, hormone crosstalk, chromatin dynamics, non-coding RNA regulation, post-translational modifications, and metabolic remodeling, in addition to cell-type-specific expression. Further evidence indicates that pathogens frequently manipulate TFs to weaken host defense, underscoring their central position in plant molecular physiology and plant-pathogen coevolution. The data emphasize that TF function is context-dependent and influenced by multilayered regulation, cell type, pathogen lifestyle, and host genetic background. This review provides a framework for understanding TFs in plant immune control and highlights TF-centered strategies for engineering durable crop resistance, along with future challenges.