Phytosulfokine-δ: A Small Peptide, but a Big Player in Symbiosis Gene Regulation

Nitrogen availability is one of the critical determinants of agricultural yield. Biological nitrogen fixation, such as legume–rhizobia symbiotic association, might function as a solution to fix nitrogen. Using phytosulfokine (PSK)-α sequences as a query, Yu et al., 2022 performed a comprehensive genome-wide search of legume species to identify PSK-δ, a divergent pentapeptide differing in single amino acid. Furthermore, PSK-δ exhibited nodule-specific expression with lower expression in the root, substantiating the nodule-specific temporal expression and suggesting its role in nodule development and nitrogen fixation. Additionally, in planta functional characterization in Medicago truncatula using overexpression and Tnt1-insertion mutant analysis indicated the role of PSK-δ in symbiotic nodulation. Interestingly, a similar phenotype of MtPSKδ mutant (mtpskδ) with that of wild-type control led to the hypothesis of its functional redundancy with PSK-α in nodule organogenesis. Further investigation regarding its position in the Nod-factor signaling pathway revealed the downstream function of PSK-δ in association with MtENOD11 in regulating nodule formation

Frontline Warrior microRNA167: A Battle of Survival

Plant pathogens such as viruses are detrimental to the survivorship of plant species. Coinfection of maize chlorotic mottle virus (MCMV) and the sugarcane mosaic virus (SCMV) causes a deadly disease in maize. An investigation by Liu et al. (2022) showed the role of Zma-miR167 in positively imparting resistance against the MCMV and SCMV. The authors identified ZmARF3 and ZmARF30 as the targets of Zma-miR167. ZmARF3 and ZmARF30 were identified as transcription factors that bind the cis-element in ZmPAO1 promoters to activate its expression. The authors showed how the Zma-miR167-ZmARF3/30-ZmPAO1 module functions differently in resistant and susceptible lines with high expression of Zma-miR167 in resistant lines correlated with the resistant phenotype. Finally, the authors concluded that MCMV-encoded p31 protein enhances ZmPAO1 enzyme activity for its survival in the host

Frontline Warrior microRNA167: A Battle of Survival

Plant pathogens such as viruses are detrimental to the survivorship of plant species. Coinfection of maize chlorotic mottle virus (MCMV) and the sugarcane mosaic virus (SCMV) causes a deadly disease in maize. An investigation by Liu et al. (2022) showed the role of Zma-miR167 in positively imparting resistance against the MCMV and SCMV. The authors identified ZmARF3 and ZmARF30 as the targets of Zma-miR167. ZmARF3 and ZmARF30 were identified as transcription factors that bind the cis-element in ZmPAO1 promoters to activate its expression. The authors showed how the Zma-miR167-ZmARF3/30-ZmPAO1 module functions differently in resistant and susceptible lines with high expression of Zma-miR167 in resistant lines correlated with the resistant phenotype. Finally, the authors concluded that MCMV-encoded p31 protein enhances ZmPAO1 enzyme activity for its survival in the host

Metabolomics in oncology

Abstract Background Oncogenic transformation alters intracellular metabolism and contributes to the growth of malignant cells. Metabolomics, or the study of small molecules, can reveal insight about cancer progression that other biomarker studies cannot. Number of metabolites involved in this process have been in spotlight for cancer detection, monitoring, and therapy. Recent Findings In this review, the “Metabolomics” is defined in terms of current technology having both clinical and translational applications. Researchers have shown metabolomics can be used to discern metabolic indicators non‐invasively using different analytical methods like positron emission tomography, magnetic resonance spectroscopic imaging etc. Metabolomic profiling is a powerful and technically feasible way to track changes in tumor metabolism and gauge treatment response across time. Recent studies have shown metabolomics can also predict individual metabolic changes in response to cancer treatment, measure medication efficacy, and monitor drug resistance. Its significance in cancer development and treatment is summarized in this review. Conclusion Although in infancy, metabolomics can be used to identify treatment options and/or predict responsiveness to cancer treatments. Technical challenges like database management, cost and methodical knowhow still persist. Overcoming these challenges in near further can help in designing new treatment régimes with increased sensitivity and specificity