Journal of Biosciences最新文献

Abstract

Increasing soil and underground water salinization with decreasing availability of fresh water has become a potential threat to sustainable crop production in arid and semi-arid areas globally. Introduction and evaluation of salt-tolerant halophytic crops is one of the sustainable ways to preserve productivity in saline ecosystems. This study was aimed to screen quinoa germplasms under high-sodium adsorption ratio (SAR) saline stress. Thirteen quinoa germplasms were evaluated under four levels [best available water (BAW), 8, 16, and 24 dSm⁻¹] of high-SAR saline water irrigation. The evaluation was carried out based on growth, yield, and ionic content parameters along with statistical tools such as multivariate analysis, salt tolerance indices, and correlation. The results showed that the salinity levels of 16 and 24 dSm⁻¹ resulted in increase of chlorophyll content relative to BAW and 8 dSm⁻¹. The germplasm CSQ2 recorded the highest proline content (163.7 mg g⁻¹ FW) at 24 dSm⁻¹. Increasing levels of salinity reduced relative water content in plant leaves, and the germplasm CSQ2 showed minimal reduction of 4% at 24 dSm⁻¹. Na⁺ and K⁺ contents in the plants increased with increasing salinity levels, while the K⁺/Na⁺ ratio decreased. The grain yield of quinoa germplasms ranged between 3.5 and 14.1 g plant⁻¹. The germplasm EC507740 recorded the highest grain yield (7.0 g plant⁻¹) followed by CSQ1 and CSQ2 at a maximum stress of 24 dSm⁻¹. Principal component analysis (PCA) and correlation elucidated that Na⁺ content in plants was negatively correlated with all the studied traits except SPAD, proline content, and K⁺ content. The different salt tolerance indices indicated that the germplasms EC507740, CSQ1, CSQ2, EC507738, and IC411825 were more stable at high-SAR salinity, while PCA showed the germplasms EC507740 and CSQ2 as the most salt-tolerant germplasms.

Lafora disease (LD) is a life-threatening autosomal recessive and progressive neurodegenerative disorder that primarily affects adolescents, resulting in mortality within a decade of onset. The symptoms of LD include epileptic seizures, ataxia, dementia, and psychosis. The underlying pathology involves the presence of abnormal glycogen inclusions in neurons and other tissues, which may contribute to neurodegeneration. LD is caused by loss-of-function mutations in either the EPM2A gene or the NHLRC1 gene. These two genes, respectively, code for laforin phosphatase and malin ubiquitin ligase, and are thought to function, as a functional complex, in diverse cellular pathways. One of the major pathways affected in LD is glycogen metabolism; defects here lead to abnormally higher levels of glycogen and its hyperphosphorylation and aggregation, resulting in the formation of Lafora inclusion bodies. Currently, there is no effective therapy for LD. Studies, particularly from animal models, provide distinct insights into the fundamental mechanisms of diseases and potential avenues for therapeutic interventions. The purpose of this review is to present a comprehensive overview of our current knowledge regarding the disease, its genetics, the animal models that have been developed, and the therapeutic strategies that are being developed based on an understanding of the disease mechanism.

Abstract

The development and progression of breast cancer (BC) depend heavily on the tumor microenvironment (TME), especially tumor infiltration leukocytes (TILs). TME-based classifications in BC remain largely unknown and need to be clarified. Using the bioinformatic analysis, we attempted to construct a prognostic nomogram based on clinical features and TME-related differentially expressed genes (DEGs). We also tried to investigate the association between the prognostic nomogram and clinical characteristics, TILs, possible signaling pathways, and response to immunotherapy in BC patients. DEGs for BC patients were identified from The Cancer Genome Atlas Breast Invasive Carcinoma database. TME-related genes were downloaded from the Immunology Database and Analysis Portal. After intersecting DEGs and TME-related genes, 3985 overlapping TME-related DEGs were selected for non-negative matrix factorization clustering, microenvironment cell populations-counter (MCP-counter), LASSO Cox regression, tumor immune dysfunction, and exclusion (TIDE) algorithm analyses. BC patients were divided into three clusters based on the TME-related DEGs and survival data, in which cluster 3 had the best overall survival (OS). Of note, cluster 3 exhibited the highest infiltration or lowest infiltration of CD³⁺ T-cells, CD⁸⁺ T-cells, cytotoxic lymphocytes, B-lymphocytes, monocytic lineage, and myeloid dendritic cells (MDCs). A total of 33 TME-related DEGs were identified as a prognostic gene signature by the LASSO regression analysis. The prognostic gene signature separated BC patients into low- and high-risk groups with significant differences in OS (p<0.01) and demonstrated powerful effectiveness (TCGA all group: 1-year area under the curve [AUC] = 0.773, 3-year AUC = 0.770, 5-year AUC = 0.792). By integrating demographic features, tumor-node metastasis (TNM) stages, and prognostic gene signature, we constructed a nomogram with better predictive value than other clinical features alone. TME-related DEGs in the low-risk BC patients (with better OS) were enriched in chemokine, cytokine–cytokine receptor interaction, and JAK-STAT and Toll-like receptor signaling pathways. BC patients in the low-risk group exhibited higher TIDE scores associated with worse immune checkpoint blockade response. A prognostic nomogram based on TME-related DEGs and clinical characteristics could predict prognosis and guide immunotherapy in BC patients.

Oculocutaneous albinism (OCA) is characterized by reduced melanin biosynthesis affecting the retina, thus impairing visual function. The disease pathology of OCA is poorly understood at the cellular level due to unavailability of suitable biological model systems. This study aimed to develop a disease-specific in vitro model for OCA type 1A, the most severe form caused by TYR (tyrosinase) gene mutations, using retinal pigment epithelium (RPE) differentiated from patient-derived human-induced pluripotent stem cells (hiPSCs). A comparative study between healthy and OCA1A RPE cells revealed that while healthy RPE cells exhibited timely onest of pigmentation during differentiation, OCA1A RPE cells failed to pigment even after an extended culture period. This observation was validated by ultrastructural studies using electron microscopy, hinting at melanosome-specific defects. Immunocytochemistry demonstrated abnormal expression patterns of melanogenesis-specific protein markers in OCA1A RPE cells, indicating reduced or absence of melanin synthesis. Next, a quantitative assay was performed to confirm the absence of melanin production in OCA1A RPE cells. Tyrosinase assay showed no activity in OCA1A compared with healthy RPE, suggesting non-functionality of TYR, further corroborated by western blot analysis showing complete absence of the protein. Gene expression by RNA sequencing of healthy and OCA1A RPE cells uncovered differential gene expression associated with lens development, visual perception, transmembrane transporter activity, and key signaling pathways. This disease-in-a-dish model of OCA1A provides an excellent platform to understand disease mechanism, identify potential therapeutic targets, and facilitate gene therapy or gene correction.

The term ‘blue economy’, first introduced in 2010, is the sustainable use of ocean resources for economic growth, jobs, ocean health, and to improve livelihoods. However, a sustainable blue economy faces various challenges in the form of global warming, ocean acidification, and lack of knowledge about the ocean; for example, 95% of the sea is still unexplored, making it more important to understand the blue economy and implement it on a global scale. Other challenges include harmful algal blooms (HABs), invasive species, coral bleaching, and thermohaline circulation. This review discusses various aspects of the blue economy like food, value-added products, offshore energy, oxygen source, mining, fisheries, carbon sequestration, and cloud seeding. The future aspects of blue economy, like sustainability, effective policies, and reducing carbon footprints and microplastics are also explored here.