Drought response revealed by chromatin organization variation and transcriptional regulation in cotton.

BACKGROUND: Cotton is a major world cash crop and an important source of natural fiber, oil, and protein. Drought stress is becoming a restrictive factor affecting cotton production. To facilitate the development of drought-tolerant cotton varieties, it is necessary to study the molecular mechanism of drought stress response by exploring key drought-resistant genes and related regulatory factors. RESULTS: In this study, two cotton varieties, ZY007 (drought-sensitive) and ZY168 (drought-tolerant), showing obvious phenotypic differences under drought stress, were selected. A total of 25,898 drought-induced genes were identified, exhibiting significant enrichment in pathways related to plant stress responses. Under drought induction, At subgenome expression bias was observed at the whole-genome level, which may be due to stronger inhibition of Dt subgenome expression. A gene co-expression module that was significantly associated with drought resistance was identified. About 90% of topologically associating domain (TAD) boundaries were stable, and 6613 TAD variation events were identified between the two varieties under drought. We identified 92 genes in ZY007 and 98 in ZY168 related to chromatin 3D structural variation and induced by drought stress. These genes are closely linked to the cotton response to drought stress through canonical hormone-responsive pathways, modulation of kinase and phosphatase activities, facilitation of calcium ion transport, and other related molecular mechanisms. CONCLUSIONS: These results lay a foundation for elucidating the molecular mechanism of the cotton drought response and provide important regulatory locus and gene resources for the future molecular breeding of drought-resistant cotton varieties.

Classification of deep and shallow groundwater wells based on machine learning in the Hebei Plain North China.

Accurately determining the extraction volumes from various aquifers is crucial for effectively managing groundwater overexploitation. A key initial step in quantifying extracted groundwater volumes involves the classification of groundwater wells as either deep or shallow. This study evaluated 881,872 groundwater wells in the Hebei Plain, applying machine learning techniques to classify wells with unknown depths. Through the hydrogeological borehole data, the groundwater wells with known depth are divided into deep wells and shallow wells. Four machine learning algorithms-Random Forest, Support Vector Machine, Logistic Regression, and Naive Bayes-were employed to classify groundwater wells with unknown depths. The accuracy of these models was validated using known-depth well classifications. The results reveal that the Random Forest algorithm exhibited the highest performance among the models, achieving an overall accuracy of 91.23%. According to the Random Forest model, 43.51% of groundwater wells with unknown depths were classified as deep, while 56.49% were classified as shallow. The study also found that wells in areas where salinity exceeds 2 g/L are primarily deep groundwater wells. These findings provide valuable technical insight for groundwater well decommissioning and facilitate the assessment of extracted volumes of deep and shallow groundwater.

Implants coating strategies for antibacterial treatment in fracture and defect models: A systematic review of animal studies.

Objective: Fracture-related infection (FRI) remains a major concern in orthopaedic trauma. Functionalizing implants with antibacterial coatings are a promising strategy in mitigating FRI. Numerous implant coatings have been reported but the preventive and therapeutic effects vary. This systematic review aimed to provide a comprehensive overview of current implant coating strategies to prevent and treat FRI in animal fracture and bone defect models. Methods: A literature search was performed in three databases: PubMed, Web of Science and Embase, with predetermined keywords and criteria up to 28 February 2023. Preclinical studies on implant coatings in animal fracture or defect models that assessed antibacterial and bone healing effects were included. Results: A total of 14 studies were included in this systematic review, seven of which used fracture models and seven used defect models. Passive coatings with bacteria adhesion resistance were investigated in two studies. Active coatings with bactericidal effects were investigated in 12 studies, four of which used metal ions including Ag+ and Cu2+; five studies used antibiotics including chlorhexidine, tigecycline, vancomycin, and gentamicin sulfate; and the other three studies used natural antibacterial materials including chitosan, antimicrobial peptides, and lysostaphin. Overall, these implant coatings exhibited promising efficacy in antibacterial effects and bone formation. Conclusion: Antibacterial coating strategies reduced bacterial infections in animal models and favored bone healing in vivo. Future studies of implant coatings should focus on optimal biocompatibility, antibacterial effects against multi-drug resistant bacteria and polymicrobial infections, and osseointegration and osteogenesis promotion especially in osteoporotic bone by constructing multi-functional coatings for FRI therapy. The translational potential of this paper: The clinical treatment of FRI is complex and challenging. This review summarizes novel orthopaedic implant coating strategies applied to FRI in preclinical studies, and offers a perspective on the future development of orthopaedic implant coatings, which can potentially contribute to alternative strategies in clinical practice.

In Situ Synthesis of an Immune-Checkpoint Blocker from Engineered Bacteria Elicits a Potent Antitumor Response.

Immune-checkpoint blockade (ICB) reinvigorates T cells from exhaustion and potentiates T-cell responses to tumors. However, most patients do not respond to ICB therapy, and only a limited response can be achieved in a "cold" tumor with few infiltrated lymphocytes. Synthetic biology can be used to engineer bacteria as controllable bioreactors to synthesize biotherapeutics in situ. We engineered attenuated Salmonella VNP20009 with synthetic gene circuits to produce PD-1 and Tim-3 scFv to block immunosuppressive receptors on exhausted T cells to reinvigorate their antitumor response. Secreted PD-1 and Tim-3 scFv bound PD-1+ Tim-3+ T cells through their targeting receptors in vitro and potentiated the T-cell secretion of IFN-γ. Engineered bacteria colonized the hypoxic core of the tumor and synthesized PD-1 and Tim-3 scFv in situ, reviving CD4+ T cells and CD8+ T cells to execute an antitumor response. The bacteria also triggered a strong innate immune response, which stimulated the expansion of IFN-γ+ CD4+ T cells within the tumors to induce direct and indirect antitumor immunity.