Genome-wide identification, evolution, and role of SPL gene family in beet (Beta vulgaris L.) under cold stress.

BACKGROUND: SPL transcription factors play vital roles in regulating plant growth, development, and abiotic stress responses. Sugar beet (Beta vulgaris L.), one of the world's main sugar-producing crops, is a major source of edible and industrial sugars for humans. Although the SPL gene family has been extensively identified in other species, no reports on the SPL gene family in sugar beet are available. RESULTS: Eight BvSPL genes were identified at the whole-genome level and were renamed based on their positions on the chromosome. The gene structure, SBP domain sequences, and phylogenetic relationship with Arabidopsis were analyzed for the sugar beet SPL gene family. The eight BvSPL genes were divided into six groups (II, IV, V, VI, VII, and VIII). Of the BvSPL genes, no tandem duplication events were found, but one pair of segmental duplications was present. Multiple cis-regulatory elements related to growth and development were identified in the 2000-bp region upstream of the BvSPL gene start codon (ATG). Using quantitative real-time polymerase chain reaction (qRT-PCR), the expression profiles of the eight BvSPL genes were examined under eight types of abiotic stress and during the maturation stage. BvSPL transcription factors played a vital role in abiotic stress, with BvSPL3 and BvSPL6 being particularly noteworthy. CONCLUSION: Eight sugar beet SPL genes were identified at the whole-genome level. Phylogenetic trees, gene structures, gene duplication events, and expression profiles were investigated. The qRT-PCR analysis indicated that BvSPLs play a substantial role in the growth and development of sugar beet, potentially participating in the regulation of root expansion and sugar accumulation.

Genome-wide identification, structural characterization and gene expression analysis of the WRKY transcription factor family in pea (Pisum sativum L.).

BACKGROUND: The WRKY gene family is one of the largest families of transcription factors in higher plants, and WRKY transcription factors play important roles in plant growth and development as well as in response to abiotic stresses; however, the WRKY gene family in pea has not been systematically reported. RESULTS: In this study, 89 pea WRKY genes were identified and named according to the random distribution of PsWRKY genes on seven chromosomes. The gene family was found to have nine pairs of tandem duplicates and 19 pairs of segment duplicates. Phylogenetic analyses of the PsWRKY and 60 Arabidopsis WRKY proteins were performed to determine their homology, and the PsWRKYs were classified into seven subfamilies. Analysis of the physicochemical properties, motif composition, and gene structure of pea WRKYs revealed significant differences in the physicochemical properties within the PsWRKY family; however, their gene structure and protein-conserved motifs were highly conserved among the subfamilies. To further investigate the evolutionary relationships of the PsWRKY family, we constructed comparative syntenic maps of pea with representative monocotyledonous and dicotyledonous plants and found that it was most recently homologous to the dicotyledonous WRKY gene families. Cis-acting element analysis of PsWRKY genes revealed that this gene family can respond to hormones, such as abscisic acid (ABA), indole-3-acetic acid (IAA), gibberellin (GA), methyl jasmonate (MeJA), and salicylic acid (SA). Further analysis of the expression of 14 PsWRKY genes from different subfamilies in different tissues and fruit developmental stages, as well as under five different hormone treatments, revealed differences in their expression patterns in the different tissues and fruit developmental stages, as well as under hormone treatments, suggesting that PsWRKY genes may have different physiological functions and respond to hormones. CONCLUSIONS: In this study, we systematically identified WRKY genes in pea for the first time and further investigated their physicochemical properties, evolution, and expression patterns, providing a theoretical basis for future studies on the functional characterization of pea WRKY genes during plant growth and development.

FtbZIP12 Positively Regulates Responses to Osmotic Stress in Tartary Buckwheat.

ABFs play a key role in regulating plant osmotic stress. However, in Tartary buckwheat, data on the role of ABF genes in osmotic stress remain limited and its associated mechanism in osmoregulation remain nebulous. Herein, a novel ABF family in Tartary buckwheat, FtbZIP12, was cloned and characterized. FtbZIP12 is a transcriptional activator located in the nucleus; its expression is induced by NaCl, mannitol, and abscisic acid (ABA). Atopic expression of FtbZIP12 in Arabidopsis promoted seed germination, reduced damage to primary roots, and improved the tolerance of seedlings to osmotic stress. The quantitative realtime polymerase chain reaction (RT-qPCR) results showed that the expressions of the typical genes related to stress, the SOS pathway, and the proline synthesis pathway in Arabidopsis were significantly (p < 0.05) upregulated under osmotic stress. FtbZIP12 improved the osmotic pressure resistance by reducing the damage caused by reactive oxygen species to plants and maintained plant homeostasis by upregulating the expression of genes related to stress, osmotic regulation, and ion homeostasis. This study identified a key candidate gene for understanding the mechanism underlying osmotic-stress-regulated function in Tartary buckwheat, thereby providing a theoretical basis for improving its yield and quality.

Potential therapeutic targets of gastric cancer explored under endogenous network modeling of clinical data.

Improvement in the survival rate of gastric cancer, a prevalent global malignancy and the leading cause of cancer-related mortality calls for more avenues in molecular therapy. This work aims to comprehend drug resistance and explore multiple-drug combinations for enhanced therapeutic treatment. An endogenous network modeling clinic data with core gastric cancer molecules, functional modules, and pathways is constructed, which is then transformed into dynamics equations for in-silicon studies. Principal component analysis, hierarchical clustering, and K-means clustering are utilized to map the attractor domains of the stochastic model to the normal and pathological phenotypes identified from the clinical data. The analyses demonstrate gastric cancer as a cluster of stable states emerging within the stochastic dynamics and elucidate the cause of resistance to anti-VEGF monotherapy in cancer treatment as the limitation of the single pathway in preventing cancer progression. The feasibility of multiple objectives of therapy targeting specified molecules and/or pathways is explored. This study verifies the rationality of the platform of endogenous network modeling, which contributes to the development of cross-functional multi-target combinations in clinical trials.

The Performances of SNAQ, GLIM, mNICE, and ASPEN for Identification of Neurocritically Ill Patients at High Risk of Developing Refeeding Syndrome.

We previously found that neurocritically ill patients are prone to refeeding syndrome (RFS), a potentially life-threatening complication. However, there is no unified or validated consensus on the screening tool for RFS so far. We aimed to validate and compare the performance of four screening tools for RFS in neurocritically ill patients. We conducted a single-center, observational, retrospective cohort study among neurocritically ill adult patients who were admitted to the neurocritical care unit (NCU), and who received enteral nutrition for 72 h or longer. They were scored on the Short Nutritional Assessment Questionnaire (SNAQ), the Global Leadership Initiative on Malnutrition (GLIM), the modified criteria of the Britain's National Institute for Health and Care Excellence (mNICE), and ASPEN Consensus Recommendations for Refeeding Syndrome (ASPEN) scales to predict RFS risk via admission data. The performance of each scale in predicting RFS was evaluated. Logistic regression analysis was used to identify the independent risk factors for RFS, and they were added to the above scales to strengthen the identification of RFS. Of the 478 patients included, 84 (17.57%) developed RFS. The sensitivity of the SNAQ and GLIM was only 20.2% (12.6-30.7%), although they had excellent specificities of 84.8% (80.8-88.1%) and 86.0% (82.1-89.2%), respectively; mNICE predicted RFS with a sensitivity of 48.8% (37.8-59.9%) and a specificity of 65.0% (60.0-69.9%); ASPEN had the highest Youden index, with a sensitivity and specificity of 53.6% (42.4-64.4%) and 64.7% (59.8-69.4%), respectively. The Area Under the receiver operating characteristic Curves (AUC) of SNAQ, GLIM, mNICE, and ASPEN to predict RFS were 0.516 (0.470-0.561), 0.533 (0.487-0.579), 0.568 (0.522-0.613), and 0.597 (0.551-0.641), respectively. We identified age, Acute Physiology and Chronic Health Evaluation II (APACHE II), Sequential Organ Failure Assessment (SOFA), and Glasgow Coma Scale (GCS) score as independent risk factors of RFS, and the combination of GCS and age can improve the AUC of ASPEN to 0.664 (0.620-0.706) for predicting RFS. SNAQ, GLIM, mNICE, and ASPEN do not perform well in identifying neurocritically ill patients at high risk of RFS, although ASPEN appears to have relatively a good validity among them. Combining GCS and age with ASPEN slightly improves RFS recognition, but it still leaves a lot of room for improvement.

Incidence and outcome of refeeding syndrome in neurocritically ill patients.

BACKGROUND & AIM: Neurocritically ill patients are more likely to be comatose and suffer from dysphagia, conditions that inevitably require nutritional support. Inappropriate nutritional support may lead to refeeding syndrome (RFS). This study aimed to explore the incidence and outcome of RFS in neurocritically ill patients. METHODS: We conducted a retrospective study among neurocritically ill patients who received total enteral nutrition for >72 h in a university-affiliated hospital. RFS was defined as the occurrence of new-onset hypophosphatemia (<0.65 mmol/L) within 72 h of the commencement of nutritional support. The primary outcome was 6-month mortality. The secondary outcomes included 30-day mortality, neurocritical care unit (NCU) stay, and hospital length of stay. RESULTS: A total of 328 patients were enrolled, and 56 (17.1%) of them developed RFS within 72 h of nutrition support. Significantly, we found that patients with high malnutrition universal screening tool (MUST) and sequential organ failure assessment (SOFA) scores were more likely to develop RFS. The occurrence of RFS was associated with a longer NCU stay, higher 30-day mortality and 6-month mortality, and poorer 6-month functional outcome. Moreover, RFS was identified as an independent risk factor for 6-month mortality. CONCLUSION: RFS is not rare in neurocritically ill patients and is more likely to occur in patients with nutritional risk and more severe conditions. RFS is an independent risk factor for 6-month mortality in neurocritically ill patients.

Distribution path robust optimization of electric vehicle with multiple distribution centers.

To identify electrical vehicle (EV) distribution paths with high robustness, insensitivity to uncertainty factors, and detailed road-by-road schemes, optimization of the distribution path problem of EV with multiple distribution centers and considering the charging facilities is necessary. With the minimum transport time as the goal, a robust optimization model of EV distribution path with adjustable robustness is established based on Bertsimas' theory of robust discrete optimization. An enhanced three-segment genetic algorithm is also developed to solve the model, such that the optimal distribution scheme initially contains all road-by-road path data using the three-segment mixed coding and decoding method. During genetic manipulation, different interlacing and mutation operations are carried out on different chromosomes, while, during population evolution, the infeasible solution is naturally avoided. A part of the road network of Xifeng District in Qingyang City is taken as an example to test the model and the algorithm in this study, and the concrete transportation paths are utilized in the final distribution scheme. Therefore, more robust EV distribution paths with multiple distribution centers can be obtained using the robust optimization model.