Lipid Production,Oxidative Status,Antioxidant Enzymes and Photosynthetic Efficiency of <i>Coccomyxa chodatii</i> SAG 216-2 in Response to Calcium Oxide Nanoparticles

The extensive use of nanoparticles (NPs) in diverse applications causes their localization to aquatic habitats, affecting the metabolic products of primary producers in aquatic ecosystems, such as algae. Synthesized calcium oxide nanoparticles (CaO NPs) are of the scarcely studied NPs. Thus, the current work proposed that the exposure to CaO NPs may instigate metabolic pathway to be higher than that of normally growing algae, and positively stimulate algal biomass. In this respect, this research was undertaken to study the exposure effect of CaO NPs (0, 20, 40, 60, 80, and 100 µg mL⁻¹ ) on the growth, photosynthesis, respiration, oxidative stress, antioxidants, and lipid production of the microalga Coccomyxa chodatii SAG 216-2. The results showed that the algal growth concomitant with chlorophyll content, photosynthesis, and calcium content increased in response to CaO NPs. The contents of biomolecules such as proteins, amino acids, and carbohydrates were also promoted by CaO NPs with variant degrees. Furthermore, lipid production was enhanced by the applied nanoparticles. CaO NPs induced the accumulation of hydrogen peroxide, while lipid peroxidation was reduced, revealing no oxidative behavior of the applied nanoparticles on alga. Also, CaO NPs have a triggering effect on the antioxidant enzymes such as superoxide dismutase, catalase, ascorbate peroxidase, and guaiacol peroxidase. The results recommended the importance of the level of 60 µg mL⁻¹ CaO NPs on lipid production (with increasing percentage of 65% compared to control) and the highest dry matter acquisition of C. chodatii. This study recommended the feasibility of an integrated treatment strategy of CaO NPs in augmenting biomass, metabolic up-regulations, and lipid accumulation in C. chodatii.     

温州地区小杓鹬的代谢产热特征

动物能量代谢相关的生理生态特征与其地理分布密切相关。为探讨温州地区迁徙鸟类小杓鹬(Numenius minutus)的代谢产热特征及体温调节,本文在环境温度(T_a)5.0～42.5℃范围内,测定了小杓鹬的代谢率(R_m,以单位时间耗氧量表示,ml/h)和体温,并计算不同环境温度的热传导。结果显示：在环境温度为5～35℃的范围内,小杓鹬的体温维持相对恒定,平均体温为(42.8±0.10)℃;热中性区为27.5～40.0℃;在热中性区温度范围内,代谢率即基础代谢率为(221.31±6.01)ml/h,是体重预期值的141%;环境温度在5.0～27.5℃范围内,代谢率与环境温度(T_a,℃)呈负相关,回归方程为R_m=587.10﹣11.78 T_a;在5.0～27.5℃的环境温度范围内,小杓鹬的热传导最低,平均为(0.11±0.00)ml/(g·h·℃),是体重预期值的212%;代谢预期比和热传导预期比的比值(F值)为1.21,表明该物种有较好的体温调节能力。小杓鹬具有较高的体温和基础代谢...     

酿酒酵母合成木栓酮及其衍生物的研究现状及展望

木栓酮及其衍生物在植物中普遍存在且种类繁多,具有丰富的生理药理学活性。木栓酮衍生物是以木栓酮为骨架经细胞色素氧化酶P450(cytochromeP450,CYP450)及UDP葡萄糖醛酸转移酶(UDP-glucuronosyltransferase, UGT)修饰而来。植物中天然木栓酮及其衍生物的含量极低,传统的萃取分离和化学合成效率低、能耗高且污染环境,因此,利用酿酒酵母作为宿主菌生产木栓酮及其衍生物是一种高效且环保的策略。本文从增加前体含量、提高酶活性和产物合成的亚细胞定位等方面介绍并展望了木栓酮在酿酒酵母中高效生产的策略,并介绍了目前几种常见的木栓酮衍生物研究现状,从根据碳骨架相似性挖掘CYP450、蛋白质工程改造CYP450和合成代谢基因簇的挖掘等方面展望了木栓酮衍生物的合成途径解析的新思路。     

代谢工程改造大肠杆菌一碳模块高效合成L-甲硫氨酸

L-甲硫氨酸又名L-蛋氨酸,是人体必需8种氨基酸之一,在饲料、医药、食品领域具有重要应用。以实验室前期构建的M2(Escherichia coli W3110?IJAHFEBC/PAM)为出发菌株,以模块化代谢工程策略构建了一株L-甲硫氨酸高产菌株。首先通过过表达亚甲基四氢叶酸还原酶(methylenetetrahydrofolate reductase,MetF)和筛选不同来源的丝氨酸羟甲基转移酶(hydroxymethyltransferase,GlyA),增强了一碳模块甲基供体的生成,优化了一碳模块。随后针对一碳模块的前体供应,过表达了胱醚裂解酶(cysteamine lyase,MalY)和半胱氨酸内运基因(fliY),有效地提高了L-高半胱氨酸和L-半胱氨酸的供应。最终摇瓶发酵L-甲硫氨酸的产量由2.8 g/L提高至4.05 g/L,5 L发酵罐中达到18.26 g/L。研究结果表明,一碳模块对L-甲硫氨酸的生物合成具有十分重要的影响,在细胞内通过优化一碳模块,可以实现L-甲硫氨酸的高效生物合成。本研究为进一步提高微生物发酵生产L-甲硫氨酸的水平奠定了基础。     

白脂素在代谢综合征、雄性生殖和运动中的研究进展

白脂素（asprosin,ASP）是新发现的一种禁食诱导的升糖激素,由脂肪细胞产生和分泌,并作用于多种组织器官,发挥重要的调控作用。ASP的异常表达,诱发了代谢综合征的发展,此外,ASP还对生殖功能具有一定影响。运动可引起ASP水平的变化,但其影响结果还存在争议。因此,长期和深层次地研究探索ASP在代谢综合征和生殖中的机理以及运动对ASP的确切作用可以为防治慢病和提升生殖潜能提供新思路。     

酶类药物现状、问题及展望

随着生物制药的迅速发展,许多酶类药物应运而生,在治疗代谢疾病、心血管疾病、癌症等诸多疾病上发挥着越来越重要的作用。但是酶类药物也存在一些不足,如潜在的免疫原性、较短的体内半衰期,以及较差的组织靶向性,影响了酶类药物的疗效和应用。为克服这些缺点,人们已开发出多种技术,如通过糖基化、聚乙二醇修饰等分子工程技术提升酶蛋白药效,另一方面酶基因疗法也已成功用于多种酶缺陷疾病的治疗。基于酶类药物的迅速发展和广泛的应用前景,本文对酶类药物的现状进行较详细的阐述,并对酶类药物的优势、所存在的问题及未来发展趋势进行分析和评述。     

Automatic Detection of Severely and Mildly Infected COVID-19 Patients with Supervised Machine Learning Models

ObjectivesWhen the prognosis of COVID-19 disease can be detected early, the intense-pressure and loss of workforce in health-services can be partially reduced. The primary-purpose of this article is to determine the feature-dataset consisting of the routine-blood-values (RBV) and demographic-data that affect the prognosis of COVID-19. Second, by applying the feature-dataset to the supervised machine-learning (ML) models, it is to identify severely and mildly infected COVID-19 patients at the time of admission.Material and methodsThe sample of this study consists of severely (n = 192) and mildly (n = 4010) infected-patients hospitalized with the diagnosis of COVID-19 between March-September, 2021. The RBV-data measured at the time of admission and age-gender characteristics of these patients were analyzed retrospectively. For the selection of the features, the minimum-redundancy-maximum-relevance (MRMR) method, principal-components-analysis and forward-multiple-logistics-regression analyzes were used. The features set were statistically compared between mild and severe infected-patients. Then, the performances of various supervised-ML-models were compared in identifying severely and mildly infected-patients using the feature set.ResultsIn this study, 28 RBV-parameters and age-variable were found as the feature-dataset. The effect of features on the prognosis of the disease has been clinically proven. The ML-models with the highest overall-accuracy in identifying patient-groups were found respectively, as follows: local-weighted-learning (LWL)-97.86%, K-star (K*)-96.31%, Naive-Bayes (NB)-95.36% and k-nearest-neighbor (KNN)-94.05%. Also, the most successful models with the highest area-under-the-receiver-operating-characteristic-curve (AUC) values in identifying patient groups were found respectively, as follows: LWL-0.95%, K*-0.91%, NB-0.85% and KNN-0.75%.ConclusionThe findings in this article have significant a motivation for the healthcare professionals to detect at admission severely and mildly infected COVID-19 patients.     

Phylogeny of Saxifraga section Saxifraga subsection Arachnoideae (Saxifragaceae) and the origin of low elevation shade-dwelling species

Saxifraga section Saxifraga subsection Arachnoideae is a lineage of 12 species distributed mainly in the European Alps. It is unusual in terms of ecological diversification by containing both high elevation species from exposed alpine habitats and low elevation species from shady habitats such as overhanging rocks and cave entrances. Our aims are to explore which of these habitat types is ancestral, and to identify the possible drivers of this remarkable ecological diversification. Using a Hybseq DNA-sequencing approach and a complete species sample we reconstructed and dated the phylogeny of subsection Arachnoideae. Using Landolt indicator values, this phylogenetic tree was used for the reconstruction of the evolution of temperature, light and soil pH requirements in this lineage. Diversification of subsection Arachnoideae started in the late Pliocene and continued through the Pleistocene. Both diversification among and within clades was largely allopatric, and species from shady habitats with low light requirements are distributed in well-known refugia. We hypothesize that low light requirements evolved when species persisting in cold-stage refugia were forced into marginal habitats by more competitive warm-stage vegetation. While we do not claim that such competition resulted in speciation, it very likely resulted in adaptive evolution.     

Development and validation of robust metabolism-related gene signature in the prognostic prediction of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common malignant tumours worldwide. Given metabolic reprogramming in tumours was a crucial hallmark, several studies have demonstrated its value in the diagnostics and surveillance of malignant tumours. The present study aimed to identify a cluster of metabolism-related genes to construct a prediction model for the prognosis of HCC. Multiple cohorts of HCC cases (466 cases) from public datasets were included in the present analysis. (GEO cohort) After identifying a list of metabolism-related genes associated with prognosis, a risk score based on metabolism-related genes was formulated via the LASSO-Cox and LASSO-pcvl algorithms. According to the risk score, patients were stratified into low- and high-risk groups, and further analysis and validation were accordingly conducted. The results revealed that high-risk patients had a significantly worse 5-year overall survival (OS) than low-risk patients in the GEO cohort. (30.0% vs. 57.8%; hazard ratio [HR], 0.411; 95% confidence interval [95% CI], 0.302–0.651; p < 0.001) This observation was confirmed in the external TCGA-LIHC cohort. (34.5% vs. 54.4%; HR 0.452; 95% CI, 0.299–0.681; p < 0.001) To promote the predictive ability of the model, risk score, age, gender and tumour stage were integrated into a nomogram. According to the results of receiver operating characteristic curves and decision curves analysis, the nomogram score possessed a superior predictive ability than conventional factors, which indicate that the risk score combined with clinicopathological features was able to achieve a robust prediction for OS and improve the individualized clinical decision making of HCC patients. In conclusion, the metabolic genes related to OS were identified and developed a metabolism-based predictive model for HCC. Through a series of bioinformatics and statistical analyses, the predictive ability of the model was approved.