Mutation of the chloroplast‐localized phosphate transporter OsPHT2;1 reduces flavonoid accumulation and UV tolerance in rice

Phosphorus (P) is an essential macronutrient required for plant development and production. The mechanisms regulating phosphate (Pi) uptake are well established, but the function of chloroplast Pi homeostasis is poorly understood in Oryza sativa (rice). PHT2;1 is one of the transporters/translocators mediating Pi import into chloroplasts. In this study, to gain insight into the role of OsPHT2;1‐mediated stroma Pi, we analyzed OsPHT2;1 function in Pi utilization and photoprotection. Our results showed that OsPHT2;1 was induced by Pi starvation and light exposure. Cell‐based assays showed that OsPHT2;1 localized to the chloroplast envelope and functioned as a low‐affinity Pi transporter. The ospht2;1 had reduced Pi accumulation, plant growth and photosynthetic rates. Metabolite profiling revealed that 52.6% of the decreased metabolites in ospht2;1 plants were flavonoids, which was further confirmed by 40% lower content of total flavonoids compared with the wild type. As a consequence, ospht2;1 plants were more sensitive to UV‐B irradiation. Moreover, the content of phenylalanine, the precursor of flavonoids, was also reduced, and was largely associated with the repressed expression of ADT1/MTR1. Furthermore, the ospht2;1 plants showed decreased grain yields at relatively high levels of UV‐B irradiance. In summary, OsPHT2;1 functions as a chloroplast‐localized low‐affinity Pi transporter that mediates UV tolerance and rice yields at different latitudes.     

Sustainability of SARS-CoV-2 Induced Humoral Immune Responses in COVID-19 Patients from Hospitalization to Convalescence Over Six Months

Virologica Sinica - Understanding the persistence of antibody in convalescent COVID-19 patients may help to answer the current major concerns such as the risk of reinfection, the protection period...     

内生Bacillus svelezensis HBB5菌株发酵盾叶薯蓣产薯蓣皂苷元的研究

目的探讨内生Bacillus svelezensis HBB5菌株发酵宿主植物盾叶薯蓣产薯蓣皂苷元的能力。方法接种内生B.svelezensis HBB5及B.subtilis ATCC 6633菌株(0.35×10~8 CFU/mL)至含盾叶薯蓣地下茎组织的液体培养基,32℃、165~185 r/min连续发酵108 h,检测发酵液细菌、pH、淀粉、麦芽糖、葡萄糖、淀粉酶(α-amylase)及薯蓣皂苷元溶出率等指标。结果内生B.svelezensis HBB5菌株有较强的酸、碱耐受力[pH(4.8±0.2)~(8.4±0.2)],相比B.subtilis ATCC 6633[pH(5.2±0.2)~(8.7±0.2)]差异不明显;前者达峰值生长量(60×10~(8±2) CFU/mL)明显高于后者(32×10~(8±2) CFU/mL)。发酵36~60 h时,B.svelezensis HBB5、B.subtilis ATCC 6633菌株发酵液的淀粉、麦芽糖、葡萄糖浓度达峰值,分别为(37.41±3.12)、(27.83±2.14)ng/mL,(21.06±1.25)、(16.54±1.08)ng/mL,(54.33±3.12)、(36.65±2.10)ng/mL,前者均高于后者。同时,B.svelezensis HBB5菌株维持高的α-amylase酶活性及薯蓣皂苷元溶出率。结论内生B.svelezensis HBB5菌株拥有较强的耐酸碱、降解淀粉、提高薯蓣皂苷元溶出的能力,为工业生产薯蓣皂苷元提供了一个新的方法。     

大连地区分泌型与非分泌型母亲母乳菌群结构的差异

目的探讨大连地区分泌型和非分泌型母亲哺乳期间母乳菌群的主要差异。方法于大连市妇幼保健院纳入42名志愿者产妇,收集其产后第6天的母乳样本。提取母乳样本DNA,并对包含rs601338和rs1047781单核酸位点多态性(SNP)的片段用聚合酶链式反应(polymerase chain reaction,PCR)检测并加以测序,以检测受试母亲岩藻糖基转移酶2基因(FUT2)的类型。采用16S rRNA高通量测序法对不同母乳样本中微生物的多样性进行分析,并且对不同母乳样本中的微生物丰度及类型展开探讨。结果母亲分泌型分布情况分析:42名志愿者中,36名母亲是分泌型,6名母亲是非分泌型。母乳菌群多样性分析:分泌型与非分泌型组母亲母乳菌群比较,发现反映组内差异的Alpha多样性指数(包括Ace、Chao1以及Shannon等)在2组间差异无统计学意义;通过使用主坐标分析,发现分泌型组和非分泌型组的距离相对较远,母乳菌群组内菌群结构类似,组间差异性较显著,说明母乳中的蛋白核心岩藻糖基化水平可明显改变母乳中菌群结构。乳汁菌群物种构成分析:从门水平分析,变形菌门与厚壁菌门为主要优势菌;从属水平分析,2组母乳菌群中的双歧杆菌属丰度差异较显著。相关性分析:分泌型母乳的菌群中相关基因的表达更强,说明分泌型母乳对菌群基因的表达有促进作用。结论母乳菌群构成在一定程度上受到母体FUT2基因类型,即母亲分泌型和非分泌型的影响。     

原发性肝癌患者肠道菌群变化与内毒素和炎性因子水平的关系

目的探讨原发性肝癌患者肠道菌群变化及其与内毒素、炎性因子水平的相关性。方法选取2018年2月至2019年10月我院收治的80例原发性肝癌患者为观察组,另选同时期80例健康体检者为对照组。观察两组对象肠道菌群情况,同时检测两组对象血清内毒素和白细胞介素6(IL-6)、白细胞介素1(IL-1)及肿瘤坏死因子α(TNF-α)水平。采用Pearson检验进行相关性分析。结果观察组患者肠道双歧杆菌、乳杆菌和拟杆菌数量低于对照组;而肠杆菌和酵母菌数量高于对照组(均P0.05)。观察组患者血清内毒素、IL-6、IL-1、TNF-α水平均高于对照组(均P0.05)。Pearson相关性分析显示,肠道双歧杆菌、乳杆菌和拟杆菌数量与血清内毒素和IL-6、IL-1、TNF-α水平呈负相关,肠道肠杆菌和酵母菌数量与内毒素和IL-6、IL-1、TNF-α水平呈正相关(均P0.05)。结论原发性肝癌患者肠道菌群紊乱与内毒素和炎症因子水平具有相关性,肠道菌群失衡可能是促进该病病情发展的原因之一。     

A conservation and rigidity based method for detecting critical protein residues

Background

Certain amino acids in proteins play a critical role in determining their structural stability and function. Examples include flexible regions such as hinges which allow domain motion, and highly conserved residues on functional interfaces which allow interactions with other proteins. Detecting these regions can aid in the analysis and simulation of protein rigidity and conformational changes, and helps characterizing protein binding and docking. We present an analysis of critical residues in proteins using a combination of two complementary techniques. One method performs in-silico mutations and analyzes the protein's rigidity to infer the role of a point substitution to Glycine or Alanine. The other method uses evolutionary conservation to find functional interfaces in proteins.

Results

We applied the two methods to a dataset of proteins, including biomolecules with experimentally known critical residues as determined by the free energy of unfolding. Our results show that the combination of the two methods can detect the vast majority of critical residues in tested proteins.

Conclusions

Our results show that the combination of the two methods has the potential to detect more information than each method separately. Future work will provide a confidence level for the criticalness of a residue to improve the accuracy of our method and eliminate false positives. Once the combined methods are integrated into one scoring function, it can be applied to other domains such as estimating functional interfaces.

     

A conservation and biophysics guided stochastic approach to refining docked multimeric proteins

Background

We introduce a protein docking refinement method that accepts complexes consisting of any number of monomeric units. The method uses a scoring function based on a tight coupling between evolutionary conservation, geometry and physico-chemical interactions. Understanding the role of protein complexes in the basic biology of organisms heavily relies on the detection of protein complexes and their structures. Different computational docking methods are developed for this purpose, however, these methods are often not accurate and their results need to be further refined to improve the geometry and the energy of the resulting complexes. Also, despite the fact that complexes in nature often have more than two monomers, most docking methods focus on dimers since the computational complexity increases exponentially due to the addition of monomeric units.

Results

Our results show that the refinement scheme can efficiently handle complexes with more than two monomers by biasing the results towards complexes with native interactions, filtering out false positive results. Our refined complexes have better IRMSDs with respect to the known complexes and lower energies than those initial docked structures.

Conclusions

Evolutionary conservation information allows us to bias our results towards possible functional interfaces, and the probabilistic selection scheme helps us to escape local energy minima. We aim to incorporate our refinement method in a larger framework which also enables docking of multimeric complexes given only monomeric structures.

     

Statins in cardiometabolic disease: what makes pitavastatin different?

The term cardiometabolic disease encompasses a range of lifestyle-related conditions, including Metabolic syndrome (MetS) and type 2 diabetes (T2D), that are characterized by different combinations of cardiovascular (CV) risk factors, including dyslipidemia, abdominal obesity, hypertension, hyperglycemia/insulin resistance, and vascular inflammation. These risk factors individually and interdependently increase the risk of CV and cerebrovascular events, and represent one of the biggest health challenges worldwide today. CV diseases account for almost 50% of all deaths in Europe and around 30% of all deaths worldwide. Furthermore, the risk of CV death is increased twofold to fourfold in people with T2D. Whilst the clinical management of CV disease has improved in Western Europe, the pandemic of obesity and T2D reduces the impact of these gains. This, together with the growing, aging population, means the number of CV deaths is predicted to increase from 17.1 million worldwide in 2004 to 23.6 million in 2030. The recommended treatment for MetS is lifestyle change followed by treatment for the individual risk factors. Numerous studies have shown that lowering low-density lipoprotein-cholesterol (LDL-C) levels using statins can significantly reduce CV risk in people with and without T2D or MetS. However, the risk of major vascular events in those attaining the maximum levels of LDL-C-reduction is only reduced by around one-third, which leaves substantial residual risk. Recent studies suggest that low high-density lipoprotein-cholesterol (HDL-C) (<1 .0 mmol/l; 40 mg/dl) and high triglyceride levels (≥1.7 mmol/l; 150 mg/dl) are independent risk factors for CV disease and that the relationship between HDL-C and CV risk persists even when on-treatment LDL-C levels are low (<1.7 mmol/l; 70 mg/dl). European guidelines highlight the importance of reducing residual risk by targeting these risk factors in addition to LDL-C. This is particularly important in patients with T2D and MetS because obesity and high levels of glycated hemoglobin are directly related to low levels of HDL-C and high triglyceride. Although most statins have a similar low-density lipoprotein-lowering efficacy, differences in chemical structure and pharmacokinetic profile can lead to variations in pleiotropic effects (for example, high-density lipoprotein-elevating efficacy), adverse event profiles, and drug-drug interactions. The choice of statin should therefore depend on the needs of the individual patient. The following reviews will discuss the potential benefits of pitavastatin versus other statins in the treatment of patients with dyslipidemia and MetS or T2D, focusing on its effects on HDL-C quantity and quality, its potential impact on atherosclerosis and CV risk, and its metabolic characteristics that reduce the risk of drug interactions. Recent controversies surrounding the potentially diabetogenic effects of statins will also be discussed.     

Predicting beta-turns in proteins using support vector machines with fractional polynomials

Background

β-turns are secondary structure type that have essential role in molecular recognition, protein folding, and stability. They are found to be the most common type of non-repetitive structures since 25% of amino acids in protein structures are situated on them. Their prediction is considered to be one of the crucial problems in bioinformatics and molecular biology, which can provide valuable insights and inputs for the fold recognition and drug design.

Results

We propose an approach that combines support vector machines (SVMs) and logistic regression (LR) in a hybrid prediction method, which we call (H-SVM-LR) to predict β-turns in proteins. Fractional polynomials are used for LR modeling. We utilize position specific scoring matrices (PSSMs) and predicted secondary structure (PSS) as features. Our simulation studies show that H-SVM-LR achieves Qtotal of 82.87%, 82.84%, and 82.32% on the BT426, BT547, and BT823 datasets respectively. These values are the highest among other β-turns prediction methods that are based on PSSMs and secondary structure information. H-SVM-LR also achieves favorable performance in predicting β-turns as measured by the Matthew's correlation coefficient (MCC) on these datasets. Furthermore, H-SVM-LR shows good performance when considering shape strings as additional features.

Conclusions

In this paper, we present a comprehensive approach for β-turns prediction. Experiments show that our proposed approach achieves better performance compared to other competing prediction methods.