AI-based spectroscopic monitoring of real-time interactions between SARS-CoV-2 and human ACE2

The novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), invades a human cell via human angiotensin-converting enzyme 2 (hACE2) as the entry, causing the severe coronavirus disease (COVID-19). The interactions between hACE2 and the spike glycoprotein (S protein) of SARS-CoV-2 hold the key to understanding the molecular mechanism to develop treatment and vaccines, yet the dynamic nature of these interactions in fluctuating surroundings is very challenging to probe by those structure determination techniques requiring the structures of samples to be fixed. Here we demonstrate, by a proof-of-concept simulation of infrared (IR) spectra of S protein and hACE2, that time-resolved spectroscopy may monitor the real-time structural information of the protein−protein complexes of interest, with the help of machine learning. Our machine learning protocol is able to identify fine changes in IR spectra associated with variation of the secondary structures of S protein of the coronavirus. Further, it is three to four orders of magnitude faster than conventional quantum chemistry calculations. We expect our machine learning protocol would accelerate the development of real-time spectroscopy study of protein dynamics.

The ongoing pandemic of COVID-19, a highly infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has posed tremendous threat to human health and well-being by having affected tens of millions of people and killed more than 1 million affected since December 2019 (1). It has spurred enormous efforts in biological and biomedical research to search for a solution to this fatal disease, which rapidly advance our knowledge about it, including the identity of the pathogen (i.e., SARS-CoV-2), the genome sequence of the virus, and the structural basis for coronavirus recognition and infection (2–5). SARS-CoV-2 recognizes human angiotensin-converting enzyme 2 (hACE2) as the entry receptor to host cells using its surface spike glycoprotein (S protein) (1). The interactions of S protein with hACE2 have been subjected to intensive investigations by several groups (6–10), which laid the foundation for comprehensive understanding of the invasion of SARS-CoV-2 into the human body at the atomic scale (11), helps the search for intermediate hosts of the coronavirus (12), and will guide the design of therapeutics and vaccines (11, 13). Since the physiological environment in which S protein and hACE2 interact is always fluctuated due to the dynamic nature of water, a dynamic picture of the interactions between them is needed for precise mechanistic understanding that will inspire modulation and application (14). Unfortunately, such information relies on real-time tracking of protein conformations, which cannot be achieved by powerful structure characterization techniques with atomic precision like X-ray diffraction and cryoelectron microscopy, because they require fixed structures in samples. It motivates us to develop alternative approaches to resolve the issue.Recently, time-resolved infrared (IR) spectroscopy techniques have realized successful monitoring of changes of secondary structure with time (15), signaling the feasibility of real-time observation of protein dynamics in ambient conditions using spectroscopy. However, to facilitate the monitoring of specific peptide fragments in a secondary structure typically requires isotope labeling (e.g., C=O in the amide of protein backbone is replaced with ¹³C=O or C=¹⁸O) in the preparation of samples, which is, unfortunately, tedious and expensive for systematic investigation on conformation changes in protein dynamics. Therefore, it is desirable to develop isotope labeling-free spectroscopy to accelerate structure study of proteins for biological and biomedical sciences. To achieve this goal, one needs to employ quantum chemistry calculations to complete spectra signal assignment and structure determination. In fact, it relies on computer simulations of various possible conformers to nail the job, which is, unfortunately, very expensive for macromolecules like proteins. One of the biggest bottleneck problems in spectroscopic measurement of proteins is lack of rapid theoretical interpretation that can timely translate spectra signals into structural information. As a result, it is nearly impossible for an experimental spectroscopic study to monitor continuous structural changes associated with protein functions. Developing a cost-effective spectra simulation protocol is a pressing task to advance the real-time spectroscopy study of protein structures.Machine learning (ML), a collection of statistics-based methods which gain prediction power from the learning of big data, has emerged as a powerful toolkit to reduce the barrier to revealing the structure−property relationship (16). It has been increasingly popular in the study of molecules and materials, such as predicting chemical reaction routes (17) and accelerating discovery of materials (18). Especially, neural networks (NN), a subclass of ML algorithms, are well recognized for handling complex nonlinear problems. NN established a predictive model for desired properties by iterative optimization of a complex high-dimensional function in a virtually infinite space of parameters. This feature makes it a transferrable tool for predicting protein spectra (19).In this article, we developed and applied a cost-effective ML protocol, to predict the IR spectra along with the kinetic process of a COVID-2019 virus (SARS-CoV-2) protein binding to hACE2. The efficient simulation of IR signals of different states of the coronavirus associated with the changes in its secondary structure is very encouraging for studying dynamic interactions between S protein of SARS-CoV-2 and human ACE2 with the help of ML techniques. This will enable a real-time spectroscopic monitoring of protein structure evolution for this deadly virus, providing valuable information for understanding its molecular mechanism, as well as developing cures and vaccines. ML should provide a cost-effective tool for simulating optical properties of SARS-CoV-2.     

复方贞术调脂方对HepG2细胞胰岛素抵抗的作用及机制研究

目的 观察复方贞术调脂方（FTZ）对胰岛素抵抗（IR） HepG2细胞的作用,为开拓FTZ的治疗范围,阐明FTZ调节糖脂代谢的机制提供实验依据.方法 用高浓度胰岛素诱导HepG2细胞使其产生胰岛素抵抗,用高、中、低3个剂量的FTZ干预后,葡萄糖氧化酶法检测HepG2细胞培养液上清液中葡萄糖的含量,实时荧光定量PCR检测HepG2细胞胰岛素信号PI-3Kp85 mRNA的表达,Western blot检测HepG2细胞胰岛素信号转导蛋白IRS1表达.结果 模型细胞培养基上清液中葡萄糖含量高于正常细胞（P＜0.05）.给予FTZ（1、25、100μg/mL）后,HepG2细胞培养基上清液中葡萄糖含量均低于模型细胞葡萄糖含量（P＜0.05）.胰岛素抵抗细胞与正常细胞相比PI-3Kp85 mRNA和IRS1的蛋白表达显著降低（P＜0.05）.给予FTZ干预后,与胰岛素抵抗细胞相比PI-3Kp85 mRNA和IRS1的蛋白表达显著增加（P＜0.05）.结论 FTZ可改善胰岛素抵抗HepG2细胞对葡萄糖的摄取.其改善胰岛素抵抗的作用机制之一可能是通过上调胰岛素信号PI-3Kp85 mRNA和IRSl蛋白质在胰岛素抵抗HepG2细胞的表达.     

电针对非酒精性脂肪性肝炎大鼠胰岛素抵抗指数水平的影响简

目的 采用电针刺激大鼠腧穴,观察其对高脂饮食所致的非酒精性脂肪肝（NASH）大鼠胰岛抵抗指数（HO-MA-IR）水平的影响.方法 40只SD大鼠随机分为正常对照组、模型组、电针组和西药组,每组10只,在16周后隔夜空腹,以2％戊巴比妥钠（45mg/kg体重）麻醉,经下腔静脉采血,分离血清.采用放射免疫分析法测定空腹血清胰岛素（FINS）,计算HOMA-IR.结果 空腹血糖（FBG）正常组与其他各组比较,差异无统计学意义（P＞0.05）;胰岛素抵抗指数水平模型组比正常组显著增高（P＜0.01）;模型组比电针组增高（P＜0.05）;电针组较之西药组有下降趋势,但二者比较无明显差异（P＞0.05）.结论 电针可能通过改善NASH大鼠HOMA-IR而发挥治疗NASH的作用.     

Treatment Patterns,HealthCare Resource Utilization and Costs in Patients with Bipolar Disorder,Newly Treated with Extended Release or Immediate Release Quetiapine Fumarate using US Healthcare Administrative Claims Data

Background

Differences in treatment patterns, health care resource use, and costs are expected among patients newly treated with quetiapine extended release (XR) or quetiapine immediate release (IR).

Objective

To compare treatment patterns, health care resource use, and costs in patients with bipolar disorder newly treated with quetiapine XR or quetiapine IR.

Methods

This was an observational, retrospective cohort study that used HealthCore Integrated Research Database–identified patients (age range, 18-64 years) with an International Classification of Disease, Ninth Revision diagnosis of bipolar disorder and ≥1 pharmacy claim for quetiapine XR or quetiapine IR between October 2, 2008, and July 31, 2010. Outcomes were as follows: patient characteristics at the index date (first claim for quetiapine XR or quetiapine IR); 12-month preindex clinical characteristics, health care resource use, and costs; and 12-month postindex treatment patterns, health care resource use, and costs, assessed using generalized linear models (adjusted for index date and preindex patient demographic characteristics, clinical characteristics, health care resource use, and costs).

Results

In total, 3049 patients with bipolar disorder were analyzed (651 in the quetiapine XR group and 2398 in the quetiapine IR group). Of patients initiating treatment with quetiapine XR, 8.8% had no change in or discontinuation of their index therapy compared with 5.7% of patients treated with quetiapine IR (adjusted odds ratio, 1.44; 95% confidence interval, 1.03-2.00; P = 0.0317). The average daily dose (adjusted mean) of quetiapine XR was higher than quetiapine IR (225 vs 175 mg/d, P < 0.0001). An average daily dose of 300 to 800 mg was reached sooner (15.6 vs 30.8 days, P = 0.0049) and in more patients (44.2% vs 27.2%, P < 0.0001) who were taking quetiapine XR compared with patients taking quetiapine IR. No differences in total health care costs were found between the cohorts; however, patients taking quetiapine XR were less likely to be hospitalized for mental health–related reasons (12.1% vs 18.3%, P = 0.0022) and incurred lower mental health–related costs (US $6686 vs US $7577, P = 0.0063) compared with patients taking quetiapine IR.

Conclusions

Treatment patterns and dosing differ in patients with bipolar disorder treated with quetiapine XR compared with those treated with quetiapine IR. Mental health–related hospitalizations and costs may be reduced in the 12 months after patients initiating treatment with quetiapine XR compared with initiating treatment with quetiapine IR.     

Tests of Dental Properties of Composite Materials Containing Nanohybrid Filler

Complex composite materials are used in many areas of dentistry. Initially, chemically hardened materials were also used, and in this group nanohybrid composites are highly valued. They are often used today, mainly for the direct reconstruction of damaged hard tooth tissue materials for rebuilding damaged tissues using indirect adhesive techniques. The research was conducted to determine the mechanical properties of materials with nanofillers. The article focuses on methods of important test methods for dental prosthetics: resilience, abrasion, wear test, impact strength, hardness, SEM, and chemical analysis. As part of this work, five different series of hybrid composites with nano-fillers were tested. The mechanical properties of composites, such as compressive strength, microhardness, flexural strength, and modulus of elasticity, depend mainly on the type, particle size, and amount of filler introduced. The obtained test results showed that the type and amount of nanofiller have a significant influence on the mechanical and tribological properties. The introduction of nanofillers allowed us to obtain higher mechanical properties compared to classic materials discussed by other researchers. The study observed a change in vibrations in the IR spectrum, which allowed a comparison of the organic structures of the studied preparations.     

Subclinical hypothyroidism is associated with metabolic syndrome and clomiphene citrate resistance in women with polycystic ovary syndrome

Background: To evaluate the possible relationship between subclinical hypothyroidism (SCH) and metabolic syndrome (MS) and the response to clomiphene citrate (CC) stimulation in women with polycystic ovary syndrome (PCOS).

Methods: One hundred and ninety-six women with PCOS were divided into two groups: (1) the SCH group with 92 patients; (2) the euthyroid (EU) group with 104 patients. Physical characteristics and metabolic parameters as well as the reaction to CC stimulating test were compared between these two groups.

Results: (1) In the SCH group, significantly higher body mass index, Ferriman–Gallwey score, serum triglyceride, insulin and glucose of oral glucose tolerance test, homeostatic model assessment-insulin resistance (HOMA-IR) and significantly lower serum high-density lipoprotein cholesterol was observed in comparison with those in the EU group (p?IR (43.5%) and MS (34.8%) in the SCH group was significantly higher than that in the EU group (p < 0.05).

Conclusions: SCH was found associated with IR, MS and CC resistance in women with PCOS. PCOS patients with SCH may have a poorer treatment response to ovulation induction with CC.     

睾酮对胰岛素诱导的肝细胞糖原合成和Akt/GSK3β磷酸化水平的影响

目的:探讨睾酮(T)在肝脏胰岛素抵抗(IR)形成过程中的作用及其分子途径。方法:将成年C57BL/6雌鼠随机分为T组(n=11)及对照组(n=10),T组小鼠每日腹腔注射T(10μg/g体质量,溶剂为蓖麻油),对照组每日腹腔注射相同体积的蓖麻油,连续给药24周后处死,分离出原代小鼠肝细胞进行体外培养,用胰岛素(Ins)处理细胞后,通过液闪法检测原代肝细胞中的糖原合成率。分别用10~(-5) mol/L和10~(-7) mol/L浓度的T溶液短时间(1 h)或长时间(36 h)处理体外培养的人肝癌细胞系BEL-7404后,再用Ins处理BEL-7404细胞,然后通过液闪法检测细胞中的糖原合成率;并通过免疫印迹法检测细胞中Akt、GSK3β蛋白的表达水平和磷酸化水平。结果:Ins对T组小鼠原代肝细胞中糖原合成的诱导作用显著低于对照组(P0.05),提示T组小鼠原代肝细胞对Ins的敏感性降低。BEL-7404细胞经T短时间(1 h)处理后,Ins对细胞中糖原合成率以及Akt和GSK3β蛋白活性的诱导作用显著提高(P0.05);但当BEL-7404细胞经高浓度T(10~(-5) mol/L)长时间(36 h)处理后,Ins对细胞中糖原合成率以及Akt和GSK3β蛋白活性的诱导作用显著降低(P0.05),提示高浓度T在短时间内能增强BEL-7404细胞对Ins的敏感性,但长时间暴露后会降低细胞对Ins的敏感性。结论:长时间的T暴露可能会降低肝细胞中Ins信号转导活性,从而干扰肝细胞对Ins的敏感性,导致IR的产生。     

葛根调节脂肪细胞糖脂代谢改善胰岛素抵抗的研究

该实验通过检测葛根对胰岛素抵抗(IR)3T3-L1脂肪细胞葡萄糖消耗,甘油三脂(TG)含量及PPARγ,ADPN,GLUT4,LPL,FABP4,FASn表达量的影响来探讨葛根调节糖脂代谢改善脂肪IR的作用机制。先采用3T3-L1前脂细胞诱导分化的成熟脂肪细胞,地塞米松诱导建立IR模型,将脂肪细胞分为正常组,IR模型组,罗格列酮阳性组,低、中、高剂量葛根含药血清组,以葡萄糖氧化酶-过氧化物酶(glucose oxidase-peroxidase,GOD-POD)法检测细胞培养液葡萄糖含量和甘油磷酸氧化酶(glycerol phosphate oxidase,GPO-POD)法测定胞内TG含量,荧光定量q PCR检测PPARγ,ADPN,GLUT4,LPL,FABP4(a P2),FASn基因mRNA水平。结果显示1μmol·L~(-1)地塞米松作用3T3-L1脂肪细胞96 h,与正常组比较,模型组葡萄糖消耗量降低(P0.01),胞内TG含量增加(P0.01),由此确认建立IR模型;与IR组比较,葛根含药血清干预IR细胞24 h葡萄糖消耗量上升(P0.01),胞内TG含量降低(P0.01),中、高剂量葛根含药血清组升高PPARγ,ADPN和GLUT4表达(P0.01),PPARγ与后两者基因表达呈现一致性。脂代谢相关基因检测结果显示仅高剂量葛根含药血清显著升高LPL表达(P0.05);各剂量葛根含药血清下调FABP4表达(P0.01);中、高剂量的葛根含药血清上调FASn基因表达(P0.01)。该实验表明葛根提高IR-3T3-L1脂肪细胞对葡萄糖的摄取能力,降低细胞内TG积聚,干预多个重要糖脂代谢基因,推测以PPARγ为中心多靶点调节糖脂代谢改善脂肪IR。