阿尔茨海默病相关核心生物标志物研究进展

阿尔茨海默病作为近年来患病率最高的中老年疾病之一,以导致患者认知功能障碍、记忆力减退、性格改变为主要表现,其神经病理学特点为老年斑和神经元内神经原纤维缠结等。此病起病隐袭,临床症状出现前即有相应的生物学标记物改变;而易检、高敏感性及高特异性的有效生物学标记物有助于疾病早期诊断和早期治疗,意义重大。通过综述目前较为成熟的阿尔茨海默病相关核心生物学标记物,以讨论其临床及科研应用价值并展望未来的发展趋势。     

阿尔茨海默病生物标志物研究进展

阿尔茨海默病(Alzheimer's disease,AD)是一种常见的神经退行性疾病,以胞外淀粉样蛋白(amyloid-β,Aβ)沉积和胞内神经纤维缠结为主要病理特征.AD发病机理尚未完全探明,并且缺乏有效的早期临床诊断方法,AD患者往往在轻度认知障碍(mild cognitive impairment,MCI)和痴...     

生物反应器技术平台及其在新药研发中的应用

<正>生物反应器是科研成果的转化及新药研发的重要技术平台。简要介绍长江学者蛋白药物重点科技创新团队在多种生物反应器技术平台的建立及其在新药研发中的应用,为以高校为科研主体,开展新型"产学研"相结合的模式,积极推动科研成果的产业转化提供一种可借鉴模式。     

RNA干扰在阿尔茨海默病研究中的应用

阿尔茨海默病(Alzheimer Disease,AD)是一种中枢神经系统退行性疾病,临床表现主要为认知功能障碍,行为异常及日常生活能力下降。主要病理特征为淀粉样蛋白沉积形成的老年斑,tau蛋白过度磷酸化导致的神经纤维缠结以及神经元丢失。目前RNA干扰(RNA interference,RNAi)技术在AD研究中的应用日趋广泛,同时也被认为在治疗和改善AD中很具潜力。本文将从RNAi与淀粉样蛋白的产生、淀粉样蛋白引发的学习记忆障碍、tau蛋白过磷酸化及其治疗方面综述RNAi在AD研究中的应用。     

甲基乙二醛在阿尔茨海默病中的作用

糖酵解毒性副产物甲基乙二醛（methylglyoxal,MG）以其高反应活性在阿尔茨海默病（Alzheimer''s disease,AD）发生发展过 程中起到了重要的作用。MG 在AD病人脑中累积并促进beta淀粉样蛋白(beta-amyloid peptide,A beta)的产生和寡聚。大量累积的MG 通 过形成晚期糖基化终末产物(advanced glycosylation end products,AGEs)加剧了神经元中tau 蛋白的过度磷酸化。研究还发现MG 和AGEs 均参与了AD 脑中活性氧（reactive oxygen species,ROS）的产生和炎症的发生发展。本文总结了MG 在AD 病理过程中 的作用,并加以综述。     

阿尔茨海默病血浆和肠道菌群中生物标志物的研究进展

阿尔茨海默病(Alzheimer’s disease, AD)是一种常见的神经退行性疾病,在老年人中患病率高.但目前其发生机制尚不明确,而且缺乏便捷的早期诊断手段,因而难以在疾病早期进行干预和治疗.近几年,随着质谱、免疫学方法等新技术的发展,外周血Aβ、P-tau、外泌体、miRNA、肠道细菌等成为AD早期诊断及病情监测的潜在生物学标志物.本文就目前AD外周血生物标志物及AD相关肠道菌群生物标志物现况进行了综述,旨在为AD的早期诊断和预测疾病进展提供参考.     

阿尔茨海默病早期诊断的体液生物标志物

阿尔茨海默病(Alzheimer’s disease,AD)是目前发病率较高的神经退行性疾病,主要临床表现为不可逆的记忆力丧失与认知功能的衰退。AD起病隐秘,不易察觉,病程长达数十年,尚无有效治疗手段,因此AD早期的诊断与干预尤为重要;但是,当前AD早期诊断缺乏灵敏、简便的检测方案。体液(特别是血液)中的生物标志物受到了越来越多的关注,可能成为AD早期诊断的有效手段。现主要综述了与AD病理进程相关的脑脊液、血液、尿液中的生物标志物,并对其应用与前景做一展望。     

脑脊液β-淀粉样蛋白/磷酸化tau蛋白比值在鉴别诊断阿尔茨海默病与血管性痴呆中的价值

目的:探讨脑脊液Aβ1-42、t-tau蛋白及p-tau181蛋白以及Aβ1-42/t-tau和Aβ1-12/p-tau181比值鉴别诊断阿尔茨海默病(AD)与血管性痴呆(VD)的临床应用价值.方法:采用酶联免疫吸附法检测AD患者、VD患者和正常对照组(NC)脑脊液中Aβ1-42、t-tau蛋白及P-tau181蛋白浓度的变化.结果:An组患者脑脊液Aβ1-42越浓度显著低于VD组和NC组,t-tan蛋白及p-tau181蛋白浓度显著高于VD组和NC组.当Aβ1-42/p-tau181比值分界值为11.3时,鉴别诊断AD与VD的敏感性和特异性分别为95%和94%.结论:脑脊液Aβ1-42、t-tau蛋白及p-tau181蛋白浓度的变化,尤其是Aβ1-42/p-tau181比值是很好的AD与VD鉴别诊断的生物学指标.     

动物生物标志物在土壤污染生态学研究中的应用

应用陆栖无脊椎动物的生物标志物对土壤生态系统中污染物的暴露和效应进行评价日益受到重视,并取得了显著的研究进展,文中介绍了溶酶体、胁迫蛋白和金属硫蛋白(MTs)3种主要生物标志物,体腔细胞内溶酶体膜稳定性用中性红保持时间(NRR)进行检测;胁迫蛋白类多采其中的Hsp70和Hsp60;金属硫蛋白不同同分异构体的定量分析可用于反映不同的金属污染胁迫,对3种生物标志物机理、特性、检测实例以及在污染土壤生态毒理诊断中的应用前景进行了评述。     

星形胶质细胞在阿尔茨海默病中的功能变化及分子机制

星形胶质细胞在脑内数量最多,分布最广,对神经元有营养支持的作用,并且能够调控神经元的活性。越来越多的证据表明星形胶质细胞激活参与阿尔茨海默病(Alzheimer's disease,AD)的发生和发展。在AD病理情况下,星形胶质细胞在多种因子如β淀粉样蛋白(beta-amyloid,Aβ)和促炎细胞因子的作用下被激活,激活的星形胶质细胞进一步释放一氧化氮(Nitric oxide,NO)和多种炎性因子增强炎症级联反应。功能失常的星形胶质细胞会促进Aβ的产生,减弱对Aβ的摄取和清除,导致Aβ聚集沉积形成老年斑。激活的星形胶质细胞释放的炎症因子还能显著增加神经元内tau蛋白的异常过度磷酸化,产生神经纤维缠结。本文对星形胶质细胞在AD中参与神经变性的功能变化和分子机制进行总结,为星形胶质细胞作为靶点预防及治疗AD提供一定的理论依据。     

Diffusion Tensor Metrics as Biomarkers in Alzheimer's Disease

Background

Although diffusion tensor imaging has been a major research focus for Alzheimer’s disease in recent years, it remains unclear whether it has sufficient stability to have biomarker potential. To date, frequently inconsistent results have been reported, though lack of standardisation in acquisition and analysis make such discrepancies difficult to interpret. There is also, at present, little knowledge of how the biometric properties of diffusion tensor imaging might evolve in the course of Alzheimer’s disease.

Methods

The biomarker question was addressed in this study by adopting a standardised protocol both for the whole brain (tract-based spatial statistics), and for a region of interest: the midline corpus callosum. In order to study the evolution of tensor changes, cross-sectional data from very mild (N = 21) and mild (N = 22) Alzheimer’s disease patients were examined as well as a longitudinal cohort (N = 16) that had been rescanned at 12 months.

Findings and Significance

The results revealed that increased axial and mean diffusivity are the first abnormalities to occur and that the first region to develop such significant differences was mesial parietal/splenial white matter; these metrics, however, remained relatively static with advancing disease indicating they are suitable as ‘state-specific’ markers. In contrast, increased radial diffusivity, and therefore decreased fractional anisotropy–though less detectable early–became increasingly abnormal with disease progression, and, in the splenium of the corpus callosum, correlated significantly with dementia severity; these metrics therefore appear ‘stage-specific’ and would be ideal for monitoring disease progression. In addition, the cross-sectional and longitudinal analyses showed that the progressive abnormalities in radial diffusivity and fractional anisotropy always occurred in areas that had first shown an increase in axial and mean diffusivity. Given that the former two metrics correlate with dementia severity, but the latter two did not, it would appear that increased axial diffusivity represents an upstream event that precedes neuronal loss.     

Longitudinal Study of CSF Biomarkers in Patients with Alzheimer's Disease

Background

The CSF biomarkers tau and Aβ42 can identify patients with AD, even during the preclinical stages. However, previous studies on longitudinal changes of tau and Aβ42 in individual patients with AD and elderly controls report somewhat inconsistent results.

Methodology/Principal Findings

We investigated the levels of tau and Aβ42 at baseline and after 1 year in 100 patients with AD. In a second cohort of 45 AD patients we measured the CSF biomarkers at baseline and after 2 years. Moreover, in 34 healthy elderly controls the CSF biomarkers were followed for 4 years. The baseline levels of tau were increased with >60% in AD patients compared to controls (p<0.001), while baseline Aβ42 levels were decreased with >50% (p<0.001). In the AD group followed for 2 years, tau increased with 16% compared to the baseline levels (p<0.05). However, the levels of tau were stable over 4 years in the controls. The levels of Aβ42 did not change significantly over time in any of the groups. In the patients with AD, tau was moderately associated with worse cognitive performance already at baseline (p<0.05).

Conclusions/Significance

Tau and Aβ42 in CSF seem to reflect the underlying disease state in both early and late stages of AD. The slight increase in tau over time observed in the patients with AD is modest when compared to the relatively large difference in absolute tau levels between AD patients and controls. Therefore, these markers maintain their usefulness as state markers over time and might serve as surrogate markers for treatment efficacy in clinical trials.     

Brain Regional Correspondence Between Alzheimer's Disease Histopathology and Biomarkers of Protein Oxidation

Abstract: Four biomarkers of neuronal protein oxidation [W/S ratio of MAL-6 spin-labeled synaptosomes, phenylhydrazine-reactive protein carbonyl content, glutamine synthetase (GS) activity, creatine kinase (CK) activity] in three brain regions [cerebellum, inferior parietal lobule (IPL), and hippocampus (HIP)] of Alzheimer's disease (AD)-demented and age-matched control subjects were assessed. These endpoints indicate that AD brain protein may be more oxidized than that of control subjects. The W/S ratios of AD hippocampal and inferior parietal synaptosomes are 30 and 46% lower, respectively, than corresponding values of tissue isolated from control brain; however, the difference between the W/S ratios of AD and control cerebellar synaptosomes is not significant. Protein carbonyl content is increased 42 and 37% in the Alzheimer's HIP and IPL regions, respectively, relative to AD cerebellum, whereas carbonyl content in control HIP and IPL is similar to that of control cerebellum. GS activity decreases an average of 27% in the AD brain; CK activity declines by 80%. The brain regional variation of these oxidation-sensitive biomarkers corresponds to established histopathological features of AD (senile plaque and neurofibrillary tangle densities) and is paralleled by an increase in immunoreactive microglia. These data indicate that senile plaque-dense regions of the AD brain may represent environments of elevated oxidative stress.     

Biomarkers in Alzheimer's disease drug development

Biomarkers may be of great value in Alzheimer's disease drug development to select the most optimal drug candidates for large and expensive phase 3 clinical trials. Biomarkers will also be important to provide evidence that a drug affects the underlying pathophysiology of the disease, which, together with a beneficial effect on the clinical course, will be essential for labeling the drug as having a disease-modifying effect.     

多聚组氨酸融合标签在蛋白药物开发中的应用

纯化技术是制约蛋白质药物开发及其产业化的关键技术之一。构建多聚组氨酸标签融合蛋白,采用固定金属离子亲和层析进行纯化,是一种高效的蛋白质纯化策略。介绍多聚组氨酸融合标签在蛋白质药物开发中的应用基础和应用概况,分析多聚组氨酸标签在融合蛋白中的位置对亲和层析纯化的影响,总结常用的多聚组氨酸融合表达方式,并对其融合表达样品的预处理、亲和层析纯化条件及其对目的蛋白药物药用安全性和有效性的影响进行探讨。     

Applications and Challenges of Machine Learning Methods in Alzheimer's Disease Multi-Source Data Analysis

BackgroundRecent development in neuroimaging and genetic testing technologies have made it possible to measure pathological features associated with Alzheimer''s disease (AD) in vivo. Mining potential molecular markers of AD from high-dimensional, multi-modal neuroimaging and omics data will provide a new basis for early diagnosis and intervention in AD. In order to discover the real pathogenic mutation and even understand the pathogenic mechanism of AD, lots of machine learning methods have been designed and successfully applied to the analysis and processing of large-scale AD biomedical data.ObjectiveTo introduce and summarize the applications and challenges of machine learning methods in Alzheimer''s disease multi-source data analysis.MethodsThe literature selected in the review is obtained from Google Scholar, PubMed, and Web of Science. The keywords of literature retrieval include Alzheimer''s disease, bioinformatics, image genetics, genome-wide association research, molecular interaction network, multi-omics data integration, and so on.ConclusionThis study comprehensively introduces machine learning-based processing techniques for AD neuroimaging data and then shows the progress of computational analysis methods in omics data, such as the genome, proteome, and so on. Subsequently, machine learning methods for AD imaging analysis are also summarized. Finally, we elaborate on the current emerging technology of multi-modal neuroimaging, multi-omics data joint analysis, and present some outstanding issues and future research directions.