Common Factors of Alzheimer’s Disease and Rheumatoid Arthritis—Pathomechanism and Treatment

The accumulation of amyloid plaques, or misfolded fragments of proteins, leads to the development of a condition known as amyloidosis, which is clinically recognized as a systemic disease. Amyloidosis plays a special role in the pathogenesis of neurodegenerative diseases such as Alzheimer’s disease (AD), Parkinson’s disease, and rheumatoid arthritis (RA). The occurrence of amyloidosis correlates with the aging process of the organism, and since nowadays, old age is determined by the comfort of functioning and the elimination of unpleasant disease symptoms in the elderly, exposure to this subject is justified. In Alzheimer’s disease, amyloid plaques negatively affect glutaminergic and cholinergic transmission and loss of sympathetic protein, while in RA, amyloids stimulated by the activity of the immune system affect the degradation of the osteoarticular bond. The following monograph draws attention to the over-reactivity of the immune system in AD and RA, describes the functionality of the blood–brain barrier as an intermediary medium between RA and AD, and indicates the direction of research to date, focusing on determining the relationship and the cause–effect link between these disorders. The paper presents possible directions for the treatment of amyloidosis, with particular emphasis on innovative therapies.     

MASSyPup — an ‘Out of the Box’ solution for the analysis of mass spectrometry data

Mass spectrometry has evolved to a key technology in the areas of metabolomics and proteomics. Centralized facilities generate vast amount of data, which frequently need to be processed off‐site. Therefore, the distribution of data and software, as well as the training of personnel in the analysis of mass spectrometry data, becomes increasingly important. Thus, we created a comprehensive collection of mass spectrometry software which can be run directly from different media such as DVD or USB without local installation. MASSyPup is based on a Linux Live distribution and was complemented with programs for conversion, visualization and analysis of mass spectrometry (MS) data. A special emphasis was put on protein analysis and proteomics, encompassing the measurement of complete proteins, the identification of proteins based on Peptide Mass Fingerprints (PMF) or LC‐MS/MS data, and de novo sequencing. Another focus was directed to the study of metabolites and metabolomics, covering the detection, identification and quantification of compounds, as well as subsequent statistical analyses. Additionally, we added software for Mass Spectrometry Imaging (MSI), including hardware support for self‐made MSI devices. MASSyPup represents a ‘ready to work’ system for teaching or MS data analysis, but also represents an ideal platform for the distribution of MS data and the development of related software. The current Live DVD version can be downloaded free of charge from http://www.bioprocess.org/massypup . Copyright © 2014 John Wiley & Sons, Ltd.     

Significance of Oligomeric and Fibrillar Species in Amyloidosis: Insights into Pathophysiology and Treatment

Amyloidosis is a term referring to a group of various protein-misfolding diseases wherein normally soluble proteins form aggregates as insoluble amyloid fibrils. How, or whether, amyloid fibrils contribute to tissue damage in amyloidosis has been the topic of debate. In vitro studies have demonstrated the appearance of small globular oligomeric species during the incubation of amyloid beta peptide (Aβ). Nerve biopsy specimens from patients with systemic amyloidosis have suggested that globular structures similar to Aβ oligomers were generated from amorphous electron-dense materials and later developed into mature amyloid fibrils. Schwann cells adjacent to amyloid fibrils become atrophic and degenerative, suggesting that the direct tissue damage induced by amyloid fibrils plays an important role in systemic amyloidosis. In contrast, there is increasing evidence that oligomers, rather than amyloid fibrils, are responsible for cell death in neurodegenerative diseases, particularly Alzheimer’s disease. Disease-modifying therapies based on the pathophysiology of amyloidosis have now become available. Aducanumab, a human monoclonal antibody against the aggregated form of Aβ, was recently approved for Alzheimer’s disease, and other monoclonal antibodies, including gantenerumab, solanezumab, and lecanemab, could also be up for approval. As many other agents for amyloidosis will be developed in the future, studies to develop sensitive clinical scales for identifying improvement and markers that can act as surrogates for clinical scales should be conducted.     

Mass spectrometry in biodefense

Potential agents for biological attacks include both microorganisms and toxins. In mass spectrometry (MS), rapid identification of potential bioagents is achieved by detecting the masses of unique biomarkers, correlated to each agent. Currently, proteins are the most reliable biomarkers for detection and characterization of both microorganisms and toxins, and MS-based proteomics is particularly well suited for biodefense applications. Confident identification of an organism can be achieved by top-down proteomics following identification of individual protein biomarkers from their tandem mass spectra. In bottom-up proteomics, rapid digestion of intact protein biomarkers is again followed by MS/MS to provide unambiguous bioagent identification and characterization. Bioinformatics obviates the need for culturing and rigorous control of experimental variables to create and use MS fingerprint libraries for various classes of bioweapons. For specific applications, MS methods, instruments and algorithms have also been developed for identification based on biomarkers other than proteins and peptides.     

Shotgun proteomics‐based clinical testing for diagnosis and classification of amyloidosis

Shotgun proteomics technology has matured in the research laboratories and is poised to enter clinical laboratories. However, the road to this transition is sprinkled with major technical unknowns such as long‐term stability of the platform, reproducibility of the technology and clinical utility over traditional antibody‐based platforms. Further, regulatory bodies that oversee the clinical laboratory operations are unfamiliar with this new technology. As a result, diagnostic laboratories have avoided using shotgun proteomics for routine diagnostics. In this perspectives article, we describe the clinical implementation of a shotgun proteomics assay for amyloid subtyping, with a special emphasis on standardizing the platform for better quality control and earning clinical acceptance. This assay is the first shotgun proteomics assay to receive regulatory approval for patient diagnosis. The blueprint of this assay can be utilized to develop novel proteomics assays for detecting numerous other disease pathologies. Copyright © 2013 John Wiley & Sons, Ltd.     

Evaluation of sample extraction methods for proteomics analysis of green algae Chlorella vulgaris

Many protein extraction methods have been developed for plant proteome analysis but information is limited on the optimal protein extraction method from algae species. This study evaluated four protein extraction methods, i.e. direct lysis buffer method, TCA‐acetone method, phenol method, and phenol/TCA‐acetone method, using green algae Chlorella vulgaris for proteome analysis. The data presented showed that phenol/TCA‐acetone method was superior to the other three tested methods with regards to shotgun proteomics. Proteins identified using shotgun proteomics were validated using sequential window acquisition of all theoretical fragment‐ion spectra (SWATH) technique. Additionally, SWATH provides protein quantitation information from different methods and protein abundance using different protein extraction methods was evaluated. These results highlight the importance of green algae protein extraction method for subsequent MS analysis and identification.     

Intact protein separation by chromatographic and/or electrophoretic techniques for top-down proteomics

Mass spectrometry used in combination with a wide variety of separation methods is the principal methodology for proteomics. In bottom-up approach, proteins are cleaved with a specific proteolytic enzyme, followed by peptide separation and MS identification. In top-down approach intact proteins are introduced into the mass spectrometer. The ions generated by electrospray ionization are then subjected to gas-phase separation, fragmentation, fragment separation, and automated interpretation of mass spectrometric and chromatographic data yielding both the molecular weight of the intact protein and the protein fragmentation pattern. This approach requires high accuracy mass measurement analysers capable of separating the multi-charged isotopic cluster of proteins, such as hybrid ion trap-Fourier transform instruments (LTQ-FTICR, LTQ-Orbitrap). Front-end separation technologies tailored for proteins are of primary importance to implement top-down proteomics. This review intends to provide the state of art of protein chromatographic and electrophoretic separation methods suitable for MS coupling, and to illustrate both monodimensional and multidimensional approaches used for LC-MS top-down proteomics. In addition, some recent progresses in protein chromatography that may provide an alternative to those currently employed are also discussed.     

Amyloidosis in Alzheimer’s Disease: Pathogeny,Etiology, and Related Therapeutic Directions

The amyloid hypothesis of Alzheimer’s disease has long been the predominant theory, suggesting that Alzheimer’s disease is caused by the accumulation of amyloid beta protein (Aβ) in the brain, leading to neuronal toxicity in the central nervous system (CNS). Because of breakthroughs in molecular medicine, the amyloid pathway is thought to be central to the pathophysiology of Alzheimer’s disease (AD). Currently, it is believed that altered biochemistry of the Aβ cycle remains a central biological feature of AD and is a promising target for treatment. This review provides an overview of the process of amyloid formation, explaining the transition from amyloid precursor protein to amyloid beta protein. Moreover, we also reveal the relationship between autophagy, cerebral blood flow, ACHE, expression of LRP1, and amyloidosis. In addition, we discuss the detailed pathogenesis of amyloidosis, including oxidative damage, tau protein, NFTs, and neuronal damage. Finally, we list some ways to treat AD in terms of decreasing the accumulation of Aβ in the brain.     

Amyloid Cross-Seeding: Mechanism,Implication, and Inhibition

Most neurodegenerative diseases such as Alzheimer’s disease, type 2 diabetes, Parkinson’s disease, etc. are caused by inclusions and plaques containing misfolded protein aggregates. These protein aggregates are essentially formed by the interactions of either the same (homologous) or different (heterologous) sequences. Several experimental pieces of evidence have revealed the presence of cross-seeding in amyloid proteins, which results in a multicomponent assembly; however, the molecular and structural details remain less explored. Here, we discuss the amyloid proteins and the cross-seeding phenomena in detail. Data suggest that targeting the common epitope of the interacting amyloid proteins may be a better therapeutic option than targeting only one species. We also examine the dual inhibitors that target the amyloid proteins participating in the cross-seeding events. The future scopes and major challenges in understanding the mechanism and developing therapeutics are also considered. Detailed knowledge of the amyloid cross-seeding will stimulate further research in the practical aspects and better designing anti-amyloid therapeutics.     

基于深度学习的保留时间预测方法的研究进展及应用

在基于液相色谱-质谱联用的蛋白质组学研究中,肽段的保留时间作为有效区分不同肽段的特征参数,可以根据肽段自身的序列等信息对其进行预测.使用预测得到的保留时间辅助质谱数据鉴定肽段序列可以提高鉴定的准确性,因此对保留时间预测的工作一直受到领域内的广泛关注.传统的保留时间预测方法通常是根据氨基酸序列计算肽段的理化性质,进而计算...     

Interpretation of mass spectrometry data for high-throughput proteomics

Recent developments in proteomics have revealed a bottleneck in bioinformatics: high-quality interpretation of acquired MS data. The ability to generate thousands of MS spectra per day, and the demand for this, makes manual methods inadequate for analysis and underlines the need to transfer the advanced capabilities of an expert human user into sophisticated MS interpretation algorithms. The identification rate in current high-throughput proteomics studies is not only a matter of instrumentation. We present software for high-throughput PMF identification, which enables robust and confident protein identification at higher rates. This has been achieved by automated calibration, peak rejection, and use of a meta search approach which employs various PMF search engines. The automatic calibration consists of a dynamic, spectral information-dependent algorithm, which combines various known calibration methods and iteratively establishes an optimised calibration. The peak rejection algorithm filters signals that are unrelated to the analysed protein by use of automatically generated and dataset-dependent exclusion lists. In the "meta search" several known PMF search engines are triggered and their results are merged by use of a meta score. The significance of the meta score was assessed by simulation of PMF identification with 10,000 artificial spectra resembling a data situation close to the measured dataset. By means of this simulation the meta score is linked to expectation values as a statistical measure. The presented software is part of the proteome database ProteinScape which links the information derived from MS data to other relevant proteomics data. We demonstrate the performance of the presented system with MS data from 1891 PMF spectra. As a result of automatic calibration and peak rejection the identification rate increased from 6% to 44%.Abbreviations 2-DE Two-dimensional gel electrophoresis - MALDI Matrix-assisted laser desorption ionisation - PMF Peptide mass fingerprinting - MS Mass spectrometry - TOF Time of flight     

Development of a DNA‐Templated Peptide Probe for Photoaffinity Labeling and Enrichment of the Histone Modification Reader Proteins

Histone post‐translational modifications (HPTMs) provide signal platforms to recruit proteins or protein complexes to regulate gene expression. Therefore, the identification of these recruited partners (readers) is essential to understand the underlying regulatory mechanisms. However, it is still a major challenge to profile these partners because their interactions with HPTMs are rather weak and highly dynamic. Herein we report the development of a HPTM dual probe based on DNA‐templated technology and a photo‐crosslinking method for the identification of HPTM readers. By using the trimethylation of histone H3 lysine 4, we demonstrated that this HPTM dual probe can be successfully utilized for labeling and enrichment of HPTM readers, as well as for the discovery of potential HPTM partners. This study describes the development of a new chemical proteomics tool for profiling HPTM readers and can be adapted for broad biomedical applications.     

The Classification of Rice Blast Resistant Seed Based on Ranman Spectroscopy and SVM

Rice blast is a serious threat to rice yield. Breeding disease-resistant varieties is one of the most economical and effective ways to prevent damage from rice blast. The traditional identification of resistant rice seeds has some shortcoming, such as long possession time, high cost and complex operation. The purpose of this study was to develop an optimal prediction model for determining resistant rice seeds using Ranman spectroscopy. First, the support vector machine (SVM), BP neural network (BP) and probabilistic neural network (PNN) models were initially established on the original spectral data. Second, due to the recognition accuracy of the Raw-SVM model, the running time was fast. The support vector machine model was selected for optimization, and four improved support vector machine models (ABC-SVM (artificial bee colony algorithm, ABC), IABC-SVM (improving the artificial bee colony algorithm, IABC), GSA-SVM (gravity search algorithm, GSA) and GWO-SVM (gray wolf algorithm, GWO)) were used to identify resistant rice seeds. The difference in modeling accuracy and running time between the improved support vector machine model established in feature wavelengths and full wavelengths (200–3202 cm⁻¹) was compared. Finally, five spectral preproccessing algorithms, Savitzky–Golay 1-Der (SGD), Savitzky–Golay Smoothing (SGS), baseline (Base), multivariate scatter correction (MSC) and standard normal variable (SNV), were used to preprocess the original spectra. The random forest algorithm (RF) was used to extract the characteristic wavelengths. After different spectral preproccessing algorithms and the RF feature extraction, the improved support vector machine models were established. The results show that the recognition accuracy of the optimal IABC-SVM model based on the original data was 71%. Among the five spectral preproccessing algorithms, the SNV algorithm’s accuracy was the best. The accuracy of the test set in the IABC-SVM model was 100%, and the running time was 13 s. After SNV algorithms and the RF feature extraction, the classification accuracy of the IABC-SVM model did not decrease, and the running time was shortened to 9 s. This demonstrates the feasibility and effectiveness of IABC in SVM parameter optimization, with higher prediction accuracy and better stability. Therefore, the improved support vector machine model based on Ranman spectroscopy can be applied to the fast and non-destructive identification of resistant rice seeds.     

Applications of Protein Biochips in Biomedical and Biotechnological Research

Progress in the development of protein‐immobilization strategies and methods has made protein biochips increasingly accessible. The integration of these assay and analysis platforms into biomedical and biotechnological research has substantially expanded the repertoire of methods available for proteomics and biomarker research and for drug development. This Minireview highlights selected developments in the application of protein biochips in these fields.     

Protein inference: A protein quantification perspective

In mass spectrometry-based shotgun proteomics, protein quantification and protein identification are two major computational problems. To quantify the protein abundance, a list of proteins must be firstly inferred from the raw data. Then the relative or absolute protein abundance is estimated with quantification methods, such as spectral counting. Until now, most researchers have been dealing with these two processes separately. In fact, the protein inference problem can be regarded as a special protein quantification problem in the sense that truly present proteins are those proteins whose abundance values are not zero. Some recent published papers have conceptually discussed this possibility. However, there is still a lack of rigorous experimental studies to test this hypothesis.In this paper, we investigate the feasibility of using protein quantification methods to solve the protein inference problem. Protein inference methods aim to determine whether each candidate protein is present in the sample or not. Protein quantification methods estimate the abundance value of each inferred protein. Naturally, the abundance value of an absent protein should be zero. Thus, we argue that the protein inference problem can be viewed as a special protein quantification problem in which one protein is considered to be present if its abundance is not zero. Based on this idea, our paper tries to use three simple protein quantification methods to solve the protein inference problem effectively. The experimental results on six data sets show that these three methods are competitive with previous protein inference algorithms. This demonstrates that it is plausible to model the protein inference problem as a special protein quantification task, which opens the door of devising more effective protein inference algorithms from a quantification perspective. The source codes of our methods are available at: http://code.google.com/p/protein-inference/.     

BagReg: Protein inference through machine learning

Protein inference from the identified peptides is of primary importance in the shotgun proteomics. The target of protein inference is to identify whether each candidate protein is truly present in the sample. To date, many computational methods have been proposed to solve this problem. However, there is still no method that can fully utilize the information hidden in the input data.In this article, we propose a learning-based method named BagReg for protein inference. The method firstly artificially extracts five features from the input data, and then chooses each feature as the class feature to separately build models to predict the presence probabilities of proteins. Finally, the weak results from five prediction models are aggregated to obtain the final result. We test our method on six public available data sets. The experimental results show that our method is superior to the state-of-the-art protein inference algorithms.     

The Ultrastructure of Tissue Damage by Amyloid Fibrils

Amyloidosis is a group of diseases that includes Alzheimer’s disease, prion diseases, transthyretin (ATTR) amyloidosis, and immunoglobulin light chain (AL) amyloidosis. The mechanism of organ dysfunction resulting from amyloidosis has been a topic of debate. This review focuses on the ultrastructure of tissue damage resulting from amyloid deposition and therapeutic insights based on the pathophysiology of amyloidosis. Studies of nerve biopsy or cardiac autopsy specimens from patients with ATTR and AL amyloidoses show atrophy of cells near amyloid fibril aggregates. In addition to the stress or toxicity attributable to amyloid fibrils themselves, the toxicity of non-fibrillar states of amyloidogenic proteins, particularly oligomers, may also participate in the mechanisms of tissue damage. The obscuration of the basement and cytoplasmic membranes of cells near amyloid fibrils attributable to an affinity of components constituting these membranes to those of amyloid fibrils may also play an important role in tissue damage. Possible major therapeutic strategies based on pathophysiology of amyloidosis consist of the following: (1) reducing or preventing the production of causative proteins; (2) preventing the causative proteins from participating in the process of amyloid fibril formation; and/or (3) eliminating already-deposited amyloid fibrils. As the development of novel disease-modifying therapies such as short interfering RNA, antisense oligonucleotide, and monoclonal antibodies is remarkable, early diagnosis and appropriate selection of treatment is becoming more and more important for patients with amyloidosis.     

Proteomic analysis of peptides tagged with dimedone and related probes

Owing to its labile nature, a new role for cysteine sulfenic acid (–SOH) modification has emerged. This oxidative modification modulates protein function by acting as a redox switch during cellular signaling. The identification of proteins that undergo this modification represents a methodological challenge, and its resolution remains a matter of current interest. The development of strategies to chemically modify cysteinyl‐containing peptides for liquid chromatography–tandem mass spectrometry (LC‐MS/MS) analysis has increased significantly within the past decade. The method of choice to selectively label sulfenic acid is based on the use of dimedone or its derivatives. For these chemical probes to be effective on a proteome‐wide level, their reactivity toward –SOH must be high to ensure reaction completion. In addition, the presence of an adduct should not interfere with electrospray ionization, the efficiency of induced dissociation in MS/MS experiments or with the identification of Cys‐modified peptides by automated database searching algorithms. Herein, we employ a targeted proteomics approach to study the electrospray ionization and fragmentation effects of different –SOH specific probes and compared them to commonly used alkylating agents. We then extend our study to a whole proteome extract using shotgun proteomic approaches. These experiments enable us to demonstrate that dimedone adducts do not interfere with electrospray by suppressing the ionization nor impede product ion assignment by automated search engines, which detect a + 138 Da increase from unmodified peptides. Collectively, these results suggest that dimedone can be a powerful tool to identify sulfenic acid modifications by high‐throughput shotgun proteomics of a whole proteome. Copyright © 2014 John Wiley & Sons, Ltd.     

Identification evidence unraveled by strict proteomics rules toward forensic samples

Snake venom is a complex mixture of proteins and peptides secreted by venomous snakes from their poison glands. Although proteomics for snake venom composition, interspecific differences, and developmental evolution has been developed for a decade, current diagnosis or identification techniques of snake venom in clinical intoxication and forensic science applications are mainly dependent on morphological and immunoassay. It could be expected that the proteomics techniques directly offer great help. This work applied a bottom-up proteomics method to identify proteins’ types and species attribution in suspected snake venom samples using ultrahigh-performance liquid chromatography–quadrupole-electrostatic field Orbitrap tandem mass spectrometric technique, and cytotoxicity assay was amended to provide a direct evidence of toxicity. Toward the suspicious samples seized in the security control, sample pretreatment (in-sol and in-gel digestion) and data acquisition (nontargeted and targeted screening) modes complemented and validated each other. We have implemented two consequent approaches in identifying the species source of proteins in the samples via the points of venom proteomics and strict forensic identification. First, we completed a workflow consisting of a proteomics database match toward an entire SWISS-PROT (date 2018-11-22) database and a result-directed specific taxonomy database. The latter was a helpful hint to compare master protein kinds and reveal the insufficiency of specific venom proteomics characterization rules. Second, we suggested strict rules for protein identification to meet the requirements of forensic science on improved identification correctness, that is, (1) peptide spectrum matches confidence, peptide confidence, and protein confidence were both high (with the false-discovery ratio less than 1%); (2) the number of unique peptides was more than or equal to two in one protein, and (3) within unique peptides, which at least 75% of the ∆m/z of the matched y and b ions were less than 5 ppm. We identified these samples as cobra venom containing 10 highly abundant proteins (P00597, P82463, P60770, Q9YGI4, P62375, P49123, P80245, P60302, P01442, and P60304) from two snake venom protein families (acid phospholipase A2 and three-finger toxins), and the most abundant proteins were cytotoxins.