The Urine Proteome Profile Is Different in Neuromyelitis Optica Compared to Multiple Sclerosis: A Clinical Proteome Study

Applications of artificial intelligence and bioinformatics methodologies in the analysis of ocular biofluid markers: a scoping review

PURPOSE

This scoping review summarizes the applications of artificial intelligence (AI) and bioinformatics methodologies in analysis of ocular biofluid markers. The secondary objective was to explore supervised and unsupervised AI techniques and their predictive accuracies. We also evaluate the integration of bioinformatics with AI tools.

METHODS

This scoping review was conducted across five electronic databases including EMBASE, Medline, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Web of Science from inception to July 14, 2021. Studies pertaining to biofluid marker analysis using AI or bioinformatics were included.

RESULTS

A total of 10,262 articles were retrieved from all databases and 177 studies met the inclusion criteria. The most commonly studied ocular diseases were diabetic eye diseases, with 50 papers (28%), while glaucoma was explored in 25 studies (14%), age-related macular degeneration in 20 (11%), dry eye disease in 10 (6%), and uveitis in 9 (5%). Supervised learning was used in 91 papers (51%), unsupervised AI in 83 (46%), and bioinformatics in 85 (48%). Ninety-eight papers (55%) used more than one class of AI (e.g. > 1 of supervised, unsupervised, bioinformatics, or statistical techniques), while 79 (45%) used only one. Supervised learning techniques were often used to predict disease status or prognosis, and demonstrated strong accuracy. Unsupervised AI algorithms were used to bolster the accuracy of other algorithms, identify molecularly distinct subgroups, or cluster cases into distinct subgroups that are useful for prediction of the disease course. Finally, bioinformatic tools were used to translate complex biomarker profiles or findings into interpretable data.

CONCLUSION

AI analysis of biofluid markers displayed diagnostic accuracy, provided insight into mechanisms of molecular etiologies, and had the ability to provide individualized targeted therapeutic treatment for patients. Given the progression of AI towards use in both research and the clinic, ophthalmologists should be broadly aware of the commonly used algorithms and their applications. Future research may be aimed at validating algorithms and integrating them in clinical practice.

Early urinary candidate biomarkers and clinical outcomes of intervention in a rat model of experimental autoimmune encephalomyelitis

Multiple sclerosis is a chronic autoimmune demyelinating disease of the central nervous system and is difficult to diagnose in early stages. Without homeostatic control, urine was reported to have the ability to accumulate early changes in the body. We expect that urinary proteome can reflect early changes in the nervous system. The early urinary proteome changes in a most employed multiple sclerosis rat model (experimental autoimmune encephalomyelitis) were analysed to explore early urinary candidate biomarkers, and early treatment of methylprednisolone was used to evaluate the therapeutic effect. Twenty-five urinary proteins were altered at day 7 when there were no clinical symptoms and obvious histological changes. Fourteen were reported to be differently expressed in the serum/cerebrospinal fluid/brain tissues of multiple sclerosis patients or animals such as angiotensinogen and matrix metallopeptidase 8. Functional analysis showed that the dysregulated proteins were associated with asparagine degradation, neuroinflammation and lipid metabolism. After the early treatment of methylprednisolone, the incidence of encephalomyelitis in the intervention group was only 1/13. This study demonstrates that urine may be a good source of biomarkers for the early detection of multiple sclerosis. These findings may provide important information for early diagnosis and intervention of multiple sclerosis in the future.

Diagnostic Potential of Two Novel Biomarkers for Neuromyelitis Optica Spectrum Disorder and Multiple Sclerosis

BACKGROUND

Currently, no tests can definitively diagnose and distinguish neuromyelitis optica spectrum disorder (NMOSD) from multiple sclerosis (MS).

METHODS

Initially, cerebrospinal fluid (CSF) proteomics were employed to uncover the novel biomarkers that differentiate NMOSD from MS into cohorts of 10 MS and 10 NMOSD patients. Subsequently, screening biomarkers were validated using an enzyme-linked immunosorbent assay method and CSF and serum samples from 20 MS patients, 20 NMOSD patients, 20 non-inflammatory neurological controls, and 20 healthy controls.

RESULTS

In study cohort, insulin-like growth factor-binding protein 7 (IGFBP7) and lysosome-associated membrane glycoprotein 2 (LAMP2) were screened. In validation cohort, serum and CSF IGFBP7 not only exhibited higher levels in MS and NMOSD patients than controls, but also had greatest area under the curve (AUC, above or equal to 0.8) in MS and NMOSD diagnoses. Serum IGFBP7 (0.945) and CSF IGFBP7 (0.890) also had the greatest AUCs for predicting MS progression, while serum LAMP2 had a moderate curve (0.720).

CONCLUSIONS

IGFBP7 was superior in diagnosing MS and NMOSD, and IGFBP7 and serum LAMP2 performed exceptionally well in predicting the MS progression. These results offered reasons for further investigations into the functions of IGFBP7 and LAMP2 in MS and NMOSD.

Proteomics in Multiple Sclerosis: The Perspective of the Clinician

Multiple sclerosis (MS) is the inflammatory demyelinating and neurodegenerative disease of the central nervous system (CNS) that affects approximately 2.8 million people worldwide. In the last decade, a new era was heralded in by a new phenotypic classification, a new diagnostic protocol and the first ever therapeutic guideline, making personalized medicine the aim of MS management. However, despite this great evolution, there are still many aspects of the disease that are unknown and need to be further researched. A hallmark of these research are molecular biomarkers that could help in the diagnosis, differential diagnosis, therapy and prognosis of the disease. Proteomics, a rapidly evolving discipline of molecular biology may fulfill this dire need for the discovery of molecular biomarkers. In this review, we aimed to give a comprehensive summary on the utility of proteomics in the field of MS research. We reviewed the published results of the method in case of the pathogenesis of the disease and for biomarkers of diagnosis, differential diagnosis, conversion of disease courses, disease activity, progression and immunological therapy. We found proteomics to be a highly effective emerging tool that has been providing important findings in the research of MS.

Biology of the Extracellular Proteasome

Proteasomes are traditionally considered intracellular complexes that play a critical role in maintaining proteostasis by degrading short-lived regulatory proteins and removing damaged proteins. Remarkably, in addition to these well-studied intracellular roles, accumulating data indicate that proteasomes are also present in extracellular body fluids. Not much is known about the origin, biological role, mode(s) of regulation or mechanisms of extracellular transport of these complexes. Nevertheless, emerging evidence indicates that the presence of proteasomes in the extracellular milieu is not a random phenomenon, but rather a regulated, coordinated physiological process. In this review, we provide an overview of the current understanding of extracellular proteasomes. To this end, we examine 143 proteomic datasets, leading us to the realization that 20S proteasome subunits are present in at least 25 different body fluids. Our analysis also indicates that while 19S subunits exist in some of those fluids, the dominant proteasome activator in these compartments is the PA28α/β complex. We also elaborate on the positive correlations that have been identified in plasma and extracellular vesicles, between 20S proteasome and activity levels to disease severity and treatment efficacy, suggesting the involvement of this understudied complex in pathophysiology. In addition, we address the considerations and practical experimental methods that should be taken when investigating extracellular proteasomes. Overall, we hope this review will stimulate new opportunities for investigation and thoughtful discussions on this exciting topic that will contribute to the maturation of the field.

HBFP: a new repository for human body fluid proteome

Body fluid proteome has been intensively studied as a primary source for disease biomarker discovery. Using advanced proteomics technologies, early research success has resulted in increasingly accumulated proteins detected in different body fluids, among which many are promising biomarkers. However, despite a handful of small-scale and specific data resources, current research is clearly lacking effort compiling published body fluid proteins into a centralized and sustainable repository that can provide users with systematic analytic tools. In this study, we developed a new database of human body fluid proteome (HBFP) that focuses on experimentally validated proteome in 17 types of human body fluids. The current database archives 11 827 unique proteins reported by 164 scientific publications, with a maximal false discovery rate of 0.01 on both the peptide and protein levels since 2001, and enables users to query, analyze and download protein entries with respect to each body fluid. Three unique features of this new system include the following: (i) the protein annotation page includes detailed abundance information based on relative qualitative measures of peptides reported in the original references, (ii) a new score is calculated on each reported protein to indicate the discovery confidence and (iii) HBFP catalogs 7354 proteins with at least two non-nested uniquely mapping peptides of nine amino acids according to the Human Proteome Project Data Interpretation Guidelines, while the remaining 4473 proteins have more than two unique peptides without given sequence information. As an important resource for human protein secretome, we anticipate that this new HBFP database can be a powerful tool that facilitates research in clinical proteomics and biomarker discovery.

Database URL:https://bmbl.bmi.osumc.edu/HBFP/

Autoantibodies Against the Complement Regulator Factor H in the Serum of Patients With Neuromyelitis Optica Spectrum Disorder

Neuromyelitis optica spectrum disorder (NMOSD) is an autoimmune inflammatory disease of the central nervous system (CNS), characterized by pathogenic, complement-activating autoantibodies against the main water channel in the CNS, aquaporin 4 (AQP4). NMOSD is frequently associated with additional autoantibodies and antibody-mediated diseases. Because the alternative pathway amplifies complement activation, our aim was to evaluate the presence of autoantibodies against the alternative pathway C3 convertase, its components C3b and factor B, and the complement regulator factor H (FH) in NMOSD. Four out of 45 AQP4-seropositive NMOSD patients (~9%) had FH autoantibodies in serum and none had antibodies to C3b, factor B and C3bBb. The FH autoantibody titers were low in three and high in one of the patients, and the avidity indexes were low. FH-IgG complexes were detected in the purified IgG fractions by Western blot. The autoantibodies bound to FH domains 19-20, and also recognized the homologous FH-related protein 1 (FHR-1), similar to FH autoantibodies associated with atypical hemolytic uremic syndrome (aHUS). However, in contrast to the majority of autoantibody-positive aHUS patients, these four NMOSD patients did not lack FHR-1. Analysis of autoantibody binding to FH19-20 mutants and linear synthetic peptides of the C-terminal FH and FHR-1 domains, as well as reduced FH, revealed differences in the exact binding sites of the autoantibodies. Importantly, all four autoantibodies inhibited C3b binding to FH. In conclusion, our results demonstrate that FH autoantibodies are not uncommon in NMOSD and suggest that generation of antibodies against complement regulating factors among other autoantibodies may contribute to the complement-mediated damage in NMOSD.

CSF proteome in multiple sclerosis subtypes related to brain lesion transcriptomes

To identify markers in the CSF of multiple sclerosis (MS) subtypes, we used a two-step proteomic approach: (i) Discovery proteomics compared 169 pooled CSF from MS subtypes and inflammatory/degenerative CNS diseases (NMO spectrum and Alzheimer disease) and healthy controls. (ii) Next, 299 proteins selected by comprehensive statistics were quantified in 170 individual CSF samples. (iii) Genes of the identified proteins were also screened among transcripts in 73 MS brain lesions compared to 25 control brains. F-test based feature selection resulted in 8 proteins differentiating the MS subtypes, and secondary progressive (SP)MS was the most different also from controls. Genes of 7 out these 8 proteins were present in MS brain lesions: GOLM was significantly differentially expressed in active, chronic active, inactive and remyelinating lesions, FRZB in active and chronic active lesions, and SELENBP1 in inactive lesions. Volcano maps of normalized proteins in the different disease groups also indicated the highest amount of altered proteins in SPMS. Apolipoprotein C-I, apolipoprotein A-II, augurin, receptor-type tyrosine-protein phosphatase gamma, and trypsin-1 were upregulated in the CSF of MS subtypes compared to controls. This CSF profile and associated brain lesion spectrum highlight non-inflammatory mechanisms in differentiating CNS diseases and MS subtypes and the uniqueness of SPMS.

Human body-fluid proteome: quantitative profiling and computational prediction

Empowered by the advancement of high-throughput bio technologies, recent research on body-fluid proteomes has led to the discoveries of numerous novel disease biomarkers and therapeutic drugs. In the meantime, a tremendous progress in disclosing the body-fluid proteomes was made, resulting in a collection of over 15 000 different proteins detected in major human body fluids. However, common challenges remain with current proteomics technologies about how to effectively handle the large variety of protein modifications in those fluids. To this end, computational effort utilizing statistical and machine-learning approaches has shown early successes in identifying biomarker proteins in specific human diseases. In this article, we first summarized the experimental progresses using a combination of conventional and high-throughput technologies, along with the major discoveries, and focused on current research status of 16 types of body-fluid proteins. Next, the emerging computational work on protein prediction based on support vector machine, ranking algorithm, and protein-protein interaction network were also surveyed, followed by algorithm and application discussion. At last, we discuss additional critical concerns about these topics and close the review by providing future perspectives especially toward the realization of clinical disease biomarker discovery.

Metabolomic Profiling in Neuromyelitis Optica Spectrum Disorder Biomarker Discovery

There is no specific test for diagnosing neuromyelitis optica spectrum disorder (NMOSD), a disabling autoimmune disease of the central nervous system. Instead, diagnosis relies on ruling out other related disorders with overlapping clinical symptoms. An urgency for NMOSD biomarker discovery is underscored by adverse responses to treatment following misdiagnosis and poor prognosis following the delayed onset of treatment. Pathogenic autoantibiotics that target the water channel aquaporin-4 (AQP4) and myelin oligodendrocyte glycoprotein (MOG) contribute to NMOSD pathology. The importance of early diagnosis between AQP4-Ab⁺ NMOSD, MOG-Ab⁺ NMOSD, AQP4-Ab⁻ MOG-Ab⁻ NMOSD, and related disorders cannot be overemphasized. Here, we provide a comprehensive data collection and analysis of the currently known metabolomic perturbations and related proteomic outcomes of NMOSD. We highlight short chain fatty acids, lipoproteins, amino acids, and lactate as candidate diagnostic biomarkers. Although the application of metabolomic profiling to individual NMOSD patient care shows promise, more research is needed.

Proteomic Approaches to Decipher Mechanisms Underlying Pathogenesis in Multiple Sclerosis Patients

Multiple sclerosis (MS) is a chronic inflammatory demyelinating and neurodegenerative disease of the central nervous system (CNS). The cause of MS is unknown, with no effective therapies available to halt the progressive neurological disability. Development of new and improvement of existing therapeutic strategies therefore require a better understanding of MS pathogenesis, especially during the progressive phase of the disease. This can be achieved through development of biomarkers that can help to identify disease pathophysiology and monitor disease progression. Proteomics is a powerful and promising tool to accelerate biomarker detection and contribute to novel therapeutics. In this review, an overview of how proteomic technology using CNS tissues and biofluids from MS patients has provided important clues to the pathogenesis of MS is provided. Current publications, pitfalls, as well as directions of future research involving proteomic approaches to understand the pathogenesis of MS are discussed.

Subsets of activated monocytes and markers of inflammation in incipient and progressed multiple sclerosis

Multiple sclerosis (MS) is an immune mediated, inflammatory and demyelinating disease of the central nervous system (CNS). Substantial evidence points toward monocytes and macrophages playing prominent roles early in disease, mediating both pro- and anti-inflammatory responses. Monocytes are subdivided into three subsets depending on the expression of CD14 and CD16, representing different stages of inflammatory activation. To investigate their involvement in MS, peripheral blood mononuclear cells from 40 patients with incipient or progressed MS and 20 healthy controls were characterized ex vivo. In MS samples, we demonstrate a highly significant increase in nonclassical monocytes (CD14+CD16++), with a concomitant significant reduction in classical monocytes (CD14++CD16-) compared with healthy controls. Also, a significant reduction in the surface expression of CD40, CD163, and CD192 was found, attributable to the upregulation of the nonclassical monocytes. In addition, significantly increased levels of human endogenous retrovirus (HERV) envelope (Env) epitopes, encoded by both HERV-H/F and HERV-W, were specifically found on nonclassical monocytes from patients with MS; emphasizing their involvement in MS disease. In parallel, serum and cerebrospinal fluid (CSF) samples were analyzed for soluble biomarkers of inflammation and neurodegeneration. For sCD163 versus CD163, no significant correlations were found, whereas highly significant correlations between levels of soluble neopterine and the intermediate monocyte (CD14++CD16+) population was found, as were correlations between levels of soluble osteopontin and the HERV Env expression on nonclassical monocytes. The results from this study emphasize the relevance of further focus on monocyte subsets, particularly the nonclassical monocytes in monitoring of inflammatory diseases.

Clinical proteomics of enervated neurons

The dynamic field of neurosciences entails ever increasing search for molecular mechanisms of disease states, especially in the domain of neurodegenerative disorders. The previous century heralded the techniques in proteomics when indexing of the human proteomes relating to various disease conditions became important. Early stage research in certain diseases or pathological conditions requires a more holistic approach of first discovering the proteins of interest for the condition. Despite its limitations, proteomics is one of the most powerful techniques available to us today to dissect the molecular scenario in a particular disease situation. In this review we will discuss about the current clinical research in neurodegenerative disorders that employ proteomics techniques. We will specifically focus on our understanding of Alzheimer’s disease, traumatic spinal cord injury and neuromyelitis optica. Discussions will include ongoing worldwide research in these areas, research in India and specifically our laboratory in these domains of neurodegenerative conditions.