Tear proteomics of orbital decompression for disfiguring exophthalmos in inactive thyroid-associated ophthalmopathy

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.

Comprehensive analysis of key genes and pathways for biological and clinical implications in thyroid-associated ophthalmopathy

Background

Thyroid-associated ophthalmopathy (TAO) is a common and organ-specific autoimmune disease. Early diagnosis and novel treatments are essential to improve the prognosis of TAO patients. Therefore, the current work was performed to identify the key genes and pathways for the biological and clinical implications of TAO through comprehensive bioinformatics analysis and a series of clinical validations.

Methods

GSE105149 and GSE185952 were obtained from the Gene Expression Omnibus (GEO) database for analysis. The data were normalized to identify the common differentially expressed genes (DEGs) between the two datasets, and the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to assess key pathways in TAO. Protein–protein interaction (PPI) networks and hub genes among the common DEGs were identified. Furthermore, we collected the general information and blood samples from 50 TAO patients and 20 healthy controls (HCs), and the expression levels of the proteins encoded by hub genes in serum were detected by enzyme-linked immunosorbent assay (ELISA). Then we further assessed the relationship between the ELISA data and the TAO development.

Results

Several common pathways, including neuroactive ligand-receptor interaction, the IL-17 signaling pathway, and the TNF signaling pathway, were identified in both datasets. In parallel, 52 common DEGs were identified. The KEGG analysis showed that these common DEGs are mainly enriched in long-term depression, the VEGF signaling pathway, the IL-17 signaling pathway, the TNF signaling pathway, and cytokine-cytokine receptor interactions. The key hub genes PRKCG, OSM, DPP4, LRRTM1, CXCL6, and CSF3R were screened out through the PPI network. As confirmation, the ELISA results indicated that protein expression levels of PRKCG, OSM, CSF3R, and DPP4 were significantly upregulated in TAO patients compared with HCs. In addition, PRKCG and DPP4 were verified to show value in diagnosing TAO, and CSF3R was found to be a valuable diagnostic marker in distinguishing active TAO from inactive TAO.

Conclusions

Inflammation- and neuromodulation-related pathways might be closely associated with TAO. Based on the clinical verification, OSM, CSF3R, CXCL6, DPP4, and PRKCG may serve as inflammation- or neuromodulation-related biomarkers for TAO, providing novel insights for the diagnosis and treatment of TAO.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08854-5.

Mass spectrometry-based tear proteomics for noninvasive biomarker discovery

The lacrimal film has attracted increasing interest in the last decades as a potential source of biomarkers of physiopathological states, due to its accessibility, moderate complexity, and responsiveness to ocular and systemic diseases. High-performance liquid chromatography-mass spectrometry (LC-MS) has led to effective approaches to tear proteomics, despite the intrinsic limitations in sample amounts. This review focuses on the recent progress in strategy and technology, with an emphasis on the potential for personalized medicine. After an introduction on lacrimal-film composition, examples of applications to biomarker discovery are discussed, comparing approaches based on pooled-sample and single-tear analysis. Then, the most critical steps of the experimental pipeline, that is, tear collection, sample fractionation, and LC-MS implementation, are discussed with reference to proteome-coverage optimization. Advantages and challenges of the alternative procedures are highlighted. Despite the still limited number of studies, tear quantitative proteomics, including single-tear investigation, could offer unique contributions to the identification of low-invasiveness, sustained-accessibility biomarkers, and to the development of personalized approaches to therapy and diagnosis.

Comparison of Different Mass Spectrometry Workflows for the Proteomic Analysis of Tear Fluid

The tear film is a multi-layer fluid that covers the corneal and conjunctival epithelia of the eye and provides lubrication, nutrients, and protection from the outside environment. Tear fluid contains a high concentration of proteins and has thus been recognized as a potential source of biomarkers for ocular disorders due to its proximity to disease sites on the ocular surface and the non-invasive nature of its collection. This is particularly true in the case of dry eye disease, which directly impacts the tear film and its components. Proteomic analysis of tear fluid is challenging mainly due to the wide dynamic range of proteins and the small sample volumes. However, recent advancements in mass spectrometry have revolutionized the field of proteomics enabling unprecedented depth, speed, and accuracy, even with small sample volumes. In this study using the Orbitrap Fusion Tribrid mass spectrometer, we compared four different mass spectrometry workflows for the proteomic analysis of tear fluid collected via Schirmer strips. We were able to establish a method of in-strip protein digestion that identified >3000 proteins in human tear samples from 11 healthy subjects. Our method offers a significant improvement in the number of proteins identified compared to previously reported methods without pooling samples.

Proteomics of Tear in Inactive Thyroid-Associated Ophthalmopathy

BACKGROUND

Thyroid-associated ophthalmopathy (TAO), one of the most common orbital diseases in adults, seriously reduces patients' quality of life. Although human tear proteomics identified many abnormal expressed proteins and proposed several pathogeneses of TAO, most of these studies focused on the active stage or mixed types in TAO. In this study we identified significantly changed proteins and preliminary revealed the potential signalling pathways and mechanisms of TAO with the late, inactive stage.

PATIENTS AND METHODS

Tears from TAO patients (n=6) with a CAS score < 3 and 6 control healthy subject were collected. The pooled tears were further fractionated using high pH reversed-phase chromatography, then submitted to LC-MS/MS and subsequent bioinformatic analysis.

RESULTS

Proteomic profiling identified 107 significantly changed proteins between the inactive stage of TAO patients and healthy cases. Among these proteins, 62 were upregulated, and 45 were downregulated in TAO cases compared to healthy individuals. Enrichment analysis revealed that the immune system, cell cycle, metabolism (carbohydrate metabolism and metabolism of cofactors and vitamins), protein synthesis and degradation might play a vital role in the progress of inactive TAO. The present investigation represents the first proteomic tear study of TAO patients in the inactive stage.

CONCLUSION

The results shed light on the differences between inactive TAO patients and healthy cases, thus enabling us to understand better the molecular mechanisms and potential targets for the treatment of inactive TAO.