Scientists from Augusta University’s Medical College of Georgia (MCG) have established a new database targeting the study of vision loss causes: the Aqueous Humor (AH) Proteome Database.
The database is the subject of recent research published in Database, The Journal of Biological Databases and Curation, offering researchers the opportunity for a better comprehensive understanding of the impact of AH on the eye.
Let’s begin with some background.
We’ll start with aqueous humor (AH)—a transparent, low-viscosity fluid with low protein concentration that continuously circulates within the ciliary body epithelium, from the posterior to anterior chamber of the eye.
Why it’s important: AH plays a key role in supplying nutrients, removing wastes, and providing oxygen to the eye’s avascular tissues.
The result: homeostasis and intraocular pressure (IOP) regulation.
Go on …
AH’s protein interactions (called proteomic composition) are known to be key for understanding cellular processes.
Even further: Investigators noted that previous research has linked specific AH proteomic composition changes to ocular disease:
- Cataracts
- Glaucoma
- Age-related macular degeneration (AMD)
- Uveitis
- Retinoblastoma
- Diabetic retinopathy (DR)
Which leads us to…?
The need for more research and access to explore and better understand AH proteins and their connection to ocular disease.
Enter the AH Proteome Database.
Explain what this is.
The AH Proteome Database is a web-based reference database consisting of commonly identified AH proteins that aims to provide “valuable insights to the vision research community, helping them to better understand physiological and pathological proteomic signatures within the AH,” according to investigators.
How was it established?
The free database was developed as part of an investigation conducted by MCG researchers, including Ashok Sharma, PhD, associate professor and director of bioinformatics core at the MGC Center for Biotechnology, and fellow researchers from the university’s Department of Ophthalmology and the Culver Vision Discovery Institute.
Note: This research was funded by the National Institutes of Health.
Gotcha. Talk more about this research.
Investigators collected 307 human AH samples, with volumes ranging from 50 to 200 µL depending on the depth of the anterior chamber.
After sample collection, researchers conducted a chart review of the study participants’ demographic and clinical information.
Note: Samples were collected from patients undergoing cataract or glaucoma surgical procedures at the University of Augusta Medical Center.
How were the samples analyzed?
Via liquid chromatography with tandem mass spectrometry (LC-MS/MS)—specifically, the Orbitrap Fusion Tribrid mass spectrometer using data-dependent acquisition in positive mode.
And protein identification?
The Proteome Discoverer software was used to process raw MS files for both protein identification and quantification, while the SequestHT algorithm aligned identified peptide sequences to the UniPro-SwissProt database (a global resource for protein sequence and functional information).
For proteins with similar peptide sequences that couldn’t be differentiated using LC-MS/MS, the researcher grouped them based on the parsimony principle (a more simplified approach).
Now how was this new database developed?
Based on the LC-MS/MS analyses, generated data was uploaded to a Microsoft platform server via the web. The researchers designed internal programming to process these data files, generating appropriate tables and adding data records to the database.
From there, they integrated “user-friendly filtering and search options for each data column,” then collected and updated all clinical data that corresponded to each of the 307 AH samples into a Microsoft Excel file before uploading to the database.
Study authors noted: The web-based database is designed to automatically update all database pages and downloadable data sets following each upload.
And the end result of this?
After all samples were uploaded, a total of 1,683 proteins were detected in >5% of the samples.
See here (page 3) for a look at the 50 most abundant proteins detected in the AH (based on their detection levels and peptide-spectrum match [PSM] count), courtesy of the investigators.
How is the protein data stratified?
The database’s ‘Protein Data’ tab provides a visual display of the MS output data for all identified proteins (plus, raw data is also accessible for download).
The data is organized by:
- Sample ID (including the UniPro ID)
- Score (providing a measure of confidence in protein identification)
- Coverage (referring to the total protein sequence matched by identified peptides)
- Number of identified proteins in the protein group
- Number of peptides and unique peptides
- PSM count (for each peptide collected and identified)
- Number of amino acids
- Protein molecular weight
- Calculated isoelectric point (pI)
What else?
A reference clinical data set also provides an analysis of the proteomic data based on clinical and demographic characteristics, such as:
- Ocular pathology (glaucoma or cataract)
- Ocular characteristics (IOP, etc)
- Comorbidities
- Current medications
So how can clinicians utilize this data?
As the database is intended to be continuously updated, clinicians may use it to “analyze commonly expressed proteins from the human AH, thereby enhancing knowledge in the field of ophthalmology,” the study authors wrote.
And where can I access the database?
Note: As of April 19, 2024, the AH Proteome Database currently includes the following clinical data:
- 349 samples
- 17068 unique proteins measured
- 194,344 total proteins measured