Identification of Comorbidities and Genomic Associations of Biochemically Analogous Retinal and Brain Tissues Using Bioinformatics Approaches

doi:10.21203/rs.3.rs-5280395/v1

The human retina and brain share similar biochemical compositions, primarily containing docosahexaenoic acid (DHA), an omega-3 fatty acid crucial to neurological functions. Scientific reports indicate that DHA is essential for brain and retinal development, aging, memory formation, synaptic membrane function, photoreceptor biogenesis, and neuroprotection. Epidemiologic studies suggest that diets rich in DHA can slow cognitive impairments, aid in the progression of Alzheimer’s disease, and promote ocular health. In this study, we identified comorbidities and genomic associations between retinal and brain tissues using bioinformatics approaches. This involved several key steps: data collection, bioinformatic analysis, comorbidity identification, and studying genomic associations. The project aimed to investigate the connection between retinal and brain disorders by examining altered genetic compositions using automated mutation discovery platforms such as DisGeNET, Cytoscape, and Karyosoft. The hypothesis is that similar biochemical compositions will lead to commonalities in genetic variations and phenotypic expressions. The data helped to understand genomic diseases affecting both the retina and the brain, such as Alzheimer’s disease and age-related macular degeneration.

Comorbidity

Bioinformatics

Eye Disease

Brain Diseases

Genomic Associations

The human retina and brain share similar biochemical compositions, both containing docosahexaenoic acid (DHA), an omega-3 fatty acid, as a primary macromolecule. They are also interconnected in neurological functions (Yeboah, Lobanova, Brush, & Agbaga, 2021). Scientific studies indicate that DHA plays a crucial role in brain and retinal development, aging, memory formation, synaptic membrane function, photoreceptor biogenesis, and neuroprotection (Stinson et al., 2017; Shindou, Hideo et al., 2017; Sugasini, Dhavamani, et al., 2020). Epidemiological research suggests that diets rich in DHA can slow down cognitive impairments and the progression of Alzheimer’s disease. A few conditions are well studied, such as neurovisual disorders like optic neuritis, or optic nerve inflammation, causes vision loss, and affects the brain (Optic Neuritis Study Group 2008). In addition, age-related eye diseases like macular degeneration, glaucoma, and diabetic retinopathy have been linked with cognitive impairment and dementia (Mody, Sankalp, and Abhishek Joshi 2023; Chan, Jane W et al. 2021; Crosby-Nwaobi, Roxanne R, et al.)

Inherited retinal diseases are a clinically and genetically heterogeneous group of disorders. Based on a diverse database, retinal diseases occur most in the U.S., with increasing annual incidences. It’s essential to be on top of the prognosis. An early and accurate diagnosis can help predict vision loss, help the patient plan their education, and choose appropriate career choices. The Pharma world focuses on modalities to deal with IRDs, provides new analyses of IRD-related genes and targets (GRID and complexity scores), and provides information to match genetic-based therapies such as gene-specific and variant-specific therapies to the appropriate individuals. One way to identify genetic predisposition to retinal diseases is by testing comorbidity with biochemically similar tissues such as brain tissues. This led us to investigate genetic associations between various brain and ocular comorbidities. Previous research has identified genetic links to brain and ocular comorbidities, primarily categorized as cerebral visual impairments (CVIs) (Chang, Kao-Jung et al., 2022). Researchers have previously classified the genetic predispositions to CVIs using genetic data. This is also receiving more attention as some of the clinical studies reported are also indicating that ocular comorbidities are also contributing to death in the US (Huang et al., 2023).

This project attempted to investigate the connection between retinal and brain disorders by studying altered genetic composition using automated mutation discovery platforms such as DisGeNET Cytoscape and Karyosoft.

We adopted a three-step methodology to explore potential comorbidities between the retina and the brain. First, we performed a comprehensive literature search using NCBI/PubMed. Next, we leveraged the DisGeNET discovery platform to investigate gene-disease and disease-disease associations. Finally, we evaluated clinical relevance through ClinVar databases and conducted gene variant analysis using Karyosoft tools.

DisGeNET Cytoscape App

The DisGeNET Cytoscape app (Janet Piñero et al. 2021) is an open-source tool designed for Cytoscape 3.x. It allows users to visualize, query, and analyze networks representing gene-disease and variant-disease associations from DisGeNET. The SQLite version of the DisGeNET database structure that supports the app is available on the DisGeNET website. A detailed tutorial on using the app can be found online. Once installed and launched, the DisGeNET control panel features two tabs to guide queries over different network views: gene-disease and variant-disease networks. These networks are bipartite graphs with two types of vertices (genes/variants and diseases) and edges connecting different kinds of vertices (e.g., a gene with a disease). Multiple edges can connect two vertices, representing various evidence for the associations, such as different database sources, publications, or DisGeNET association types.

DisGeNET is a comprehensive discovery platform that houses one of the largest publicly available collections of genes and variants associated with human diseases. It integrates data from expert-curated repositories, GWAS catalogs, animal models, and scientific literature. The current version, DisGeNET v7.0, includes 1,134,942 gene-disease associations (GDAs) involving 21,671 genes and 30,170 diseases, disorders, traits, and clinical or abnormal human phenotypes. Additionally, it contains 369,554 variant-disease associations (VDAs) between 194,515 variants and 14,155 diseases, traits, and phenotypes.

In this project, the web interface’s Search and Browse functionalities were utilized to study gene-disease and disease-disease associations by computing the number of shared genes and variants between retinal and brain diseases. Disease-disease associations (DDAs) were explored through the search panel by searching for both diseases.

Curated data in DisGeNET includes gene-disease associations (GDAs) provided by expert-curated resources. This involves data annotation, publication, and presentation to maintain its value over time and remain available for reuse and preservation. Several vital resources contribute to this curated data: UniProt/SwissProt offers curated information on protein sequences, structures, and functions; the Comparative Toxicogenomics Database (CTD) provides manually curated data on gene-disease relationships, focusing on the effects of environmental chemicals on humans; Orphanet serves as a reference portal for rare diseases and orphan drugs; The Clinical Genome Resource (ClinGen) aims to define the clinical relevance of genes and variants for precision medicine and research; Genomics England PanelApp is a publicly available knowledge base for creating, storing, and querying virtual gene panels related to human disorders; The Cancer Genome Interpreter (CGI) identifies known oncogenic alterations; and PsyGeNET is a resource for exploring psychiatric diseases and their associated genes.

Gene Variant using NCBI.

NCBI databases were accessed using the NCBI website. The search bar was used to enter the gene name (ABCA4 & HUWE1 ) and/or accession number. An appropriate database (nucleotide) was selected, and relevant search results were navigated between formats like Gen Bank or FASTA for easier viewing. Further, for variant viewing, the gene name, chromosomal location, or variant ID was entered into the search box of the variation viewer. The results were filtered, and results were sorted based on criteria like clinical significance, molecular consequence, or population frequency. We picked the Sequence Read Archive (SRA) human database that is maintained by the National Center for Biotechnology Information (NCBI) and stores high-throughput sequencing data from various sequencing technologies like Illumina, Ion Torrent, and PacBio [The Sequence Read Archive (SRA) (nih.gov)]. However, we picked only Illumina data as it is one of the most widely used sequencing technologies known for its high throughput and accuracy. We chose a DNA source (sequencing data originates from DNA samples), where sequencing data was generated using a paired-end sequencing approach. Both ends of a DNA fragment are sequenced, providing more information and improving the accuracy of the sequence assembly. The downloadable file extracted was studied using the Karyosoft suite.

Variant analysis in Human Database using NCBI and Karyosoft Tools

In this study, we utilized Karyosoft’s Variants, a web-based no-code platform for discovering SNPs and Indels. This platform employs an accelerated Genome Analysis Toolkit (GATK) workflow optimized for GPU-based cloud servers, allowing the parallel processing of up to 8 samples simultaneously, significantly reducing analysis time. The Variants platform automates the discovery and interpretation of SNPs and Indels.

We also used Karyosoft’s Variants Mining Studio, a web-based no-code platform that serves as a central repository for the millions of discovered SNPs and Indels. Typically, the SRR number was generated from the NCBI SRA database, with selection criteria focused on a curated human database backed by clinical data. This unique identifier, assigned to a specific sequencing run (SRR) in the NCBI Sequence Read Archive (SRA), was used to upload VCF files of SNPs and Indels into the Variants Mining Studio. This platform simplifies the management and analysis of genomic variants, such as SNPs and Indels, in genes associated with the co-morbidity of retinal and brain diseases. It was used to identify variants by gene name, annotation effect, chromosome number, and homozygous and heterozygous states.

The DisGeNET Cytoscape app helped us visualize, query, and analyze gene-disease and variant-disease association networks. DisGeNET Cytoscape app involves Network Visualization, which allows users to create and customize networks based on genes, diseases, and variants. After taking it in, users can query the database for specific genes, diseases, or variants and analyze the relationships between them. The app supports the integration of external data sources, enabling comprehensive network annotation. In addition, users can interactively explore the genetic associations and pathways involved in various diseases (Figure 1).

Research on retinal diseases and associated genes using the complete DisGeNET database, curated data, and the CTD human database revealed significant findings. The significance of hits was determined by DPI, DSI, and Pli scores associated with the protein hits. A total of 714 significant genes related to retinal diseases were identified from DisGeNET’s integrated database. Only 401 could be classified into protein classes, revealing 19 proteins, including enzymes, G protein-coupled receptors, signaling proteins, kinases, transporters, enzyme modulators, ion channels, and transcription factors. The complex network of diseased genes was refined using only curated and CTD human databases, resulting in 27 significant hits. This study also focused on comorbidities associated with retinal and brain diseases, examining brain diseases similarly (Figure 2).

Research on brain diseases and associated genes using the complete DisGeNET database, curated data, and the CTD human database revealed significant findings. The significance of hits was determined by DPI, DSI, and PLI scores associated with the protein hits. A total of 345 significant genes were identified as associated with brain diseases from DisGeNET’s integrated database (Figure 3). Of these, only 215 could be classified into protein classes, revealing 11 different protein classes. The complex network of diseased genes was further refined using only curated and CTD human databases, resulting in 25 significant hits. The overlap of protein classes with retinal diseases indicated that enzymes, G protein-coupled receptors, transporters, and transcription factors could be comorbidity targets ((Figure 4).

The Comorbidity Gene association chart between retinal and complete DBs is a complicated mix of comorbidity gene association charts between Retinal and Brain Diseases (Gene Disease Association (GDA) using the Curated Human CTD database) (Figure 5). A curated human database is a structured data repository that experts have carefully selected, organized, and annotated. This process involves significant human effort to ensure the data is accurate, relevant, and easily accessible. These databases are often used in scientific research, healthcare, and other fields where high-quality, reliable data is crucial. We obtained 66 hits from the curated database, but only two hits were extracted from the curated human database (Figure 6). Firstly, the ABCA4 gene, which is crucial for eye health, particularly in the retina. It encodes a protein that helps remove toxic substances from photoreceptor cells, which are vital for vision. These substances are byproducts of phototransduction, where light is converted into electrical signals sent to the brain. Mutations in the ABCA4 gene are associated with several retinal diseases, such as Stargardt disease, which causes progressive vision loss. The proper function of this gene is essential for maintaining photoreceptor health and preventing the accumulation of toxic substances that can damage these cells. While the ABCA4 gene primarily affects the eyes, its role in the brain is indirect. The brain processes the electrical signals generated by phototransduction into visual information. Therefore, any disruption in the ABCA4 gene’s function can indirectly impact how the brain processes visual information. This study enabled the identification of clinically relevant variants in the ABCA4 gene for gene mutations that cause comorbidity. This method allows for comprehensive analysis of the ABCA4 gene to detect various mutations mapped to chromosome 1, including insertions and deletions (INDELs) and SNPs (Table 1). Secondly, The HUWE1 gene, which is essential for the development and function of the nervous system, including both the brain and retina. It plays a vital role in the proliferation and differentiation of neural progenitor cells, crucial for neuron formation during brain development. Additionally, HUWE1 regulates the release of neurotransmitters, the chemicals responsible for transmitting signals between neurons, ensuring proper brain function and communication within the nervous system. Variants or mutations in the HUWE1 gene have been linked to intellectual disabilities and developmental delays, underscoring its significance in cognitive development. While the primary focus has been on the brain, HUWE1 also impacts the retina, with some individuals exhibiting retinal abnormalities due to HUWE1 mutations, which can affect vision. Some eye-related issues linked to HUWE1 variants may include strabismus, hypermetropia, astigmatism, and retinopathy. An NCBI search using the SRA database for HUWE1 using the following user query: “HUWE1 [All Fields] AND "Homo sapiens"[orgn] AND ("biomol dna"[Properties] AND "library layout paired"[Properties] AND "platform Illumina"[Properties]) “ revealed 16 variants hits. We investigated these variants for SNPs and Indel. We identified 50 SNPs and 300 Indels potentially associated with co-morbidity.

Table 1: Significant SNPs and Indel Mutants of ABC4R gene identified in this study.

Mutation	Chromosome	Position	Reference nucleotide	Alternate Nucleotide
SNPs	NC_000001.11	94112992	T	C
SNPs	NC_000001.11	94112992	T	C
SNPs	NC_000001.11	94005519	A	G
SNPs	NC_000001.11	94005519	A	G
IN DEL	NC_000001.11	94021729	AAC	A
IN DEL	NC_000001.11	94021729	AAC	A
SNPs	NC_000001.11	94011676	T	G
SNPs	NC_000001.11	94011676	T	G
SNPs	NC_000001.11	94111412	T	C
SNPs	NC_000001.11	94111412	T	C
SNPs	NC_000001.11	94030374	T	C
SNPs	NC_000001.11	94030374	T	C
SNPs	NC_000001.11	94078720	G	A
SNPs	NC_000001.11	94078720	G	A
SNPs	NC_000001.11	94062695	C	T
SNPs	NC_000001.11	94062695	C	T
SNPs	NC_000001.11	94005493	T	C
SNPs	NC_000001.11	94005493	T	C
SNPs	NC_000001.11	94045779	A	T
SNPs	NC_000001.11	94045779	A	T
SNPs	NC_000001.11	94078677	G	A
SNPs	NC_000001.11	94078677	G	A
SNPs	NC_000001.11	94041311	G	C
SNPs	NC_000001.11	94041311	G	C
SNPs	NC_000001.11	94030983	C	T
SNPs	NC_000001.11	94030983	C	T
SNPs	NC_000001.11	94108497	C	A
SNPs	NC_000001.11	94108497	C	A
SNPs	NC_000001.11	94062807	C	T
SNPs	NC_000001.11	94062807	C	T
Indel	NC_000001.11	94078770	GT	G
Indel	NC_000001.11	94078770	GT	G
SNPs	NC_000001.11	94048856	T	G
SNPs	NC_000001.11	94048856	T	G

Table 1: Significant SNPs and Indel Mutants of the HUWE1 gene identified in this study.

Mutation	Chromosome	Position	Reference nucleotide	Alternate Nucleotide
Indel	NC_000023.11	53647928	C	CTTTTTTTGCTTTGATTTT
Indel	NC_000023.11	53647927	T	TTGG
Indel	NC_000023.11	53650957	T	TA
Indel	NC_000023.11	53650960	GCA	G
Indel	NC_000023.11	53587497	T	TCTTGGTGAATGTATTGAAAAATATATGTA
SNPs	NC_000023.11	53572016	C	A

This study underscores the significant biochemical and genetic connections between the human retina and brain, primarily through the shared presence of docosahexaenoic acid (DHA). We identified comorbidities and genomic associations between retinal and brain tissues through bioinformatics approaches. Using automated mutation discovery platforms such as DisGeNET, Cytoscape, and Karyosoft facilitated the exploration of gene-disease and disease-disease associations, revealing common genetic variations and phenotypic expressions. The data supported our hypothesis that similar biochemical compositions would lead to commonalities in genetic variations and phenotypic expressions. The study provides valuable insights into genomic diseases affecting the retina and the brain, such as Alzheimer’s disease and age-related macular degeneration. Understanding these connections can enhance early diagnosis, prognosis, and the development of targeted therapies for these interconnected disorders. Future research should continue exploring these associations to elucidate further the genetic underpinnings of comorbid retinal and brain diseases.

Ethics Approval and Consent to Participate

Not applicable

Consent for Publication

Not applicable

Availability of Data and Materials

NCBI datasets were used for the study.

Competing Interests

The authors declare that they have no competing interests.

Funding

Not Applicable

Author’s Contributions

Both authors worked equally on data mining and analysis. RB was the major contributor to writing the manuscript. Both authors read and approved the final manuscript.

Acknowledgments

We would like to thank the creators of DisGeNET and Cytoscape for creating the tools.

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No competing interests reported.

Identification of Comorbidities and Genomic Associations of Biochemically Analogous Retinal and Brain Tissues Using Bioinformatics Approaches

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