Using Computational Drug-Gene Analysis to Identify Novel Therapeutic Candidates for Retinal Neuroprotection.

Retinal cell death is responsible for irreversible vision loss in many retinal disorders. No commercially approved treatments are currently available to attenuate retinal cell loss and preserve vision. We seek to identify chemicals/drugs with thoroughly-studied biological functions that possess neuroprotective effects in the retina using a computational bioinformatics approach. We queried the National Center for Biotechnology Information (NCBI) to identify genes associated with retinal neuroprotection. Enrichment analysis was performed using ToppGene to identify compounds related to the identified genes. This analysis constructs a Pharmacome from multiple drug-gene interaction databases to predict compounds with statistically significant associations to genes involved in retinal neuroprotection. Compounds with known deleterious effects (e.g., asbestos, ethanol) or with no clinical indications (e.g., paraquat, ozone) were manually filtered. We identified numerous drug/chemical classes associated to multiple genes implicated in retinal neuroprotection using a systematic computational approach. Anti-diabetics, lipid-lowering medicines, and antioxidants are among the treatments anticipated by this analysis, and many of these drugs could be readily repurposed for retinal neuroprotection. Our technique serves as an unbiased tool that can be utilized in the future to lead focused preclinical and clinical investigations for complex processes such as neuroprotection, as well as a wide range of other ocular pathologies.

Absence of Gut Microbiota Is Associated with RPE/Choroid Transcriptomic Changes Related to Age-Related Macular Degeneration Pathobiology and Decreased Choroidal Neovascularization.

Studies have begun to reveal significant connections between the gut microbiome and various retinal diseases, including age-related macular degeneration (AMD). As critical supporting tissues of the retina, the retinal pigment epithelium (RPE) and underlying choroid play a critical role in retinal homeostasis and degeneration. However, the relationship between the microbiome and RPE/choroid remains poorly understood, particularly in animal models of AMD. In order to better elucidate this role, we performed high-throughput RNA sequencing of RPE/choroid tissue in germ-free (GF) and specific pathogen-free (SPF) mice. Furthermore, utilizing a specialized laser-induced choroidal neovascularization (CNV) model that we developed, we compared CNV size and inflammatory response between GF and SPF mice. After correction of raw data, 660 differentially expressed genes (DEGs) were identified, including those involved in angiogenesis regulation, scavenger and cytokine receptor activity, and inflammatory response-all of which have been implicated in AMD pathogenesis. Among lasered mice, the GF group showed significantly decreased CNV lesion size and microglial infiltration around CNV compared to the SPF group. Together, these findings provide evidence for a potential gut-RPE/choroidal axis as well as a correlation with neovascular features of AMD.

Exploring the functional impact of alternative splicing on human protein isoforms using available annotation sources.

In recent years, the emphasis of scientific inquiry has shifted from whole-genome analyses to an understanding of cellular responses specific to tissue, developmental stage or environmental conditions. One of the central mechanisms underlying the diversity and adaptability of the contextual responses is alternative splicing (AS). It enables a single gene to encode multiple isoforms with distinct biological functions. However, to date, the functions of the vast majority of differentially spliced protein isoforms are not known. Integration of genomic, proteomic, functional, phenotypic and contextual information is essential for supporting isoform-based modeling and analysis. Such integrative proteogenomics approaches promise to provide insights into the functions of the alternatively spliced protein isoforms and provide high-confidence hypotheses to be validated experimentally. This manuscript provides a survey of the public databases supporting isoform-based biology. It also presents an overview of the potential global impact of AS on the human canonical gene functions, molecular interactions and cellular pathways.

Lynx: a knowledge base and an analytical workbench for integrative medicine.

Lynx (http://lynx.ci.uchicago.edu) is a web-based database and a knowledge extraction engine. It supports annotation and analysis of high-throughput experimental data and generation of weighted hypotheses regarding genes and molecular mechanisms contributing to human phenotypes or conditions of interest. Since the last release, the Lynx knowledge base (LynxKB) has been periodically updated with the latest versions of the existing databases and supplemented with additional information from public databases. These additions have enriched the data annotations provided by Lynx and improved the performance of Lynx analytical tools. Moreover, the Lynx analytical workbench has been supplemented with new tools for reconstruction of co-expression networks and feature-and-network-based prioritization of genetic factors and molecular mechanisms. These developments facilitate the extraction of meaningful knowledge from experimental data and LynxKB. The Service Oriented Architecture provides public access to LynxKB and its analytical tools via user-friendly web services and interfaces.

Anatomical differences of the protein profile in the rabbit sclera during growth.

We aimed to investigate the proteome changes in anatomical regions of sclera during growth and development of the rabbit. Sclera from New Zealand white rabbits of three ages (1 month, 2 months and 6 months) was dissected into three segments - anterior, equatorial, and posterior. A total of 36 samples were divided into groups by age and anatomical region. Tryptic digests of total proteins were analyzed by liquid chromatography followed by tandem mass spectrometry (LC-MS/MS). Label-free quantification based on spectral counts was used to determine the relative protein abundance and identify proteins with statistically significant differences between age groups or anatomical regions of the sclera. Western blotting was performed to validate some of the differentially expressed proteins. A total of 840 non-redundant proteins was identified in the sclera at different ages and regions with protein and peptide false discovery rate (FDR) at ≤1.0% and ≤0.1%, respectively. Differentially expressed proteins were identified by comparing age or anatomical region. Among these, periostin showed decreasing abundance with age, while myocilin, latent-transforming growth factor beta-binding protein 2, hyaluronan, proteoglycan link protein 1 and selenbp1 showed increasing abundance with age. In mature rabbits, alcohol dehydrogenase showed region-related differences in the sclera. Periostin showed an age-related decrease while selenbp1 showed an age-related increase in abundance in the anterior region. Vitronectin and extracellular superoxide dismutase had greater expression with age in the equatorial and posterior regions, respectively. The age related differential expression of periostin and selenbp1 was confirmed by western blotting. In conclusion, the protein profile of sclera showed age- and region-related differences. The differential protein profiles provide a baseline for understanding changes in the protein expression in the young and mature rabbit that appears to show regional changes. The changes observed in the present study add to the existing knowledge about regional alterations in biomechanical properties of sclera during growth.

A RESTful API for accessing microbial community data for MG-RAST.

Metagenomic sequencing has produced significant amounts of data in recent years. For example, as of summer 2013, MG-RAST has been used to annotate over 110,000 data sets totaling over 43 Terabases. With metagenomic sequencing finding even wider adoption in the scientific community, the existing web-based analysis tools and infrastructure in MG-RAST provide limited capability for data retrieval and analysis, such as comparative analysis between multiple data sets. Moreover, although the system provides many analysis tools, it is not comprehensive. By opening MG-RAST up via a web services API (application programmers interface) we have greatly expanded access to MG-RAST data, as well as provided a mechanism for the use of third-party analysis tools with MG-RAST data. This RESTful API makes all data and data objects created by the MG-RAST pipeline accessible as JSON objects. As part of the DOE Systems Biology Knowledgebase project (KBase, http://kbase.us) we have implemented a web services API for MG-RAST. This API complements the existing MG-RAST web interface and constitutes the basis of KBase's microbial community capabilities. In addition, the API exposes a comprehensive collection of data to programmers. This API, which uses a RESTful (Representational State Transfer) implementation, is compatible with most programming environments and should be easy to use for end users and third parties. It provides comprehensive access to sequence data, quality control results, annotations, and many other data types. Where feasible, we have used standards to expose data and metadata. Code examples are provided in a number of languages both to show the versatility of the API and to provide a starting point for users. We present an API that exposes the data in MG-RAST for consumption by our users, greatly enhancing the utility of the MG-RAST service.

Anti-inflammatory therapy after selective laser trabeculoplasty: a randomized, double-masked, placebo-controlled clinical trial.

PURPOSE: To investigate the effect of anti-inflammatory therapy on selective laser trabeculoplasty (SLT) outcomes. DESIGN: Randomized, double-masked, placebo-controlled trial. PARTICIPANTS: Patients with primary open-angle or pseudo-exfoliation glaucoma. METHODS: Patients undergoing SLT were randomized to receive placebo (artificial tears), prednisolone acetate 1%, or ketorolac tromethamine 0.5% eye drops 4 times per day for 5 days commencing immediately after SLT. MAIN OUTCOME MEASURES: Change in intraocular pressure (IOP) from baseline to the 1-month post-SLT visit. RESULTS: Mean change in IOP at the 1-month primary outcome time point, as well as all other time points, was not significantly different among groups (P = 0.99). Likewise, a repeated-measures, mixed-effects model did not find significant differences in IOP outcome at the 1-month time point (P = 0.95). The IOP was reduced in all groups at the 1-month post-SLT time point and all other time points, and no significant differences were found between groups using separate unadjusted cross-sectional analyses of variance (P > 0.15 for analyses at all time points). Treatment failure rates were not different among groups (P = 0.75), and at 1 year after SLT, the percentage of patients maintaining a 20% IOP reduction ranged from 18% to 22% in the 3 study groups. CONCLUSIONS: Anti-inflammatory therapy after SLT does not seem to substantially influence the IOP-lowering effect of SLT. In this study of patients with low baseline IOP, SLT showed limited efficacy in achieving a sustained reduction in IOP.

Suitability of novel 3D-printed and coated vessels for water calorimetry.

BACKGROUND: In water calorimetry, absolute dose to water is determined by measuring radiation-induced temperature rises. In conventional water calorimeters, temperature detectors are housed in handmade glass vessels that are filled with high-purity water, thus mitigating radiation-induced exo/endothermic chemical reactions of impurities that would otherwise introduce additional heat gain/loss, known as heat defect. Being hand-crafted, these glass vessels may suffer from imperfections, have shape and design constraints, are often backordered, and can be prohibitively expensive. PURPOSE: The purpose of this work is to determine suitability of 3D-printed plastic vessels that are further coated for use in water calorimetry applications, and to study their stability and characterize their associated heat defect correction factor ( k hd ) ${k_{{\mathrm{hd}}}})$ . This novel vessel production technique would allow for cost-effective rapid construction of vessels that can be produced with high accuracy and designs that are simply not practical with current glass vessel construction techniques. This in turn enables water calorimetry applications in many novel radiation delivery modalities, which may include spherical vessels in GammaKnife ICON water calorimetry as an example. METHODS: Eight vessels were 3D-printed using Accura ClearVue in an SLA 3D-printer. Two vessels were coated with Parylene C and four were coated with Parylene N. The water calorimetry preparation procedures followed for these vessels was identical to that of our traditional glass-vessels (i.e., same cleaning procedures, same high purity water, and same saturation procedures with high purity hydrogen gas). The performance of each vessel was characterized using our in-house built water calorimeter in an Elekta Versa using both 6 MV flattening filter-free (FFF) and 18 MV beams. The stability of the coating as function of time and accumulated dose was evaluated through repeated measurements. k hd ${k_{{\mathrm{hd}}}}\;$ of each vessel was determined through cross-comparisons against an Exradin A1SL ionization chamber with direction calibration link to Canada's primary standard laboratory. RESULTS: k HD ${k_{{\mathrm{HD}}}}\;$ of the two uncoated vessels differed by 2.8% under a 6 MV FFF beam. Vessels coated with Parylenes resulted in a stable and reproducible heat defect for both energies. An overall k hd ${k_{{\mathrm{hd}}}}$ of 1.001 ± 0.010 and 1.005 ± 0.010 were obtained for Parylene N and Parylene C coated vessels respectively. All Parylene coated vessels showed agreement, within the established uncertainties, to the zero-heat defect observed in a hydrogen-saturated glass vessel system. An additional long-term study (17 days) of a Parylene N vessel showed no change in response with accumulated dose and time. Electron microscopy images of a Parylene N coated vessel showed a uniform intact coating after repeated irradiations. CONCLUSIONS: An uncoated 3D-printed vessel is not viable for water calorimetry because it exhibits an unstable vessel-dependent heat defect. However, applying a Parylene coating stabilizes the heat defect, suggesting that coated 3D-printed vessels may be suitable for use in water calorimetry. This method facilitates the creation of intricate vessel shapes, which can be efficiently manufactured using 3D printing.

A platform-independent method for detecting errors in metagenomic sequencing data: DRISEE.

We provide a novel method, DRISEE (duplicate read inferred sequencing error estimation), to assess sequencing quality (alternatively referred to as "noise" or "error") within and/or between sequencing samples. DRISEE provides positional error estimates that can be used to inform read trimming within a sample. It also provides global (whole sample) error estimates that can be used to identify samples with high or varying levels of sequencing error that may confound downstream analyses, particularly in the case of studies that utilize data from multiple sequencing samples. For shotgun metagenomic data, we believe that DRISEE provides estimates of sequencing error that are more accurate and less constrained by technical limitations than existing methods that rely on reference genomes or the use of scores (e.g. Phred). Here, DRISEE is applied to (non amplicon) data sets from both the 454 and Illumina platforms. The DRISEE error estimate is obtained by analyzing sets of artifactual duplicate reads (ADRs), a known by-product of both sequencing platforms. We present DRISEE as an open-source, platform-independent method to assess sequencing error in shotgun metagenomic data, and utilize it to discover previously uncharacterized error in de novo sequence data from the 454 and Illumina sequencing platforms.

Using Advanced Bioinformatics Tools to Identify Novel Therapeutic Candidates for Proliferative Vitreoretinopathy.

Purpose: Proliferative vitreoretinopathy (PVR) is the dreaded cause of failure following retinal detachment repair; however, no cures or preventative therapies exist to date. The purpose of this study was to use bioinformatics tools to identify drugs or compounds that interact with biomarkers and pathways involved in PVR pathogenesis that could be eligible for further testing for the prevention and treatment of PVR. Methods: We queried PubMed to compile a comprehensive list of genes described in PVR to date from human studies, animal models, and genomic studies found in the National Center for Biotechnology Information database. Gene enrichment analysis was performed using ToppGene on PVR-related genes against drug-gene interaction databases to construct a pharmacome and estimate the statistical significance of overrepresented compounds. Compounds with no clinical indications were filtered out from the resulting drug lists. Results: Our query identified 34 unique genes associated with PVR. Out of 77,146 candidate drugs or compounds in the drug databases, our analysis revealed multiple drugs and compounds that have significant interactions with genes involved in PVR, including antiproliferatives, corticosteroids, cardiovascular agents, antioxidants, statins, and micronutrients. Top compounds, including curcumin, statins, and cardiovascular agents such as carvedilol and enalapril, have well-established safety profiles and potentially could be readily repurposed for PVR. Other significant compounds such as prednisone and methotrexate have shown promising results in ongoing clinical trials for PVR. Conclusions: This bioinformatics approach of studying drug-gene interactions can identify drugs that may affect genes and pathways implicated in PVR. Predicted bioinformatics studies require further validation by preclinical or clinical studies; however, this unbiased approach could identify potential candidates among existing drugs and compounds that could be repurposed for PVR and guide future investigations. Translational Relevance: Novel repurposable drug therapies for PVR can be found using advanced bioinformatics models.

Identifying novel candidate compounds for therapeutic strategies in retinopathy of prematurity via computational drug-gene association analysis.

Purpose: Retinopathy of prematurity (ROP) is the leading cause of preventable childhood blindness worldwide. Although interventions such as anti-VEGF and laser have high success rates in treating severe ROP, current treatment and preventative strategies still have their limitations. Thus, we aim to identify drugs and chemicals for ROP with comprehensive safety profiles and tolerability using a computational bioinformatics approach. Methods: We generated a list of genes associated with ROP to date by querying PubMed Gene which draws from animal models, human studies, and genomic studies in the NCBI database. Gene enrichment analysis was performed on the ROP gene list with the ToppGene program which draws from multiple drug-gene interaction databases to predict compounds with significant associations to the ROP gene list. Compounds with significant toxicities or without known clinical indications were filtered out from the final drug list. Results: The NCBI query identified 47 ROP genes with pharmacologic annotations present in ToppGene. Enrichment analysis revealed multiple drugs and chemical compounds related to the ROP gene list. The top ten most significant compounds associated with ROP include ascorbic acid, simvastatin, acetylcysteine, niacin, castor oil, penicillamine, curcumin, losartan, capsaicin, and metformin. Antioxidants, NSAIDs, antihypertensives, and anti-diabetics are the most common top drug classes derived from this analysis, and many of these compounds have potential to be readily repurposed for ROP as new prevention and treatment strategies. Conclusion: This bioinformatics analysis creates an unbiased approach for drug discovery by identifying compounds associated to the known genes and pathways of ROP. While predictions from bioinformatic studies require preclinical/clinical studies to validate their results, this technique could certainly guide future investigations for pathologies like ROP.

Imbalanced gut microbiota predicts and drives the progression of nonalcoholic fatty liver disease and nonalcoholic steatohepatitis in a fast-food diet mouse model.

Nonalcoholic fatty liver disease (NAFLD) is multifactorial in nature, affecting over a billion people worldwide. The gut microbiome has emerged as an associative factor in NAFLD, yet mechanistic contributions are unclear. Here, we show fast food (FF) diets containing high fat, added cholesterol, and fructose/glucose drinking water differentially impact short- vs. long-term NAFLD severity and progression in conventionally-raised, but not germ-free mice. Correlation and machine learning analyses independently demonstrate FF diets induce early and specific gut microbiota changes that are predictive of NAFLD indicators, with corresponding microbial community instability relative to control-fed mice. Shotgun metagenomics showed FF diets containing high cholesterol elevate fecal pro-inflammatory effectors over time, relating to a reshaping of host hepatic metabolic and inflammatory transcriptomes. FF diet-induced gut dysbiosis precedes onset and is highly predictive of NAFLD outcomes, providing potential insights into microbially-based pathogenesis and therapeutics.

Oral Metformin Inhibits Choroidal Neovascularization by Modulating the Gut-Retina Axis.

Purpose: Emerging data indicate that metformin may prevent the development of age-related macular degeneration (AMD). Whereas the underlying mechanisms of metformin's anti-aging properties remain undetermined, one proposed avenue is the gut microbiome. Using the laser-induced choroidal neovascularization (CNV) model, we investigate the effects of oral metformin on CNV, retinal pigment epithelium (RPE)/choroid transcriptome, and gut microbiota. Methods: Specific pathogen free (SPF) male mice were treated via daily oral gavage of metformin 300 mg/kg or vehicle. Male mice were selected to minimize sex-specific differences to laser induction and response to metformin. Laser-induced CNV size and macrophage/microglial infiltration were assessed by isolectin and Iba1 immunostaining. High-throughput RNA-seq of the RPE/choroid was performed using Illumina. Fecal pellets were analyzed for gut microbiota composition/pathways with 16S rRNA sequencing/shotgun metagenomics, as well as microbial-derived metabolites, including small-chain fatty acids and bile acids. Investigation was repeated in metformin-treated germ-free (GF) mice and antibiotic-treated/GF mice receiving fecal microbiota transplantation (FMT) from metformin-treated SPF mice. Results: Metformin treatment reduced CNV size (P < 0.01) and decreased Iba1+ macrophage/microglial infiltration (P < 0.005). One hundred forty-five differentially expressed genes were identified in the metformin-treated group (P < 0.05) with a downregulation in pro-angiogenic genes Tie1, Pgf, and Gata2. Furthermore, metformin altered the gut microbiome in favor of Bifidobacterium and Akkermansia, with a significant increase in fecal levels of butyrate, succinate, and cholic acid. Metformin did not suppress CNV in GF mice but colonization of microbiome-depleted mice with metformin-derived FMT suppressed CNV. Conclusions: These data suggest that oral metformin suppresses CNV, the hallmark lesion of advanced neovascular AMD, via gut microbiome modulation.

Plasma metabolites with mechanistic and clinical links to the neurovascular disease cavernous angioma.

BACKGROUND: Cavernous angiomas (CAs) affect 0.5% of the population, predisposing to serious neurologic sequelae from brain bleeding. A leaky gut epithelium associated with a permissive gut microbiome, was identified in patients who develop CAs, favoring lipid polysaccharide producing bacterial species. Micro-ribonucleic acids along with plasma levels of proteins reflecting angiogenesis and inflammation were also previously correlated with CA and CA with symptomatic hemorrhage. METHODS: The plasma metabolome of CA patients and CA patients with symptomatic hemorrhage was assessed using liquid-chromatography mass spectrometry. Differential metabolites were identified using partial least squares-discriminant analysis (p < 0.05, FDR corrected). Interactions between these metabolites and the previously established CA transcriptome, microbiome, and differential proteins were queried for mechanistic relevance. Differential metabolites in CA patients with symptomatic hemorrhage were then validated in an independent, propensity matched cohort. A machine learning-implemented, Bayesian approach was used to integrate proteins, micro-RNAs and metabolites to develop a diagnostic model for CA patients with symptomatic hemorrhage. RESULTS: Here we identify plasma metabolites, including cholic acid and hypoxanthine distinguishing CA patients, while arachidonic and linoleic acids distinguish those with symptomatic hemorrhage. Plasma metabolites are linked to the permissive microbiome genes, and to previously implicated disease mechanisms. The metabolites distinguishing CA with symptomatic hemorrhage are validated in an independent propensity-matched cohort, and their integration, along with levels of circulating miRNAs, enhance the performance of plasma protein biomarkers (up to 85% sensitivity and 80% specificity). CONCLUSIONS: Plasma metabolites reflect CAs and their hemorrhagic activity. A model of their multiomic integration is applicable to other pathologies.

Cavernous angiomas (CAs) are clusters of abnormal blood vessels found in the brain or spinal cord. A blood test that could identify people with CAs that have recently bled would help determine who need surgery or closer medical monitoring. We looked at the blood of people with CAs to compare the levels of metabolites, a type of small molecule produced within the body, in those who had recently bled and those who had not. We found that some metabolites may contribute to CA and have an impact on CA symptoms. Monitoring the levels of these metabolites can determine whether there had been a recent bleed. In the future, drugs or other therapies could be developed that would block or change the levels of these molecules and possibly be used to treat CA disease.

Short-read reading-frame predictors are not created equal: sequence error causes loss of signal.

BACKGROUND: Gene prediction algorithms (or gene callers) are an essential tool for analyzing shotgun nucleic acid sequence data. Gene prediction is a ubiquitous step in sequence analysis pipelines; it reduces the volume of data by identifying the most likely reading frame for a fragment, permitting the out-of-frame translations to be ignored. In this study we evaluate five widely used ab initio gene-calling algorithms-FragGeneScan, MetaGeneAnnotator, MetaGeneMark, Orphelia, and Prodigal-for accuracy on short (75-1000 bp) fragments containing sequence error from previously published artificial data and "real" metagenomic datasets. RESULTS: While gene prediction tools have similar accuracies predicting genes on error-free fragments, in the presence of sequencing errors considerable differences between tools become evident. For error-containing short reads, FragGeneScan finds more prokaryotic coding regions than does MetaGeneAnnotator, MetaGeneMark, Orphelia, or Prodigal. This improved detection of genes in error-containing fragments, however, comes at the cost of much lower (50%) specificity and overprediction of genes in noncoding regions. CONCLUSIONS: Ab initio gene callers offer a significant reduction in the computational burden of annotating individual nucleic acid reads and are used in many metagenomic annotation systems. For predicting reading frames on raw reads, we find the hidden Markov model approach in FragGeneScan is more sensitive than other gene prediction tools, while Prodigal, MGA, and MGM are better suited for higher-quality sequences such as assembled contigs.

Connecting genotype to phenotype in the era of high-throughput sequencing.

BACKGROUND: The development of next generation sequencing technology is rapidly changing the face of the genome annotation and analysis field. One of the primary uses for genome sequence data is to improve our understanding and prediction of phenotypes for microbes and microbial communities, but the technologies for predicting phenotypes must keep pace with the new sequences emerging. SCOPE OF REVIEW: This review presents an integrated view of the methods and technologies used in the inference of phenotypes for microbes and microbial communities based on genomic and metagenomic data. Given the breadth of this topic, we place special focus on the resources available within the SEED Project. We discuss the two steps involved in connecting genotype to phenotype: sequence annotation, and phenotype inference, and we highlight the challenges in each of these steps when dealing with both single genome and metagenome data. MAJOR CONCLUSIONS: This integrated view of the genotype-to-phenotype problem highlights the importance of a controlled ontology in the annotation of genomic data, as this benefits subsequent phenotype inference and metagenome annotation. We also note the importance of expanding the set of reference genomes to improve the annotation of all sequence data, and we highlight metagenome assembly as a potential new source for complete genomes. Finally, we find that phenotype inference, particularly from metabolic models, generates predictions that can be validated and reconciled to improve annotations. GENERAL SIGNIFICANCE: This review presents the first look at the challenges and opportunities associated with the inference of phenotype from genotype during the next generation sequencing revolution. This article is part of a Special Issue entitled: Systems Biology of Microorganisms.

Using Advanced Bioinformatics Tools to Identify Novel Therapeutic Candidates for Age-Related Macular Degeneration.

Purpose: Age-related macular degeneration (AMD) is the most common cause of aging-related blindness in the developing world. Although medications can slow progressive wet AMD, currently, no drugs to treat dry-AMD are available. We use a systems or in silico biology analysis to identify chemicals and drugs approved by the Food and Drug Administration for other indications that can be used to treat and prevent AMD. Methods: We queried National Center for Biotechnology Information to identify genes associated with AMD, wet AMD, dry AMD, intermediate AMD, and geographic atrophy to date. We combined genes from various AMD subtypes to reflect distinct stages of disease. Enrichment analysis using the ToppGene platform predicted molecules that can influence AMD genes. Compounds without clinical indications or with deleterious effects were manually filtered. Results: We identified several drug/chemical classes that can affect multiple genes involved in AMD. The drugs predicted from this analysis include antidiabetics, lipid-lowering agents, and antioxidants, which could theoretically be repurposed for AMD. Metformin was identified as the drug with the strongest association with wet AMD genes and is among the top candidates in all dry AMD subtypes. Curcumin, statins, and antioxidants are also among the top drugs correlating with AMD-risk genes. Conclusions: We use a systematic computational process to discover potential therapeutic targets for AMD. Our systematic and unbiased approach can be used to guide targeted preclinical/clinical studies for AMD and other ocular diseases. Translational Relevance: Advanced bioinformatics models identify novel chemicals and approved drug candidates that can be efficacious for different subtypes of AMD.

High-Fat Diet Alters the Retinal Pigment Epithelium and Choroidal Transcriptome in the Absence of Gut Microbiota.

Relationships between retinal disease, diet, and the gut microbiome have started to emerge. In particular, high-fat diets (HFDs) are associated with the prevalence and progression of several retinal diseases, including age-related macular degeneration (AMD) and diabetic retinopathy (DR). These effects are thought to be partly mediated by the gut microbiome, which modulates interactions between diet and host homeostasis. Nevertheless, the effects of HFDs on the retina and adjacent retinal pigment epithelium (RPE) and choroid at the transcriptional level, independent of gut microbiota, are not well-understood. In this study, we performed the high-throughput RNA-sequencing of germ-free (GF) mice to explore the transcriptional changes induced by HFD in the RPE/choroid. After filtering and cleaning the data, 649 differentially expressed genes (DEGs) were identified, with 616 genes transcriptionally upregulated and 33 genes downregulated by HFD compared to a normal diet (ND). Enrichment analysis for gene ontology (GO) using the DEGs was performed to analyze over-represented biological processes in the RPE/choroid of GF-HFD mice relative to GF-ND mice. GO analysis revealed the upregulation of processes related to angiogenesis, immune response, and the inflammatory response. Additionally, molecular functions that were altered involved extracellular matrix (ECM) binding, ECM structural constituents, and heparin binding. This study demonstrates novel data showing that HFDs can alter RPE/choroid tissue transcription in the absence of the gut microbiome.

Bacteroidota and Lachnospiraceae integration into the gut microbiome at key time points in early life are linked to infant neurodevelopment.

The early life microbiome plays critical roles in host development, shaping long-term outcomes including brain functioning. It is not known which initial infant colonizers elicit optimal neurodevelopment; thus, this study investigated the association between gut microbiome succession from the first week of life and head circumference growth (HCG), the earliest validated marker for neurodevelopment. Fecal samples were collected weekly from a preterm infant cohort during their neonatal intensive care unit stay and subjected to 16S rRNA gene sequencing for evaluating gut microbiome composition, in conjunction with clinical data and head circumference measurements. Preterm infants with suboptimal HCG trajectories had a depletion in the abundance/prevalence of Bacteroidota and Lachnospiraceae, independent of morbidity and caloric restriction. The severity of gut microbiome depletion matched the timing of significant HCG pattern separation between study groups at 30-week postmenstrual age demonstrating a potential mediating relationship resultant from clinical practices. Consideration of the clinical variables indicated that optimal infant microbiome succession is primarily driven by dispersal limitation (i.e., delivery mode) and secondarily by habitat filtering (i.e., antibiotics and enteral feeding). Bacteroidota and Lachnospiraceae are known core taxa of the adult microbiome, with roles in dietary glycan foraging, beneficial metabolite production and immunity, and our work provides evidence that their integration into the gut microbiome needs to occur early for optimal neurodevelopment.

High-Fat Diet Alters the Retinal Transcriptome in the Absence of Gut Microbiota.

The relationship between retinal disease, diet, and the gut microbiome has shown increasing importance over recent years. In particular, high-fat diets (HFDs) are associated with development and progression of several retinal diseases, including age-related macular degeneration (AMD) and diabetic retinopathy. However, the complex, overlapping interactions between diet, gut microbiome, and retinal homeostasis are poorly understood. Using high-throughput RNA-sequencing (RNA-seq) of whole retinas, we compare the retinal transcriptome from germ-free (GF) mice on a regular diet (ND) and HFD to investigate transcriptomic changes without influence of gut microbiome. After correction of raw data, 53 differentially expressed genes (DEGs) were identified, of which 19 were upregulated and 34 were downregulated in GF-HFD mice. Key genes involved in retinal inflammation, angiogenesis, and RPE function were identified. Enrichment analysis revealed that the top 3 biological processes affected were regulation of blood vessel diameter, inflammatory response, and negative regulation of endopeptidase. Molecular functions altered include endopeptidase inhibitor activity, protease binding, and cysteine-type endopeptidase inhibitor activity. Human and mouse pathway analysis revealed that the complement and coagulation cascades are significantly affected by HFD. This study demonstrates novel data that diet can directly modulate the retinal transcriptome independently of the gut microbiome.