Gut mycobiome dysbiosis in rats showing retinal changes indicative of diabetic retinopathy.

The current study compared the gut mycobiomes of diabetic rats generated by a streptozotocin chemical challenge, diabetic rats with retinal changes and normal control rats over a period of 4 months. Sustained increase in blood sugar levels (>150 mg/dL) confirmed the induction of diabetes. Histology and immunohistochemistry were used to identify changes in the retinal tissues in the diabetic rats indicative of the animals progressing into diabetic retinopathy. Gut mycobiomes generated using faecal DNA, indicated dysbiosis at the genus level in both diabetic (DM) and diabetic rats with retinal changes (DRC) when compared with the control rats. In Tables 3-6 the specific genera that were significantly increased/decreased in DM1 and DM2 and in DRC1 and DRC2 respectively compared to the respective controls CT1-CT4 rats are listed. Further, the mycobiomes of the DM and DRC rats separated into distinct clusters following heat-map analysis of the discriminating genera. In addition, ß-diversity analysis separated the mycobiomes of DM and DRC rats from that of the control rats, but the mycobiomes of diabetic rats and diabetic rats with retinal changes showed an overlap. Based on the inferred functions of the discriminating genera in the mycobiomes, we speculated that increase in pathogenic fungi might contribute to the inflammatory status both in diabetic rats and rats showing retinal changes.

Intraocular Viral Communities Associated With Post-fever Retinitis.

The virome of ocular fluids is naive. The results of this study highlight the virome in the vitreous fluid of the eye of individuals without any ocular infection and compare it with the virome of the vitreous fluid of individuals with retinitis. A total of 1,016,037 viral reads were generated from 25 vitreous fluid samples comprising control and post-fever retinitis (PFR) samples. The top 10 viral families in the vitreous fluids comprised of Myoviridae, Siphoviridae, Phycodnaviridae, Herpesviridae, Poxviridae, Iridoviridae, Podoviridae, Retroviridae, Baculoviridae, and Flaviviridae. Principal coordinate analysis and heat map analysis clearly discriminated the virome of the vitreous fluid of the controls from that of the PFR virome. The abundance of 10 viral genera increased significantly in the vitreous fluid virome of the post-fever retinitis group compared with the control group. Genus Lymphocryptovirus, comprising the human pathogen Epstein-Barr virus (EBV) that is also implicated in ocular infections was significantly abundant in eight out of the nine vitreous fluid viromes of post-fever retinitis group samples compared with the control viromes. Human viruses, such as Hepacivirus, Circovirus, and Kobuvirus, were also significantly increased in abundance in the vitreous fluid viromes of post-fever retinitis group samples compared with the control viromes. The Kyoto Encyclopedia of Genes and Genomes (KEGG) functional analysis and the network analysis depicted an increase in the immune response by the host in the post-fever retinitis group compared with the control group. All together, the results of the study indicate changes in the virome in the vitreous fluid of patients with the post-fever retinitis group compared to the control group.

Dysbiosis in the Gut Microbiome in Streptozotocin-Induced Diabetes Rats and Follow-Up During Retinal Changes.

Purpose: To analyze the gut bacterial microbiome of streptozotocin-induced diabetic rats and rats with retinal changes. Methods: Induction of diabetes was confirmed by an increase in blood sugar (>150 mg/dL), and the progression of diabetes with retinal changes was assessed by histology and immunohistochemistry of retinal sections. Microbiomes were generated using fecal DNA, and the V3-V4 amplicons were sequenced and analyzed by QIIME and R. Results: Dysbiosis in the gut microbiome of diabetic rats and diabetic rats with retinal changes was observed at the phylum and genus levels compared with the control rats. Heat-map analysis based on the differentially abundant genera indicated that the microbiomes of controls and diabetic rats separated into two distinct clusters. The majority of the microbiomes in diabetic rats with retinal changes also formed a distinct cluster from the control rats. ß-diversity analysis separated the microbiome of control rats from the microbiome of diabetic rats and diabetic rats with retinal changes, but the microbiomes of diabetic rats and diabetic rats with retinal changes showed an overlap. Functional analysis indicated that the enhanced inflammation in diabetic rats showing retinal changes could be ascribed to a decrease in anti-inflammatory bacteria and an increase in pathogenic and proinflammatory bacteria. Conclusions: This study showed that the gut bacterial microbiome in diabetic rats with retinal changes was different compared with control rats. The results could help develop novel therapeutics for diabetics and diabetic individuals with retinal changes.

Forecast of Outpatient Visits to a Tertiary Eyecare Network in India Using the EyeSmart Electronic Medical Record System.

India is home to 1.3 billion people. The geography and the magnitude of the population present unique challenges in the delivery of healthcare services. The implementation of electronic health records and tools for conducting predictive modeling enables opportunities to explore time series data like patient inflow to the hospital. This study aims to analyze expected outpatient visits to the tertiary eyecare network in India using datasets from a domestically developed electronic medical record system (eyeSmart™) implemented across a large multitier ophthalmology network in India. Demographic information of 3,384,157 patient visits was obtained from eyeSmart EMR from August 2010 to December 2017 across the L.V. Prasad Eye Institute network. Age, gender, date of visit and time status of the patients were selected for analysis. The datapoints for each parameter from the patient visits were modeled using the seasonal autoregressive integrated moving average (SARIMA) modeling. SARIMA (0,0,1)(0,1,7)7 provided the best fit for predicting total outpatient visits. This study describes the prediction method of forecasting outpatient visits to a large eyecare network in India. The results of our model hold the potential to be used to support the decisions of resource planning in the delivery of eyecare services to patients.

Alterations in the conjunctival surface bacterial microbiome in bacterial keratitis patients.

Microbial keratitis is an infectious disease of the eye, in which the cornea is inflamed. Under severe conditions, keratitis can lead to significant loss of vision and enucleation of the eye. Ocular trauma is the major risk factor causing keratitis and microorganisms viz., bacteria, fungi, viruses are the causative agents. The current study characterized the conjunctival bacterial microbiomes of healthy individuals and individuals with bacterial keratitis (BK) and assessed whether ocular microbiome dysbiosis is prevalent in BK patients. Ocular bacterial microbiomes were generated from the conjunctival swabs of healthy controls (HC-SW) and conjunctival swabs (BK-SW) and corneal scrapings (BK-CR) of BK patients using V3-V4 amplicon sequencing and data analysed using QIIME and R software. The Alpha diversity indices, diversity and abundance of different phyla and genera, heat map analysis, NMDS plots and inferred functional pathway analysis clearly discriminated the bacterial microbiomes of conjunctival swabs of healthy controls from that of BK patients. Preponderance of negative interactions in the hub genera were observed in BK-CR and BK-SW compared to HC-SW. In addition, a consistent increase in the abundance of pathogenic bacteria, as inferred from published literature, was observed in the conjunctiva of BK patients compared to HC and this may be related to causing or exacerbating ocular surface inflammation. This is the first study demonstrating dysbiosis in the ocular bacterial microbiome of conjunctiva of bacterial keratitis patients compared to the eye of healthy controls. The bacterial microbiome associated with the corneal scrapings of keratitis individuals is also described for the first time.

Automated Categorization of Systemic Disease and Duration From Electronic Medical Record System Data Using Finite-State Machine Modeling: Prospective Validation Study.

BACKGROUND: One of the major challenges in the health care sector is that approximately 80% of generated data remains unstructured and unused. Since it is difficult to handle unstructured data from electronic medical record systems, it tends to be neglected for analyses in most hospitals and medical centers. Therefore, there is a need to analyze unstructured big data in health care systems so that we can optimally utilize and unearth all unexploited information from it. OBJECTIVE: In this study, we aimed to extract a list of diseases and associated keywords along with the corresponding time durations from an indigenously developed electronic medical record system and describe the possibility of analytics from the acquired datasets. METHODS: We propose a novel, finite-state machine to sequentially detect and cluster disease names from patients' medical history. We defined 3 states in the finite-state machine and transition matrix, which depend on the identified keyword. In addition, we also defined a state-change action matrix, which is essentially an action associated with each transition. The dataset used in this study was obtained from an indigenously developed electronic medical record system called eyeSmart that was implemented across a large, multitier ophthalmology network in India. The dataset included patients' past medical history and contained records of 10,000 distinct patients. RESULTS: We extracted disease names and associated keywords by using the finite-state machine with an accuracy of 95%, sensitivity of 94.9%, and positive predictive value of 100%. For the extraction of the duration of disease, the machine's accuracy was 93%, sensitivity was 92.9%, and the positive predictive value was 100%. CONCLUSIONS: We demonstrated that the finite-state machine we developed in this study can be used to accurately identify disease names, associated keywords, and time durations from a large cohort of patient records obtained using an electronic medical record system.

Gut mycobiomes are altered in people with type 2 Diabetes Mellitus and Diabetic Retinopathy.

Studies have documented dysbiosis in the gut mycobiome in people with Type 2 diabetes mellitus (T2DM). However, it is not known whether dysbiosis in the gut mycobiome of T2DM patients would be reflected in people with diabetic retinopathy (DR) and if so, is the observed mycobiome dysbiosis similar in people with T2DM and DR. Gut mycobiomes were generated from healthy controls (HC), people with T2DM and people with DR through Illumina sequencing of ITS2 region. Data were analysed using QIIME and R software. Dysbiotic changes were observed in people with T2DM and DR compared to HC at the phyla and genera level. Mycobiomes of HC, T2DM and DR could be discriminated by heat map analysis, Beta diversity analysis and LEfSE analysis. Spearman correlation of fungal genera indicated more negative correlation in HC compared to T2DM and DR mycobiomes. This study demonstrates dysbiosis in the gut mycobiomes in people with T2DM and DR compared to HC. These differences were significant both at the phyla and genera level between people with T2DM and DR as well. Such studies on mycobiomes may provide new insights and directions to identification of specific fungi associated with T2DM and DR and help developing novel therapies for Diabetes Mellitus and DR.

Mycobiome changes in the vitreous of post fever retinitis patients.

Fungi have been associated with various diseases of the eye like keratitis, uveitis and endophthalmitis. Despite this fact, fungal microbiome (mycobiome) studies compared to the bacterial microbiome studies have remained neglected. In the present study, using metagenomic sequencing, the mycobiomes of the vitreous of healthy control individuals (VC, n = 15) and individuals with post fever retinitis + non-PFR uveitis (PFR+, n = 9) were analysed and compared. The results indicated that Ascomycota was the most predominant phylum in both VC and PFR+ groups. Further, at the genera level it was observed that the abundance of 17 fungal genera were significantly different in post fever retinitis (PFR, n = 6) group compared to control group. Of these 17 genera, it was observed that 14 genera were relatively more abundant in PFR group and the remaining 3 genera in the VC group. Genus Saccharomyces, a commensal of the gut and skin, was predominantly present in the vitreous of both the cohorts, however it was significantly less abundant in PFR group. Further, significant increase in the genera that have a pathogenic interaction with the host were observed in PFR group. On the whole the mycobiome in both the groups differed significantly and formed two distinct clusters in the heatmap and Principal co-ordinate analysis. These results demonstrate significant changes in the mycobiome from the vitreous of post fever retinitis patients compared to healthy controls thus implying that dysbiotic changes in the fungal vitreous microbiome are associated with PFR.

Comparison of the Vitreous Fluid Bacterial Microbiomes between Individuals with Post Fever Retinitis and Healthy Controls.

Ocular microbiome research has gained momentum in the recent past and has provided new insights into health and disease conditions. However, studies on sight threatening intraocular inflammatory diseases have remained untouched. In the present study, we attempted to identify the bacterial microbiome associated with post fever retinitis using a metagenomic sequencing approach. For this purpose, bacterial ocular microbiomes were generated from vitreous samples collected from control individuals (VC, n = 19) and individuals with post fever retinitis (PFR, n = 9), and analysed. The results revealed 18 discriminative genera in the microbiomes of the two cohorts out of which 16 genera were enriched in VC and the remaining two in PFR group. These discriminative genera were inferred to have antimicrobial, anti-inflammatory, and probiotic function. Only two pathogenic bacteria were differentially abundant in 20% of the PFR samples. PCoA and heatmap analysis showed that the vitreous microbiomes of VC and PFR formed two distinct clusters indicating dysbiosis in the vitreous bacterial microbiomes. Functional assignments and network analysis also revealed that the vitreous bacterial microbiomes in the control group exhibited more evenness in the bacterial diversity and several bacteria had antimicrobial function compared to the PFR group.

Phylogenetic Grouping of Human Ocular Escherichia coli Based on Whole-Genome Sequence Analysis.

Escherichia coli is a predominant bacterium in the intestinal tracts of animals. Phylogenetically, strains have been classified into seven phylogroups, A, B1, B2, C, D, E, and F. Pathogenic strains have been categorized into several pathotypes such as Enteropathogenic (EPEC), Enterotoxigenic (ETEC), Enteroinvasive (EIEC), Enteroaggregative (EAEC), Diffusely adherent (DAEC), Uropathogenic (UPEC), Shiga-toxin producing (STEC) or Enterohemorrhagic (EHEC) and Extra-intestinal pathogenic E. coli (ExPEC). E. coli also survives as a commensal on the ocular surface. However, under conditions of trauma and immune-compromised states, E. coli causes conjunctivitis, keratitis, endopthalmitis, dacyrocystitis, etc. The phylogenetic affiliation and the pathotype status of these ocular E. coli strains is not known. For this purpose, the whole-genome sequencing of the 10 ocular E. coli strains was accomplished. Based on whole-genome SNP variation, the ocular E. coli strains were assigned to phylogenetic groups A (two isolates), B2 (seven isolates), and C (one isolate). Furthermore, results indicated that ocular E. coli originated either from feces (enteropathogenic and enterotoxigenic), urine (uropathogenic), or from extra-intestinal sources (extra-intestinal pathogenic). A high concordance was observed between the presence of AMR (Antimicrobial Resistance) genes and antibiotic resistance in the ocular E. coli strains. Furthermore, several virulent genes (fimB to fimI, papB to papX, etc.) and prophages (Enterobacteria phage HK97, Enterobacteria phage P1, Escherichia phage D108 etc.) were unique to ocular E. coli. This is the first report on a whole-genome analysis of ocular E. coli strains.

Allergic eye disease in children and adolescents seeking eye care in India: Electronic medical records driven big data analytics report II.

PURPOSE: To describe the demographics, clinical presentation and risk-factors of allergic eye disease (AED) in children and adolescents presenting to a multi-tier ophthalmology hospital network in India. METHODS: This was a hospital-based cross-sectional study of 259,969 new patients (≤21 years of age) presenting between 2010 and 2018. Patients with a clinical diagnosis of AED were considered as cases. Data were collected using an electronic medical record system. Multiple logistic regression with odds ratios (OR) was performed to identify the high risk-factors of AED. RESULTS: Overall, 26,309 (10.1%) children and adolescents were diagnosed with AED. The prevalence of AED was 0.3%, 6.6%, 18.3%, 15.8%, 8.1%, and 4.9% in infancy (<1 years), toddlerhood (1-2 years), early childhood (3-5 years), middle childhood (6-11 years), early adolescence (12-18 years) and late adolescence (18-21 years), respectively. Most patients with AED presented in spring and summer. Palpebral vernal keratoconjunctivitis was the commonest clinical form, seen in 42.8% of eyes. Corneal scarring, keratoconus, limbal stem cell deficiency and shield ulcers were seen in 2.4%, 1.4%, 0.4% and 0.3% of eyes, respectively. Male sex (OR 2.05); early and middle childhood age groups (OR 66.6; 59.1); higher socio-economic class (OR 1.46) and history of systemic allergy (OR 3.74) were identified as high-risk factors of AED. CONCLUSION: About a tenth of the children and adolescents seeking eye care in India are affected by AED, which commonly affects boys with atopy, from middle to higher income families during their early to middle childhood and shows a self-limiting trend by late adolescence.

Implicating Dysbiosis of the Gut Fungal Microbiome in Uveitis, an Inflammatory Disease of the Eye.

Purpose: In this study, the gut fungal microbiome of uveitis (UVT) patients was generated and compared with healthy controls (HC) to identify dysbiosis in UVT patients and ascertain the role of gut fungal microbiome in disease pathology. Methods: In the present study, gut fungal microbiomes were analyzed in the fecal samples of HC (n = 24) and UVT patients (n = 14) using high-throughput Illumina sequencing of ITS2 region of the fungal ribosomal RNA. QIIME and R software were used for data analysis. Results: The gut fungal richness and diversity were significantly decreased in UVT patients compared to HC. Our analyses showed enrichment of several pathogenic fungi including Malassezia restricta, Candida albicans, Candida glabrata, and Aspergillus gracilis in UVT patients. Heatmap and discriminatory OTUs further confirmed the disparities between UVT and HC microbiomes. Conclusions: This is the first study demonstrating dysbiosis in the gut fungal communities of UVT patients indicating the importance of fungal microbiome in the disease pathology. These initial findings might warrant further investigation into the fungal microbiome, especially interactions between fungal and bacterial that then might give further insight into how probiotics or fecal transplants might benefit.

The Human Ocular Surface Fungal Microbiome.

Purpose: To enumerate the ocular surface fungal microbiome of healthy human eyes by using next-generation sequencing (NGS). Methods: Tarsal and fornix conjunctiva from the lower and upper lids of both eyes of healthy individuals were swabbed in duplicate separately. A total of 34 samples were collected from both the eyes of 17 individuals, which were used for the generation of ocular surface fungal microbiomes by NGS. Twenty-four swabs were used for the detection of culturable fungi by the conventional cultivable method. Microbiome generation involved DNA extraction, internal transcribed spacer 2 (ITS2) amplification, library preparation, amplicon sequencing, taxonomic assignment of sequences, diversity analyses, and identification of genera. Results: The cultivable method detected fungi in 3 out of 24 (12.5 %) ocular surface swabs, whereas NGS identified fungi in 25 of the 34 (73.5 %) swabs. In the cultivable method Aspergillus was the only genus detected, whereas NGS detected 65 distinct genera with 12 to 24 genera per microbiome. Genera Aspergillus, Setosphaeria, Malassezia, and Haematonectria were present in the 25 eyes in which fungi were detected. Alpha diversity in the two eyes was similar and sex had no effect, but Chao1 and Simpson indices were altered by age. Conclusions: This study explored the ocular surface fungal microbiome of healthy individuals using NGS and identified a greater degree of diversity of fungi than with the conventional cultivable method. It was observed that several fungal genera were associated with the healthy conjunctiva.

Dysbiosis in the Gut Bacterial Microbiome of Patients with Uveitis, an Inflammatory Disease of the Eye.

Uveitis (UVT), an inflammatory disease of the eye significantly contributes to vision impairment and blindness. Uveitis is associated with systemic infectious and autoimmune diseases, but in most cases, the aetiology remains unidentified. Dysbiosis in the gut microbiome has been implicated in autoimmune diseases, inflammatory diseases, cancers and mental disorders. In a mice model of autoimmune UVT, it was observed that manipulating the gut microbiome reduces the inflammation and disease severity. Further, alterations in the bacterial gut microbiome and their metabolites were reported in UVT patients from a Chinese cohort. Hence, it is worth comparing the bacterial gut microbiome of UVT patients with that of healthy controls (HC) to ascertain whether dysbiosis of the gut microbiome has implications in UVT. Our analyses showed reduced diversity of several anti-inflammatory organisms including Faecalibacterium, Bacteroides, Lachnospira, Ruminococcus and members of Lachnospiraceae and Ruminococcaceae families, and enrichment of Prevotella (proinflammatory) and Streptococcus (pathogenic) OTUs in UVT microbiomes compared to HC. In addition, decrease in probiotic and antibacterial organisms was observed in UVT compared to HC microbiomes. Heatmap and PCoA plots also indicated significant variations in the microbiomes of UVT versus HC. This is the first study demonstrating dysbiosis in the gut bacterial communities of UVT patients in an Indian cohort and suggests a role of the gut microbiome in the pathophysiology of UVT.