|Year : 2022 | Volume
| Issue : 3 | Page : 36-45
Role of gene sequencing for the diagnosis, tracking and prevention of ocular infections
Rajapandian Siva Ganesa Karthikeyan1, Gunasekaran Rameshkumar2, Prajna Lalitha2
1 Research and Development Centre, Velammal Medical College Hospital and Research Institute, Madurai, Tamil Nadu, India
2 Department of Ocular Microbiology, Aravind Eye Hospital, Madurai, Tamil Nadu, India
|Date of Submission||19-Sep-2022|
|Date of Acceptance||29-Sep-2022|
|Date of Web Publication||11-Nov-2022|
Department of Ocular Microbiology, Aravind Eye Hospital, No-1, Anna Nagar, Madurai - 626 020, Tamil Nadu
Source of Support: None, Conflict of Interest: None
Next-generation sequencing (NGS) is a robust platform which can be employed for pathogen detection. It has the potential to identify pathogens in an unbiased nature including bacteria, fungus, parasites, DNA/RNA viruses and even newer novel pathogens which have been previously reported. The high sensitivity of this technology makes it suitable for ophthalmology applications that rely on very small clinical specimen volumes. Currently, the diagnosis of ocular pathogens relies mainly on conventional procedures such as culturing and microscopy. Traditional methods lack sensitivity, which can be compensated by the application of molecular tools, such as polymerase chain reaction and NGS. In this review, we will look at how NGS can be used to treat ocular infectious diseases such as keratitis, conjunctivitis, trachoma, dacryocystitis, lacrimal sac infection, endophthalmitis and uveitis, with added information on the limitations, advantages and disadvantages on the ocular implication of this NGS technology. NGS-based approaches increase the sensitivity of pathogen detection as well as have the potential to improve healthcare services. NGS still requires advancement in the data analysis tools, establishing standards and reducing turnaround time. We are now closer to realising the full potential of this high-throughput technology. Importantly, NGS also delivers additional data which gives a unique opportunity to explore potential biomarkers of disease pathogenicity or underlying genetic predisposition.
Keywords: Eye infections, next-generation sequencing, polymerase chain reaction
|How to cite this article:|
Karthikeyan RS, Rameshkumar G, Lalitha P. Role of gene sequencing for the diagnosis, tracking and prevention of ocular infections. J Acad Clin Microbiol 2022;24, Suppl S1:36-45
|How to cite this URL:|
Karthikeyan RS, Rameshkumar G, Lalitha P. Role of gene sequencing for the diagnosis, tracking and prevention of ocular infections. J Acad Clin Microbiol [serial online] 2022 [cited 2022 Dec 8];24, Suppl S1:36-45. Available from: https://www.jacmjournal.org/text.asp?2022/24/3/36/360979
| Introduction|| |
Ocular infection remains one of the major health burdens on our society, especially corneal infection (keratitis) and intraocular infection (endophthalmitis). A variety of pathogens, including viruses, bacteria, fungi and parasites, can cause eye infection. Globally, infectious corneal ulcers constitute up to 3.5% of all cases of blindness. Eye infection without proper clinical intervention will lead to vision loss or even surgical removal of the eye. Further, vision loss leads to an enormous financial burden on the affected family and society due to productivity losses. The source of these infections can occur through the normal flora or external pathogens which gain entrance to the eye through trauma or any invasive procedures such as trabeculectomy, intravitreal injections, cataract surgery, vitreous or retinal surgery or even through an endogenous route via bloodstream infection. The diagnosis of ocular pathogens is extremely difficult due to the limited quantity of specimen retrieved for diagnosis. This limitation will restrict the application of multiple diagnostic tests to be performed. In general, the ophthalmologist relies on the clinical diagnosis based on the unique signs and symptoms for the initial diagnosis and treatment, which is not reliable most of the time., For providing the proper and accurate treatment, rapid and specific identification of the pathogens is very essential.
The need for more sensitive and rapid diagnostic tests for ocular infection has been felt most intensely by ophthalmologists, as the infectious aetiologies frequently elude detection with traditional culture and microscopy. Recent advancements in molecular technology will soon confront the standard microbiology diagnostic test. In this concern, next-generation sequencing (NGS) has received the greatest attention from clinicians and researchers. The holy grail of diagnosis is the development of single tests that are rapid and reliable with added information about understanding the disease pathogenesis. NGS provides an all-in-one package of unbiased pathogen identification with the added potential to study antimicrobial susceptibilities, pathogen virulence, molecular epidemiology and strain typing. NGS has opened up new avenues and raised awareness of vast microbial diversity in addition to pathogenic species found in human specimens, including ocular tissue.,, In addition to pathogen detection in infectious samples, NGS offers additional genetic information, including strain identification and evolution prediction, which are essential for understanding epidemiology, identifying new novel species and also antimicrobial resistance.
| Conventional Mode of Diagnosis|| |
The gold standard diagnosis is mainly reliant on microscopic observation and the culture of the infected specimen. The sensitivity and specificity are highly variable and mainly depend on the type of specimen as well as the pathogens to be detected. The conventional identification methods include cultures, which mainly rely on viable organisms present in the specimen, combined with microscopic morphological identification through staining and serological identification of pathogen antigens. The positivity of the culture for the bacterial and fungal growth in the suspected ocular infection ranges from 40% to 70%.,,, These conventional methods take more time, especially for identifying fungal pathogens, which requires at least 48 h and takes up to two to four weeks for sporulation, and up to 10 days for anaerobic organisms. The advancements in microscopes, especially the confocal technology, have led to the application of the in vivo confocal imaging of the cornea. Confocal imaging is a non-invasive rapid process that can easily identify certain pathogens, especially parasites (Acanthamoeba) and filamentous fungi.,,, In cases of Acanthamoeba keratitis, in vivo confocal imaging had 100% sensitivity in identifying pathogens compared with cultures, which had only 33% sensitive.
The major difficulty with this technology is that, even though they can detect the presence of pathogens, species-level identification is not possible. The more time it takes for diagnosis, the more destruction of the ocular tissues occurs. Unless the pathogens are identified earlier, optimal therapy cannot be provided promptly to save the eye and prevent any irreversible visual complications. Gene sequencing methods such as targeted Sanger sequencing or high-throughput NGS are more promising than conventional methods in providing an accurate and rapid diagnosis in considerably less time. Although such with advancements, traditional microbiology's constraints remain mostly connected to the necessity to cultivate the organism. A significant number of pathogens were missed due to the constraints of difficulties in culturing slow growing, fastidious nature or prior use of the antibiotics., Even after the growth of the organisms in culture, the identification and characterisation of antibiotic nature complicates the further diagnosis required.
| Molecular Diagnostic Techniques|| |
The most significant barrier to ocular infection diagnosis is the scarcity of diagnostic specimens. Adding to the complications, there is no reliable way to confirm the presence of viable organisms in the culture-negative cases. The molecular methods, due to their higher sensitivity, require only very small-volume size tissues. Because of this significant advantage, molecular tools are increasingly being used to diagnose ocular infections. The main methods used for diagnosis include polymerase chain reaction (PCR), Sanger sequencing and, more recently, more advanced NGS.,
| Polymerase Chain Reaction|| |
In past decades, PCR has proven to be a powerful tool, especially for the diagnosis of ocular infections. PCR relies on the pathogens' specific genetic markers, which can be amplified and used for pathogen detection. PCR is highly sensitive and rapid detecting in pathogens compared to conventional culturing. PCR relies on primer sequences that are highly specific for a particular genetic target in a pathogen. This method is widely employed for detecting ocular pathogens including Cytomegalovirus (CMV), herpes simplex virus (HSV), varicella-zoster virus (VZV), Toxoplasma gondii,, Mycobacterium tuberculosis, and even adenovirus. PCR had the added advantage of detecting pathogens even in culture-negative cases. As reported earlier, a study showed that the PCR had a 95% positive rate in detecting bacterial and fungal pathogens using directed 16S or 18S PCR, respectively, in compared with only a 50% culture-positive rate. The low positive rate of culture may be due to the poor sensitivity or limited group of pathogenic organisms that can grow efficiently in the provided nutrient media.
For the identification of bacterial and fungal species, PCR targeting the 16S rRNA and 28S rRNA or internal transcribed spacer region is used, followed by Sanger sequencing.,, Further including more specific target genes for sequencing for multilocus sequence typing enhances the specificity for identifying the more closely related species, subspecies or clade identification., The major limitation of this technology is that it relies on primers designed to detect only specific pathogens which limits the identification of unknown pathogens. This limitation can be overcome by the use of NGS technology, which can identify any genetic markers present in the samples non-specifically.
| Next-Generation Sequencing|| |
NGS is the upcoming and newer method which is currently employed widely for the identification of the pathogens in many infections. Sanger sequencing, which is considered the precursor to the NGS technology, relies on DNA polymerase-mediated chain termination due to the insertion of dideoxynucleotide chain terminators with fluorescent nucleotides. The limitations of the Sanger sequencing include the possibility of sequencing only one DNA strand to a maximum of 1000 bp, high cost, labour intensity and prolonged turnaround time. NGS technology in general encompasses a wide range of high-throughput sequencing technologies capable of parallel sequencing of large numbers of nucleic acids within a single specimen., Irrespective of the NGS platform and the samples, the workflow remains almost similar.
NGS is based on high-throughput sequencing technology, which can sequence a large number of small DNA fragments (150–500 bases) in a single reaction., NGS technology is capable of outputting millions of reads or DNA base pairs in a few hours. The technology is also versatile: specimens include body fluids, tissues and even laser-microdissected formalin-fixed and paraffin-embedded (FFPE) tissue can be used., The major limitation with this technology is that the specimen used for the diagnosis will contain the majority of human DNA fragments from the patient and only a small minor fraction will contain (0.1%–8%) the pathogenic organism. The contaminating human DNA can be removed by pre-treatment with saponin or other chemicals or human DNA sequence-specific probes or via Cas9 nuclease digestion. All these methods can reduce human DNA considerably, but there is also a loss of pathogen nucleic acid to an extent. Even without these treatments, this limitation can be overcome by the use of powerful bioinformatic tools to filter and differentiate human DNA from pathogens. Automation in NGS technologies has significantly reduced costs, making it the technique of choice for most applications.
| Next-Generation Sequencing Approach for Diagnosis|| |
NGS can provide an unbiased approach to detect any genomic material both DNA and RNA present in a specimen, which is especially useful for pathogen detection in culture-negative or microscopy-negative ocular diseases. There are a number of NGS platforms that are commercially available, with the key difference in the sequencing methodology, reagents, sequence read length and error rate. General approach for the NGS application in the ocular diagnosis is straightforward and follows a similar workflow irrespective of the NGS platform used [Figure 1]. The implications of NGS for the identification of pathogens can be approached by either Targeted Amplicon Sequencing (TAS) or microbial whole-genome sequencing (MWGS).
|Figure 1: Common next generation sequencing workflow strategy for the analysis of host or pathogen genomes in the ocular samples|
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| Targeted Next-Generation Sequencing versus Microbial Whole-Genome Sequencing Methodologies|| |
Both of these procedures are commonly used for diagnosis, and each has its own set of benefits and drawbacks. For the targeted amplified sequencing (TAS), PCR is performed targeting conserved genetic regions such as 16S rRNA for bacteria,, and 18S rRNA and other targets for eukaryotes,, followed by attaching a smaller DNA fragment for barcoding to identify the samples followed by sequencing using the NGS platform. This method is employed to either identify multiple organisms in a single sample, such as bacteria and fungus, or identify specific pathogens in large numbers of samples.
In contrast, MWGS sequences all the available nucleic acids within the given sample. This indiscriminate approach will provide a culture-independent tool for clinical diagnostics,, which can be highly instrumental in identifying novel organisms. It also has the potential to detect bacteria, fungi, parasites and both DNA and RNA viruses, from a minimal sample volume using intraocular fluid., MWGS is a valuable tool for detecting pathogens in critically ill patients when other methods of identification have failed.,,,,, In addition, this will also provide additional genetic information regarding the pathogenesis or even antimicrobial resistance-associated genes. The MWGS approach is generally recommended for clinical specimens with minimal microbial diversity.
Both these approaches have their own advantages, but in terms of diagnosis, TAS is an appropriate tool, which is also a more efficient and cost-effective way to investigate multiple pathogens non-specifically. In the TAS approach, adding unique barcodes to each sample during the library preparation will enable pooling multiple samples and sequencing them simultaneously in a single sequencing run. After sequencing, each sample read can be separated based on the barcode for the data analysis. The sample pooling provides greater advantages, which increase the number of samples to be analysed in a single run without increasing cost or test duration.
| Ocular Applications of Next-Generation Sequencing|| |
The use of NGS technology in the treatment of ocular diseases is becoming increasingly common as the technology becomes more accessible and less expensive. There are a number of reports about polymicrobial eye infections,,,,,,,,,, and some of them associated with normal environmental organisms., Whenever there is a polymicrobial infection or unsuspected pathogens, the conventional methods will fail considerably in the identification of multiple organisms.
| Next-Generation Sequencing in the Diagnosis of Ocular Surface Infections|| |
The external infection in cornea, conjunctiva and sclera has a greater advantage for direct observation and sample collection. Irrespective of this advantage, the isolation of the viable pathogen from the infected surface, especially the cornea, is only 60%. Clinically differentiating infectious from non-infectious inflammatory scleritis is highly challenging. The NGS application is ideal for the diagnostic application for ocular surface infection. The greater challenge will be the differentiation of normal microflora from the actual pathogenic infection.
| Keratitis|| |
Li et al. evaluated the implication of the NGS in 16 cases of keratitis using the formalin-fixed tissue section. Their methodology used ten-micron thick tissue sections from FFPE specimens. The DNA was extracted and obtained at 20–46 million/sample using an Illumina NextSeq platform. Most of the sequences they obtained were human DNA, and only 1.7% represented microbial sequences. They found four samples infected with bacteria, five fungal-infected samples, three Acanthamoeba infections and three viral infections.
Contact lens-associated keratitis infections are due to the contamination of the contact lens or storage solution with diverse environmental bacteria in addition to the actual pathogens. These microbial communities are difficult to culture, mainly due to inactivation by the antimicrobial and preservatives in the storage solutions. Even though they are not detected, there are greater chances that they may prevail in the clinical samples. Eguchi et al. applied a 16S rDNA library analysis to the CL storage case of a patient with Pseudomonas aeruginosa keratitis. The culture methods only identified three isolates, such as Bacillus sp., Pseudomonas putida and P. aeruginosa, in the CL storage solution. Whereas the 16S rDNA library analysis detected 23 genera, indicating the contact lens (CL) storage case was contaminated with a greater number of bacteria. These uncultivable coexisting organisms might have a role in keratitis pathophysiology. Further, additional investigation by the team identified more bacteria coexisting with the CL storage cases from patients with Acanthamoeba keratitis. Acanthamoeba are known to have symbiotic associations with the bacteria, which might have an influence on the disease.,
| Lacrimal Sac Infections|| |
Eguchi et al. and their team employed the MiSeq 3000 platform (Illumina, Inc., Tokyo, Japan) for metagenomic microbiome analysis using NGS in lacrimal sac secretion. In their study, they included two acute dacryocystitis patients and one chronic dacryocystitis patient. They identified that both the NGS and the culture had similar identification of bacterial groups in the two acute dacryocystitis cases. In the case of chronic dacryocystitis, NGS identified Veillonella sp. (Gram-negative anaerobes) in addition to Haemophilus parainfluenzae and Staphylococcus sp. identified by both culture and NGS. These findings suggest that NGS has an advantage over traditional culture methods in identifying difficult-to-grow pathogens in ocular infection. Dacryocystitis is an infection of the lacrimal sac known to have a higher microbial load, leading to disease pathology. Mycoplasma sp. is one of the most difficult to culture or diagnose in acute dacryocystitis, which can be identified by metagenomic analysis. Further polymicrobial infections were also reported in 30% of the cases with multiple isolates. The polymicrobial infection should be considered when deciding on an antimicrobial treatment. An additional proof-of-concept study conducted by Seitzman et al., using total RNA extracted from the swabs collected from keratitis, scleritis and conjunctivitis patients. They used HiSeq 4000 or NovaSeq using 150-nucleotide paired-end sequencing and analysed the data using an in-house developed pipeline to identify potential pathogens. Furthermore, they used the contralateral unaffected eye as a control for background subtraction to rule out common microflora. The organism is considered a pathogen only if it has the most abundant reads after the background subtraction. They identified a wide variety of pathogenic organisms in this study, including HSV-1, adenovirus, Acanthamoeba and Aspergillus species, which correlate with microbiologic and molecular testing. A similar study by Lalitha et al. using extracted RNA from conjunctival swabs identified 86% (12 out of 14 eyes tested) positive for human pathogens with the most common abundance of adenovirus and also identified the very rare and unusual fungal pathogen, Vittaforma corneae.
| Trachoma|| |
Trachoma is one of the severe infections of the eye caused by ocular Chlamydia trachomatis. There is an increase in the application of whole-genome sequencing (WGS) of C. trachomatis.,, Last et al. completed a genome-wide association study on 81 ocular C. trachomatis isolates using the Illumina GAII or HiSeq 2000 platform to identify genomic markers for trachoma disease severity. Their study identified a greater diversity of the circulating C. trachomatis ocular pathogen than expected in trachoma-endemic West African communities. Alkhidir et al. did a similar investigation, using WGS to sequence 20 ocular C. trachomatis isolates from Gadarif State, Sudan. They used the Illumina NextSeq platform for sequencing and successfully sequenced all 20 samples with high-quality reads. They selected 12 isolates that had more than 98% genome coverage for further analysis. The phylogenetic analysis reveals that all these isolates grouped closely with the subclade within the T2-trachoma clade but were phylogenetically distinct from isolates from other geographic regions. In addition, this study also revealed the absence of macrolide resistance alleles, which indicates that the macrolide antibiotic group is still effective for treatment. Azithromycin is the drug of choice for the treatment of C. trachomatis, but even after prolonged treatment, the ocular infections often persist., This might be due to the other co-founding factors that may contribute to antibiotic resistance. Both these studies demonstrated the WGS potential in terms of epidemiological understanding as well as disease outcome.
| Next-Generation Sequencing in the Diagnosis of Intraocular Infections|| |
Aqueous and vitreous fluids are primarily used for diagnosis of intraocular infections such as uveitis, retinitis, and invasive or endogenous ocular infection. The small volume of specimens collected for diagnosis is one of the reasons for the low diagnostic positivity for identifying pathogens.,, A proof-of-concept study by Doan et al. demonstrated that MWGS can accurately detect viral (HSV-1), fungal (Cryptococcus neoformans) and protozoan (T. gondii) infections. In addition, they also identified the presence of rubella virus in one subject who had chronic idiopathic bilateral uveitis for more than 16 years. Their study demonstrated the unbiased nature of the MWGS system in detecting fungi, parasites and DNA and RNA viruses in minimal samples. Using metagenomic DNA sequencing (DNAseq), the same team was able to identify intraocular pathogens in vitreous samples that were negative for CMV, VZV, HSV and T. gondii through PCR. In this study, they employed the Illumina HiSeq 4000 platform with an average read depth of 15 × 106 reads/sample. The author used their in-house developed software for analysing the NGS data using the National Center for Biotechnology Information as a reference database. They identified eight samples that were positive for one or more of the following pathogens such as CMV,, human herpesvirus-6, HSV-2, HTLV-1, Klebsiella pneumoniae and Candida dubliniensis. All of these pathogens have been previously reported to be associated with intraocular infection.
Lee et al. employed targeted 16S amplification in ocular fluids of patients with infectious endophthalmitis. In this study, they identified that the sequencing results are in good concordance with the bacterial and fungal cultures in the seven control patients, with additional findings of the presence of Streptococcus and Pseudomonas sp. in culture-negative specimens. In another study by Lee et al., using whole genome sequencing, found no correlation between the S. epidermidis burden and endophthalmitis severity. Further, they also identified the presence of non-pathogenic human anellovirus, torque teno virus (TTV) in all the culture-negative samples, 57.1% positivity in the culture-positive specimens and none in the control samples. TTV is mostly reported in patients with an altered immune system and also has a significantly higher prevalence in endophthalmitis patients who have undergone multiple ocular invasive procedures such as intraocular injection or surgery, as well as in seasonal hyperacute panuveitis patients. The patient with the TTV also had a higher risk of second vitrectomy, suggesting that the presence of the TTV indicates the severity of the ocular disease, which can be considered a biomarker. Similarly, Seitzman et al identified an unculturable gram-negative bacterium Capnocytophaga in aqueous fluid using metagenomic sequencing. Deshmukh et al. amplified the V3-V4 regions of the bacterial 16S rRNA and the ITS2 region of bacterial and fungus, respectively, followed by sequencing on an Illumina HiSeq 2500 Machine on 34 patients with endophthalmitis and thirty non-infectious controls with presumed infectious endophthalmitis. In their research, they reported that conventional culture-based diagnosis was only 44% (15/34) positive while NGS positivity was 88% (30/34) positive. In addition, NGS identified a mixed infection in two patients, with bacteria in 26 and fungal infection in 2 patients. NGS outperformed the culture methods with additional identification of Streptococcus sp., Staphylococcus sp., Pseudomonas sp., Gemella sp., Haemophilus sp. and Acinetobacter sp. in the culture-negative cases. NGS had a specificity of 100% when compared to the 20% with culture, but the sensitivity remained almost similar between both methods (87.5% and 88%, respectively).
Another major advantage of NGS is that, in addition to pathogen detection, by employing the bioinformatic tools, simultaneously analysing mutations in genes is also possible. In their study, Doan et al. were able to detect mutations in the UL97 gene coding for phosphotransferase that confer resistance to Ganciclovir and Valganciclovir, and similar findings were reported by Kirstahler et al., in post-operative endophthalmitis patients. In going further, Gonzales et al. identified the lymphoma associated with infectious aetiology of both Epstein–Barr virus and human herpesvirus-8 in one patient and the S243N mutation in MYD88, which has been associated with B-cell lymphoma in another patient. Both of these diagnoses are only possible with NGS because these markers are not primary targets for conventional diagnosis. All these recent findings suggest that the mNGS application can extend far beyond the infectious diagnosis and can be applied to non-infectious ocular diseases.
| Alternate Approach for Pathogen Detection|| |
DNA-based molecular tools, including NGS, cannot distinguish between viable and non-viable organisms. This problem can be overcome by RNA sequencing, with the added advantage of detecting RNA viruses., In the case of infection, the presence of a pathogen can also be detected indirectly by monitoring the RNA profile or specific gene expression. This approach has proven to be complementary to direct diagnosis when isolation of the pathogens is not possible or inaccessible for collecting specimens. This approach has proven to be effective in identifying unique transcriptome profiles in peripheral blood in respiratory patients., The effectiveness of the RNA sequence was also proven recently by Wecker et al., who showed the identification of more than 150 microRNAs in the very small volume of aqueous samples of patients undergoing cataract surgery. This alternative strategy still requires more studies and it has the potential to be employed as the alternate methods whenever the direct detection of pathogens is difficult.
| Conclusion|| |
There are a number of studies that confirmed that NGS results are concordant with the standard diagnostic methods, including cultures, microscopy and PCR. Further, NGS has also proven to be effective in small-volume samples such as aqueous and vitreous fluids, various body tissues, other body fluids and even treated specimens such as formalin-embedded or frozen specimens. NGS has shown that it has the ability to greatly enhance the detection of infectious pathogens in an unbiased manner, allowing it to detect bacteria, fungus, protozoan and even DNA/RNA viruses. In addition, NGS can provide a plethora of information such as antibiotic resistance, virulence nature, epidemiological prevalence and even gene mutation associated with other diseases. NGS-based approach will definitely complement the present ophthalmology diagnostic strategy. Even though NGS is the latest and still evolving technology, analysis and interpretation of the results is still a daunting task. It is still unwise to make diagnostic decisions exclusively based on NGS data, and all results, including those from conventional microbiological methods, must be thoroughly interpreted for a better understanding. NGS has proven to be highly sensitive, which further adds to the problem of amplifying or picking up the contamination. The ocular surface has a rich microflora in addition to pathogens that can interfere with the sample collection and have a huge impact on diagnostic results. In order to overcome those issues, lots of quality of checkpoints should be placed in each and every step. Data processing in the NGS still requires major advancement and simplification, and the sheer volume of data can overwhelm ophthalmologists and microbiologists. In most clinical settings, NGS is still prohibitively costly, and to become a standard routine test, the cost of testing must be decreased further.
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Conflicts of interest
There are no conflicts of interest.
| References|| |
Henry CR, Flynn HW Jr., Miller D, Forster RK, Alfonso EC. Infectious keratitis progressing to endophthalmitis: A 15-year study of microbiology, associated factors, and clinical outcomes. Ophthalmology 2012;119:2443-9.
Flaxman SR, Bourne RR, Resnikoff S, Ackland P, Braithwaite T, Cicinelli MV, et al.
Global causes of blindness and distance vision impairment 1990-2020: A systematic review and meta-analysis. Lancet Glob Health 2017;5:e1221-34.
Keay L, Edwards K, Dart J, Stapleton F. Grading contact lens-related microbial keratitis: Relevance to disease burden. Optom Vis Sci 2008;85:531-7.
Dahlgren MA, Lingappan A, Wilhelmus KR. The clinical diagnosis of microbial keratitis. Am J Ophthalmol 2007;143:940-4.
Dalmon C, Porco TC, Lietman TM, Prajna NV, Prajna L, Das MR, et al.
The clinical differentiation of bacterial and fungal keratitis: A photographic survey. Invest Ophthalmol Vis Sci 2012;53:1787-91.
Ung L, Bispo PJ, Shanbhag SS, Gilmore MS, Chodosh J. The persistent dilemma of microbial keratitis: Global burden, diagnosis, and antimicrobial resistance. Surv Ophthalmol 2019;64:255-71.
Gu W, Miller S, Chiu CY. Clinical metagenomic next-generation sequencing for pathogen detection. Annu Rev Pathol 2019;14:319-38.
Forbes JD, Knox NC, Peterson CL, Reimer AR. Highlighting clinical metagenomics for enhanced diagnostic decision-making: A step towards wider implementation. Comput Struct Biotechnol J 2018;16:108-20.
Kitsios GD. Translating lung microbiome profiles into the next-generation diagnostic gold standard for pneumonia: A clinical investigator's perspective. mSystems 2018;3:e00153-17.
Fournier PE, Drancourt M, Colson P, Rolain JM, La Scola B, Raoult D. Modern clinical microbiology: New challenges and solutions. Nat Rev Microbiol 2013;11:574-85.
Badiee P, Nejabat M, Alborzi A, Keshavarz F, Shakiba E. Comparative study of Gram stain, potassium hydroxide smear, culture and nested PCR in the diagnosis of fungal keratitis. Ophthalmic Res 2010;44:251-6.
Zhao G, Zhai H, Yuan Q, Sun S, Liu T, Xie L. Rapid and sensitive diagnosis of fungal keratitis with direct PCR without template DNA extraction. Clin Microbiol Infect 2014;20:O776-82.
Ferrer C, Alió JL. Evaluation of molecular diagnosis in fungal keratitis. Ten years of experience. J Ophthalmic Inflamm Infect 2011;1:15-22.
Doan T, Pinsky BA. Current and future molecular diagnostics for ocular infectious diseases. Curr Opin Ophthalmol 2016;27:561-7.
Sharma S. Diagnosis of infectious diseases of the eye. Eye (Lond) 2012;26:177-84.
Eleinen KG, Mohalhal AA, Elmekawy HE, Abdulbaki AM, Sherif AM, El-Sherif RH, et al.
Polymerase chain reaction-guided diagnosis of infective keratitis – A hospital-based study. Curr Eye Res 2012;37:1005-11.
Al-Mujaini A, Al-Kharusi N, Thakral A, Wali UK. Bacterial keratitis: Perspective on epidemiology, clinico-pathogenesis, diagnosis and treatment. Sultan Qaboos Univ Med J 2009;9:184-95.
Villani E, Baudouin C, Efron N, Hamrah P, Kojima T, Patel SV, et al. In vivo
confocal microscopy of the ocular surface: From bench to bedside. Curr Eye Res 2014;39:213-31.
Austin A, Lietman T, Rose-Nussbaumer J. Update on the management of infectious keratitis. Ophthalmology 2017;124:1678-89.
Kanavi MR, Javadi M, Yazdani S, Mirdehghanm S. Sensitivity and specificity of confocal scan in the diagnosis of infectious keratitis. Cornea 2007;26:782-6.
Goh JW, Harrison R, Hau S, Alexander CL, Tole DM, Avadhanam VS. Comparison of In vivo
confocal microscopy, PCR and culture of corneal scrapes in the diagnosis of Acanthamoeba keratitis. Cornea 2018;37:480-5.
Staley JT, Konopka A. Measurement of in situ
activities of nonphotosynthetic microorganisms in aquatic and terrestrial habitats. Annu Rev Microbiol 1985;39:321-46.
Nakamura S, Maeda N, Miron IM, Yoh M, Izutsu K, Kataoka C, et al.
Metagenomic diagnosis of bacterial infections. Emerg Infect Dis 2008;14:1784-6.
Goldberg B, Sichtig H, Geyer C, Ledeboer N, Weinstock GM. Making the leap from research laboratory to clinic: Challenges and opportunities for next-generation sequencing in infectious disease diagnostics. mBio 2015;6:e01888-15.
Besser J, Carleton HA, Gerner-Smidt P, Lindsey RL, Trees E. Next-generation sequencing technologies and their application to the study and control of bacterial infections. Clin Microbiol Infect 2018;24:335-41.
Taravati P, Lam D, Van Gelder RN. Role of molecular diagnostics in ocular microbiology. Curr Ophthalmol Rep 2013;1:10. [doi 1007/s40135-013-0025-1].
Doan T, Acharya NR, Pinsky BA, Sahoo MK, Chow ED, Banaei N, et al.
Metagenomic DNA sequencing for the diagnosis of intraocular infections. Ophthalmology 2017;124:1247-8.
Nasr Esfahani B, Moghim S, Ghasemian Safaei H, Moghoofei M, Sedighi M, Hadifar S. Phylogenetic analysis of prevalent tuberculosis and non-tuberculosis mycobacteria in Isfahan, Iran, based on a 360 BP sequence of the RPOB gene. Jundishapur J Microbiol 2016;9:e30763.
Duan H, Liu G, Wang X, Fu Y, Liang Q, Shang Y, et al.
Evaluation of the ribosomal protein S1 gene (Rpsa) as a novel biomarker for mycobacterium species identification. Biomed Res Int 2015;2015:271728.
Gopalkrishna V, Ganorkar NN, Patil PR. Identification and molecular characterization of adenovirus types (HAdV-8, HAdV-37, HAdV-4, HAdV-3) in an epidemic of keratoconjunctivitis occurred in Pune, Maharashtra, Western India. J Med Virol 2016;88:2100-5.
Kim E, Chidambaram JD, Srinivasan M, Lalitha P, Wee D, Lietman TM, et al
. Prospective comparison of microbial culture and polymerase chain reaction in the diagnosis of corneal ulcer. Am J Ophthalmol 2008;146:714-23.
Appavu SP, Prajna L, Rajapandian SG. Genotyping and phylogenetic analysis of Pythium insidiosum causing human corneal ulcer. Med Mycol 2020;58:211-8.
Gnanam H, Rajapandian SG, Gunasekaran R, Roshni Prithiviraj S, Ravindran RS, Sen S, et al.
Molecular identification of Nocardia
species causing endophthalmitis using multilocus sequence analysis (MLSA): A 10-year perspective. J Med Microbiol 2020;69:728-38.
Roshni Prithiviraj S, Rajapandian SGK, Gnanam H, Gunasekaran R, Mariappan P, Sankalp Singh S, et al.
Clinical presentations, genotypic diversity and phylogenetic analysis of Acanthamoeba
species causing keratitis. J Med Microbiol 2020;69:87-95.
Sanger F, Nicklen S, Coulson AR. DNA sequencing with chain-terminating inhibitors. Proc Natl Acad Sci U S A 1977;74:5463-7.
Racsa LD, DeLeon-Carnes M, Hiskey M, Guarner J. Identification of bacterial pathogens from formalin-fixed, paraffin-embedded tissues by using 16S sequencing: Retrospective correlation of results to clinicians' responses. Hum Pathol 2017;59:132-8.
Deurenberg RH, Bathoorn E, Chlebowicz MA, Couto N, Ferdous M, García-Cobos S, et al.
Application of next generation sequencing in clinical microbiology and infection prevention. J Biotechnol 2017;243:16-24.
Li Z, Breitwieser FP, Lu J, Jun AS, Asnaghi L, Salzberg SL, et al.
Identifying corneal infections in formalin-fixed specimens using next generation sequencing. Invest Ophthalmol Vis Sci 2018;59:280-8.
Hasan MR, Rawat A, Tang P, Jithesh PV, Thomas E, Tan R, et al.
Depletion of human DNA in spiked clinical specimens for improvement of sensitivity of pathogen detection by next-generation sequencing. J Clin Microbiol 2016;54:919-27.
Gu W, Crawford ED, O'Donovan BD, Wilson MR, Chow ED, Retallack H, et al.
Depletion of abundant sequences by hybridization (DASH): Using Cas9 to remove unwanted high-abundance species in sequencing libraries and molecular counting applications. Genome Biol 2016;17:41.
Salipante SJ, Sengupta DJ, Rosenthal C, Costa G, Spangler J, Sims EH, et al.
Rapid 16S rRNA next-generation sequencing of polymicrobial clinical samples for diagnosis of complex bacterial infections. PLoS One 2013;8:e65226.
Mardis ER. Next-generation sequencing platforms. Annu Rev Anal Chem (Palo Alto Calif) 2013;6:287-303.
Fadrosh DW, Ma B, Gajer P, Sengamalay N, Ott S, Brotman RM, et al.
An improved dual-indexing approach for multiplexed 16S rRNA gene sequencing on the Illumina MiSeq platform. Microbiome 2014;2:6.
Janda JM, Abbott SL. 16S rRNA gene sequencing for bacterial identification in the diagnostic laboratory: Pluses, perils, and pitfalls. J Clin Microbiol 2007;45:2761-4.
De Filippis F, Laiola M, Blaiotta G, Ercolini D. Different amplicon targets for sequencing-based studies of fungal diversity. Appl Environ Microbiol 2017;83:e00905-17.
Banos S, Lentendu G, Kopf A, Wubet T, Glöckner FO, Reich M. A comprehensive fungi-specific 18S rRNA gene sequence primer toolkit suited for diverse research issues and sequencing platforms. BMC Microbiol 2018;18:190.
Lefterova MI, Suarez CJ, Banaei N, Pinsky BA. Next-Generation sequencing for infectious disease diagnosis and management: A report of the association for molecular pathology. J Mol Diagn 2015;17:623-34.
Chiu CY, Miller SA. Clinical metagenomics. Nat Rev Genet 2019;20:341-55.
Pallen MJ, Loman NJ, Penn CW. High-throughput sequencing and clinical microbiology: Progress, opportunities and challenges. Curr Opin Microbiol 2010;13:625-31.
Doan T, Wilson MR, Crawford ED, Chow ED, Khan LM, Knopp KA, et al.
Illuminating uveitis: Metagenomic deep sequencing identifies common and rare pathogens. Genome Med 2016;8:90.
Wilson MR, Naccache SN, Samayoa E, Biagtan M, Bashir H, Yu G, et al.
Actionable diagnosis of neuroleptospirosis by next-generation sequencing. N Engl J Med 2014;370:2408-17.
Brown JR, Bharucha T, Breuer J. Encephalitis diagnosis using metagenomics: Application of next generation sequencing for undiagnosed cases. J Infect 2018;76:225-40.
Fukui Y, Aoki K, Okuma S, Sato T, Ishii Y, Tateda K. Metagenomic analysis for detecting pathogens in culture-negative infective endocarditis. J Infect Chemother 2015;21:882-4.
Imai A, Gotoh K, Asano Y, Yamada N, Motooka D, Fukushima M, et al.
Comprehensive metagenomic approach for detecting causative microorganisms in culture-negative infective endocarditis. Int J Cardiol 2014;172:e288-9.
Lelouvier B, Servant F, Delobel P, Courtney M, Elbaz M, Amar J. Identification by highly sensitive 16S metagenomic sequencing of an unusual case of polymicrobial bacteremia. J Infect 2017;75:278-80.
Gyarmati P, Kjellander C, Aust C, Kalin M, Öhrmalm L, Giske CG. Bacterial Landscape of bloodstream infections in neutropenic patients via high throughput sequencing. PLoS One 2015;10:e0135756.
Comez AT, Koklu A, Akcali A. Chronic dacryocystitis secondary to Stenotrophomonas maltophilia
and Staphylococcus aureus
mixed infection. BMJ Case Rep 2014;2014:bcr2014203642.
Hayashi Y, Eguchi H, Toibana T, Mitamura Y, Yaguchi T. Polymicrobial sclerokeratitis caused by Scedosporium apiospermum and Aspergillus
cibarius. Cornea 2014;33:875-7.
Ray M, Nigel LC, Tan AM. Triple infection keratitis. Eye Contact Lens 2014;40:123-6.
Pathengay A, Karosekar S, Raju B, Sharma S, Das T, Hyderabad Endophthalmitis Research Group. Microbiologic spectrum and susceptibility of isolates in scleral buckle infection in India. Am J Ophthalmol 2004;138:663-4.
Wong T, Ormonde S, Gamble G, McGhee CN. Severe infective keratitis leading to hospital admission in New Zealand. Br J Ophthalmol 2003;87:1103-8.
Brook I, Frazier EH. Aerobic and anaerobic microbiology of dacryocystitis. Am J Ophthalmol 1998;125:552-4.
Edens C, Liebich L, Halpin AL, Moulton-Meissner H, Eitniear S, Zgodzinski E, et al.
Mycobacterium chelonae eye infections associated with humidifier use in an outpatient LASIK clinic – Ohio, 2015. MMWR Morb Mortal Wkly Rep 2015;64:1177.
Eguchi H, Miyamoto T, Kuwahara T, Mitamura S, Mitamura Y. Infectious conjunctivitis caused by Pseudomonas aeruginosa
isolated from a bathroom. BMC Res Notes 2013;6:245.
McLeod SD, Kolahdouz-Isfahani A, Rostamian K, Flowers CW, Lee PP, McDonnell PJ. The role of smears, cultures, and antibiotic sensitivity testing in the management of suspected infectious keratitis. Ophthalmology 1996;103:23-8.
Amann RI, Ludwig W, Schleifer KH. Phylogenetic identification and in situ
detection of individual microbial cells without cultivation. Microbiol Rev 1995;59:143-69.
Eguchi H, Hotta F, Kuwahara T, Imaohji H, Miyazaki C, Hirose M, et al.
Diagnostic approach to ocular infections using various techniques from conventional culture to next-generation sequencing analysis. Cornea 2017;36 Suppl 1:S46-52.
Bottone EJ, Madayag RM, Qureshi MN. Acanthamoeba keratitis: Synergy between amebic and bacterial cocontaminants in contact lens care systems as a prelude to infection. J Clin Microbiol 1992;30:2447-50.
Seitzman GD, Hinterwirth A, Zhong L, Cummings S, Chen C, Driver TH, et al.
Metagenomic deep sequencing for the diagnosis of corneal and external disease infections. Ophthalmology 2019;126:1724-6.
Lalitha P, Seitzman GD, Kotecha R, Hinterwirth A, Chen C, Zhong L, et al.
Unbiased pathogen detection and host gene profiling for conjunctivitis. Ophthalmology 2019;126:1090-4.
Seth-Smith HM, Harris SR, Skilton RJ, Radebe FM, Golparian D, Shipitsyna E, et al.
Whole-genome sequences of Chlamydia trachomatis
directly from clinical samples without culture. Genome Res 2013;23:855-66.e.
Christiansen MT, Brown AC, Kundu S, Tutill HJ, Williams R, Brown JR, et al.
Whole-genome enrichment and sequencing of Chlamydia trachomatis
directly from clinical samples. BMC Infect Dis 2014;14:591.
Brown AC, Christiansen MT. Whole-Genome enrichment using RNA Probes and sequencing of Chlamydia trachomatis
directly from clinical samples. Methods Mol Biol 2017;1616:1-22.
Last AR, Pickering H, Roberts CH, Coll F, Phelan J, Burr SE, et al.
Population-based analysis of ocular Chlamydia trachomatis
in trachoma-endemic West African communities identifies genomic markers of disease severity. Genome Med 2018;10:15.
Alkhidir AA, Holland MJ, Elhag WI, Williams CA, Breuer J, Elemam AE, et al.
Whole-genome sequencing of ocular Chlamydia trachomatis
isolates from Gadarif State, Sudan. Parasit Vectors 2019;12:518.
West ES, Munoz B, Mkocha H, Holland MJ, Aguirre A, Solomon AW, et al.
Mass treatment and the effect on the load of Chlamydia trachomatis
infection in a trachoma-hyperendemic community. Invest Ophthalmol Vis Sci 2005;46:83-7.
Nash SD, Stewart AE, Zerihun M, Sata E, Gessese D, Melak B, et al.
Ocular Chlamydia trachomatis
infection under the surgery, antibiotics, facial cleanliness, and environmental improvement strategy in Amhara, Ethiopia, 2011-2015. Clin Infect Dis 2018;67:1840-6.
Borroni D, Romano V, Kaye SB, Somerville T, Napoli L, Fasolo A, et al.
Metagenomics in ophthalmology: Current findings and future prospectives. BMJ Open Ophthalmol 2019;4:e000248.
Deshmukh D, Joseph J, Chakrabarti M, Sharma S, Jayasudha R, Sama KC, et al.
New insights into culture negative endophthalmitis by unbiased next generation sequencing. Sci Rep 2019;9:844.
Doan T, Akileswaran L, Andersen D, Johnson B, Ko N, Shrestha A, et al.
Paucibacterial microbiome and resident DNA Virome of the healthy conjunctiva. Invest Ophthalmol Vis Sci 2016;57:5116-26.
Sahoo MK, Lefterova MI, Yamamoto F, Waggoner JJ, Chou S, Holmes SP, et al.
Detection of Cytomegalovirus drug resistance mutations by next-generation sequencing. J Clin Microbiol 2013;51:3700-10.
Barzon L, Lavezzo E, Militello V, Toppo S, Palù G. Applications of next-generation sequencing technologies to diagnostic virology. Int J Mol Sci 2011;12:7861-84.
Lee AY, Akileswaran L, Tibbetts MD, Garg SJ, Van Gelder RN. Identification of torque teno virus in culture-negative endophthalmitis by representational deep DNA sequencing. Ophthalmology 2015;122:524-30.
Lee CS, Hong B, Kasi SK, Aderman C, Talcott KE, Adam MK, et al.
Prognostic utility of whole-genome sequencing and polymerase chain reaction tests of ocular fluids in postprocedural endophthalmitis. Am J Ophthalmol 2020;217:325-34.
Doberer K, Schiemann M, Strassl R, Haupenthal F, Dermuth F, Görzer I, et al.
Torque teno virus for risk stratification of graft rejection and infection in kidney transplant recipients-A prospective observational trial. Am J Transplant 2020;20:2081-90.
Naik P, Dave VP, Joseph J. Detection of torque teno virus (TTV) and TTV-Like Minivirus in patients with presumed infectious endophthalmitis in India. PLoS One 2020;15:e0227121.
Smits SL, Manandhar A, van Loenen FB, van Leeuwen M, Baarsma GS, Dorrestijn N, et al. High prevalence of anelloviruses in vitreous fluid of children with seasonal hyperacute panuveitis. J Infect Dis 2012;205:1877-84.
Seitzman GD, Thulasi P, Hinterwirth A, Chen C, Shantha J, Doan T. Capnocytophaga Keratitis: Clinical presentation and use of metagenomic deep sequencing for diagnosis. Cornea 2019;38:246-8.
Kirstahler P, Bjerrum SS, Friis-Møller A, la Cour M, Aarestrup FM, Westh H, et al.
Genomics-Based identification of microorganisms in human ocular body fluid. Sci Rep 2018;8:4126.
Gonzales J, Doan T, Shantha JG, Bloomer M, Wilson MR, DeRisi JL, et al.
Metagenomic deep sequencing of aqueous fluid detects intraocular lymphomas. Br J Ophthalmol 2018;102:6-8.
Parnell GP, McLean AS, Booth DR, Armstrong NJ, Nalos M, Huang SJ, et al.
A distinct influenza infection signature in the blood transcriptome of patients with severe community-acquired pneumonia. Crit Care 2012;16:R157.
Tsalik EL, Henao R, Nichols M, Burke T, Ko ER, McClain MT, et al.
Host gene expression classifiers diagnose acute respiratory illness etiology. Sci Transl Med 2016;8:322ra11.
Wecker T, Hoffmeier K, Plötner A, Grüning BA, Horres R, Backofen R, et al.
MicroRNA profiling in aqueous humor of individual human eyes by next-generation sequencing. Invest Ophthalmol Vis Sci 2016;57:1706-13.