Metataxonomics of Internal Transcribed Spacer Amplicons in Cerebrospinal Fluid for Diagnosing and Genotyping of Cryptococcal Meningitis
Cryptococcal meningitis, caused primarily by Cryptococcus neoformans, is a life-threatening fungal infection of the central nervous system (CNS) with high mortality rates, particularly among immunocompromised individuals such as those with AIDS. Rapid and accurate diagnosis is critical for effective treatment, yet conventional methods like India Ink staining, fungal culture, and antigen detection suffer from limitations in sensitivity, speed, and operator dependency. This study explored the utility of metataxonomics—a high-throughput sequencing approach targeting the Internal Transcribed Spacer (ITS) region of fungal ribosomal DNA (rDNA)—to diagnose and genotype Cryptococcus directly from cerebrospinal fluid (CSF) samples, bypassing the need for culture-based isolation.
Methodology Overview
The study included 15 CSF samples: 11 from patients with clinically suspected cryptococcal meningitis and 4 non-infectious controls. Samples were collected from three hospitals in China between December 2017 and December 2018. All suspected cases were confirmed through microscopy or culture. For metataxonomic analysis, DNA was extracted using a refined protocol involving centrifugation, phosphate buffer saline (PBS) washing, heat inactivation, mechanical disruption via bead-beating, and purification with the QIAamp DNA Mini Kit.
The ITS1 region was amplified using universal primers ITS1 (CTTGGTCATTTAGAGGAAGTAA) and ITS2 (GCTGCGTTCTTCATCGATGC). Amplicons were sequenced on the Illumina MiSeq platform, generating paired-end 250 bp reads. Bioinformatics processing involved trimming low-quality reads (Q-score <20, length <100 bp), merging paired sequences with FLASH2, and removing chimeras and singletons using UPARSE. Operational Taxonomic Units (OTUs) were clustered at 97% similarity and aligned to the UNITE fungal database for taxonomic assignment. Cryptococcus genotypes were determined by comparing ITS sequences to reference strains (ITS types 1–7). Statistical differences between groups were assessed using permutational multivariate analysis (PERMANOVA).
Key Findings
Sequencing Data Quality and Pathogen Detection
Between 30,000 and 340,000 high-quality reads were obtained from infected samples, while controls yielded only 8–60 reads. Over 99% of reads in infected samples mapped to Cryptococcus, with relative abundances ranging from 95.90% to 99.97% (Figure 1A–C). Low-abundance fungal taxa (<1.41%) included Myrothecium roridum, Alternaria spp., and Guehomyces pullulans, likely representing environmental contamination or commensal species. Controls showed negligible fungal signals, confirming CSF sterility in non-infectious cases.
Genotyping and Validation
All 11 infected samples were identified as ITS type 1 (C. neoformans var. grubii), consistent with the dominant molecular type (VNI/VNII) in clinical settings. Sanger sequencing of cultured isolates validated the metataxonomic results, with 100% concordance in ITS1 sequences (Figure 3A–B). Additional confirmation using the CAP59 gene—a species-specific marker for C. neoformans—further supported the diagnosis (Figure 3C).
Statistical Significance
PERMANOVA revealed a striking distinction between infected and control groups (R² = 0.65869, P = 0.0014), underscoring the reliability of ITS metataxonomics in differentiating true infections from background noise (Figure 2).
Advantages of ITS Metataxonomics
- Superior Sensitivity: The method detected Cryptococcus at concentrations as low as 0.5 pg DNA (~20–30 cells), outperforming microscopy and culture, which require higher pathogen burdens.
- Rapid Turnaround: Sequencing and analysis were completed within days, contrasting with the weeks-long wait for culture results.
- Culture-Independent Genotyping: Direct genotyping from CSF bypassed the need for isolate cultivation, a significant advantage in resource-limited settings or for non-culturable strains.
- Comprehensive Pathogen Profiling: While Cryptococcus dominated, the approach also identified rare fungi, hinting at polymicrobial interactions or contamination pathways.
Challenges and Insights
- Contamination Considerations: Low-level Cryptococcus reads in controls were attributed to laboratory or reagent contamination. RPM ratios (sample vs. negative controls) and order-of-magnitude differences in read counts helped distinguish true pathogens.
- Fungal Diversity in CSF: The unexpected presence of plant pathogens like Myrothecium and Alternaria raises questions about microbial translocation across the blood-brain barrier or olfactory pathways, warranting further investigation.
Clinical and Epidemiological Implications
The study highlights ITS metataxonomics as a robust tool for diagnosing cryptococcal meningitis and elucidating pathogen diversity. ITS type 1 predominance aligns with global epidemiological patterns, where C. neoformans var. grubii (serotype A) is the most common cause of CNS infections in immunocompromised hosts. Future applications could include outbreak surveillance, antifungal resistance profiling, and monitoring treatment responses.
Limitations and Future Directions
The small sample size (n=11) and focus on a single geographic region limit generalizability. Expanding to diverse populations and incorporating shotgun metagenomics could enhance resolution for detecting mixed infections or novel variants. Additionally, correlating fungal community shifts with clinical outcomes may uncover prognostic biomarkers.
Conclusion
This study establishes ITS metataxonomics as a transformative diagnostic and genotyping tool for cryptococcal meningitis. By combining high sensitivity, rapid processing, and culture independence, the approach addresses critical gaps in current diagnostics. As sequencing costs decline and bioinformatics pipelines mature, metataxonomics is poised to become a standard in clinical mycology, improving patient outcomes through timely and precise pathogen identification.
doi.org/10.1097/CM9.0000000000000541
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