Adeliza Mae L. Realingo

Leptospirosis is a highly endemic neglected tropical disease in the Philippines, where conventional diagnostic methods such as the microscopic agglutination test (MAT) provide limited information on circulating Leptospira and are affected by serological cross-reactivity. This study evaluated the utility of shotgun metagenomic next-generation sequencing (mNGS) for pathogen detection and species-level characterization using 20 archived serum-derived total nucleic acid extracts collected from suspected leptospirosis cases. Samples were previously tested by quantitative real-time PCR (qPCR) and MAT, and sequenced using the Illumina MiSeq platform. Host-derived reads were removed using BWA and SAMtools prior to taxonomic profiling with KrakenUniq. Species-level Leptospira assignments were retained based on predefined KrakenUniq filtering criteria, including a minimum taxReads threshold and evidence of taxon-specific unique k-mers. Leptospira taxReads were detected in 58.8% (n=10/17) of qPCR-positive samples. Notably, mNGS detected pathogenic Leptospira in 60% (n=6/10) qPCR-positive but MAT-negative samples, demonstrating its potential to complement conventional serology for etiological characterization. MAT reactivity was predominantly against L. biflexa serovar Patoc (n=4/5), with one sample reacting to L. interrogans serovar Autumnalis in the MAT-positive samples. Meanwhile, mNGS identified a predominance of L. interrogans, with additional detection of L. borgpetersenii and L. kirschneri. The absence of detectable Leptospira in some qPCR-positive samples underscores the sensitivity limitations of mNGS in low-burden or archived samples. Overall, mNGS provided species-level information beyond conventional diagnostics and supports its potential application in Leptospira genomic surveillance in the Philippines. IMPACT STATEMENT: Leptospirosis remains an important public health concern in the Philippines, but genomic information on human Leptospira infections is limited. Shotgun metagenomic sequencing provided species-level information beyond routine qPCR and MAT. Leptospira interrogans predominated, alongside other pathogenic species. These findings establish an initial genomic baseline for Leptospira in Philippine clinical specimens and support future surveillance of strain and serovar diversity and transmission.

Leptospirosis remains a public health concern in the Philippines. Conventional diagnostic methods, including the microscopic agglutination test (MAT) and qPCR, are routinely used for outbreak response and surveillance. However, these methods often yield discordant results due to cross-reactivity, limited sensitivity, or lack of species-level resolution. To address these diagnostic gaps, this study optimized the Boonsilp 16S rRNA PCR assay and applied Sanger sequencing for accurate species identification of Leptospira in 92 archived DNA samples collected between 2018 and 2020. The sensitivity and specificity of the optimized assay were compared with those of MAT and qPCR. Species-level identification was confirmed via sequencing, and a phylogenetic tree was constructed. Among the 92 samples, 46 (50.0%) tested positive by qPCR, 39 (42.4%) by MAT, and 67 (72.8%) by at least one of the two methods. The optimized Boonsilp assay detected Leptospira in 23 samples (25.0%), of which 22 were also qPCR positive. Twenty-one samples were confirmed as L. interrogans, one as L. borgpetersenii, and one as an unclassified Leptospira species. One sample undetected by both MAT and qPCR tested positive using the optimized assay. Compared to the composite reference, the Boonsilp assay showed 32.8% sensitivity and 96.0% specificity. Phylogenetic analysis revealed multiple L. interrogans strains, including those closely related to reference sequences of Copenhageni, Manilae, and Canicola. While the optimized Boonsilp PCR assay demonstrates diagnostic value as an adjunct molecular tool to qPCR and MAT supporting species-level identification during outbreak surveillance, this warrants further validation in freshly isolated DNA samples.

Co-infection with multiple severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants, though rare, may have clinical and public health implications, including facilitating variant recombination. Early detection of co-infections is, therefore, crucial. In this study, we report two probable cases of co-infection identified during routine genomic surveillance. Initially suspected as cross-contamination due to the presence of private mutations and nucleotide mixtures flagged by Nextclade and bammix, the samples were re-extracted and re-sequenced after workspace decontamination, yet the anomalies persisted. To investigate further, we developed a bioinformatics pipeline (Katmon) incorporating various tools such as Freyja, with lineage abundance results that illustrated the presence of multiple variants, and VirStrain, which confirmed inconsistent lineage assignments. We also visualized the alternative allele fractions for each lineage-defining mutation and amplicon, showing evidence of two variants, Delta and Omicron, co-existing within a single amplicon. Amplicon sorting effectively separated reads corresponding to the two variants, and the resulting consensus sequences aligned with their respective lineage assignments. These findings suggest that the first sample, PH-RITM-1395, involved a Delta–Omicron co-infection, while the second sample, PH-RITM-4146, probably contains both a co-infection and a recombinant variant. To further support the second sample’s recombinant nature, we employed sc2rf, which identified Delta–Omicron breakpoints. Retrospective analysis of 1,078 samples from July 2021 to July 2022, encompassing the period of co-circulation of different variants in the Philippines, flagged four additional co-infection cases, including Delta–Omicron and Beta–Omicron, suggesting a lower bound co-infection prevalence of 0.27% and 0.19%, respectively. Furthermore, the pipeline was used to test previously identified co-infections of different variants from different countries. Our findings underscore the critical importance of real-time genomic surveillance and advanced bioinformatics pipelines in detecting SARS-CoV-2 co-infections and variant recombination.

Co-infection with multiple SARS-CoV-2 variants, though rare, may have clinical and public health implications, including facilitating variant recombination. Early detection of co-infections is therefore crucial. In this study, we report two probable cases of co-infection identified during routine genomic surveillance. Initially suspected as cross-contamination due to the presence of private mutations and nucleotide mixtures flagged by Nextclade and bammix, the samples were re-extracted and resequenced after workspace decontamination, yet the anomalies persisted. To investigate further, we developed a bioinformatics pipeline (Katmon) incorporating various tools such as Freyja, with lineage abundance results that illustrated presence of multiple variants, and VirStrain, which confirmed inconsistent lineage assignments. We also visualised the alternative allele fractions for each lineage-defining mutation and amplicon, showing evidence of two variants — Delta and Omicron — co-existing within a single amplicon. Amplicon sorting effectively separated reads corresponding to the two variants, and the resulting consensus sequences aligned with their respective lineage assignments. These findings suggest that the first sample, PH-RITM-1395, involved a Delta-Omicron co-infection, while the second sample, PH-RITM-4146, contains both a co-infection and a recombinant variant. To further support the second sample’s recombinant nature, we employed sc2rf, which identified Delta-Omicron breakpoints. Retrospective analysis of 1,078 samples from July 2021 to July 2022, encompassing the period of co-circulation of different variants in the Philippines, flagged four additional co-infection cases, including Delta-Omicron and Beta-Omicron suggesting a lower bound co-infection rate of 0.27%, and 0.19%, respectively. Our findings underscore the critical importance of real-time genomic surveillance and advanced bioinformatics pipelines in detecting SARS-CoV-2 co-infections and variant recombination.