Biology subjects

Joshi, D. J.

Publications and source records attributed to Joshi, D. J..

3 recordsLinked to original sources

bioRxiv2026

Immunological imprinting shapes the cross-reactive antibody responses to the KP.2 and LP.8.1 vaccine doses

The emergence of the SARS-CoV-2 Omicron BA.2.86 subvariant, a lineage derived from the BA.2 strain, led to the 2024-2025 COVID-19 vaccine update to include KP.2 or related JN.1-lineage spike antigens. We evaluated the magnitude, breadth, and durability of humoral immune responses following a single KP.2 vaccine dose in a longitudinal cohort of 21 individuals up to six months. KP.2 vaccination increased spike-specific binding and neutralizing antibodies against the ancestral WA1 strain, alongside the BA.5, XBB.1.5, and KP.2 variants. Power law modeling estimated half-lives for WA1- and KP.2-specific IgG responses at 770 and 248 days, respectively. Additionally, the KP.2 dose increased IgG1 and IgG4 subclasses more than IgG2 and IgG3 responses to both spike proteins. Serum depletion experiments using WA1 or KP.2 proteins demonstrated most vaccine-elicited antibodies were cross-reactive. Consequently, KP.2 vaccine-induced antibodies retained broad neutralizing activity against recently circulating Omicron subvariants (BA.2.86, KP.3.1.1, XEC, LP.8.1, LF.7, XFG.3.12, PQ.1, BA.3.2.1, and RE.2). Using a live virus neutralization assay, XFG.3.12 showed the greatest reduction in neutralizing titers relative to KP.2 (4.2-fold). In a small subset, an LP.8.1 vaccine dose increased neutralizing activity against the matched variant while maintaining WA1 and KP.2 cross-reactivity, but only modestly increased antibodies to divergent variants BA.3.2.1 and RE.2. Ultimately, these data indicate the KP.2 mRNA vaccine generates durable, cross-reactive responses against current Omicron subvariants. However, ongoing spike evolution impacts neutralization of emerging lineages, highlighting the need for continued viral monitoring and timely vaccine updates. IMPORTANCESARS-CoV-2 continues to evolve, raising ongoing concerns about how well updated vaccines protect against emerging variants. This study evaluates antibody responses after KP.2 spike mRNA vaccine dose and shows that a single dose induces durable and broadly cross-reactive immunity against both earlier strains and recently circulating Omicron subvariants. Despite this breadth, reduced neutralizing activity against certain emerging variants indicates that ongoing antigenic changes can impact vaccine induced antibody effectiveness. These findings provide insight into how current vaccines perform over time and highlight the need to track viral evolution and update vaccine antigens to maintain broad protection against severe disease, hospitalization, and death.

immunology↗

bioRxiv2023

HQAlign: Aligning nanopore reads for SV detection using current-level modeling

MotivationDetection of structural variants (SV) from the alignment of sample DNA reads to the reference genome is an important problem in understanding human diseases. Long reads that can span repeat regions, along with an accurate alignment of these long reads play an important role in identifying novel SVs. Long read sequencers such as nanopore sequencing can address this problem by providing very long reads but with high error rates, making accurate alignment challenging. Many errors induced by nanopore sequencing have a bias because of the physics of the sequencing process and proper utilization of these error characteristics can play an important role in designing a robust aligner for SV detection problems. In this paper, we design and evaluate HQAlign, an aligner for SV detection using nanopore sequenced reads. The key ideas of HQAlign include (i) using basecalled nanopore reads along with the nanopore physics to improve alignments for SVs (ii) incorporating SV specific changes to the alignment pipeline (iii) adapting these into existing state-of-the-art long read aligner pipeline, minimap2 (v2.24), for efficient alignments. ResultsWe show that HQAlign captures about 4 - 6% complementary SVs across different datasets which are missed by minimap2 alignments while having a standalone performance at par with minimap2 for real nanopore reads data. For the common SV calls between HQAlign and minimap2, HQAlign improves the start and the end breakpoint accuracy for about 10 - 50% of SVs across different datasets. Moreover, HQAlign improves the alignment rate to 89.35% from minimap2 85.64% for nanopore reads alignment to recent telomere-to-telomere CHM13 assembly, and it improves to 86.65% from 83.48% for nanopore reads alignment to GRCh37 human genome. Availabilityhttps://github.com/joshidhaivat/HQAlign.git

bioinformatics↗

bioRxiv2019

QAlign: Aligning nanopore reads accurately using current-level modeling

MotivationEfficient and accurate alignment of DNA / RNA sequence reads to each other or to a reference genome / transcriptome is an important problem in genomic analysis. Nanopore sequencing has emerged as a major sequencing technology and many long-read aligners have been designed for aligning nanopore reads. However, the high error rate makes accurate and efficient alignment difficult. Utilizing the noise and error characteristics inherent in the sequencing process properly can play a vital role in constructing a robust aligner. In this paper, we design QAlign, a pre-processor that can be used with any long-read aligner for aligning long reads to a genome / transcriptome or to other long reads. The key idea in QAlign is to convert the nucleotide reads into discretized current levels that capture the error modes of the nanopore sequencer before running it through a sequence aligner. ResultsWe show that QAlign is able to improve alignment rates from around 80% up to 90% with nanopore reads when aligning to the genome. We also show that QAlign improves the average overlap quality by 9.2%, 2.5% and 10.8% in three real datasets for read-to-read alignment. Read-to-transcriptome alignment rates are improved from 51.6% to 75.4% and 82.6% to 90% in two real datasets. Availabilityhttps://github.com/joshidhaivat/QAlign.git

bioinformatics↗