Biology subjects

Tekinsoy, M.

Publications and source records attributed to Tekinsoy, M..

4 recordsLinked to original sources

bioRxiv2026

Long-term Humoral Immune Dynamics After SARS-CoV-2 Vaccination and Infection in Healthcare Workers: The VaCoMRI Study

ObjectivesThe VaCoMRI study tracked a healthcare worker cohort from 2020 through June 2024 to characterize the long-term sustainability of vaccine- and infection-induced SARS-CoV-2 immunity. MethodsFollowing an initial anti-nucleocapsid (N) IgG screening of 4,554 employees in early 2020, participants were monitored regularly from the BNT162b2 rollout (December 2020). Serum collected at predefined intervals after vaccination and breakthrough infection (BTI) was analyzed for anti-N, quantitative anti-spike (anti-S) IgG, and surrogate viral neutralizing antibody (sVNT) titers. ResultsOver a median follow-up of 1,180 days, 142 healthcare workers contributed longitudinal samples; 66.2% experienced one BTI and 14.1% a second. In previously seronegative individuals, anti-N titers decreased 2.3-fold between 3 and 6 months post-first BTI, with seropositivity dropping from 82% to 40.4% (median time to seronegativity: 179 days). Conversely, prior anti-N seropositivity was associated with 4.5-fold higher anti-N IgG levels during follow-up, and a second BTI led to 3-fold higher titers at 3 months. Before BTI, the third vaccine dose significantly enhanced antibody persistence versus the second dose; at 9 months, titers remained 7.2-fold higher for sVNT and 3.9-fold higher for anti-S IgG. Initial seropositivity also predicted 2.9-fold higher sVNT levels during follow-up. ConclusionsRepeated vaccination and infection synergistically induced robust, durable spike-directed hybrid immunity. Conversely, rapid anti-N IgG waning severely limits its reliability for the retrospective serosurveillance of SARS-CoV-2 exposure in occupational settings.

microbiology↗

bioRxiv2026

Viability of engineered AAVs via protein language models

Capsid engineering has greatly improved the performance of recombinant AAV vectors used for gene therapy. One commonly used strategy is the insertion of a short, 7-mer, peptide into surface-exposed loops to modify receptor interactions and enhance cell entry. While effective in receptor retargeting and improved transduction, these insertions might destabilize the capsid protein, hinder assembly, and thus limit production. While previous attempts have used deep mutational scanning and AI to predict which insertions are viable, there is lack in understanding the structural consequences of these peptide insertions at the amino-acid level. Here we combined experiments, deep sequencing and large protein language models to gain insight on the impact of 7-mer insertions on the VR-VIII region. We first characterize the biochemical properties of viable insertions, thus identifying which residues are well tolerated, and which should instead be avoided. We then focus on the nearby context of those insertions, by studying the effect of the linkers, either for highly diverse libraries or for individual variants known for their efficiency. Next, we study the broader context, by extending our analysis to the whole capsid sequence, and identifying regions that can tolerate insertions without long-ranged structural deformations that could affect capsid functionality. We conclude with a cross-serotype comparison and a viability analysis of tens of previously engineered variants. Our work showcases how AI can uncover structure-function rules governing the success of engineered AAV capsids.

bioinformatics↗

bioRxiv2024

Optimal sequencing depth for measuring the concentrations of molecular barcodes

In combinatorial genetic engineering experiments, next-generation sequencing (NGS) allows for measuring the concentrations of barcoded or mutated genes within highly diverse libraries. When designing and interpreting these experiments, sequencing depths are thus important parameters to take into account. Service providers follow established guidelines to determine NGS depth depending on the type of experiment, such as RNA sequencing or whole genome sequencing. However, guidelines specifically tailored for measuring barcode concentrations have not yet reached an accepted consensus. To address this issue, we combine the analysis of NGS datasets from barcoded libraries with a mathematical model taking into account the PCR amplification in library preparation. We demonstrate on several datasets that noise in the NGS counts increases with the sequencing depth; consequently, beyond certain limits, deeper sequencing does not improve the precision of measuring barcode concentrations. We propose, as rule of thumb, that the optimal sequencing depth should be about ten times the initial amount of barcoded DNA molecules before any amplification step.

bioinformatics↗

bioRxiv2023

In-silico monitoring of directed evolution convergence to unveil best performing variants with credibility score

Directed evolution (DE) is a versatile protein-engineering strategy, successfully applied to a range of proteins, including enzymes, antibodies, and viral vectors. However, DE can be time-consuming and costly, as it typically requires many rounds of selection to identify desired mutants. Next-generation sequencing allows monitoring of millions of variants during DE and can be leveraged to reduce the number of selection rounds. Unfortunately the noisy nature of the sequencing data impedes the estimation of the performance of individual variants. Here, we propose ACIDES that combines statistical inference and in-silico simulations to improve performance estimation in DE by providing accurate statistical scores. We tested ACIDES first on a novel random-peptide-insertion experiment and then on several public datasets from DE of viral vectors and phage-display. ACIDES allows experimentalists to reliably estimate variant performance on the fly and can aid protein engineering pipelines in a range of applications, including gene therapy.

bioinformatics↗