Biology subjects

Kaderali, L.

Publications and source records attributed to Kaderali, L..

7 recordsLinked to original sources

bioRxiv2026

First community challenge for automated virus taxonomy

The rapid rate of virus discovery renders manual curation by taxonomy experts increasingly impractical, creating a need for reliable software that can reproducibly assign viral contigs to taxa at all fifteen ranks of the virus taxonomy. We led an open community challenge for the computational taxonomic classification of viruses and assembled a dataset of virus sequences combining expert-curated and metagenomic sequences. Seventeen teams contributed a total of thirty-four automated, fully reproducible classification pipelines. Most tools correctly assigned viruses belonging to established species, genera, or families, but viruses that are unclassified at those lower ranks remain challenging. This study provides datasets, open-source software, novel approaches, and recommendations to benchmark computational taxonomic classification of viruses, and support organizing the many viruses discovered in big omics data.

microbiology↗

bioRxiv2026

Quantitative modeling of SARS-CoV-2 replication reveals phase-specific bottlenecks and antiviral targets

SARS-CoV-2 replication depends on a tightly coordinated series of intracellular processes that remain incompletely quantified. Here, we integrated high-resolution time-resolved measurements of viral RNA, protein expression, and infectious virion production with mechanistic mathematical modeling to obtain a quantitative description of the viral replication cycle in human lung cells. Using transcriptomic, proteomic, and infectivity data collected over the first 24 hours of infection, we calibrated an ordinary differential equation model that captures genomic and subgenomic RNA synthesis, viral protein production, virion assembly, and virus release. The model accurately reproduced the observed replication dynamics and enabled estimation of kinetic parameters that are difficult to measure experimentally. Sensitivity analysis identified viral RNA replication and non-structural protein maturation as dominant determinants of viral replication efficiency. To assess predictive power, the model was challenged with independent antiviral perturbation experiments using remdesivir, nirmatrelvir, and montelukast. Model predictions closely matched experimentally observed treatment responses and correctly reproduced drug interaction effects during combination therapy. Furthermore, comparison of alternative mechanistic hypotheses supported NSP5 rather than NSP1 as the primary antiviral target of montelukast. Together, these results establish a predictive framework for dissecting intracellular coronavirus replication and evaluating antiviral intervention strategies.

microbiology↗

bioRxiv2026

MICA: Model-Informed Change-point Analysis

Change point detection is critical for identifying structural transitions in time series data. While most existing methods focus on changes in statistical properties of the data such as the mean or variance, many real-world systems are governed by dynamical models in which changes occur in model parameters. We introduce MICA, an algorithm that detects change points by minimizing the discrepancy between model simulations with a given dynamical model and observed data. The method integrates binary segmentation with a genetic algorithm to identify both the timing and nature of model parameter changes. MICA simultaneously estimates segment-specific and global parameters alongside change points, offering enhanced flexibility and interpretability. We demonstrate its utility on synthetic datasets and real-world scenarios, including COVID-19 epidemiological modeling, under policy interventions, and the analysis of generator cooling systems in wind turbines to monitor operational status. While illustrated using differential and difference equation models, MICA is model-agnostic and applicable to any simulatable system, making it broadly useful for applications requiring accurate tracking of structural dynamics.

systems biology↗

bioRxiv2024

Jaeger: an accurate and fast deep-learning tool to detect bacteriophage sequences

Viruses are integral to every biome on Earth, yet we still need a more comprehensive picture of their identity and global distribution. Global metagenomics sequencing efforts revealed the genomic content of tens of thousands of environmental samples, however identifying the viral sequences in these datasets remains challenging due to their vast genomic diversity. Here, we address identifying bacteriophage sequences in unlabeled sequencing data. In a recent benchmarking paper, we observed that existing deep-learning tools show a high true positive rate, but may also produce many false positives when confronted with divergent sequences. To tackle this challenge, we introduce Jaeger, a novel deep-learning method designed specifically for identifying bacteriophage genome fragments. Extensive benchmarking on the IMG/VR database and real-world metagenomes reveals Jaegers consistent high sensitivity (0.87) and precision (0.92). Applying Jaeger to over 16,000 metagenomic assemblies from the MGnify database yielded over five million putative phage contigs. On average, Jaeger is around 20 times faster than the other state-of-the-art methods. Jaeger is available at https://github.com/MGXlab/Jaeger.

bioinformatics↗

bioRxiv2022

High Resolution Kinetic Characterization and Dynamic Mathematical Modeling of the RIG-I Signaling Pathway and the Antiviral Responses

The pattern recognition receptor RIG-I is essential for the recognition of viral dsRNA and the activation of a cell-autonomous antiviral response. Upon stimulation, RIG-I triggers a signaling cascade leading to the expression of cytokines, most prominently type I and III interferons (IFNs). IFNs are secreted and signal in an auto- and paracrine manner to trigger the expression of a large variety of IFN-stimulated genes, which in concert establish an antiviral state of the cell. While the topology of this pathway has been studied quite intensively, the dynamics, particularly of the RIG-I-mediated IFN induction, is much less understood. Here, we employed electroporation-based transfection to synchronously activate the RIG-I signaling pathway, enabling us to characterize the kinetics and dynamics of cell-intrinsic innate immune signaling to virus infections. By employing an A549 IFNAR1/IFNLR deficient cell line, we could analyze the difference between the primary RIG-I signaling phase and the secondary signaling phase downstream of the IFN receptors. We further used our quantitative data to set up and calibrate a comprehensive dynamic mathematical model of the RIG-I and IFN signaling pathways. This model accurately predicts the kinetics of signaling events downstream of dsRNA recognition by RIG-I as well as the feedback and signal amplification by secreted IFN and JAK/STAT signaling. We have furthermore investigated the impact of various viral immune antagonists on the signaling dynamics experimentally, and we utilized the here described modelling approach to simulate and in silico study these critical virus-host interactions. Our work provides a comprehensive insight into the signaling events occurring early upon virus infection and opens up new avenues to study and disentangle the complexity of the host-virus interface.

immunology↗

bioRxiv2022

Mathematical modeling of plus-strand RNA virus replication to identify broad-spectrum antiviral treatment strategies

Plus-strand RNA viruses are the largest group of viruses. Many are human pathogens that inflict a socio-economic burden. Interestingly, plus-strand RNA viruses share remarkable similarities in their replication. A hallmark of plus-strand RNA viruses is the remodeling of intracellular membranes to establish replication organelles (so-called "replication factories"), which provide a protected environment for the replicase complex, consisting of the viral genome and proteins necessary for viral RNA synthesis. In the current study, we investigate pan-viral similarities and virus-specific differences in the life cycle of this highly relevant group of viruses. We first measured the kinetics of viral RNA, viral protein, and infectious virus particle production of hepatitis C virus (HCV), dengue virus (DENV), and coxsackievirus B3 (CVB3) in the immuno-compromised Huh7 cell line and thus without perturbations by an intrinsic immune response. Based on these measurements, we developed a detailed mathematical model of the replication of HCV, DENV, and CVB3 and show that only small virus-specific changes in the model were necessary to describe the in vitro dynamics of the different viruses. Our model correctly predicted virus-specific mechanisms such as host cell translation shut off and different kinetics of replication organelles. Further, our model suggests that the ability to suppress or shut down host cell mRNA translation may be a key factor for in vitro replication efficiency which may determine acute self-limited or chronic infection. We further analyzed potential broad-spectrum antiviral treatment options in silico and found that targeting viral RNA translation, especially polyprotein cleavage, and viral RNA synthesis may be the most promising drug targets for all plus-strand RNA viruses. Moreover, we found that targeting only the formation of replicase complexes did not stop the viral replication in vitro early in infection, while inhibiting intracellular trafficking processes may even lead to amplified viral growth. Author summaryPlus-strand RNA viruses comprise a large group of related and medically relevant viruses. The current global pandemic of COVID-19 caused by the SARS-coronavirus-2 as well as the constant spread of diseases such as dengue and chikungunya fever show the necessity of a comprehensive and precise analysis of plus-strand RNA virus infections. Plus-strand RNA viruses share similarities in their life cycle. To understand their within-host replication strategies, we developed a mathematical model that studies pan-viral similarities and virus-specific differences of three plus-strand RNA viruses, namely hepatitis C, dengue, and coxsackievirus. By fitting our model to in vitro data, we found that only small virus-specific variations in the model were required to describe the dynamics of all three viruses. Furthermore, our model predicted that ribosomes involved in viral RNA translation seem to be a key player in plus-strand RNA replication efficiency, which may determine acute or chronic infection outcome. Furthermore, our in-silico drug treatment analysis suggests that targeting viral proteases involved in polyprotein cleavage, in combination with viral RNA replication, may represent promising drug targets with broad-spectrum antiviral activity.

microbiology↗

bioRxiv2021

Convergent use of phosphatidic acid for Hepatitis C virus and SARS-CoV-2 replication organelle formation

Double membrane vesicles (DMVs) are used as replication organelles by phylogenetically and biologically distant pathogenic RNA viruses such as hepatitis C virus (HCV) and severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Viral DMVs are morphologically analogous to DMVs formed during autophagy, and although the proteins required for DMV formation are extensively studied, the lipids driving their biogenesis are largely unknown. Here we show that production of the lipid phosphatidic acid (PA) by acylglycerolphosphate acyltransferase (AGPAT) 1 and 2 in the ER is important for DMV biogenesis in viral replication and autophagy. Using DMVs in HCV-replicating cells as model, we found that AGPATs are recruited to and critically contribute to HCV replication and DMV formation. AGPAT1/2 double knockout also impaired SARS-CoV-2 replication and the formation of autophagosome-like structures. By using correlative light and electron microscopy, we observed the relocalization of AGPAT proteins to HCV and SARS-CoV-2 induced DMVs. In addition, an intracellular PA sensor accumulated at viral DMV formation sites, consistent with elevated levels of PA in fractions of purified DMVs analyzed by lipidomics. Apart from AGPATs, PA is generated by alternative pathways via phosphotidylcholine (PC) and diacylglycerol (DAG). Pharmacological inhibition of these synthesis pathways also impaired HCV and SARS-CoV-2 replication as well as formation of autophagosome-like DMVs. These data identify PA as an important lipid used for replication organelle formation by HCV and SARS-CoV-2, two phylogenetically disparate viruses causing very different diseases, i.e. chronic liver disease and COVID-19, respectively. In addition, our data argue that host-targeting therapy aiming at PA synthesis pathways might be suitable to attenuate replication of these viruses. One Sentence SummaryPhosphatidic acid is important for the formation of double membrane vesicles, serving as replication organelles of hepatitis C virus and SARS-CoV-2, and offering a possible host-targeting strategy to treat SARS-CoV-2 infection.

microbiology↗