Biology subjects

Yakimovich, A.

Publications and source records attributed to Yakimovich, A..

16 recordsLinked to original sources

bioRxiv2026

Agentic-AI-ready genome-wide poxvirus-host interaction screen refined by a protein language model

Recent mpox outbreaks highlight the necessity to understand the interactions between poxviruses and the human host. These can be discovered systematically through screening for host genes involved in infection at the single-cell level using RNA interference. However, off-target effects and assay noise obscure true gene-phenotype relationships, hampering the discovery of therapeutically relevant targets. Here, we show that integrating protein-protein interaction information derived from a protein language model boosts the discovery of vaccinia virus-host interactions. We propose ICARus - a positive-unlabelled read-out refinement framework to achieve this. Our approach enhances the identification of human genes with potential antiviral function. We provide the raw and refined read-outs of a genome-wide screen for vaccinia virus host factors as an agentic-AI-enabled community resource. Our findings provide a generalisable strategy for robust hit prioritisation in functional screens, accelerating discovery across complex biological systems.

systems biology↗

bioRxiv2026

DeepDiffusion: a Physics-Informed Neural Network for Heterogeneous Facilitated 1D Diffusion

Single-molecule fluorescence microscopy combined with optical tweezers has enabled the direct observation of diffusing proteins on tethered DNA. These and complementary techniques reveal facilitated one-dimensional diffusion as a common functional mechanism for numerous DNA-binding proteins, with a wide range of heterogeneous diffusive behaviours arising from different DNA-binding modes. However, detailed investigations have been limited by the lack of methods to detect such heterogeneous diffusion. We have developed DeepDiffusion, a physics-informed neural network model for estimating the instantaneous diffusion at each point along a single-molecule trajectory. We show, using synthetic trajectories, that DeepDiffusion can accurately detect subtle changes in diffusion even when challenged with large underlying errors. DeepDiffusion can recapitulate previously characterised heterogeneity in experimental data and reveal mechanistic details of facilitated diffusion that are inaccessible to current methods. We expect DeepDiffusion to become a powerful tool for single-molecule researchers, allowing them to investigate the diffusion of their proteins of interest in unprecedented detail.

biophysics↗

bioRxiv2026

Granularity screening identifies candidate genes involved in vaccinia virus induced LC3 lipidation

Autophagy is a catabolic process used for the degradation of organelles and proteins. Macroautophagy involves the formation of autophagosomes and subsequent fusion with lysosomes to mediate cargo degradation. It also functions as a cellular defence mechanism, known as xenophagy, during infection. Previous studies show that different viruses manipulate the autophagy pathway of the host cell to assure successful replication and/or virion assembly. Vaccinia virus (VACV), the prototypic poxvirus, replicates exclusively in the cytoplasm of host cells. It is known that VACV infection causes LC3 lipidation and prevents autophagosome formation, yet the double membrane vesicles formed during autophagy do not serve as the source of the mature VACV membrane. To date the viral protein(s) causing increased LC3 lipidation have not been identified. Here we developed an image-based screening approach based on LC3 granularity to identify candidate VACV genes affecting its lipidation. We identify several candidate viral membrane proteins as effectors of LC3 lipidation, suggesting that the interplay between VACV and autophagy is more directed than previously thought.

cell biology↗

bioRxiv2024

A Benchmark for Virus Infection Reporter Virtual Staining in Fluorescence and Brightfield Microscopy

Detecting virus-infected cells in light microscopy requires a reporter signal commonly achieved by immunohistochemistry or genetic engineering. While classification-based machine learning approaches to the detection of virus-infected cells have been proposed, their results lack the nuance of a continuous signal. Such a signal can be achieved by virtual staining. Yet, while this technique has been rapidly growing in importance, the virtual staining of virus-infected cells remains largely uncharted. In this work, we propose a benchmark and datasets to address this. We collate microscopy datasets, containing a panel of viruses of diverse biology and reporters obtained with a variety of magnifications and imaging modalities. Next, we explore the virus infection reporter virtual staining (VIRVS) task employing U-Net and pix2pix architectures as prototypical regressive and generative models. Together our work provides a comprehensive benchmark for VIRVS, as well as defines a new challenge at the interface of Data Science and Virology.

bioinformatics↗

bioRxiv2024

PyPlaque: an Open-source Python Package for Phenotypic Analysis of Virus Plaque Assays

Virological plaque assays are the primary method for quantifying infectious particles in a suspension, achieved by incubating a serial dilution of the virus with a monolayer of indicator cells. Existing software tools for quantification of plaque assay images lack modularity, show measurements disagreements or are closed-source - a common hurdle in BioImage analysis. We introduce PyPlaque, an open-source Python package focusing on flexibility and modularity rather than a bulky graphic user interface. Unlike previous methods, an abstracted architecture using object-oriented programming allows accommodation of various experimental containers and specimen carriers as data structures while focusing on phenotype-specific information. Aligned with the logical flow of experimental design and desired quantifications, it delivers insights at multiple granularity levels, facilitating detailed analysis. We demonstrate how this approach allows to focus on alleviating the disagreement in measurements. Furthermore, similar design is generalisable to diverse datasets in various biological contexts that fit our structural paradigm.

bioinformatics↗

bioRxiv2024

Denoising, Deblurring, and optical Deconvolution for cryo-ET and light microscopy with a physics-informed deep neural network DeBCR

Computational image-quality enhancement for microscopy (deblurring, denoising, and optical deconvolution) provides researchers with detailed information on samples. Recent general-purpose deep learning solutions advanced in this task. Yet, without consideration of the underlying physics, they may yield unrealistic and non-existent details and distortions during image restoration, requiring domain expertise to discern true features from artifacts. Furthermore, the large expressive capacity of general-purpose deep learning models requires more resources to train and use in applications. We introduce DeBCR, a physics-informed deep learning model based on wavelet theory to enhance microscopy images. DeBCR is a light model with a fast runtime and without hallucinations. We evaluated the image restoration performance of DeBCR and 12 current state-of-the-art models over 6 datasets spanning crucial modalities in advanced light microscopy and cryo-electron tomography. Leveraging optic models, DeBCR demonstrates superior performance in denoising, optical deconvolution, and deblurring tasks across both LM and cryo-ET modalities.

bioinformatics↗

bioRxiv2021

A tandem segmentation-classification approach for the localization of morphological predictors of C. elegans lifespan and motility

C. elegans is an established model organism for studying genetic and drug effects on ageing, many of which are conserved in humans. It is also an important model for basic research, and C. elegans pathologies is a new emerging field. Here we develop a proof-of-principal convolutional neural network-based platform to segment C. elegans and extract features that might be useful for lifespan prediction. We use a dataset of 734 worms tracked throughout their lifespan and classify worms into long-lived and short-lived. We designed WormNet - a convolutional neural network (CNN) to predict the worm lifespan class based on young adult images (day 1 - day 3 old adults) and showed that WormNet, as well as, InceptionV3 CNN can successfully classify lifespan. Based on U-Net architecture we develop HydraNet CNNs which allow segmenting worms accurately into anterior, mid-body and posterior parts. We combine HydraNet segmentation, WormNet prediction and the class activation map approach to determine the segments most important for lifespan classification. Such a tandem segmentation-classification approach shows posterior part of the worm might be more important for classifying long-lived worms. Our approach can be useful for the acceleration of anti-ageing drug discovery and for studying C. elegans pathologies.

bioinformatics↗

bioRxiv2021

Vaccinia virus subverts xenophagy through phosphorylation and nuclear targeting of p62

Autophagy is an essential degradation program required to maintain cell homeostasis. Amongst its many functions is the engulfment and destruction of cytosolic pathogens, termed Xenophagy. Not surprisingly, many pathogens have adapted various strategies to circumvent or co-opt autophagic degradation during infection. For poxviruses, it is known that infection activates autophagy, which however is not required for successful replication. Despite the fact that these complex viruses replicate exclusively in the cytoplasm, autophagy-mediated control of poxvirus infection has not been explored. Using the prototypic poxvirus, vaccinia virus (VACV), we show that over-expression of the xenophagy receptors p62, NDP52 and Tax1Bp1 restricts poxvirus infection. While NDP52 and Tax1Bp1 were degraded, p62 was found to initially target cytoplasmic virions before being shunted to the nucleus. Nuclear translocation of p62 during infection was dependent upon p62 NLS2, and VACV kinase mediated phosphorylation of associated p62 residues T269/S272. These results indicate that VACV actively targets the xenophagy receptor p62 during the early stages of infection to avoid destruction, and further suggest that poxviruses exhibit a unique multi-layered control of autophagy in order to facilitate cytoplasmic replication.

microbiology↗

bioRxiv2020

Vaccinia virus hijacks ESCRT-mediated multivesicular body formation for virus egress

Unlike most enveloped viruses, poxvirus egress is a complex process whereby cytoplasmic single membrane-bound virions are wrapped in a cell-derived double membrane. These triple membrane-bound particles, termed intracellular enveloped virions (IEVs), are then released from infected cells by fusion. While the wrapping double membrane is thought to be derived from virus-modified trans-Golgi or early endosomal cisternae, the cellular factors that regulate virus wrapping remain largely undefined. To identify novel cell factors required for this process the prototypic poxvirus, vaccinia virus (VACV), was subjected to a high-throughput RNAi screen directed against cellular membrane trafficking proteins. Focusing on the endosomal sorting complexes required for transport (ESCRT), we demonstrate that ESCRT-III and VPS4 are required for packaging of virus into multivesicular bodies (MVBs). EM-based characterization of these MVB-IEVs showed that they account for half of IEV production indicating that MVBs serve as a second major source of VACV wrapping membrane. These data support a model whereby, in addition to cisternae-based wrapping, VACV hijacks ESCRT-mediated MVB formation to facilitate virus egress and spread.

cell biology↗

bioRxiv2020

The FDA-approved drug Nelfinavir inhibits lytic cell-free transmission of human adenoviruses

Adenoviruses (AdVs) are prevalent and give rise to chronic and recurrent disease. The human AdV (HAdV) species B and C, such as HAdV-C2, C5 and B14, cause respiratory disease, and constitute a health threat for immuno-compromised individuals. HAdV-Cs are well known for lysing cells, owing to the E3 CR1-{beta}-encoded adenovirus death protein (ADP). We previously reported a high-throughput image-based screening framework and identified an inhibitor of HAdV-C2 multi-round infection, Nelfinavir Mesylate. Nelfinavir is the active ingredient of Viracept, an FDA-approved inhibitor of the human immuno-deficiency virus (HIV) aspartyl protease, and used to treat acquired immunodeficiency syndrome (AIDS). It is not effective against single round HAdV infections. Here, we show that Nelfinavir inhibits the lytic cell-free transmission of HAdV, indicated by the suppression of comet-shaped infection foci in cell culture. Comet-shaped foci occur upon convection-based transmission of cell-free viral particles from an infected cell to neighbouring uninfected cells. HAdV lacking ADP was insensitive to Nelfinavir, but gave rise to comet-shaped foci indicating that ADP enhances but is not required for cell lysis. This was supported by the notion that HAdV-B14 and B14p1 lacking ADP were highly sensitive to Nelfinavir, although HAdV-A31, B3, B7, B11, B16, B21, D8, D30 or D37 were less sensitive. Conspicuously, Nelfinavir uncovered slow-growing round-shaped HAdV-C2 foci, independent of neutralizing antibodies in the medium, indicative of non-lytic cell-to-cell transmission. Our study demonstrates the repurposing potential of Nelfinavir with post-exposure efficacy against different HAdVs, and describes an alternative non-lytic cell-to-cell transmission mode of HAdV. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=181 SRC="FIGDIR/small/098061v1_ufig1.gif" ALT="Figure 1"> View larger version (85K): org.highwire.dtl.DTLVardef@8aa32corg.highwire.dtl.DTLVardef@1dea943org.highwire.dtl.DTLVardef@13aa2fborg.highwire.dtl.DTLVardef@a23352_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1.C_FLOATNO C_FIG

microbiology↗

bioRxiv2020

A full cycle anti-viral drug screen identifies a clinical compound against adenovirus infection

Human adenoviruses (HAdVs) are fatal to immuno-suppressed people, but no effective anti-HAdV therapy is available. Here, we present a novel image-based high-throughput screening (HTS) platform, which scores the full viral replication cycle from virus entry to dissemination of progeny. We analysed 1,280 small molecular weight compounds of the Prestwick Chemical Library (PCL) for interference with HAdV-C2 infection in a quadruplicate, blinded format, and included robust image analyses, and hit filtering. We present the entire set of the screening data including all the images, image analyses and data processing pipelines. The data are made available at the Image Data Repository (IDR) 1, accession number idr0081. Our screen identified Nelfinavir mesylate as an inhibitor of HAdV-C2 multi-round plaque formation, but not single round infection. Nelfinavir has been FDA-approved for anti-retroviral therapy in humans. Our results underscore the power of image-based full cycle infection assays in identifying viral inhibitors with clinical potential.

microbiology↗

bioRxiv2019

In vivo control of Toxoplasma gondii by zebrafish macrophages

Toxoplasma gondii is an obligate intracellular parasite capable of invading any nucleated cell. Three main clonal lineages (type I, II, III) exist and murine models have driven the understanding of general and strain-specific immune mechanisms underlying Toxoplasma infection. However, murine models are limited for studying parasite-leukocyte interactions in vivo, and discrepancies exist between cellular immune responses observed in mouse versus human cells. Here, we develop a zebrafish infection model to study the innate immune response to Toxoplasma in vivo. By infecting the zebrafish hindbrain ventricle, and using high-resolution microscopy techniques coupled with computer vision driven automated image analysis, we reveal that Toxoplasma invades and replicates inside a parasitophorous vacuole to which type I and III parasites recruit host cell mitochondria. We show that type II and III strains maintain a higher infectious burden than type I strains. To understand how parasites are being cleared in vivo, we analyzed Toxoplasma-macrophage interactions using time-lapse and correlative light and electron microscopy. Strikingly, macrophages are recruited to the infection site and play a key role in Toxoplasma control. These results highlight in vivo control of Toxoplasma by macrophages, and illuminate the possibility to exploit zebrafish for discoveries within the field of parasite immunity.

microbiology↗

bioRxiv2019

Mimicry embedding for advanced neuralnetwork training of 3D biomedical micrographs

The use of deep neural networks (DNNs) for analysis of complex biomedical images shows great promise but is hampered by a lack of large verified datasets for rapid network evolution. Here we present a novel \"mimicry embedding\" strategy for rapid application of neural network architecture-based analysis of biomedical imaging datasets. Embedding of a novel biological dataset, such that it mimics a verified dataset, enables efficient deep learning and seamless architecture switching. We apply this strategy across various microbiological phenotypes; from super-resolved viruses to in vivo parasitic infections. We demonstrate that mimicry embedding enables efficient and accurate analysis of three-dimensional microscopy datasets. The results suggest that transfer learning from pre-trained network data may be a powerful general strategy for analysis of heterogeneous biomedical imaging datasets.

bioinformatics↗

bioRxiv2019

Deep learning of virus infections reveals mechanics of lytic cells

Imaging across scales gives insight into disease mechanisms in organisms, tissues and cells. Yet, rare infection phenotypes, such as virus-induced cell lysis have remained difficult to study. Here, we developed fixed and live cell imaging modalities and a deep learning approach to identify herpesvirus and adenovirus infections in the absence of virus-specific stainings. Procedures comprises staining of infected nuclei with DNA-dyes, fluorescence microscopy, and validation by virus-specific live-cell imaging. Deep learning of multi-round infection phenotypes identified hallmarks of adenovirus-infected cell nuclei. At an accuracy of >95%, the procedure predicts two distinct infection outcomes 20 hours prior to lysis, nonlytic (nonspreading) and lytic (spreading) infections. Phenotypic prediction and live-cell imaging revealed a faster enrichment of GFP-tagged virion proteins in lytic compared to nonlytic infected nuclei, and distinct mechanics of lytic and nonlytic nuclei upon laser-induced ruptures. The results unleash the power of deep learning based prediction in unraveling rare infection phenotypes.

cell biology↗

bioRxiv2018

An Artificial Intelligence Workflow for Defining Host-Pathogen Interactions

For image-based infection biology, accurate unbiased quantification of host-pathogen interactions is essential, yet often performed manually or using limited enumeration employing simple image analysis algorithms based on image segmentation. Host protein recruitment to pathogens is often refractory to accurate automated assessment due to its heterogeneous nature. An intuitive intelligent image analysis program to assess host protein recruitment within general cellular pathogen defense is lacking. We present HRMAn (Host Response to Microbe Analysis), an open-source image analysis platform based on machine learning algorithms and deep learning. We show that HRMAn has the capability to learn phenotypes from the data, without relying on researcher-based assumptions. Using Toxoplasma gondii and Salmonella typhimurium we demonstrate HRMAns capacity to recognize, classify and quantify pathogen killing, replication and cellular defense responses.

microbiology↗

bioRxiv2018

Label-free digital holographic microscopy reveals virus-induced cytopathic effects in live cells

Cytopathic effects (CPEs) are a hallmark of infections. CPEs can be observed by phase contrast or fluorescence light microscopy, albeit at the cost of phototoxicity. We report that digital holo-tomographic microscopy (DHTM) reveals distinct patterns of virus infections in live cells with minimal perturbation. DHTM is label-free, and records the phase shift of low energy light passing through the specimen on a transparent surface. DHTM infers a 3-dimensional (3D) tomogram based on the refractive index (RI). By measuring RI and computing the refractive index gradient (RIG) values DHTM unveils on optical heterogeneity in cells upon virus infection. We find that vaccinia virus (VACV), herpes simplex virus (HSV) and rhinovirus (RV) infections progressively and distinctly increased RIG. VACV, but not HSV and RV infection induced oscillations of cell volume, while all three viruses altered cytoplasmic membrane dynamics, and induced apoptotic features akin to the chemical compound staurosporin, but with virus-specific signatures. In sum, we introduce DHTM for quantitative label-free microscopy in infection research, and uncover virus-type specific changes and CPE in living cells at minimal interference.

cell biology↗