Biology subjects

Liu, D.

Publications and source records attributed to Liu, D..

At least 163 records · Page 9Linked to original sources

bioRxiv2024

DeepAssembly2: A Web Server for Protein Complex Structure Assembly Based on Domain-Domain Interactions

Proteins often perform biological functions by forming complexes, thereby accurately predicting the structure of protein complexes is crucial to understanding and mastering their functions, as well as facilitating drug discovery. Protein monomeric structure prediction has made a breakthrough in recent years, but the accurate prediction of complex structure remains a challenge. In this work, we present DeepAssembly2, a web server for automatically assembling protein complex structure based on domain-domain interactions. First, the features are constructed according to the input complex sequence and monomeric structures, then these features are used to predict the inter-chain residue distance through a deep learning model, and finally, the complex structure is assembled under the guidance of inter-chain residue distances. Compared with the previously developed version, DeepAssembly2 is trained on a newly constructed inter-chain domain-domain interaction dataset. Meanwhile, several important features have been added, such as Interface Residue Propensity and Ultrafast Shape Recognition. In addition, we introduced the inter-chain residue distance from the AlphaFold-Multimer model to further improve the accuracy. Finally, we also integrate our recently developed model quality assessment method to select the output models. The performance of DeepAssembly2 is significantly improved compared with the previous version, providing a solution for large protein complex structure modeling through divide-and-conquer assembly strategy, and is expected to provide new insights and an effective tool for drug development, vaccine design, etc. The web server of DeepAssembly2 is freely available at http://zhanglab-bioinf.com/DeepAssembly/.

bioinformatics↗

bioRxiv2024

Distinct Binding Modes of Inter-Spike Cross-Linking Suggest a Supplementary Mechanism for SARS-CoV-2 Antibody Neutralization

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its Omicron subvariants drastically amplifies transmissibility, infectivity, and immune escape, mainly due to their resistance to most neutralizing antibodies. Thus, exploring the mechanisms underlying antibody evasion is crucial. Although the full-length native form of antibody, immunoglobulin G (IgG), offers valuable insights into the neutralization, structural investigations primarily focus on the fragment of antigen-binding (Fab). Here, we employ single-particle cryo-electron microscopy (cryo-EM) to characterize a W328-6H2 antibody, in its native IgG form complexed with severe acute respiratory syndrome (SARS), severe acute respiratory syndrome coronavirus 2 wild-type (WT) and Omicron variant BA.1 spike protein (S). Three high-resolution structures reveal that the full-length IgG forms a centered head-to-head dimer of trimer when bound fully stoichiometrically with both SARS and WT S, while adopting a distinct offset configuration with Omicron BA.1 S. Combined with functional assays, our results suggest that, beyond the binding affinity between the RBD epitope and Fab, the higher-order architectures of S trimer and full-length IgG play an additional role in neutralization, elucidate a potential molecular basis of Omicron immune escape, and expand our understanding on antibody evasion mechanisms.

immunology↗

bioRxiv2024

PLM-interact: extending protein language models to predict protein-protein interactions

Computational prediction of protein structure from amino acid sequences alone has been achieved with unprecedented accuracy, yet the prediction of protein-protein interactions (PPIs) remains an outstanding challenge. Here we assess the ability of protein language models (PLMs), routinely applied to protein folding, to be retrained for PPI prediction. Existing PPI prediction models that exploit PLMs use a pre-trained PLM feature set, ignoring that the proteins are physically interacting. Our novel method, PLM-interact, goes beyond a single protein, jointly encoding protein pairs to learn their relationships, analogous to the next-sentence prediction task from natural language processing. This approach provides a significant improvement in performance: Trained on human-human PPIs, PLM-interact predicts mouse, fly, worm, E. coli and yeast PPIs, with 16-28% improvements in AUPR compared with state-of-the-art PPI models. Additionally, it can detect changes that disrupt or cause PPIs and be applied to virus-host PPI prediction. Our work demonstrates that large language models can be extended to learn the intricate relationships among biomolecules from their sequences alone.

bioinformatics↗

bioRxiv2024

The open-closed mod-minimizer algorithm

Sampling algorithms that deterministically select a subset of k-mers are an important building block in bioinformatics applications. For example, they are used to index large textual collections, like DNA, and to compare sequences quickly. In such applications, a sampling algorithm is required to select one k-mer out of every window of w consecutive k-mers. The folklore and most used scheme is the random minimizer that selects the smallest k-mer in the window according to some random order. This scheme is remarkably simple and versatile, and has a density (expected fraction of selected k-mers) of 2/(w + 1). In practice, lower density leads to faster methods and smaller indexes, and it turns out that the random minimizer is not the best one can do. Indeed, some schemes are known to approach optimal density 1/w when k [->] {infty}, like the recently introduced mod-minimizer (Groot Koerkamp and Pibiri, WABI 2024). In this work, we study methods that achieve low density when k [≤] w. In this small-k regime, a practical method with provably better density than the random minimizer is the miniception (Zheng et al., Bioinformatics 2021). This method can be elegantly described as sampling the smallest closed sycnmer (Edgar, PeerJ 2021) in the window according to some random order. We show that extending the miniception to prefer sampling open syncmers yields much better density. This new method - the open-closed minimizer - offers improved density for small k [≤] w while being as fast to compute as the random minimizer. Compared to methods based on decycling sets, that achieve very low density in the small-k regime, our method has comparable density while being computationally simpler and intuitive. Furthermore, we extend the mod-minimizer to improve density of any scheme that works well for small k to also work well when k > w is large. We hence obtain the open-closed mod-minimizer, a practical method that improves over the mod-minimizer for all k.

bioinformatics↗

bioRxiv2024

Physical-aware model accuracy estimation for protein complex using deep learning method

With the breakthrough of AlphaFold2 on monomers, the research focus of structure prediction has shifted to protein complexes, driving the continued development of new methods for multimer structure prediction. Therefore, it is crucial to accurately estimate quality scores for the multimer model independent of the used prediction methods. In this work, we propose a physical-aware deep learning method, DeepUMQA-PA, to evaluate the residue-wise quality of protein complex models. For the input complex model, the residue-based contact area and orientation features were first constructed using Voronoi tessellation, representing the potential physical interactions and hydrophobic properties. Then, the relationship between local residues and the overall complex topology as well as the inter-residue evolutionary information are characterized by geometry-based features, protein language model embedding representation, and knowledge-based statistical potential features. Finally, these features are fed into a fused network architecture employing equivalent graph neural network and ResNet network to estimate residue-wise model accuracy. Experimental results on the CASP15 test set demonstrate that our method outperforms the state-of-the-art method DeepUMQA3 by 3.69% and 3.49% on Pearson and Spearman, respectively. Notably, our method achieved 16.8% and 15.5% improvement in Pearson and Spearman, respectively, for the evaluation of nanobody-antigens. In addition, DeepUMQA-PA achieved better MAE scores than AlphaFold-Multimer and AlphaFold3 self-assessment methods on 43% and 50% of the targets, respectively. All these results suggest that physical-aware information based on the area and orientation of atom-atom and atom-solvent contacts has the potential to capture sequence-structure-quality relationships of proteins, especially in the case of flexible proteins. The DeepUMQA-PA server is freely available at http://zhanglab-bioinf.com/DeepUMQA-PA/.

bioinformatics↗

bioRxiv2024

Stratified analysis identifies HIF-2α as a therapeutic target for highly immune-infiltrated melanomas

While immune-checkpoint blockade (ICB) has revolutionized treatment of metastatic melanoma over the last decade, the identification of broadly applicable robust biomarkers has been challenging, driven in large part by the heterogeneity of ICB regimens and patient and tumor characteristics. To disentangle these features, we performed a standardized meta-analysis of eight cohorts of patients treated with anti-PD-1 (n=290), anti-CTLA-4 (n=175), and combination anti-PD-1/anti-CTLA-4 (n=51) with RNA sequencing of pre-treatment tumor and clinical annotations. Stratifying by immune-high vs -low tumors, we found that surprisingly, high immune infiltrate was a biomarker for response to combination ICB, but not anti-PD-1 alone. Additionally, hypoxia-related signatures were associated with non-response to anti-PD-1, but only amongst immune infiltrate-high melanomas. In a cohort of scRNA-seq of patients with metastatic melanoma, hypoxia also correlated with immunosuppression and changes in tumor-stromal communication in the tumor microenvironment (TME). Clinically actionable targets of hypoxia signaling were also uniquely expressed across different cell types. We focused on one such target, HIF-2, which was specifically upregulated in endothelial cells and fibroblasts but not in immune cells or tumor cells. HIF-2 inhibition, in combination with anti-PD-1, enhanced tumor growth control in pre-clinical models, but only in a more immune-infiltrated melanoma model. Our work demonstrates how careful stratification by clinical and molecular characteristics can be leveraged to derive meaningful biological insights and lead to the rational discovery of novel clinical targets for combination therapy.

cancer biology↗

bioRxiv2024

Monocytes-derived cxcl12 guides a directional migration of blood vessels in zebrafish

BackgroundSprouting blood vessels, reaching the aimed location, and establishing the proper connections are vital for building vascular networks. Such biological processes are subject to precise molecular regulation. So far, the mechanistic insights into understanding how blood vessels grow to the correct position are limited. In particular, the guiding cues and the signaling-originating cells remain elusive. MethodsLive imaging analysis was used to observe the vascular developmental process of zebrafish. Whole-mount in situ hybridization and fluorescent in situ hybridization were used to detect the expression profiles of the genes. Single-cell sequencing analysis was conducted to identify the guiding protein and its originating cells. ResultsTaking advantage of live imaging analysis, we described a directional blood vessel migration in the vascularization process of zebrafish pectoral fins. We demonstrated that pectoral fin vessel c (PFVc) migrated over long distances and was anastomosed with the second pair of intersegmental vessels (ISVs). Furthermore, we found the cxcl12a-cxcr4a axis specifically guided this long-distance extension of PFVc-ISV, and either inhibition or over-expression of cxcl12a-cxcr4a signaling both mislead the growth of PFVc to ectopic areas. Finally, based on an analysis of single-cell sequencing data, we revealed that a population of monocytes expresses the Cxcl12a, which guides the migration of the vascular sprout. ConclusionsOur study identified Cxcl12a as the signaling molecule for orchestrating organotypic-specific long-distance migration and anastomosis of the pectoral fin vessel and ISVs in zebrafish. We discovered a specific cluster of gata1-positive monocytes that are responsible for expressing Cxcl12a. The findings offer novel insights into the mechanisms underlying organotypic vascularization in vertebrates.

developmental biology↗

bioRxiv2024

Understanding the epidemiology and pathogenesis of Mycobacterium tuberculosis with non-redundant pangenome and population genetics

Tuberculosis is a major public health threat demanding more than one million lives every year. Many challenges exist to defeat this deadly infectious disease which address the importance of a thorough understanding of the biology of the causative agent Mycobacterium tuberculosis (MTB). We generated a non-redundant pangenome of 420 epidemic MTB strains from China. We estimate that MTB strains have a pangenome of 4,278 genes encoding 4,183 proteins, of which 3,438 of which are core genes. However, due to 99,694 interruptions in 2,447 coding genes, only 1,651 may be translated in all samples, which dramatically reduces the number of active core genes. Of these interruptions, 67,315 (67.52%) could be classified by various genetic variations detected by currently available tools, and more than half of them are due to structure variations, mostly small indels. We further describe differential evolutionary patterns of genes under the influences of selective pressure, population structure and background selection. While selective pressure is ubiquitous among these coding genes, evolutionary adaptations primarily occur in 1,313 genes. Genes located in the cell wall and membrane region are under the strongest selective pressure, while biological processes including regulation of transcription, translation and regulation of growth are under strongest background selection in MTB. The metabolism of fatty acids may be an outstanding example of evolutionary adaption for MTB under current selective pressure. This study provides a comprehensive view on the genetic diversity and evolution patterns of coding genes in MTB which may deepen our understanding of its epidemiology and pathogenicity.

microbiology↗

bioRxiv2024

Human-mouse proteomics reveals the shared pathways in Alzheimer's disease and delayed protein turnover in the amyloidome

Murine models of Alzheimers disease (AD) are crucial for elucidating disease mechanisms but have limitations in fully representing AD molecular complexities. We comprehensively profiled age-dependent brain proteome and phosphoproteome (n > 10,000 for both) across multiple mouse models of amyloidosis. We identified shared pathways by integrating with human metadata, and prioritized novel components by multi-omics analysis. Collectively, two commonly used models (5xFAD and APP-KI) replicate 30% of the human protein alterations; additional genetic incorporation of tau and splicing pathologies increases this similarity to 42%. We dissected the proteome-transcriptome inconsistency in AD and 5xFAD mouse brains, revealing that inconsistent proteins are enriched within amyloid plaque microenvironment (amyloidome). Determining the 5xFAD proteome turnover demonstrates that amyloid formation delays the degradation of amyloidome components, including A{beta}-binding proteins and autophagy/lysosomal proteins. Our proteomic strategy defines shared AD pathways, identify potential new targets, and underscores that protein turnover contributes to proteome-transcriptome discrepancies during AD progression.

biochemistry↗

bioRxiv2024

m6A-mediated epi-transcriptomic dysregulation underlies synaptic dysfunction in fragile X syndrome

Fragile X syndrome (FXS), the leading genetic cause of intellectual disability, arises from FMR1 gene silencing and loss of the FMRP protein. N6-methyladenosine (m6A) is a prevalent mRNA modification essential for post-transcriptional regulation. FMRP is known to bind to and regulate the stability of m6A-containing transcripts. However, how loss of FMRP impacts on transcriptome-wide m6A modifications in FXS patients remains unknown. To answer this question, we generated cortical neurons differentiated from induced pluripotent stem cells (iPSC) derived from healthy subjects and FXS patients. In electrophysiology recordings, we validated that synaptic and neuronal network defects in iPSC-derived FXS neurons corresponded to the clinical EEG data of the patients from which the corresponding iPSC line was derived. In analysis of transcriptome-wide methylation, we show that FMRP deficiency led to increased translation of m6A writers, resulting in hypermethylation that primarily affecting synapse-associated transcripts and increased mRNA decay. Conversely, in the presence of an m6A writer inhibitor, synaptic defects in FXS neurons were rescued. Taken together, our findings uncover that an FMRP-dependent epi-transcriptomic mechanism contributes to FXS pathogenesis by disrupting m6A modifications in FXS, suggesting a promising avenue for m6A- targeted therapies.

neuroscience↗

bioRxiv2024

DEMINERS enables clinical metagenomics and comparative transcriptomic analysis by increasing throughput and accuracy of nanopore direct RNA sequencing

Nanopore direct RNA sequencing (DRS) advances RNA biology but is limited by relatively low basecalling accuracy, low throughput, yet high RNA input and costs. Here we introduce a novel DRS toolkit, DEMINERS, which integrates an RNA multiplexing experimental workflow, a machine-learning barcode classifier based on Random Forest and a novel basecaller built on an optimized convolutional neural network providing an additional species-specific training module. With the increased accuracy in barcode classification and basecalling, DEMINERS can demultiplex up to 24 samples and the required RNA input and running time are both substantially reduced. We demonstrated the applications of DEMINERS in clinical metagenomics, cancer transcriptomics and parallel comparison of transcriptomic features in different biological conditions, revealing altered airway microbial diversity in COVID-19 and a potential role of m6A in increasing transcriptomic diversity in glioma and the mature blood-stage of malaria parasites. Overall, DEMINERS is a simple, robust, high-throughput DRS method for accurately estimating transcript levels, poly(A) lengths, and mutation and RNA modification heterogeneity at single-read level, with minimal sequencing biases.

bioinformatics↗

bioRxiv2024

PROPERMAB: an integrative framework for in silico prediction of antibody developability using machine learning

Selection of lead therapeutic molecules is often driven predominantly by pharmacological efficacy and safety. Candidate developability, such as biophysical properties that affect the formulation of the molecule into a product, is usually evaluated only toward the end of the drug development pipeline. The ability to evaluate developability properties early in the process of antibody therapeutic development could accelerate the timeline from discovery to clinic and save considerable resources. In silico predictive approaches, such as machine learning models, which map molecules to predictions of developability properties could offer a cost-effective and high-throughput alternative to experiments for antibody developability assessment. We developed a computational framework, PROPERMAB, for large-scale and efficient in silico prediction of developability properties for monoclonal antibodies, using custom molecular features and machine learning modeling. We demonstrate the power of PROPERMAB by using it to develop models to predict antibody hydrophobic interaction chromatography retention time and high-concentration viscosity. We further show that structure-derived features can be rapidly and accurately predicted directly from sequences by pre-training simple models for molecular features, thus providing the ability to scale these approaches to repertoire-scale sequence datasets.

bioinformatics↗

bioRxiv2024

A novel approach for the quantification of single-cell adhesion dynamics from microscopy images

BackgroundCell adhesion, that is the ability to attach to a given substrate, is a key property of cancer cells, as it relates to their potential for dissemination and metastasis. The in vitro assays used to measure it, however, are characterized by several drawbacks, including low temporal resolution and limited procedural standardisation which reduce their usefulness and accuracy. ResultsIn this work, we propose an alternative analytical approach, based on live-cell imaging data, that yields comprehensive information on cell adhesion dynamics at the single-cell level. It relies on a segmentation routine, to identify the pixels belonging to each cell from time-lapse microscopy images acquired during the adhesion process. A tracking algorithm then enables the study of individual cell adhesion dynamics over time. The increased resolution afforded by this method was instrumental for the identification of cell division prior to attachment and the co-existence of markedly different proliferation rates across the culture, previously unidentified patterns of behaviour in the adhesion process. Finally, we generalize our method by substituting the segmentation algorithm of the instrument used to acquire the images, with a custom-made one, showing that this approach can be integrated within routine laboratory analytical procedures and does not necessarily require high-performance microscopy and imaging setups. ConclusionsOur new analytical approach improves the in vitro quantification of cell adhesion, enabling the study of this process with high temporal resolution and increased level of detail. The extension of the analysis to the single-cell level, additionally, uncovered the role of population variability and proliferation in this process. The simple and cost-effective procedure here described enables the accurate characterisation of cell adhesion. Beside improving our understanding of adhesion dynamics, its results could support the development of treatments targeting the ability of cancer cells to adhere to surrounding tissues.

cell biology↗

bioRxiv2024

Gene encoding CC-NBS-LRR protein on rye chromosome 1RS confers wheat stripe rust resistance

Stripe rust, a globally widespread disease, stands as one of the most significant threats to wheat cultivation. The 1BL/1RS translocation, renowned for its robust resistance to both rust and powdery mildew, has historically played an important role in wheat breeding and production. The gene for resistance to stripe rust on the 1RS is known as Yr9 and plays an important role in the production of wheat, but over the course of long-term breeding had lost its resistance due to the evolution of stripe rust towards greater and greater virulence. In this paper, we cloned the stripe rust resistance gene, Yr9, from triticale by genetic mapping approach. The Yr9 encodes a typical nucleotide-binding leucine-rich repeat (NLR) protein. Both transgenic and overexpression of Yr9 in highly stripe rust susceptible wheat varieties conferred complete resistance to the stripe rust races CYR17 and partial resistance to the stripe rust races CYR32, CYR33, and CYR34. In addition, the Yr9 allele in the 1BL/1RS translocation line also showed the same level of resistance to stripe rust. Both two alleles loses resistance when deployed in the field or inoculated with mixed physiological races collected from the field. Our findings provide valuable insights for breeders to strategically incorporate disease resistance genes and provides a foundation for further understanding how pathogenic bacteria might evolve to evade recognition via NLR type proteins. SignificanceThe 1BL/1RS translocation between wheat and rye is the most successful case of exogenous gene application in plant genetic improvement and has been used in wheat breeding for over 50 years. Here we report the cloning of a stripe rust resistance gene Yr9 located on rye chromosome 1RS using a triticale population. The Yr9 encodes a coiled-coil nucleotide-binding site leucine-rich repeat (CC-NBS-LRR) protein that show complete resistance to the stripe rust races CYR17 and partial resistance to the stripe rust races CYR32, CYR33, and CYR34, albeit demonstrating susceptibility under field conditions. Our findings position Yr9 as an ideal candidate gene to study the mechanism of inactivation of disease resistance genes as a result of pathogen evolution.

genetics↗

bioRxiv2024

Microbicidal Mechanisms for Light-Activated Molecular Nanomachines in Mycobacterium smegmatis: A Model for Pathogenic Bacteria

There is a global health crisis of antimicrobial resistance, with over a million deaths annually attributed to antimicrobial-resistant pathogens, and mycobacterial infections are a major cause of antimicrobial-resistant infections, leading to more deaths than any other single infectious agent. Notably, the rise of multidrug-resistant (MDR), extensively drug-resistant (XDR), and totally drug-resistant (TDR) strains of Mycobacterium tuberculosis led to higher mortality rates and challenge all existing antibiotic regimens. Light-activated molecular nanomachines (MNMs) represent a promising class of broad-spectrum antimicrobial agents that could help counter this rise in antimicrobial resistance. Addressing a key knowledge gap, this study explores the mechanisms of action for MNMs in Mycobacterium smegmatis, a surrogate model for pathogenic mycobacteria. We show that fast rotor MNMs kill up to 97% of M. smegmatis and co-localize with the bacteria as part of their mechanism of action. The ability to translate these observations to pathogenic mycobacteria was demonstrated by the ability of MNMs to kill 93.5% of M. tuberculosis under similar conditions. These findings suggest that MNMs may provide innovative sustainable antimicrobial agents for the treatment of drug-resistant mycobacterial infections. Graphical AbstractBacteria exposed to MNMs have two distinct outcomes when activated by 365 nm light. Slow motors (MNM 2 and 4) have no rotational action, remains outside the bacteria and have little to no effect on bacterial viability. Whereas fast motors (MNM 1 and 3) co-localize and embed into the bacterial cell wall causing disruptions that lead to a significant reduction in bacterial viability. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=89 SRC="FIGDIR/small/616754v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@70991corg.highwire.dtl.DTLVardef@541a36org.highwire.dtl.DTLVardef@18504bcorg.highwire.dtl.DTLVardef@1fa08c3_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2024

APOE Protects Against Severe Infection with Mycobacterium tuberculosis by Restraining Production of Neutrophil Extracellular Traps

While neutrophils are the predominant cell type in the lungs of humans with active tuberculosis (TB), they are relatively scarce in the lungs of most strains of mice that are used to study the disease. However, similar to humans, neutrophils account for approximately 45% of CD45+ cells in the lungs of Apoe-/- mice on a high-cholesterol (HC) diet following infection with Mycobacterium tuberculosis (Mtb). We hypothesized that the susceptibility of Apoe-/- HC mice might arise from an unrestrained feed-forward loop in which production of neutrophil extracellular traps (NETs) stimulates production of type I interferons by pDCs which in turn leads to the recruitment and activation of more neutrophils, and demonstrated that depleting neutrophils, depleting plasmacytoid dendritic cells (pDCs), or blocking type I interferon signaling, improved the outcome of infection. In concordance with these results, we found that Mtb-infected in Apoe-/-HC mice produce high levels of LTB4 and 12-HETE, two eicosanoids known to act as neutrophil chemoattractants and showed that blocking leukotriene B4 (LTB4) receptor signaling also improved the outcome of tuberculosis. While production of NETs has been associated with severe tuberculosis in other mouse models and in humans, a causative role for NETs in the pathology has not been directly established. We demonstrate that blocking the activation of peptidylarginine deiminase 4 (PAD4), an enzyme critical to NET formation, leads to fewer NETs in the lungs and, strikingly, completely reverses the hypersusceptibility of Apoe-/-HC mice to tuberculosis.

immunology↗

bioRxiv2024

Systematic empirical evaluation of individual base editing targets: validating therapeutic targets in USH2A and comparison of methods

Base editing shows promise for the correction of human mutations at a higher efficiency than other repair methods and is especially attractive for mutations in large genes that are not amenable to gene augmentation therapy. Here, we demonstrate a comprehensive workflow for in vitro screening of potential therapeutic base editing targets for the USH2A gene and empirically validate the efficiency of adenine and cytosine base editor/guide combinations for correcting 35 USH2A mutations. Editing efficiency and bystander edits are compared between different target templates (plasmids versus transgenes) and assays (Next generation sequencing versus Sanger), as well as comparisons between unbiased empirical results and computational predictions. Based on these observations, practical assay recommendations are discussed. Finally, a humanized knock-in mouse model was created with the best-performing target, the nonsense mutation c.11864G>A p.(Trp3955*). Split-intein AAV9 delivery of editing reagents resulted in the restoration of USH2A protein and a correction rate of 65 {+/-} 3% at the mutant base pair and of 52 {+/-} 3% excluding bystander amino acid changes. This efficiency compares favorably to a prior genome editing strategy tested in the retina that completed a clinical trial and demonstrates the effectiveness of this overall strategy to identify and test base editing reagents with the potential for human therapeutic applications.

genetics↗

bioRxiv2024

Development and validation of a computational tool to predict treatment outcomes in cells from High-Grade Serous Ovarian Cancer patients

Treatment of High-Grade Serous Ovarian Cancer (HGSOC) is often ineffective due to frequent late-stage diagnosis and development of resistance to therapy. Timely selection of the most effective (combination of) drug(s) for each patient would improve outcomes, however the tools currently available to clinicians are poorly suited to the task. We here present a computational simulator capable of recapitulating cell response to treatment in ovarian cancer. The technical development of the in silico framework is described, together with its validation on both cell lines and patient-derived laboratory models. A calibration procedure to identify the parameters that best recapitulate each patients response is also presented. Our results support the use of this tool in preclinical research, to provide relevant insights into HGSOC behaviour and progression. They also provide a proof of concept for its use as a personalised medicine tool and support disease monitoring and treatment selection.

bioengineering↗