Biology subjects

Lio, P.

Publications and source records attributed to Lio, P..

At least 19 recordsLinked to original sources

bioRxiv2026

Interpreting Graph Neural Networks for Antibacterial Drug Repurposing against Acinetobacter baumannii

Antimicrobial resistance in Acinetobacter baumannii, whose carbapenem-resistant strains are classified by the World Health Organization as a critical priority, has motivated machine-learning screens for new antibacterials, but translating predictions into chemical hypotheses requires evaluating the molecular features on which a model relies. A graph neural network ensemble achieves competitive predictive performance against a descriptor-augmented reference model on the same antibacterial screen while using only atom features and connectivity, allowing interpretation to focus on the molecular graph without a separate input of handcrafted descriptors. The atom-level explanations identify features supporting the model's activity predictions under perturbation and restoration, while comparisons with equally sized perturbations within each molecule distinguish piperazine as the most consistently supported recurring contributor to predicted activity. Prospective screening of the Drug Repurposing Hub followed by developability and safety assessment yields a ranked shortlist for experimental follow-up and leads to testing of CE3F4, which reduced A. baumannii growth by 57% at 0.25 mM in a preliminary assay with a non-monotonic concentration--response profile.

bioinformatics↗

bioRxiv2026

Five hundred million years of methylation: tracing the mutational origins of vertebrate genome composition

Methylation-associated deamination removes CpG from vertebrate genomes, but how it affects the dinucleotide profile remains unresolved. We analysed 753 vertebrate and 481 invertebrate genomes to test whether CpG loss defines a compositional axis and to identify its strongest signature. CpG depletion was the dominant axis of vertebrate dinucleotide variation. Unexpectedly, its strongest between-genome correlate was AG/CT rather than the direct mutational product TpG/CpA, which showed the expected dataset-wide mass balance but varied little among genomes. A forward-evolution model based on measured seven-nucleotide human germline substitution rates produced neither CpG depletion nor AG/CT enrichment when methylated-CpG mutability was excluded. Adding one CpG-specific mutability term, calibrated only to the mammalian CpG ratio, reproduced both features, identifying AG/CT as a second-order consequence of the context-dependent mutation network. Within genomes, CpG depletion was strongest in transposable elements and weakened with distance from them. Across vertebrates, the axis followed Amniota more closely than endothermy and was associated with an expanded GC-rich isochore compartment. A Machine Learning analysis shows that CpG depletion and AG/CT were the principal features separating vertebrates from invertebrates, in which both were markedly attenuated. Thus, a methylation-associated axis organises vertebrate dinucleotide composition, and its strongest marker is not the immediate product of CpG deamination.

genomics↗

bioRxiv2026

Deep learning enables cross-species annotation and attribution of ageing states in haematopoietic stem and immune cells

Mouse single-cell ageing studies provide experimentally controlled age contrasts, but using mouse-labelled data to annotate human ageing states is limited by species, donor and assay effects in sparse transcriptomic and chromatin profiles. We developed a cross-species annotation workflow that treats mouse-to-human prediction as a target-validated domain-adaptation problem. The workflow uses orthologue-aligned features, a residual encoder, an age classifier and a species discriminator trained with two-phase adversarial optimisation, and couples prediction with stability-based gene attribution. In haematopoietic stem cells (HSCs), the model achieved held-out human AUROCs of 0.933 in scRNA-seq and 0.953 in scATAC-seq. In an independent CD8+ T-cell scRNA-seq setting, the held-out human AUROC was 0.941. Ablation analyses indicated that residual connections, ELU activation and two-phase training improved predictive performance and attribution stability. Consensus attributions from DeepLIFT, Integrated Gradients and saliency recovered conserved ageing-associated genes with greater cross-species overlap than differential expression alone. In a COVID-19 convalescent cohort, severe disease in younger adults was associated with a higher fraction of CD8+ cells classified as old-like by the pretrained model. These results support a reproducible framework for testing, interpreting and releasing cross-species single-cell ageing models, while highlighting the need for target-domain validation when mouse labels are transferred to human data.

bioinformatics↗

bioRxiv2026

De novo design of RNA pseudoknots with deep learning

RNA design has been hindered by the limited accuracy of 3D structure prediction. Here, we show that intricate RNA structures can be generated with current deep learning tools through accurate de novo design of pseudoknot secondary structures. In an Eterna competition involving 57 pseudoknots, generative AI methods matched experienced human designers in solving most blind challenges, evaluated by single-nucleotide-resolution chemical mapping, compensatory mutagenesis, and cryogenic electron microscopy. AI-generated molecules with accurate secondary structures formed well-ordered 3D folds stabilized by noncanonical tertiary interactions not modeled during design. Success was guided by an RNet foundation model trained on prior chemical mapping data, suggesting that some difficult RNA design tasks may be tractable without first solving RNA 3D structure prediction.

biophysics↗

bioRxiv2026

LLMsFold: Integrating Large Language Models and Biophysical Simulations for De Novo Drug Design

The discovery of novel small molecules is challenging because of the vastness of chemical space and the complexity of protein-ligand interactions, leading to low success rates and time-consuming workflows. Here, we present LLMsFold, a computational framework that combines Large Language Models (LLMs) and biophysical foundation tools to design and validate new small molecules targeting pathogenic proteins. The pipeline starts by identifying viable binding pockets on a target protein through geometry-based pocket detection. A 70-billion-parameter transformer model from the LlaMA family then generates candidate molecules as SMILES strings under prompt constraints that enforce drug-likeness. Each molecule is evaluated by Boltz-2, a diffusion-based model for protein-ligand co-folding that predicts bound 3D structure and binding affinity. Promising candidates are iteratively optimized through a reinforcement learning loop that prioritizes high predicted affinity and synthetic accessibility. We demonstrate the approach on two challenging targets: ACVR1 (Activin A Receptor Type 1), implicated in fibrodysplasia ossificans progressiva (FOP), and CD19, a surface antigen expressed on most B-cell lymphoma and leukemia cells. Top candidates show strong in silico binding predictions and favorable drug-like profiles. All code and models are made available to support reproducibility and further development.

bioinformatics↗

bioRxiv2026

Systematic discovery of a topical bacterial consortium that targets Staphylococcus aureus to treat atopic dermatitis

Atopic dermatitis (AD) flares are frequently accompanied by Staphylococcus aureus overgrowth and activation of quorum sensing-regulated virulence pathways that amplify inflammation and barrier dysfunction. Because commensal members of the skin microbiome can inhibit S. aureus colonization and virulence, we hypothesized that a consortium sourced from healthy human skin could therapeutically target S. aureus and ameliorate AD. We curated 180 skin-derived bacterial strains and used kChip, an ultrahigh-throughput coculture platform, to profile S. aureus physiological response to over four million combinations consisting of two, three, or seven cocultured strains. This screening identified Ensemble No.2 (ENS-002), a three-strain consortium that strongly suppressed S. aureus growth and virulence in follow-up microtiter assays and a Reconstructed Human Epidermis S. aureus Activity (RHESA) model. ENS-002 is now undergoing development as a topical live biotherapeutic product (LBP) treatment for AD.

microbiology↗

bioRxiv2025

Generative inverse design of RNA structure and function with gRNAde

The design of RNA molecules with bespoke three-dimensional structures and functions is a central goal in synthetic biology and biotechnology. However, progress has been limited by the challenges of designing complex tertiary interactions such as pseudoknots, as well as engineering catalytic functions--problems that have remained largely intractable for automated methods. Here we present a high-throughput generative AI pipeline for inverse design of RNA structure and function. Central to the pipeline is gRNAde, an RNA language model conditioned on 3D backbone structures and sequence constraints. We have validated the gRNAde pipeline in a community-wide, blinded RNA design competition on the Eterna platform, where it proved able to design complex pseudoknotted RNAs at success rates matching that of human experts (95%), while significantly outperforming other physics- and AI-based automated algorithms (70%). We further demonstrate gRNAdes capabilities by generatively designing functional RNA polymerase ribozymes (RPR) with nearly 20% sequence divergence from the wild type RPR, discovering highly active variants at mutational distances inaccessible to rational design or adaptive walks by directed evolution. gRNAde thus provides an experimentally validated, open-source platform for automated design of complex RNA structures and accelerated engineering of complex RNA functions, providing a step towards programmable RNA catalysts and nanostructures. Open-source code: github.com/chaitjo/geometric-rna-design

synthetic biology↗

bioRxiv2025

Nanoplasmonic SERS reveals previously uncharacterised indole derivative in E. coli metabolism

Tryptophanase (TnaA) is a promiscuous enzyme which regulates the production of amino acid-derived metabolites, essential for bacterial communication. TnaA is known to convert tryptophan into indole, an important signalling molecule influencing a range of behaviours - from bacterial quorum sensing to urinary tract infections (UTI) - but other conversion routes remain obscure. Here, we show that nanoplasmonic surface-enhanced Raman spectroscopy (SERS) combined with targeted genetic subtraction enables the label-free, cellular characterisation and structural identification of TnaA-derived metabolites directly from Escherichia coli culture solutions, without isolation or purification. We systematically profile the SERS spectra of wild-type and tnaA gene knockout strains (E. coli BW25113 and the uropathogenic 536) supplemented with each of the twenty amino acids, uncovering a previously unreported TnaA-dependent metabolic signature, I*, whose Raman fingerprint does not match free indole or any common indole derivative characterised by mass spectrometry. Using isotopomer-labelled substrates together with structural reference compounds, we perform label-free structural deduction of I* directly from SERS spectra. We find I*s structure is consistent with an intact indole ring carrying a C3 substituent, likely produced indirectly from intracellular tryptophan. These findings introduce a novel indole metabolite, raise new hypotheses regarding the biological role of TnaA-mediated byproducts in bacterial signalling and virulence, and demonstrate that nanoplasmonic SERS provides a powerful framework for probing enzyme activity and bioactive metabolite production directly from living bacterial cultures with nanomolar sensitivity and structural specificity.

microbiology↗

bioRxiv2025

Uncertainty-Aware Tau Detection in Progressive Supranuclear Palsy Using Object Detection Models

Abnormal tau accumulation is a hallmark of neurodegenerative tauopathies such as Progressive Supranuclear Palsy (PSP). Traditional post-mortem assessments rely on manual lesion annotation, which is time-consuming and subjective. Existing machine learning methods typically involve multi-stage, feature-based pipelines, resulting in limited scalability and reliance on handcrafted features. This work evaluates deep learning-based object detection (OD) as a unified, end-to-end alternative for automating tau lesion identification and classification. We fine-tuned YOLO and Faster R-CNN models on a dataset of 29,852 tau lesions from 16 PSP brain slides, achieving mAP@50 of 0.702. To address the limitations of conventional OD models, which assume overconfident spatial predictions, we integrated Monte Carlo Dropout into both architectures to estimate spatial and label uncertainty. We evaluated detection uncertainty using Probability-based Detection Quality. Our results demonstrate that incorporating uncertainty offers a more comprehensive evaluation of detection reliability, complementing traditional metrics like mAP. For clinical application, we integrate a non-stochastic YOLO model into QuPath for whole-slide inference. Downstream clinical validation was conducted, including SHAP-based saliency analysis and correlation between model-predicted tau densities and PSP Rating Scale scores, a clinical disease severity measure. The alignment of detection outputs with pathological features and clinical severity underscores the clinical utility of OD models. Overall, this work highlights the complementary strengths of deep object detectors and uncertainty quantification, offering a path toward scalable, interpretable, and clinically meaningful tau pathology analysis. Code is available at https://github.com/Pidongg/dl_histopathology.

bioinformatics↗

bioRxiv2025

Integration of Bioinformatics and Machine Learning to Characterize Fusobacterium nucleatum's Pathogenicity

Fusobacterium nucleatum has been found to be associated with cancer lesions in both oral and colon cancers. Although important studies have dissected the clinical aspects of its remarkable pathogenicity, there is a lack of molecular studies. This study aimed to computationally predict potential pathogenicity islands in F. nucleatum ATCC 25586, with candidate functional relation-ships among PAI-encoded proteins, and generate testable hypotheses regarding iron-dependent virulence mechanisms. We employed an integrative bioinformatics pipeline combining genomic is-land prediction (IslandViewer), promoter analysis (PePPER), codon adaptation index calculation, protein interaction prediction (STRING), co-expression network inference (bnlearn), structural modeling (AlphaFold-Multimer), and genome-scale metabolic modeling (CarveMe/COBRApy). Computational predication was integrated with published literature to formulate mechanistic hy-potheses. Our analysis identified three candidate genomic islands, with Region 2 (PAI2; 1,496,613 to 1,523,855 bp) exhibiting the characteristics most consistent with a functional pathogenicity island, including predicted mobile genetic elements, putative toxin-antitoxin systems, and strong promoter motifs. We propose a mechanistic hypothesis linking hemolysin-mediated iron acquisi-tion to cancer promotion through oxidative stress and Hippo pathway modulation. Our work has two immediate and important benefits: the improved understanding of the biological processes that shape the pathogenicity and evolution of Fusobacterium nucleatum at the molecular level and the improved ability to integrate and automate the state-of-the-art bioinformatics tools and machine learning approaches in the inference of the mechanistic interpretability of a pathogenic phenotype.

microbiology↗

bioRxiv2025

Accelerating Biomolecular Modeling with AtomWorks andRF3

Deep learning methods trained on protein structure databases have revolutionized biomolecular structure prediction, but developing and training new models remains a considerable challenge. To facilitate the development of new models, we present AtomWorks: a broadly applicable data framework for developing state-of-the-art biomolecular foundation models spanning diverse tasks, including structure prediction, generative protein design, and fixed backbone sequence design. We use AtomWorks to train RosettaFold-3 (RF3), a structure prediction network capable of predicting arbitrary biomolecular complexes with an improved treatment of chirality that narrows the performance gap between closed-source AlphaFold3 (AF3) and existing open-source implementations. We expect that AtomWorks will accelerate the next generation of open-source biomolecular machine learning models and that RF3 will be broadly useful as a structure prediction tool. To this end, we release the AtomWorks framework (https://github.com/RosettaCommons/atomworks), together with curated training data, code and model weights for RF3 (https://github.com/RosettaCommons/modelforge) under a permissive BSD license.

biochemistry↗

bioRxiv2025

Proteomizer: Leveraging the Transcriptome-Proteome Mismatch to Infer Novel Gene Regulatory Relations

The correlation between transcriptomic (Tx) and proteomic (Px) profiles remains modest, typically around r = 0.5 across genes and r = 0.3 across samples, limiting the utility of transcriptomic data as a proxy for protein abundance. To address this, we introduce Proteomizer, a deep learning platform designed to infer a samples Px landscape from its Tx and miRNomic (Mx) profiles. Trained on 8,613 matched Tx-Mx-Px samples from TCGA and CPTAC, Proteomizer achieved a Tx-Px correlation of r = 0.68, representing the highest performance reported to date for this task. We further developed a Monte Carlo simulation framework to evaluate the impact of proteomization on differential expression analysis. Proteomizer substantially improved the accuracy of differential gene expression detection, with p-value precision increasing by up to 62-fold, and by as much as six orders of magnitude for a subset of genes enriched in mitochondrial and ribosomal functions. However, performance gains did not generalize to unseen tissue types or datasets generated using different protocols. Finally, we applied explainable AI (XAI) techniques to identify regulatory relations contributing to Tx-Px discrepancies. Our predictions from 100 highly annotated genes were cross-compared against by a literature-based biological knowledge graph of 322 million annotations: our explainers achieved a ROC-AUC of 0.74 in predicting miRNA-gene downregulation interactions. To our knowledge, this is the first study to systematically evaluate the biological relevance, limitations, and interpretability of proteomization models, establishing Proteomizer as a state-of-the-art tool for multiomic integration and hypothesis generation. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=199 HEIGHT=200 SRC="FIGDIR/small/660946v1_ufig1.gif" ALT="Figure 1"> View larger version (54K): org.highwire.dtl.DTLVardef@59b64dorg.highwire.dtl.DTLVardef@1cf82a4org.highwire.dtl.DTLVardef@89fdb6org.highwire.dtl.DTLVardef@16fe109_HPS_FORMAT_FIGEXP M_FIG C_FIG miRNA: microRNA; Mx: miRNome; MS: mass spectrometry; PTM: post-translational modifications; Px: proteome; Px: predicted proteome; r: Pearson correlation coefficient; RBP: RNA binding proteins; ROC-AUC: area under the receiver operating characteristic curve; RPPA: reverse-phase protein array; TF: transcription factors; Tx: transcriptome; XAI: explainable artificial intelligence.

bioinformatics↗

bioRxiv2025

GhostBuster: A Deep-Learning-based, Literature-Unbiased Gene Prioritization Tool for Gene Annotation Prediction

All genes are not equal before literature. Despite the explosion of genomic data, a significant proportion of human protein-coding genes remain poorly characterized ("ghost genes"). Due to sociological dynamics in research, scientific literature disproportionately focuses on already well-annotated genes, reinforcing existing biases (bandwagon effect). This literature bias often permeates machine learning (ML) models trained on gene annotation tasks, leading to predictions that favor well-studied genes. Consequently, standard ML performance metrics may overestimate biological relevance by overfitting literature-derived patterns. To address this challenge, we developed GhostBuster, an encoder-decoder ML platform designed to predict gene functions, disease associations and interactions while minimizing literature bias. We first compared the impact of biased (Gene Ontology) versus unbiased training datasets (LINCS, TCGA, STRING). While literature-biased sources yielded higher ML metrics, they also amplified bias by prioritizing well-characterized genes. In contrast, models trained on unbiased datasets were 2-3x more effective at identifying recently discovered gene annotations. Notably, one of the unbiased channels (TCGA), combined minimal amounts of literature bias with robust performance, at a test ROC-AUC of 0.8-0.95. We demonstrate that GhostBuster can be applied to predict novel gene functions, refine pathway memberships, and prioritize intergenic GWAS hits. As the first ML framework explicitly designed to counteract literature bias, GhostBuster offers a powerful tool for uncovering the roles of understudied genes in cellular function, disease, and molecular networks. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=197 SRC="FIGDIR/small/660948v1_ufig1.gif" ALT="Figure 1"> View larger version (59K): org.highwire.dtl.DTLVardef@5318eforg.highwire.dtl.DTLVardef@18a2f99org.highwire.dtl.DTLVardef@13b0b87org.highwire.dtl.DTLVardef@171d3e0_HPS_FORMAT_FIGEXP M_FIG C_FIG GCN: graph convolutional neural network; GeneOntol.: Gene Ontology; LINCS: Library of Integrated Network-Based Cellular Signatures; MLP: multi-layer perceptron (deep learning); PPI: (physical) protein-protein interactions; SVM: support vector machine; TCGA: The Cancer Genome Atlas; y/n: yes or no (binary classification).

bioinformatics↗

bioRxiv2025

Multiplexed, scalable analog recording of gene regulation dynamics over weeks using intracellular protein tapes

Gene expression is constantly regulated by gene regulatory networks that consist of multiple regulatory components to mediate cellular functions. An ideal tool for analyzing gene regulation processes would provide simultaneous measurements of the dynamics of many components in the gene regulatory network, but existing methodologies fall short of simultaneously tracking the dynamics of components over long periods of time. Here, we present CytoTape--a genetically encoded, modular, and scalable analog recorder for continuous, multiplexed in situ recording of gene regulation dynamics over multiple days and weeks at single-cell resolution. CytoTape consists of a flexible, thread-like, elongating intracellular protein self-assembly engineered via AI-guided rational design. Gene regulation dynamics, together with timestamps for reconstruction of the continuous time axis, are directly encoded via distinct molecular tags distributed along single CytoTape assemblies in live cells, to be readout at scale after fixation via standard immunofluorescence imaging. CytoTape recorders are modularly designed to record gene expression driven by a variety of activity-dependent promoters. We demonstrated the utility of CytoTape in mammalian embryonic kidney cells, cancer cells, glial cells, and neurons, achieving simultaneous recording of five cell plasticity-associated transcription factor activities and immediate early gene expression levels, namely CREB, c-fos, Arc, Egr1, and Npas4 activities, within single cells in a spatiotemporally scalable manner. CytoTape revealed complex waveforms and nonlinear temporal couplings among these cellular activities, enabling investigations of how gene regulation histories and intrinsic signaling states shape transcriptional logics. We envision CytoTape to have broad applications in both basic and disease-related cell biology research.

synthetic biology↗

bioRxiv2025

Topography Aware Optimal Transport for Alignment of Spatial Omics Data

Understanding the spatial organization of tissues is essential for uncovering cellular communication, developmental processes, and disease mechanisms. Recent advances in spatial omics technologies have provided unprecedented insight into tissue spatial organization, but challenges remain in aligning spatial slices and integrating complementary single-cell and spatial data. Here, we propose TOAST (Topography-aware Optimal Alignment of Spatially-resolved Tissues), a novel OT-based framework that extends the classical Fused Gromov-Wasserstein (FGW) objective to more comprehensively model the heterogeneity of local molecular interactions. By introducing spatial coherence, quantified through the entropy of local neighborhoods, and neighborhood consistency, which preserves the expression profiles of neighboring spots, TOASTs objective function significantly improves the alignment of spatially resolved tissue slices and the mapping between single-cell and spatial data. Through comprehensive evaluations on both simulated and real-world datasets, including human brain cortex Visium data, Axolotl brain Stereo-seq data, mouse embryo seqFISH data, and Imaging Mass Cytometry from multiple cancer types, we demonstrate that our method consistently outperforms traditional FGW and other OT-based alignment methods. Specifically, TOAST improves the accuracy of spatial slice alignment, better preserves cell type compositions, recovers lineage trajectories in developmental brain data, and reconstructs spatial relationships in spatial transcriptomics mouse embryo data. By integrating spatial constraints into OT, our framework provides a principled approach to enhance the biological interpretability of spatially resolved omics data and facilitate multimodal data integration.

bioinformatics↗

bioRxiv2025

Genome-wide DNA methylation study reveals specific signatures in the affected arterial tissue of giant cell arteritis patients

ObjectivesGiant cell arteritis (GCA) is a large-vessel vasculitis, potentially causing complications such as blindness and strokes. This study aims to gain insights into the pathogenesis of GCA by identifying specific DNA methylation signatures in the arterial tissue of patients with this vasculitis. MethodsDNA methylation profiling was analyzed in 79 temporal artery biopsy samples (69 patients with GCA and 10 controls) by performing an epigenome-wide association study (EWAS). Differential analysis was performed to identify differentially methylated positions (DMPs) and regions (DMRs). Lastly, we compared our findings with previous transcriptomics and epigenomics studies on GCA-affected arteries. ResultsEWAS identified 3,644 DMPs (FDR < 0.05, |{Delta}{beta}| > 0.3), indicating a profound alteration within GCA-affected arterial tissue. These DMPs were annotated to 1,517 potentially dysregulated genes. 282 additional genes were identified by annotation of significant DMRs. Pathway enrichment analysis revealed a significant alteration of inflammatory mechanisms, such as interleukins 2 and 7, as well as pathways related to vascular remodeling. Omics study comparison revealed 37 genes consistently affected across datasets, many of them linked to immune signaling and T cell regulation. Notably, markers of exhausted T cells, including SLAMF6 and HAVCR2, were present among them. ConclusionsOur study identified GCA-specific DNA methylation signatures in arterial tissue, revealing disrupted inflammatory and vascular pathways, and suggesting the involvement of exhausted T cells in this condition. These findings offer new insights into GCA pathogenesis and provide new potential targets for the treatment of this debilitating disease. Key messagesO_ST_ABSWhat is already known on this topicC_ST_ABS{square} Affected arteries by giant cell arteritis (GCA) exhibit unique epigenetic signatures, reflecting significant disruptions in gene regulation. However, small sample sizes have constrained the clinical translation and broader interpretation of these findings, leaving key gaps in understanding GCA pathogenesis and identifying therapeutic targets. What this study adds{square} This study provides the largest-to-date epigenome-wide DNA methylation profiling in GCA-affected arteries, uncovering thousands of epigenetic changes and revealing profound disruptions in inflammatory and vascular pathways, including IL-2, IL-7, and CXCR4 signaling. {square}We compiled 37 genes consistently affected across different omics datasets in GCA-affected arteries, providing a robust list of candidates for further research into GCA pathogenesis and treatment. {square}Our findings provide evidence of T cell exhaustion in GCA-affected arteries, supported by consistent changes in key markers such as SLAMF6 and HAVCR2 (TIM-3), suggesting a novel mechanism of immune dysregulation in GCA. How this study might affect research, practice or policy{square} This study nominates exhausted T cells, the NLRP3 inflammasome, and CXCR4 signaling as novel contributors in GCA pathogenesis, suggesting new therapeutic targets for further research in the treatment of this disease.

immunology↗

bioRxiv2024

Interaction between gene expression and morphokinetic parameters in undisturbed human embryo culture

The clinical in vitro fertilisation (IVF) need for optimal culture conditions and improved selection of human embryos for transfer led to the development of time-lapse systems built into incubators to allow a stable, well-defined and undisturbed culture environment with continual monitoring of embryo development. Clinical data suggest that both undisturbed culture of embryos and selection algorithms based on time-lapse morphometric parameters can potentially improve embryo development and live birth rates. However, there remains a need to validate and understand the mechanisms underlying the potential benefits of the time-lapse technology in IVF. In this study, we compared the gene expression of human embryos grown in an undisturbed time-lapse system with a conventional incubator and showed that there is no fundamental difference in the developmental program of the undisturbed embryos, which provides important reassurance concerning the time-lapse technology. We then performed a network-based integrative analysis based on the undisturbed blastocyst transcriptomes and identified time-lapse parameter correlated genes. These showed cross talk with identified embryo development gene functional modules, suggesting potential molecular mechanisms underlying the clinical predictive capabilities of embryo time-lapse morphokinetic parameters for subsequent live birth. This study suggests methodologies for assessing the impact of additional predictive correlates of value for optimal embryo development and understanding their mechanisms of action.

developmental biology↗

bioRxiv2024

Microdroplet screening rapidly profiles a biocatalyst to enable its AI-assisted engineering

Engineering enzymes for increased efficiency is key to enabling sustainable, green biocatalytic production processes in the chemical and pharmaceutical industries. This challenge can be tackled from two angles: by directed evolution, based on labour-intensive experimental testing of enzyme variant libraries, or by computational methods, where data-dependent algorithms relating sequence and function are used to predict biocatalyst improvements. Here, we combine both approaches into a two-week, low-cost workflow, in which ultra-high throughput screening of a library of imine reductases (IREDs) in microfluidic devices provides not only selected hits, but also long-read sequence data linked to fitness scores of >17 thousand enzyme variants. We demonstrate the engineering of an IRED for chiral amine synthesis by mapping its local fitness landscape in one go, ready to be used for interpretation and extrapolation by protein engineers with the help of machine learning (ML). We calculate position-dependent mutability and combinability scores of mutations and comprehensively illuminate a complex interplay of mutations driven by synergistic, often positively epistatic effects. When interpreted by easy-to-use regression and tree-based ML algorithms designed for random whole-gene mutagenesis data, 3-fold improved hits initially obtained from experimental screening are extrapolated further to give another order of magnitude improvement (23-fold in kcat) after testing only a handful of designed mutants. Predictions succeed in >80% of cases. The catalytic features discovered in one IRED are shown to be portable and confer activity on IREDs with [~]50% homology. Our campaigns yield biocatalytically efficient IREDs and are paradigmatic for future enzyme engineering efforts that rely on large sequence-function maps, profiling how a biocatalyst responds to mutation. In the age of predictive biology, these maps will chart the way to improved function by exploiting the synergy of rapid experimental screening combined with ML evaluation and extrapolation.

synthetic biology↗