Biology subjects

Fu, L.

Publications and source records attributed to Fu, L..

At least 55 records · Page 3Linked to original sources

bioRxiv2024

The level of endothelial glycocalyx maturity modulates interactions with charged nano-materials

Nanomaterials have been extensively investigated for their potential in delivering therapeutics to target tissues, but few have advanced to clinical application. The luminal surface of endothelial cells that line blood vessels are covered by a glycocalyx, a complex extracellular matrix rich in anionic glycans. However, the role of this glycocalyx in governing nanomaterial-cell interactions is often overlooked. In this study, we demonstrate that gold nanoparticles functionalized with branched polyethyleneimine (AuNP+) bind to primary human endothelial cells expressing either a developing or mature glycocalyx, with the interaction involving hyaluronan and heparan sulfate. Notably, the mature glycocalyx decreases the toxicity of AuNP+. In contrast, lipoic acid-functionalized gold nanoparticles (AuNP-) bind to endothelial cells with a developing glycocalyx, but not a mature glycocalyx. To further investigate this phenomenon, we studied charged polymers, including poly(arginine) (polyR) and poly(glutamic acid) (polyE). PolyE does not associate with endothelial cells regardless of glycocalyx maturity, but when glycans are enzymatically degraded, it can bind to the cells. Conversely, polyR associates with endothelial cells irrespective of glycocalyx maturity or glycan degradation. These findings highlight the intricate relationship between nanomaterial charge and presentation in interactions with endothelial cells, offering insights for modulating nanomaterial interactions with the blood vessel wall.

bioengineering↗

bioRxiv2024

RORγt-dependent antigen-presenting cells direct regulatory T cell-mediated tolerance to food antigen

The gastrointestinal tract is continuously exposed to foreign antigens in food and commensal microbes with potential to induce adaptive immune responses. Peripherally induced T regulatory (pTreg) cells are essential for mitigating inflammatory responses to these agents1-4. While ROR{gamma}t+ antigen-presenting cells (ROR{gamma}t-APCs) were shown to program gut microbiota-specific pTreg5-7, their definition remains incomplete, and the APC responsible for food tolerance has remained elusive. Here, we identify a distinct subset of ROR{gamma}t-APCs, designated tolerogenic dendritic cells (tDC), required for differentiation of both food- and microbiota-specific pTreg cells and for establishment of oral tolerance. tDC development and function require expression of the transcription factors Prdm16 and ROR{gamma}t, as well as a unique Rorc(t) cis-regulatory element. Gene expression, chromatin accessibility, and surface marker analysis establish tDC as myeloid in origin, distinct from ILC3, and sharing epigenetic profiles with classical DC. Upon genetic perturbation of tDC, we observe a substantial increase in food antigen-specific T helper 2 (Th2) cells in lieu of pTreg, leading to compromised tolerance in mouse models of asthma and food allergy. Single-cell analyses of freshly resected mesenteric lymph nodes from a human organ donor, as well as multiple specimens of human intestine and tonsil, reveal candidate tDC with co-expression of PRDM16 and RORC and an extensive transcriptome shared with mice, highlighting an evolutionarily conserved role across species. Our findings suggest that a better understanding of how tDC develop and how they regulate T cell responses to food and microbial antigens could offer new insights into developing therapeutic strategies for autoimmune and allergic diseases as well as organ transplant tolerance.

immunology↗

bioRxiv2024

Two mechanisms of photoenergy regulation revealed by kinetic behaviors of chlorophyll fluorescence during light adaptation

Photochemical reactions were analyzed and modeled to observe photoenergy regulation mechanisms in PSII from measured chlorophyll fluorescence (ChlF) kinetics. Two pH-driven mechanisms were revealed: One is the PsbS-mediated non-photochemical quenching (NPQ) of activated antennae (A*), as commonly understood, and the other is a disruption of energy transfer from A* to P680. Representing the latter with a conformational change from complex formation of zeaxanthin and lutein with antennae closely described measured ChlF from initially dark-adapted state to light-adapted state and measured NPQ variations. Analysis based on the model indicates that zeaxanthin and lutein lead to slow protracted reductions in P680* and ChlF via the second mechanism without strong influence by pH or PsbS. Protonation of PsbS plays a major role via the first mechanism in responding to fast changes in illumination. The research provides insights into the mechanisms for photoenergy regulation in PSII and a kinetic model with broadened applicability.

bioengineering↗

bioRxiv2024

Comparative genomics of macaques and integrated insights into genetic variation and population history

The crab-eating macaques (Macaca fascicularis) and rhesus macaques (M. mulatta) are widely studied nonhuman primates in biomedical and evolutionary research. Despite their significance, the current understanding of the complex genomic structure in macaques and the differences between species requires substantial improvement. Here, we present a complete genome assembly of a crab-eating macaque and 20 haplotype-resolved macaque assemblies to investigate the complex regions and major genomic differences between species. Segmental duplication in macaques is [~]42% lower, while centromeres are [~]3.7 times longer than those in humans. The characterization of [~]2 Mbp fixed genetic variants and [~]240 Mbp complex loci highlights potential associations with metabolic differences between the two macaque species (e.g., CYP2C76 and EHBP1L1). Additionally, hundreds of alternative splicing differences show post-transcriptional regulation divergence between these two species (e.g., PNPO). We also characterize 91 large-scale genomic differences between macaques and humans at a single-base-pair resolution and highlight their impact on gene regulation in primate evolution (e.g., FOLH1 and PIEZO2). Finally, population genetics recapitulates macaque speciation and selective sweeps, highlighting potential genetic basis of reproduction and tail phenotype differences (e.g., STAB1, SEMA3F, and HOXD13). In summary, the integrated analysis of genetic variation and population genetics in macaques greatly enhances our comprehension of lineage-specific phenotypes, adaptation, and primate evolution, thereby improving their biomedical applications in human diseases.

genomics↗

bioRxiv2024

Circadian rhythm disruption alters mammary gland morphology and accelerates cold aggressive tumorigenesis through a LILRB4-dependent pathway

Epidemiological studies have shown that circadian rhythm disruption (CRD) is associated with the risk of breast cancer. However, the role of CRD in mammary gland morphology and aggressive basal mammary tumorigenesis and the molecular mechanisms underlying CRD and cancer risk remain unknown. To investigate the effect of CRD on aggressive tumorigenesis, a genetically engineered mouse model that recapitulates the human basal type of breast cancer was used for this study. The effect of CRD on mammary gland morphology was investigated using wild-type mice model. The impact of CRD on the tumor microenvironment was investigated using the tumors from LD12:12 and CRD mice via scRNA seq. ScRNA seq was substantiated by multiplexing immunostaining, flow cytometry, and realtime PCR. The effect of LILRB4 immunotherapy on CRD-induced tumorigenesis was also investigated. Here we identified the impact of CRD on basal tumorigenesis and mammary gland morphology and identified the role of LILRB4 on CRD-induced lung metastasis. We found that chronic CRD disrupted mouse mammary gland morphology and increased tumor burden, and lung metastasis and induced an immunosuppressive tumor microenvironment by enhancing LILRB4a expression. Moreover, CRD increased the M2-macrophage and regulatory T-cell populations but decreased the M1-macrophage, and dendritic cell populations. Furthermore, targeted immunotherapy against LILRB4 reduced CRD-induced immunosuppressive microenvironment and lung metastasis. These findings identify and implicate LILRB4a as a link between CRD and aggressive mammary tumorigenesis. This study also establishes the potential role of the targeted LILRB4a immunotherapy as an inhibitor of CRD-induced lung metastasis.

cancer biology↗

bioRxiv2024

Ultraviolet B acts as a dietary restriction mimetic by targeting mitochondrial bioenergetics.

Ultraviolet (UV) light is a common environmental stimulus, and UV exposure confers health benefits, with cellular targets still unclear. Here, we show that ultraviolet B (UVB) exposure alters mitochondrial bioenergetics in C. elegans and human skin fibroblasts triggering loss of membrane potential, mitochondrial fission and calcium release. This initial stress is followed by a recovery process relying on mitochondrial biogenesis and fusion, which prevents lasting mitochondrial damage. Strikingly, the transient decline of ATP synthesis caused by UVB-induced mitochondrial changes triggers a swift metabolic re-wiring response that resembles effects of dietary restriction (DR) at the organismal and molecular levels. Both recovery from UVB and DR-mimetic UVB effects require mitochondrial fusion, and we found that dysfunction of fusion during aging abrogates UVB benefits and sensitizes old nematodes to UVB toxicity. Finally, UVB irradiation of the skin was effective in inducing organismal fasting-like phenomena in proof-of-concept tests in young mice. We thus uncovered a novel evolutionary conserved cellular mechanism connecting UV light and metabolism. Our findings illuminate potential DR-mimetic properties of UVB and explain late life-specific UVB intolerance.

cell biology↗

bioRxiv2024

CD81+ fibroblasts, a unique subpopulation with accelerated cellular senescence, exaggerate inflammation and activate neutrophils via C3/C3aR1 axis in periodontitis

Periodontitis, a prevalent inflammatory disease worldwide, poses a significant economic burden on society and the country. Previous research has established a connection between cellular senescence and periodontitis. However, the role and mechanism of cell senescence in the progression of periodontitis have not been thoroughly investigated. This study aimed to explore the involvement of cellular senescence in the pathogenesis of periodontitis and determine the underlying mechanisms. Our findings demonstrated that senescent cells accumulated during the progress of periodontitis. Moreover, several scRNA-seq analysis suggested that gingival fibroblasts were the main cell population undergoing cellular senescence during periodontitis, which helps mitigate tissue damage and bone loss. Furthermore, we identified a high expression of CD81 in the senescent gingival fibroblast population. These cells were found to actively contribute to inflammation through their potent pro-inflammatory metabolic activity and secretion of SASP-related factors. Additionally, they recruited neutrophils via the C3/C3aR1 pathway, indirectly sustaining the inflammatory response. Senolytics via Navitoclax successfully alleviated inflammation and bone loss in periodontitis and administration of metformin could alleviate alleviated inflammation and bone loss in periodontitis through inhibiting cellular senescence. These results provide valuable insights into the cellular and molecular basis of periodontitis-induced tissue damage, highlighting the significance of fibroblast senescence. In conclusion, our study sheds light on the relationship between CD81 and cellular senescence, suggesting its potential as a therapeutic target for periodontitis.

immunology↗

bioRxiv2023

KGRACDA: A Model Based on Knowledge Graph from Recursion and Attention Aggregation for CircRNA-disease Association Prediction

CircRNA has been found to exhibit with human diseases, rendering the investigation of the circRNA-disease association (CDA) of significant importance. Deep learning provides a novel avenue for predicting CDA, however, due to the limited availability of comprehensive CDA datasets, most existing methods operate by directly reasoning over triplets of embedded entities and relations, neglecting the explicit capture local depth information. In this study, we propose a novel approach called Knowledge Graph-Enhanced Recursive Aggregation for CDA (KGRACDA), which enables the querying of explicit local structures surrounding triplets, thereby serving as crucial evidence for predicting CDA. First, we construct a comprehensive CDA knowledge graph utilizing a large-scale, multi-source heterogeneous dataset. Subsequently, we develop a graph neural network using recursive techniques and employ attention aggregation to generate CDA prediction scores. Experimental results demonstrate that KGRACDA surpasses other state-of-the-art methods by explicitly capturing local depth information related to CDA. To improve the convenience of CDA prediction, we have updated an interactive web platform named HNRBase v2.0, which provides rich visualization of circRNA data, and allows users to easily download data and predict CDA using KGRACDA. The code and datasets are publicly available at https://github.com/maoyuanma/KGRACDA.

bioinformatics↗

bioRxiv2023

Use of dye sensitizers for increased photoacoustic mechanosensation

The photoacoustic effect refers to the generation of pressure waves in matter stimulated by light[1]. In the context of radiology (i.e., photoacoustic imaging) waves generated by pulsed laser light are detected by an ultrasound transducer[2-4]. It has been shown that photoacoustic waves produce a mechanical, tactile sensation in humans on bare skin[5]. In a series of psychophysical experiments, performed with both medical grade and off-the-shelf pulsed light systems, participants could detect, categorically describe, and discern the direction of travel of pulsed optical stimuli with the use of a dye as an optical absorber on the skin. To a large extent, the sensations were perceived as localized vibration on the glabrous surface of the fingers, when sensitized with the thin film of dye. This form of sensory stimulation demonstrates an enhanced non-contact, non-optogenetic, in situ activation of the mechanosensory system. This modality of sensation may provide a tool that leads to new insights in psychology, neuroscience, mechanobiology, and the health sciences. Finally, it has many advantageous characteristics for human interaction with artificial environments, as optical signals can be projected onto the skin across distances.

neuroscience↗

bioRxiv2023

DeltaAI: Semi-Autonomous Tissue Grossing Measurements and Recommendations using Neural Radiance Fields for Rapid, Complete Intraoperative Histological Assessment of Tumor Margins

Mohs Micrographic Surgery (MMS) aims to excise cutaneous cancer with real-time margin analysis. However, manual tissue grossing and analysis can be inefficient, so we propose DeltaAI, a novel workflow that utilizes Neural Radiance Fields (NeRF) to enable rapid tissue grossing and generate a 3D model in an augmented reality (AR) environment. In our study, we captured 30-second videos of 17 MMS specimens using a photogrammetry turntable and cellphone camera. Preprocessing the tissues with segmentation models, we created a dataset of 923, 360-degree-view, images per video (17 videos). Using COLMAP, we estimated poses for sparse tissue reconstructions and trained the NeRF model for 3D volumetric tissue renderings. The results demonstrated that DeltaAI generated more accurate and complete 360-degree, 3D tissue renderings compared to previous models, while also achieving significantly faster runtimes. Our proposed semi-autonomous NeRF-based workflow has the potential to enhance the speed of MMS specimen processing, measurement, report generation, and margin assessment. It can inform real-time grossing decisions, automate the export of electronic health record data, and facilitate time-efficient and complete cancer excisions. Moreover, DeltaAI can contribute to the wider adoption of AI technology in clinical settings by improving tissue modeling for manual grossing.

pathology↗

bioRxiv2023

BertNDA: a Model Based on Graph-Bert and Multi-scale Information Fusion for ncRNA-disease Association Prediction

Non-coding RNAs (ncRNAs) are a class of RNA molecules that lack the ability to encode proteins in human cells, yet play crucial roles in various biological process. Understanding these relationships and how different ncRNAs interact with each other to affect diseases can vastly contribute to their diagnosis, prevention, and treatment. However, predicting tertiary interactions between ncRNA-disease associations by utilizing structural information across multiple scales remains a challenging task. It should be noted that research on predicting tertiary interaction between trinary ncRNA-disease associations is scarce, highlighting the need for further studies in this area. In this work, we propose a predictive framework, called BertNDA, which aims to predict association between miRNA, lncRNA and disease. The framework employs Laplace transform of graph structure and WL (Weisfeiler-Lehman) absolute role coding to extract global information. Local information is identified by the connectionless subgraph which aggregates neighbor feature. Moreover, an EMLP (Element-wise MLP) structure is designed to fuse the multi-scale feature representation of nodes. Furtherly, feature representation is encoded by using a Transformer-encoder structure, the prediction-layer outputs the final correlation between miRNA, lncRNA and diseases. The 5-fold cross-validation result furtherly demonstrates that BertNDA outperforms the state-of-the-art method in predicting assignment. Furthermore, an online prediction platform that embeds our prediction model is designed for users to experience. Overall, our model provides an efficient, accurate, and comprehensive tool for predicting ncRNA-disease associations. The code of our method is available in: https://github.com/zhiweining/BertNDA-main.

bioinformatics↗

bioRxiv2023

Progesterone is an Inducement of Heritable Pulmonary Arterial Hypertension with BMPR2 Mutation

BackgroundBone morphogenetic protein type II receptor (BMPR2) gene mutation accounts for 80% of patients with heritable pulmonary artery hypertension (HPAH), and female mutation carriers have significantly higher penetrance rate than males. The inducement of HPAH penetrance and the mechanism of sex differential penetrance are still elusive. MethodsWe infected or transfected pulmonary artery smooth muscle cells (PASMCs) with shBMPR2 lentivirus or siBMPR2 to simulate the pathologic condition of BMPR2 heterozygous mutation and treated them with progesterone. The HPAH patient-derived induced pluripotent stem cells (iPSCs) were induced into vascular smooth muscle cells (VSMCs) to further verify the cellular phenotype. Wild-type flox+/- female mice and SM22-cre BMPR2 flox+/- female mice (CKO mice) were administered with 1-month progesterone, and their phenotype of PAH was evaluated by right heart catheterization and histopathological examination. ResultsProgesterone promoted the proliferation of PASMCs with BMPR2-knockdownby activating ERK pathway via progesterone receptor (PGR). Activated ERK not only upregulated the phosphorylation and elevation of cMYC, but also induced the transcription of endothelin (EDN1) by promoting the nuclear entry of c-JUN and combination on the its promoter region. Similar results were confirmed by iPSCs-VSMCs experiment. CKO mice developed PAH spontaneously and had increased expression of EDN1, which was further aggravated by exogenous progesterone. ConclusionsProgesterone might be an inducement of HPAH penetrance caused by BMPR2 mutation, accounting for sex differential penetrance.

molecular biology↗

bioRxiv2023

KGETCDA: an efficient representation learning framework based on knowledge graph encoder from transformer for predicting circRNA-disease associations

Recent studies have demonstrated the significant role that circRNA plays in the progression of human diseases. Identifying circRNA-disease associations (CDA) in an efficient manner can offer crucial insights into disease diagnosis. While traditional biological experiments can be time-consuming and labor-intensive, computational methods have emerged as a viable alternative in recent years. However, these methods are often limited by data sparsity and their inability to explore high-order information. In this paper, we introduce a novel method named Knowledge Graph Encoder from Transformer for predicting CDA (KGETCDA). Specifically, KGETCDA first integrates more than 10 databases to construct a large heterogeneous non-coding RNA dataset, which contains multiple relationships between circRNA, miRNA, lncRNA and disease. Then, a biological knowledge graph is created based on this dataset and Transformer-based knowledge representation learning and attentive propagation layers are applied to obtain high-quality embeddings with accurately captured high-order interaction information. Finally, multilayer perceptron is utilized to predict the matching scores of CDA based on their embeddings. Our empirical results demonstrate that KGETCDA significantly outperforms other state-of-the-art models. To enhance user experience, we have developed an interactive web-based platform named HNRBase that allows users to visualize, download data and make predictions using KGETCDA with ease.

bioinformatics↗

bioRxiv2023

CircRNA-disease inference using deep ensemble model based on triple association

Accumulating evidence indicates more and more circular RNAs (i.e. circRNAs) have played a vital role in regulating gene expression and are related to diseases through different biological procedures. Predicting circRNA-disease associations helps to conjecture possible disease related circRNA and facilitate human disease diagnosis and downstream treatment. Nevertheless, little effort was made to uncover the interaction between various diseases and circRNAs. In our work, human circRNA-disease association network is first generated using known miRNA-circRNA interactions and disease related miRNA (microRNA) information. Then we further integrated this information to compute similarity scores between human diseases and circRNAs. Here, we proposed one deep ensemble model called DeepInteract, which first used two stacked auto-encoders to explore hidden features utilizing similarity information, and adopted a 3-layer neuron network to predict the final association. Our method is capable of capturing more complex non-linear features comparing to other approaches. Our results indicate the proposed method is superior to other previous competitors. Many prediction results have been validated by some biological experiments using our model.

bioinformatics↗

bioRxiv2023

Neuronal Transcriptome Disruption, Tau Accumulation and Synapse Loss in Alzheimer's Knock-in Mice Require Cellular Prion Protein

BackgroundCellular prion protein (PrPC) is a high-affinity cell-surface receptor for Amyloid-{beta} oligomers (A{beta}o). In certain overexpression models of Alzheimers Disease (AD), pharmacology and genetics demonstrate its essential role for synaptic plasticity impairment, memory deficits and synapse loss. However, PrPCs role in AD-related phenotypes with endogenous expression levels, its role in tau accumulation and its effect on imaging biomarkers are unknown. The necessity of PrPC for transcriptomic alterations driven by A{beta} across cell types is unexplored. MethodsThe role of PrPC was examined as a function of age in homozygous AppNL-G-F/hMapt double knock-in mice (DKI). Phenotypes of AppNL-G-F/hMapt mice with a deletion of Prnp expression (DKI; Prnp-/-) were compared with DKI mice with intact Prnp, mice with a targeted deletion of Prnp (Prnp-/-), and mice with intact Prnp (WT). Phenotypes examined included behavioral deficits, synapse loss by PET imaging, synapse loss by immunohistology, tau pathology, gliosis, inflammatory markers, and snRNA-seq transcriptomic profiling. ResultsBy 9 months age, DKI mice showed learning and memory impairment, but DKI; Prnp-/- and Prnp-/- groups were indistinguishable from WT. Synapse loss in DKI brain, measured by [18F]SynVesT-1 SV2A PET or anti-SV2A immunohistology, was prevented by Prnp deletion. Accumulation of Tau phosphorylated at aa 217 and 202/205, C1q tagging of synapses, and dystrophic neurites were all increased in DKI mice but each decreased to WT levels with Prnp deletion. In contrast, astrogliosis, microgliosis and A{beta} levels were unchanged between DKI and DKI; Prnp-/- groups. Single-nuclei transcriptomics revealed differential expression in neurons and glia of DKI mice relative to WT. For DKI; Prnp-/- mice, the majority of neuronal genes differentially expressed in DKI mice were no longer significantly altered relative to WT, but most glial DKI-dependent gene expression changes persisted. The DKI-dependent neuronal genes corrected by Prnp deletion associated bioinformatically with synaptic function. Additional genes were uniquely altered only in the Prnp-/-or the DKI; Prnp-/- groups. ConclusionsA functional Prnp gene is required in AppNL-G-F/hMapt double knock-in mice for synapse loss, phospho-tau accumulation and neuronal gene expression. These data support the efficacy of targeting the A{beta}o-PrPC interaction to prevent A{beta}o-neurotoxicity and pathologic tau accumulation in AD.

neuroscience↗

bioRxiv2022

Disulfide stabilization reveals conserved dynamic features between SARS-CoV-1 and SARS-CoV-2 spikes.

SARS-CoV-2 spike protein (S) is structurally dynamic and has been observed by cryo-EM to adopt a variety of prefusion conformations that can be categorized as locked, closed and open. The locked conformations feature tightly packed trimers with structural elements incompatible with RBD in "up" position. For SARS-CoV-2 S, it has been shown that the locked conformations are transient under neutral pH. Probably due to their transience, locked conformations remain largely uncharacterized for SARS-CoV-1 S. Intriguingly, locked conformations were the only conformations captured for S proteins of bat and pangolin origin SARS-related coronaviruses. In this study, we introduced x1, x2, and x3 disulfides into SARS-CoV-1 S. Some of these disulfides have been shown to preserve rare locked conformations when introduced to SARS-CoV-2 S. Introduction of these disulfides allowed us to image a variety of locked and other rare conformations for SARS-CoV-1 S by cryo-EM. We identified bound cofactors and structural features that are associated with SARS-CoV-1 S locked conformations. We compare newly determined structures to other available spike structures of Sarbecoviruses to identify conserved features and discuss their possible functions.

biochemistry↗

bioRxiv2022

LncDLSM: Identification of Long Non-coding RNAs with Deep Learning-based Sequence Model

Long non-coding RNAs (LncRNAs) serve a vital role in regulating gene expressions and other biological processes. Differentiation of lncRNAs from protein-coding transcripts helps researchers dig into the mechanism of lncRNA formation and its downstream regulations related to various diseases. Previous works have been proposed to identify lncRNAs, including traditional bio-sequencing and machine learning approaches. Considering the tedious work of biological characteristic-based feature extraction procedures and inevitable artifacts during bio-sequencing processes, those lncRNA detection methods are not always satisfactory. Hence, in this work, we presented lncDLSM, a deep learning-based framework differentiating lncRNA from other protein-coding transcripts without dependencies on prior biological knowledge. lncDLSM is a helpful tool for identifying lncRNAs compared with other biological feature-based machine learning methods and can be applied to other species by transfer learning achieving satisfactory results. Further experiments showed that different species display distinct boundaries among distributions corresponding to the homology and the specificity among species, respectively. An online web server is provided to the community for easy use and efficient identification of lncRNA, available at http://39.106.16.168/lncDLSM.

bioinformatics↗

bioRxiv2022

Protein engineering via Bayesian optimization-guided evolutionary algorithm and robotic experiments

Protein engineering aims to find top functional sequences in a vast design space. For such an expensive "black-box" function optimization problem, Bayesian optimization is a principled sample-efficient approach, which is guided by a surrogate model of the objective function. Unfortunately, Bayesian optimization is computationally intractable with the vast search space. Even worse, it proposes sequences sequentially, making it incompatible with batched wet-lab measurement. Here, we report a scalable and batched method, Bayesian Optimization-guided EVOlutionary (BO-EVO) algorithm, to guide multiple rounds of robotic experiments to explore protein fitness landscapes of combinatorial mutagenesis libraries. We first examined various design specifications based on an empirical landscape of protein G domain B1. Then, BO-EVO was successfully generalized to another empirical landscape of an Escherichia coli kinase PhoQ, as well as simulated NK landscapes with up to moderate epistasis. This approach was then applied to guide robotic library creation and screening to engineer enzyme specificity of RhlA, a key biosynthetic enzyme for rhamnolipid biosurfactants. A 4.8-fold improvement in producing a target rhamnolipid congener was achieved after examining less than 1% of all possible mutants after 4 iterations. Overall, BO-EVO proves to be an efficient and general approach to guide combinatorial protein engineering without prior knowledge.

synthetic biology↗