Biology subjects

Zhao, H.

Publications and source records attributed to Zhao, H..

At least 217 records · Page 12Linked to original sources

bioRxiv2024

Development of a tick-borne encephalitis virus nanoparticle vaccine utilizing envelope dimer

Tick-borne encephalitis virus (TBEV) is primarily transmitted to humans through tick bites, leading to symptoms of encephalitis with a fatality rate ranging from 1% to 30%, depending on the virus subtype. Currently, only inactivated virus vaccines are available for human use, though break-through infections can still occur. Therefore, developing new vaccines against TBEV is crucial. In this study, we designed and characterized a novel nanoparticle-based TBEV envelope (E) dimer vaccine. We successfully expressed and purified the TBEV E dimer by engineering disulfide bond mutants, and animal experiments demonstrated that the E dimer protein elicited a stronger immunogenic responses compared to the E monomer protein. Further, antibody depletion experiment confirmed that the E dimer protein effectively mimics the virion surface structure, inducing robust humoral immunity targeted at neutralizing epitopes. We also presented the TBEV E dimer on the surface of the nanoparticle Mi3 using the SpyCatcher-SpyTag system, with animal experiment showing that this TBEV E dimer nanoparticle vaccine elicited a potent humoral immune response. These findings offer new insights into the immunogenicity of the TBEV E dimer and suggest that the nanoparticle-based TBEV E dimer vaccine represents a promising and highly effective candidate for TBEV immunization.

microbiology↗

bioRxiv2024

Inferring Cell-Type-Specific Co-Expressed Genes from Single Cell Data

Cell-type-specific gene co-expression networks are widely used to characterize gene relationships. Although many methods have been developed to infer such co-expression networks from single-cell data, the lack of consideration of false positive control in many evaluations and downstream analyses may lead to incorrect conclusions because higher reproducibility, higher functional coherence, and a larger overlap with known biological networks may not imply better performance if the false positives are not well controlled. In this study, we systematically compared two distinct criteria for selecting correlated gene pairs from single-cell data, p-value versus correlation strength. We found that the use of p-values instead of correlation strength is more robust for both selecting meaningful gene pairs and for the fair benchmarking of co-expression estimation methods. To make this approach universally applicable, we extended and validated a simulation method that can efficiently and reliably generate empirical p-values for co-expression estimation methods that do not have corresponding or well-controlled p-values. Furthermore, we demonstrated that a fair comparison of the estimation methods requires adjusting for the varying number of gene pairs they identified and accounting for the inherent expression-level biases within ground truth biological networks. Our study provides a practical guide for researchers to select reliable correlated gene pairs for downstream study and establishes a more rigorous standard for the evaluation and comparison of gene co-expression network estimation methods.

bioinformatics↗

bioRxiv2024

Single-cell atlas of developing mouse palates reveals cellular and molecular transitions in periderm cell fate

Cleft palate is one of the most common congenital craniofacial disorders that affects childrens appearance and oral functions. Investigating the transcriptomics during palatogenesis is crucial for comprehending the etiology of this disorder and facilitating prenatal molecular diagnosis. However, there is limited knowledge about the single-cell differentiation dynamics during mid- and late-palatogenesis, specifically regarding the subpopulations and developmental trajectories of periderm, a rare but critical cell population. Here we explore the single-cell landscape of mouse developing palates from E10.5 to E16.5. We systematically depict the single-cell transcriptomics of mesenchymal and epithelial cells during palatogenesis, including subpopulations and differentiation dynamics. Additionally, we identify four subclusters of palatal periderm and construct two distinct trajectories of cell fates for periderm cells. Our findings reveal that Claudins and Arhgap29 play a role in the non-stick function of the periderm before the palatal shelves contact, and Pitx2 mediates the adhesion of periderm during the contact of opposing palatal shelves. Furthermore, we demonstrate that epithelial-mesenchymal transition (EMT), apoptosis, and migration collectively contribute to the degeneration of periderm cells in the medial epithelial seam. Taken together, our study suggests a novel model of periderm development during palatogenesis and delineates the cellular and molecular transitions in periderm cell determination.

genomics↗

bioRxiv2024

Embryonic Mast Cells Arise from Cpa3-expressing Precursors Independent of Granulocyte-Monocyte Progenitors

Most of the mast cells (MCs) in connective tissues, such as skin, are long-lived embryonic-derived immune cells that play important roles in host defense and various immunological diseases, including allergies. Their embryonic origin and ontogeny remain to be fully studied since several overlapping waves of embryonic hematopoiesis have been shown to give rise to these cells. Here, combining single-cell RNA sequencing and new genetic fate mapping models, we identified a Cpa3-expressing population sequentially appearing in the yolk sac, fetal liver, and peripheral tissues which gives rise to dermal MCs during embryonic days 11.5 to 14.5. Using in vitro differentiation and in vivo transplant methods, we identified a Ter119-F4/80- CD45+CD117+CD16/32+CD135-CD115-Ly6C-CD34lo population as potential fetal liver MC precursors. Fate mapping with Cpa3CreERT2and Zbtb16CreERT2 models, as well as the granulocyte-monocyte progenitor (GMP) models Ms4a3Cre and ElaneCre, demonstrated that MCs arise from Cpa3+ progenitors rather than Ms4a3+ or Elane+ GMPs. A corresponding population with a similar developmental trajectory was also identified in human early yolk sac and fetal liver, suggesting a conserved MC developmental program across species. These findings delineate the detailed developmental path of MCs in embryos, permitting future functional studies in immunity and development. HighlightsO_LICpa3-expressing cells in the yolk sac and fetal liver contain mast cell precursors C_LIO_LICpa3CreERT2 labels embryonic mast cells and their precursors C_LIO_LIEmbryonic mast cells arise through Cpa3+ mast cell precursors, but not Elane+/Ms4a3+ GMPs C_LIO_LIThe seeding of MCPs to embryonic skin slows down after E14.5 C_LI

immunology↗

bioRxiv2024

Efficient Sampling of PROTAC-Induced Ternary Complexes

Proteolysis targeting chimeras (PROTACs) are bifunctional small molecules that recruit an E3 ligase to a target protein, leading to ubiquitin transfer and subsequent proteasomal degradation. The formation of ternary complexes is a crucial step in PROTAC-induced protein degradation, and gaining structural insights is essential for rational PROTAC design. In this study, we present a novel approach for efficiently sampling PROTAC-induced ternary complexes, which has been validated using 40 co-crystallized ternary complex structures. In comparison to protein-protein docking-based integrative approaches, our method achieved an impressive success rate of 97% and 50% retrospectively, measured by C-RMSD to the crystal structure within 10 and 4 [A], respectively, with an average CPU time of 4 hours. Notably, utilizing unbound protein structures, the C-RMSD values between the predicted and experimental structures were consistently within 7 [A] across six WDR5-PROTAC-VHL ternary structures. Our open-source software enables the modeling of ternary structures in a single step and holds promise for enhancing PROTAC design efforts. TOC O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=141 SRC="FIGDIR/small/619573v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@c380e9org.highwire.dtl.DTLVardef@14478c8org.highwire.dtl.DTLVardef@559e67org.highwire.dtl.DTLVardef@1128491_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2024

Phage CRISPR-like regulatory RNAs silence bacterial adaptive and innate immunity

CRISPR RNAs (crRNAs) encoded by CRISPR arrays direct Cas nucleases against viruses. Some CRISPR-Cas loci also contain mini-arrays encoding crRNA-like RNAs (crlRNAs), which modulate transcription of cas or associated toxin genes that safeguard CRISPR-Cas activity. Here, we report that phages hijack these host crlRNA regulatory circuits to suppress bacterial immunity. Viral crlRNAs compete with host crRNAs for Cas protein binding and reprogram Cas proteins to silence their own expression or suppress transcription of CRISPR-safeguarding toxins. These anti-defense crlRNAs are found across diverse phages and plasmids, frequently organized into multiplexed transcriptional units resembling CRISPR arrays, and often synergize with co-encoded anti-CRISPR proteins to disable hierarchical host immune responses. Furthermore, we demonstrate that these compact viral crlRNA architectures can be harnessed for efficient mammalian genome editing. Our findings uncover widespread viral hijacking of host Cas proteins via crlRNAs, highlighting the recurrent exaptation of CRISPR-Cas regulatory functions to counter multilayered bacterial immune defenses.

microbiology↗

bioRxiv2024

Chemoenzymatic Synthesis Planning Guided by Reaction Type Score

Thanks to the growing interests in computer-aided synthesis planning (CASP), a wide variety of retrosynthesis and retrobiosynthesis tools have been developed in the past decades. However, synthesis planning tools for multi-step chemoenzymatic reactions are still rare despite the widespread use of enzymatic reactions in chemical synthesis. Herein we report a reaction type score (RTscore)-guided chemoenzymatic synthesis planning (RTS-CESP) strategy. Briefly, the RTscore is trained using a text-based convolutional neural network (TextCNN) to distinguish synthesis reactions from decomposition reactions and evaluate synthesis efficiency. Once multiple chemical synthesis routes are generated by a retrosynthesis tool for a target molecule, RTscore is used to rank them and find the step(s) that can be replaced by enzymatic reactions to improve synthesis efficiency. As proof of concept, RTS-CESP was applied to 10 molecules with known chemoenzymatic synthesis routes in literature and was able to predict all of them with six being the top-ranked routes. Moreover, RTS-CESP was employed for 1000 molecules in the boutique database and was able to predict the chemoenzymatic synthesis routes for 554 molecules, outperforming ASKCOS, a state-of-the-art chemoenzymatic synthesis planning tool. Finally, RTS-CESP was used to design a new chemoenzymatic synthesis route for the FDA-approved drug Alclofenac, which was shorter than literature-reported route and have been experimentally validated.

bioinformatics↗

bioRxiv2024

A unified framework for drug-target interaction prediction by semantic-guided meta-path method

Drug-target interaction (DTI) prediction plays a crucial role in drug development, impacting areas such as virtual screening, drug repurposing, and the identification of potential drug side effects. Despite significant efforts dedicated to improving DTI prediction, existing methods still struggle with the challenges posed by the high sparsity of DTI datasets and the complexity of capturing heterogeneous information in biological networks. To address these challenges, we propose a unified framework for DTI prediction based on a semantics-guided meta-path walk. Specifically, we first pre-train drug and protein embeddings to capture their semantic information. This semantic information is then leveraged to guide a meta-path-based random walk on the biological heterogeneous network, generating sequences of interactions. These sequences are used to compute embedding features via a heterogeneous skip-gram model, which are subsequently fed into downstream tasks to predict DTIs. SGMDTI achieves substantial performance improvement over other state-of-the-art methods for drug-target interaction prediction. Moreover, it excels in the cold-start scenario, which is often a challenging case in DTI prediction. These results indicate the effectiveness of our approach in predicting drug-target interactions.Experimental datasets and experimental codes can be found in https://github.com/HYLPRC/SGMDTI

bioinformatics↗

bioRxiv2024

Light modulates glucose and lipid homeostasis via the sympathetic nervous system

Light is an important environmental factor for vision, and for diverse physiological and psychological functions. Light can also modulate glucose metabolism. Here, we show that in mice, light is critical for glucose and lipid homeostasis by regulating the sympathetic nervous system, independent of circadian disruption. Light deprivation from birth elicits insulin hypersecretion, glucagon hyposecretion, lower gluconeogenesis, and reduced lipolysis by 6- 8 weeks, in male, but not, female mice. These metabolic defects are consistent with blunted sympathetic activity, and indeed, sympathetic responses to a cold stimulus are significantly attenuated in dark-reared mice. Further, long-term dark rearing leads to body weight gain, insulin resistance, and glucose intolerance. Notably, metabolic dysfunction can be partially alleviated by 5 weeks exposure to a regular light-dark cycle. These studies provide insight into circadian-independent mechanisms by which light directly influences whole-body physiology and inform new approaches for understanding metabolic disorders linked to aberrant environmental light conditions. TeaserLight exerts direct circadian-independent effects on glucose and lipid metabolism.

neuroscience↗

bioRxiv2024

Catabolism of extracellular glutathione supplies amino acids to support tumor growth

Restricting amino acids from tumors is an emerging therapeutic strategy with significant promise. While typically considered an intracellular antioxidant with tumor-promoting capabilities, glutathione (GSH) is a tripeptide of cysteine, glutamate, and glycine that can be catabolized, yielding amino acids. The extent to which GSH-derived amino acids are essential to cancers is unclear. Here, we find that GSH catabolism promotes tumor growth. We show that depletion of intracellular GSH does not perturb tumor growth, and extracellular GSH is highly abundant in the tumor microenvironment, highlighting the potential importance of GSH outside of tumors. We find supplementation with GSH can rescue cancer cell survival and growth in cystine-deficient conditions, and this rescue is dependent on the catabolic activity of {gamma}-glutamyltransferases (GGTs). Finally, pharmacologic targeting of GGTs activity prevents the breakdown of circulating GSH, lowers tumor cysteine levels, and slows tumor growth. Our findings indicate a non-canonical role for GSH in supporting tumors by acting as a reservoir of amino acids. Depriving tumors of extracellular GSH or inhibiting its breakdown is potentially a therapeutically tractable approach for patients with cancer. Further, these findings change our view of GSH and how amino acids, including cysteine, are supplied to cells.

cancer biology↗

bioRxiv2024

scMODAL: A general deep learning framework for comprehensive single-cell multi-omics data alignment with feature links

Recent advancements in single-cell technologies have enabled comprehensive characterization of cellular states through transcriptomic, epigenomic, and proteomic profiling at single-cell resolution. These technologies have significantly deepened our understanding of cell functions and disease mechanisms from various omics perspectives. As these technologies evolve rapidly and data resources expand, there is a growing need for computational methods that can integrate information from different modalities to facilitate joint analysis of single-cell multi-omics data. However, integrating single-cell omics datasets presents unique challenges due to varied feature correlations and technology-specific limitations. To address these challenges, we introduce scMODAL, a deep learning framework tailored for single-cell multi-omics data alignment using feature links. scMODAL integrates datasets with limited known positively correlated features, leveraging neural networks and generative adversarial networks to align cell embeddings and preserve feature topology. Our experiments demonstrate scMODALs effectiveness in removing unwanted variation, preserving biological information, and accurately identifying cell subpopulations across diverse datasets. scMODAL not only advances integration tasks but also supports downstream analyses such as feature imputation and feature relationship inference, offering a robust solution for advancing single-cell multi-omics research.

bioinformatics↗

bioRxiv2024

Multi-omics analysis reveals the mechanism of BnC07MYB3a is involved in seed coat color in Brassica napus L.

In rapeseed (Brassica napus), yellow-seeded varieties accumulate less flavonoid pigments (anthocyanins/proanthocyanidins) in their seed coats compared with black-seeded varieties. The yellow-seeded trait is associated with greatly improved seed oil yield, quality, and commercial value. Many R2R3 MYB activators have been characterized in rapeseed, but how MYB-type repressors affect pigment biosynthesis is not yet fully understood. In this study, we performed transcriptome sequencing and metabolomic analysis of B. napus varieties with extreme differences in seed coat color, combined with weighted gene co-expression network analysis. This analysis identified an R2R3-MYB-type transcription factor, BnC07MYB3a (BnaC07G0178800ZS), as a candidate regulator of the yellow-seeded trait in B. napus. Overexpressing BnC07MYB3a in Arabidopsis thaliana and B. napus downregulated the expression of flavonoid biosynthetic genes, resulting in significantly lower anthocyanin and proanthocyanidin accumulation than in the wild-type and a lighter seed coat color in transgenic plants. BnC07MYB3a directly binds to the promoter of the TRANSPARENT TESTA (TT) gene BnTT6 and represses its expression. BnC07MYB3a also physically interacts with BnA06bHLH92a and the WD40 transcription factor TRANSPARENT TESTA GLABRA1 (BnTTG1), suggesting that they might form a previously unidentified MYB-bHLH-WD40 transcription factor complex. Our results reveal the molecular mechanism and regulatory network of BnC07MYB3a in determining seed coat color in B. napus and provide a genetic resource for breeding yellow-seeded cultivars of B. napus.

molecular biology↗

bioRxiv2024

Mapping and engineering RNA-controlled architecture of the multiphase nucleolus

Biomolecular condensates are key features of intracellular compartmentalization. As the most prominent nuclear condensate in eukaryotes, the nucleolus is a layered multiphase liquid-like structure and the site of ribosome biogenesis. In the nucleolus, ribosomal RNAs (rRNAs) are transcribed and processed, undergoing multiple maturation steps that ultimately result in formation of the ribosomal small subunit (SSU) and large subunit (LSU). However, how rRNA processing is coupled to the layered nucleolar organization is poorly understood due to a lack of tools to precisely monitor and perturb nucleolar rRNA processing dynamics. Here, we developed two complementary approaches to spatiotemporally map rRNA processing and engineer de novo nucleoli. Using sequencing in parallel with imaging, we found that rRNA processing steps are spatially segregated, with sequential maturation of rRNA required for its outward movement through nucleolar phases. Furthermore, by generating synthetic de novo nucleoli through an engineered rDNA plasmid system in cells, we show that defects in SSU processing can alter the ordering of nucleolar phases, resulting in inside-out nucleoli and preventing rRNA outflux, while LSU precursors are necessary to build the outermost layer of the nucleolus. These findings demonstrate how rRNA is both a scaffold and substrate for the nucleolus, with rRNA acting as a programmable blueprint for the multiphase architecture that facilitates assembly of an essential molecular machine.

cell biology↗

bioRxiv2024

INSPIRE: interpretable, flexible and spatially-aware integration of multiple spatial transcriptomics datasets from diverse sources

Recent advances in spatial transcriptomics technologies have led to a growing number of diverse datasets, offering unprecedented opportunities to explore tissue organizations and functions within spatial contexts. However, it remains a significant challenge to effectively integrate and interpret these data, often originating from different samples, technologies, and developmental stages. In this paper, we present INSPIRE, a deep learning method for integrative analyses of multiple spatial transcriptomics datasets to address this challenge. With designs of graph neural networks and an adversarial learning mechanism, INSPIRE enables spatially informed and adaptable integration of data from varying sources. By incorporating non-negative matrix factorization, INSPIRE uncovers interpretable spatial factors with corresponding gene programs, revealing tissue architectures, cell type distributions and biological processes. We demonstrate the capabilities of INSPIRE by applying it to human cortex slices from different samples, mouse brain slices with complementary views, mouse hippocampus and embryo slices generated through different technologies, and spatiotemporal organogenesis atlases containing half a million spatial spots. INSPIRE shows superior performance in identifying detailed biological signals, effectively borrowing information across distinct profiling technologies, and elucidating dynamical changes during embryonic development. Furthermore, we utilize INSPIRE to build 3D models of tissues and whole organisms from multiple slices, demonstrating its power and versatility.

bioinformatics↗

bioRxiv2024

AI-Augmented R-Group Exploration in Medicinal Chemistry

Efficient R-group exploration in the vast chemical space, enabled by increasingly available building blocks or generative AI, remains an open challenge. Here, we developed an enhanced Free-Wilson QSAR model embedding R-groups by atom-centric pharmacophoric features. Regioisomers of R-groups can be distinguished by explicitly accounting for the atomic positions. Good predictivity is observed consistently across 12 public datasets. Integrated into an open-source program, we showcase its application in performing classic Free-Wilson analysis as well as R-group exploration in uncharted chemical space.

bioinformatics↗

bioRxiv2024

A multidimensional Pan-cancer analysis of CDKN1A identifies CDKN1A as an Immunological and Prognostic Biomarker

CDKN1A/p21 is well recognized for its role in cell cycle regulation and genomic stability. However, its functions in the Tumor microenvironment (TME) and tumor immunity are not yet fully understood. Hereby, we explored CDKN1A expression and immunological/prognostic values via various databases and analytical methods including cBioPortal, Kaplan-Meier, UCSCXenaShiny, TIMER, Single-cell RNA sequencing (scRNA-seq) analysis, etc. In addition, we explored different approaches including CCK8, EdU, Colony formation, Drug sensitivity and Annixin-V assay to explore the influence of p21 in proliferative capacity in cancer cells. We found that CDKN1A is lowly expressed in BLCA, BRCA, COAD, KICH, LUAD, LUSC, PRAD, READ and STAD compared to normal samples, whereas it is highly expressed in CHOL, HNSC, KIRC, KIRP and THCA compared to normal cohorts. CDKN1A expression is significantly correlated with overall survival, disease-specific survival, disease-free survival and progression-free interval different cancer types. Additionally, CDKN1A is associated with CD4+ T cell, CD8+ T cell, Neutrophil, Macrophage and Myeloid dendritic cell infiltration in diverse cancer types. Functional experiments reveal that p21 overexpression leads to a significant reduction in proliferative capacity, facilitates cell apoptosis and senescence in multiple cancer cell lines. In contrast, silenced p21 facilitates cell growth and wound closure, prevent cell senescence in different cancer cell lines. In conclusion, our findings suggest that CDKN1A may serve as a valuable prognostic and immunotherapeutic marker in diverse cancer.

cancer biology↗

bioRxiv2024

Design of diverse, functional mitochondrial targeting sequences across eukaryotic organisms using variational autoencoder

Mitochondria play a key role in energy production and cellular metabolism, making them a promising target for metabolic engineering and disease treatment. However, despite the known influence of passenger proteins on localization efficiency, only a few protein-localization tags have been characterized for mitochondrial targeting. To address this limitation, we exploited Variational Autoencoder (VAE), an unsupervised deep learning framework, to design novel mitochondrial targeting sequences (MTSs). In silico analysis revealed that a high fraction of generated peptides are functional and possess features important for mitochondrial targeting. Additionally, we devised a sampling scheme to indirectly address biases arising from the differences in mitochondrial protein import machinery and characterized artificial MTSs in four eukaryotic organisms. These sequences displayed significant diversity, sharing less than 60% sequence identity with MTSs in the UniProt database. Moreover, we trained a separate VAE and employed latent space interpolation to design dual targeting sequences capable of targeting both mitochondria and chloroplasts, shedding light on their evolutionary origins. As a proof-of-concept, we demonstrate the application of these artificial MTSs in increasing titers of 3-hydroxypropionic acid through pathway compartmentalization and improving 5-aminolevulinate synthase delivery by 1.62-fold and 4.76-fold, respectively. Overall, our work not only demonstrates the potential of generative artificial intelligence in designing novel, functional mitochondrial targeting sequences but also highlights their utility in engineering mitochondria for both fundamental research and practical applications in biology.

synthetic biology↗

bioRxiv2024

Gain-of-function study reveals the pleiotropic roles of serine protease HtrA in Borrelia burgdorferi

High-temperature requirement protease A (HtrA) is a family of serine proteases degrading misfolded and damaged proteins that are toxic to bacteria. The Lyme disease agent Borrelia burgdorferi encodes a single HtrA (BbHtrA). Previous studies have shown that BbHtrA is a key virulence determinant of B. burgdorferi as a deletion mutant of htrA ({Delta}htrA) fails to establish infection in mice. However, previous complementation could only restore protein expression but not infectivity in mice. In this report, we first identify the native promoter of BbHtrA which allows us to construct a fully complemented{Delta} htrA strain. Follow up promoter activity analysis reveals that BbHtrA is likely dually regulated by the house keeping sigma factor RpoD and the alternative sigma factor RpoS. The{Delta} htrA mutant exhibits growth defect upon entering the mid-log to stationary phase especially at high temperatures. Microscopic analysis further demonstrates that the absence of htrA induces extensive cell death. Additionally, the{Delta} htrA mutant has defects in cell locomotion as the expression of several key chemotaxis proteins are significantly downregulated. Cryo-electron tomography imaging of htrA mutant further reveals that deletion of htrA disrupts flagellar homeostasis. The failure of{Delta} htrA to establish an infection in mice is likely due to repressed expression of BosR and RpoS at the transcriptional level which ultimately causes dysregulation of the RpoS-induced virulence factors. Collectively, we conclude that the expression of htrA is finely tuned which is critical for its pleiotropic roles in the regulation of motility, stress response, and virulence gene expression in B. burgdorferi. IMPORTANCELyme borreliosis is the most commonly reported vector-borne illnesses in the United States, which is caused by Borrelia burgdorferi. As the enzootic pathogen alternates between the tick vector and mammalian hosts, adaptation to drastically different growth milieu is imperative to its survival. Hence, robust alteration of gene expression and proper quality control on protein synthesis and turnover are pivotal for its fitness. The family of HtrA serine proteases is mainly responsible for the maintenance of protein homeostasis particularly under stressful conditions. The significance of this report is to decode how BbHtrA contributes to the fitness of B. burgdorferi. BbHtrA is essential for mammalian host infection but little is known about its regulatory mechanism as well as its contribution to the virulence of B. burgdorferi. By deciphering the regulatory elements involved in the expression of BbHtrA, we are one step closer to comprehending its significance in the pathophysiology of B. burgdorferi.

microbiology↗