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PubMed2026

Engineering Protein Stability with Small Molecules: A Review of the ecDHFR Destabilizing Domain System.

The E. coli dihydrofolate reductase (ecDHFR) destabilizing domain (DD) is a versatile post-translational tool for the conditional control of protein stability via ligand-induced stabilization. In this system, a DD-tagged protein is rapidly degraded by the proteasome unless stabilized by the antibiotic trimethoprim (TMP), allowing for conditional control of protein abundance. The ecDHFR-DD system has been successfully applied across diverse biological systems, including yeast, invertebrate models such as Drosophila, and mammalian cells, to study a broad spectrum of cellular and developmental processes. Compared with DNA- and RNA-based regulatory approaches, post-translational systems offer faster response times and more precise control, making them valuable for processes that require tight, reversible regulation. In this review, we synthesize current knowledge on the mechanisms, performance, and optimization of the ecDHFR-DD system across organisms and evaluate its advantages and limitations relative to most conditional gene expression systems. We also highlight emerging opportunities for applying the system across diverse areas, ranging from functional genomics and synthetic biology to biomedical research. Additionally, we discuss its potential application in applied biological systems, such as pest and vector management, positioning the ecDHFR-DD system as a broadly applicable platform for the precise and tunable control of protein function across diverse disciplines.

Tetrahydrofolate Dehydrogenase↗

PubMed2026

Decoding Nonlinearities in AAV-Based Gene Therapy Using PBPK Modelling.

The objective of this research was to develop a physiologically based pharmacokinetic (PBPK) model for AAV-based gene therapy, which can capture the nonlinearity observed in both viral vector and transgene product pharmacokinetics (PK) across a wide range of doses, while accounting for the effect of immunogenicity. To develop the PBPK model, previously published PK data generated in mice using AAV8 vector containing the transgene for a non-binding monoclonal antibody was used. Immunocompetent mice were administered with AAV at a wide range of doses (1E8, 1E9, 5E9, 1E10, 2E10, 1E11, 2E11, 1E12, and 1E13vg per mouse), and the PK of transgene and transgene product (i.e., antibody) in plasma and/or tissue was collected. The nonlinearity in transgene product concentrations was characterized using a saturable production process and a concentration-dependent antibody elimination rate was used to characterize the effect of anti-drug antibody (ADA) on transgene product. The model successfully described the PK of both the vector and the transgene product across all dose levels and accurately captured the sigmoidal dose-exposure-response relationship for AAV. Notably, the model described a dose-dependent ADA response, with the high dose group exhibiting an earlier onset and faster rate of transgene product elimination. Lower dose group showed delayed onset and minimal ADA-mediated elimination of transgene product. Overall, the PBPK model presented here effectively characterizes vector and transgene product kinetics in mice and demonstrates utility in preclinical-to-clinical translation and dose optimization of AAV-based gene therapies.

Animals↗

PubMed2026

Genomic signatures associated with epidemiologically defined high-risk pathogenic Escherichia coli isolates identified by interpretable machine learning.

Pathogenic Escherichia coli is a major cause of foodborne illness worldwide and includes strains capable of causing severe disease. To establish a genome-informed framework for foodborne outbreak surveillance, we analyzed 1,029 E. coli isolates from clinical, food, livestock, and environmental sources using whole-genome sequencing. Pathogenic isolates obtained from human clinical cases or linked to documented outbreaks were classified as epidemiologically defined high-risk (EpiHR), whereas the remaining pathogenic isolates were classified as non-EpiHR. Virulence-associated genomic features were extracted using a bioinformatics pipeline, and four machine learning (ML) algorithms, including gradient boosting machine, random forest (RF), and support vector machines with linear and radial basis function kernels, were evaluated. Among them, the RF model showed the best performance, achieving an area under the curve (AUC) of 0.98 and accuracy of 0.93 in 10-fold cross-validation. Additional leave-one-group-out validation showed retained discrimination across held-out sequence types and serotypes, although performance was reduced when isolates were grouped by isolation source. Evaluation using an independent test dataset of 1,908 publicly available pathogenic E. coli genomes showed an AUC of 0.97 and a sensitivity of 0.98. Feature importance analysis using Shapley additive explanations identified influential predictive features, including traT, etpB, and enterotoxin-associated genes. A reduced 10-feature model achieved an AUC of 0.79 in the independent test dataset, supporting its exploratory use for future simplified screening approaches. These results indicate that genome-based ML provides a sensitive framework for surveillance-oriented prioritization of EpiHR pathogenic E. coli isolates, with model predictions interpreted together with epidemiological information.

Escherichia coli↗

PubMed2026

Phytoplasma-plant interactions: effector-mediated host reprogramming, hormonal crosstalk, metabolic alterations and plant-mediated vector manipulation.

Phytoplasmas are wall-less, phloem-restricted bacterial pathogens that infect over 1,000 plant species, causing substantial losses in agriculture, horticulture, and forestry worldwide. Despite their reduced genomes and limited metabolic autonomy, these obligate parasites colonize diverse hosts through secreted effector proteins that extensively reprogram plant development, metabolism, immune signalling, and vector interactions. Advances in genomics, transcriptomics, proteomics, metabolomics, and functional studies have substantially clarified the molecular basis of phytoplasma pathogenicity and symptom development. This review synthesizes current understanding of phytoplasma-plant interactions, covering phytoplasma biology, genome evolution, and the infection cycle across plant and insect vector hosts. We examine the molecular functions of key effectors, SAP11, SAP54/PHYL1, SAP05, TENGU, SWP1, and recently identified virulence factors, focusing on how they target host transcription factors, phytohormone networks, protein degradation pathways, and immune responses to promote colonization and disease progression. We further discuss how phytoplasma infection disrupts phytohormone signalling, primary and secondary metabolism, and developmental programs to produce characteristic disease symptoms, with particular attention to pathogen-induced changes in host volatiles and nutritional quality that alter vector behaviour and enhance transmission. Finally, we summarize insights from multi-omics studies and emerging management strategies, including CRISPR-based genome editing, RNAi, rapid molecular diagnostics, resistant cultivars, microbiome-based approaches, and sustainable vector control, and highlight key knowledge gaps and priorities for developing effective, environmentally sustainable phytoplasma disease management.

Phytoplasma↗

PubMed2025

CSGL: chemical synthesis graph learning for molecule representation.

MOTIVATION: Molecule representation learning (MRL) translates molecules into a real vector space, serving as input to downstream tasks in biology, chemistry, and computer science. This article introduces a chemical synthesis graph learning (CSGL) framework, which enhances MRL by considering both the atomic structures of molecules and their roles in chemical reactions through a hierarchical graph representation. Specifically, molecules are first modeled based on their molecular graphs, which capture atomic-level structural information. They are then further refined using a chemical synthesis graph, where nodes represent reactant and product molecule sets, and edges encode chemical transformations between reactants and products (e.g. changes in molecular structures). CSGL optimizes molecular embeddings of reactant and product nodes in a fashion that ensures the embeddings conform to a chemical balance constraint. RESULTS: Experimental results show that our method CSGL achieves strong performance on a variety of tasks, including product prediction, reaction classification, and molecular property prediction. AVAILABILITY AND IMPLEMENTATION: https://github.com/li-2023/CSGL.

Machine Learning↗

PubMed2025

A general framework to over-express tRNA-derived fragments from their parental tRNAs in mammalian cells.

tRNA-derived fragments (tRFs), generated from the cleavage of mature or precursor tRNAs are a category of regulatory noncoding RNAs with diverse functions in physiological or pathophysiological conditions. Here we describe a framework for the over-expression of tRFs from their parental tRNAs in mammalian cells. The process involves bioinformatics analysis to identify specific tRNAs that produce the tRF, PCR amplification of corresponding tRNA genes, and insertion into expression vectors. Transfection is carried out in HEK293T cells and detection of tRFs is achieved through northern blotting and dual luciferase reporter assays. In the latter, a complementary sequence to the tRF of interest is inserted into the luciferase reporter. By observing the reduction in luciferase activity, we can validate the expression of tRFs. This method enables precise study of tRF functions and their roles in cellular processes.

Humans↗

PubMed2025

Clustering individuals using INMTD: a novel versatile multi-view embedding framework integrating omics and imaging data.

MOTIVATION: Combining omics and images can lead to a more comprehensive clustering of individuals than classic single-view approaches. Among the various approaches for multi-view clustering, nonnegative matrix tri-factorization (NMTF) and nonnegative Tucker decomposition (NTD) are advantageous in learning low-rank embeddings with promising interpretability. Besides, there is a need to handle unwanted drivers of clusterings (i.e. confounders). RESULTS: In this work, we introduce a novel multi-view clustering method based on NMTF and NTD, named INMTD, which integrates omics and 3D imaging data to derive unconfounded subgroups of individuals. According to the adjusted Rand index, INMTD outperformed other clustering methods on a synthetic dataset with known clusters. In the application to real-life facial-genomic data, INMTD generated biologically relevant embeddings for individuals, genetics, and facial morphology. By removing confounded embedding vectors, we derived an unconfounded clustering with better internal and external quality; the genetic and facial annotations of each derived subgroup highlighted distinctive characteristics. In conclusion, INMTD can effectively integrate omics data and 3D images for unconfounded clustering with biologically meaningful interpretation. AVAILABILITY AND IMPLEMENTATION: INMTD is freely available at https://github.com/ZuqiLi/INMTD.

Cluster Analysis↗

PubMed2026

Adenoviral Vectors in Gene Therapy: A Detailed Overview.

Adenoviral vectors (AdVs) represent one of the most extensively researched platforms in the realm of gene therapy, providing advantages such as high transduction efficiency, large transgene capacity, and broad tropism. This review provides a detailed and structured overview of AdVs, highlighting their biology, gene delivery mechanisms, clinical applications, and challenges limiting their broader therapeutic applicability. The study also explores recent progress in vector engineering, such as rare serotypes, capsid modifications, third-generation vectors, as well as strategies for immune modulation and toxicity reduction. AdVs are used in therapies for genetic disorders, oncology, and vaccinology, alongside innovations such as CRISPR-Cas9, nanotechnology, and artificial intelligence design. Nevertheless, persistent hurdles, including vector immunogenicity, hepatotoxicity, scalability, and the lack of durable expression, prevent widespread clinical use. This review consolidates current knowledge and presents a future perspective on how AdVs may evolve as powerful, adaptable, and precise tools in modern gene therapy. By contextualizing strengths and unresolved challenges, this work aims to give researchers and clinicians a balanced foundation for evaluating their future roles in translational medicine.

Humans↗

PubMed2014

Genome-wide computational analysis reveals cardiomyocyte-specific transcriptional Cis-regulatory motifs that enable efficient cardiac gene therapy.

Gene therapy is a promising emerging therapeutic modality for the treatment of cardiovascular diseases and hereditary diseases that afflict the heart. Hence, there is a need to develop robust cardiac-specific expression modules that allow for stable expression of the gene of interest in cardiomyocytes. We therefore explored a new approach based on a genome-wide bioinformatics strategy that revealed novel cardiac-specific cis-acting regulatory modules (CS-CRMs). These transcriptional modules contained evolutionary-conserved clusters of putative transcription factor binding sites that correspond to a "molecular signature" associated with robust gene expression in the heart. We then validated these CS-CRMs in vivo using an adeno-associated viral vector serotype 9 that drives a reporter gene from a quintessential cardiac-specific α-myosin heavy chain promoter. Most de novo designed CS-CRMs resulted in a >10-fold increase in cardiac gene expression. The most robust CRMs enhanced cardiac-specific transcription 70- to 100-fold. Expression was sustained and restricted to cardiomyocytes. We then combined the most potent CS-CRM4 with a synthetic heart and muscle-specific promoter (SPc5-12) and obtained a significant 20-fold increase in cardiac gene expression compared to the cytomegalovirus promoter. This study underscores the potential of rational vector design to improve the robustness of cardiac gene therapy.

Animals↗

PubMed2026

A structural bridge between dengue virus tandem xrRNAs facilitates coordination of exonuclease resistance.

Orthoflavivirus RNA genomes resist host 5'-3' exoribonucleases to produce subgenomic flaviviral RNAs (sfRNAs). This resistance is conferred by exoribonuclease-resistant RNA (xrRNA) structures within the viral 3' untranslated region that often occur in tandem, and whose function can be coupled. In dengue virus serotype 2 (DENV2), this coupling results in changing patterns of sfRNA identity and abundance associated with the ability of the virus to adapt to host vs. vector infections. The physical basis of this coupling was unknown. Using a combination of virology, biochemistry, bioinformatics, structural biology, and biophysics, we explored the structural and sequence determinants of tandem xrRNA coupling in DENV2. We discovered that the spatial proximity, order, and structural integrity of the tandem xrRNAs are all important for coupling. Furthermore, an unpaired A-rich linker that lies between the two xrRNAs is essential in stabilizing a specific structure that correlates to coupling. This A-rich sequence likely forms tertiary contacts with an adjacent stem-loop structure to form a physical bridge between the two xrRNAs, a finding that is supported by a mid-resolution cryo-electron microscopy (cryo-EM) map of the DENV2 tandem xrRNAs. Disruption of the structure of this bridge by mutation changes the relative orientation or spacing between the tandem xrRNAs, which is correlated to their functional coupling. These findings help provide an explanation for the coupling between tandem xrRNAs, suggesting a new mechanistic hypothesis in which the two tandem xrRNAs can simultaneously encounter Xrn1.IMPORTANCEDengue virus (DENV) generates non-coding subgenomic flaviviral RNAs (sfRNAs) that affect several cellular pathways and are important for successful infection. These sfRNAs are formed by structured RNA elements in the viral genome called exoribonuclease-resistant RNAs (xrRNAs), which fold into a distinct three-dimensional topology to block degradation by host cell exoribonucleases and often occur in tandem. Specific patterns of sfRNAs made during infection are important for host vs. vector fitness, and in DENV2, this pattern depends on functional coupling between tandem xrRNAs. However, the source of this functional coupling was unknown. We determined that an unpaired A-rich linker between the tandem xrRNAs is necessary for creating a structural bridge between the tandem xrRNAs. This bridge appears to favor a specific orientation between the tandem xrRNAs that is correlated to coupling and therefore to the patterns and relative abundance of sfRNAs produced during infection.

Dengue Virus↗

PubMed2026

Comet assay analysis of multigenerational genomic instability (F0-F2) in Aedes aegypti exposed to gamma radiation in Sterile Insect Technique.

The use of irradiation in the Sterile Insect Technique (SIT) is a sustainable and environmentally friendly strategy for controlling Aedes aegypti populations by the release of sterile males. However, the potential toxic effects of radiation on mosquito genetic material, as well as the heritability of such damage, remain insufficiently understood. In this study, we evaluated gamma radiation-induced DNA damage (20, 30, 40, and 50 Gy) in male pupae (F0 generation) and assessed the persistence of these effects in subsequent generations (F1 and F2) using the comet assay in hemocytes. In the parental generation, a significant dose-response relationship was observed, with increasing radiation doses associated with higher damage index and damage frequency (p < 0.05). In the F1 generation, both larvae and adults exhibited significantly greater DNA damage than the control group, particularly at doses of 30 and 40 Gy, supporting the inheritance of radiation-induced genomic instability. In the F2 generation, genotoxic effects were attenuated, although residual damage remained detectable in adults, suggesting partial recovery of genomic stability, possibly influenced by DNA repair mechanisms and/or selective pressures. No viable offspring were obtained at 50 Gy, confirming the sterilizing efficacy of higher doses. Integration of comet assay results with micronucleus data and reproductive parameters reinforces the association between DNA damage, mutagenic effects, and reduced fertility. These findings indicate that radiation-induced genotoxic effects may persist beyond the irradiated generation but tend to decline across generations. Overall, this study provides insights into the balance between achieving sterility and preserving biological quality in SIT programs, contributing to optimizing radiation doses and enhancing the safety and efficacy of vector control strategies.

Comet assay↗

PubMed2026

289th ENMC international workshop: assessing and managing emerging AAV related toxicities after gene therapy for neuromuscular disorders, 26 - 28 September 2025, Hoofddorp, The Netherlands.

Adeno-associated virus (AAV) mediated gene therapies has emerged as a potentially transformative treatment approaches for neuromuscular disorders, with two FDA-approved products now in widespread clinical use: onasemnogene abeparvovec (Zolgensma) for spinal muscular atrophy and delandistrogene moxeparvovec-rokl (Elevidys) for Duchenne Muscular Dystrophy. However, severe and occasionally fatal adverse events affecting vital organs, including the blood, liver, muscle, and heart, have emerged in both clinical trials and real-world post marketing settings. The 289th European NeuroMuscular Centre (ENMC) workshop convened 38 participants from patient advocacy groups, industry, and preclinical and clinical research groups to collaboratively review these toxicities, their underlying mechanisms, and potential mitigation and monitoring strategies. Discussions addressed the clinical spectrum and biological drivers of these events, the respective roles of innate and adaptive immunity, the contribution of specific vector characteristics as well as of the specific disease and recipient. The application of risk stratification and immunosuppressive regimens for prevention, monitoring, and management were considered. Emerging toxicities, including capillary leak syndrome, endothelial and dorsal root ganglia injuries, were reviewed alongside corresponding preclinical data from non-human primates. Participants agreed on the need to harmonize standard operating procedures, clinical guidelines, and data-sharing practices, and endorsed collaborative initiatives to proactively address critical gaps and unresolved key questions through a patient-centered framework.

Adaptive immune response↗

PubMed2026

Pilot metaproteomic profiling reveals bacterial diversity and potential medical and veterinary relevance of tick microbiomes in northern Algeria.

Ticks are major ectoparasites and vectors of pathogens affecting humans, livestock, and wildlife. They harbor diverse microbial communities that may influence tick biology and interactions with microorganisms; however, functional information on tick-associated microbiomes remains limited, particularly in North Africa. In this pilot study, we applied a metaproteomic approach based on high-resolution tandem mass spectrometry to characterize bacterial communities associated with three tick species collected in Algeria: Rhipicephalus sanguineus sensu lato, Hyalomma aegyptium, and Hyalomma dromedarii. Peptide spectra were assigned to taxa using a two-step database search strategy based on NCBInr, and bacterial composition and relative abundance were compared across tick species and sampling locations. A total of 40 bacterial genera belonging to 32 families and four phyla were identified. Microbiome composition differed significantly between tick genera and collection locations, suggesting an influence of species-specific and geographical factors on microbial community structure. Dominant genera included Streptomyces, Bacillus, Clostridium, Escherichia, Flavobacterium, Paenibacillus, and Providencia. Peptides related to Coxiella spp. were frequently detected, consistent with previous reports of Coxiella-like endosymbionts in ticks. This pilot study provides a first metaproteomic characterization of tick-associated communities in Algeria. The results reveal species- and location-associated differences in microbial composition and highlight the potential of metaproteomics for exploring tick-associated microbiomes in North Africa.

Animals↗

PubMed2025

Habitat radiomics predicts occult lymph node metastasis and uncovers immune microenvironment of head and neck cancer.

BACKGROUND: Occult lymph node metastasis (LNM) is a key prognostic factor for patients with head and neck squamous cell carcinoma (HNSCC). This study was to establish radiomics models derived from intratumoral, peritumoral, and habitat regions for identifying occult LNM in HNSCC. METHODS: Patients with pathologically confirmed HNSCC from three medical Centers (from March 2014 to April 2024) and The Cancer Genome Atlas (TCGA) were enrolled. Center 1 was split into training (n = 330) and internal test sets (n = 154), while Center 2 and Center 3 served as the external test set (n = 183). Genomic set (n = 50) from TCGA and single-cell RNA sequencing set (n = 6) from Center 1 were used for biological analysis. We used the intratumoral, peritumoral, and habitat volumes of interest (VOIs) to extract radiomics features, respectively. Based on Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF) classifiers, nine radiomics models were built to confirm the optimal predictive performance. The best-performing model, along with clinical-radiologic data, was combined to develop a hybrid model. The log-rank test was used to evaluate the model's prognostic performance. Additionally, bulk and single-cell RNA sequencing were applied for investigating the biological mechanisms underlying the optimal model. RESULTS: The RF-habitat radiomics model showed the best performance, achieving AUCs of 0.835-0.919 across all datasets. Survival analysis further confirmed the prognostic value of the RF-habitat radiomics model. The RF-habitat radiomics model and the hybrid model notably surpassed the clinical model in predictive performance. Moreover, the RF-habitat radiomics model was associated with the abundance level of exhaustion-associated CD8 + T cells, uncovering the immune microenvironment characteristics contributing to occult LNM in HNSCC. CONCLUSIONS: The RF-habitat radiomics model demonstrated excellent performance for predicting occult LNM in HNSCC across three cohorts, providing a non-invasive solution for occult LNM. Furthermore, radiogenomic analysis further revealed the biological associations of the model, primarily related to T cell dysfunction.

Humans↗

PubMed2026

From spillover to systems: evidence gaps in One Health preparedness for emerging infectious diseases in Latin America and the Caribbean.

Latin America and the Caribbean are a global hotspot for emerging and re-emerging infectious diseases, yet regional One Health preparedness remains uneven and incompletely operationalized. This narrative Mini Review synthesizes evidence published mainly between 2015 and 2026 on One Health preparedness for emerging infectious diseases in the region, emphasizing how environmental disruption and climate change shape zoonotic and vector-borne spillover risk. Available regional surveys suggest broad professional familiarity with the One Health concept but limited operational implementation, with environmental health frequently identified as the least-integrated domain. We argue that spillover risk-and the failure to detect and contain spillover once it occurs-should be understood as a system-level outcome shaped by ecological disruption, socioeconomic vulnerability, surveillance capacity, and governance, rather than as an isolated biological event: deforestation, agricultural and extractive expansion-including illegal mining and logging-unplanned urbanization, and climate variability generate new human-animal-vector interfaces, while fragmented governance, uneven and poorly decentralized laboratory capacity, and limited reservoir and environmental surveillance leave these interfaces unmonitored. Environmental and climatic drivers are robustly linked to spillover, although the pathways are disease-specific rather than universal, and socioeconomic vulnerability concentrates the resulting burden in Indigenous, rural, and marginalized populations. We identify priority gaps in integrated surveillance, decentralized diagnostics, genomic capacity, reservoir ecology, governance, financing, and equity, and propose an agenda for anticipatory, climate-informed, and context-sensitive preparedness.

Latin America↗

PubMed2025

Multichassis Expression of Cyanobacterial and Other Bacterial Biosynthetic Gene Clusters.

Heterologous expression of biosynthetic gene clusters (BGCs) is a powerful strategy for natural product (NP) discovery, yet achieving consistent expression across microbial hosts remains challenging. Here, we developed cross-phyla vector systems enabling the expression of BGCs from cyanobacteria and other bacterial origins in Gram-negative Escherichia coli, Gram-positive Bacillus subtilis, and two model cyanobacterial strains including unicellular Synechocystis PCC 6803 and filamentous Anabaena sp. PCC 7120. Following validation using constitutive and inducible expression of the enhanced yellow fluorescent protein (eYFP), we applied these vectors to express the shinorine and violacein BGCs in all four hosts. Promoter tuning, substrate feeding, BGC refactoring, and inducible control enhanced NP production and mitigated host toxicity. Notably, we demonstrated that B. subtilis can serve as a chassis for cyanobacterial NP BGC expression. Our results provide versatile expression platforms for probing BGC function and accelerating natural product discovery from diverse cyanobacterial and other bacterial lineages.

Multigene Family↗

PubMed2026

An Integrated Machine Learning and Genomic Framework for Precise Detection of Gastric Cancer.

This study presents a novel integrative approach for the analysis of high-dimensional gene expression data, leveraging the complementary strengths of unsupervised clustering and supervised classification. Using K-means clustering, the data set is stratified into three distinct clusters, revealing intrinsic biological patterns and relationships. The resulting cluster assignments are subsequently used as pseudolabels to train machine learning models, including support vector machines, random forest, and a stacking ensemble classifier. To validate and enhance the robustness of clustering, complementary methods, such as hierarchical clustering and density-based spatial clustering of applications with noise (DBSCAN), are used, with results visualized through principal component analysis-driven dimensionality reduction. The high predictive accuracy achieved by the classifiers underlines the separability and reliability of the identified clusters. Furthermore, feature importance analysis highlighted key genetic determinants within each cluster, offering actionable insights into potential biomarkers and critical genomic features. This framework bridges the gap between exploratory unsupervised learning and predictive supervised modeling, providing a scalable and interpretable method for analyzing complex genomic data sets. Its applicability extends to biomarker discovery, patient stratification, and other precision medicine applications, emphasizing its utility in advancing genomic research and clinical practice.

Humans↗

PubMed2026

Development of an arabinose-inducible gene expression system for nontuberculous mycobacteria.

Nontuberculous mycobacteria (NTM) are emerging pathogens for which genetic tools remain limited. Here, we developed an arabinose-inducible gene expression system based on a modified pBAD24 vector adapted for mycobacterial hosts. The vector carries replication origins for mycobacteria and Escherichia coli, as well as selectable markers compatible with NTM. In Mycobacterium abscessus (Mycobacteroides abscessus), the system enabled dose-dependent induction of target gene expression by arabinose, as demonstrated by increased antibiotic resistance and quantitative RT-PCR analysis. Although basal expression was observed in the absence of arabinose, expression levels were tunable across arabinose concentrations. The system was also functional in Mycobacterium smegmatis (Mycolicibacterium smegmatis) and Mycobacterium bovis BCG, although the degree of basal expression varied among host species. These results establish a tunable inducible expression system for mycobacteria and provide a useful genetic tool for studies of NTM biology.

Arabinose↗