FEBS Lett. 2023 Dec 17. doi: 10.1002/1873-3468.14790. Online ahead of print.

ABSTRACT

Insulin-responsive vesicles (IRVs) deliver the glucose transporter Glut4 to the plasma membrane in response to activation of the insulin signaling cascade: insulin receptor-IRS-PI3 kinase-Akt-TBC1D4-Rab10. Previous studies have shown that Akt, TBC1D4, and Rab10 are compartmentalized on the IRVs. Although functionally significant, the mechanism of Akt association with the IRVs remains unknown. Using pull-down assays, immunofluorescence microscopy, and cross-linking, we have found that Akt may be recruited to the IRVs via the interaction with the juxtamembrane domain of the cytoplasmic C-terminus of sortilin, a major IRV protein. Overexpression of full-length sortilin increases insulin-stimulated phosphorylation of TBC1D4 and glucose uptake in adipocytes, while overexpression of the cytoplasmic tail of sortilin has the opposite effect. Our findings demonstrate that the IRVs represent both a scaffold and a target of insulin signaling.

PMID:38105115 | DOI:10.1002/1873-3468.14790

J Adv Res. 2023 Dec 15:S2090-1232(23)00396-X. doi: 10.1016/j.jare.2023.12.009. Online ahead of print.

ABSTRACT

INTRODUCTION: Previous lipidomics studies have identified various lipid predictors for cardiovascular risk, however, with limited predictive increment, sometimes using too many predictor variables at the expense of practical efficiency.

OBJECTIVES: To search for lipid predictors of future coronary heart disease (CHD) with stronger predictive power and efficiency to guide primary intervention.

METHODS: We conducted a prospective nested case-control study involving 1,621 incident CHD cases and 1:1 matched controls. Lipid profiling of 161 lipid species for baseline fasting plasma was performed by liquid chromatography-mass spectrometry.

RESULTS: In search of CHD predictors, seven lipids were selected by elastic-net regression during over 90% of 1000 cross-validation repetitions, and the derived composite lipid score showed an adjusted odds ratio of 3.75 (95% confidence interval: 3.15, 4.46) per standard deviation increase. Addition of the lipid score into traditional risk model increased c-statistic to 0.736 by an increment of 0.077 (0.063, 0.092). From the seven lipids, we found mediation of CHD risk from baseline diabetes through sphingomyelin (SM) 41:1b with a considerable mediation proportion of 36.97% (P <0.05). We further found that the positive associations of phosphatidylcholine (PC) 36:0a, SM 41:1b, lysophosphatidylcholine (LPC) 18:0 and LPC 20:3 were more pronounced among participants with higher exposure to fine particulate matter or its certain components, also to ozone for LPC 18:0 and LPC 20:3, while the negative association of cholesteryl ester (CE) 18:2 was attenuated with higher black carbon exposure (P <0.05).

CONCLUSION: We identified seven lipid species with greatest incremental predictive value so-far achieved for incident CHD, and also found novel biomarkers for CHD risk stratification among individuals with diabetes or heavy air pollution exposure.

PMID:38104795 | DOI:10.1016/j.jare.2023.12.009

J Am Chem Soc. 2023 Dec 17. doi: 10.1021/jacs.3c07325. Online ahead of print.

ABSTRACT

Lassa virus is a negative-strand RNA virus with only four structural proteins that causes periodic outbreaks in West Africa. The nucleoprotein (NP) encapsidates the viral genome, forming ribonucleoprotein complexes (RNPs) together with the viral RNA and the L protein. RNPs must be continuously restructured during viral genome replication and transcription. The Z protein is important for membrane recruitment of RNPs, viral particle assembly, and budding and has also been shown to interact with the L protein. However, the interaction of NP, viral RNA, and Z is poorly understood. Here, we characterize the interactions between Lassa virus NP, Z, and RNA using structural mass spectrometry. We identify the presence of RNA as the driver for the disassembly of ring-like NP trimers, a storage form, into monomers to subsequently form higher order RNA-bound NP assemblies. We locate the interaction site of Z and NP and demonstrate that while NP binds Z independently of the presence of RNA, this interaction is pH-dependent. These data improve our understanding of RNP assembly, recruitment, and release in Lassa virus.

PMID:38104324 | DOI:10.1021/jacs.3c07325

J Proteome Res. 2023 Nov 21. doi: 10.1021/acs.jproteome.3c00536. Online ahead of print.

ABSTRACT

This study describes a new release of the Arabidopsis thaliana PeptideAtlas proteomics resource (build 2023-10) providing protein sequence coverage, matched mass spectrometry (MS) spectra, selected post-translational modifications (PTMs), and metadata. 70 million MS/MS spectra were matched to the Araport11 annotation, identifying ∼0.6 million unique peptides and 18,267 proteins at the highest confidence level and 3396 lower confidence proteins, together representing 78.6% of the predicted proteome. Additional identified proteins not predicted in Araport11 should be considered for the next Arabidopsis genome annotation. This release identified 5198 phosphorylated proteins, 668 ubiquitinated proteins, 3050 N-terminally acetylated proteins, and 864 lysine-acetylated proteins and mapped their PTM sites. MS support was lacking for 21.4% (5896 proteins) of the predicted Araport11 proteome: the "dark" proteome. This dark proteome is highly enriched for E3 ligases, transcription factors, and for certain (e.g., CLE, IDA, PSY) but not other (e.g., THIONIN, CAP) signaling peptides families. A machine learning model trained on RNA expression data and protein properties predicts the probability that proteins will be detected. The model aids in discovery of proteins with short half-life (e.g., SIG1,3 and ERF-VII TFs) and for developing strategies to identify the missing proteins. PeptideAtlas is linked to TAIR, tracks in JBrowse, and several other community proteomics resources.

PMID:38104260 | DOI:10.1021/acs.jproteome.3c00536

Biophys J. 2023 Dec 15:S0006-3495(23)04149-8. doi: 10.1016/j.bpj.2023.12.011. Online ahead of print.

ABSTRACT

Protein structure predictions from deep learning models like AlphaFold2, despite their remarkable accuracy, are likely insufficient for direct use in downstream tasks like molecular docking. The functionality of such models could be improved with a combination of increased accuracy and physical intuition. We propose a new method to train deep learning protein structure prediction models using molecular dynamics force fields to work toward these goals. Our custom PyTorch loss function, OpenMM-Loss, represents the potential energy of a predicted structure. OpenMM-Loss can be applied to any all-atom representation of protein structure capable of mapping into our software package, SidechainNet. We demonstrate our method's efficacy by finetuning OpenFold. We show that subsequently predicted protein structures, both before and after a relaxation procedure, exhibit comparable accuracy while displaying lower potential energy and improved structural quality as assessed by MolProbity metrics.

PMID:38104241 | DOI:10.1016/j.bpj.2023.12.011

Mol Ther. 2023 Dec 15:S1525-0016(23)00678-0. doi: 10.1016/j.ymthe.2023.12.014. Online ahead of print.

ABSTRACT

Missense mutations account for ∼50% of pathogenic mutations in human genetic diseases, and most lack effective treatments. Gene therapies, gene editing, and RNA therapies, including transfer RNA (tRNA) modalities, are common strategies for genetic disease treatments. However, reported tRNA therapies are for nonsense mutations only. It has not been explored how tRNAs can be engineered to correct missense mutations. Here, we describe missense-correcting tRNAs (mc-tRNAs) as a potential therapeutic for correcting pathogenic missense mutations. Mc-tRNAs are engineered tRNAs charged with one amino acid but read codons of another in translation. We first developed a series of fluorescent protein-based reporters that indicate the successful correction of missense mutations via restoration of fluorescence. We engineered mc-tRNAs that effectively corrected Serine and Arginine missense mutations in the reporters and confirmed the amino acid substitution by mass spectrometry and mc-tRNA expression by sequencing. We examined the transcriptome response to mc-tRNA expression and found some mc-tRNAs induced minimum transcriptomic changes. Furthermore, we applied an mc-tRNA to rescue a pathogenic CAPN3 Arg-to-Gln mutant involved in limb-girdle muscular dystrophy type 2A. These results establish a versatile pipeline for mc-tRNA engineering and demonstrate the potential of mc-tRNA as an alternative therapeutic platform for the treatment of genetic disorders.

PMID:38104240 | DOI:10.1016/j.ymthe.2023.12.014

Curr Opin Biotechnol. 2023 Dec 15;85:103051. doi: 10.1016/j.copbio.2023.103051. Online ahead of print.

ABSTRACT

N-acetyl-L-aspartic acid (NAA) is a prominent amino acid derivative primarily associated with vertebrate brain metabolism. This review delineates the critical role of NAA across various cell types and its significance in pathophysiological contexts, including Canavan disease and cancer metabolism. Although traditionally linked with myelination and aspartoacylase-driven carbon donation, its significance as a carbon source for myelination remains debated. Evidence suggests that intact NAA might substantially impact cellular signaling, particularly processes such as histone acetylation. Beyond the brain, NAA metabolism's relevance is evident in diverse tissues, such as adipocytes, immune cells, and notably, cancer cells. In several cancer types, there is an observed upregulation of NAA synthesis accompanied by a simultaneous downregulation of its degradation. This pattern highlights the potential signaling role of intact NAA in disease.

PMID:38103520 | DOI:10.1016/j.copbio.2023.103051

STAR Protoc. 2023 Dec 8;4(4):102651. doi: 10.1016/j.xpro.2023.102651. Online ahead of print.

ABSTRACT

Biological systems inherently span multiple levels, which can pose challenges in spatial representation for modelers. We present a protocol that utilizes colored Petri nets to construct and analyze biological models of systems, encompassing both unilevel and multilevel scenarios. We detail a modeling workflow exploiting the PetriNuts platform comprising a set of tools linked together via common file formats. We describe steps for modeling preparation, component-level modeling and analysis, followed by system-level modeling and analysis, and model use.

PMID:38103198 | DOI:10.1016/j.xpro.2023.102651

Mol Plant. 2023 Dec 14:S1674-2052(23)00402-1. doi: 10.1016/j.molp.2023.12.008. Online ahead of print.

ABSTRACT

Cell walls in plants, particularly forest trees, are the major carbon sink of the terrestrial ecosystem. Chemical and biosynthetic features of plant cell walls were revealed early on, focusing mostly on herbaceous model species. Recent developments in genomics, transcriptomics, epigenomics, transgenesis and associated analytical techniques are enabling novel insights into formation of woody cell walls. Here, we review multilevel regulation of cell wall biosynthesis in forest tree species. We highlight current approaches to engineering cell walls as potential feedstock for materials and energy and survey reported field tests of such engineered transgenic trees. We outline opportunities and challenges in future research to better understand cell type biogenesis for more efficient wood cell-wall modification and utilization for biomaterials or for enhanced carbon capture and storage.

PMID:38102833 | DOI:10.1016/j.molp.2023.12.008

Biophys J. 2023 Dec 15:S0006-3495(23)04148-6. doi: 10.1016/j.bpj.2023.12.010. Online ahead of print.

ABSTRACT

Quantitative understanding of cellular processes, such as cell cycle and differentiation, is impeded by various forms of complexity ranging from myriad molecular players and their multilevel regulatory interactions, cellular evolution with multiple intermediate stages, lack of elucidation of cause-effect relationships among the many system players, and the computational complexity associated with the profusion of variables and parameters. In this paper, we present a modeling framework based on the cybernetic concept that biological regulation is inspired by objectives embedding rational strategies for dimension reduction, process stage specification through the system dynamics, and innovative causal association of regulatory events with the ability to predict the evolution of the dynamical system. The elementary step of the modeling strategy involves stage-specific objective functions that are computationally-determined from experiments, augmented with dynamical network computations involving end point objective functions, mutual information, change point detection, and maximal clique centrality. We demonstrate the power of the method through application to the mammalian cell cycle, which involves thousands of biomolecules engaged in signaling, transcription, and regulation. Starting with a fine-grained transcriptional description obtained from RNA sequencing measurements, we develop an initial model, which is then dynamically modeled using the cybernetic-inspired method (CIM), based on the strategies described above. The CIM is able to distill the most significant interactions from a multitude of possibilities. In addition to capturing the complexity of regulatory processes in a mechanistically causal and stage-specific manner, we identify the functional network modules, including novel cell cycle stages. Our model is able to predict future cell cycles consistent with experimental measurements. We posit that this innovative framework has the promise to extend to the dynamics of other biological processes, with a potential to provide novel mechanistic insights.

PMID:38102827 | DOI:10.1016/j.bpj.2023.12.010

Protein Eng Des Sel. 2023 Dec 15:gzad023. doi: 10.1093/protein/gzad023. Online ahead of print.

ABSTRACT

Numerous cellular functions rely on protein-protein interactions. Efforts to comprehensively characterize them remain challenged however by the diversity of molecular recognition mechanisms employed within the proteome. Deep learning has emerged as a promising approach for tackling this problem by exploiting both experimental data and basic biophysical knowledge about protein interactions. Here, we review the growing ecosystem of deep learning methods for modeling protein interactions, highlighting the diversity of these biophysically-informed models and their respective trade-offs. We discuss recent successes in using representation learning to capture complex features pertinent to predicting protein interactions and interaction sites, geometric deep learning to reason over protein structures and predict complex structures, and generative modeling to design de novo protein assemblies. We also outline some of the outstanding challenges and promising new directions. Opportunities abound to discover novel interactions, elucidate their physical mechanisms, and engineer binders to modulate their functions using deep learning and, ultimately, unravel how protein interactions orchestrate complex cellular behaviors.

PMID:38102755 | DOI:10.1093/protein/gzad023

BMC Genomics. 2023 Dec 15;24(1):780. doi: 10.1186/s12864-023-09877-2.

ABSTRACT

BACKGROUND: The bHLH transcription factor family is named after the basic helix-loop-helix (bHLH) domain that is a characteristic element of their members. Understanding the function and characteristics of this family is important for the examination of a wide range of functions. As the availability of genome sequences and transcriptome assemblies has increased significantly, the need for automated solutions that provide reliable functional annotations is emphasised.

RESULTS: A phylogenetic approach was adapted for the automatic identification and functional annotation of the bHLH transcription factor family. The bHLH_annotator, designed for the automated functional annotation of bHLHs, was implemented in Python3. Sequences of bHLHs described in literature were collected to represent the full diversity of bHLH sequences. Previously described orthologs form the basis for the functional annotation assignment to candidates which are also screened for bHLH-specific motifs. The pipeline was successfully deployed on the two Arabidopsis thaliana accessions Col-0 and Nd-1, the monocot species Dioscorea dumetorum, and a transcriptome assembly of Croton tiglium. Depending on the applied search parameters for the initial candidates in the pipeline, species-specific candidates or members of the bHLH family which experienced domain loss can be identified.

CONCLUSIONS: The bHLH_annotator allows a detailed and systematic investigation of the bHLH family in land plant species and classifies candidates based on bHLH-specific characteristics, which distinguishes the pipeline from other established functional annotation tools. This provides the basis for the functional annotation of the bHLH family in land plants and the systematic examination of a wide range of functions regulated by this transcription factor family.

PMID:38102570 | DOI:10.1186/s12864-023-09877-2

Sci Rep. 2023 Dec 15;13(1):22342. doi: 10.1038/s41598-023-49157-3.

ABSTRACT

Influenza viruses undergo rapid evolutionary changes, which requires continuous surveillance to monitor for genetic and potential antigenic changes in circulating viruses that can guide control and prevention decision making. We sequenced and phylogenetically analyzed A(H1N1)pdm09 virus genome sequences obtained from specimens collected from hospitalized patients of all ages with or without pneumonia between 2009 and 2018 from seven sentinel surveillance sites across Kenya. We compared these sequences with recommended vaccine strains during the study period to infer genetic and potential antigenic changes in circulating viruses and associations of clinical outcome. We generated and analyzed a total of 383 A(H1N1)pdm09 virus genome sequences. Phylogenetic analyses of HA protein revealed that multiple genetic groups (clades, subclades, and subgroups) of A(H1N1)pdm09 virus circulated in Kenya over the study period; these evolved away from their vaccine strain, forming clades 7 and 6, subclades 6C, 6B, and 6B.1, and subgroups 6B.1A and 6B.1A1 through acquisition of additional substitutions. Several amino acid substitutions among circulating viruses were associated with continued evolution of the viruses, especially in antigenic epitopes and receptor binding sites (RBS) of circulating viruses. Disease severity declined with an increase in age among children aged < 5 years. Our study highlights the necessity of timely genomic surveillance to monitor the evolutionary changes of influenza viruses. Routine influenza surveillance with broad geographic representation and whole genome sequencing capacity to inform on prioritization of antigenic analysis and the severity of circulating strains are critical to improved selection of influenza strains for inclusion in vaccines.

PMID:38102198 | DOI:10.1038/s41598-023-49157-3

NPJ Syst Biol Appl. 2023 Dec 15;9(1):62. doi: 10.1038/s41540-023-00319-z.

ABSTRACT

Systemic lupus erythematosus (SLE) and primary Sjögren's syndrome (pSS) share clinical as well as pathogenic similarities. Although previous studies suggest various abnormalities in different immune cell compartments, dedicated cell-type specific transcriptomic signatures are often masked by patient heterogeneity. Here, we performed transcriptional profiling of isolated CD4, CD8, CD16 and CD19 lymphocytes from pSS and SLE patients upon T cell stimulation, in addition to a steady-state condition directly after blood drawing, in total comprising 581 sequencing samples. T cell stimulation, which induced a pronounced inflammatory response in all four cell types, gave rise to substantial re-modulation of lymphocyte subsets in the two autoimmune diseases compared to healthy controls, far exceeding the transcriptomic differences detected at steady-state. In particular, we detected cell-type and disease-specific down-regulation of a range of pro-inflammatory cytokine and chemokine pathways. Such differences between SLE and pSS patients are instrumental for selective immune targeting by future therapies.

PMID:38102122 | DOI:10.1038/s41540-023-00319-z

Nat Commun. 2023 Dec 15;14(1):8372. doi: 10.1038/s41467-023-44081-6.

ABSTRACT

ATP-dependent chromatin remodeling SWI/SNF complexes exist in three subcomplexes: canonical BAF (cBAF), polybromo BAF (PBAF), and a newly described non-canonical BAF (ncBAF). While cBAF and PBAF regulate fates of multiple cell types, roles for ncBAF in hematopoietic stem cells (HSCs) have not been investigated. Motivated by recent discovery of disrupted expression of BRD9, an essential component of ncBAF, in multiple cancers, including clonal hematopoietic disorders, we evaluate here the role of BRD9 in normal and malignant HSCs. BRD9 loss enhances chromatin accessibility, promoting myeloid lineage skewing while impairing B cell development. BRD9 significantly colocalizes with CTCF, whose chromatin recruitment is augmented by BRD9 loss, leading to altered chromatin state and expression of myeloid-related genes within intact topologically associating domains. These data uncover ncBAF as critical for cell fate specification in HSCs via three-dimensional regulation of gene expression and illuminate roles for ncBAF in normal and malignant hematopoiesis.

PMID:38102116 | DOI:10.1038/s41467-023-44081-6

Brief Bioinform. 2023 Nov 22;25(1):bbad462. doi: 10.1093/bib/bbad462.

ABSTRACT

Protein-ligand interactions are increasingly profiled at high-throughput, playing a vital role in lead compound discovery and drug optimization. Accurate prediction of binding pose and binding affinity constitutes a pivotal challenge in advancing our computational understanding of protein-ligand interactions. However, inherent limitations still exist, including high computational cost for conformational search sampling in traditional molecular docking tools, and the unsatisfactory molecular representation learning and intermolecular interaction modeling in deep learning-based methods. Here we propose a geometry-aware attention-based deep learning model, GAABind, which effectively predicts the pocket-ligand binding pose and binding affinity within a multi-task learning framework. Specifically, GAABind comprehensively captures the geometric and topological properties of both binding pockets and ligands, and employs expressive molecular representation learning to model intramolecular interactions. Moreover, GAABind proficiently learns the intermolecular many-body interactions and simulates the dynamic conformational adaptations of the ligand during its interaction with the protein through meticulously designed networks. We trained GAABind on the PDBbindv2020 and evaluated it on the CASF2016 dataset; the results indicate that GAABind achieves state-of-the-art performance in binding pose prediction and shows comparable binding affinity prediction performance. Notably, GAABind achieves a success rate of 82.8% in binding pose prediction, and the Pearson correlation between predicted and experimental binding affinities reaches up to 0.803. Additionally, we assessed GAABind's performance on the severe acute respiratory syndrome coronavirus 2 main protease cross-docking dataset. In this evaluation, GAABind demonstrates a notable success rate of 76.5% in binding pose prediction and achieves the highest Pearson correlation coefficient in binding affinity prediction compared with all baseline methods.

PMID:38102069 | DOI:10.1093/bib/bbad462

Cell Metab. 2023 Dec 8:S1550-4131(23)00421-7. doi: 10.1016/j.cmet.2023.11.010. Online ahead of print.

ABSTRACT

Common genetic variants in glucokinase regulator (GCKR), which encodes GKRP, a regulator of hepatic glucokinase (GCK), influence multiple metabolic traits in genome-wide association studies (GWASs), making GCKR one of the most pleiotropic GWAS loci in the genome. It is unclear why. Prior work has demonstrated that GCKR influences the hepatic cytosolic NADH/NAD+ ratio, also referred to as reductive stress. Here, we demonstrate that reductive stress is sufficient to activate the transcription factor ChREBP and necessary for its activation by the GKRP-GCK interaction, glucose, and ethanol. We show that hepatic reductive stress induces GCKR GWAS traits such as increased hepatic fat, circulating FGF21, and circulating acylglycerol species, which are also influenced by ChREBP. We define the transcriptional signature of hepatic reductive stress and show its upregulation in fatty liver disease and downregulation after bariatric surgery in humans. These findings highlight how a GCKR-reductive stress-ChREBP axis influences multiple human metabolic traits.

PMID:38101397 | DOI:10.1016/j.cmet.2023.11.010

Microbiol Res. 2023 Dec 9;279:127572. doi: 10.1016/j.micres.2023.127572. Online ahead of print.

ABSTRACT

The filamentous cyanobacterium Limnospira platensis, formerly known as Arthrospira platensis or spirulina, is one of the most commercially important species of microalgae. Due to its high nutritional value, pharmacological and industrial applications it is extensively cultivated on a large commercial scale. Despite its widespread use, its precise manipulation is still under development due to the lack of effective genetic protocols. Genetic transformation of Limnospira has been attempted but the methods reported have not been generally reproducible in other laboratories. Knowledge of the transformation defense mechanisms is essential for understanding its physiology and for broadening their applications. With the aim to understand more about the genetic defenses of L. platensis, in this work we have identified the restriction-modification and CRISPR-Cas systems and we have cloned and characterized thirteen methylases. In parallel, we have also characterized the methylome and orphan methyltransferases using genome-wide analysis of DNA methylation patterns and RNA-seq. The identification and characterization of these enzymes will be a valuable resource to know how this strain avoids being genetically manipulated and for further genomics studies.

PMID:38101163 | DOI:10.1016/j.micres.2023.127572

Am J Hosp Palliat Care. 2023 Dec 15:10499091231213359. doi: 10.1177/10499091231213359. Online ahead of print.

ABSTRACT

Background: Opioid therapy is critical for pain relief for most hospice patients but may be limited by adverse side effects. Combining medical cannabis with opioids may help mitigate adverse effects while maintaining effective pain relief. Aim: This single-arm study investigated the impact of combined medical cannabis/opioid therapy on pain relief, opioid dose, appetite, respiratory function, well-being, nausea, and adverse events in hospice inpatients. Design: Adult hospice inpatients using scheduled oral, parenteral, or transdermal opioids for pain were administered standardized oral medical cannabis, 40 mg CBD/1.5 mg THC or 80 mg CBD/3 mg THC. Descriptive statistics detailed demographic and clinical baseline characteristics, the Mann-Whitney test compared outcomes, and the longitudinal mixed effects regression model analyzed longitudinal effects of combined therapy. Setting/Participants: Sixty-six inpatients at The Connecticut Hospital, Inc. were assessed over 996 treatment days; average age was 68.2 ± 12.9 years, 90.9% were white. Cancer was the most common diagnosis. Results: The medical cannabis/opioid combination showed a significant longitudinal reduction in pain intensity (P = .0029) and a non-significant trend toward lower opioid doses. Well-being, appetite, nausea, and respiratory function showed non-statistically significant changes. Three patients (4.5%) experienced minor, reversible adverse events potentially related to medical cannabis. No serious or life-threatening adverse events were seen. Conclusion: Combination medical cannabis/opioid therapy showed statistically significant pain relief and may have the potential for reducing opioid dose and mitigating opioid toxicity, offering a safe pain management alternative to opioids alone for patients in end-of-life care settings, and warrants further investigation in larger controlled trials.

PMID:38100655 | DOI:10.1177/10499091231213359

ACS Synth Biol. 2023 Dec 15. doi: 10.1021/acssynbio.3c00622. Online ahead of print.

ABSTRACT

Halomonas bluephagenesis is a halophilic bacterium capable of efficiently producing polyhydroxyalkanoates and other valuable chemicals through high salinity open fermentation, offering an appealing platform for next-generation industrial biotechnology. Various techniques have been developed to engineer Halomonas bluephagenesis, each with its inherent shortcomings. Genome editing methods often entail complex and time-consuming processes, while flexible expression systems relying on plasmids necessitate the use of antibiotics. In this study, we developed a stable recombinant plasmid vector, pHbPBC, based on a novel hbpB/hbpC toxin-antitoxin system found within the endogenous plasmid of Halomonas bluephagenesis. Remarkably, pHbPBC exhibited exceptional stability during 7 days of continuous subculture, eliminating the need for antibiotics or other selection pressures. This stability even rivaled genomic integration, all while achieving higher levels of heterologous expression. Our research introduces a novel approach for genetically modifying and harnessing nonmodel halophilic bacteria, contributing to the advancement of next-generation industrial biotechnology.

PMID:38100561 | DOI:10.1021/acssynbio.3c00622