Ex vivo gene editing and cell therapy for hereditary tyrosinemia type 1.

BACKGROUND: We previously demonstrated the successful use of in vivo CRISPR gene editing to delete 4-hydroxyphenylpyruvate dioxygenase (HPD) to rescue mice deficient in fumarylacetoacetate hydrolase (FAH), a disorder known as hereditary tyrosinemia type 1 (HT1). The aim of this study was to develop an ex vivo gene-editing protocol and apply it as a cell therapy for HT1. METHODS: We isolated hepatocytes from wild-type (C57BL/6J) and Fah-/- mice and then used an optimized electroporation protocol to deliver Hpd-targeting CRISPR-Cas9 ribonucleoproteins into hepatocytes. Next, hepatocytes were transiently incubated in cytokine recovery media formulated to block apoptosis, followed by splenic injection into recipient Fah-/- mice. RESULTS: We observed robust engraftment and expansion of transplanted gene-edited hepatocytes from wild-type donors in the livers of recipient mice when transient incubation with our cytokine recovery media was used after electroporation and negligible engraftment without the media (mean: 46.8% and 0.83%, respectively; p=0.0025). Thus, the cytokine recovery medium was critical to our electroporation protocol. When hepatocytes from Fah-/- mice were used as donors for transplantation, we observed 35% and 28% engraftment for Hpd-Cas9 ribonucleoproteins and Cas9 mRNA, respectively. Tyrosine, phenylalanine, and biochemical markers of liver injury normalized in both Hpd-targeting Cas9 ribonucleoprotein and mRNA groups independent of induced inhibition of Hpd through nitisinone, indicating correction of disease indicators in Fah-/- mice. CONCLUSIONS: The successful liver cell therapy for HT1 validates our protocol and, despite the known growth advantage of HT1, showcases ex vivo gene editing using electroporation in combination with liver cell therapy to cure a disease model. These advancements underscore the potential impacts of electroporation combined with transplantation as a cell therapy.

Exploring the substrate spectrum of phylogenetically distinct bacterial polyesterases.

The rapid escalation of plastic waste accumulation presents a significant threat of the modern world, demanding an immediate solution. Over the last years, utilization of the enzymatic machinery of various microorganisms has emerged as an environmentally friendly asset in tackling this pressing global challenge. Thus, various hydrolases have been demonstrated to effectively degrade polyesters. Plastic waste streams often consist of a variety of different polyesters, as impurities, mainly due to wrong disposal practices, rendering recycling process challenging. The elucidation of the selective degradation of polyesters by hydrolases could offer a proper solution to this problem, enhancing the recyclability performance. Towards this, our study focused on the investigation of four bacterial polyesterases, including DaPUase, IsPETase, PfPHOase, and Se1JFR, a novel PETase-like lipase. The enzymes, which were biochemically characterized and structurally analyzed, demonstrated degradation ability of synthetic plastics. While a consistent pattern of polyesters' degradation was observed across all enzymes, Se1JFR stood out in the degradation of PBS, PLA, and polyether PU. Additionally, it exhibited comparable results to IsPETase, a benchmark mesophilic PETase, in the degradation of PCL and semi-crystalline PET. Our results point out the wide substrate spectrum of bacterial hydrolases and underscore the significant potential of PETase-like enzymes in polyesters degradation.

Metabolic Modulation of Kynurenine Based on Kynureninase-Loaded Nanoparticle Depot Overcomes Tumor Immune Evasion in Cancer Immunotherapy.

Evading recognition of immune cells is a well-known strategy of tumors used for their survival. One of the immune evasion mechanisms is the synthesis of kynurenine (KYN), a metabolite of tryptophan, which suppresses the effector T cells. Therefore, lowering the KYN concentration can be an efficient antitumor therapy by restoring the activity of immune cells. Recently, kynureninase (KYNase), which is an enzyme transforming KYN into anthranilate, was demonstrated to show the potential to decrease KYN concentration and inhibit tumor growth. However, due to the limited bioavailability and instability of proteins in vivo, it has been challenging to maintain the KYNase concentration sufficiently high in the tumor microenvironment (TME). Here, we developed a nanoparticle system loaded with KYNase, which formed a Biodegradable and Implantable Nanoparticle Depot named 'BIND' following subcutaneous injection. The BIND sustainably supplied KYNase around the TME while located around the tumor, until it eventually degraded and disappeared. As a result, the BIND system enhanced the proliferation and cytokine production of effector T cells in the TME, followed by tumor growth inhibition and increased mean survival. Finally, we showed that the BIND carrying KYNase significantly synergized with PD-1 blockade in three mouse models of colon cancer, breast cancer, and melanoma.

An empirical assessment of the influence of digital transformation on sports corporate sustainability.

The trend of digital transformation fosters enterprise change, helps cultivate enterprises' own competitive advantages and is crucial to the advancement of sports enterprises' sustainable development in the framework of the emerging digital economy as a national strategy. However, there have been few empirical studies on the microlevel of digital transformation and its impact on the sustainability of sports organizations. Therefore, the sustainable growth dynamic model is used to construct indicators of corporate sustainability by referencing 48 sports corporations listed on Shanghai and Shenzhen A-shares markets and the New Third Board in China from 2012 to 2021. The intrinsic relationship between digital transformation and the sustainable development of sports enterprises and the underlying mechanism of action are explored by constructing a panel fixed effects model, a chain mediating effects model, and a panel threshold model. The most important contribution is as follows: To provide a useful reference for analyzing enterprise digital transformation, a more complete indicator indicating the extent of corporate digital transformation is built. The micro viewpoint broadens our awareness of sustainable development in sports organizations and deepens our understanding of the interaction model between sustainable development and enterprise digital transformation. This study provides methodical evidence and insights for an accurate understanding of digital transformation for sustainable enterprise development, looking into the "black box" of the mechanism between digital transformation and sustainable business development. The results show that digital transformation significantly aids sports enterprises in their pursuit of long-term sustainability. Heterogeneity tests demonstrate the pivotal role of digital transformation in advancing the sustained growth of sports firms and high-tech sports enterprises situated in the eastern region of China. Regarding transmission mechanisms, the chain mediating effect of enterprises' digital transformation on improved technological innovation and TFP, which in turn promote long-term business growth, has yet to be validated. Further examination exposes that within the context of the correlation between digital transformation and the sustainability of corporations, there is a single threshold effect based on financing restrictions and operational costs and a double threshold effect based on operational efficiency.

Cue-driven microbial cooperation and communication: evolving quorum sensing with honest signaling.

BACKGROUND: Quorum sensing (QS) is the ability of microorganisms to assess local clonal density by measuring the extracellular concentration of signal molecules that they produce and excrete. QS is also the only known way of bacterial communication that supports the coordination of within-clone cooperative actions requiring a certain threshold density of cooperating cells. Cooperation aided by QS communication is sensitive to cheating in two different ways: laggards may benefit from not investing in cooperation but enjoying the benefit provided by their cooperating neighbors, whereas Liars explicitly promise cooperation but fail to do so, thereby convincing potential cooperating neighbors to help them, for almost free. Given this double vulnerability to cheats, it is not trivial why QS-supported cooperation is so widespread among prokaryotes. RESULTS: We investigated the evolutionary dynamics of QS in populations of cooperators for whom the QS signal is an inevitable side effect of producing the public good itself (cue-based QS). Using spatially explicit agent-based lattice simulations of QS-aided threshold cooperation (whereby cooperation is effective only above a critical cumulative level of contributions) and three different (analytical and numerical) approximations of the lattice model, we explored the dynamics of QS-aided threshold cooperation under a feasible range of parameter values. We demonstrate three major advantages of cue-driven cooperation. First, laggards cannot wipe out cooperation under a wide range of reasonable environmental conditions, in spite of an unconstrained possibility to mutate to cheating; in fact, cooperators may even exclude laggards at high cooperation thresholds. Second, lying almost never pays off, if the signal is an inevitable byproduct (i.e., the cue) of cooperation; even very cheap fake signals are selected against. And thirdly, QS is most useful if local cooperator densities are the least predictable, i.e., if their lattice-wise mean is close to the cooperation threshold with a substantial variance. CONCLUSIONS: Comparing the results of the four different modeling approaches indicates that cue-driven threshold cooperation may be a viable evolutionary strategy for microbes that cannot keep track of past behavior of their potential cooperating partners, in spatially viscous and in well-mixed environments alike. Our model can be seen as a version of the famous greenbeard effect, where greenbeards coexist with defectors in a evolutionarily stable polymorphism. Such polymorphism is maintained by the condition-dependent trade-offs of signal production which are characteristic of cue-based QS.

Genomic characterization and related functional genes of γ- poly glutamic acid producing Bacillus subtilis.

γ- poly glutamic acid (γ-PGA), a high molecular weight polymer, is synthesized by microorganisms and secreted into the extracellular space. Due to its excellent performance, γ-PGA has been widely used in various fields, including food, biomedical and environmental fields. In this study, we screened natto samples for two strains of Bacillus subtilis N3378-2at and N3378-3At that produce γ-PGA. We then identified the γ-PGA synthetase gene cluster (PgsB, PgsC, PgsA, YwtC and PgdS), glutamate racemase RacE, phage-derived γ-PGA hydrolase (PghB and PghC) and exo-γ-glutamyl peptidase (GGT) from the genome of these strains. Based on these γ-PGA-related protein sequences from isolated Bacillus subtilis and 181 B. subtilis obtained from GenBank, we carried out genotyping analysis and classified them into types 1-5. Since we found B. amyloliquefaciens LL3 can produce γ-PGA, we obtained the B. velezensis and B. amyloliquefaciens strains from GenBank and classified them into types 6 and 7 based on LL3. Finally, we constructed evolutionary trees for these protein sequences. This study analyzed the distribution of γ-PGA-related protein sequences in the genomes of B. subtilis, B. velezensis and B. amyloliquefaciens strains, then the evolutionary diversity of these protein sequences was analyzed, which provided novel information for the development and utilization of γ-PGA-producing strains.

Predictive modeling for breast cancer classification in the context of Bangladeshi patients by use of machine learning approach with explainable AI.

Breast cancer has rapidly increased in prevalence in recent years, making it one of the leading causes of mortality worldwide. Among all cancers, it is by far the most common. Diagnosing this illness manually requires significant time and expertise. Since detecting breast cancer is a time-consuming process, preventing its further spread can be aided by creating machine-based forecasts. Machine learning and Explainable AI are crucial in classification as they not only provide accurate predictions but also offer insights into how the model arrives at its decisions, aiding in the understanding and trustworthiness of the classification results. In this study, we evaluate and compare the classification accuracy, precision, recall, and F1 scores of five different machine learning methods using a primary dataset (500 patients from Dhaka Medical College Hospital). Five different supervised machine learning techniques, including decision tree, random forest, logistic regression, naive bayes, and XGBoost, have been used to achieve optimal results on our dataset. Additionally, this study applied SHAP analysis to the XGBoost model to interpret the model's predictions and understand the impact of each feature on the model's output. We compared the accuracy with which several algorithms classified the data, as well as contrasted with other literature in this field. After final evaluation, this study found that XGBoost achieved the best model accuracy, which is 97%.

Semantic segmentation of urban environments: Leveraging U-Net deep learning model for cityscape image analysis.

Semantic segmentation of cityscapes via deep learning is an essential and game-changing research topic that offers a more nuanced comprehension of urban landscapes. Deep learning techniques tackle urban complexity and diversity, which unlocks a broad range of applications. These include urban planning, transportation management, autonomous driving, and smart city efforts. Through rich context and insights, semantic segmentation helps decision-makers and stakeholders make educated decisions for sustainable and effective urban development. This study investigates an in-depth exploration of cityscape image segmentation using the U-Net deep learning model. The proposed U-Net architecture comprises an encoder and decoder structure. The encoder uses convolutional layers and down sampling to extract hierarchical information from input images. Each down sample step reduces spatial dimensions, and increases feature depth, aiding context acquisition. Batch normalization and dropout layers stabilize models and prevent overfitting during encoding. The decoder reconstructs higher-resolution feature maps using "UpSampling2D" layers. Through extensive experimentation and evaluation of the Cityscapes dataset, this study demonstrates the effectiveness of the U-Net model in achieving state-of-the-art results in image segmentation. The results clearly shown that, the proposed model has high accuracy, mean IOU and mean DICE compared to existing models.

Metabolism-triggered sensor array aided by machine learning for rapid identification of pathogens.

Chemical-nose strategy has achieved certain success in the discrimination and identification of pathogens. However, this strategy usually relies on non-specific interactions, which are prone to be significantly disturbed by the change of environment thus limiting its practical usefulness. Herein, we present a novel chemical-nose sensing approach leveraging the difference in the dynamic metabolic variation during peptidoglycan metabolism among different species for rapid pathogen discrimination. Pathogens were first tethered with clickable handles through metabolic labeling at two different acidities (pH = 5 and 7) for 20 and 60 min, respectively, followed by click reaction with fluorescence up-conversion nanoparticles to generate a four-dimensional signal output. This discriminative multi-dimensional signal allowed eight types of model bacteria to be successfully classified within the training set into strains, genera, and Gram phenotypes. As the difference in signals of the four sensing channels reflects the difference in the amount/activity of enzymes involved in metabolic labeling, this strategy has good anti-interference capability, which enables precise pathogen identification within 2 h with 100% accuracy in spiked urinary samples and allows classification of unknown species out of the training set into the right phenotype. The robustness of this approach holds significant promise for its widespread application in pathogen identification and surveillance.

Efficient Conversion of Stevioside to Rebaudioside M in Saccharomyces cerevisiae by a Engineering Hydrolase System and Prolonging the Growth Cycle.

Rebaudioside (Reb) M is an important sweetener with high sweetness, but its low content in Stevia rebaudiana and low catalytic capacity of the glycosyltransferases in heterologous microorganisms limit its production. In order to improve the catalytic efficiency of the conversion of stevioside to Reb M by Saccharomyces cerevisiae, several key issues must be resolved including knocking out endogenous hydrolases, enhancing glycosylation, and extending the enzyme catalytic process. Herein, endogenous glycosyl hydrolase SCW2 was knocked out in S. cerevisiae. The glycosylation process was enhanced by screening glycosyltransferases, and UGT91D2 from S. rebaudiana was identified as the optimum glycosyltransferase. The UDP-glucose supply was enhanced by overexpressing UGP1, and co-expressing UGT91D2 and UGT76G1 achieved efficient conversion of stevioside to Reb M. In order to extend the catalytic process, the silencing information regulator 2 (SIR2) which can prolong the growth cycle of S. cerevisiae was introduced. Finally, combining these modifications produced 12.5 g/L Reb M and the yield reached 77.9% in a 5 L bioreactor with 10.0 g/L stevioside, the highest titer from steviol glycosides to Reb M reported to date. The engineered strain could facilitate the industrial production of Reb M, and the strategies provide references for the production of steviol glycosides.

Identification and Biological Evaluation of a Novel Small-Molecule Inhibitor of Ricin Toxin.

The plant-derived toxin ricin is classified as a type 2 ribosome-inactivating protein (RIP) and currently lacks effective clinical antidotes. The toxicity of ricin is mainly due to its ricin toxin A chain (RTA), which has become an important target for drug development. Previous studies have identified two essential binding pockets in the active site of RTA, but most existing inhibitors only target one of these pockets. In this study, we used computer-aided virtual screening to identify a compound called RSMI-29, which potentially interacts with both active pockets of RTA. We found that RSMI-29 can directly bind to RTA and effectively attenuate protein synthesis inhibition and rRNA depurination induced by RTA or ricin, thereby inhibiting their cytotoxic effects on cells in vitro. Moreover, RSMI-29 significantly reduced ricin-mediated damage to the liver, spleen, intestine, and lungs in mice, demonstrating its detoxification effect against ricin in vivo. RSMI-29 also exhibited excellent drug-like properties, featuring a typical structural moiety of known sulfonamides and barbiturates. These findings suggest that RSMI-29 is a novel small-molecule inhibitor that specifically targets ricin toxin A chain, providing a potential therapeutic option for ricin intoxication.

Developing an Improved Cycle Architecture for AI-Based Generation of New Structures Aimed at Drug Discovery.

Drug discovery involves a crucial step of optimizing molecules with the desired structural groups. In the domain of computer-aided drug discovery, deep learning has emerged as a prominent technique in molecular modeling. Deep generative models, based on deep learning, play a crucial role in generating novel molecules when optimizing molecules. However, many existing molecular generative models have limitations as they solely process input information in a forward way. To overcome this limitation, we propose an improved generative model called BD-CycleGAN, which incorporates BiLSTM (bidirectional long short-term memory) and Mol-CycleGAN (molecular cycle generative adversarial network) to preserve the information of molecular input. To evaluate the proposed model, we assess its performance by analyzing the structural distribution and evaluation matrices of generated molecules in the process of structural transformation. The results demonstrate that the BD-CycleGAN model achieves a higher success rate and exhibits increased diversity in molecular generation. Furthermore, we demonstrate its application in molecular docking, where it successfully increases the docking score for the generated molecules. The proposed BD-CycleGAN architecture harnesses the power of deep learning to facilitate the generation of molecules with desired structural features, thus offering promising advancements in the field of drug discovery processes.

Biosensor and machine learning-aided engineering of an amaryllidaceae enzyme.

A major challenge to achieving industry-scale biomanufacturing of therapeutic alkaloids is the slow process of biocatalyst engineering. Amaryllidaceae alkaloids, such as the Alzheimer's medication galantamine, are complex plant secondary metabolites with recognized therapeutic value. Due to their difficult synthesis they are regularly sourced by extraction and purification from the low-yielding daffodil Narcissus pseudonarcissus. Here, we propose an efficient biosensor-machine learning technology stack for biocatalyst development, which we apply to engineer an Amaryllidaceae enzyme in Escherichia coli. Directed evolution is used to develop a highly sensitive (EC50 = 20 µM) and specific biosensor for the key Amaryllidaceae alkaloid branchpoint 4'-O-methylnorbelladine. A structure-based residual neural network (MutComputeX) is subsequently developed and used to generate activity-enriched variants of a plant methyltransferase, which are rapidly screened with the biosensor. Functional enzyme variants are identified that yield a 60% improvement in product titer, 2-fold higher catalytic activity, and 3-fold lower off-product regioisomer formation. A solved crystal structure elucidates the mechanism behind key beneficial mutations.

Machine learning-aided design and screening of an emergent protein function in synthetic cells.

Recently, utilization of Machine Learning (ML) has led to astonishing progress in computational protein design, bringing into reach the targeted engineering of proteins for industrial and biomedical applications. However, the design of proteins for emergent functions of core relevance to cells, such as the ability to spatiotemporally self-organize and thereby structure the cellular space, is still extremely challenging. While on the generative side conditional generative models and multi-state design are on the rise, for emergent functions there is a lack of tailored screening methods as typically needed in a protein design project, both computational and experimental. Here we describe a proof-of-principle of how such screening, in silico and in vitro, can be achieved for ML-generated variants of a protein that forms intracellular spatiotemporal patterns. For computational screening we use a structure-based divide-and-conquer approach to find the most promising candidates, while for the subsequent in vitro screening we use synthetic cell-mimics as established by Bottom-Up Synthetic Biology. We then show that the best screened candidate can indeed completely substitute the wildtype gene in Escherichia coli. These results raise great hopes for the next level of synthetic biology, where ML-designed synthetic proteins will be used to engineer cellular functions.

SARS-CoV-2 virulence factor ORF3a blocks lysosome function by modulating TBC1D5-dependent Rab7 GTPase cycle.

SARS-CoV-2, the causative agent of COVID-19, uses the host endolysosomal system for entry, replication, and egress. Previous studies have shown that the SARS-CoV-2 virulence factor ORF3a interacts with the lysosomal tethering factor HOPS complex and blocks HOPS-mediated late endosome and autophagosome fusion with lysosomes. Here, we report that SARS-CoV-2 infection leads to hyperactivation of the late endosomal and lysosomal small GTP-binding protein Rab7, which is dependent on ORF3a expression. We also observed Rab7 hyperactivation in naturally occurring ORF3a variants encoded by distinct SARS-CoV-2 variants. We found that ORF3a, in complex with Vps39, sequesters the Rab7 GAP TBC1D5 and displaces Rab7 from this complex. Thus, ORF3a disrupts the GTP hydrolysis cycle of Rab7, which is beneficial for viral production, whereas the Rab7 GDP-locked mutant strongly reduces viral replication. Hyperactivation of Rab7 in ORF3a-expressing cells impaired CI-M6PR retrieval from late endosomes to the trans-Golgi network, disrupting the biosynthetic transport of newly synthesized hydrolases to lysosomes. Furthermore, the tethering of the Rab7- and Arl8b-positive compartments was strikingly reduced upon ORF3a expression. As SARS-CoV-2 egress requires Arl8b, these findings suggest that ORF3a-mediated hyperactivation of Rab7 serves a multitude of functions, including blocking endolysosome formation, interrupting the transport of lysosomal hydrolases, and promoting viral egress.

Evaluation of the Cytosolic Uptake of HaloTag Using a pH-Sensitive Dye.

The efficient cytosolic delivery of proteins is critical for advancing novel therapeutic strategies. Current delivery methods are severely limited by endosomal entrapment, and detection methods lack sophistication in tracking the fate of delivered protein cargo. HaloTag, a commonly used protein in chemical biology and a challenging delivery target, is an exceptional model system for understanding and exploiting cellular delivery. Here, we employed a combinatorial strategy to direct HaloTag to the cytosol. We established the use of Virginia Orange, a pH-sensitive fluorophore, and Janelia Fluor 585, a similar but pH-agnostic fluorophore, in a fluorogenic assay to ascertain protein localization within human cells. Using this assay, we investigated HaloTag delivery upon modification with cell-penetrating peptides, carboxyl group esterification, and cotreatment with an endosomolytic agent. We found efficacious cytosolic entry with two distinct delivery methods. This study expands the toolkit for detecting the cytosolic access of proteins and highlights that multiple intracellular delivery strategies can be used synergistically to effect cytosolic access. Moreover, HaloTag is poised to serve as a platform for the delivery of varied cargo into human cells.

Genetic features of patients with MPS type IIIB: Description of five pathogenic gene variations.

BACKGROUND: There are four distinct forms of Sanfilippo syndrome (MPS type III), each of which is an autosomal lysosomal storage disorder. These forms are caused by abnormalities in one of four lysosomal enzymes. This study aimed to identify possible genetic variants that contribute to Sanfilippo IIIB in 14 independent families in Southwest Iran. METHODS: Patients were included if their clinical features and enzyme assay results were suggestive. The patients were subsequently subjected to Sanger Sequencing to screen for Sanfilippo-related genes. Additional investigations have been conducted using various computational analyses to determine the probable functional effects of diagnosed variants. RESULTS: Five distinct variations were identified in the NAGLU gene. This included two novel variants in two distinct families and three previously reported variants in 12 distinct families. All of these variations were recognized as pathogenic using the MutationTaster web server. In silico analysis showed that all detected variants affected protein structural stability; four destabilized protein structures, and the fifth variation had the opposite effect. CONCLUSION: In this study, two novel variations in the NAGLU gene were identified. The results of this study positively contribute to the mutation diversity of the NAGLU gene. To identify new disease biomarkers and therapeutic targets, precision medicine must precisely characterize and account for genetic variations. New harmful gene variants are valuable for updating gene databases concerning Sanfilippo disease variations and NGS gene panels. This may also improve genetic counselling for rapid risk examinations and disease surveillance.

GnRH Induces Citrullination of the Cytoskeleton in Murine Gonadotrope Cells.

Peptidylarginine deiminases (PADs or PADIs) catalyze the conversion of positively charged arginine to neutral citrulline, which alters target protein structure and function. Our previous work established that gonadotropin-releasing hormone agonist (GnRHa) stimulates PAD2-catalyzed histone citrullination to epigenetically regulate gonadotropin gene expression in the gonadotrope-derived LßT2 cell line. However, PADs are also found in the cytoplasm. Given this, we used mass spectrometry (MS) to identify additional non-histone proteins that are citrullinated following GnRHa stimulation and characterized the temporal dynamics of this modification. Our results show that actin and tubulin are citrullinated, which led us to hypothesize that GnRHa might induce their citrullination to modulate cytoskeletal dynamics and architecture. The data show that 10 nM GnRHa induces the citrullination of ß-actin, with elevated levels occurring at 10 min. The level of ß-actin citrullination is reduced in the presence of the pan-PAD inhibitor biphenyl-benzimidazole-Cl-amidine (BB-ClA), which also prevents GnRHa-induced actin reorganization in dispersed murine gonadotrope cells. GnRHa induces the citrullination of ß-tubulin, with elevated levels occurring at 30 min, and this response is attenuated in the presence of PAD inhibition. To examine the functional consequence of ß-tubulin citrullination, we utilized fluorescently tagged end binding protein 1 (EB1-GFP) to track the growing plus end of microtubules (MT) in real time in transfected LßT2 cells. Time-lapse confocal microscopy of EB1-GFP reveals that the MT average lifetime increases following 30 min of GnRHa treatment, but this increase is attenuated by PAD inhibition. Taken together, our data suggest that GnRHa-induced citrullination alters actin reorganization and MT lifetime in gonadotrope cells.

Changes in an enzyme ensemble during catalysis observed by high-resolution XFEL crystallography.

Enzymes populate ensembles of structures necessary for catalysis that are difficult to experimentally characterize. We use time-resolved mix-and-inject serial crystallography at an x-ray free electron laser to observe catalysis in a designed mutant isocyanide hydratase (ICH) enzyme that enhances sampling of important minor conformations. The active site exists in a mixture of conformations, and formation of the thioimidate intermediate selects for catalytically competent substates. The influence of cysteine ionization on the ICH ensemble is validated by determining structures of the enzyme at multiple pH values. Large molecular dynamics simulations in crystallo and time-resolved electron density maps show that Asp17 ionizes during catalysis and causes conformational changes that propagate across the dimer, permitting water to enter the active site for intermediate hydrolysis. ICH exhibits a tight coupling between ionization of active site residues and catalysis-activated protein motions, exemplifying a mechanism of electrostatic control of enzyme dynamics.

Characterization of two novel hydrolases from Sphingopyxis sp. DBS4 for enantioselective degradation of chiral herbicide diclofop-methyl.

Diclofop-methyl, an aryloxyphenoxypropionate (AOPP) herbicide, is a chiral compound with two enantiomers. Microbial detoxification and degradation of various enantiomers is garnering immense research attention. However, enantioselective catabolism of diclofop-methyl has been rarely explored, especially at the molecular level. This study cloned two novel hydrolase genes (dcmA and dcmH) in Sphingopyxis sp. DBS4, and characterized them for diclofop-methyl degradation. DcmA, a member of the amidase superfamily, exhibits 26.1-45.9% identity with functional amidases. Conversely, DcmH corresponded to the DUF3089 domain-containing protein family (a family with unknown function), sharing no significant similarity with other biochemically characterized proteins. DcmA exhibited a broad spectrum of substrates, with preferential hydrolyzation of (R)-(+)-diclofop-methyl, (R)-(+)-quizalofop-ethyl, and (R)-(+)-haloxyfop-methyl. DcmH also preferred (R)-(+)-quizalofop-ethyl and (R)-(+)-haloxyfop-methyl degradation while displaying no apparent enantioselective activity towards diclofop-methyl. Using site-directed mutagenesis and molecular docking, it was determined that Ser175 was the fundamental residue influencing DcmA's activity against the two enantiomers of diclofop-methyl. For the degradation of AOPP herbicides, DcmA is an enantioselective amidase that has never been reported in research. This study provided novel hydrolyzing enzyme resources for the remediation of diclofop-methyl in the environment and deepened the understanding of enantioselective degradation of chiral AOPP herbicides mediated by microbes.