Misregulation of bromotyrosine compromises fertility in male Drosophila.

Biological regulation often depends on reversible reactions such as phosphorylation, acylation, methylation, and glycosylation, but rarely halogenation. A notable exception is the iodination and deiodination of thyroid hormones. Here, we report detection of bromotyrosine and its subsequent debromination during Drosophila spermatogenesis. Bromotyrosine is not evident when Drosophila express a native flavin-dependent dehalogenase that is homologous to the enzyme responsible for iodide salvage from iodotyrosine in mammals. Deletion or suppression of the dehalogenase-encoding condet (cdt) gene in Drosophila allows bromotyrosine to accumulate with no detectable chloro- or iodotyrosine. The presence of bromotyrosine in the cdt mutant males disrupts sperm individualization and results in decreased fertility. Transgenic expression of the cdt gene in late-staged germ cells rescues this defect and enhances tolerance of male flies to bromotyrosine. These results are consistent with reversible halogenation affecting Drosophila spermatogenesis in a process that had previously eluded metabolomic, proteomic, and genomic analyses.

Impairing hydrolase transport machinery prevents human melanoma metastasis.

Metastases are the major cause of cancer-related death, yet, molecular weaknesses that could be exploited to prevent tumor cells spreading are poorly known. Here, we found that perturbing hydrolase transport to lysosomes by blocking either the expression of IGF2R, the main receptor responsible for their trafficking, or GNPT, a transferase involved in the addition of the specific tag recognized by IGF2R, reduces melanoma invasiveness potential. Mechanistically, we demonstrate that the perturbation of this traffic, leads to a compensatory lysosome neo-biogenesis devoided of degradative enzymes. This regulatory loop relies on the stimulation of TFEB transcription factor expression. Interestingly, the inhibition of this transcription factor playing a key role of lysosome production, restores melanomas' invasive potential in the absence of hydrolase transport. These data implicate that targeting hydrolase transport in melanoma could serve to develop new therapies aiming to prevent metastasis by triggering a physiological response stimulating TFEB expression in melanoma.

Nontraditional Roles of Magnesium Ions in Modulating Sav2152: Insight from a Haloacid Dehalogenase-like Superfamily Phosphatase from Staphylococcus aureus.

Methicillin-resistant Staphylococcus aureus (MRSA) infection has rapidly spread through various routes. A genomic analysis of clinical MRSA samples revealed an unknown protein, Sav2152, predicted to be a haloacid dehalogenase (HAD)-like hydrolase, making it a potential candidate for a novel drug target. In this study, we determined the crystal structure of Sav2152, which consists of a C2-type cap domain and a core domain. The core domain contains four motifs involved in phosphatase activity that depend on the presence of Mg2+ ions. Specifically, residues D10, D12, and D233, which closely correspond to key residues in structurally homolog proteins, are responsible for binding to the metal ion and are known to play critical roles in phosphatase activity. Our findings indicate that the Mg2+ ion known to stabilize local regions surrounding it, however, paradoxically, destabilizes the local region. Through mutant screening, we identified D10 and D12 as crucial residues for metal binding and maintaining structural stability via various uncharacterized intra-protein interactions, respectively. Substituting D10 with Ala effectively prevents the interaction with Mg2+ ions. The mutation of D12 disrupts important structural associations mediated by D12, leading to a decrease in the stability of Sav2152 and an enhancement in binding affinity to Mg2+ ions. Additionally, our study revealed that D237 can replace D12 and retain phosphatase activity. In summary, our work uncovers the novel role of metal ions in HAD-like phosphatase activity.

Discovery and characterization of two novel polyethylene terephthalate hydrolases: One from a bacterium identified in human feces and one from the Streptomyces genus.

Polyethylene terephthalate (PET) is widely used for various industrial applications. However, owing to its extremely slow breakdown rate, PET accumulates as plastic trash, which negatively affects the environment and human health. Here, we report two novel PET hydrolases: PpPETase from Pseudomonas paralcaligenes MRCP1333, identified in human feces, and ScPETase from Streptomyces calvus DSM 41452. These two enzymes can decompose various PET materials, including semicrystalline PET powders (Cry-PET) and low-crystallinity PET films (gf-PET). By structure-guided engineering, two variants, PpPETaseY239R/F244G/Y250G and ScPETaseA212C/T249C/N195H/N243K were obtained that decompose Cry-PET 3.1- and 1.9-fold faster than their wild-type enzymes, respectively. The co-expression of ScPETase and mono-(2-hydroxyethyl) terephthalate hydrolase from Ideonella sakaiensis (IsMHETase) resulted in 1.4-fold more degradation than the single enzyme system. This engineered strain degraded Cry-PET and gf-PET by more than 40% and 6%, respectively, after 30 d. The concentrations of terephthalic acid (TPA) in the Cry-PET and gf-PET degradation products were 37.7% and 25.6%, respectively. The discovery of these two novel PET hydrolases provides opportunities to create more powerful biocatalysts for PET biodegradation.

Rational design to improve the catalytic efficiency and stability of arginine deiminase.

Arginine deiminase (ADI) has garnered significant interest because of its ability to objectively eradicate cancer cells and produce L-citrulline. To meet the production demands, this study focused on enhancing the enzyme activity and thermal stability of ADI. In this study, 24 ADI mutants were obtained through computer aid site-specific mutation in the ADI of Enterobacter faecalis. Notably, the specific enzyme activities of F44W, N163P, E220I, E220L, N318E, A336G, T340I, and N382F increased, reaching 1.33-2.53 times that of the original enzyme. This study confirmed that site-specific mutations are critical for optimizing enzyme function. Additionally, the F44W, N163P, E220I, T340I, and A336G mutants demonstrated good thermal stability. The optimal pH for mutant F44W increased to 8, whereas mutants E220I, I244V, A336G, T340I, and N328F maintained an optimal pH of 7.5. Conversely, the M109L, N163P, E220L, I244L, and N318E mutants shad an optimal pH of 7. This study revealed that mutant enzymes with increased activity were more likely to contain mutation sites situated near the four loops associated with catalytic residues, whereas mutations at the dimer junction sites had a higher tendency to enhance enzyme stability. These findings contribute to the development of ADI industrial applications and its modifications.

Protein Citrullination by Peptidyl Arginine Deiminase/Arginine Deiminase Homologs in Members of the Human Microbiota and Its Recognition by Anti-Citrullinated Protein Antibodies.

The human microbiome exists throughout the body, and it is essential for maintaining various physiological processes, including immunity, and dysbiotic events, which are associated with autoimmunity. Peptidylarginine deiminase (PAD) enzymes can citrullinate self-proteins related to rheumatoid arthritis (RA) that induce the production of anti-citrullinated protein antibodies (ACPAs) and lead to inflammation and joint damage. The present investigation was carried out to demonstrate the expression of homologs of PADs or arginine deiminases (ADs) and citrullinated proteins in members of the human microbiota. To achieve the objective, we used 17 microbial strains and specific polyclonal antibodies (pAbs) of the synthetic peptide derived from residues 100-200 of human PAD2 (anti-PAD2 pAb), and the recombinant fragment of amino acids 326 and 611 of human PAD4 (anti-PAD4 pAb), a human anti-citrulline pAb, and affinity ACPAs of an RA patient. Western blot (WB), enzyme-linked immunosorbent assay (ELISA), elution, and a test with Griess reagent were used. This is a cross-sectional case-control study on patients diagnosed with RA and control subjects. Inferential statistics were applied using the non-parametric Kruskal-Wallis test and Mann-Whitney U test generated in the SPSS program. Some members of phyla Firmicutes and Proteobacteria harbor homologs of PADs/ADs and citrullinated antigens that are reactive to the ACPAs of RA patients. Microbial citrullinome and homolog enzymes of PADs/ADs are extensive in the human microbiome and are involved in the production of ACPAs. Our findings suggest a molecular link between microorganisms of a dysbiotic microbiota and RA pathogenesis.

Recombinant Arginine Deiminase from Levilactobacillus brevis Inhibits the Growth of Stomach Cancer Cells, Possibly by Activating the Intrinsic Apoptosis Pathway.

The anticancer potential of Levilactobacillus brevis KU15176 against the stomach cancer cell line AGS has been reported previously. In this study, we aimed to analyze the genome of L. brevis KU15176 and identify key genes that may have potential anticancer properties. Among potential anticancer molecules, the role of arginine deiminase (ADI) in conferring an antiproliferative functionality was confirmed. In vitro assay against AGS cell line confirmed that recombinant ADI from L. brevis KU15176 (ADI_br, 5 µg/mL), overexpressed in E. coli BL21 (DE3), exerted an inhibitory effect on AGS cell growth, resulting in a 65.32% reduction in cell viability. Moreover, the expression of apoptosis-related genes, such as bax, bad, caspase-7, and caspase-3, as well as the activity of caspase-9 in ADI_br-treated AGS cells, was higher than those in untreated (culture medium-only) cells. The cell-scattering behavior of ADI_br-treated cells showed characteristics of apoptosis. Flow cytometry analyses of AGS cells treated with ADI_br for 24 and 28 h revealed apoptotic rates of 11.87 and 24.09, respectively, indicating the progression of apoptosis in AGS cells after ADI_br treatment. This study highlights the potential of ADI_br as an effective enzyme for anticancer applications.

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.

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%.

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.

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.

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.

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.

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.

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.

Mitochondrial enzyme FAHD1 reduces ROS in osteosarcoma.

This study investigated the impact of overexpressing the mitochondrial enzyme Fumarylacetoacetate hydrolase domain-containing protein 1 (FAHD1) in human osteosarcoma epithelial cells (U2OS) in vitro. While the downregulation or knockdown of FAHD1 has been extensively researched in various cell types, this study aimed to pioneer the exploration of how increased catalytic activity of human FAHD1 isoform 1 (hFAHD1.1) affects human cell metabolism. Our hypothesis posited that elevation in FAHD1 activity would lead to depletion of mitochondrial oxaloacetate levels. This depletion could potentially result in a decrease in the flux of the tricarboxylic acid (TCA) cycle, thereby accompanied by reduced ROS production. In addition to hFAHD1.1 overexpression, stable U2OS cell lines were established overexpressing a catalytically enhanced variant (T192S) and a loss-of-function variant (K123A) of hFAHD1. It is noteworthy that homologs of the T192S variant are present in animals exhibiting increased resistance to oxidative stress and cancer. Our findings demonstrate that heightened activity of the mitochondrial enzyme FAHD1 decreases cellular ROS levels in U2OS cells. However, these results also prompt a series of intriguing questions regarding the potential role of FAHD1 in mitochondrial metabolism and cellular development.

Crystal structure of human peptidylarginine deiminase type VI (PAD6) provides insights into its inactivity.

Human peptidylarginine deiminase isoform VI (PAD6), which is predominantly limited to cytoplasmic lattices in the mammalian oocytes in ovarian tissue, is essential for female fertility. It belongs to the peptidylarginine deiminase (PAD) enzyme family that catalyzes the conversion of arginine residues to citrulline in proteins. In contrast to other members of the family, recombinant PAD6 was previously found to be catalytically inactive. We sought to provide structural insight into the human homologue to shed light on this observation. We report here the first crystal structure of PAD6, determined at 1.7 Šresolution. PAD6 follows the same domain organization as other structurally known PAD isoenzymes. Further structural analysis and size-exclusion chromatography show that PAD6 behaves as a homodimer similar to PAD4. Differential scanning fluorimetry suggests that PAD6 does not coordinate Ca2+ which agrees with acidic residues found to coordinate Ca2+ in other PAD homologs not being conserved in PAD6. The crystal structure of PAD6 shows similarities with the inactive state of apo PAD2, in which the active site conformation is unsuitable for catalytic citrullination. The putative active site of PAD6 adopts a non-productive conformation that would not allow protein-substrate binding due to steric hindrance with rigid secondary structure elements. This observation is further supported by the lack of activity on the histone H3 and cytokeratin 5 substrates. These findings suggest a different mechanism for enzymatic activation compared with other PADs; alternatively, PAD6 may exert a non-enzymatic function in the cytoplasmic lattice of oocytes and early embryos.

Substrate promiscuity of xenobiotic-transforming hydrolases from stream biofilms impacted by treated wastewater.

Organic contaminants enter aquatic ecosystems from various sources, including wastewater treatment plant effluent. Freshwater biofilms play a major role in the removal of organic contaminants from receiving water bodies, but knowledge of the molecular mechanisms driving contaminant biotransformations in complex stream biofilm (periphyton) communities remains limited. Previously, we demonstrated that biofilms in experimental flume systems grown at higher ratios of treated wastewater (WW) to stream water displayed an increased biotransformation potential for a number of organic contaminants. We identified a positive correlation between WW percentage and biofilm biotransformation rates for the widely-used insect repellent, N,N-diethyl-meta-toluamide (DEET) and a number of other wastewater-borne contaminants with hydrolyzable moieties. Here, we conducted deep shotgun sequencing of flume biofilms and identified a positive correlation between WW percentage and metagenomic read abundances of DEET hydrolase (DH) homologs. To test the causality of this association, we constructed a targeted metagenomic library of DH homologs from flume biofilms. We screened our complete metagenomic library for activity with four different substrates, including DEET, and a subset thereof with 183 WW-related organic compounds. The majority of active hydrolases in the metagenomic library preferred aliphatic and aromatic ester substrates while, remarkably, only a single reference enzyme was capable of DEET hydrolysis. Of the 626 total enzyme-substrate combinations tested, approximately 5% were active enzyme-substrate pairs. Metagenomic DH family homologs revealed a broad substrate promiscuity spanning 22 different compounds when summed across all enzymes tested. We biochemically characterized the most promiscuous and active enzymes identified based on metagenomic analysis from uncultivated Rhodospirillaceae and Planctomycetaceae. In addition to characterizing new DH family enzymes, we exemplified a framework for linking metagenome-guided hypothesis generation with experimental validation. Overall, this study expands the scope of known enzymatic contaminant biotransformations for metagenomic hydrolases from WW-receiving stream biofilm communities.