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Ovarian estrogen exerts both positive and negative feedback control over luteinizing hormone (LH) secretion during the ovulatory cycle. Estrogen receptor (ER) [alpha] but not ER[beta] knockout mice lack estrogen feedback. Thus, estrogen feedback appears to be primarily mediated by ER[alpha]. However, it is now recognized that, in addition to binding to estrogen response elements (EREs) in DNA to alter target gene transcription, ER[alpha] signals through ERE-independent or nonclassical pathways, and the relative contributions of these pathways in conveying estrogen feedback remain unknown. Previously we created a knockin mouse model expressing a mutant form of ER[alpha] (AA) with ablated ERE-dependent but intact ERE-independent activity. Breeding this allele onto the ER[alpha]-null (-/-) background, we examine the ability of ERE-independent ER[alpha] signaling pathways to convey estrogen feedback regulation of the female hypothalamic-pituitary axis in vivo. ER[[alpha].sup.-/AA] exhibited 69.9% lower serum LH levels compared with ER[[alpha].sup.-/-] mice. Additionally, like wild type, ER[[alpha].sup.-/AA] mice exhibited elevated LH after ovariectomy (OVX). Furthermore, the post-OVX rise in serum LH was significantly suppressed by estrogen treatment in OVX ERa-/AA mice. However, unlike wild type, both ER[[alpha].sup.-/AA] and ER[[alpha].sup.-/-] mice failed to exhibit estrous cyclicity, spontaneous ovulation, or an afternoon LH surge response to estrogen. These results indicate that ERE-independent ER[alpha] signaling is sufficient to convey a major portion of estrogen's negative feedback actions, whereas positive feedback and spontaneous ovulatory cyclicity require ERE-dependent ER[alpha] signaling. neuroendocrine | reproduction

BackgroundResponses to conventional donor lymphocyte infusion for postallogeneic hematopoietic cell transplantation (HCT) relapse are typically poor. Natural killer (NK) cell-based therapy is a promising modality to treat post-HCT relapse.MethodsWe initiated this ongoing phase I trial of adoptively transferred cytokine-induced memory-like (CIML) NK cells in patients with myeloid malignancies who relapsed after haploidentical HCT. All patients received a donor-derived NK cell dose of 5 to 10 million cells/kg after lymphodepleting chemotherapy, followed by systemic IL-2 for 7 doses. High-resolution profiling with mass cytometry and single-cell RNA sequencing characterized the expanding and persistent NK cell subpopulations in a longitudinal manner after infusion.ResultsIn the first 6 enrolled patients on the trial, infusion of CIML NK cells led to a rapid 10- to 50-fold in vivo expansion that was sustained over months. The infusion was well tolerated, with fever and pancytopenia as the most common adverse events. Expansion of NK cells was distinct from IL-2 effects on endogenous post-HCT NK cells, and not dependent on CMV viremia. Immunophenotypic and transcriptional profiling revealed a dynamic evolution of the activated CIML NK cell phenotype, superimposed on the natural variation in donor NK cell repertoires.ConclusionGiven their rapid expansion and long-term persistence in an immune-compatible environment, CIML NK cells serve as a promising platform for the treatment of posttransplant relapse of myeloid disease. Further characterization of their unique in vivo biology and interaction with both T cells and tumor targets will lead to improvements in cell-based immunotherapies.Trial RegistrationClinicalTrials.gov NCT04024761.FundingDunkin' Donuts, NIH/National Cancer Institute, and the Leukemia and Lymphoma Society. [ABSTRACT FROM AUTHOR]

Diffusion-weighted imaging (DWI) is a magnetic resonance imaging (MRI) technique that provides information about the Brownian motion of water molecules within biological tissues. DWI plays a crucial role in stroke imaging and oncology, but its diagnostic value can be compromised by the inherently low signal-to-noise ratio (SNR). Conventional supervised deep learning-based denoising techniques encounter challenges in this domain as they necessitate noise-free target images for training. This work presents a novel approach for denoising and evaluating DWI scans in a self-supervised manner, eliminating the need for ground-truth data. By leveraging an adapted version of Stein's unbiased risk estimator (SURE) and exploiting a phase-corrected combination of repeated acquisitions, we outperform both state-of-the-art self-supervised denoising methods and conventional non-learning-based approaches. Additionally, we demonstrate the applicability of our proposed approach in accelerating DWI scans by acquiring fewer image repetitions. To evaluate denoising performance, we introduce a self-supervised methodology that relies on analyzing the characteristics of the residual signal removed by the denoising approaches., (© 2024. The Author(s).)

Guanylate binding proteins (GBPs) are an evolutionarily ancient family of proteins that are widely distributed among eukaryotes. They belong to the dynamin superfamily of GTPases, and their expression can be partially induced by interferons (IFNs). GBPs are involved in the cell-autonomous innate immune response against bacterial, parasitic and viral infections. Evolutionary studies have shown that GBPs exhibit a pattern of gene gain and loss events, indicative for the birth-and-death model of evolution. Most species harbor large GBP gene clusters that encode multiple paralogs. Previous functional and in-depth evolutionary studies have mainly focused on murine and human GBPs. Since rabbits are another important model system for studying human diseases, we focus here on lagomorphs to broaden our understanding of the multifunctional GBP protein family by conducting evolutionary analyses and performing a molecular and functional characterization of rabbit GBPs. We observed that lagomorphs lack GBP3, 6 and 7 . Furthermore, Leporidae experienced a loss of GBP2 , a unique duplication of GBP5 and a massive expansion of GBP4 . Gene expression analysis by reverse transcriptase quantitative polymerase chain reaction (RT-qPCR) and transcriptome data revealed that leporid GBP expression varied across tissues. Overexpressed rabbit GBPs localized either uniformly and/or discretely to the cytoplasm and/or to the nucleus. Oryctolagus cuniculus (oc)GBP5L1 and rarely ocGBP5L2 were an exception, colocalizing with the trans-Golgi network (TGN). In addition, four ocGBPs were IFN-inducible and only ocGBP5L2 inhibited furin activity. In conclusion, from an evolutionary perspective, lagomorph GBPs experienced multiple gain and loss events, and the molecular and functional characteristics of ocGBP suggest a role in innate immunity., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Schelle, Côrte-Real, Fayyaz, del Pozo Ben, Shnipova, Petersen, Lotke, Menon, Matzek, Pfaff, Pinheiro, Marques, Melo-Ferreira, Popper, Esteves, Sauter, Abrantes and Baldauf.)

Thermal noise caused by the imaged object is an intrinsic limitation in magnetic resonance imaging (MRI), resulting in an impaired clinical value of the acquisitions. Recently, deep learning (DL)-based denoising methods achieved promising results by extracting complex feature representations from large data sets. Most approaches are trained in a supervised manner by directly mapping noisy to noise-free ground-truth data and, therefore, require extensive paired data sets, which can be expensive or infeasible to obtain for medical imaging applications. In this work, a DL-based denoising approach is investigated which operates on complex-valued reconstructed magnetic resonance (MR) images without noise-free target data. An extension of Stein's unbiased risk estimator (SURE) and spatially resolved noise maps quantifying the noise level with pixel accuracy were employed during the training process. Competitive denoising performance was achieved compared to supervised training with mean squared error (MSE) despite optimizing the model without noise-free target images. The proposed DL-based method can be applied for MR image enhancement without requiring noise-free target data for training. Integrating the noise maps as an additional input channel further enables the regulation of the desired level of denoising to adjust to the preference of the radiologist., (© 2023. The Author(s).)

Objective. Incorporating computed tomography (CT) reconstruction operators into differentiable pipelines has proven beneficial in many applications. Such approaches usually focus on the projection data and keep the acquisition geometry fixed. However, precise knowledge of the acquisition geometry is essential for high quality reconstruction results. In this paper, the differentiable formulation of fan-beam CT reconstruction is extended to the acquisition geometry. Approach. The CT fan-beam reconstruction is analytically derived with respect to the acquisition geometry. This allows to propagate gradient information from a loss function on the reconstructed image into the geometry parameters. As a proof-of-concept experiment, this idea is applied to rigid motion compensation. The cost function is parameterized by a trained neural network which regresses an image quality metric from the motion-affected reconstruction alone. Main results. The algorithm improves the structural similarity index measure (SSIM) from 0.848 for the initial motion-affected reconstruction to 0.946 after compensation. It also generalizes to real fan-beam sinograms which are rebinned from a helical trajectory where the SSIM increases from 0.639 to 0.742. Significance. Using the proposed method, we are the first to optimize an autofocus-inspired algorithm based on analytical gradients. Next to motion compensation, we see further use cases of our differentiable method for scanner calibration or hybrid techniques employing deep models., (Creative Commons Attribution license.)

Due to the promise of more sustainable recycling of plastics through biocatalytic degradation, the search for and engineering of polyester hydrolases have become a thriving field of research. Furthermore, among other methods, halo formation assays have become popular for the detection of polyester-hydrolase activity. However, established halo-formation assays are limited in their ability to screen for thermostable enzymes, which are particularly important for efficient plastic degradation. The incubation of screening plates at temperatures above 50 °C leads to cell lysis and death. Therefore, equivalent master plates are commonly required to maintain and identify the active strains found on the screening plates. This replica plating procedure necessitates 20- to 60-fold more plates than our method, assuming the screened library is transferred to 384-well microtiter plates or 96-well microtiter plates, respectively, to organize the colonies in a retraceable manner, thus significantly lowering throughput. Here, we describe a halo formation assay that is designed to screen thermostable polyesterases independent of master plates and colony replication, thereby markedly reducing the workload and increasing the throughput., (© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

TfCa, a promiscuous carboxylesterase from Thermobifida fusca , was found to hydrolyze polyethylene terephthalate (PET) degradation intermediates such as bis(2-hydroxyethyl) terephthalate (BHET) and mono-(2-hydroxyethyl)-terephthalate (MHET). In this study, we elucidated the structures of TfCa in its apo form, as well as in complex with a PET monomer analogue and with BHET. The structure-function relationship of TfCa was investigated by comparing its hydrolytic activity on various ortho- and para-phthalate esters of different lengths. Structure-guided rational engineering of amino acid residues in the substrate-binding pocket resulted in the TfCa variant I69W/V376A (WA), which showed 2.6-fold and 3.3-fold higher hydrolytic activity on MHET and BHET, respectively, than the wild-type enzyme. TfCa or its WA variant was mixed with a mesophilic PET depolymerizing enzyme variant [ Ideonella sakaiensis PETase ( Is PETase) PM] to degrade PET substrates of various crystallinity. The dual enzyme system with the wild-type TfCa or its WA variant produced up to 11-fold and 14-fold more terephthalate (TPA) than the single Is PETase PM, respectively. In comparison to the recently published chimeric fusion protein of Is PETase and MHETase, our system requires 10% Is PETase and one-fourth of the reaction time to yield the same amount of TPA under similar PET degradation conditions. Our simple dual enzyme system reveals further advantages in terms of cost-effectiveness and catalytic efficiency since it does not require time-consuming and expensive cross-linking and immobilization approaches., Competing Interests: The authors declare no competing financial interest., (© 2022 The Authors. Published by American Chemical Society.)

Enzymatic hydrolysis holds great promise for plastic waste recycling and upcycling. The interfacial catalysis mode, and the variability of polymer specimen properties under different degradation conditions, add to the complexity and difficulty of understanding polymer cleavage and engineering better biocatalysts. We present a systemic approach to studying the enzyme-catalyzed surface erosion of poly(ethylene terephthalate) (PET) while monitoring/controlling operating conditions in real time with simultaneous detection of mass loss and changes in viscoelastic behavior. PET nanofilms placed on water showed a porous morphology and a thickness-dependent glass transition temperature ( T g ) between 40°C and 44°C, which is >20°C lower than the T g of bulk amorphous PET. Hydrolysis by a dual-enzyme system containing thermostabilized variants of Ideonella sakaiensis PETase and MHETase resulted in a maximum depolymerization of 70% in 1 h at 50°C. We demonstrate that increased accessible surface area, amorphization, and T g reduction speed up PET degradation while simultaneously lowering the threshold for degradation-induced crystallization., Competing Interests: The authors declare no competing interests., (© 2022 The Author(s).)

Low-dose computed tomography (CT) denoising algorithms aim to enable reduced patient dose in routine CT acquisitions while maintaining high image quality. Recently, deep learning (DL)-based methods were introduced, outperforming conventional denoising algorithms on this task due to their high model capacity. However, for the transition of DL-based denoising to clinical practice, these data-driven approaches must generalize robustly beyond the seen training data. We, therefore, propose a hybrid denoising approach consisting of a set of trainable joint bilateral filters (JBFs) combined with a convolutional DL-based denoising network to predict the guidance image. Our proposed denoising pipeline combines the high model capacity enabled by DL-based feature extraction with the reliability of the conventional JBF. The pipeline's ability to generalize is demonstrated by training on abdomen CT scans without metal implants and testing on abdomen scans with metal implants as well as on head CT data. When embedding RED-CNN/QAE, two well-established DL-based denoisers in our pipeline, the denoising performance is improved by 10%/82% (RMSE) and 3%/81% (PSNR) in regions containing metal and by 6%/78% (RMSE) and 2%/4% (PSNR) on head CT data, compared to the respective vanilla model. Concluding, the proposed trainable JBFs limit the error bound of deep neural networks to facilitate the applicability of DL-based denoisers in low-dose CT pipelines., (© 2022. The Author(s).)

Background: Gestational diabetes mellitus is one of the most frequent pregnancy complications with a global prevalence of 13.4% in 2021. Pregnant women with COVID-19 and gestational diabetes mellitus are 3.3 times more likely to be admitted to an intensive care unit than women without gestational diabetes mellitus. Data on the association of gestational diabetes mellitus with maternal and neonatal pregnancy outcomes in pregnant women with SARS-CoV-2 infection are lacking., Objective: This study aimed to investigate whether gestational diabetes mellitus is an independent risk factor for adverse maternal and fetal and neonatal outcomes in pregnant women with COVID-19., Study Design: The COVID-19-Related Obstetric and Neonatal Outcome Study is a registry-based multicentric prospective observational study from Germany and Linz, Austria. Pregnant women with clinically confirmed COVID-19 were enrolled between April 3, 2020, and August 24, 2021, at any stage of pregnancy. Obstetricians and neonatologists of 115 hospitals actively provided data to the COVID-19-Related Obstetric and Neonatal Outcome Study. For collecting data, a cloud-based electronic data platform was developed. Women and neonates were observed until hospital discharge. Information on demographic characteristics, comorbidities, medical history, COVID-19-associated symptoms and treatments, pregnancy, and birth outcomes were entered by the local sites. Information on the periconceptional body mass index was collected. A primary combined maternal endpoint was defined as (1) admission to an intensive care unit (including maternal mortality), (2) viral pneumonia, and/or (3) oxygen supplementation. A primary combined fetal and neonatal endpoint was defined as (1) stillbirth at ≥24 0/7 weeks of gestation, (2) neonatal death ≤7 days after delivery, and/or (3) transfer to a neonatal intensive care unit. Multivariable logistic regression analysis was performed to evaluate the modulating effect of gestational diabetes mellitus on the defined endpoints., Results: Of the 1490 women with COVID-19 (mean age, 31.0±5.2 years; 40.7% nulliparous), 140 (9.4%) were diagnosed with gestational diabetes mellitus; of these, 42.9% were treated with insulin. Overall, gestational diabetes mellitus was not associated with an adverse maternal outcome (odds ratio, 1.50; 95% confidence interval, 0.88-2.57). However, in women who were overweight or obese, gestational diabetes mellitus was independently associated with the primary maternal outcome (adjusted odds ratio, 2.69; 95% confidence interval, 1.43-5.07). Women who were overweight or obese with gestational diabetes mellitus requiring insulin treatment were found to have an increased risk of a severe course of COVID-19 (adjusted odds ratio, 3.05; 95% confidence interval, 1.38-6.73). Adverse maternal outcomes were more common when COVID-19 was diagnosed with or shortly after gestational diabetes mellitus diagnosis than COVID-19 diagnosis before gestational diabetes mellitus diagnosis (19.6% vs 5.6%; P<.05). Maternal gestational diabetes mellitus and maternal preconception body mass index of ≥25 kg/m 2 increased the risk of adverse fetal and neonatal outcomes (adjusted odds ratio, 1.83; 95% confidence interval, 1.05-3.18). Furthermore, overweight and obesity (irrespective of gestational diabetes mellitus status) were influential factors for the maternal (adjusted odds ratio, 1.87; 95% confidence interval, 1.26-2.75) and neonatal (adjusted odds ratio, 1.81; 95% confidence interval, 1.32-2.48) primary endpoints compared with underweight or normal weight., Conclusion: Gestational diabetes mellitus, combined with periconceptional overweight or obesity, was independently associated with a severe maternal course of COVID-19, especially when the mother required insulin and COVID-19 was diagnosed with or after gestational diabetes mellitus diagnosis. These combined factors exhibited a moderate effect on neonatal outcomes. Women with gestational diabetes mellitus and a body mass index of ≥25 kg/m 2 were a particularly vulnerable group in the case of COVID-19., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Thermophilic polyester hydrolases (PES-H) have recently enabled biocatalytic recycling of the mass-produced synthetic polyester polyethylene terephthalate (PET), which has found widespread use in the packaging and textile industries. The growing demand for efficient PET hydrolases prompted us to solve high-resolution crystal structures of two metagenome-derived enzymes (PES-H1 and PES-H2) and notably also in complex with various PET substrate analogues. Structural analyses and computational modeling using molecular dynamics simulations provided an understanding of how product inhibition and multiple substrate binding modes influence key mechanistic steps of enzymatic PET hydrolysis. Key residues involved in substrate-binding and those identified previously as mutational hotspots in homologous enzymes were subjected to mutagenesis. At 72 °C, the L92F/Q94Y variant of PES-H1 exhibited 2.3-fold and 3.4-fold improved hydrolytic activity against amorphous PET films and pretreated real-world PET waste, respectively. The R204C/S250C variant of PES-H1 had a 6.4 °C higher melting temperature than the wild-type enzyme but retained similar hydrolytic activity. Under optimal reaction conditions, the L92F/Q94Y variant of PES-H1 hydrolyzed low-crystallinity PET materials 2.2-fold more efficiently than LCC ICCG, which was previously the most active PET hydrolase reported in the literature. This property makes the L92F/Q94Y variant of PES-H1 a good candidate for future applications in industrial plastic recycling processes., Competing Interests: The authors declare no competing financial interest., (© 2022 The Authors. Published by American Chemical Society.)

Polybutylene adipate terephthalate (PBAT) is a biodegradable alternative to polyethylene and can be broadly used in various applications. These polymers can be degraded by hydrolases of terrestrial and aquatic origin. In a previous study, we identified tandem PETase-like hydrolases (Ples) from the marine microbial consortium I1 that were highly expressed when a PBAT blend was supplied as the only carbon source. In this study, the tandem Ples, Ple628 and Ple629, were recombinantly expressed and characterized. Both enzymes are mesophilic and active on a wide range of oligomers. The activities of the Ples differed greatly when model substrates, PBAT-modified polymers or PET nanoparticles were supplied. Ple629 was always more active than Ple628. Crystal structures of Ple628 and Ple629 revealed a structural similarity to other PETases and can be classified as member of the PETases IIa subclass, α/β hydrolase superfamily. Our results show that the predicted functions of Ple628 and Ple629 agree with the bioinformatic predictions, and these enzymes play a significant role in the plastic degradation by the consortium., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Meyer Cifuentes, Wu, Zhao, Liu, Neumann-Schaal, Pfaff, Barys, Li, Gao, Han, Bornscheuer, Wei and Öztürk.)

Plastic waste imposes a serious problem to the environment and society. Hence, strategies for a circular plastic economy are demanded. One strategy is the engineering of polyester hydrolases toward higher activity for the biotechnological recycling of polyethylene terephthalate (PET). To provide tools for the rapid characterization of PET hydrolases and the detection of degradation products like terephthalic acid (TPA), we coupled a carboxylic acid reductase (CAR) and the luciferase LuxAB. CAR converted TPA into the corresponding aldehydes in Escherichia coli , which yielded bioluminescence that not only semiquantitatively reflected amounts of TPA in hydrolysis samples but is suitable as a high-throughput screening assay to assess PET hydrolase activity. Furthermore, the CAR-catalyzed synthesis of terephthalaldehyde was combined with a reductive amination cascade in a one-pot setup yielding the corresponding diamine, suggesting a new strategy for the transformation of TPA as a product obtained from PET biodegradation., Competing Interests: The authors declare no competing interests., (© 2022 The Authors.)

Polyethylene terephthalate (PET) is the most widespread synthetic polyester, having been utilized in textile fibers and packaging materials for beverages and food, contributing considerably to the global solid waste stream and environmental plastic pollution. While enzymatic PET recycling and upcycling have recently emerged as viable disposal methods for a circular plastic economy, only a handful of benchmark enzymes have been thoroughly described and subjected to protein engineering for improved properties over the last 16 years. By analyzing the specific material properties of PET and the reaction mechanisms in the context of interfacial biocatalysis, this Perspective identifies several limitations in current enzymatic PET degradation approaches. Unbalanced enzyme-substrate interactions, limited thermostability, and low catalytic efficiency at elevated reaction temperatures, and inhibition caused by oligomeric degradation intermediates still hamper industrial applications that require high catalytic efficiency. To overcome these limitations, successful protein engineering studies using innovative experimental and computational approaches have been published extensively in recent years in this thriving research field and are summarized and discussed in detail here. The acquired knowledge and experience will be applied in the near future to address plastic waste contributed by other mass-produced polymer types (e.g., polyamides and polyurethanes) that should also be properly disposed by biotechnological approaches., Competing Interests: The authors declare no competing financial interest., (© 2022 The Authors. Published by American Chemical Society.)

Polyethylene terephthalate (PET) is a mass-produced petroleum-based synthetic polymer. Enzymatic PET degradation using, for example, Ideonella sakaiensis PETase ( Is PETase) can be a more environmentally friendly and energy-saving alternative to the chemical recycling of PET. However, Is PETase is a mesophilic enzyme with an optimal reaction temperature lower than the glass transition temperature ( T g ) of PET, where the amorphous polymers can be readily accessed for enzymatic breakdown. In this study, we used error-prone PCR to generate a mutant library based on a thermostable triple mutant (TM) of Is PETase. The library was screened against the commercially available polyester-polyurethane Impranil DLN W 50 for more thermostable Is PETase variants, yielding four variants with higher melting points. The most promising Is PETaseTM K95N/F201I variant had a 5.0°C higher melting point than Is PETaseTM. Although this variant showed a slightly lower activity on PET at lower incubation temperatures, its increased thermostability makes it a more active PET hydrolase at higher reaction temperatures up to 60°C. Several other variants were compared and combined with selected previously published Is PETase mutants in terms of thermostability and hydrolytic activity against PET nanoparticles and amorphous PET films. Our findings indicate that thermostability is one of the most important characteristics of an effective PET hydrolase., Competing Interests: The authors declare no conflict of interest. No experiments involving animals or humans were performed in the context of this study., (© 2021 The Authors. Engineering in Life Sciences published by Wiley‐VCH GmbH.)