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Chromatin-binding proteins are critical regulators of cell state in haematopoiesis 1 , 2 . Acute leukaemias driven by rearrangement of the mixed lineage leukaemia 1 gene ( KMT2A r) or mutation of the nucleophosmin gene ( NPM1 ) require the chromatin adapter protein menin, encoded by the MEN1 gene, to sustain aberrant leukaemogenic gene expression programs 3 – 5 . In a phase 1 first-in-human clinical trial, the menin inhibitor revumenib, which is designed to disrupt the menin–MLL1 interaction, induced clinical responses in patients with leukaemia with KMT2A r or mutated NPM1 (ref. 6 ). Here we identified somatic mutations in MEN1 at the revumenib–menin interface in patients with acquired resistance to menin inhibition. Consistent with the genetic data in patients, inhibitor–menin interface mutations represent a conserved mechanism of therapeutic resistance in xenograft models and in an unbiased base-editor screen. These mutants attenuate drug–target binding by generating structural perturbations that impact small-molecule binding but not the interaction with the natural ligand MLL1, and prevent inhibitor-induced eviction of menin and MLL1 from chromatin. To our knowledge, this study is the first to demonstrate that a chromatin-targeting therapeutic drug exerts sufficient selection pressure in patients to drive the evolution of escape mutants that lead to sustained chromatin occupancy, suggesting a common mechanism of therapeutic resistance. Somatic mutations in MEN1 are identified in patients with leukaemia treated with a novel chromatin-targeting therapy, and the mechanism by which these mutations mediate therapeutic resistance is characterized.

Chemical inducers of proximity (CIPs) stabilize biomolecular interactions, often causing an emergent rewiring of cellular biochemistry. While rational design strategies can expedite the discovery of heterobifunctional CIPs, monovalent, molecular glue-like CIPs have relied predominantly on serendipity. Envisioning a prospective approach to discover molecular glues for a pre-selected target, we hypothesized that pre-existing ligands could be systematically decorated with chemical modifications to empirically discover protein-ligand surfaces that are tuned to cooperatively engage another protein interface. Here, we used sulfur(VI)-fluoride exchange (SuFEx)-based high-throughput chemistry (HTC) to install 3,163 structurally diverse chemical building blocks onto ENL and BRD4 ligands and then screened the crude products for degrader activity. This revealed dHTC1, a potent, selective, and stereochemistry-dependent degrader of ENL. It recruits CRL4 to ENL through an extended interface of protein-protein and protein-ligand contacts, but only after pre-forming the ENL:dHTC1 complex. We also characterized two structurally distinct BRD4 degraders, including dHTC3, a molecular glue that selectively dimerizes the first bromodomain of BRD4 to SCF , an E3 ligase not previously accessible for chemical rewiring. Altogether, this study introduces HTC as a facile tool to discover new CIPs and actionable cellular effectors of proximity pharmacology.

Reprogramming complex cellular metabolism requires simultaneous regulation of multigene expression. Ex-situ cloning-based methods are commonly used, but the target gene number and combinatorial library size are severely limited by cloning and transformation efficiencies. In-situ methods such as multiplex automated genome engineering (MAGE) depends on high-efficiency transformation and incorporation of heterologous DNA donors, which are limited to few microorganisms. Here, we describe a Base Editor-Targeted and Template-free Expression Regulation (BETTER) method for simultaneously diversifying multigene expression. BETTER repurposes CRISPR-guided base editors and in-situ generates large numbers of genetic combinations of diverse ribosome binding sites, 5’ untranslated regions, or promoters, without library construction, transformation, and incorporation of DNA donors. We apply BETTER to simultaneously regulate expression of up to ten genes in industrial and model microorganisms Corynebacterium glutamicum and Bacillus subtilis . Variants with improved xylose catabolism, glycerol catabolism, or lycopene biosynthesis are respectively obtained. This technology will be useful for large-scale fine-tuning of multigene expression in both genetically tractable and intractable microorganisms. To obtain optimal yield and productivity in bioproduction, expression of pathway genes must be appropriately coordinated. Here, the authors report repurposing of base editors for simultaneous regulation of multiple gene expression and demonstrate its application in industrially important and model microorganisms.

Acetyl-CoA is a fundamental metabolite for all life on Earth, and is also a key starting point for the biosynthesis of a variety of industrial chemicals and natural products. Here we design and construct a Synthetic Acetyl-CoA (SACA) pathway by repurposing glycolaldehyde synthase and acetyl-phosphate synthase. First, we design and engineer glycolaldehyde synthase to improve catalytic activity more than 70-fold, to condense two molecules of formaldehyde into one glycolaldehyde. Second, we repurpose a phosphoketolase to convert glycolaldehyde into acetyl-phosphate. We demonstrated the feasibility of the SACA pathway in vitro, achieving a carbon yield ~50%, and confirmed the SACA pathway by 13 C-labeled metabolites. Finally, the SACA pathway was verified by cell growth using glycolaldehyde, formaldehyde and methanol as supplemental carbon source. The SACA pathway is proved to be the shortest, ATP-independent, carbon-conserving and oxygen-insensitive pathway for acetyl-CoA biosynthesis, opening possibilities for producing acetyl-CoA-derived chemicals from one-carbon resources in the future. The microbial synthesis of carbon-containing compounds from single carbon precursors is desirable, yet designed pathways to achieve this goal overlap with host metabolism. Here the authors design a de novo metabolic pathway to assimilate formaldehyde into acetyl-CoA that does not overlap with known metabolic networks.

The flavonoid extract from Erigeron breviscapus , breviscapine, has increasingly been used to treat cardio- and cerebrovascular diseases in China for more than 30 years, and plant supply of E. breviscapus is becoming insufficient to satisfy the growing market demand. Here we report an alternative strategy for the supply of breviscapine by building a yeast cell factory using synthetic biology. We identify two key enzymes in the biosynthetic pathway (flavonoid-7- O -glucuronosyltransferase and flavone-6-hydroxylase) from E. breviscapus genome and engineer yeast to produce breviscapine from glucose. After metabolic engineering and optimization of fed-batch fermentation, scutellarin and apigenin-7- O -glucuronide, two major active ingredients of breviscapine, reach to 108 and 185 mg l –1 , respectively. Our study not only introduces an alternative source of these valuable compounds, but also provides an example of integrating genomics and synthetic biology knowledge for metabolic engineering of natural compounds. Breviscapine is the flavonoid extract from medical plant Erigeron breviscapus for the treatment of cardio- and cerebrovascular disease. Here, the authors identify the key enzymes of the biosynthetic pathway from the plant genome and engineer yeast to produce breviscapine from glucose.

Background The association between dietary flavonoids and fatty liver disease is still controversial. This study investigated the link between dietary flavonoids intake and metabolic-associated fatty liver disease (MAFLD) and metabolic dysfunction-associated steatotic liver disease (MASLD). Methods The study utilized data from the National Health and Nutrition Examination Survey cycles of 2007–2010 and 2017–2018. The relationship between dietary flavonoids intake and the prevalence of MAFLD/MASLD was evaluated using multivariate logistic regression models. Subgroup and population attributable fraction were employed to investigate the prevalence of MAFLD/MASLD in different smoking status groups. Results The study included 5,645 participants. The fully adjusted multivariate logistic regression model indicated no significant association between ln flavonoids and MAFLD/MASLD ( p  > 0.05). Restricted cubic spline analysis identified a nonlinear relationship between ln flavonoids and MAFLD/MASLD, with 4.747 and 4.409 as the turning points, respectively. Subgroup and population attributable fraction analyses revealed that the negative association between flavonoids and MAFLD/MASLD is particularly significant in non-smokers. Mediation analysis indicated that the low-grade inflammation played a crucial role in the association. The study’s robustness was validated through sensitivity analyses. Conclusions Our study highlighted a U-shaped association between ln flavonoids and MAFLD/MASLD, influenced by low-grade inflammation. Encouraging a flavonoid-rich diet is crucial for managing MAFLD/MASLD in non-smokers.

The primary purpose of the Loving-kindness and Compassion Meditations (LKCM) in Buddhism was the cultivation of kindness, but many modern LKCM trainings focused on happiness, and even used the “kindness for happiness” strategy that advocate cultivation of kindness for the benefit of happiness. This study investigated whether cultivating kindness was lesser desired than enhancing happiness for potential trainees, and it impacts on LKCM training. Study 1 recruited 583 university students, study 2 involved 1075 participants from a 4-week online LKCM training. The measures included interest in meditation trainings that focused on emotional happiness, kind attitudes and other effects. Two studies cohesively supported kind attitudes were the least desired effects, and study 2 showed that higher interest in meditations on Subjective well-being predicted increases in personal happiness. In summary, this study provided first evidence that trainees’ preference on potential effects of LKCM existed and linked with effects of training. It suggested the hedonic bias in modern positive psychology is facilitated by trainees, and encouraged further attention in the philosophical and ethical issues in the trainings. The intervention program has been retrospectively registered with the PRS on May 17, 2024, under registration number NCT06424951.

Based on the ventilation characteristics of the gob-side entry retaining face, a mathematical model of spontaneous combustion in the gob-side entry retaining face is established. From the overburden caving of the goaf along the goaf retaining roadway, the development characteristics of rock strata and residual coal fissures in the goaf are summarized and analyzed. In addition, by using numerical simulation software, the effects of normal mining period, goaf retaining roadway as return air roadway, air leakage prevention, and nitrogen injection measures in goaf on spontaneous combustion in goaf are studied, and the distribution characteristics of flow field, oxygen concentration field and temperature field in goaf are obtained. The results show that the mining of the Geng 20 working face has a significant impact on the Geng 19 coal seam. The Geng 19 coal seam is in the range of fracture zone, and the fracture is well developed. Furthermore, the permeability coefficient of the Geng 19 coal seam increases sharply, air leakage in the goaf is increased along the goaf retaining roadway, and the range of oxygen concentration is enlarged, which results in a temperature rise in the goaf. Therefore, air leakage measures are proposed along the goaf to inject 900 m3/h of nitrogen into the goaf, which can prevent the spontaneous combustion of coal left over in the goaf. In addition, according to the characteristics of fracture development and numerical simulation of spontaneous combustion in goaf under the goaf retaining roadway, the hierarchical prevention, control, and fire extinguishing technology system of the goaf retaining roadway is constructed.

In this study, a deep learning method named U-net neural network is utilized to calibrate the gridded forecast of surface air temperature from the Global Ensemble Forecasting System (GEFS), with forecast lead times of 1–7 days in Xinjiang. The calibration performance of U-net is compared with three conventional postprocessing methods: unary linear regression (ULR), the decaying averaging method (DAM) and Quantile Mapping (QM). Results show that biases of the raw GEFS forecasts are mainly distributed in the Altai Mountains, the Junggar Basin, the Tarim Basin and the Kunlun Mountains. The four postprocessing methods effectively improve the forecast skills for all lead times, whereas U-net shows the best correction performance with the lowest mean absolute error (MAE) and the highest hit rate of 2°C (HR2) and pattern correlation coefficient (PCC). The U-net model considerably reduces the warm biases of the raw forecasts. The skill improvement magnitudes are greater in southern than northern Xinjiang, showing a higher mean absolute error skill score (MAESS). Furthermore, in order to distinguish the error sources of each forecasting scheme and to reveal their capabilities of calibrating errors of different sources, the error decomposition analysis is carried out based on the mean square errors. It shows that the bias term is the leading source of error in the raw forecasts, and barely changes as the lead time increases, which is mainly distributed in Tarim Basin and Kunlun Mountains. All four forecast calibrations effectively reduce the bias and distribution error of the raw forecasts, but only the U-net significantly reduces the sequence error.

PurposeThe purpose of this paper is to provide a shorter time cost, high-accuracy fault diagnosis method for water pumps. Water pumps are widely used in industrial equipment and their fault diagnosis is gaining increasing attention. Considering the time-consuming empirical mode decomposition (EMD) method and the more efficient classification provided by the convolutional neural network (CNN) method, a novel classification method based on incomplete empirical mode decomposition (IEMD) and dual-input dual-channel convolutional neural network (DDCNN) composite data is proposed and applied to the fault diagnosis of water pumps.Design/methodology/approachThis paper proposes a data preprocessing method using IEMD combined with mel-frequency cepstrum coefficient (MFCC) and a neural network model of DDCNN. First, the sound signal is decomposed by IEMD to get numerous intrinsic mode functions (IMFs) and a residual (RES). Several IMFs and one RES are then extracted by MFCC features. Ultimately, the obtained features are split into two channels (IMFs one channel; RES one channel) and input into DDCNN.FindingsThe Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection (MIMII dataset) is used to verify the practicability of the method. Experimental results show that decomposition into an IMF is optimal when taking into account the real-time and accuracy of the diagnosis. Compared with EMD, 51.52% of data preprocessing time, 67.25% of network training time and 63.7% of test time are saved and also improve accuracy.Research limitations/implicationsThis method can achieve higher accuracy in fault diagnosis with a shorter time cost. Therefore, the fault diagnosis of equipment based on the sound signal in the factory has certain feasibility and research importance.Originality/valueThis method provides a feasible method for mechanical fault diagnosis based on sound signals in industrial applications.