Inflammatory responses, leading to fibrosis and potential host rejection, significantly hinder the long-term success and widespread adoption of biomedical implants. The ability to control and investigated macrophage inflammatory responses at the implant-macrophage interface would be critical for reducing chronic inflammation and improving tissue integration. Nonetheless, the systematic investigation of how surface topography affects macrophage polarization is typically complicated by the restricted complexity of accessible nanostructures, difficulties in achieving exact control, and biased preselection of experimental parameters. In response to these problems, we developed a large-scale, high-content combinatorial biophysical cue (CBC) array for enabling high-throughput screening (HTS) of the effects of nanotopography on macrophage polarization and subsequent inflammatory processes. Our CBC array, created utilizing the dynamic laser interference lithography (DLIL) technology, contains over 1 million nanotopographies, ranging from nanolines and nanogrids to intricate hierarchical structures with dimensions ranging from 100 nm to several microns. Using machine learning (ML) based on the Gaussian process regression algorithm, we successfully identified certain topographical signals that either repress (pro-M2) or stimulate (pro-M1) macrophage polarization. The upscaling of these nanotopographies for further examination has shown mechanisms such as cytoskeletal remodeling and ROCK-dependent epigenetic activation to be critical to the mechanotransduction pathways regulating macrophage fate. Thus, we have also developed a platform combining advanced DLIL nanofabrication techniques, HTS, ML-driven prediction of nanobio interactions, and mechanotransduction pathway evaluation. In short, our developed platform technology not only improves our ability to investigate and understand nanotopography-regulated macrophage inflammatory responses but also holds great potential for guiding the design of nanostructured coatings for therapeutic biomaterials and biomedical implants.

Pulmonary fibrosis poses a significant health threat with very limited therapeutic options available. In this study, we reported the enhanced expression of mesenchymal homobox 1 (MEOX1) in pulmonary fibrosis patients, especially in their fibroblasts and endothelial cells, and confirmed MEOX1 as a central orchestrator in the activation of profibrotic genes. By high-throughput screening, we identified Ailanthone (AIL) from a natural compound library as the first small molecule capable of directly targeting and suppressing MEOX1. AIL demonstrated the ability to inhibit both the activation of fibroblasts and endothelial-to-mesenchymal transition of endothelial cells when challenged by transforming growth factor-β1 (TGF-β1). In an animal model of bleomycin-induced pulmonary fibrosis, AIL effectively mitigated the fibrotic process and restored respiratory functions. Mechanistically, AIL acted as a suppressor of MEOX1 by disrupting the interaction between the transcription factor JUN and the promoter of MEOX1, thereby inhibiting MEOX1 expression and activity. In summary, our findings pinpointed MEOX1 as a cell-specific and clinically translatable target in fibrosis. Moreover, we demonstrated the potent anti-fibrotic effect of AIL in pulmonary fibrosis, specifically through the suppression of JUN-dependent MEOX1 activation.

1, 3-propanediol is an important monomer for the production of polytrimethylene terephthalate (PTT). Currently, it is mainly produced by microbial fermentation, which, however, has low production efficiency. To address this problem, this study employed atmospheric room temperature plasma (ARTP) mutagenesis technology and high-throughput screening to obtain a strain with high tolerance to osmotic pressure, which achieved a 1, 3-propanediol titer of 87 g/L. Furthermore, the gene expression elements suitable for Klebsiella pneumoniae were screened, and metabolic engineering was employed to block redundant metabolic pathways (deletion of ldhA, budA, and aldA) and enhance the synthesis pathway (overexpression of dhaB and yqhD). The titer of 1, 3-propanediol produced by the engineered strain increased to 107 g/L. Finally, in a 5 L fermenter, the optimal strain KP-FMME-6 achieved a 1, 3-propanediol titer of 118 g/L, with a glycerol conversion rate of 42% and productivity of 2.46 g/(h·L), after optimization of the fermentation parameters. This study provides a reference for the industrial production of 1, 3-propanediol.

BACKGROUND: Base editing is a powerful tool for artificial evolution to create allelic diversity and improve agronomic traits. However, the great evolutionary potential for every sgRNA target has been overlooked. And there is currently no high-throughput method for generating and characterizing as many changes in a single target as possible based on large mutant pools to permit rapid gene directed evolution in plants.
RESULTS: In this study, we establish an efficient germline-specific evolution system to screen beneficial alleles in Arabidopsis which could be applied for crop improvement. This system is based on a strong egg cell-specific cytosine base editor and the large seed production of Arabidopsis, which enables each T1 plant with unedited wild type alleles to produce thousands of independent T2 mutant lines. It has the ability of creating a wide range of mutant lines, including those containing atypical base substitutions, and as well providing a space- and labor-saving way to store and screen the resulting mutant libraries. Using this system, we efficiently generate herbicide-resistant EPSPS, ALS, and HPPD variants that could be used in crop breeding.
CONCLUSIONS: Here, we demonstrate the significant potential of base editing-mediated artificial evolution for each sgRNA target and devised an efficient system for conducting deep evolution to harness this potential.

In recent years, genetic circuit-based regulation of metabolic flux in microbial cell factories has received significant attention. In this review, we describe a pipeline for the design and construction of genetic circuits for metabolic flux optimization. In particular, we summarize the recent advances in computationally assisted prediction of critical metabolic nodes and genetic circuit design automation. Further, we introduce strategies for constructing high-performance genetic circuits. We also summarize the latest applications of genetic circuits in the dynamic regulation of metabolism and high-throughput screening. Finally, we discuss the challenges and prospects associated with the design and construction of sophisticated genetic circuits. Through this review, we aim to provide a theoretical basis for designing and constructing high-performance genetic circuits to optimize metabolic flux.

Foodborne diseases are major public health problems globally. Metagenomics has emerged as a widely used tool for pathogen screening. In this study, we conducted an updated Tn5 transposase-assisted RNA/DNA hybrid co-tagmentation (TRACE) library construction approach. To address the detection of prevalent known foodborne viruses and the discovery of unknown pathogens, we employed both specific primers and oligo-T primers during reverse transcription. The method was validated using clinical samples confirmed by RT-qPCR and compared with standard RNA-seq library construction methods. The mapping-based approach enabled the retrieval of nearly complete genomes (>95%) for the majority of virus genome segments (86 out of 88, 97.73%), with a mean coverage depth of 21,494.53× (ranging from 77.94× to 55,688.58×). Co-infection phenomena involving prevalent genotypes of Norovirus with Astrovirus and Human betaherpesvirus 6B were observed in two samples. The updated TRACE-seq exhibited superior performance in viral reads percentages compared to standard RNA-seq library preparation methods. This updated method has expanded its target pathogens beyond solely Norovirus to include other prevalent foodborne viruses. The feasibility and potential effectiveness of this approach were then evaluated as an alternative method for surveilling foodborne viruses, thus paving the way for further exploration into whole-genome sequencing of viruses.

During the deamination and amination processes of meso-diaminopimelate dehydrogenase (meso-DAPDH) from Symbiobacterium thermophilum (StDAPDH), residue R71 was observed to display distinct functions. H154 has been proposed as a basic residue that facilitates water molecules to attack the D-chiral carbon of meso-DAP during deamination. Inspired by the phenomenon of R71, the effects of H154 during deamination and amination were investigated in this study with the goal of enhancing the amination activities of StDAPDH. Single site saturation mutagenesis indicated that almost all of the H154 mutants completely lost their deamination activity towards meso-DAP. However, some H154 variants showed enhanced kcat/Km values towards pyruvic acid and other bulky 2-keto acids, such as 2-oxovaleric acid, 4-methyl-2-oxopentanoic acid, 2-ketobutyric acid, and 3-methyl-2-oxobutanoic acid. When combined with the previously reported W121L/H227I mutant, triple mutants with significantly improved kcat/Km values (2.4-, 2.5-, 2.5-, and 4.0-fold) towards these 2-keto acids were obtained. Despite previous attempts, mutations at the H154 site did not yield the desired results. Moreover, this study not only recognizes the distinctive impact of H154 on both the deamination and amination reactions, but also provides guidance for further high-throughput screening in protein engineering and understanding the catalytic mechanism of StDAPDH.

Halide perovskites have gained considerable attention in materials science due to their exceptional optoelectronic properties, including high absorption coefficients, excellent charge-carrier mobilities, and tunable band gaps, which make them highly promising for applications in photovoltaics, light-emitting diodes, synapses, and other optoelectronic devices. However, challenges such as long-term stability and lead toxicity hinder large-scale commercialization. Computational methods have become essential in this field, providing insights into material properties, enabling the efficient screening of large chemical spaces, and accelerating discovery processes through high-throughput screening and machine learning techniques. This review further discusses the role of computational tools in the accelerated discovery of high-performance halide perovskite materials, like the double perovskites A2BX6 and A2BB\'X6, zero-dimensional perovskite A3B2X9, and novel halide perovskite ABX6. This review provides significant insights into how computational methods have accelerated the discovery of high-performance halide perovskite. Challenges and future perspectives are also presented to stimulate further research progress.

The extensive use of chemical pesticides, such as herbicides, has resulted in significant environmental pollution. Microbial degradation represents a crucial approach for managing this pesticide-associated pollution, with enrichment culturing serving as a method for isolating pesticide-degrading microorganisms. However, the efficiency of this strategy is limited, often yielding only a few isolated strains. In this study, a new mineral salt medium (MSM) was developed, and a high-throughput method was used for screening pendimethalin-degrading bacteria by measuring the bacterial growth in the MSM. The utilization of this method resulted in the isolation of 56 pendimethalin-degrading bacteria from approximately 2000 bacterial strains, including 37 Bacillus spp., 10 Alcaligenes spp., 5 Pseudomonas spp., and other 4 strains identified for the first time as pendimethalin-degrading strains. This method may hold promise not only for isolating bacterial strains capable of degrading other pesticides but also for facilitating the utilization of the substantial bacterial strains stored in bacterial banks.

Emerging contaminants pose a potential risk to aquatic ecosystems in the Pearl River Basin, China, owing to the high population density and active industry. This study investigated samples from eight sewage treatment plants, and five surface water bodies of related watersheds. To screen the risk of emerging contaminants (ECs), and clarify their sources, this study calculated the risk quotient of detected chemical and performed source identification/apportionment using the positive matrix factorization method. In total, 149 organic pollutants were identified. Pharmaceuticals showed significant concentrations in sewage treatment plant samples (120.87 ng/L), compared with surface water samples (1.13 ng/L). The ecological risk assessment identified three chemicals with a heightened risk to aquatic organisms: fipronil sulfide, caffeine, and roxithromycin. Four principal sources of contaminants were identified: pharmaceutical wastewater, domestic sewage, medical effluent, and agricultural runoff. Pharmaceutical wastewater was the primary contributor (60.4 %), to the cumulative EC concentration and to ECs in sewage treatment plant effluent. Agricultural drainage was the main source of ECs in surface water. This study provides a strategy to obtain comprehensive information on the aquatic risks and potential sources of EC species in areas affected by artificial activities, which is of substantial importance to pollutant management and control.