LeTetR Positively Regulates 3-Hydroxylation of the Antifungal HSAF and Its Analogs in \u3ci\u3eLysobacter enzymogenes\u3c/i\u3e OH11

The biocontrol agent Lysobacter enzymogenes OH11 produces several structurally distinct antibiotic compounds, including the antifungal HSAF (Heat Stable Antifungal Factor) and alteramides, along with their 3-dehydroxyl precursors (3-deOH). We previously showed that the 3-hydroxylation is the final step of the biosynthesis and is also a key structural moiety for the antifungal activity. However, the procedure through which OH11 regulates the 3-hydroxylation is still not clear. In OH11, the gene orf3232 was predicted to encode a TetR regulator (LeTetR) with unknown function. Here, we deleted orf3232 and found that the LeTetR mutant produced very little HSAF and alteramides, while the 3-deOH compounds were not significantly affected. The production of HSAF and alteramides was restored in orf3232-complemented mutant. qRT-PCR showed that the deletion of orf3232 impaired the transcription of a putative fatty acid hydroxylase gene, orf2195, but did not directly affect the expression of the HSAF biosynthetic gene cluster (hsaf ). When an enzyme extract from E. coli expressing the fatty acid hydroxylase gene, hsaf -orf7, was added to the LeTetR mutant, the production of HSAF and alteramides increased by 13–14 fold. This study revealed a rare function of the TetR family regulator, which positively controls the final step of the antifungal biosynthesis and thus controls the antifungal activity of the biocontrol agent

Spermidine-Regulated Biosynthesis of Heat-Stable Antifungal Factor (HSAF) in Lysobacter enzymogenes OH11

Heat-Stable Antifungal Factor (HSAF) and its analogs are antifungal natural products produced by the biocontrol agent Lysobacter enzymogenes. The production of HSAF is greatly influenced by environmental stimuli and nutrients, but the underlying molecular mechanism is mostly unclear. Here, we found that HSAF production in L. enzymogenes OH11 is strictly controlled by spermidine, which is the most prevalent triamine in bacteria. When added into OH11 cultures, spermidine regulated the production of HSAF and analogs in a concentration-dependent manner. To verify the role of spermidine, we deleted LeSDC and LeADC genes, encoding S-adenosylmethionine decarboxylase and arginine decarboxylase, respectively, that are the key enzymes for spermidine biosynthesis. Both deletion mutants produced barely detectable spermidine and HSAF including its analogs, whereas the antifungals production was restored by exogenous spermidine. The results showed that the OH11 cells must maintain a proper spermidine homeostasis for the antifungals production. Indeed, the expression level of the key HSAF biosynthetic genes was significantly impaired in LeSDC and LeADC mutants, and exogenous spermidine restored the gene expression level in the mutants. Ornithine is a key substrate for HSAF biosynthesis, and OH11 genome contains arg1 and arg2 genes, encoding arginases that convert arginine to ornithine. While the expression of arg1 and arg2 was affected slightly upon mutation of LeSDC and LeADC, exogenous spermidine significantly increased the arginase gene expression in LeSDC and LeADC mutants. Together, the data revealed a previously unrecognized mechanism, in which spermidine controls antibiotic production through controlling both the biosynthetic genes and the substrate-production genes

Brush Your Text: Synthesize Any Scene Text on Images via Diffusion Model

Recently, diffusion-based image generation methods are credited for their remarkable text-to-image generation capabilities, while still facing challenges in accurately generating multilingual scene text images. To tackle this problem, we propose Diff-Text, which is a training-free scene text generation framework for any language. Our model outputs a photo-realistic image given a text of any language along with a textual description of a scene. The model leverages rendered sketch images as priors, thus arousing the potential multilingual-generation ability of the pre-trained Stable Diffusion. Based on the observation from the influence of the cross-attention map on object placement in generated images, we propose a localized attention constraint into the cross-attention layer to address the unreasonable positioning problem of scene text. Additionally, we introduce contrastive image-level prompts to further refine the position of the textual region and achieve more accurate scene text generation. Experiments demonstrate that our method outperforms the existing method in both the accuracy of text recognition and the naturalness of foreground-background blending.Comment: Accepted to AAAI 2024. Code: https://github.com/ecnuljzhang/brush-your-tex

Systematic optimization for production of the anti-MRSA antibiotics WAP-8294A in an engineered strain of Lysobacter enzymogenes

WAP-8294A is a group of cyclic lipodepsipeptides and considered as the first-in-class new chemical entity with potent activity against methicillin-resistant Staphylococcus aureus. One of the roadblocks in developing the WAP-8294A antibiotics is the very low yield in Lysobacter. Here, we carried out a systematic investigation of the nutritional and environmental conditions in an engineered L. enzymogenes strain for the optimal production of WAP-8294A. We developed an activity-based simple method for quick screening of various factors, which enabled us to optimize the culture conditions. With the method, we were able to improve the WAP-8294A yield by 10-fold in small-scale cultures and approximately 15-fold in scale-up fermentation. Additionally, we found the ratio of WAP-8294A2 to WAP-8294A1 in the strains could be manipulated through medium optimization. The development of a practical method for yield improvement in Lysobacter will facilitate the ongoing basic research and clinical studies to develop WAP- 8294A into true therapeutics

Production of the siderophore lysochelin in rich media through maltose-promoted high-density growth of Lysobacter sp. 3655

Siderophores are produced by bacteria in iron-restricted conditions. However, we found maltose could induce the biosynthesis of the siderophore lysochelin in Lysobacter sp. 3655 in rich media that are not compatible with siderophore production. Maltose markedly promoted cell growth, with over 300% increase in cell density (OD600) when LB medium was added with maltose (LBM). While lysochelin was not detectable when OD600 in LBM was below 5.0, the siderophore was clearly produced when OD600 reached 7.5 and dramatically increased when OD600 was 15.0. Coincidently, the transcription of lysochelin biosynthesis genes was remarkably enhanced following the increase of OD600. Conversely, the iron concentration in the cell culture dropped to 1.2 μM when OD600 reached 15.0, which was 6-fold lower than that in the starting medium. Moreover, mutants of the maltose-utilizing genes (orf2677 and orf2678) or quorum-sensing related gene orf644 significantly lowered the lysochelin yield. Transcriptomics analysis showed that the iron-utilizing/up-taking genes were up-regulated under high cell density. Accordingly, the transcription of lysochelin biosynthetic genes and the yield of lysochelin were stimulated when the iron-utilizing/up-taking genes were deleted. Finally, lysochelin biosynthesis was positively regulated by a TetR regulator (ORF3043). The lysochelin yield in orf3043 mutant decreased to 50% of that in the wild type and then restored in the complementary strain. Together, this study revealed a previously unrecognized mechanism for lysochelin biosynthetic regulation, by which the siderophore could still be massively produced in Lysobacter even grown in a rich culture medium. This finding could find new applications in large-scale production of siderophores in bacteria

YUAN 2.0: A Large Language Model with Localized Filtering-based Attention

In this work, we develop and release Yuan 2.0, a series of large language models with parameters ranging from 2.1 billion to 102.6 billion. The Localized Filtering-based Attention (LFA) is introduced to incorporate prior knowledge of local dependencies of natural language into Attention. A data filtering and generating system is presented to build pre-training and fine-tuning dataset in high quality. A distributed training method with non-uniform pipeline parallel, data parallel, and optimizer parallel is proposed, which greatly reduces the bandwidth requirements of intra-node communication, and achieves good performance in large-scale distributed training. Yuan 2.0 models display impressive ability in code generation, math problem-solving, and chatting compared with existing models. The latest version of YUAN 2.0, including model weights and source code, is accessible at Github

A five-collagen-based risk model in lung adenocarcinoma: prognostic significance and immune landscape

As part of the tumor microenvironment (TME), collagen plays a significant role in cancer fibrosis formation. However, the collagen family expression profile and clinical features in lung adenocarcinoma (LUAD) are poorly understood. The objective of the present work was to investigate the expression pattern of genes from the collagen family in LUAD and to develop a predictive signature based on collagen family. The Cancer Genome Atlas (TCGA) samples were used as the training set, and five additional cohort samples obtained from the Gene Expression Omnibus (GEO) database were used as the validation set. A predictive model based on five collagen genes, including COL1A1, COL4A3, COL5A1, COL11A1, and COL22A1, was created by analyzing samples from the TCGA cohort using LASSO Cox analysis and univariate/multivariable Cox regression. Using Collagen-Risk scores, LUAD patients were then divided into high- and low-risk groups. KM survival analysis showed that collagen signature presented a robust prognostic power. GO and KEGG analyses confirmed that collagen signature was associated with extracellular matrix organization, ECM-receptor interaction, PI3K-Akts and AGE-RAGE signaling activation. High-risk patients exhibited a considerable activation of the p53 pathway and cell cycle, according to GSEA analysis. The Collage-Risk model showed unique features in immune cell infiltration and tumor-associated macrophage (TAM) polarization of the TME. Additionally, we deeply revealed the association of collagen signature with immune checkpoints (ICPs), tumor mutation burden (TMB), and tumor purity. We first constructed a reliable prognostic model based on TME principal component—collagen, which would enable clinicians to treat patients with LUAD more individually

Enhancing regulatory T-cell function via inhibition of high mobility group box 1 protein signaling in immune thrombocytopenia

Primary immune thrombocytopenia (ITP) is the most common acquired autoimmune bleeding disorder. Abnormally increased levels of High Mobility Group Box 1 (HMGB1) protein associate with thrombocytopenia and therapeutic outcome in ITP. Previous studies proposed that a natural inhibitor of HMGB1, 18β-glycyrrhetinic acid (18β-GA), could be used for its anti-inflammatory and immune-modulatory effects, although its ability to correct immune balance in ITP is unclear. In this study, we showed that plasma HMGB1 correlated negatively with platelet counts in ITP patients, and confirmed that 18β-GA stimulated the production of regulatory T cells (Treg), restored the balance of CD4+ T-cell subsets and enhanced the suppressive function of Treg through blocking the effect on HMGB1 in patients with ITP. HMGB1 short hairpin RNA interference masked the effect of 18β-GA in Treg of ITP patients. Furthermore, we found that 18β-GA alleviated thrombocytopenia in mice with ITP. Briefly, anti-CD61 immune-sensitized splenocytes were transferred into severe combined immunodeficient mice to induce a murine model of severe ITP. The proportion of circulating Treg increased significantly, while the level of plasma HMGB1 and serum antiplatelet antibodies decreased significantly in ITP mice along 18β-GA treatment. In addition, 18β-GA reduced phagocytic activity of macrophages towards platelets both in ITP patients and ITP mice. These results indicate that 18β-GA has the potential to restore immune balance in ITP via inhibition of HMGB1 signaling. In short, this study reveals the role of HMGB1 in ITP, which may serve as a potential target for thrombocytopenia therapy

ddPCR provides a sensitive test compared with GeneXpert MTB/RIF and mNGS for suspected Mycobacterium tuberculosis infection

IntroductionThe Metagenomics next-generation sequencing (mNGS) and GeneXpert MTB/RIF assay (Xpert) exhibited a sensitivity for tuberculosis (TB) diagnostic performance. Research that directly compared the clinical performance of ddPCR analysis, mNGS, and Xpert in mycobacterium tuberculosis complex (MTB) infection has not been conducted.MethodsThe study aimed to evaluate the diagnostic performance of ddPCR compared to mNGS and Xpert for the detection of MTB in multiple types of clinical samples. The final clinical diagnosis was used as the reference standard.ResultsOut of 236 patients with suspected active TB infection, 217 underwent synchronous testing for tuberculosis using ddPCR, Xpert, and mNGS on direct clinical samples. During follow-up, 100 out of 217 participants were diagnosed with MTB infection. Compared to the clinical final diagnosis, ddPCR produced the highest sensitivity of 99% compared with mNGS (86%) and Xpert (64%) for all active MTB cases. DiscussionTwenty-two Xpert-negative samples were positive in mNGS tests, which confirmed the clinical diagnosis results from ddPCR and clinical manifestation, radiologic findings. Thirteen mNGS-negative samples were positive in ddPCR assays, which confirmed the clinical final diagnosis.ddPCR provides a higher sensitive compared to Xpert and mNGS for MTB diagnosis, as defined by the high concordance between ddPCR assay and clinical final diagnosis