Krüppel-like factor (KLF)-10 is an important transcriptional regulator of TGF-β1 signaling in both CD8(+) and CD4(+) T lymphocytes. In the present study, we demonstrate a novel role for KLF10 in the regulation of TGFβRII expression with functional relevance in macrophage differentiation and activation. We first show that transfer of KLF10(-/-) bone marrow-derived macrophages into wild-type (WT) mice leads to exacerbation of experimental colitis. At the cell biological level, using two phenotypic strategies, we show that KLF10-deficient mice have an altered colonic macrophage phenotype with higher frequency of proinflammatory LyC6(+)MHCII(+) cells and a reciprocal decrease of the anti-inflammatory LyC6(-)MHCII(+) subset. Additionally, the anti-inflammatory CD11b(+)CX3CR1(hi) subset of colonic macrophages is significantly decreased in KLF10(-/-) compared with WT mice under inflammatory conditions. Molecularly, CD11b(+) colonic macrophages from KLF10(-/-) mice exhibit a proinflammatory cytokine profile with increased production of TNF-α and lower production of IL-10 in response to LPS stimulation. Because KLF10 is a transcription factor, we explored how this protein may regulate macrophage function. Consequently, we analyzed the expression of TGFβRII expression in colonic macrophages and found that, in the absence of KLF10, macrophages express lower levels of TGFβRII and display an attenuated Smad-2 phosphorylation following TGF-β1 stimulation. We further show that KLF10 directly binds to the TGFβRII promoter in macrophages, leading to enhanced gene expression through histone H3 acetylation. Collectively, our data reveal a critical role for KLF10 in the epigenetic regulation of TGFβRII expression in macrophages and the acquisition of a "regulatory" phenotype that contributes to intestinal mucosal homeostasis.
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http://dx.doi.org/10.1152/ajpgi.00309.2015 | DOI Listing |
Brief Bioinform
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School of Engineering, Westlake University, No. 600 Dunyu Road, 310030 Zhejiang, P.R. China.
Single-cell RNA sequencing (scRNA-seq) offers remarkable insights into cellular development and differentiation by capturing the gene expression profiles of individual cells. The role of dimensionality reduction and visualization in the interpretation of scRNA-seq data has gained widely acceptance. However, current methods face several challenges, including incomplete structure-preserving strategies and high distortion in embeddings, which fail to effectively model complex cell trajectories with multiple branches.
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Biotherapeutics Molecule Discovery, Boehringer Ingelheim Pharmaceutical Inc., 900 Ridgebury Road, Ridgefield, CT 06877, United States.
Antibody generation requires the use of one or more time-consuming methods, namely animal immunization, and in vitro display technologies. However, the recent availability of large amounts of antibody sequence and structural data in the public domain along with the advent of generative deep learning algorithms raises the possibility of computationally generating novel antibody sequences with desirable developability attributes. Here, we describe a deep learning model for computationally generating libraries of highly human antibody variable regions whose intrinsic physicochemical properties resemble those of the variable regions of the marketed antibody-based biotherapeutics (medicine-likeness).
View Article and Find Full Text PDFBrief Bioinform
November 2024
Guangdong Provincial Key Laboratory of Mathematical and Neural Dynamical Systems, Great Bay University, No. 16 Daxue Rd, Songshanhu District, Dongguan, Guangdong, 523000, China.
Multimodal omics provide deeper insight into the biological processes and cellular functions, especially transcriptomics and proteomics. Computational methods have been proposed for the integration of single-cell multimodal omics of transcriptomics and proteomics. However, existing methods primarily concentrate on the alignment of different omics, overlooking the unique information inherent in each omics type.
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January 2025
Key Laboratory of Jiangxi Province for Transfusion Medicine, Department of Blood Transfusion, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi 330006, China.
The tRNA-derived small RNAs (tsRNAs) are a new class of non coding RNAs, which are stable in body fluids and can be used as potential biomarkers for disease diagnosis. However, the exact value of tsRNAs in the diagnosis of tuberculosis (TB) is still unclear. The objective of the present study was to evaluate the performance of the serum tsRNAs biosignature to distinguish between active TB, healthy controls, latent TB infection, and other respiratory diseases.
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