Biology subjects

Tien, H.-F.

Publications and source records attributed to Tien, H.-F..

3 recordsLinked to original sources

bioRxiv2025

Granulocyte Derived Resistin Inhibits Monocyte Maturation and Induces Immune Suppression in CMML

Chronic myelomonocytic leukaemia (CMML) is a haematological malignancy characterised by overlapping myeloid dysplasia and proliferation with persisting monocytosis. While monocytes are the cardinal malignant cell type in CMML, as a stem cell neoplasm the disease clone comprises most lineages and differentiation stages, including granulocytes. To investigate the pathogenic contribution of granulocytes in CMML maintenance and progression, we performed phenotypic, transcriptomic and functional characterization of CMML granulocytes. Compared with healthy age-matched controls, CMML granulocytes exhibit defective maturation with reduced granularity and phagocytic capacity. Transcriptome analysis revealed activation of pathways linked to proliferation, Myc activity and inflammation. Notably, RETN, which encodes the inflammatory mediator resistin, was upregulated approximately 100-fold in CMML granulocytes; but not differentially expressed in CMML PBMNCs, sorted monocytes, or stem and progenitor cells compared to their healthy counterparts. Accordingly, resistin protein levels were 10-fold higher in plasma from CMML patients and higher plasma resistin levels correlate with poor overall survival and AML-free survival. Remarkably, exposure of healthy monocytes to exogenous recombinant resistin inhibited monocyte maturation and macrophage differentiation. Transcriptome analysis of resistin treated monocytes revealed that resistin induces gene signatures related to immune suppression and myeloid-derived suppressor cell phenotype. We found SEMA4A to be a downstream target of resistin and overexpressed in CMML monocytes. Consistent with known roles for SEMA4A, CMML patients displayed higher percentage of Tregs and elevated Th2/Th1 ratio compared with healthy controls and percentage of Tregs corresponding with associated resistin levels. Furthermore, we demonstrated that resistin directly skews the Th2/Th1 ratio via binding to monocytes. In conclusion, we showed that immature granulocytes in CMML produce high levels of resistin, which contributes to defective monocyte maturation and immune suppression.

cancer biology↗

bioRxiv2024

A mutant ASXL1-EHMT complex contributes to heterochromatin dysfunction in clonal hematopoiesis and chronic monomyelocytic leukemia

ASXL1 is one of the three most frequently mutated genes in age-related clonal hematopoiesis (CH), alongside DNMT3A and TET2. CH can progress to myeloid malignancies including chronic monomyelocytic leukemia (CMML), and is also strongly associated with inflammatory cardiovascular disease and all-cause mortality in humans. DNMT3A and TET2 regulate DNA methylation and demethylation pathways respectively, and loss-of-function mutations in these genes reduce DNA methylation in heterochromatin, allowing de-repression of silenced elements in heterochromatin. In contrast, the mechanisms that connect mutant ASXL1 and CH are not yet fully understood. CH/CMML-associated ASXL1 mutations encode C-terminally truncated proteins that enhance the deubiquitinase activity of the ASXL-BAP1 "PR-DUB" deubiquitinase complex, which removes mono-ubiquitin from H2AK119Ub. Here we show that ASXL1 mutant proteins interact with the EHMT1-EHMT2 methyltransferase complex, which generates H3K9me1 and me2, the latter a repressive modification in constitutive heterochromatin. Compared to cells from age-matched wildtype mice, we found that expanded myeloid cells from old ([≥]18-month-old) Asxl1tm/+ mice, a heterozygous knock-in mouse model of CH, display genome-wide decreases of H3K9me2, H3K9me3 and H2AK119Ub as well as an associated increase in expression of transposable elements (TEs) and satellite repeats. Increased TE expression was also observed in monocytes from ASXL1-mutant CMML patients compared to monocytes from healthy controls. Our data suggest that mutant ASXL1 proteins compromise the integrity of both constitutive and facultative heterochromatin in an age-dependent manner, by reducing the levels of H3K9me2/3 and H2AK119Ub. This increase in TE expression correlated with increased expression of nearby genes, including many interferon-inducible (inflammation-associated) genes (ISGs). Significance StatementAge-related clonal hematopoiesis (CH) is a premalignant condition associated with inflammatory cardiovascular disease. ASXL1 mutations are very frequent in CH. We show that ASXL1 interacts with EHMT1 and EHMT2, H3K9 methyltransferases that deposit H3K9me1 and me2. Using a mouse model of mutant ASXL1 to recapitulate CH, we found that old ASXL1-mutant mice showed marked expansion of myeloid cells in bone marrow, with decreased H3K9me2/3 and increased expression of transposable elements (TEs) in heterochromatin. In humans, ASXL1-mutant CH progresses to chronic monomyelocytic leukemia (CMML); CMML patient samples showed striking upregulation of many TE families, suggesting that ASXL1 mutations compromise heterochromatin integrity, hence causing derepression of TEs. Targeting heterochromatin-associated proteins and TEs might counter the progression of CH, CMML and other myeloid malignancies.

cancer biology↗

bioRxiv2023

Annotation-Free Deep Learning for Predicting Gene Mutations from Whole Slide Images of Acute Myeloid Leukemia

The rapid development of deep learning in recent years has revolutionized the field of medical image processing, including the applications of using high-resolution whole slide images (WSIs) in acute myeloid leukemia (AML) diagnosis. Although the potential of characterizing gene mutations directly from WSIs has been demonstrated in some cancers, it still faces challenges due to image resolutions and manual annotations. To address this, we propose a deep learning model based on multiple instance learning (MIL) with ensemble learning to predict gene mutations from AML annotation-free WSIs. Our deep learning model offers a promising solution for gene mutation prediction on NPM1 mutations and FLT3 -ITD without the need for patch-level or cell-level manual annotations, reducing the manpower and time costs associated with traditional supervised learning approaches. The dataset of 572 WSIs from AML patients that we used to train our MIL models is currently the largest independent database with both WSI and genetic mutation information. By leveraging upsampling and ensemble learning techniques, our final model achieved an AUC of 0.90 for predicting NPM1 mutations and 0.81 for FLT3 -ITD. This confirms the feasibility of directly obtaining gene mutation data through WSIs without the need for expert annotation and training involvement. Our study also compared the proportional representation of cell types before and after applying the MIL model, finding that blasts are consistently important indicators for gene mutation predictions, with their proportion increasing in mutated WSIs and decreasing in non-mutated WSIs after MIL application. These enhancements, leading to more precise predictions, have brought AML WSI analysis one step closer to being utilized in clinical practice.

bioinformatics↗