Biology subjects

Johnson, N. A.

Publications and source records attributed to Johnson, N. A..

4 recordsLinked to original sources

bioRxiv2024

The CCL17-CCR4 axis is critical for mutant STAT6-mediated microenvironmental remodelling and therapeutic resistance in Relapsed/Refractory Diffuse Large B Cell Lymphoma

Relapsed and refractory Diffuse Large B Cell Lymphoma (rrDLBCL) presents a significant challenge in hematology-oncology, with approximately 30-40% of DLBCL patients experiencing relapse or resistance to treatment. This underscores the urgent need to better understand the molecular mechanisms governing therapeutic resistance. Signal Transducer and Activator of Transcription 6 (STAT6) has been previously identified as a gene with recurrent D419 gain-of-function mutations in rrDLCBL. When STAT6D419 mutations are present in DLBCL tumour cells, we have demonstrated that transcription of the chemokine CCL17 (aka TARC) is increased, and tumours have increased infiltration of CD4+ T cells. However, the significance of increased T cell infiltration had not been determined. In the present study, we developed a mouse model of STAT6D419N mutant DLBCL, that recapitulates the critical features of human STAT6D419 mutant DLBCL, including increased expression of phospho-STAT6, increased CD4+ T cell invasion, and resistance to doxorubicin treatment. With this model, we found CD4+ T cells in STAT6D419N tumours have higher expression of the receptor for CCL17, CCR4. Using ex vivo functional assays we demonstrate that STAT6D419N tumour cells are directly chemoattractive to CCR4+ CD4+ T cells, and when CCR4 is inhibited using a small molecule antagonist, CD4+ T cells in STAT6D419N tumours are reduced and STAT6D419N tumours regain therapeutic sensitivity to doxorubicin. Using PhenoCycler imaging of human rrDLBCL samples, we find that STAT6D419 tumours indeed have increased expression of phospho-STAT6+ and increased cellular interactions between phospho-STAT6+ tumour cells and CD4+/ CCR4+ CD4+ T cells. Thus, our data identify CCR4 as an attractive therapeutic target in STAT6D419 mutant rrDLBCL.

cancer biology↗

bioRxiv2023

Tunable PhenoCycler Imaging of the Murine Pre-Clinical Tumour Microenvironments

The tumour microenvironment (TME) consists of tumour-supportive immune cells, endothelial cells, and fibroblasts. PhenoCycler, a high-plex single cell imaging platform, is used to characterize the complexity of the TME. Here, we used PhenoCycler to spatially resolve the TME of 8 routinely employed pre-clinical models of lymphoma, breast cancer, and melanoma. Our data reveal distinct TMEs in the different cancer models that were imaged, and show that cell-cell contacts differ depending on the tumour type examined. For instance, we found that the immune infiltration in a murine model of melanoma is altered in cellular organization in melanomas that become resistant to PD-1 therapy, with depletions in a number of cell-cell interactions. Furthermore, we provide detailed pipelines for the conjugation of antibodies that are optimized for PhenoCycler staining of murine FFPE tissues specifically, alongside open-source data analysis procedures. Overall, this is a valuable resource study seamlessly adaptable to any field of research involving murine models.

cancer biology↗

bioRxiv2023

A data-driven evaluation of Arabidopsis-centric research and the model species concept

The selection of Arabidopsis as a model organism played a pivotal role in advancing genomic science, firmly establishing the cornerstone of today s plant molecular biology. Competing frameworks to select an agricultural- or ecological-based model species, or to decentralize plant science and study a multitude of diverse species, were selected against in favor of building core knowledge in a species that would facilitate genome-enabled research that could assumedly be transferred to other plants. Here, we examine the ability of models based on Arabidopsis gene expression data to predict tissue identity in other flowering plant species. Comparing different machine learning algorithms, models trained and tested on Arabidopsis data achieved near perfect precision and recall values using the K-Nearest Neighbor method, whereas when tissue identity is predicted across the flowering plants using models trained on Arabidopsis data, precision values range from 0.69 to 0.74 and recall from 0.54 to 0.64, depending on the algorithm used. Below-ground tissue is more predictable than other tissue types, and the ability to predict tissue identity is not correlated with phylogenetic distance from Arabidopsis. This suggests that gene expression signatures rather than marker genes are more valuable to create models for tissue and cell type prediction in plants. Our data-driven results highlight that, in hindsight, the assertion that knowledge from Arabidopsis is translatable to other plants is not always true. Considering the current landscape of abundant sequencing data and computational resources, it may be prudent to reevaluate the scientific emphasis on Arabidopsis and to prioritize the exploration of plant diversity.

plant biology↗

bioRxiv2019

Repression of LKB1 by miR-17~92 sensitizes MYC-dependent lymphoma to biguanide treatment

Cancer cells display metabolic plasticity to survive metabolic and energetic stresses in the tumor microenvironment, prompting the need for tools to target tumor metabolism. Cellular adaptation to energetic stress is coordinated in part by signaling through the Liver Kinase B1 (LKB1)-AMP-activated protein kinase (AMPK) pathway. Reducing LKB1-AMPK signaling exposes metabolic vulnerabilities in tumor cells with potential for therapeutic targeting. Here we describe that miRNA-mediated silencing of LKB1 (mediated by the oncogenic miRNA cluster miR-17[~]92) confers sensitivity of lymphoma cells to mitochondrial inhibition by biguanides. Using both classic (phenformin) and novel (IM156) biguanides, we demonstrate that Myc+ lymphoma cells with elevated miR-17[~]92 expression display increased sensitivity to biguanide treatment both in cell viability assays in vitro and tumor growth assays in vivo. This increased biguanide sensitivity is driven by miR-17-dependent silencing of LKB1, which results in reduced AMPK activation in response to bioenergetic stress. Mechanistically, biguanide treatment inhibits TCA cycle metabolism and mitochondrial respiration in miR-17[~]92-expressing tumor cells, targeting their metabolic vulnerability. Finally, we demonstrate a direct correlation between miR-17[~]92 expression and biguanide sensitivity in human cancer cells. Our results identify miR-17[~]92 expression as a potential biomarker for biguanide sensitivity in hematological malignancies and solid tumors. One Sentence SummarymiR-17[~]92 expression in Myc+ tumors sensitizes cancer cells to biguanide treatment by disrupting bioenergetic stability in lymphoma cells.

cancer biology↗