Summary
Despite the inclusion of inherited myeloid malignancies as a separate entity in the World Health Organization Classification, many established predisposing loci continue to lack functional ...characterization. While germline mutations in the DNA repair factor ERCC excision repair 6 like 2 (ERCC6L2) give rise to bone marrow failure and acute myeloid leukaemia, their consequences on normal haematopoiesis remain unclear. To functionally characterise the dual impact of germline ERCC6L2 loss on human primary haematopoietic stem/progenitor cells (HSPCs) and mesenchymal stromal cells (MSCs), we challenged ERCC6L2‐silenced and patient‐derived cells ex vivo. Here, we show for the first time that ERCC6L2‐deficiency in HSPCs significantly impedes their clonogenic potential and leads to delayed erythroid differentiation. This observation was confirmed by CIBERSORTx RNA‐sequencing deconvolution performed on ERCC6L2‐silenced erythroid‐committed cells, which demonstrated higher proportions of polychromatic erythroblasts and reduced orthochromatic erythroblasts versus controls. In parallel, we demonstrate that the consequences of ERCC6L2‐deficiency are not limited to HSPCs, as we observe a striking phenotype in patient‐derived and ERCC6L2‐silenced MSCs, which exhibit enhanced osteogenesis and suppressed adipogenesis. Altogether, our study introduces a valuable surrogate model to study the impact of inherited myeloid mutations and highlights the importance of accounting for the influence of germline mutations in HSPCs and their microenvironment.
Summary
A model is presented describing the gene regulatory network surrounding three similar NAC transcription factors that have roles in Arabidopsis leaf senescence and stress responses. ANAC019, ...ANAC055 and ANAC072 belong to the same clade of NAC domain genes and have overlapping expression patterns. A combination of promoter DNA/protein interactions identified using yeast 1‐hybrid analysis and modelling using gene expression time course data has been applied to predict the regulatory network upstream of these genes. Similarities and divergence in regulation during a variety of stress responses are predicted by different combinations of upstream transcription factors binding and also by the modelling. Mutant analysis with potential upstream genes was used to test and confirm some of the predicted interactions. Gene expression analysis in mutants of ANAC019 and ANAC055 at different times during leaf senescence has revealed a distinctly different role for each of these genes. Yeast 1‐hybrid analysis is shown to be a valuable tool that can distinguish clades of binding proteins and be used to test and quantify protein binding to predicted promoter motifs.
Cells naïve to stress can display the effects of stress, such as DNA damage and apoptosis, when they are exposed to signals from stressed cells; this phenomenon is known as the bystander effect. We ...previously showed that bystander effect induced by ionising radiation are mediated by extracellular vesicles (EVs). Bystander effect can also be induced by other types of stress, including heat shock, but it is unclear whether EVs are involved. Here we show that EVs released from heat shocked cells are also able to induce bystander damage in unstressed populations. Naïve cells treated with media conditioned by heat shocked cells showed higher levels of DNA damage and apoptosis than cells treated with media from control cells. Treating naïve cells with EVs derived from media conditioned by heat shocked cells also induced a bystander effect when compared to control, with DNA damage and apoptosis increasing whilst the level of cell viability was reduced. We demonstrate that treatment of naïve cells with heat shocked cell-derived EVs leads to greater invasiveness in a trans-well Matrigel assay. Finally, we show that naïve cells treated with EVs from heat-shocked cells are more likely to survive a subsequent heat shock, suggesting that these EVs mediate an adaptive response. We propose that EVs released following stress mediate an intercellular response that leads to apparent stress in neighbouring cells but also greater robustness in the face of a subsequent insult.
Next Generation Sequencing (NGS) has dramatically improved the flexibility and outcomes of cancer research and clinical trials, providing highly sensitive and accurate high-throughput platforms for ...large-scale genomic testing. In contrast to whole-genome (WGS) or whole-exome sequencing (WES), targeted genomic sequencing (TS) focuses on a panel of genes or targets known to have strong associations with pathogenesis of disease and/or clinical relevance, offering greater sequencing depth with reduced costs and data burden. This allows targeted sequencing to identify low frequency variants in targeted regions with high confidence, thus suitable for profiling low-quality and fragmented clinical DNA samples. As a result, TS has been widely used in clinical research and trials for patient stratification and the development of targeted therapeutics. However, its transition to routine clinical use has been slow. Many technical and analytical obstacles still remain and need to be discussed and addressed before large-scale and cross-centre implementation. Gold-standard and state-of-the-art procedures and pipelines are urgently needed to accelerate this transition. In this review we first present how TS is conducted in cancer research, including various target enrichment platforms, the construction of target panels, and selected research and clinical studies utilising TS to profile clinical samples. We then present a generalised analytical workflow for TS data discussing important parameters and filters in detail, aiming to provide the best practices of TS usage and analyses.
The inclusion of familial myeloid malignancies as a separate disease entity in the revised WHO classification has renewed efforts to improve the recognition and management of this group of at risk ...individuals. Here we report a cohort of 86 acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS) families with 49 harboring germline variants in 16 previously defined loci (57%). Whole exome sequencing in a further 37 uncharacterized families (43%) allowed us to rationalize 65 new candidate loci, including genes mutated in rare hematological syndromes (ADA, GP6, IL17RA, PRF1 and SEC23B), reported in prior MDS/AML or inherited bone marrow failure series (DNAH9, NAPRT1 and SH2B3) or variants at novel loci (DHX34) that appear specific to inherited forms of myeloid malignancies. Altogether, our series of MDS/AML families offer novel insights into the etiology of myeloid malignancies and provide a framework to prioritize variants for inclusion into routine diagnostics and patient management.
Myelodysplastic syndromes (MDS) are hematopoietic stem cell (HSC) malignancies characterized by ineffective hematopoiesis, with increased incidence in older individuals. Here we analyze the ...transcriptome of human HSCs purified from young and older healthy adults, as well as MDS patients, identifying transcriptional alterations following different patterns of expression. While aging-associated lesions seem to predispose HSCs to myeloid transformation, disease-specific alterations may trigger MDS development. Among MDS-specific lesions, we detect the upregulation of the transcription factor DNA Damage Inducible Transcript 3 (DDIT3). Overexpression of DDIT3 in human healthy HSCs induces an MDS-like transcriptional state, and dyserythropoiesis, an effect associated with a failure in the activation of transcriptional programs required for normal erythroid differentiation. Moreover, DDIT3 knockdown in CD34
cells from MDS patients with anemia is able to restore erythropoiesis. These results identify DDIT3 as a driver of dyserythropoiesis, and a potential therapeutic target to restore the inefficient erythroid differentiation characterizing MDS patients.
Determining the tissue- and disease-specific circuit of biological pathways remains a fundamental goal of molecular biology. Many components of these biological pathways still remain unknown, ...hindering the full and accurate characterization of biological processes of interest. Here we describe ACSNI, an algorithm that combines prior knowledge of biological processes with a deep neural network to effectively decompose gene expression profiles (GEPs) into multi-variable pathway activities and identify unknown pathway components. Experiments on public GEP data show that ACSNI predicts cogent components of mTOR, ATF2, and HOTAIRM1 signaling that recapitulate regulatory information from genetic perturbation and transcription factor binding datasets. Our framework provides a fast and easy-to-use method to identify components of signaling pathways as a tool for molecular mechanism discovery and to prioritize genes for designing future targeted experiments (https://github.com/caanene1/ACSNI).
Display omitted
•ACSNI uses deep neural network to decompose transcriptomic measurements•ACSNI maps pathway components to target biological or clinical phenotypes•ACSNI robustly extracts regulatory output of transcription factors•ACSNI enables annotation of genes with unknown functions
Methods that group genes into functional units to quantify pathway activities are critical in the analysis of biological systems. Although many components of biological pathways have been described in detail, these tend to be limited to well-studied genes. In contrast, the majority of possible components remain unexplored. Here, we present a machine-learning tool for constructing and predicting tissue-specific components of biological pathways from large biological datasets. Our algorithm, ACSNI, can tackle incomplete pathway descriptions and enhance current pathway analysis methods' performance. We anticipate that, by dissecting the complex signals in biological data in a flexible and context-specific manner, ACSNI can facilitate the full characterization of physiological systems of interest.
ACSNI software presents a flexible approach to discover unknown components of a biological pathway. We demonstrate through simulation and biological user cases that ASCNI has superior performance in inferring pathway activities compared to other approaches. In particular, it is able to identify unknown components of pathways in a tissue-specific manner, facilitating the full characterization of physiological systems of interest.
PDF
