Cooperating, congenital neutropenia-associated Csf3r and Runx1 mutations activate pro-inflammatory signaling and inhibit myeloid differentiation of mouse HSPCs

Proteome-wide base editor screens to assess phosphorylation site functionality in high-throughput

Signaling pathways that drive gene expression are typically depicted as having a dozen or so landmark phosphorylation and transcriptional events. In reality, thousands of dynamic post-translational modifications (PTMs) orchestrate nearly every cellular function, and we lack technologies to find causal links between these vast biochemical pathways and genetic circuits at scale. Here, we describe "signaling-to-transcription network" mapping through the development of PTM-centric base editing coupled to phenotypic screens, directed by temporally-resolved phosphoproteomics. Using T cell activation as a model, we observe hundreds of unstudied phosphorylation sites that modulate NFAT transcriptional activity. We identify the phosphorylation-mediated nuclear localization of the phosphatase PHLPP1 which promotes NFAT but inhibits NFκB activity. We also find that specific phosphosite mutants can alter gene expression in subtle yet distinct patterns, demonstrating the potential for fine-tuning transcriptional responses. Overall, base editor screening of PTM sites provides a powerful platform to dissect PTM function within signaling pathways.

RUNX1 mutations mitigate quiescence to promote transformation of hematopoietic progenitors in Fanconi anemia

Many inherited bone marrow failure syndromes (IBMFSs) present a high risk of transformation to myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML). During transformation of IBMFSs, hematopoietic stem and progenitor cells (HSPCs) with poor fitness gain ectopic, dysregulated self-renewal secondary to somatic mutations via undefined mechanisms. Here, in the context of the prototypical IBMFS Fanconi anemia (FA), we performed multiplexed gene editing of mutational hotspots in MDS-associated genes in human induced pluripotent stem cells (iPSCs) followed by hematopoietic differentiation. We observed aberrant self-renewal and impaired differentiation of HSPCs with enrichment of RUNX1 insertions and deletions (indels), generating a model of IBMFS-associated MDS. We observed that compared to the failure state, FA MDS cells show mutant RUNX1-mediated blunting of the G1/S cell cycle checkpoint that is normally activated in FA in response to DNA damage. RUNX1 indels also lead to activation of innate immune signaling, which stabilizes the homologous recombination (HR) effector BRCA1, and this pathway can be targeted to abrogate viability and restore sensitivity to genotoxins in FA MDS. Together, these studies develop a paradigm for modeling clonal evolution in IBMFSs, provide basic understanding of the pathogenesis of MDS, and uncover a therapeutic target in FA-associated MDS.

Myeloid neoplasms and clonal hematopoiesis from the RUNX1 perspective

RUNX1 is a critical transcription factor for the emergence of definitive hematopoiesis and the precise regulation of adult hematopoiesis. Dysregulation of its regulatory network causes aberrant hematopoiesis. Recurrent genetic alterations in RUNX1, including chromosomal translocations and mutations, have been identified in both inherited and sporadic diseases. Recent genomic studies have revealed a vast mutational landscape surrounding genetic alterations in RUNX1. Accumulating pieces of evidence also indicate the leukemogenic role of wild-type RUNX1 in certain situations. Based on these efforts, part of the molecular mechanisms of disease development as a consequence of dysregulated RUNX1-regulatory networks have become increasingly evident. This review highlights the recent advances in the field of RUNX1 research and discusses the critical roles of RUNX1 in hematopoiesis and the pathobiological function of its alterations in the context of disease, particularly myeloid neoplasms, and clonal hematopoiesis.

An ensemble machine learning model based on multiple filtering and supervised attribute clustering algorithm for classifying cancer samples

BACKGROUND

Machine learning is one kind of machine intelligence technique that learns from data and detects inherent patterns from large, complex datasets. Due to this capability, machine learning techniques are widely used in medical applications, especially where large-scale genomic and proteomic data are used. Cancer classification based on bio-molecular profiling data is a very important topic for medical applications since it improves the diagnostic accuracy of cancer and enables a successful culmination of cancer treatments. Hence, machine learning techniques are widely used in cancer detection and prognosis.

METHODS

In this article, a new ensemble machine learning classification model named Multiple Filtering and Supervised Attribute Clustering algorithm based Ensemble Classification model (MFSAC-EC) is proposed which can handle class imbalance problem and high dimensionality of microarray datasets. This model first generates a number of bootstrapped datasets from the original training data where the oversampling procedure is applied to handle the class imbalance problem. The proposed MFSAC method is then applied to each of these bootstrapped datasets to generate sub-datasets, each of which contains a subset of the most relevant/informative attributes of the original dataset. The MFSAC method is a feature selection technique combining multiple filters with a new supervised attribute clustering algorithm. Then for every sub-dataset, a base classifier is constructed separately, and finally, the predictive accuracy of these base classifiers is combined using the majority voting technique forming the MFSAC-based ensemble classifier. Also, a number of most informative attributes are selected as important features based on their frequency of occurrence in these sub-datasets.

RESULTS

To assess the performance of the proposed MFSAC-EC model, it is applied on different high-dimensional microarray gene expression datasets for cancer sample classification. The proposed model is compared with well-known existing models to establish its effectiveness with respect to other models. From the experimental results, it has been found that the generalization performance/testing accuracy of the proposed classifier is significantly better compared to other well-known existing models. Apart from that, it has been also found that the proposed model can identify many important attributes/biomarker genes.

Genome-Wide Association Studies for the Concentration of Albumin in Colostrum and Serum in Chinese Holstein

Albumin can be of particular benefit in fighting infections for newborn calves due to its anti-inflammatory and anti-oxidative stress properties. To identify the candidate genes related to the concentration of albumin in colostrum and serum, we collected the colostrum and blood samples from 572 Chinese Holstein cows within 24 h after calving and measured the concentration of albumin in the colostrum and serum using the ELISA methods. The cows were genotyped with GeneSeek 150 K chips (containing 140,668 single nucleotide polymorphisms; SNPs). After quality control, we performed GWASs via GCTA software with 91,620 SNPs and 563 cows. Consequently, 9 and 7 genome-wide significant SNPs (false discovery rate (FDR) at 1%) were identified. Correspondingly, 42 and 206 functional genes that contained or were approximate to (±1 Mbp) the significant SNPs were acquired. Integrating the biological process of these genes and the reported QTLs for immune and inflammation traits in cattle, 3 and 12 genes were identified as candidates for the concentration of colostrum and serum albumin, respectively; these are RUNX1, CBR1, OTULIN,CDK6, SHARPIN, CYC1, EXOSC4, PARP10, NRBP2, GFUS, PYCR3, EEF1D, GSDMD, PYCR2 and CXCL12. Our findings provide important information for revealing the genetic mechanism behind albumin concentration and for molecular breeding of disease-resistance traits in dairy cattle.