Genome-wide association study on 13 167 individuals identifies regulators of blood CD34+cell levels.

Stem cell transplantation is a cornerstone in the treatment of blood malignancies. The most common method to harvest stem cells for transplantation is by leukapheresis, requiring mobilization of CD34+ hematopoietic stem and progenitor cells (HSPCs) from the bone marrow into the blood. Identifying the genetic factors that control blood CD34+ cell levels could reveal new drug targets for HSPC mobilization. Here we report the first large-scale, genome-wide association study on blood CD34+ cell levels. Across 13 167 individuals, we identify 9 significant and 2 suggestive associations, accounted for by 8 loci (PPM1H, CXCR4, ENO1-RERE, ITGA9, ARHGAP45, CEBPA, TERT, and MYC). Notably, 4 of the identified associations map to CXCR4, showing that bona fide regulators of blood CD34+ cell levels can be identified through genetic variation. Further, the most significant association maps to PPM1H, encoding a serine/threonine phosphatase never previously implicated in HSPC biology. PPM1H is expressed in HSPCs, and the allele that confers higher blood CD34+ cell levels downregulates PPM1H. Through functional fine-mapping, we find that this downregulation is caused by the variant rs772557-A, which abrogates an MYB transcription factor-binding site in PPM1H intron 1 that is active in specific HSPC subpopulations, including hematopoietic stem cells, and interacts with the promoter by chromatin looping. Furthermore, PPM1H knockdown increases the proportion of CD34+ and CD34+90+ cells in cord blood assays. Our results provide the first large-scale analysis of the genetic architecture of blood CD34+ cell levels and warrant further investigation of PPM1H as a potential inhibition target for stem cell mobilization.

The insertion/deletion polymorphism rs201494641 at ITGA9 influences blood CD34+ cell levels by altering ZNF384 binding.

Not available.

AliGater: a framework for the development of bioinformatic pipelines for large-scale, high-dimensional cytometry data.

Motivation: AliGater is an open-source framework to accelerate the development of bioinformatic pipelines for the analysis of large-scale, high-dimensional flow cytometry data. AliGater provides a Python package for automatic feature extraction workflows, as well as building blocks to construct analysis pipelines. Results: We illustrate the use of AliGater in a high-resolution flow cytometry-based genome-wide association study on 46 immune cell populations in 14 288 individuals. Availability and implementation: Source code and documentation at https://github.com/LudvigEk/aligater and https://aligater.readthedocs.io.

Accelerating target deconvolution for therapeutic antibody candidates using highly parallelized genome editing.

Therapeutic antibodies are transforming the treatment of cancer and autoimmune diseases. Today, a key challenge is finding antibodies against new targets. Phenotypic discovery promises to achieve this by enabling discovery of antibodies with therapeutic potential without specifying the molecular target a priori. Yet, deconvoluting the targets of phenotypically discovered antibodies remains a bottleneck; efficient deconvolution methods are needed for phenotypic discovery to reach its full potential. Here, we report a comprehensive investigation of a target deconvolution approach based on pooled CRISPR/Cas9. Applying this approach within three real-world phenotypic discovery programs, we rapidly deconvolute the targets of 38 of 39 test antibodies (97%), a success rate far higher than with existing approaches. Moreover, the approach scales well, requires much less work, and robustly identifies antibodies against the major histocompatibility complex. Our data establish CRISPR/Cas9 as a highly efficient target deconvolution approach, with immediate implications for the development of antibody-based drugs.

Essential genes shape cancer genomes through linear limitation of homozygous deletions.

The landscape of somatic acquired deletions in cancer cells is shaped by positive and negative selection. Recurrent deletions typically target tumor suppressor, leading to positive selection. Simultaneously, loss of a nearby essential gene can lead to negative selection, and introduce latent vulnerabilities specific to cancer cells. Here we show that, under basic assumptions on positive and negative selection, deletion limitation gives rise to a statistical pattern where the frequency of homozygous deletions decreases approximately linearly between the deletion target gene and the nearest essential genes. Using DNA copy number data from 9,744 human cancer specimens, we demonstrate that linear deletion limitation exists and exposes deletion-limiting genes for seven known deletion targets (CDKN2A, RB1, PTEN, MAP2K4, NF1, SMAD4, and LINC00290). Downstream analysis of pooled CRISPR/Cas9 data provide further evidence of essentiality. Our results provide further insight into how the deletion landscape is shaped and identify potentially targetable vulnerabilities.