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A comprehensive Proteomic Investigation of Ebf1 Heterozygosity in pro-B lymphocytes utilizing Data Independent Acquisition (DIA) Yaarub Raji Musa, Soeren Boller, Monika Puchalska, Rudolf Grosschedl, and Gerhard Mittler J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.7b00369 • Publication Date (Web): 28 Nov 2017 Downloaded from http://pubs.acs.org on November 29, 2017
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A Comprehensive Proteomic Investigation of Ebf1 Heterozygosity in pro-B lymphocytes Utilizing Data Independent Acquisition (DIA) Yaarub R. Musa1, Sören Boller2, Monika Puchalska1, Rudolf Gorsschedl2, Gerhard Mittler1* 1 Proteomics Facility, Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany. 2 Department of Molecular and Cellular Immunology, Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany. KEYWORDS: DDA, DIA, PRM, EBF1, Pro-B lymphocyte, transcription factor
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ABSTRACT Early B cell factor 1 (EBF1) is one of the key transcription factors required for orchestrating B-cell lineage development. Although studies have shown that Ebf1 haploinsufficiency is involved in the development of leukemia, no study has been conducted that characterizes the global effect of Ebf1 heterozygosity on the proteome of pro-B lymphocytes. Here, we employ both DIA (Data Independent Acquisition) and shotgun DDA (Data Dependent Acquisition) workflows for profiling proteins that are differently expressed between Ebf1+/+ and Ebf1+/cells. Both DDA and DIA were able to reveal the downregulation of the EBF1 transcription factor in Ebf1+/- pro-B lymphocytes. Further examination of differentially expressed proteins by DIA revealed that, similar to EBF1, the expression of other B-cell lineage regulators, such as TCF3 and Pax5, is also down-regulated in Ebf1 heterozygous cells. Functional DIA analysis of differentially expressed proteins showed that EBF1 heterozygosity resulted in the deregulation of at least 8 transcription factors involved in lymphopoiesis, and to the deregulation of key proteins playing crucial roles in survival, development and differentiation of pro-B lymphocytes. Introduction: Transcription factors (TFs) are typically DNA-binding proteins, whose gene regulatory capabilities are of vital importance for defining cell-type identity in multicellular organisms. Despite their biological significance, our understanding of TF function on a proteome-wide scale is still limited. Although qualitative information is widely available in the literature, reliable TF protein abundance measurements were rarely conducted, because studies have usually relied on mRNA expression data to approximate their protein levels (1). This can in part be explained by the fact that TFs are low abundance proteins (2, 3). This makes quantitative analyses a substantial challenge, considering the complexity of metazoan proteomes. Accurate quantitative measurement of TF abundance is vital for the comprehensive understanding of regulatory mechanisms and effects (1). For example, several reports indicate that the ability of transcription factors to exert their regulatory function is in
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part determined by their nuclear abundance (4-6). In biomedicine, accurate quantitative TF measurements are also crucial, as aberration in TF expression levels is responsible for a variety of disorders (7). Early B cell factor 1 (EBF1) is a TF that is highly conserved in multicellular organisms (8) and plays a key role in the complex regulatory network that drives B cell differentiation (9, 10). Deletion of Ebf1 by targeted gene inactivation results in a developmental block in early B cell development at the transition from the pre-pro-B to pro-B cell stage (11). EBF1 activates genes crucial for B cell development. It acts in concert with Pax5 to implement B lineage commitment by repressing genes important for alternative hematopoietic lineages, such as T lymphocytes and macrophages (12-17). Notably, the amount of EBF1 plays a critical role. This is shown in heterozygous knock-out mice that lack a complete block, but display a 50% reduction of pro-B cells in the bone marrow and a decrease in B cell numbers by 10 to 30% in the spleen, compared to wildtype mice (10, 11). Furthermore, inactivation of one Ebf1 allele, together with other mutations, has been shown to promote leukemia (18-22). Until today, shotgun and targeted proteomics constitute the main mass spectrometry (MS) methodologies used for quantitative analysis of transcription factors. On one hand, shotgun proteomics analyses (via DDA: data dependent acquisition) allow an in-depth characterization of complex proteomes, though the stochastic nature of this approach and the high rate of missing values are the main drawbacks hampering detection of low abundance proteins and imposing challenges during data analysis. On the other hand, targeted proteomics approaches, such as multiple reaction monitoring (MRM) and parallel reaction monitoring (PRM), offer high sensitivity, reproducibility and precision. However, they require a priori knowledge of selected proteotypic peptides and only a limited number of proteins can be analyzed in a single experiment. Recently, data independent acquisition (DIA) has evolved into a highly promising MS-acquisition strategy for comprehensive and reproducible profiling of complex biological proteomes. In DIA, the fragmentation spectra of all tryptic peptides belonging to the most frequent peptide mass range (e.g. m/z 400-1200) are acquired in each cycle time, without a pre-selection of the precursor ions. This leads to an unbiased recording of multiplexed fragmentation spectra covering the entire set of peptide precursors in a given sample (23). Hence, this unbiased approach offers the potential to overcome limitations associated with DDA, namely the semi-stochastic nature and the relatively high rate of missing quantitative data points.
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To date, the application of DIA workflows has not been reported in TF proteomic studies. In the present study, we examined and compared the utility of DDA (using MaxQuant) and DIA (using Biognosys Spectronaut software) for investigating the precise relative quantification of EBF1 in Ebf1+/+ and Ebf1+/- pro-B lymphocytes in a TF model system where the gene dosage is reduced by 50%. To our knowledge, our study characterizes, for the first time, the downregulation of EBF1 protein and its effects on the relative expression of other proteins on a proteome-wide scale in heterozygous pro-B lymphocytes.
Experimental Section Extended description of methods can be found in supplementary methods
Cell Cultivation and Sample Preparation Ebf1+/+ and Ebf1+/- cells were cultured on OP9 feeder cells in RPMI medium (supplemented with 10% FCS, 1% penicillin/streptomycin/glutamine, 50µM beta-mercaptoethanol and 5ng/ml interleukin 7 (IL7)). In order to compare identical developmental stages, both cell types, Ebf1+/+ and Ebf1+/-, additionally carry a Rag2 deletion (Rag2-/-), which blocks them at the same developmental stage. Protein sample preparation was carried out as described in Kulak et al. (24) with minor modification. All samples used for DIA and PRM analyses were spiked with HRM (hyper reaction monitoring) kit and iRT kit peptides (Biognosys), respectively, according to the manufacturer’s instructions. Construction of DIA Spectral Library and setup of PRM assays Spectral libraries were generated by Spectronaut version 9.0 utilizing MaxQuant results as an input as described in Muntel et al. (25). Six DDA runs (three measurements from each genotype) were acquired using a QExactive Plus instrument and data were searched using MaxQuant as described below (section: mass spectrometry data analysis). Selected proteins were quantified using the parallel reaction monitoring (PRM) approach. Five proteins were selected for PRM analyses (EBF1/COE1, Pax5, TCF3/E2 alpha, BCL2 and TNPO3). PRM assays were constructed with the help of SpectroDive (Biognosys) software using the DIA spectral library. For the purpose of PRM relative quantification, only
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proteotypic peptides (in the case of EBF1 protein) or “top 2” peptides with specific criteria were considered for quantification. Briefly, for a peptide to be considered for quantification, the peptide had to meet the following criteria: (1) peptide q-value
