Rapid label‐free quantitative analysis of the E. coli BL21(DE3) inner membrane proteome

Biological membranes define cells and cellular compartments and are essential in regulating bidirectional flow of chemicals and signals. Characterizing their protein content therefore is required to determine their function, nevertheless, the comprehensive determination of membrane‐embedded sub‐proteomes remains challenging. Here, we experimentally characterized the inner membrane proteome (IMP) of the model organism E. coli BL21(DE3). We took advantage of the recent extensive re‐annotation of the theoretical E. coli IMP regarding the sub‐cellular localization of all its proteins. Using surface proteolysis of IMVs with variable chemical treatments followed by nanoLC‐MS/MS analysis, we experimentally identified ～45% of the expressed IMP in wild type E. coli BL21(DE3) with 242 proteins reported here for the first time. Using modified label‐free approaches we quantified 220 IM proteins. Finally, we compared protein levels between wild type cells and those over‐synthesizing the membrane‐embedded translocation channel SecYEG proteins. We propose that this proteomics pipeline will be generally applicable to the determination of IMP from other bacteria.     

Computational and informatics strategies for identification of specific protein interaction partners in affinity purification mass spectrometry experiments

Analysis of protein interaction networks and protein complexes using affinity purification and mass spectrometry (AP/MS) is among most commonly used and successful applications of proteomics technologies. One of the foremost challenges of AP/MS data is a large number of false-positive protein interactions present in unfiltered data sets. Here we review computational and informatics strategies for detecting specific protein interaction partners in AP/MS experiments, with a focus on incomplete (as opposite to genome wide) interactome mapping studies. These strategies range from standard statistical approaches, to empirical scoring schemes optimized for a particular type of data, to advanced computational frameworks. The common denominator among these methods is the use of label-free quantitative information such as spectral counts or integrated peptide intensities that can be extracted from AP/MS data. We also discuss related issues such as combining multiple biological or technical replicates, and dealing with data generated using different tagging strategies. Computational approaches for benchmarking of scoring methods are discussed, and the need for generation of reference AP/MS data sets is highlighted. Finally, we discuss the possibility of more extended modeling of experimental AP/MS data, including integration with external information such as protein interaction predictions based on functional genomics data.     

Identification of survival-related alternative splicing signatures in acute myeloid leukemia

Aberrant RNA alternative splicing (AS) variants play critical roles in tumorigenesis and prognosis in human cancers. Here, we conducted a comprehensive profiling of aberrant AS events in acute myeloid leukemia (AML). RNA AS profile, including seven AS types, and the percent spliced in (PSI) value for each patient were generated by SpliceSeq using RNA-seq data from TCGA. Univariate followed by multivariate Cox regression analysis were used to identify survival-related AS events and develop the AS signatures. A nomogram was developed, and its predictive efficacy was assessed. About 27,892 AS events and 3,178 events were associated with overall survival (OS) after strict filtering. Parent genes of survival-associated AS events were mainly enriched in leukemia-associated processes including chromatin modification, autophagy, and T-cell receptor signaling pathway. The 10 AS signature based on seven types of AS events showed better efficacy in predicting OS of patients than those built on a single AS event type. The area under curve (AUC) value of the 10 AS signature for 3-year OS was 0.91. Gene set enrichment analysis (GSEA) confirmed that these survival-related AS events contribute to AML progression. Moreover, the nomogram showed good predictive performance for patient''s prognosis. Finally, the correlation network of AS variants with splicing factor genes found potential important regulatory genes in AML. The present study presented a systematic analysis of survival-related AS events and developed AS signatures for predicting the patient’s survival. Further studies are needed to validate the signatures in independent AML cohorts and might provide a promising perspective for developing therapeutic targets.     

Proteogenomics approaches for studying cancer biology and their potential in the identification of acute myeloid leukemia biomarkers

Introduction: Mass spectrometry (MS)-based proteomics has become an indispensable tool for the characterization of the proteome and its post-translational modifications (PTM). In addition to standard protein sequence databases, proteogenomics strategies search the spectral data against the theoretical spectra obtained from customized protein sequence databases. Up to date, there are no published proteogenomics studies on acute myeloid leukemia (AML) samples.

Areas covered: Proteogenomics involves the understanding of genomic and proteomic data. The intersection of both datatypes requires advanced bioinformatics skills. A standard proteogenomics workflow that could be used for the study of AML samples is described. The generation of customized protein sequence databases as well as bioinformatics tools and pipelines commonly used in proteogenomics are discussed in detail.

Expert commentary: Drawing on evidence from recent cancer proteogenomics studies and taking into account the public availability of AML genomic data, the interpretation of present and future MS-based AML proteomic data using AML-specific protein sequence databases could discover new biological mechanisms and targets in AML. However, proteogenomics workflows including bioinformatics guidelines can be challenging for the wide AML research community. It is expected that further automation and simplification of the bioinformatics procedures might attract AML investigators to adopt the proteogenomics strategy.     

Molecular dissection of valproic acid effects in acute myeloid leukemia identifies predictive networks

Histone deacetylase inhibitors (HDACIs) like valproic acid (VPA) display activity in leukemia models and induce tumor-selective cytotoxicity against acute myeloid leukemia (AML) blasts. As there are limited data on HDACIs effects, we aimed to dissect VPA effects in vitro using myeloid cell lines with the idea to integrate findings with in vivo data from AML patients treated with VPA additionally to intensive chemotherapy (n = 12). By gene expression profiling we identified an in vitro VPA response signature enriched for genes/pathways known to be implicated in cell cycle arrest, apoptosis, and DNA repair. Following VPA treatment in vivo, gene expression changes in AML patients showed concordant results with the in vitro VPA response despite concomitant intensive chemotherapy. Comparative miRNA profiling revealed VPA-associated miRNA expression changes likely contributing to a VPA-induced reversion of deregulated gene expression. In addition, we were able to define markers predicting VPA response in vivo such as CXCR4 and LBH. These could be validated in an independent cohort of VPA and intensive chemotherapy treated AML patients (n = 114) in which they were inversely correlated with relapse-free survival. In summary, our data provide new insights into the molecular mechanisms of VPA in myeloid blasts, which might be useful in further advancing HDAC inhibition based treatment approaches in AML.     

A quantitative proteomics analysis of MCF7 breast cancer stem and progenitor cell populations

Accumulating evidence has demonstrated that breast cancers are initiated and develop from a small population of stem‐like cells termed cancer stem cells (CSCs). These cells are hypothesized to mediate tumor metastasis and contribute to therapeutic resistance. However, the molecular regulatory networks responsible for maintaining CSCs in an undifferentiated state have yet to be elucidated. In this study, we used CSC markers to isolate pure breast CSCs fractions (ALDH+ and CD44+CD24‐ cell populations) and the mature luminal cells (CD49f‐EpCAM+) from the MCF7 cell line. Proteomic analysis was performed on these samples and a total of 3304 proteins were identified. A label‐free quantitative method was applied to analyze differentially expressed proteins. Using the criteria of greater than twofold changes and p value <0.05, 305, 322 and 98 proteins were identified as significantly different in three pairwise comparisons of ALDH+ versus CD44+CD24‐, ALDH+ versus CD49f‐EpCAM+ and CD44+CD24‐ versus CD49f‐EpCAM+, respectively. Pathway analysis of differentially expressed proteins by Ingenuity Pathway Analysis (IPA) revealed potential molecular regulatory networks that may regulate CSCs. Selected differential proteins were validated by Western blot assay and immunohistochemical staining. The use of proteomics analysis may increase our understanding of the underlying molecular mechanisms of breast CSCs. This may be of importance in the future development of anti‐CSC therapeutics.     

Nucleophosmin mutations in acute myeloid leukemia: A tale of protein unfolding and mislocalization

Nucleophosmin (NPM1) is an abundant, ubiquitously expressed protein mainly localized at nucleoli but continuously shuttling between nucleus and cytoplasm. NPM1 plays a role in several cellular functions, including ribosome biogenesis and export, centrosome duplication, chromatin remodeling, DNA repair, and response to stress stimuli. Much of the interest in this protein arises from its relevance in human malignancies. NPM1 is frequently overexpressed in solid tumors and is the target of several chromosomal translocations in hematologic neoplasms. Notably, NPM1 has been characterized as the most frequently mutated gene in acute myeloid leukemia (AML). Mutations alter the C‐terminal DNA‐binding domain of the protein and result in its aberrant nuclear export and stable cytosolic localization. In this review, we focus on the leukemia‐associated NPM1 C‐terminal domain and describe its structure, function, and the effect exerted by leukemic mutations. Finally, we discuss the possibility to target NPM1 for the treatment of cancer and, in particular, of AML patients with mutated NPM1 gene.     

The role of statistical power analysis in quantitative proteomics

Designing an experiment for quantitative proteomic analysis is not a trivial task. One of the key factors influencing the success of such studies is the number of biological replicates included in the analysis. This, along with the measured variation will determine the statistical power of the analysis. Presented is a simple yet powerful analysis to determine the appropriate sample size required for reliable and reproducible results, based on the total variation (technical and biological). This approach can also be applied retrospectively for the interpretation of results as it takes into account both significance (p value) and quantitative difference (fold change) of the results.     

Serum-free generation and quantification of functionally active Leukemia-derived DC is possible from malignant blasts in acute myeloid leukemia and myelodysplastic syndromes

Functional dendritic cells (DC) are professional antigen presenting cells (APC) and can be generated in vitro from leukemic cells from acute myeloid leukemia AML patients, giving rise to APC of leukemic origin presenting leukemic antigens (DC_leu). We have already shown that DC can be successfully generated from AML and myeloplastic syndromes (MDS) cells in serum-free standard medium (X-vivo + GM-CSF + IL-4 +TNF + FL) in 10–14 days. In this study, we present that DC counts generated from mononuclear cells (MNC) varied between 20% (from 55 MDS samples), 34% (from 100 AML samples) and 25% (from 38 healthy MNC samples) medium. Between 53% and 58% of DC are mature CD83⁺ DC. DC harvests were highest in monocytoid FAB types (AML-M4/M5, MDS-CMML) and independent from cytogenetic risk groups, demonstrating that DC-based strategies can be applied for patients with all cytogenetic risk groups. Proof of the clonal derivation of DC generated was obtained in five AML and four MDS cases with a combined FISH/immunophenotype analysis (FISH-IPA): The clonal numerical chromosome aberrations of the diseases were regularly codetectable with DC markers; however, not with all clonal cells being convertible to leukemia-derived DC_leu (on average, 53% of blasts in AML or MDS). To the contrary, not all DC generated carried the clonal aberration (on average, 51% of DC). In 41 AML and 13 MDS cases with a suitable antigen expression, we could confirm FISH-IPA data by Flow cytometry: although DC_leu are regularly detectable, on average only 57% of blasts in AML and 64% of blasts in MDS were converted to DC_leu. After coculture with DC in mixed lymphocyte reactions (MLR), autologous T cells from AML and MDS patients proliferate and upregulate costimulatory receptors. The specific lysis of leukemic cells by autologous T cells could be demonstrated in three cases with AML in a Fluorolysis assay. In six cases with only few DC_leu or few vital T cells available after the DC/MLR procedure, no lysis of allogeneic or autologous leukemic cells was seen, pointing to the crucial role of both partners in the lysis process. We conclude: (1) the generation of DC is regularly possible in AML and also in MDS under serum-free conditions. (2) Clonal/leukemia-derived DC_leu can be regularly generated from MDS and AML-MNC; however, not with all blasts being converted to DC_leu and not all DC generated carrying leukemic markers. We recommend to select DC_leu for vaccinations or ex vivo T-cell activations to avoid contaminations with non-converted blasts and non-leukemia-derived DC and to improve the harvest of specific, anti-leukemic T cells. DC and DC-primed T cells could provide a practical strategy for the immunotherapy of AML and MDS.     

Overexpression of long noncoding RNA HOXA‐AS2 predicts an adverse prognosis and promotes tumorigenesis via SOX4/PI3K/AKT pathway in acute myeloid leukemia

Long noncoding RNAs (lncRNAs) play important roles in diverse cellular processes and carcinogenesis. Homeobox A cluster antisense RNA 2 (HOXA‐AS2) is a 1,048‐basepairs lncRNA located between human HOXA3 and HOXA4 genes, whose overactivation was previously found to promote the proliferation and invasion of solid tumors. However, its clinical and biological roles in acute myeloid leukemia (AML) remain unclear. This study showed that HOXA‐AS2 was overexpressed in AML patients. In addition, the increased HOXA‐AS2 expression was correlated with higher white blood cell and bone marrow blast counts, unfavorable karyotype classification, more measurable residual disease positivity, and earlier death. There was also a tendency toward inferior survival in patients with high HOXA‐AS2 expression, and HOXA‐AS2 was an independent prognostic factor among the normal‐karyotype AMLs. Furthermore, the results of in vitro study showed that silencing HOXA‐AS2 significantly inhibited the growth of leukemic cells by inducing G1/G0‐phase arrest and apoptosis. Further analysis demonstrated that silencing HOXA‐AS2 suppressed the phosphorylation level of PI3K and AKT, which thereafter promoted the expression of P21 and P27. Moreover, it was suggested that the sex‐determining region Y‐box 4 (SOX4), which is closely involved in the PI3K/AKT pathway, might be one of the major downstream targets of HOXA‐AS2. Silencing HOXA‐AS2 decreased the expression of SOX4, whereas the upregulation of SOX4 partially abrogated the inhibitory effect of silencing HOXA‐AS2 on leukemic cells. In conclusion, these findings suggest that HOXA‐AS2 probably functions as an oncogene via SOX4/PI3K/AKT pathway and might be a useful biomarker for the prognostic prediction in AML patients, providing a potential therapeutic target for AML.