Phosphoproteomics to Characterize Host Response During Influenza A Virus Infection of Human Macrophages.

Influenza A viruses cause infections in the human respiratory tract and give rise to annual seasonal outbreaks, as well as more rarely dreaded pandemics. Influenza A viruses become quickly resistant to the virus-directed antiviral treatments, which are the current main treatment options. A promising alternative approach is to target host cell factors that are exploited by influenza viruses. To this end, we characterized the phosphoproteome of influenza A virus infected primary human macrophages to elucidate the intracellular signaling pathways and critical host factors activated upon influenza infection. We identified 1675 phosphoproteins, 4004 phosphopeptides and 4146 nonredundant phosphosites. The phosphorylation of 1113 proteins (66%) was regulated upon infection, highlighting the importance of such global phosphoproteomic profiling in primary cells. Notably, 285 of the identified phosphorylation sites have not been previously described in publicly available phosphorylation databases, despite many published large-scale phosphoproteome studies using human and mouse cell lines. Systematic bioinformatics analysis of the phosphoproteome data indicated that the phosphorylation of proteins involved in the ubiquitin/proteasome pathway (such as TRIM22 and TRIM25) and antiviral responses (such as MAVS) changed in infected macrophages. Proteins known to play roles in small GTPase-, mitogen-activated protein kinase-, and cyclin-dependent kinase- signaling were also regulated by phosphorylation upon infection. In particular, the influenza infection had a major influence on the phosphorylation profiles of a large number of cyclin-dependent kinase substrates. Functional studies using cyclin-dependent kinase inhibitors showed that the cyclin-dependent kinase activity is required for efficient viral replication and for activation of the host antiviral responses. In addition, we show that cyclin-dependent kinase inhibitors protect IAV-infected mice from death. In conclusion, we provide the first comprehensive phosphoproteome characterization of influenza A virus infection in primary human macrophages, and provide evidence that cyclin-dependent kinases represent potential therapeutic targets for more effective treatment of influenza infections.

Systematic Identification of MicroRNAs That Impact on Proliferation of Prostate Cancer Cells and Display Changed Expression in Tumor Tissue.

BACKGROUND: Systematic approaches to functionally identify key players in microRNA (miRNA)-target networks regulating prostate cancer (PCa) proliferation are still missing. OBJECTIVE: To comprehensively map miRNA regulation of genes relevant for PCa proliferation through phenotypic screening and tumor expression data. DESIGN, SETTING, AND PARTICIPANTS: Gain-of-function screening with 1129 miRNA molecules was performed in five PCa cell lines, measuring proliferation, viability, and apoptosis. These results were integrated with changes in miRNA expression from two cohorts of human PCa (188 tumors in total). For resulting miRNAs, the predicted targets were collected and analyzed for patterns with gene set enrichment analysis, and for their association with biochemical recurrence free survival. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Rank product statistical analysis was used to evaluate miRNA effects in phenotypic screening and for expression differences in the prostate tumor cohorts. Expression data were analyzed using the significance analysis of microarrays (SAM) method and the patient material was subjected to Kaplan-Meier statistics. RESULTS AND LIMITATIONS: Functional screening identified 25 miRNAs increasing and 48 miRNAs decreasing cell viability. Data integration resulted in 14 miRNAs, with aberrant expression and effect on proliferation. These miRNAs are predicted to regulate >3700 genes, of which 28 were found up-regulated and 127 down-regulated in PCa compared with benign tissue. Seven genes, FLNC, MSRB3, PARVA, PCDH7, PRNP, RAB34, and SORBS1, showed an inverse association to their predicted miRNA, and were identified to significantly correlate with biochemical recurrence free survival in PCa patients. CONCLUSIONS: A systematic in vitro screening approach combined with in vivo expression and gene set enrichment analysis provide unbiased means for revealing novel miRNA-target links, possibly driving the oncogenic processes in PCa. PATIENT SUMMARY: This study identified novel regulatory molecules, which impact on PCa proliferation and are aberrantly expressed in clinical tumors. Thus, our study reveals regulatory nodes with potential for therapy.

Label-free quantitative phosphoproteomics with novel pairwise abundance normalization reveals synergistic RAS and CIP2A signaling.

Hyperactivated RAS drives progression of many human malignancies. However, oncogenic activity of RAS is dependent on simultaneous inactivation of protein phosphatase 2A (PP2A) activity. Although PP2A is known to regulate some of the RAS effector pathways, it has not been systematically assessed how these proteins functionally interact. Here we have analyzed phosphoproteomes regulated by either RAS or PP2A, by phosphopeptide enrichment followed by mass-spectrometry-based label-free quantification. To allow data normalization in situations where depletion of RAS or PP2A inhibitor CIP2A causes a large uni-directional change in the phosphopeptide abundance, we developed a novel normalization strategy, named pairwise normalization. This normalization is based on adjusting phosphopeptide abundances measured before and after the enrichment. The superior performance of the pairwise normalization was verified by various independent methods. Additionally, we demonstrate how the selected normalization method influences the downstream analyses and interpretation of pathway activities. Consequently, bioinformatics analysis of RAS and CIP2A regulated phosphoproteomes revealed a significant overlap in their functional pathways. This is most likely biologically meaningful as we observed a synergistic survival effect between CIP2A and RAS expression as well as KRAS activating mutations in TCGA pan-cancer data set, and synergistic relationship between CIP2A and KRAS depletion in colony growth assays.

MicroRNA-135b regulates ERα, AR and HIF1AN and affects breast and prostate cancer cell growth.

MicroRNAs (miRNAs) regulate a wide range of cellular signaling pathways and biological processes in both physiological and pathological states such as cancer. We have previously identified miR-135b as a direct regulator of androgen receptor (AR) protein level in prostate cancer (PCa). We wanted to further explore the relationship of miR-135b to hormonal receptors, particularly estrogen receptor α (ERα). Here we show that miR-135b expression is lower in ERα-positive breast tumors as compared to ERα-negative samples in two independent breast cancer (BCa) patient cohorts (101 and 1302 samples). Additionally, the miR-135b expression is higher in AR-low PCa patient samples (47 samples). We identify ERα as a novel miR-135b target by demonstrating miR-135b binding to the 3'UTR of the ERα and decreased ERα protein and mRNA level upon miR-135b overexpression in BCa cells. MiR-135b reduces proliferation of ERα-positive BCa cells MCF-7 and BT-474 as well as AR-positive PCa cells LNCaP and 22Rv1 when grown in 2D. To identify other genes regulated by miR-135b we performed gene expression studies and found a link to the hypoxia inducible factor 1α (HIF1α) pathway. We show that miR-135b influences the protein level of the inhibitor for hypoxia inducible factor 1α (HIF1AN) and is able to bind to HIF1AN 3'UTR. Our study demonstrates that miR-135b regulates ERα, AR and HIF1AN protein levels through interaction with their 3'UTR regions, and proliferation in ERα-positive BCa and AR-positive PCa cells.

Statistical detection of quantitative protein biomarkers provides insights into signaling networks deregulated in acute myeloid leukemia.

The increasing coverage and sensitivity of LC-MS/MS-based proteomics have expanded its applications in systems medicine. In particular, label-free quantitation approaches are enabling biomarker discovery in terms of statistical comparison of proteomic profiles across large numbers of clinical samples. However, it still remains poorly understood how much protein markers can add novel insights compared to markers derived from mRNA transcriptomic profiling. Using paired label-free LC-MS/MS and gene expression microarray measurements from primary samples of patients with acute myeloid leukemia (AML), we demonstrate here that while the quantitative proteomic and transcriptomic profiles were highly correlated, in general, the marker panels showing statistically significant expression changes across the disease and healthy groups were profoundly different between protein and mRNA levels. In particular, the proteomic assay enabled unique links to known leukemic processes, which were missed when using the transcriptomic profiling alone, as well as identified additional links to metabolic regulators and chromatin remodelers, such as GPX1, fumarate hydratase, and SET oncogene, which have subsequently been evaluated in independent AML samples. Overall, these results highlighted the complementary and informative view obtained from the quantitative LC-MS/MS approach into the AML deregulated signaling networks.

A community effort to assess and improve drug sensitivity prediction algorithms.

Predicting the best treatment strategy from genomic information is a core goal of precision medicine. Here we focus on predicting drug response based on a cohort of genomic, epigenomic and proteomic profiling data sets measured in human breast cancer cell lines. Through a collaborative effort between the National Cancer Institute (NCI) and the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we analyzed a total of 44 drug sensitivity prediction algorithms. The top-performing approaches modeled nonlinear relationships and incorporated biological pathway information. We found that gene expression microarrays consistently provided the best predictive power of the individual profiling data sets; however, performance was increased by including multiple, independent data sets. We discuss the innovations underlying the top-performing methodology, Bayesian multitask MKL, and we provide detailed descriptions of all methods. This study establishes benchmarks for drug sensitivity prediction and identifies approaches that can be leveraged for the development of new methods.

Making sense of large-scale kinase inhibitor bioactivity data sets: a comparative and integrative analysis.

We carried out a systematic evaluation of target selectivity profiles across three recent large-scale biochemical assays of kinase inhibitors and further compared these standardized bioactivity assays with data reported in the widely used databases ChEMBL and STITCH. Our comparative evaluation revealed relative benefits and potential limitations among the bioactivity types, as well as pinpointed biases in the database curation processes. Ignoring such issues in data heterogeneity and representation may lead to biased modeling of drugs' polypharmacological effects as well as to unrealistic evaluation of computational strategies for the prediction of drug-target interaction networks. Toward making use of the complementary information captured by the various bioactivity types, including IC50, K(i), and K(d), we also introduce a model-based integration approach, termed KIBA, and demonstrate here how it can be used to classify kinase inhibitor targets and to pinpoint potential errors in database-reported drug-target interactions. An integrated drug-target bioactivity matrix across 52,498 chemical compounds and 467 kinase targets, including a total of 246,088 KIBA scores, has been made freely available.