Data independent acquisition (DIA) mass spectrometry is an emerging technique that offers more complete detection and quantification of peptides and proteins across multiple samples. DIA allows fragment-level quantification, which can be considered as repeated measurements of the abundance of the corresponding peptides and proteins in the downstream statistical analysis. However, few statistical approaches are available for aggregating these complex fragment-level data into peptide- or protein-level statistical summaries. In this work, we describe a software package, mapDIA, for statistical analysis of differential protein expression using DIA fragment-level intensities. The workflow consists of three major steps: intensity normalization, peptide/fragment selection, and statistical analysis. First, mapDIA offers normalization of fragment-level intensities by total intensity sums as well as a novel alternative normalization by local intensity sums in retention time space. Second, mapDIA removes outlier observations and selects peptides/fragments that preserve the major quantitative patterns across all samples for each protein. Last, using the selected fragments and peptides, mapDIA performs model-based statistical significance analysis of protein-level differential expression between specified groups of samples. Using a comprehensive set of simulation datasets, we show that mapDIA detects differentially expressed proteins with accurate control of the false discovery rates. We also describe the analysis procedure in detail using two recently published DIA datasets generated for 14-3-3β dynamic interaction network and prostate cancer glycoproteome.

The increased collection of high-dimensional data in various fields has raised a strong interest in clustering algorithms and variable selection procedures. In this paper, we propose a model-based method that addresses the two problems simultaneously. We introduce a latent binary vector to identify discriminating variables and use Dirichlet process mixture models to define the cluster structure. We update the variable selection index using a Metropolis algorithm and obtain inference on the cluster structure via a split-merge Markov chain Monte Carlo technique. We explore the performance of the methodology on simulated data and illustrate an application with a  microarray study.

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MicroRNA is an endogenous, small RNA controlling multiple target genes and playing roles in various biological processes including tumorigenesis. Here, we addressed the function of miR-155 using LC-MS/MS-based metabolic profiling of miR-155 deficient breast cancer cells. Our results revealed the loss of miR-155 hampers glucose uptake and glycolysis, via the down-regulation of glucose transporters and metabolic enzymes including HK2, PKM2, and LDHA. We showed this is due to the down-regulation of cMYC, controlled through phosphoinositide-3-kinase regulatory subunit alpha (PIK3R1)-PDK1/AKT-FOXO3a pathway. UTR analysis of the PIK3R1 and FOXO3a indicated miR-155 directly represses these genes. A stable expression of miR-155 in patient-derived cells (PDCs) showed activated glucose metabolism whereas a stable inhibition of miR-155 reduced in vivo tumor growth with retarded glucose metabolism. Furthermore, analysis of 50 triple-negative breast cancer (TNBC) specimens and specific uptake value (SUV) of PET images revealed a positive correlation between miR-155 level and glucose usage in human breast tumors via PIK3R1-PDK/AKT-FOXO3a-cMYC axis. Collectively, these data demonstrate the miR-155 is a key regulator of glucose metabolism in breast cancer.