Latent protein trees

Unbiased, label-free proteomics is becoming a powerful technique for measuring protein expression in almost any biological sample. The output of these measurements after preprocessing is a collection of features and their associated intensities for each sample. Subsets of features within the data are from the same peptide, subsets of peptides are from the same protein, and subsets of proteins are in the same biological pathways, therefore, there is the potential for very complex and informative correlational structure inherent in these data. Recent attempts to utilize this data often focus on the identification of single features that are associated with a particular phenotype that is relevant to the experiment. However, to date, there have been no published approaches that directly model what we know to be multiple different levels of correlation structure. Here we present a hierarchical Bayesian model which is specifically designed to model such correlation structure in unbiased, label-free proteomics. This model utilizes partial identification information from peptide sequencing and database lookup as well as the observed correlation in the data to appropriately compress features into latent proteins and to estimate their correlation structure. We demonstrate the effectiveness of the model using artificial/benchmark data and in the context of a series of proteomics measurements of blood plasma from a collection of volunteers who were infected with two different strains of viral influenza.Comment: Published in at http://dx.doi.org/10.1214/13-AOAS639 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Automated analysis of quantitative image data using isomorphic functional mixed models, with application to proteomics data

Image data are increasingly encountered and are of growing importance in many areas of science. Much of these data are quantitative image data, which are characterized by intensities that represent some measurement of interest in the scanned images. The data typically consist of multiple images on the same domain and the goal of the research is to combine the quantitative information across images to make inference about populations or interventions. In this paper we present a unified analysis framework for the analysis of quantitative image data using a Bayesian functional mixed model approach. This framework is flexible enough to handle complex, irregular images with many local features, and can model the simultaneous effects of multiple factors on the image intensities and account for the correlation between images induced by the design. We introduce a general isomorphic modeling approach to fitting the functional mixed model, of which the wavelet-based functional mixed model is one special case. With suitable modeling choices, this approach leads to efficient calculations and can result in flexible modeling and adaptive smoothing of the salient features in the data. The proposed method has the following advantages: it can be run automatically, it produces inferential plots indicating which regions of the image are associated with each factor, it simultaneously considers the practical and statistical significance of findings, and it controls the false discovery rate.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS407 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

explorase: Multivariate Exploratory Analysis and Visualization for Systems Biology

The datasets being produced by high-throughput biological experiments, such as microarrays, have forced biologists to turn to sophisticated statistical analysis and visualization tools in order to understand their data. We address the particular need for an open-source exploratory data analysis tool that applies numerical methods in coordination with interactive graphics to the analysis of experimental data. The software package, known as explorase, provides a graphical user interface (GUI) on top of the R platform for statistical computing and the GGobi software for multivariate interactive graphics. The GUI is designed for use by biologists, many of whom are unfamiliar with the R language. It displays metadata about experimental design and biological entities in tables that are sortable and filterable. There are menu shortcuts to the analysis methods implemented in R, including graphical interfaces to linear modeling tools. The GUI is linked to data plots in GGobi through a brush tool that simultaneously colors rows in the entity information table and points in the GGobi plots.

A discrete cluster of urinary biomarkers discriminates between active systemic lupus erythematosus patients with and without glomerulonephritis.

BackgroundManagement of lupus nephritis (LN) would be greatly aided by the discovery of biomarkers that accurately reflect changes in disease activity. Here, we used a proteomics approach to identify potential urinary biomarkers associated with LN.MethodsUrine was obtained from 60 LN patients with paired renal biopsies, 25 active non-LN SLE patients, and 24 healthy controls. Using Luminex, 128 analytes were quantified and normalized to urinary creatinine levels. Data were analyzed by linear modeling and non-parametric statistics, with corrections for multiple comparisons. A second cohort of 33 active LN, 16 active non-LN, and 30 remission LN SLE patients was used to validate the results.ResultsForty-four analytes were identified that were significantly increased in active LN as compared to active non-LN. This included a number of unique proteins (e.g., TIMP-1, PAI-1, PF4, vWF, and IL-15) as well as known candidate LN biomarkers (e.g., adiponectin, sVCAM-1, and IL-6), that differed markedly (>4-fold) between active LN and non-LN, all of which were confirmed in the validation cohort and normalized in remission LN patients. These proteins demonstrated an enhanced ability to discriminate between active LN and non-LN patients over several previously reported biomarkers. Ten proteins were found to significantly correlate with the activity score on renal biopsy, eight of which strongly discriminated between active proliferative and non-proliferative/chronic renal lesions.ConclusionsA number of promising urinary biomarkers that correlate with the presence of active renal disease and/or renal biopsy changes were identified and appear to outperform many of the existing proposed biomarkers

Mammary molecular portraits reveal lineage-specific features and progenitor cell vulnerabilities.

The mammary epithelium depends on specific lineages and their stem and progenitor function to accommodate hormone-triggered physiological demands in the adult female. Perturbations of these lineages underpin breast cancer risk, yet our understanding of normal mammary cell composition is incomplete. Here, we build a multimodal resource for the adult gland through comprehensive profiling of primary cell epigenomes, transcriptomes, and proteomes. We define systems-level relationships between chromatin-DNA-RNA-protein states, identify lineage-specific DNA methylation of transcription factor binding sites, and pinpoint proteins underlying progesterone responsiveness. Comparative proteomics of estrogen and progesterone receptor-positive and -negative cell populations, extensive target validation, and drug testing lead to discovery of stem and progenitor cell vulnerabilities. Top epigenetic drugs exert cytostatic effects; prevent adult mammary cell expansion, clonogenicity, and mammopoiesis; and deplete stem cell frequency. Select drugs also abrogate human breast progenitor cell activity in normal and high-risk patient samples. This integrative computational and functional study provides fundamental insight into mammary lineage and stem cell biology

In-depth proteomics identifies a role for autophagy in controlling reactive oxygen species mediated endothelial permeability

Endothelial cells (ECs) form the inner layer of blood vessels and physically separate the blood from the surrounding tissue. To support tissues with nutrients and oxygen, the endothelial monolayer is semipermeable. When EC permeability is altered, blood vessels are not functional, and this is associated with disease. A comprehensive knowledge of the mechanisms regulating EC permeability is key in developing strategies to target this mechanism in pathologies. Here we have used an in vitro model of human umbilical vein endothelial cells mimicking the formation of a physiologically permeable vessel and performed time-resolved in-depth molecular profiling using stable isotope labeling by amino acids in cell culture mass spectrometry (MS)-proteomics. Autophagy is induced when ECs are assembled into a physiologically permeable monolayer. By using siRNA and drug treatment to block autophagy in combination with functional assays and MS proteomics, we show that ECs require autophagy flux to maintain intracellular reactive oxygen species levels, and this is required to maintain the physiological permeability of the cells