A comprehensive evaluation of popular proteomics software workflows for label-free proteome quantification and imputation

Label-free mass spectrometry (MS) has developed into an important tool applied in various fields of biological and life sciences. Several software exist to process the raw MS data into quantified protein abundances, including open source and commercial solutions. Each software includes a set of unique algorithms for different tasks of the MS data processing workflow. While many of these algorithms have been compared separately, a thorough and systematic evaluation of their overall performance is missing. Moreover, systematic information is lacking about the amount of missing values produced by the different proteomics software and the capabilities of different data imputation methods to account for them.In this study, we evaluated the performance of five popular quantitative label-free proteomics software workflows using four different spike-in data sets. Our extensive testing included the number of proteins quantified and the number of missing values produced by each workflow, the accuracy of detecting differential expression and logarithmic fold change and the effect of different imputation and filtering methods on the differential expression results. We found that the Progenesis software performed consistently well in the differential expression analysis and produced few missing values. The missing values produced by the other software decreased their performance, but this difference could be mitigated using proper data filtering or imputation methods. Among the imputation methods, we found that the local least squares (lls) regression imputation consistently increased the performance of the software in the differential expression analysis, and a combination of both data filtering and local least squares imputation increased performance the most in the tested data sets.© The Author 2017. Published by Oxford University Press.</p

A practical comparison of methods for detecting transcription factor binding sites in ChIP-seq experiments

Background: Chromatin immunoprecipitation coupled with massively parallel sequencing (ChIPseq)is increasingly being applied to study transcriptional regulation on a genome-wide scale. Whilenumerous algorithms have recently been proposed for analysing the large ChIP-seq datasets, theirrelative merits and potential limitations remain unclear in practical applications.Results: The present study compares the state-of-the-art algorithms for detecting transcriptionfactor binding sites in four diverse ChIP-seq datasets under a variety of practical research settings.First, we demonstrate how the biological conclusions may change dramatically when the differentalgorithms are applied. The reproducibility across biological replicates is then investigated as aninternal validation of the detections. Finally, the predicted binding sites with each method arecompared to high-scoring binding motifs as well as binding regions confirmed in independent qPCRexperiments.Conclusions: In general, our results indicate that the optimal choice of the computationalapproach depends heavily on the dataset under analysis. In addition to revealing valuableinformation to the users of this technology about the characteristics of the binding site detectionapproaches, the systematic evaluation framework provides also a useful reference to thedevelopers of improved algorithms for ChIP-seq data

HIF prolyl hydroxylase PHD3 regulates translational machinery and glucose metabolism in clear cell renal cell carcinoma

Background: A key feature of clear cell renal cell carcinoma (ccRCC) is the inactivation of the von Hippel-Lindau tumour suppressor protein (pVHL) that leads to the activation of hypoxia-inducible factor (HIF) pathway also in well-oxygenated conditions. Important regulator of HIF-a, prolyl hydroxylase PHD3, is expressed in high amounts in ccRCC. Although several functions and downstream targets for PHD3 in cancer have been suggested, the role of elevated PHD3 expression in ccRCC is not clear.Methods: To gain insight into the functions of high PHD3 expression in ccRCC, we used PHD3 knockdown by siRNA in 786-O cells under normoxic and hypoxic conditions and performed discovery mass spectrometry (LC-MS/MS) of the purified peptide samples. The LC-MS/MS results were analysed by label- free quantification of proteome data using a peptide-level expression-change averaging procedure and subsequent gene ontology enrichment analysis.Results: Our data reveals an intriguingly widespread effect of PHD3 knockdown with 91 significantly regulated proteins. Under hypoxia, the response to PHD3 silencing was wider than under normoxia illustrated by both the number of regulated proteins and by the range of protein expression levels. The main cellular functions regulated by PHD3 expression were glucose metabolism, protein translation and messenger RNA (mRNA) processing. PHD3 silencing led to downregulation of most glycolytic enzymes from glucose transport to lactate production supported by the reduction in extracellular acidification and lactate production and increase in cellular oxygen consumption rate. Moreover, upregulation of mRNA processing-related proteins and alteration in a number of ribosomal proteins was seen as a response to PHD3 silencing. Further studies on upstream effectors of the translational machinery revealed a possible role for PHD3 in regulation of mTOR pathway signalling.Conclusions: Our findings suggest crucial involvement of PHD3 in the maintenance of key cellular functions including glycolysis and protein synthesis in ccRCC

Metagenomics analysis of gut microbiota in response to diet intervention and gestational diabetes in overweight and obese women: a randomised, double-blind, placebo-controlled clinical trial

Objective: Gut microbiota and diet are known to contribute to human metabolism. We investigated whether the metagenomic gut microbiota composition and function changes over pregnancy are related to gestational diabetes mellitus (GDM) and can be modified by dietary supplements, fish oil and/or probiotics.Design: The gut microbiota of 270 overweight/obese women participating in a mother-infant clinical study were analysed with metagenomics approach in early (mean gestational weeks 13.9) and late (gestational weeks 35.2) pregnancy. GDM was diagnosed with a 2 hour 75 g oral glucose tolerance test.Results: Unlike women with GDM, women without GDM manifested changes in relative abundance of bacterial species over the pregnancy, particularly those receiving the fish oil + probiotics combination. The specific bacterial species or function did not predict the onset of GDM nor did it differ according to GDM status, except for the higher abundance of Ruminococcus obeum in late pregnancy in the combination group in women with GDM compared with women without GDM. In the combination group, weak decreases over the pregnancy were observed in basic bacterial housekeeping functions.Conclusions: The specific gut microbiota species do not contribute to GDM in overweight/obese women. Nevertheless, the GDM status may disturb maternal gut microbiota flexibility and thus limit the capacity of women with GDM to respond to diet, as evidenced by alterations in gut microbiota observed only in women without GDM. These findings may be important when considering the metabolic complications during pregnancy, but further studies with larger populations are called for to verify the findings.</p

Quantitative proteomics analysis of the nuclear fraction of human CD4+ cells in the early phases of IL-4-induced Th2 differentiation

We used stable isotope labeling with 4-plex iTRAQ (isobaric tags for relative and absolute quantification) reagents and LC-MS/MS to investigate proteomic changes in the nucleus of activated human CD4(+) cells during the early stages of Th2 cell differentiation. The effects of IL-4 stimulation upon activated naïve CD4(+) cells were measured in the nuclear fractions from 6 and 24 h in three biological replicates, each using pooled cord blood samples derived from seven or more individuals. In these analyses, in the order of 800 proteins were detected with two or more peptides and quantified in three biological replicates. In addition to consistent differences observed with the nuclear localization/expression of established human Th2 and Th1 markers, there were changes that suggested the involvement of several proteins either only recently reported or otherwise not known in this context. These included SATB1 and among the novel changes detected and validated an IL-4-induced increase in the level of YB1. This unique data set from human cord blood CD4(+) T cells details an extensive list of protein determinations that compares with and complements previous data determined from the Jurkat cell nucleus.</p

An unbiased in vitro screen for activating epidermal growth factor receptor mutations

Cancer tissues harbor thousands of mutations, and a given oncogene may be mutated at hundreds of sites. Yet, only a few of these mutations have been functionally tested. Here, we describe an unbiased platform for the functional characterization of thousands of variants of a single receptor tyrosine kinase (RTK) gene in a single assay. Our in vitro screen for activating mutations (iSCREAM) platform enabled rapid analysis of mutations conferring gain-of-function RTK activity promoting clonal growth. The screening strategy included a somatic model of cancer evolution and utilized a library of 7,216 randomly mutated epidermal growth factor receptor (EGFR) single-nucleotide variants, that were tested in murine lymphoid Ba/F3 cells. These cells depend on exogenous interleukin-3 (IL-3) for growth, but this dependency can be compensated by ectopic EGFR overexpression, enabling selection for gain-of-function EGFR mutants. Analysis of the enriched mutants revealed EGFR A702V, a novel activating variant that structurally stabilized the EGFR kinase dimer interface and conferred sensitivity to kinase inhibition by afatinib. As proof of concept for our approach, we recapitulated clinical observations and identified the EGFR L858R as the major enriched EGFR variant. Altogether iSCREAM enabled robust enrichment of 21 variants from a total of 7,216 EGFR mutations. These findings indicate the power of this screening platform for unbiased identification of activating RTK variants that are enriched under selection pressure in a model of cancer heterogeneity and evolution

Early suppression of immune response pathways characterizes children with prediabetes in genome-wide gene expression profiling

Type 1 diabetes (T1D) is caused by autoimmune destruction of insulin-producing pancreatic p cells in the islets of Langerhans. Although defects in various T cell subsets have been linked to the disease pathogenesis, mechanisms initiating or enhancing the autoimmunity in prediabetes remain poorly understood. To unravel genes and molecular pathways affected by the diabetes-associated autoimmunity, we investigated transcriptomic profiles of prospective whole-blood samples from children who have developed T1D-associated autoantibodies and eventually clinical T1D. Gene-level investigation of the data showed systematic differential expression of 520 probesets. A network-based analysis revealed then a highly significant down-regulated network of genes involved in antigen presentation as well as T-cell receptor and insulin signaling. Finally, detection of dynamic changes in the affected pathways at the early or late phases of autoimmunity showed down-regulation of several novel T1D-associated pathways as well as known key components of immune response. The longitudinal genome-wide data generated in the present study allows the detection of dynamic changes relevant to the disease that may be completely missed in conventional cross-sectional studies or in genome-wide association studies. Taken together, our analysis showed systemic high-level repression of immune response pathways associated with T1D autoimmunity. (C) 2010 Elsevier Ltd. All rights reserved.</p

Predicting Skeletal Muscle and Whole-Body Insulin Sensitivity Using NMR-Metabolomic Profiling

Purpose: Abnormal lipoprotein and amino acid profiles are associated with insulin resistance and may help to identify this condition. The aim of this study was to create models estimating skeletal muscle and whole-body insulin sensitivity using fasting metabolite profiles and common clinical and laboratory measures.Material and Methods: The cross-sectional study population included 259 subjects with normal or impaired fasting glucose or type 2 diabetes in whom skeletal muscle and whole-body insulin sensitivity (M-value) were measured during euglycemic hyperinsulinemic clamp. Muscle glucose uptake (GU) was measured directly using [F-18]FDG-PET. Serum metabolites were measured using nuclear magnetic resonance (NMR) spectroscopy. We used linear regression to build the models for the muscle GU (Muscle-insulin sensitivity index [ISI]) and M-value (whole-body [WB]-ISI). The models were created and tested using randomly selected training (n = 173) and test groups (n = 86). The models were compared to common fasting indices of insulin sensitivity, homeostatic model assessment-insulin resistance (HOMA-IR) and the revised quantitative insulin sensitivity check index (QUICKI).Results: WB-ISI had higher correlation with actual M-value than HOMA-IR or revised QUICKI (rho = 0.83 vs -0.67 and 0.66; P < 0.05 for both comparisons), whereas the correlation of Muscle-ISI with the actual skeletal muscle GU was not significantly stronger than HOMA-IR's or revised QUICKI's (rho = 0.67 vs -0.58 and 0.59; both nonsignificant) in the test dataset.Conclusion: Muscle-ISI and WB-ISI based on NMR-metabolomics and common laboratory measurements from fasting serum samples and basic anthropometrics are promising rapid and inexpensive tools for determining insulin sensitivity in at-risk individuals. (C) Endocrine Society 2020