Biomarker discovery and redundancy reduction towards classification using a multi-factorial MALDI-TOF MS T2DM mouse model dataset

Diabetes like many diseases and biological processes is not mono-causal. On the one hand multifactorial studies with complex experimental design are required for its comprehensive analysis. On the other hand, the data from these studies often include a substantial amount of redundancy such as proteins that are typically represented by a multitude of peptides. Coping simultaneously with both complexities (experimental and technological) makes data analysis a challenge for Bioinformatics

A quantitative mass spectrometry-based approach to monitor the dynamics of endogenous chromatin-associated protein complexes.

Understanding the dynamics of endogenous protein-protein interactions in complex networks is pivotal in deciphering disease mechanisms. To enable the in-depth analysis of protein interactions in chromatin-associated protein complexes, we have previously developed a method termed RIME (Rapid Immunoprecipitation Mass spectrometry of Endogenous proteins). Here, we present a quantitative multiplexed method (qPLEX-RIME), which integrates RIME with isobaric labelling and tribrid mass spectrometry for the study of protein interactome dynamics in a quantitative fashion with increased sensitivity. Using the qPLEX-RIME method, we delineate the temporal changes of the Estrogen Receptor alpha (ERα) interactome in breast cancer cells treated with 4-hydroxytamoxifen. Furthermore, we identify endogenous ERα-associated proteins in human Patient-Derived Xenograft tumours and in primary human breast cancer clinical tissue. Our results demonstrate that the combination of RIME with isobaric labelling offers a powerful tool for the in-depth and quantitative characterisation of protein interactome dynamics, which is applicable to clinical samples

Reproducible Cancer Biomarker Discovery in SELDI-TOF MS Using Different Pre-Processing Algorithms

BACKGROUND: There has been much interest in differentiating diseased and normal samples using biomarkers derived from mass spectrometry (MS) studies. However, biomarker identification for specific diseases has been hindered by irreproducibility. Specifically, a peak profile extracted from a dataset for biomarker identification depends on a data pre-processing algorithm. Until now, no widely accepted agreement has been reached. RESULTS: In this paper, we investigated the consistency of biomarker identification using differentially expressed (DE) peaks from peak profiles produced by three widely used average spectrum-dependent pre-processing algorithms based on SELDI-TOF MS data for prostate and breast cancers. Our results revealed two important factors that affect the consistency of DE peak identification using different algorithms. One factor is that some DE peaks selected from one peak profile were not detected as peaks in other profiles, and the second factor is that the statistical power of identifying DE peaks in large peak profiles with many peaks may be low due to the large scale of the tests and small number of samples. Furthermore, we demonstrated that the DE peak detection power in large profiles could be improved by the stratified false discovery rate (FDR) control approach and that the reproducibility of DE peak detection could thereby be increased. CONCLUSIONS: Comparing and evaluating pre-processing algorithms in terms of reproducibility can elucidate the relationship among different algorithms and also help in selecting a pre-processing algorithm. The DE peaks selected from small peak profiles with few peaks for a dataset tend to be reproducibly detected in large peak profiles, which suggests that a suitable pre-processing algorithm should be able to produce peaks sufficient for identifying useful and reproducible biomarkers

Histone deacetylase (HDAC) inhibitors in recent clinical trials for cancer therapy

Heritable changes in gene expression that are not based upon alterations in the DNA sequence are defined as epigenetics. The most common mechanisms of epigenetic regulation are the methylation of CpG islands within the DNA and the modification of amino acids in the N-terminal histone tails. In the last years, it became evident that the onset of cancer and its progression may not occur only due to genetic mutations but also because of changes in the patterns of epigenetic modifications. In contrast to genetic mutations, which are almost impossible to reverse, epigenetic changes are potentially reversible. This implies that they are amenable to pharmacological interventions. Therefore, a lot of work in recent years has focussed on the development of small molecule enzyme inhibitors like DNA-methyltransferase inhibitors or inhibitors of histone-modifying enzymes. These may reverse misregulated epigenetic states and be implemented in the treatment of cancer or other diseases, e.g., neurological disorders. Today, several epigenetic drugs are already approved by the FDA and the EMEA for cancer treatment and around ten histone deacetylase (HDAC) inhibitors are in clinical development. This review will give an update on recent clinical trials of the HDAC inhibitors used systemically that were reported in 2009 and 2010 and will present an overview of different biomarkers to monitor the biological effects

Structural insights into the catalysis and regulation of E3 ubiquitin ligases

Covalent attachment (conjugation) of one or more ubiquitin molecules to protein substrates governs numerous eukaryotic cellular processes, including apoptosis, cell division and immune responses. Ubiquitylation was originally associated with protein degradation, but it is now clear that ubiquitylation also mediates processes such as protein–protein interactions and cell signalling depending on the type of ubiquitin conjugation. Ubiquitin ligases (E3s) catalyse the final step of ubiquitin conjugation by transferring ubiquitin from ubiquitin-conjugating enzymes (E2s) to substrates. In humans, more than 600 E3s contribute to determining the fates of thousands of substrates; hence, E3s need to be tightly regulated to ensure accurate substrate ubiquitylation. Recent findings illustrate how E3s function on a structural level and how they coordinate with E2s and substrates to meticulously conjugate ubiquitin. Insights regarding the mechanisms of E3 regulation, including structural aspects of their autoinhibition and activation are also emerging

The stability of multitrophic communities under habitat loss

Habitat loss (HL) affects species and their interactions, ultimately altering community dynamics. Yet, a challenge for community ecology is to understand how communities with multiple interaction types—hybrid communities—respond to HL prior to species extinctions. To this end, we develop a model to investigate the response of hybrid terrestrial communities to two types of HL: random and contiguous. Our model reveals changes in stability—temporal variability in population abundances—that are dependent on the spatial configuration of HL. Our findings highlight that habitat area determines the variability of populations via changes in the distribution of species interaction strengths. The divergent responses of communities to random and contiguous HL result from different constraints imposed on individuals’ mobility, impacting diversity and network structure in the random case, and destabilising communities by increasing interaction strength in the contiguous case. Analysis of intermediate HL suggests a gradual transition between the two extreme cases

Planck 2015 results I. Overview of products and scientific results

The European Space Agency's Planck satellite, which is dedicated to studying the early Universe and its subsequent evolution, was launched on 14 May 2009. It scanned the microwave and submillimetre sky continuously between 12 August 2009 and 23 October 2013. In February 2015, ESA and the Planck Collaboration released the second set of cosmology products based on data from the entire Planck mission, including both temperature and polarization, along with a set of scientific and technical papers and a web-based explanatory supplement. This paper gives an overview of the main characteristics of the data and the data products in the release, as well as the associated cosmological and astrophysical science results and papers. The data products include maps of the cosmic microwave background (CMB), the thermal Sunyaev-Zeldovich effect, diffuse foregrounds in temperature and polarization, catalogues of compact Galactic and extragalactic sources (including separate catalogues of Sunyaev-Zeldovich clusters and Galactic cold clumps), and extensive simulations of signals and noise used in assessing uncertainties and the performance of the analysis methods. The likelihood code used to assess cosmological models against the Planck data is described, along with a CMB lensing likelihood. Scientific results include cosmological parameters derived from CMB power spectra, gravitational lensing, and cluster counts, as well as constraints on inflation, non-Gaussianity, primordial magnetic fields, dark energy, and modified gravity, and new results on low-frequency Galactic foregrounds