Is label-free LC-MS/MS ready for biomarker discovery?

Label-free LC-MS methods are attractive for high-throughput quantitative proteomics, as the sample processing is straightforward and can be scaled to a large number of samples. Label-free methods therefore facilitate biomarker discovery in studies involving dozens of clinical samples. However, despite the increased popularity of label-free workflows, there is a hesitance in the research community to use it in clinical proteomics studies. Therefore, we here discuss pros and cons of label free LC-MS/MS for biomarker discovery, and delineate the main prerequisites for its successful employment. Furthermore, we cite studies where label-free LC-MS/MS was successfully used to identify novel biomarkers, and foresee an increased acceptance of label-free techniques by the proteomics community in the near future. This article is protected by copyright. All rights reserved

Intact salicylic acid signalling is required for potato defence against the necrotrophic fungus Alternaria solani.

Background In order to get global molecular understanding of one of the most important crop diseases worldwide, we investigated compatible and incompatible interactions between Phytophthora infestans and potato (Solanum tuberosum). We used the two most field-resistant potato clones under Swedish growing conditions, which have the greatest known local diversity of P. infestans populations, and a reference compatible cultivar. Results Quantitative label-free proteomics of 51 apoplastic secretome samples (PXD000435) in combination with genome-wide transcript analysis by 42 microarrays (E-MTAB-1515) were used to capture changes in protein abundance and gene expression at 6, 24 and 72 hours after inoculation with P. infestans. To aid mass spectrometry analysis we generated cultivar-specific RNA-seq data (E-MTAB-1712), which increased peptide identifications by 17%. Components induced only during incompatible interactions, which are candidates for hypersensitive response initiation, include a Kunitz-like protease inhibitor, transcription factors and an RCR3-like protein. More secreted proteins had lower abundance in the compatible interaction compared to the incompatible interactions. Based on this observation and because the well-characterized effector-target C14 protease follows this pattern, we suggest 40 putative effector targets. Conclusions In summary, over 17000 transcripts and 1000 secreted proteins changed in abundance in at least one time point, illustrating the dynamics of plant responses to a hemibiotroph. Half of the differentially abundant proteins showed a corresponding change at the transcript level. Many putative hypersensitive and effector-target proteins were single representatives of large gene families

Plasma lipid composition and risk of developing cardiovascular disease.

We tested whether characteristic changes of the plasma lipidome in individuals with comparable total lipids level associate with future cardiovascular disease (CVD) outcome and whether 23 validated gene variants associated with coronary artery disease (CAD) affect CVD associated lipid species

Identification of common genetic risk variants for autism spectrum disorder

Autism spectrum disorder (ASD) is a highly heritable and heterogeneous group of neurodevelopmental phenotypes diagnosed in more than 1% of children. Common genetic variants contribute substantially to ASD susceptibility, but to date no individual variants have been robustly associated with ASD. With a marked sample-size increase from a unique Danish population resource, we report a genome-wide association meta-analysis of 18,381 individuals with ASD and 27,969 controls that identified five genome-wide-significant loci. Leveraging GWAS results from three phenotypes with significantly overlapping genetic architectures (schizophrenia, major depression, and educational attainment), we identified seven additional loci shared with other traits at equally strict significance levels. Dissecting the polygenic architecture, we found both quantitative and qualitative polygenic heterogeneity across ASD subtypes. These results highlight biological insights, particularly relating to neuronal function and corticogenesis, and establish that GWAS performed at scale will be much more productive in the near term in ASD

Genomic Relationships, Novel Loci, and Pleiotropic Mechanisms across Eight Psychiatric Disorders

Genetic influences on psychiatric disorders transcend diagnostic boundaries, suggesting substantial pleiotropy of contributing loci. However, the nature and mechanisms of these pleiotropic effects remain unclear. We performed analyses of 232,964 cases and 494,162 controls from genome-wide studies of anorexia nervosa, attention-deficit/hyper-activity disorder, autism spectrum disorder, bipolar disorder, major depression, obsessive-compulsive disorder, schizophrenia, and Tourette syndrome. Genetic correlation analyses revealed a meaningful structure within the eight disorders, identifying three groups of inter-related disorders. Meta-analysis across these eight disorders detected 109 loci associated with at least two psychiatric disorders, including 23 loci with pleiotropic effects on four or more disorders and 11 loci with antagonistic effects on multiple disorders. The pleiotropic loci are located within genes that show heightened expression in the brain throughout the lifespan, beginning prenatally in the second trimester, and play prominent roles in neurodevelopmental processes. These findings have important implications for psychiatric nosology, drug development, and risk prediction.Peer reviewe

Quality Control for high-throughput Quantitative Proteomics - Harnessing the potential of label-free LC-MS

Multiplex protein quantification, proteomics, is essential for uncovering new biomarkers and understanding biology. Liquid chromatography coupled to mass spectrometry (LC-MS) is the predominant technique for these measurements. In this thesis, optimization and quality control strategies for different phases of the LC-MS pipeline have been developed. Due to the inherent complexity and dynamic range of the proteome, pre-fractionation is commonly employed in a general LC-MS setting to uncover low-abundant proteins. In paper I, we performed a (qualitative) comparative study of the proteins identified in a shotgun setup. It was found that SDS-PAGE, coupled to tryptic digestion, had the largest yield. In paper II, we introduced a quantitative quality control method on peptide level for the relatively computationally demanding, but experimentally high-throughput, label-free LC-MS workflow, where we discovered both considerable differences as well as complementary properties between software solutions. The two integral parts of label-free data processing, feature detection (peptide quantification) and alignment (peptide identity propagation between samples), were further examined in papers III and IV. The complementary quality of software modules discovered in paper II was utilized in paper III, where we showed that a combination of different feature detection methods results in higher peptide coverage beneficial for downstream statistical inference. Since the establishment of proteomics as a high-throughput science, large-scale bioinformatics has become necessity. In paper IV, an alignment algorithm was developed where parameters are estimated on the fly from underlying data, an important step towards avoiding turning data processing into the future bottleneck of proteomics. Finally, in paper V, we performed an extensive evaluation of the relative potential of discovery and validation LC-MS (shotgun and SRM), based on the method introduced in paper II. Extensive data handling optimization was performed from normalization to quantification and statistical implications were assessed. In summary, we have shown that large-scale label-free LC-MS can be combined with equally high-throughput quality control to create a competitive option in the hunt for biomarkers

A Breast Cell Atlas: Organelle analysis of the MDA-MB-231 cell line by density-gradient fractionation using isotopic marking and label-free analysis

Protein translocation between organelles in the cell is an important process that regulates many cellular functions. However, organelles can rarely be isolated to purity so several methods have been developed to analyse the fractions obtained by density gradient centrifugation. We present an analysis of the distribution of proteins amongst organelles in the human breast cell line, MDA-MB-231 using two approaches: an isotopic labelling and a label-free approach

Data processing methods and quality control strategies for label-free LC-MS protein quantification.

Protein quantification using different LC-MS techniques is becoming a standard practice. However, with a multitude of experimental setups to choose from, as well as a wide array of software solutions for subsequent data processing, it is non-trivial to select the most appropriate workflow for a given biological question. In this review, we highlight different issues that need to be addressed by software for quantitative LC-MS experiments and describe different approaches that are available. With focus on label-free quantification, examples are discussed both for LC-MS/MS and LC-SRM data processing. We further elaborate on current quality control methodology for performing accurate protein quantification experiments. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era

Quantitative proteomics and transcriptomics of potato in response to Phytophthora infestans in compatible and incompatible interactions

Background: In order to get global molecular understanding of one of the most important crop diseases worldwide, we investigated compatible and incompatible interactions between Phytophthora infestans and potato (Solanum tuberosum). We used the two most field-resistant potato clones under Swedish growing conditions, which have the greatest known local diversity of P. infestans populations, and a reference compatible cultivar.Results: Quantitative label-free proteomics of 51 apoplastic secretome samples (PXD000435) in combination with genome-wide transcript analysis by 42 microarrays (E-MTAB-1515) were used to capture changes in protein abundance and gene expression at 6, 24 and 72 hours after inoculation with P. infestans. To aid mass spectrometry analysis we generated cultivar-specific RNA-seq data (E-MTAB-1712), which increased peptide identifications by 17%. Components induced only during incompatible interactions, which are candidates for hypersensitive response initiation, include a Kunitz-like protease inhibitor, transcription factors and an RCR3-like protein. More secreted proteins had lower abundance in the compatible interaction compared to the incompatible interactions. Based on this observation and because the well-characterized effector-target C14 protease follows this pattern, we suggest 40 putative effector targets.Conclusions: In summary, over 17000 transcripts and 1000 secreted proteins changed in abundance in at least one time point, illustrating the dynamics of plant responses to a hemibiotroph. Half of the differentially abundant proteins showed a corresponding change at the transcript level. Many putative hypersensitive and effector-target proteins were single representatives of large gene families