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Background: Circulating insulin and insulin-like growth factor-1 (IGF-1) concentrations are positively correlated with adiposity. However, the causal effects of insulin and IGF-1 on adiposity are unclear. Methods: We performed two-sample Mendelian randomization analyses to estimate the likely causal effects of fasting insulin and IGF-1 on relative childhood adiposity and adult body mass index (BMI). To improve accuracy and biological interpretation, we applied Steiger filtering (to avoid reverse causality) and ‘biological effect’ filtering of fasting insulin and IGF-1 associated variants. Results: Fasting insulin-increasing alleles (35 variants also associated with higherfasting glucose, indicative of insulin resistance) were associated with lower relative childhood adiposity (P= 3.8 × 10−3) and lower adult BMI (P= 1.4 × 10−5). IGF-1-increasing alleles also associated with tallerchildhood height (351 variants indicative of greater IGF-1 bioaction) showed no association with relative childhood adiposity (P= 0.077) or adult BMI (P= 0.562). Conversely, IGF-1-increasing alleles also associated with shorterchildhood height (306 variants indicative of IGF-1 resistance) were associated with lower relative childhood adiposity (P= 6.7 × 10−3), but effects on adult BMI were inconclusive. Conclusions: Genetic causal modelling indicates negative effects of insulin resistance on childhood and adult adiposity, and negative effects of IGF-1 resistance on childhood adiposity. Our findings demonstrate the need to distinguish between bioaction and resistance when modelling variants associated with biomarker concentrations.

Pubertal timing varies considerably and is associated with later health outcomes. We performed multi-ancestry genetic analyses on ~800,000 women, identifying 1,080 signals for age at menarche. Collectively, these explained 11% of trait variance in an independent sample. Women at the top and bottom 1% of polygenic risk exhibited ~11 and ~14-fold higher risks of delayed and precocious puberty, respectively. We identified several genes harboring rare loss-of-function variants in ~200,000 women, including variants in ZNF483, which abolished the impact of polygenic risk. Variant-to-gene mapping approaches and mouse gonadotropin-releasing hormone neuron RNA sequencing implicated 665 genes, including an uncharacterized G-protein-coupled receptor, GPR83, which amplified the signaling of MC3R, a key nutritional sensor. Shared signals with menopause timing at genes involved in DNA damage response suggest that the ovarian reserve might signal centrally to trigger puberty. We also highlight body size-dependent and independent mechanisms that potentially link reproductive timing to later life disease.

Precision medicine focuses on adapting care to the individual profile of patients, for example, accounting for their unique genetic makeup. Being able to account for the effect of genetic variation on the proteome holds great promise toward this goal. However, identifying the protein products of genetic variation using mass spectrometry has proven very challenging. Here we show that the identification of variant peptides can be improved by the integration of retention time and fragmentation predictors into a unified proteogenomic pipeline. By combining these intrinsic peptide characteristics using the search-engine post-processor Percolator, we demonstrate improved discrimination power between correct and incorrect peptide-spectrum matches. Our results demonstrate that the drop in performance that is induced when expanding a protein sequence database can be compensated, hence enabling efficient identification of genetic variation products in proteomics data. We anticipate that this enhancement of proteogenomic pipelines can provide a more refined picture of the unique proteome of patients and thereby contribute to improving patient care.

The timing of parturition is crucial for neonatal survival and infant health. Yet, its genetic basis remains largely unresolved. We present a maternal genome-wide meta-analysis of gestational duration (n= 195,555), identifying 22 associated loci (24 independent variants) and an enrichment in genes differentially expressed during labor. A meta-analysis of preterm delivery (18,797 cases, 260,246 controls) revealed seven associated loci and large genetic similarities with gestational duration. Analysis of the parental transmitted and nontransmitted alleles (n= 136,833) shows that 15 of the gestational duration genetic variants act through the maternal genome, whereas 7 act both through the maternal and fetal genomes and 2 act only via the fetal genome. Finally, the maternal effects on gestational duration show signs of antagonistic pleiotropy with the fetal effects on birth weight: maternal alleles that increase gestational duration have negative fetal effects on birth weight. The present study provides insights into the genetic effects on the timing of parturition and the complex maternal–fetal relationship between gestational duration and birth weight.

A well-functioning placenta is essential for fetal and maternal health throughout pregnancy. Using placental weight as a proxy for placental growth, we report genome-wide association analyses in the fetal (n= 65,405), maternal (n= 61,228) and paternal (n= 52,392) genomes, yielding 40 independent association signals. Twenty-six signals are classified as fetal, four maternal and three fetal and maternal. A maternal parent-of-origin effect is seen near KCNQ1. Genetic correlation and colocalization analyses reveal overlap with birth weight genetics, but 12 loci are classified as predominantly or only affecting placental weight, with connections to placental development and morphology, and transport of antibodies and amino acids. Mendelian randomization analyses indicate that fetal genetically mediated higher placental weight is causally associated with preeclampsia risk and shorter gestational duration. Moreover, these analyses support the role of fetal insulin in regulating placental weight, providing a key link between fetal and placental growth.

Mass spectrometry-based proteomics is a high-throughput technology generating ever-larger amounts of data per project. However, storing, processing, and interpreting these data can be a challenge. A key element in simplifying this process is the development of interactive frameworks focusing on visualization that can greatly simplify both the interpretation of data and the generation of new knowledge. Here we present PeptideShaker Online, a user-friendly web-based framework for the identification of mass spectrometry-based proteomics data, from raw file conversion to interactive visualization of the resulting data. Storage and processing of the data are performed via the versatile Galaxy platform (through SearchGUI, PeptideShaker, and moFF), while the interaction with the results happens via a locally installed web server, thus enabling researchers to process and interpret their own data without requiring advanced bioinformatics skills or direct access to compute-intensive infrastructures. The source code, additional documentation, and a fully functional demo is available at https://github.com/barsnes-group/peptide-shaker-online.

Randomization is used in experimental design to reduce the prevalence of unanticipated confounders. Complete randomization can however create imbalanced designs, for example, grouping all samples of the same condition in the same batch. Block randomization is an approach that can prevent severe imbalances in sample allocation with respect to both known and unknown confounders. This feature provides the reader with an introduction to blocking and randomization, and insights into how to effectively organize samples during experimental design, with special considerations with respect to proteomics.

Recurrent copy number variations (CNVs) are common causes of neurodevelopmental disorders (NDDs) and associated with a range of psychiatric traits. These CNVs occur at defined genomic regions that are particularly prone to recurrent deletions and duplications and often exhibit variable expressivity and incomplete penetrance. Robust estimates of the population prevalence and inheritance pattern of recurrent CNVs associated with neurodevelopmental disorders (NDD CNVs) are lacking. Here we perform array-based CNV calling in 12,252 mother–father–child trios from the Norwegian Mother, Father, and Child Cohort Study (MoBa) and analyse the inheritance pattern of 26 recurrent NDD CNVs in 13 genomic regions. We estimate the total prevalence of recurrent NDD CNVs (duplications and deletions) in live-born children to 0.48% (95% C.I.: 0.37–0.62%), i.e., ~1 in 200 newborns has either a deletion or duplication in these NDDs associated regions. Approximately a third of the newborn recurrent NDD CNVs (34%, N?=?20/59) are de novo variants. We provide prevalence estimates and inheritance information for each of the 26 NDD CNVs and find higher prevalence than previously reported for 1q21.1 deletions (~1:2000), 15q11.2 duplications (~1:4000), 15q13.3 microdeletions (~1:2500), 16p11.2 proximal microdeletions (~1:2000) and 17q12 deletions (~1:4000) and lower than previously reported prevalence for the 22q11.2 deletion (~1:12,000). In conclusion, our analysis of an unselected and representative population of newborns and their parents provides a clearer picture of the rate of recurrent microdeletions/duplications implicated in neurodevelopmental delay. These results will provide an important resource for genetic diagnostics and counseling.

A comprehensive characterization of the molecular processes controlling cell fate decisions is essential to derive stable progenitors and terminally differentiated cells that are functional from human pluripotent stem cells (hPSCs). Here, we report the use of quantitative proteomics to describe early proteome adaptations during hPSC differentiation toward pancreatic progenitors. We report that the use of unbiased quantitative proteomics allows the simultaneous profiling of numerous proteins at multiple time points, and is a valuable tool to guide the discovery of signaling events and molecular signatures underlying cellular differentiation. We also monitored the activity level of pathways whose roles are pivotal in the early pancreas differentiation, including the Hippo signaling pathway. The quantitative proteomics data set provides insights into the dynamics of the global proteome during the transition of hPSCs from a pluripotent state toward pancreatic differentiation. Proteome adaptations occur in waves during early stages of human pluripotent stem cell (hPSC) differentiation toward pancreatic progenitors. In this study, we utilized quantitative mass spectrometry‐based proteomics, an extremely useful tool, for the discovery of novel molecular signatures of intermediate stages during early pancreatic differentiation. Here, we uncovered a potential regulatory role of the Hippo signaling pathway during hPSC differentiation toward pancreatic progenitors.

The Nordic region, comprising primarily Denmark, Estonia, Finland, Iceland, Norway and Sweden, has many of the necessary characteristics for being at the forefront of genome-based precision medicine. These include egalitarian and universal healthcare, expertly curated patient and population registries, biobanks, large population-based prospective cohorts linked to registries and biobanks, and a widely embraced sense of social responsibility that motivates public engagement in biomedical research. However, genome-based precision medicine can be achieved only through coordinated action involving all actors in the healthcare sector. Now is an opportune time to organize scientists in the Nordic region, together with other stakeholders including patient representatives, governments, pharmaceutical companies, academic institutions and funding agencies, to initiate a Nordic Precision Medicine Initiative. We present a roadmap for how this organization can be created. The Initiative should facilitate research, clinical trials and knowledge transfer to meet regional and global health challenges.

Birth weight variation is influenced by fetal and maternal genetic and non-genetic factors, and has been reproducibly associated with future cardio-metabolic health outcomes. In expanded genome-wide association analyses of own birth weight (n= 321,223) and offspring birth weight (n= 230,069 mothers), we identified 190 independent association signals (129 of which are novel). We used structural equation modeling to decompose the contributions of direct fetal and indirect maternal genetic effects, then applied Mendelian randomization to illuminate causal pathways. For example, both indirect maternal and direct fetal genetic effects drive the observational relationship between lower birth weight and higher later blood pressure: maternal blood pressure-raising alleles reduce offspring birth weight, but only direct fetal effects of these alleles, once inherited, increase later offspring blood pressure. Using maternal birth weight-lowering genotypes to proxy for an adverse intrauterine environment provided no evidence that it causally raises offspring blood pressure, indicating that the inverse birth weight–blood pressure association is attributable to genetic effects, and not to intrauterine programming.

Biochemical pathways are commonly used as a reference to conduct functional analysis on biomedical omics data sets, where experimental results are mapped to knowledgebases comprising known molecular interactions collected from the literature. Due to their central role, the content of the functional knowledgebases directly influences the outcome of pathway analyses. In this study, we investigate the structure of the current pathway knowledge, as exemplified by Reactome, discuss the consequences for biological interpretation, and outline possible improvements in the use of pathway knowledgebases. By providing a view of the underlying protein interaction network, we aim to help pathway analysis users manage their expectations and better identify possible artifacts in the results.

Mass-spectrometry-based proteomics has become the standard approach for identifying and quantifying proteins. A vital step consists of analyzing experimentally generated mass spectra to identify the underlying peptide sequences for later mapping to the originating proteins. We here present the latest developments in SearchGUI, a common open-source interface for the most frequently used freely available proteomics search and de novo engines that has evolved into a central component in numerous bioinformatics workflows.

The first stable version of the Proteomics Standards Initiative mzIdentML open data standard (version 1.1) was published in 2012—capturing the outputs of peptide and protein identification software. In the intervening years, the standard has become well-supported in both commercial and open software, as well as a submission and download format for public repositories. Here we report a new release of mzIdentML (version 1.2) that is required to keep pace with emerging practice in proteome informatics. New features have been added to support: (1) scores associated with localization of modifications on peptides; (2) statistics performed at the level of peptides; (3) identification of cross-linked peptides; and (4) support for proteogenomics approaches. In addition, there is now improved support for the encoding of de novosequencing of peptides, spectral library searches, and protein inference. As a key point, the underlying XML schema has only undergone very minor modifications to simplify as much as possible the transition from version 1.1 to version 1.2 for implementers, but there have been several notable updates to the format specification, implementation guidelines, controlled vocabularies and validation software. mzIdentML 1.2 can be described as backwards compatible, in that reading software designed for mzIdentML 1.1 should function in most cases without adaptation. We anticipate that these developments will provide a continued stable base for software teams working to implement the standard. All the related documentation is accessible at http://www.psidev.info/mzidentml.

In the current study, we conducted a quantitative in-depth proteome and deglycoproteome analysis of cerebrospinal fluid (CSF) from relapsing-remitting multiple sclerosis (RRMS) and neurological controls using mass spectrometry and pathway analysis. More than 2000 proteins and 1700 deglycopeptides were quantified, with 484 proteins and 180 deglycopeptides significantly changed between pools of RRMS and pools of controls. Approximately 300 of the significantly changed proteins were assigned to various biological processes including inflammation, extracellular matrix organization, cell adhesion, immune response, and neuron development. Ninety-six significantly changed deglycopeptides mapped to proteins that were not found changed in the global protein study. In addition, four mapped to the proteins oligo-myelin glycoprotein and noelin, which were found oppositely changed in the global study. Both are ligands to the nogo receptor, and the glycosylation of these proteins appears to be affected by RRMS. Our study gives the most extensive overview of the RRMS affected processes observed from the CSF proteome to date, and the list of differential proteins will have great value for selection of biomarker candidates for further verification.

The use of proteomics bioinformatics substantially contributes to an improved understanding of proteomes, but this novel and in-depth knowledge comes at the cost of increased computational complexity. Parallelization across multiple computers, a strategy termed distributed computing, can be used to handle this increased complexity; however, setting up and maintaining a distributed computing infrastructure requires resources and skills that are not readily available to most research groups. Here we propose a free and open-source framework named Pladipus that greatly facilitates the establishment of distributed computing networks for proteomics bioinformatics tools. Pladipus is straightforward to install and operate thanks to its user-friendly graphical interface, allowing complex bioinformatics tasks to be run easily on a network instead of a single computer. As a result, any researcher can benefit from the increased computational efficiency provided by distributed computing, hence empowering them to tackle more complex bioinformatics challenges. Notably, it enables any research group to perform large-scale reprocessing of publicly available proteomics data, thus supporting the scientific community in mining these data for novel discoveries.

Modern analytical techniques provide an unprecedented insight to biomedical samples, allowing an in depth characterization of cells or body fluids, to the level of genes, transcripts, peptides, proteins, metabolites, or metallic ions. The fine grained picture provided by such approaches holds the promise for a better understanding of complex pathologies, and consequently the personalization of diagnosis, prognosis and treatment procedures. In practice however, technical limitations restrict the resolution of the acquired data, and thus of downstream biomedical inference. As a result, the study of complex diseases like leukemia and other types of cancer is impaired by the high heterogeneity of pathologies as well as patient profiles. In this review, we propose an introduction to the general approach of characterizing samples and inferring biomedical results. We highlight the main limitations of the technique with regards to complex and heterogeneous pathologies, and provide ways to overcome these by improving the ability of experiments in discriminating samples.

Hypertensive nephrosclerosis is one of the most frequent causes of chronic kidney failure. Proteome analysis potentially improves the pathophysiological understanding and diagnostic precision of this disorder. In the present exploratory study, we investigated experimental nephrosclerosis in the two-kidney, one-clip (2K1C) hypertensive rat model.

Patients with carboxyl-ester lipase-maturity-onset diabetes of the young (CEL-MODY) display distinct disease stages toward the development of monogenic diabetes and exocrine pancreatic disease. The finding of differentially increased proteins, some related to MAPK signaling, in a discovery proteomics study of secretin-stimulated duodenal juice in three CEL-MODY patients, prompted us to monitor their abundance in an extensive number of CEL-MODY subjects at different disease stages and controls using targeted proteomics. In the current study, we demonstrate the feasibility of selected reaction monitoring assays to quantify protein levels in secretin-stimulated duodenal juice. Furthermore, we define a set of five peptides for potential use as diagnostic tests in CEL-MODY patients. Finally, we propose a further set of seven proteins with a likely pathogenic role in CEL-MODY disease progression.

De novosequencing is a popular technique in proteomics for identifying peptides from tandem mass spectra without having to rely on a protein sequence database. Despite the strong potential of de novosequencing algorithms, their adoption threshold remains quite high. We here present a user-friendly and lightweight graphical user interface called DeNovoGUI for running parallelized versions of the freely available de novosequencing software PepNovo+, greatly simplifying the use of de novosequencing in proteomics. Our platform-independent software is freely available under the permissible Apache2 open source license. Source code, binaries, and additional documentation are available at http://denovogui.googlecode.com.

One of the most important physiological platelet inhibitors is endothelium-derived prostacyclin which stimulates the platelet cyclic adenosine monophosphate/protein kinase A (cAMP/PKA)–signaling cascade and inhibits virtually all platelet-activating key mechanisms. Using quantitative mass spectrometry, we analyzed time-resolved phosphorylation patterns in human platelets after treatment with iloprost, a stable prostacyclin analog, for 0, 10, 30, and 60 seconds to characterize key mediators of platelet inhibition and activation in 3 independent biological replicates. We quantified over 2700 different phosphorylated peptides of which 360 were significantly regulated upon stimulation. This comprehensive and time-resolved analysis indicates that platelet inhibition is a multipronged process involving different kinases and phosphatases as well as many previously unanticipated proteins and pathways.

Antiplatelet treatment is of fundamental importance in combatting functions/dysfunction of platelets in the pathogenesis of cardiovascular and inflammatory diseases. Dysfunction of anucleate platelets is likely to be completely attributable to alterations in posttranslational modifications and protein expression. We therefore examined the proteome of platelets highly purified from fresh blood donations, using elaborate protocols to ensure negligible contamination by leukocytes, erythrocytes, and plasma. Using quantitative mass spectrometry, we created the first comprehensive and quantitative human platelet proteome, comprising almost 4000 unique proteins, estimated copy numbers for ∼ 3700 of those, and assessed intersubject (4 donors) as well as intrasubject (3 different blood samples from 1 donor) variations of the proteome. For the first time, our data allow for a systematic and weighted appraisal of protein networks and pathways in human platelets, and indicate the feasibility of differential and comprehensive proteome analyses from small blood donations. Because 85% of the platelet proteome shows no variation between healthy donors, this study represents the starting point for disease-oriented platelet proteomics. In the near future, comprehensive and quantitative comparisons between normal and well-defined dysfunctional platelets, or between platelets obtained from donors at various stages of chronic cardiovascular and inflammatory diseases will be feasible.