Bioinformatics pipeline for the systematic mining genomic and proteomic variation linked to rare diseases: The example of monogenic diabetes

Monogenic diabetes is characterized as a group of diseases caused by rare variants in single genes. Like for other rare diseases, multiple genes have been linked to monogenic diabetes with different measures of pathogenicity, but the information on the genes and variants is not unified among different resources, making it challenging to process them informatically. We have developed an automated pipeline for collecting and harmonizing data on genetic variants linked to monogenic diabetes. Furthermore, we have translated variant genetic sequences into protein sequences accounting for all protein isoforms and their variants. This allows researchers to consolidate information on variant genes and proteins linked to monogenic diabetes and facilitates their study using proteomics or structural biology. Our open and flexible implementation using Jupyter notebooks enables tailoring and modifying the pipeline and its application to other rare diseases.

Smoking during pregnancy and its effect on placental weight: a Mendelian randomization study

BACKGROUND

The causal relationship between maternal smoking in pregnancy and reduced offspring birth weight is well established and is likely due to impaired placental function. However, observational studies have given conflicting results on the association between smoking and placental weight. We aimed to estimate the causal effect of newly pregnant mothers quitting smoking on their placental weight at the time of delivery.

METHODS

We used one-sample Mendelian randomization, drawing data from the Avon Longitudinal Study of Parents and Children (ALSPAC) (N = 690 to 804) and the Norwegian Mother, Father and Child Cohort Study (MoBa) (N = 4267 to 4606). The sample size depends on the smoking definition used for different analyses. The analysis was performed in pre-pregnancy smokers only, due to the specific role of the single-nucleotide polymorphism (SNP) rs1051730 (CHRNA5 - CHRNA3 - CHRNB4) in affecting smoking cessation but not initiation.

RESULTS

Fixed effect meta-analysis showed a 182 g [95%CI: 29,335] higher placental weight for pre-pregnancy smoking mothers who continued smoking at the beginning of pregnancy, compared with those who stopped smoking. Using the number of cigarettes smoked per day in the first trimester as the exposure, the causal effect on placental weight was 11 g [95%CI: 1,21] per cigarette per day. Similarly, smoking at the end of pregnancy was causally associated with higher placental weight. Using the residuals of birth weight regressed on placental weight as the outcome, we showed evidence of lower offspring birth weight relative to the placental weight, both for continuing smoking at the start of pregnancy as well as continuing smoking throughout pregnancy (change in z-score birth weight adjusted for z-score placental weight: -0.8 [95%CI: -1.6,-0.1]).

CONCLUSION

Our results suggest that continued smoking during pregnancy causes higher placental weights.

Rare copy number variation in autoimmune Addison's disease

Autoimmune Addison's disease (AAD) is a rare but life-threatening endocrine disorder caused by an autoimmune destruction of the adrenal cortex. A previous genome-wide association study (GWAS) has shown that common variants near immune-related genes, which mostly encode proteins participating in the immune response, affect the risk of developing this condition. However, little is known about the contribution of copy number variations (CNVs) to AAD susceptibility. We used the genome-wide genotyping data from Norwegian and Swedish individuals (1,182 cases and 3,810 controls) to investigate the putative role of CNVs in the AAD aetiology. Although the frequency of rare CNVs was similar between cases and controls, we observed that larger deletions (>1,000 kb) were more common among patients (OR = 4.23, 95% CI 1.85-9.66, p = 0.0002). Despite this, none of the large case-deletions were conclusively pathogenic, and the clinical presentation and an AAD-polygenic risk score were similar between cases with and without the large CNVs. Among deletions exclusive to individuals with AAD, we highlight two ultra-rare deletions in the genes LRBA and BCL2L11, which we speculate might have contributed to the polygenic risk in these carriers. In conclusion, rare CNVs do not appear to be a major cause of AAD but further studies are needed to ascertain the potential contribution of rare deletions to the polygenic load of AAD susceptibility.

Genome-wide association study of placental weight identifies distinct and shared genetic influences between placental and fetal growth

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.

Automated splitting into batches for observational biomedical studies with sequential processing

Experimental design usually focuses on the setting where treatments and/or other aspects of interest can be manipulated. However, in observational biomedical studies with sequential processing, the set of available samples is often fixed, and the problem is thus rather the ordering and allocation of samples to batches such that comparisons between different treatments can be made with similar precision. In certain situations, this allocation can be done by hand, but this rapidly becomes impractical with more challenging cohort setups. Here, we present a fast and intuitive algorithm to generate balanced allocations of samples to batches for any single-variable model where the treatment variable is nominal. This greatly simplifies the grouping of samples into batches, makes the process reproducible, and provides a marked improvement over completely random allocations. The general challenges of allocation and why good solutions can be hard to find are also discussed, as well as potential extensions to multivariable settings.

Retention Time and Fragmentation Predictors Increase Confidence in Identification of Common Variant Peptides

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.

Extending protein interaction networks using proteoforms and small molecules

MOTIVATION

Biological network analysis for high-throughput biomedical data interpretation relies heavily on topological characteristics. Networks are commonly composed of nodes representing genes or proteins that are connected by edges when interacting. In this study, we use the rich information available in the Reactome pathway database to build biological networks accounting for small molecules and proteoforms modeled using protein isoforms and post-translational modifications to study the topological changes induced by this refinement of the network representation.

RESULTS

We find that improving the interactome modeling increases the number of nodes and interactions, but that isoform and post-translational modification annotation is still limited compared to what can be expected biologically. We also note that small molecule information can distort the topology of the network due to the high connectedness of these molecules, which does not necessarily represent the reality of biology. However, by restricting the connections of small molecules to the context of biochemical reactions, we find that these improve the overall connectedness of the network and reduce the prevalence of isolated components and nodes. Overall, changing the representation of the network alters the prevalence of articulation points and bridges globally but also within and across pathways. Hence, some molecules can gain or lose in biological importance depending on the level of detail of the representation of the biological system, which might in turn impact network-based studies of diseases or druggability.

AVAILABILITY AND IMPLEMENTATION

Networks are constructed based on data publicly available in the Reactome Pathway knowledgebase: reactome.org.

Smoking during pregnancy and its effect on placental weight: A Mendelian randomization study

Background

The causal relationship between maternal smoking in pregnancy and reduced offspring birth weight is well established and is likely due to impaired placental function. However, observational studies have given conflicting results on the association between smoking and placental weight. We aimed to estimate the causal effect of newly pregnant mothers quitting smoking on their placental weight at the time of delivery.

Methods

We used one-sample Mendelian randomization, drawing data from the Avon Longitudinal Study of Parents and Children (ALSPAC) (up to N = 805) and the Norwegian Mother, Father and Child Cohort Study (MoBa) (up to N = 4475). The analysis was performed in pre-pregnancy smokers only, due to the specific role of the genetic instrument SNP rs1051730 (CHRNA5 - CHRNA3 - CHRNB4) in affecting smoking cessation but not initiation.

Results

Fixed effect meta-analysis showed a 175 g [95%CI: 16, 334] higher placental weight for pre-pregnancy smoking mothers who continued smoking at the beginning of pregnancy, compared with those who stopped smoking. Using the number of cigarettes smoked per day in the first trimester as the exposure, the causal estimate was a 12 g [95%CI: 2,22] higher placental weight per cigarette per day. Results were similar when the smoking exposures were measured at the end of pregnancy. Using the residuals of birth weight regressed on placental weight as the outcome, we showed weak evidence of lower offspring birth weight relative to the placental weight for continuing smoking.

Conclusion

Our results suggest that continued smoking during pregnancy causes higher placental weights.

Understanding the genetic complexity of puberty timing across the allele frequency spectrum

Pubertal timing varies considerably and has been associated with a range of health outcomes in later life. To elucidate the underlying biological mechanisms, we performed multi-ancestry genetic analyses in ~800,000 women, identifying 1,080 independent signals associated with age at menarche. Collectively these loci explained 11% of the trait variance in an independent sample, with women at the top and bottom 1% of polygenic risk exhibiting a ~11 and ~14-fold higher risk of delayed and precocious pubertal development, respectively. These common variant analyses were supported by exome sequence analysis of ~220,000 women, identifying several genes, including rare loss of function variants in ZNF483 which abolished the impact of polygenic risk. Next, we implicated 660 genes in pubertal development using a combination of in silico variant-to-gene mapping approaches and integration with dynamic gene expression data from mouse embryonic GnRH neurons. This included an uncharacterized G-protein coupled receptor GPR83, which we demonstrate amplifies signaling of MC3R, a key sensor of nutritional status. Finally, we identified several genes, including ovary-expressed genes involved in DNA damage response that co-localize with signals associated with menopause timing, leading us to hypothesize that the ovarian reserve might signal centrally to trigger puberty. Collectively these findings extend our understanding of the biological complexity of puberty timing and highlight body size dependent and independent mechanisms that potentially link reproductive timing to later life disease.

Genetic effects on the timing of parturition and links to fetal birth weight

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 six 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.

Finding haplotypic signatures in proteins

BACKGROUND

The nonrandom distribution of alleles of common genomic variants produces haplotypes, which are fundamental in medical and population genetic studies. Consequently, protein-coding genes with different co-occurring sets of alleles can encode different amino acid sequences: protein haplotypes. These protein haplotypes are present in biological samples and detectable by mass spectrometry, but they are not accounted for in proteomic searches. Consequently, the impact of haplotypic variation on the results of proteomic searches and the discoverability of peptides specific to haplotypes remain unknown.

FINDINGS

Here, we study how common genetic haplotypes influence the proteomic search space and investigate the possibility to match peptides containing multiple amino acid substitutions to a publicly available data set of mass spectra. We found that for 12.42% of the discoverable amino acid substitutions encoded by common haplotypes, 2 or more substitutions may co-occur in the same peptide after tryptic digestion of the protein haplotypes. We identified 352 spectra that matched to such multivariant peptides, and out of the 4,582 amino acid substitutions identified, 6.37% were covered by multivariant peptides. However, the evaluation of the reliability of these matches remains challenging, suggesting that refined error rate estimation procedures are needed for such complex proteomic searches.

CONCLUSIONS

As these procedures become available and the ability to analyze protein haplotypes increases, we anticipate that proteomics will provide new information on the consequences of common variation, across tissues and time.

Spatial Environment Affects HNF4A Mutation-Specific Proteome Signatures and Cellular Morphology in hiPSC-Derived β-Like Cells

Studies of monogenic diabetes are particularly useful because we can gain insight into the molecular events of pancreatic β-cell failure. Maturity-onset diabetes of the young 1 (MODY1) is a form of monogenic diabetes caused by a mutation in the HNF4A gene. Human-induced pluripotent stem cells (hiPSCs) provide an excellent tool for disease modeling by subsequently directing differentiation toward desired pancreatic islet cells, but cellular phenotypes in terminally differentiated cells are notoriously difficult to detect. Re-creating a spatial (three-dimensional [3D]) environment may facilitate phenotype detection. We studied MODY1 by using hiPSC-derived pancreatic β-like patient and isogenic control cell lines in two different 3D contexts. Using size-adjusted cell aggregates and alginate capsules, we show that the 3D context is critical to facilitating the detection of mutation-specific phenotypes. In 3D cell aggregates, we identified irregular cell clusters and lower levels of structural proteins by proteome analysis, whereas in 3D alginate capsules, we identified altered levels of glycolytic proteins in the glucose sensing apparatus by proteome analysis. Our study provides novel knowledge on normal and abnormal function of HNF4A, paving the way for translational studies of new drug targets that can be used in precision diabetes medicine in MODY.

Characterization of the genetic architecture of infant and early childhood body mass index

Early childhood obesity is a growing global concern; however, the role of common genetic variation on infant and child weight development is unclear. Here, we identify 46 loci associated with early childhood body mass index at specific ages, matching different child growth phases, and representing four major trajectory patterns. We perform genome-wide association studies across 12 time points from birth to 8 years in 28,681 children and their parents (27,088 mothers and 26,239 fathers) in the Norwegian Mother, Father and Child Cohort Study. Monogenic obesity genes are overrepresented near identified loci, and several complex association signals near LEPR, GLP1R, PCSK1 and KLF14 point towards a major influence for common variation affecting the leptin-melanocortin system in early life, providing a link to putative treatment strategies. We also demonstrate how different polygenic risk scores transition from birth to adult profiles through early child growth. In conclusion, our results offer a fine-grained characterization of a changing genetic landscape sustaining early childhood growth.

PeptideShaker Online: A User-Friendly Web-Based Framework for the Identification of Mass Spectrometry-Based Proteomics Data

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.

Placental weight centiles adjusted for age, parity and fetal sex

INTRODUCTION

The weight of the placenta can be indicative of efficacy in nutrient and oxygen supply. Furthermore, it has been suggested that a measure of the placenta's ability to adequately supply nutrients to the fetus can be found in the relationship between birth weight and placental weight expressed as a ratio. Our aim was to develop age adjusted placenta weight and birth weight to placenta weight ratio reference curves that are stratified by maternal parity and fetal sex.

METHODS

We included singleton, non-anomalous births with a gestational age inclusive of 28 + 0 weeks to 42 + 6 weeks. Excluded were pregnancies of multiplicity, fetuses with congenital abnormalities, stillbirths and pregnancies that had placental complications (ie placenta previa or abruption). Generalised additive model for location, shape and scale (GAMLSS) was used to fit reference curves.

RESULTS

We stratified 97,882 pregnancies by maternal nulliparity status and fetal sex. Extensive assessment model goodness-of-fit showed appropriate modeling and accurate fit to the four parameters of distribution. Our results show accurate model fit of the reference curves to the data. We demonstrated that the influence that parity has on the placenta weight is far greater than that exerted by fetal sex, and that the difference is dependent on gestational age.

DISCUSSION

This is the largest presentation of age and parity adjusted placenta weight and feto-placental weight ratio reference ranges to date. The difference observed between nulliparous and multiparous pregnancies could be explained by biological memory and the remnants of maternal endo-myometrial vascularity after the first pregnancy.

Autozygosity mapping and time-to-spontaneous delivery in Norwegian parent-offspring trios

Parental genetic relatedness may lead to adverse health and fitness outcomes in the offspring. However, the degree to which it affects human delivery timing is unknown. We use genotype data from ≃25 000 parent-offspring trios from the Norwegian Mother, Father and Child Cohort Study to optimize runs of homozygosity (ROH) calling by maximizing the correlation between parental genetic relatedness and offspring ROHs. We then estimate the effect of maternal, paternal and fetal autozygosity and that of autozygosity mapping (common segments and gene burden test) on the timing of spontaneous onset of delivery. The correlation between offspring ROH using a variety of parameters and parental genetic relatedness ranged between −0.2 and 0.6, revealing the importance of the minimum number of genetic variants included in an ROH and the use of genetic distance. The optimized compared to predefined parameters showed a ≃45% higher correlation between parental genetic relatedness and offspring ROH. We found no evidence of an effect of maternal, paternal nor fetal overall autozygosity on spontaneous delivery timing. Yet, through autozygosity mapping, we identified three maternal loci TBC1D1, SIGLECs and EDN1 gene regions reducing the median time-to-spontaneous onset of delivery by ≃2–5% (P-value < 2.3 × 10⁻⁶). We also found suggestive evidence of a fetal locus at 3q22.2, near the RYK gene region (P-value = 2.0 × 10⁻⁶). Autozygosity mapping may provide new insights on the genetic determinants of delivery timing beyond traditional genome-wide association studies, but particular and rigorous attention should be given to ROH calling parameter selection.

Leptin Receptor Signaling Regulates Protein Synthesis Pathways and Neuronal Differentiation in Pluripotent Stem Cells

The role of leptin receptor (OB-R) signaling in linking pluripotency with growth and development and the consequences of dysfunctional leptin signaling on progression of metabolic disease is poorly understood. Using a global unbiased proteomics approach we report that embryonic fibroblasts (MEFs) carrying the db/db mutation exhibit metabolic abnormalities, while their reprogrammed induced pluripotent stem cells (iPSCs) show altered expression of proteins involved in embryonic development. An upregulation in expression of eukaryotic translation initiation factor 4e (Eif4e) and Stat3 binding to the Eif4e promoter was supported by enhanced protein synthesis in mutant iPSCs. Directed differentiation of db/db iPSCs toward the neuronal lineage showed defects. Gene editing to correct the point mutation in db/db iPSCs using CRISPR-Cas9, restored expression of neuronal markers and protein synthesis while reversing the metabolic defects. These data imply a direct role for OB-R in regulating metabolism in embryonic fibroblasts and key developmental pathways in iPSCs.

•

Pluripotency markers are decreased in db/db iPSCs (lacking functional OB-R)

•

Mouse db/db iPSCs exhibit higher protein synthesis mediated by the Stat3/Eif4e axis

•

OB-R signaling regulates neuronal development markers—NOGGIN, NESTIN, GFAP

•

CRISPR correction reverses defects in db/db iPSCs

Importance of Block Randomization When Designing Proteomics Experiments

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.

Novel loci for childhood body mass index and shared heritability with adult cardiometabolic traits

The genetic background of childhood body mass index (BMI), and the extent to which the well-known associations of childhood BMI with adult diseases are explained by shared genetic factors, are largely unknown. We performed a genome-wide association study meta-analysis of BMI in 61,111 children aged between 2 and 10 years. Twenty-five independent loci reached genome-wide significance in the combined discovery and replication analyses. Two of these, located near NEDD4L and SLC45A3, have not previously been reported in relation to either childhood or adult BMI. Positive genetic correlations of childhood BMI with birth weight and adult BMI, waist-to-hip ratio, diastolic blood pressure and type 2 diabetes were detected (Rg ranging from 0.11 to 0.76, P-values <0.002). A negative genetic correlation of childhood BMI with age at menarche was observed. Our results suggest that the biological processes underlying childhood BMI largely, but not completely, overlap with those underlying adult BMI. The well-known observational associations of BMI in childhood with cardio-metabolic diseases in adulthood may reflect partial genetic overlap, but in light of previous evidence, it is also likely that they are explained through phenotypic continuity of BMI from childhood into adulthood.

Anatomy and evolution of database search engines-a central component of mass spectrometry based proteomic workflows

Sequence database search engines are bioinformatics algorithms that identify peptides from tandem mass spectra using a reference protein sequence database. Two decades of development, notably driven by advances in mass spectrometry, have provided scientists with more than 30 published search engines, each with its own properties. In this review, we present the common paradigm behind the different implementations, and its limitations for modern mass spectrometry datasets. We also detail how the search engines attempt to alleviate these limitations, and provide an overview of the different software frameworks available to the researcher. Finally, we highlight alternative approaches for the identification of proteomic mass spectrometry datasets, either as a replacement for, or as a complement to, sequence database search engines.

Proteome and Phosphoproteome Changes Associated with Prognosis in Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is a hematological cancer that mainly affects the elderly. Although complete remission (CR) is achieved for the majority of the patients after induction and consolidation therapies, nearly two-thirds relapse within a short interval. Understanding biological factors that determine relapse has become of major clinical interest in AML. We utilized liquid chromatography tandem mass spectrometry (LC-MS/MS) to identify the protein changes and protein phosphorylation events associated with AML relapse in primary cells from 41 AML patients at time of diagnosis. Patients were defined as relapse-free if they had not relapsed within a five-year clinical follow-up after AML diagnosis. Relapse was associated with increased expression of RNA processing proteins and decreased expression of V-ATPase proteins. We also observed an increase in phosphorylation events catalyzed by cyclin-dependent kinases (CDKs) and casein kinase 2 (CSK2). The biological relevance of the proteome findings was supported by cell proliferation assays using inhibitors of V-ATPase (bafilomycin), CSK2 (CX-4945), CDK4/6 (abemaciclib) and CDK2/7/9 (SNS-032). While bafilomycin preferentially inhibited the cells from relapse patients, the kinase inhibitors were less efficient in these cells. This suggests that therapy against the upregulated kinases could also target the factors inducing their upregulation rather than their activity. This study, therefore, presents markers that could help predict AML relapse and direct therapeutic strategies.

PDV: an integrative proteomics data viewer

SUMMARY

Data visualization plays critical roles in proteomics studies, ranging from quality control of MS/MS data to validation of peptide identification results. Herein, we present PDV, an integrative proteomics data viewer that can be used to visualize a wide range of proteomics data, including database search results, de novo sequencing results, proteogenomics files, MS/MS data in mzML/mzXML format and data from public proteomics repositories. PDV is a lightweight visualization tool that enables intuitive and fast exploration of diverse, large-scale proteomics datasets on standard desktop computers in both graphical user interface and command line modes.

AVAILABILITY AND IMPLEMENTATION

PDV software and the user manual are freely available at http://pdv.zhang-lab.org. The source code is available at https://github.com/wenbostar/PDV and is released under the GPL-3 license.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Dynamic proteome profiling of human pluripotent stem cell-derived pancreatic progenitors

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.

A trans-ancestral meta-analysis of genome-wide association studies reveals loci associated with childhood obesity

Although hundreds of genome-wide association studies-implicated loci have been reported for adult obesity-related traits, less is known about the genetics specific for early-onset obesity and with only a few studies conducted in non-European populations to date. Searching for additional genetic variants associated with childhood obesity, we performed a trans-ancestral meta-analysis of 30 studies consisting of up to 13 005 cases (≥95th percentile of body mass index (BMI) achieved 2-18 years old) and 15 599 controls (consistently <50th percentile of BMI) of European, African, North/South American and East Asian ancestry. Suggestive loci were taken forward for replication in a sample of 1888 cases and 4689 controls from seven cohorts of European and North/South American ancestry. In addition to observing 18 previously implicated BMI or obesity loci, for both early and late onset, we uncovered one completely novel locus in this trans-ancestral analysis (nearest gene, METTL15). The variant was nominally associated with only the European subgroup analysis but had a consistent direction of effect in other ethnicities. We then utilized trans-ancestral Bayesian analysis to narrow down the location of the probable causal variant at each genome-wide significant signal. Of all the fine-mapped loci, we were able to narrow down the causative variant at four known loci to fewer than 10 single nucleotide polymorphisms (SNPs) (FAIM2, GNPDA2, MC4R and SEC16B loci). In conclusion, an ethnically diverse setting has enabled us to both identify an additional pediatric obesity locus and further fine-map existing loci.

PathwayMatcher: proteoform-centric network construction enables fine-granularity multiomics pathway mapping

BACKGROUND

Mapping biomedical data to functional knowledge is an essential task in bioinformatics and can be achieved by querying identifiers (e.g., gene sets) in pathway knowledge bases. However, the isoform and posttranslational modification states of proteins are lost when converting input and pathways into gene-centric lists.

FINDINGS

Based on the Reactome knowledge base, we built a network of protein-protein interactions accounting for the documented isoform and modification statuses of proteins. We then implemented a command line application called PathwayMatcher (github.com/PathwayAnalysisPlatform/PathwayMatcher) to query this network. PathwayMatcher supports multiple types of omics data as input and outputs the possibly affected biochemical reactions, subnetworks, and pathways.

CONCLUSIONS

PathwayMatcher enables refining the network representation of pathways by including proteoforms defined as protein isoforms with posttranslational modifications. The specificity of pathway analyses is hence adapted to different levels of granularity, and it becomes possible to distinguish interactions between different forms of the same protein.

Software engineering for scientific big data analysis

The increasing complexity of data and analysis methods has created an environment where scientists, who may not have formal training, are finding themselves playing the impromptu role of software engineer. While several resources are available for introducing scientists to the basics of programming, researchers have been left with little guidance on approaches needed to advance to the next level for the development of robust, large-scale data analysis tools that are amenable to integration into workflow management systems, tools, and frameworks. The integration into such workflow systems necessitates additional requirements on computational tools, such as adherence to standard conventions for robustness, data input, output, logging, and flow control. Here we provide a set of 10 guidelines to steer the creation of command-line computational tools that are usable, reliable, extensible, and in line with standards of modern coding practices.

Maternal and fetal genetic effects on birth weight and their relevance to cardio-metabolic risk factors

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.

Effect of disease-associated SLC9A9 mutations on protein-protein interaction networks: implications for molecular mechanisms for ADHD and autism

Na⁺/H⁺ Exchanger 9 (NHE9) is an endosomal membrane protein encoded by the Solute Carrier 9A, member 9 gene (SLC9A9). SLC9A9 has been implicated in attention deficit hyperactivity disorder (ADHD), autism spectrum disorders (ASDs), epilepsy, multiple sclerosis and cancers. To better understand the function of NHE9 and the effects of disease-associated variants on protein-protein interactions, we conducted a quantitative analysis of the NHE9 interactome using co-immunoprecipitation and isobaric labeling-based quantitative mass spectrometry. We identified 100 proteins that interact with NHE9. These proteins were enriched in known functional pathways for NHE9: the endocytosis, protein ubiquitination and phagosome pathways, as well as some novel pathways including oxidative stress, mitochondrial dysfunction, mTOR signaling, cell death and RNA processing pathways. An ADHD-associated mutation (A409P) significantly altered NHE9's interactions with a subset of proteins involved in caveolae-mediated endocytosis and MAP2K2-mediated downstream signaling. An ASD nonsense mutation in SLC9A9, R423X, produced no-detectable amount of NHE9, suggesting the overall loss of NHE9 functional networks. In addition, seven of the NHE9 interactors are products of known autism candidate genes (Simons Foundation Autism Research Initiative, SFARI Gene) and 90% of the NHE9 interactome overlap with SFARI protein interaction network PIN (p < 0.0001), supporting the role of NHE9 interactome in ASDs molecular mechanisms. Our results provide a detailed understanding of the functions of protein NHE9 and its disrupted interactions, possibly underlying ADHD and ASDs. Furthermore, our methodological framework proved useful for functional characterization of disease-associated genetic variants and suggestion of druggable targets.

Genome-wide association study of offspring birth weight in 86 577 women identifies five novel loci and highlights maternal genetic effects that are independent of fetal genetics

Genome-wide association studies of birth weight have focused on fetal genetics, whereas relatively little is known about the role of maternal genetic variation. We aimed to identify maternal genetic variants associated with birth weight that could highlight potentially relevant maternal determinants of fetal growth. We meta-analysed data on up to 8.7 million SNPs in up to 86 577 women of European descent from the Early Growth Genetics (EGG) Consortium and the UK Biobank. We used structural equation modelling (SEM) and analyses of mother–child pairs to quantify the separate maternal and fetal genetic effects. Maternal SNPs at 10 loci (MTNR1B, HMGA2, SH2B3, KCNAB1, L3MBTL3, GCK, EBF1, TCF7L2, ACTL9, CYP3A7) were associated with offspring birth weight at P < 5 × 10⁻⁸. In SEM analyses, at least 7 of the 10 associations were consistent with effects of the maternal genotype acting via the intrauterine environment, rather than via effects of shared alleles with the fetus. Variants, or correlated proxies, at many of the loci had been previously associated with adult traits, including fasting glucose (MTNR1B, GCK and TCF7L2) and sex hormone levels (CYP3A7), and one (EBF1) with gestational duration. The identified associations indicate that genetic effects on maternal glucose, cytochrome P450 activity and gestational duration, and potentially on maternal blood pressure and immune function, are relevant for fetal growth. Further characterization of these associations in mechanistic and causal analyses will enhance understanding of the potentially modifiable maternal determinants of fetal growth, with the goal of reducing the morbidity and mortality associated with low and high birth weights.

Analyzing the Structure of Pathways and Its Influence on the Interpretation of Biomedical Proteomics Data Sets

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.

A Potential Golden Age to Come-Current Tools, Recent Use Cases, and Future Avenues for De Novo Sequencing in Proteomics

In shotgun proteomics, peptide and protein identification is most commonly conducted using database search engines, the method of choice when reference protein sequences are available. Despite its widespread use the database-driven approach is limited, mainly because of its static search space. In contrast, de novo sequencing derives peptide sequence information in an unbiased manner, using only the fragment ion information from the tandem mass spectra. In recent years, with the improvements in MS instrumentation, various new methods have been proposed for de novo sequencing. This review article provides an overview of existing de novo sequencing algorithms and software tools ranging from peptide sequencing to sequence-to-protein mapping. Various use cases are described for which de novo sequencing was successfully applied. Finally, limitations of current methods are highlighted and new directions are discussed for a wider acceptance of de novo sequencing in the community.

Proceedings of the EuBIC developer's meeting 2018

The inaugural European Bioinformatics Community (EuBIC) developer's meeting was held from January 9th to January 12th 2018 in Ghent, Belgium. While the meeting kicked off with an interactive keynote session featuring four internationally renowned experts in the field of computational proteomics, its primary focus were the hands-on hackathon sessions which featured six community-proposed projects revolving around three major topics: Here, we present an overview of the scientific program of the EuBIC developer's meeting and provide a starting point for follow-up on the covered projects.

SearchGUI: A Highly Adaptable Common Interface for Proteomics Search and de Novo Engines

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.

BioContainers: an open-source and community-driven framework for software standardization

Motivation

BioContainers (biocontainers.pro) is an open-source and community-driven framework which provides platform independent executable environments for bioinformatics software. BioContainers allows labs of all sizes to easily install bioinformatics software, maintain multiple versions of the same software and combine tools into powerful analysis pipelines. BioContainers is based on popular open-source projects Docker and rkt frameworks, that allow software to be installed and executed under an isolated and controlled environment. Also, it provides infrastructure and basic guidelines to create, manage and distribute bioinformatics containers with a special focus on omics technologies. These containers can be integrated into more comprehensive bioinformatics pipelines and different architectures (local desktop, cloud environments or HPC clusters).

Availability and Implementation

The software is freely available at github.com/BioContainers/.

A de novo Ser111Thr variant in aquaporin-4 in a patient with intellectual disability, transient signs of brain ischemia, transient cardiac hypertrophy, and progressive gait disturbance

Aquaporin-4, encoded by AQP4, is the major water channel in the central nervous system and plays an important role in the brain's water balance, including edema formation and clearance. Using genomic copy-number analysis and trio-exome sequencing, we investigated a male patient with intellectual disability, hearing loss, and progressive gait dysfunction and found a de novo missense change Ser111Thr in AQP4 as the only suspicious finding. Perinatally, signs of brain ischemia were detected in relation to acute collapse 2 h after birth that resolved a few days later. At the age of 3 mo, cardiac hypertrophy was detected that persisted through childhood but was completely resolved by age 16. In theory, this neurodevelopmental disorder with transient cardiomyopathy could be caused by a disturbance of cellular water balance. Ser111 is an extremely conserved residue in the short cytoplasmic loop between AQP4 transmembrane helix 2 and 3, present across all AQP isoforms from plants to mammals, and it does not appear to be a phosphorylation site. We found that the Ser111Thr change does not affect water permeability or protein stability, suggesting another and possibly regulatory role. Although causality remains unproven, this case study draws attention to AQP4 as a candidate gene for a unique developmental disorder and to a specific serine as a residue of possibly great functional importance in many AQPs.

An Accessible Proteogenomics Informatics Resource for Cancer Researchers

Proteogenomics has emerged as a valuable approach in cancer research, which integrates genomic and transcriptomic data with mass spectrometry-based proteomics data to directly identify expressed, variant protein sequences that may have functional roles in cancer. This approach is computationally intensive, requiring integration of disparate software tools into sophisticated workflows, challenging its adoption by nonexpert, bench scientists. To address this need, we have developed an extensible, Galaxy-based resource aimed at providing more researchers access to, and training in, proteogenomic informatics. Our resource brings together software from several leading research groups to address two foundational aspects of proteogenomics: (i) generation of customized, annotated protein sequence databases from RNA-Seq data; and (ii) accurate matching of tandem mass spectrometry data to putative variants, followed by filtering to confirm their novelty. Directions for accessing software tools and workflows, along with instructional documentation, can be found at z.umn.edu/canresgithub. Cancer Res; 77(21); e43-46. ©2017 AACR.

The mzIdentML Data Standard Version 1.2, Supporting Advances in Proteome Informatics

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 novo sequencing 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.

Label-free analysis of human cerebrospinal fluid addressing various normalization strategies and revealing protein groups affected by multiple sclerosis

The aims of the study were to: (i) identify differentially regulated proteins in cerebrospinal fluid (CSF) between multiple sclerosis (MS) patients and non-MS controls; (ii) examine the effect of matching the CSF samples on either total protein amount or volume, and compare four protein normalization strategies for CSF protein quantification. CSF from MS patients (n = 37) and controls (n = 64), consisting of other noninflammatory neurological diseases (n = 50) and non neurological spinal anesthetic subjects (n = 14), were analyzed using label-free proteomics, quantifying almost 800 proteins. In total, 122 proteins were significantly regulated (p < 0.05), where 77 proteins had p-value <0.01 or AUC value >0.75. Hierarchical clustering indicated that there were two main groups of MS patients, those with increased levels of inflammatory response proteins and decreased levels of proteins involved in neuronal tissue development (n = 30), and those with normal protein levels for both of these protein groups (n = 7). The main subgroup of controls clustering with the MS patients showing increased inflammation and decreased neuronal tissue development were patients suffering from chronic fatigue. Our data indicate that the preferable way to quantify proteins in CSF is to first match the samples on total protein amount and then normalize the data based on the median intensities, preferably from the CNS-enriched proteins.

In-Depth Cerebrospinal Fluid Quantitative Proteome and Deglycoproteome Analysis: Presenting a Comprehensive Picture of Pathways and Processes Affected by Multiple Sclerosis

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.

A Simple Workflow for Large Scale Shotgun Glycoproteomics

Targeting subproteomes is a good strategy to decrease the complexity of a sample, for example in body fluid biomarker studies. Glycoproteins are proteins with carbohydrates of varying size and structure attached to the polypeptide chain, and it has been shown that glycosylation plays essential roles in several vital cellular processes, making glycosylation a particularly interesting field of study. Here, we describe a method for the enrichment of glycosylated peptides from trypsin digested proteins in human cerebrospinal fluid. We also describe how to perform the data analysis on the mass spectrometry data for such samples, focusing on site-specific identification of glycosylation sites, using user friendly open source software.

Exploring the potential of public proteomics data

In a global effort for scientific transparency, it has become feasible and good practice to share experimental data supporting novel findings. Consequently, the amount of publicly available MS-based proteomics data has grown substantially in recent years. With some notable exceptions, this extensive material has however largely been left untouched. The time has now come for the proteomics community to utilize this potential gold mine for new discoveries, and uncover its untapped potential. In this review, we provide a brief history of the sharing of proteomics data, showing ways in which publicly available proteomics data are already being (re-)used, and outline potential future opportunities based on four different usage types: use, reuse, reprocess, and repurpose. We thus aim to assist the proteomics community in stepping up to the challenge, and to make the most of the rapidly increasing amount of public proteomics data.

Interpretation of Quantitative Shotgun Proteomic Data

In quantitative proteomics, large lists of identified and quantified proteins are used to answer biological questions in a systemic approach. However, working with such extensive datasets can be challenging, especially when complex experimental designs are involved. Here, we demonstrate how to post-process large quantitative datasets, detect proteins of interest, and annotate the data with biological knowledge. The protocol presented can be achieved without advanced computational knowledge thanks to the user-friendly Perseus interface (available from the MaxQuant website, www.maxquant.org ). Various visualization techniques facilitating the interpretation of quantitative results in complex biological systems are also highlighted.