Marko Jovanovic

Protein Identification False Discovery Rates for Very Large Proteomics Data Sets Generated by Tandem Mass Spectrometry

Comprehensive characterization of a proteome is a fundamental goal in proteomics. To achieve saturation coverage of a proteome or specific subproteome via tandem mass spectrometric identification of tryptic protein sample digests, proteomics data sets are growing dramatically in size and heterogeneity. The trend toward very large integrated data sets poses so far unsolved challenges to control the uncertainty of protein identifications going beyond well established confidence measures for peptide-spectrum matches. We present MAYU, a novel strategy that reliably estimates false discovery rates for protein identifications in large scale data sets. We validated and applied MAYU using various large proteomics data sets. The data show that the size of the data set has an important and previously underestimated impact on the reliability of protein identifications. We particularly found that protein false discovery rates are significantly elevated compared with those of peptide-spectrum matches. The function provided by MAYU is critical to control the quality of proteome data repositories and thereby to enhance any study relying on these data sources. The MAYU software is available as standalone software and also integrated into the Trans-Proteomic Pipeline.

Comparative functional analysis of the Caenorhabditis elegans and Drosophila melanogaster proteomes

The nematode Caenorhabditis elegans is a popular model system in genetics, not least because a majority of human disease genes are conserved in C. elegans. To generate a comprehensive inventory of its expressed proteome, we performed extensive shotgun proteomics and identified more than half of all predicted C. elegans proteins. This allowed us to confirm and extend genome annotations, characterize the role of operons in C. elegans, and semiquantitatively infer abundance levels for thousands of proteins. Furthermore, for the first time to our knowledge, we were able to compare two animal proteomes (C. elegans and Drosophila melanogaster). We found that the abundances of orthologous proteins in metazoans correlate remarkably well, better than protein abundance versus transcript abundance within each organism or transcript abundances across organisms; this suggests that changes in transcript abundance may have been partially offset during evolution by opposing changes in protein abundance.

Comparative analysis reveals conserved protein phosphorylation networks implicated in multiple diseases.

Comparing the human phosphoproteome to that of flies, worms, and yeast reveals insight into evolution and disease. Phosphorylation Networks in Disease and Evolution Insights into the evolution of protein phosphorylation were revealed by combining the results from two computational analyses—a sequence-alignment approach and a kinase-substrate network alignment approach. The two approaches yielded different, but somewhat overlapping, sets of conserved phosphoproteins among humans and the model organisms. The first provided a set of genes encoding phosphoproteins that had positionally conserved phosphorylation sites, whereas the second included many functionally conserved phosphoproteins that lacked this positional conservation. Enrichment analysis of the genes identified through the kinase-substrate network approach suggested that genes encoding phosphorylated signaling hubs were enriched in disease-associated genes (defined by Online Mendelian Inheritance in Man), and both approaches showed that genes encoding conserved phosphoproteins were enriched in genes associated with cancer. The functional annotation of the two gene sets suggested that positional conservation is common in regions that are structurally constrained, such as those regulated by allosteric interactions, and that the kinase-substrate network method may aid in analyzing fast-evolving signaling processes, where functional conservation does not require positional conservation. The analysis also suggests that conserved regulatory networks may be involved in different diseases. Protein kinases enable cellular information processing. Although numerous human phosphorylation sites and their dynamics have been characterized, the evolutionary history and physiological importance of many signaling events remain unknown. Using target phosphoproteomes determined with a similar experimental and computational pipeline, we investigated the conservation of human phosphorylation events in distantly related model organisms (fly, worm, and yeast). With a sequence-alignment approach, we identified 479 phosphorylation events in 344 human proteins that appear to be positionally conserved over ~600 million years of evolution and hence are likely to be involved in fundamental cellular processes. This sequence-alignment analysis suggested that many phosphorylation sites evolve rapidly and therefore do not display strong evolutionary conservation in terms of sequence position in distantly related organisms. Thus, we devised a network-alignment approach to reconstruct conserved kinase-substrate networks, which identified 778 phosphorylation events in 698 human proteins. Both methods identified proteins tightly regulated by phosphorylation as well as signal integration hubs, and both types of phosphoproteins were enriched in proteins encoded by disease-associated genes. We analyzed the cellular functions and structural relationships for these conserved signaling events, noting the incomplete nature of current phosphoproteomes. Assessing phosphorylation conservation at both site and network levels proved useful for exploring both fast-evolving and ancient signaling events. We reveal that multiple complex diseases seem to converge within the conserved networks, suggesting that disease development might rely on common molecular networks.

Protein significance analysis in selected reaction monitoring (SRM) measurements

Selected reaction monitoring (SRM) is a targeted mass spectrometry technique that provides sensitive and accurate protein detection and quantification in complex biological mixtures. Statistical and computational tools are essential for the design and analysis of SRM experiments, particularly in studies with large sample throughput. Currently, most such tools focus on the selection of optimized transitions and on processing signals from SRM assays. Little attention is devoted to protein significance analysis, which combines the quantitative measurements for a protein across isotopic labels, peptides, charge states, transitions, samples, and conditions, and detects proteins that change in abundance between conditions while controlling the false discovery rate. We propose a statistical modeling framework for protein significance analysis. It is based on linear mixed-effects models and is applicable to most experimental designs for both isotope label-based and label-free SRM workflows. We illustrate the utility of the framework in two studies: one with a group comparison experimental design and the other with a time course experimental design. We further verify the accuracy of the framework in two controlled data sets, one from the NCI-CPTAC reproducibility investigation and the other from an in-house spike-in study. The proposed framework is sensitive and specific, produces accurate results in broad experimental circumstances, and helps to optimally design future SRM experiments. The statistical framework is implemented in an open-source R-based software package SRMstats, and can be used by researchers with a limited statistics background as a stand-alone tool or in integration with the existing computational pipelines.

A quantitative targeted proteomics approach to validate predicted microRNA targets in C. elegans

Efficient experimental strategies are needed to validate computationally predicted microRNA (miRNA) target genes. Here we present a large-scale targeted proteomics approach to validate predicted miRNA targets in Caenorhabditis elegans. Using selected reaction monitoring (SRM), we quantified 161 proteins of interest in extracts from wild-type and let-7 mutant worms. We demonstrate by independent experimental downstream analyses such as genetic interaction, as well as polysomal profiling and luciferase assays, that validation by targeted proteomics substantially enriched for biologically relevant let-7 interactors. For example, we found that the zinc finger protein ZTF-7 was a bona fide let-7 miRNA target. We also validated predicted miR-58 targets, demonstrating that this approach is adaptable to other miRNAs. We propose that targeted mass spectrometry can be applied generally to validate candidate lists generated by computational methods or in large-scale experiments, and that the described strategy should be readily adaptable to other organisms.

RIP-chip-SRM—a new combinatorial large-scale approach identifies a set of translationally regulated bantam/miR-58 targets in C. elegans

MicroRNAs (miRNAs) are small, noncoding RNAs that negatively regulate gene expression. As miRNAs are involved in a wide range of biological processes and diseases, much effort has been invested in identifying their mRNA targets. Here, we present a novel combinatorial approach, RIP-chip-SRM (RNA-binding protein immunopurification + microarray + targeted protein quantification via selected reaction monitoring), to identify de novo high-confidence miRNA targets in the nematode Caenorhabditis elegans. We used differential RIP-chip analysis of miRNA-induced silencing complexes from wild-type and miRNA mutant animals, followed by quantitative targeted proteomics via selected reaction monitoring to identify and validate mRNA targets of the C. elegans bantam homolog miR-58. Comparison of total mRNA and protein abundance changes in mir-58 mutant and wild-type animals indicated that the direct bantam/miR-58 targets identified here are mainly regulated at the level of protein abundance, not mRNA stability.

The HUPO initiative on Model Organism Proteomes, iMOP

The community working on model organisms is growing steadily and the number of model organisms for which proteome data are being generated is continuously increasing. To standardize efforts and to make optimal use of proteomics data acquired from model organisms, a new Human Proteome Organisation (HUPO) initiative on model organism proteomes (iMOP) was approved at the HUPO Ninth Annual World Congress in Sydney, 2010. iMOP will seek to stimulate scientific exchange and disseminate HUPO best practices. The needs of model organism researchers for central databases will be better represented, catalyzing the integration of proteomics and organism‐specific databases. Full details of iMOP activities, members, tools and resources can be found at our website http://www.imop.uzh.ch/ and new members are invited to join us.

Cooperative target mRNA destabilization and translation inhibition by miR-58 microRNA family in C. elegans.

In animals, microRNAs frequently form families with related sequences. The functional relevance of miRNA families and the relative contribution of family members to target repression have remained, however, largely unexplored. Here, we used the Caenorhabditis elegans miR-58 miRNA family, composed primarily of the four highly abundant members miR-58.1, miR-80, miR-81, and miR-82, as a model to investigate the redundancy of miRNA family members and their impact on target expression in an in vivo setting. We found that miR-58 family members repress largely overlapping sets of targets in a predominantly additive fashion. Progressive deletions of miR-58 family members lead to cumulative up-regulation of target protein and RNA levels. Phenotypic defects could only be observed in the family quadruple mutant, which also showed the strongest change in target protein levels. Interestingly, although the seed sequences of miR-80 and miR-58.1 differ in a single nucleotide, predicted canonical miR-80 targets were efficiently up-regulated in the mir-58.1 single mutant, indicating functional redundancy of distinct members of this miRNA family. At the aggregate level, target binding leads mainly to mRNA degradation, although we also observed some degree of translational inhibition, particularly in the single miR-58 family mutants. These results provide a framework for understanding how miRNA family members interact to regulate target mRNAs.