A human interactome in three quantitative dimensions organized by stoichiometries and abundances.

The organization of a cell emerges from the interactions in protein networks. The interactome is critically dependent on the strengths of interactions and the cellular abundances of the connected proteins, both of which span orders of magnitude. However, these aspects have not yet been analyzed globally. Here, we have generated a library of HeLa cell lines expressing 1,125 GFP-tagged proteins under near-endogenous control, which we used as input for a next-generation interaction survey. Using quantitative proteomics, we detect specific interactions, estimate interaction stoichiometries, and measure cellular abundances of interacting proteins. These three quantitative dimensions reveal that the protein network is dominated by weak, substoichiometric interactions that play a pivotal role in defining network topology. The minority of stable complexes can be identified by their unique stoichiometry signature. This study provides a rich interaction dataset connecting thousands of proteins and introduces a framework for quantitative network analysis.

The SMAD2/3 interactome reveals that TGFß controls m6A mRNA methylation in pluripotency.

The TGFß pathway has essential roles in embryonic development, organ homeostasis, tissue repair and disease. These diverse effects are mediated through the intracellular effectors SMAD2 and SMAD3 (hereafter SMAD2/3), whose canonical function is to control the activity of target genes by interacting with transcriptional regulators. Therefore, a complete description of the factors that interact with SMAD2/3 in a given cell type would have broad implications for many areas of cell biology. Here we describe the interactome of SMAD2/3 in human pluripotent stem cells. This analysis reveals that SMAD2/3 is involved in multiple molecular processes in addition to its role in transcription. In particular, we identify a functional interaction with the METTL3-METTL14-WTAP complex, which mediates the conversion of adenosine to N6-methyladenosine (m6A) on RNA. We show that SMAD2/3 promotes binding of the m6A methyltransferase complex to a subset of transcripts involved in early cell fate decisions. This mechanism destabilizes specific SMAD2/3 transcriptional targets, including the pluripotency factor gene NANOG, priming them for rapid downregulation upon differentiation to enable timely exit from pluripotency. Collectively, these findings reveal the mechanism by which extracellular signalling can induce rapid cellular responses through regulation of the epitranscriptome. These aspects of TGFß signalling could have far-reaching implications in many other cell types and in diseases such as cancer.

Activin/nodal signaling and NANOG orchestrate human embryonic stem cell fate decisions by controlling the H3K4me3 chromatin mark.

Stem cells can self-renew and differentiate into multiple cell types. These characteristics are maintained by the combination of specific signaling pathways and transcription factors that cooperate to establish a unique epigenetic state. Despite the broad interest of these mechanisms, the precise molecular controls by which extracellular signals organize epigenetic marks to confer multipotency remain to be uncovered. Here, we use human embryonic stem cells (hESCs) to show that the Activin-SMAD2/3 signaling pathway cooperates with the core pluripotency factor NANOG to recruit the DPY30-COMPASS histone modifiers onto key developmental genes. Functional studies demonstrate the importance of these interactions for correct histone 3 Lys4 trimethylation and also self-renewal and differentiation. Finally, genetic studies in mice show that Dpy30 is also necessary to maintain pluripotency in the pregastrulation embryo, thereby confirming the existence of similar regulations in vivo during early embryonic development. Our results reveal the mechanisms by which extracellular factors coordinate chromatin status and cell fate decisions in hESCs.

The CRAPome: a contaminant repository for affinity purification-mass spectrometry data.

Affinity purification coupled with mass spectrometry (AP-MS) is a widely used approach for the identification of protein-protein interactions. However, for any given protein of interest, determining which of the identified polypeptides represent bona fide interactors versus those that are background contaminants (for example, proteins that interact with the solid-phase support, affinity reagent or epitope tag) is a challenging task. The standard approach is to identify nonspecific interactions using one or more negative-control purifications, but many small-scale AP-MS studies do not capture a complete, accurate background protein set when available controls are limited. Fortunately, negative controls are largely bait independent. Hence, aggregating negative controls from multiple AP-MS studies can increase coverage and improve the characterization of background associated with a given experimental protocol. Here we present the contaminant repository for affinity purification (the CRAPome) and describe its use for scoring protein-protein interactions. The repository (currently available for Homo sapiens and Saccharomyces cerevisiae) and computational tools are freely accessible at http://www.crapome.org/.

A quantitative proteomics tool to identify DNA-protein interactions in primary cells or blood.

Interactions between transcription factors and genomic DNA, and in particular their impact on disease and cell fate, have been extensively studied on a global level using techniques based on next-generation sequencing. These approaches, however, do not allow an unbiased study of protein complexes that bind to certain DNA sequences. DNA pulldowns from crude lysates combined with quantitative mass spectrometry were recently introduced to close this gap. Established protocols, however, are restricted to cell lines because they are based on metabolic labeling or require large amounts of material. We introduce a high-throughput-compatible DNA pulldown that combines on-bead digestion with direct dimethyl labeling or label-free protein quantification. We demonstrate that our method can efficiently identify transcription factors binding to their consensus DNA motifs in extracts from primary foreskin fibroblasts and peripheral blood mononuclear cells (PBMCs) freshly isolated from human donors. Nuclear proteomes with absolute quantification of nearly 7000 proteins in K562 cells and PBMCs clearly link differential interactions to differences in protein abundance, hence stressing the importance of selecting relevant cell extracts for any interaction in question. As shown for rs6904029, a SNP highly associated with chronic lymphocytic leukemia, our approach can provide invaluable functional data, for example, through integration with GWAS.

Genomics meets proteomics: identifying the culprits in disease.

Genome-wide association studies (GWAS) revealed genomic risk loci that potentially have an impact on disease and phenotypic traits. This extensive resource holds great promise in providing novel directions for personalized medicine, including disease risk prediction, prevention and targeted medication. One of the major challenges that researchers face on the path between the initial identification of an association and precision treatment of patients is the comprehension of the biological mechanisms that underlie these associations. Currently, the focus to solve these questions lies on the integrative analysis of system-wide data on global genome variation, gene expression, transcription factor binding, epigenetic profiles and chromatin conformation. The generation of this data mainly relies on next-generation sequencing. However, due to multiple recent developments, mass spectrometry-based proteomics now offers additional, by the GWAS field so far hardly recognized possibilities for the identification of functional genome variants and, in particular, for the identification and characterization of (differentially) bound protein complexes as well as physiological target genes. In this review, we introduce these proteomics advances and suggest how they might be integrated in post-GWAS workflows. We argue that the combination of highly complementary techniques is powerful and can provide an unbiased, detailed picture of GWAS loci and their mechanistic involvement in disease.

Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast.

Mass spectrometry is a powerful technology for the analysis of large numbers of endogenous proteins. However, the analytical challenges associated with comprehensive identification and relative quantification of cellular proteomes have so far appeared to be insurmountable. Here, using advances in computational proteomics, instrument performance and sample preparation strategies, we compare protein levels of essentially all endogenous proteins in haploid yeast cells to their diploid counterparts. Our analysis spans more than four orders of magnitude in protein abundance with no discrimination against membrane or low level regulatory proteins. Stable-isotope labelling by amino acids in cell culture (SILAC) quantification was very accurate across the proteome, as demonstrated by one-to-one ratios of most yeast proteins. Key members of the pheromone pathway were specific to haploid yeast but others were unaltered, suggesting an efficient control mechanism of the mating response. Several retrotransposon-associated proteins were specific to haploid yeast. Gene ontology analysis pinpointed a significant change for cell wall components in agreement with geometrical considerations: diploid cells have twice the volume but not twice the surface area of haploid cells. Transcriptome levels agreed poorly with proteome changes overall. However, after filtering out low confidence microarray measurements, messenger RNA changes and SILAC ratios correlated very well for pheromone pathway components. Systems-wide, precise quantification directly at the protein level opens up new perspectives in post-genomics and systems biology.

Large-scale de novo prediction of physical protein-protein association.

Information about the physical association of proteins is extensively used for studying cellular processes and disease mechanisms. However, complete experimental mapping of the human interactome will remain prohibitively difficult in the near future. Here we present a map of predicted human protein interactions that distinguishes functional association from physical binding. Our network classifies more than 5 million protein pairs predicting 94,009 new interactions with high confidence. We experimentally tested a subset of these predictions using yeast two-hybrid analysis and affinity purification followed by quantitative mass spectrometry. Thus we identified 462 new protein-protein interactions and confirmed the predictive power of the network. These independent experiments address potential issues of circular reasoning and are a distinctive feature of this work. Analysis of the physical interactome unravels subnetworks mediating between different functional and physical subunits of the cell. Finally, we demonstrate the utility of the network for the analysis of molecular mechanisms of complex diseases by applying it to genome-wide association studies of neurodegenerative diseases. This analysis provides new evidence implying TOMM40 as a factor involved in Alzheimer's disease. The network provides a high-quality resource for the analysis of genomic data sets and genetic association studies in particular. Our interactome is available via the hPRINT web server at: www.print-db.org.

Extracting gene function from protein-protein interactions using Quantitative BAC InteraCtomics (QUBIC).

Large-scale proteomic screens are increasingly employed for placing genes into specific pathways. Therefore generic methods providing a physiological context for protein-protein interaction studies are of great interest. In recent years many protein-protein interactions have been determined by affinity purification followed by mass spectrometry (AP-MS). Among many different AP-MS approaches, the recently developed Quantitative BAC InteraCtomics (QUBIC) approach is particularly attractive as it uses tagged, full-length baits that are expressed under endogenous control. For QUBIC large cell line collections expressing tagged proteins from BAC transgenes or gene trap loci have been developed and are freely available. Here we describe detailed workflows on how to obtain specific protein binding partners with high confidence under physiological conditions. The methods are based on fast, streamlined and generic purification procedures followed by single run liquid chromatography-mass spectrometric analysis. Quantification is achieved either by the stable isotope labeling of amino acids in cell culture (SILAC) method or by a 'label-free' procedure. In either case data analysis is performed by using the freely available MaxQuant environment. The QUBIC approach enables biologists with access to high resolution mass spectrometry to perform small and large-scale protein interactome mappings.

Stable isotope labeling by amino acids in cell culture (SILAC) and proteome quantitation of mouse embryonic stem cells to a depth of 5,111 proteins.

Embryonic stem (ES) cells are pluripotent cells isolated from mammalian preimplantation embryos. They are capable of differentiating into all cell types and therefore hold great promise in regenerative medicine. Here we show that murine ES cells can be fully SILAC (stable isotope labeling by amino acids in cell culture)-labeled when grown feeder-free during the last phase of cell culture. We fractionated the SILAC-labeled ES cell proteome by one-dimensional gel electrophoresis and by isoelectric focusing of peptides. High resolution analysis on a linear ion trap-orbitrap instrument (LTQ-Orbitrap) at sub-ppm mass accuracy resulted in confident identification and quantitation of more than 5,000 distinct proteins. This is the largest quantified proteome reported to date and contains prominent stem cell markers such as OCT4, NANOG, SOX2, and UTF1 along with the embryonic form of RAS (ERAS). We also quantified the proportion of the ES cell proteome present in cytosolic, nucleoplasmic, and membrane/chromatin fractions. We compared two different preparation approaches, cell fractionation followed by one-dimensional gel separation and in-solution digestion of total cell lysate combined with isoelectric focusing, and found comparable proteome coverage with no apparent bias for any functional protein classes for either approach. Bioinformatics analysis of the ES cell proteome revealed a broad distribution of cellular functions with overrepresentation of proteins involved in proliferation. We compared the proteome with a recently published map of chromatin states of promoters in ES cells and found excellent correlation between protein expression and the presence of active and repressive chromatin marks.

High confidence determination of specific protein-protein interactions using quantitative mass spectrometry.

In recent years, interactions between proteins have successfully been determined by mass spectrometry. A limitation of this technology has been the need for extensive purification, which restricts throughput and implies a tradeoff between specificity and the ability to detect weak or transient interactions. Quantitative proteomics sidesteps this problem by directly comparing specific and control pull-downs. Specific interaction partners are revealed by their quantitative ratios rather than by gel-based visualization and can be retrieved from a vast excess of background proteins. This principle is revolutionizing the protein interaction field as demonstrated by recent applications in fields as diverse as tyrosine signaling pathways, cell adhesion, and chromatin biology.

Peptide separation with immobilized pI strips is an attractive alternative to in-gel protein digestion for proteome analysis.

Complex protein mixtures have traditionally been separated by 2-DE. Görg introduced IPGs as the first dimension of protein separation. In recent years, MS-based proteomics has increasingly become the method of choice for identifying and quantifying large number of proteins. In that technology, to decrease analyte complexity, proteins are often separated by 1-D SDS-gel electrophoresis before online MS analysis. Here, we investigate a recently introduced device for peptide separation with IPGs (Agilent OFFGEL). Loading capacity for optimal peptide focusing is below 100 microg and--similar to 2-D gels--IEF is more efficient in the acidic than the basic pH region. The 24-well fractionation format resulted in about 40% additional peptide identifications but less than 20% additional protein identifications than the 12-well format. Compared to in-gel digestion, peptide IEF consistently identified a third more proteins with equal number of fractions. Low protein starting amounts (10 microg) still resulted in deep proteome coverage. Advantages of the in-gel format include better reliability and robustness. Considering its superior performance, diminished sample and work-up requirements, peptide IEF will become a method of choice for sample preparation in proteomics.

How much peptide sequence information is contained in ion trap tandem mass spectra?

Matching peptide tandem mass spectra to their cognate amino acid sequences in databases is a key step in proteomics. It is usually performed by assigning a score to a spectrum-sequence combination. De novo sequencing or partial de novo sequencing is useful for organisms without sequenced genome or for peptides with unexpected modifications. Here we use a very large, high accuracy proteomic dataset to investigate how much peptide sequence information is present in tandem mass spectra generated in a linear ion trap (LTQ). More than 400,000 identified tandem mass spectra from a single human cancer cell line project were assigned to 26,896 distinct peptide sequences. The average absolute fragment mass accuracy is 0.102 Da. There are on average about four complementary b- and y-ions; both series are equally represented but y ions are 2- to 3-fold more intense up to mass 1000. Half of all spectra contain uninterrupted b- or y-ion series of at least six amino acids and combining b- and y-ion information yields on average seven amino acid sequences. These sequences are almost always unique in the human proteome, even without using any precursor or peptide sequence tag information. Thus, optimal de novo sequencing algorithms should be able to obtain substantial sequence information in at least half of all cases.

Epigenetic Repression of Androgen Receptor Transcription in Mutation-Negative Androgen Insensitivity Syndrome (AIS Type II).

Context: Inactivating mutations within the AR gene are present in only ~40% of individuals with clinically and hormonally diagnosed androgen insensitivity syndrome (AIS). Previous studies revealed the existence of an AR gene mutation-negative group of patients with AIS who have compromised androgen receptor (AR) function (AIS type II). Objective: To investigate whether AIS type II can be due to epigenetic repression of AR transcription. Design: Quantification of AR mRNA and AR proximal promoter CpG methylation levels in genital skin-derived fibroblasts (GFs) derived from patients with AIS type II and control individuals. Setting: University hospital endocrine research laboratory. Patients: GFs from control individuals (n = 11) and patients with AIS type II (n = 14). Main Outcome Measure(s): Measurement of AR mRNA and AR promoter CpG methylation as well as activity of AR proximal promoter in vitro. Results: Fifty-seven percent of individuals with AIS type II (n = 8) showed a reduced AR mRNA expression in their GFs. A significant inverse correlation was shown between AR mRNA abundance and methylation at two consecutive CpGs within the proximal AR promoter. Methylation of a 158-bp-long region containing these CpGs was sufficient to severely reduce reporter gene expression. This region was bound by the runt related transcription factor 1 (RUNX1). Ectopic expression of RUNX1 in HEK293T cells was able to inhibit reporter gene expression through this region. Conclusions: Aberrant CpGs methylation within the proximal AR promoter plays an important role in the control of AR gene expression and may result in AIS type II. We suggest that transcriptional modifiers, such as RUNX1, could play roles therein offering new perspectives for understanding androgen-mediated endocrine diseases.

The non-coding variant rs1800734 enhances DCLK3 expression through long-range interaction and promotes colorectal cancer progression.

Genome-wide association studies have identified a great number of non-coding risk variants for colorectal cancer (CRC). To date, the majority of these variants have not been functionally studied. Identification of allele-specific transcription factor (TF) binding is of great importance to understand regulatory consequences of such variants. A recently developed proteome-wide analysis of disease-associated SNPs (PWAS) enables identification of TF-DNA interactions in an unbiased manner. Here we perform a large-scale PWAS study to comprehensively characterize TF-binding landscape that is associated with CRC, which identifies 731 allele-specific TF binding at 116 CRC risk loci. This screen identifies the A-allele of rs1800734 within the promoter region of MLH1 as perturbing the binding of TFAP4 and consequently increasing DCLK3 expression through a long-range interaction, which promotes cancer malignancy through enhancing expression of the genes related to epithelial-to-mesenchymal transition.

A covalent and cleavable antibody-DNA conjugation strategy for sensitive protein detection via immuno-PCR.

Immuno-PCR combines specific antibody-based protein detection with the sensitivity of PCR-based quantification through the use of antibody-DNA conjugates. The production of such conjugates depends on the availability of quick and efficient conjugation strategies for the two biomolecules. Here, we present an approach to produce cleavable antibody-DNA conjugates, employing the fast kinetics of the inverse electron-demand Diels-Alder reaction between tetrazine and trans-cyclooctene (TCO). Our strategy consists of three steps. First, antibodies are functionalized with chemically cleavable NHS-s-s-tetrazine. Subsequently, double-stranded DNA is functionalized with TCO by enzymatic addition of N3-dATP and coupling to trans-Cyclooctene-PEG12-Dibenzocyclooctyne (TCO-PEG12-DBCO). Finally, conjugates are quickly and efficiently obtained by mixing the functionalized antibodies and dsDNA at low molar ratios of 1:2. In addition, introduction of a chemically cleavable disulphide linker facilitates release and sensitive detection of the dsDNA after immuno-staining. We show specific and sensitive protein detection in immuno-PCR for human epidermal stem cell markers, ITGA6 and ITGB1, and the differentiation marker Transglutaminase 1 (TGM1). We anticipate that the production of chemically cleavable antibody-DNA conjugates will provide a solid basis for the development of multiplexed immuno-PCR experiments and immuno-sequencing methodologies.

The Hematopoietic Transcription Factors RUNX1 and ERG Prevent AML1-ETO Oncogene Overexpression and Onset of the Apoptosis Program in t(8;21) AMLs.

The t(8;21) acute myeloid leukemia (AML)-associated oncoprotein AML1-ETO disrupts normal hematopoietic differentiation. Here, we have investigated its effects on the transcriptome and epigenome in t(8,21) patient cells. AML1-ETO binding was found at promoter regions of active genes with high levels of histone acetylation but also at distal elements characterized by low acetylation levels and binding of the hematopoietic transcription factors LYL1 and LMO2. In contrast, ERG, FLI1, TAL1, and RUNX1 bind at all AML1-ETO-occupied regulatory regions, including those of the AML1-ETO gene itself, suggesting their involvement in regulating AML1-ETO expression levels. While expression of AML1-ETO in myeloid differentiated induced pluripotent stem cells (iPSCs) induces leukemic characteristics, overexpression increases cell death. We find that expression of wild-type transcription factors RUNX1 and ERG in AML is required to prevent this oncogene overexpression. Together our results show that the interplay of the epigenome and transcription factors prevents apoptosis in t(8;21) AML cells.

Genome-wide binding of MBD2 reveals strong preference for highly methylated loci.

MBD2 is a subunit of the NuRD complex that is postulated to mediate gene repression via recruitment of the complex to methylated DNA. In this study we adopted an MBD2 tagging-approach to study its genome wide binding characteristics. We show that in vivo MBD2 is mainly recruited to CpG island promoters that are highly methylated. Interestingly, MBD2 binds around 1 kb downstream of the transcription start site of a subset of ∼ 400 CpG island promoters that are characterized by the presence of active histone marks, RNA polymerase II (Pol2) and low to medium gene expression levels and H3K36me3 deposition. These tagged-MBD2 binding sites in MCF-7 show increased methylation in a cohort of primary breast cancers but not in normal breast samples, suggesting a putative role for MBD2 in breast cancer.

A polymorphic enhancer near GREM1 influences bowel cancer risk through differential CDX2 and TCF7L2 binding.

A rare germline duplication upstream of the bone morphogenetic protein antagonist GREM1 causes a Mendelian-dominant predisposition to colorectal cancer (CRC). The underlying disease mechanism is strong, ectopic GREM1 overexpression in the intestinal epithelium. Here, we confirm that a common GREM1 polymorphism, rs16969681, is also associated with CRC susceptibility, conferring ∼20% differential risk in the general population. We hypothesized the underlying cause to be moderate differences in GREM1 expression. We showed that rs16969681 lies in a region of active chromatin with allele- and tissue-specific enhancer activity. The CRC high-risk allele was associated with stronger gene expression, and higher Grem1 mRNA levels increased the intestinal tumor burden in Apc(Min) mice. The intestine-specific transcription factor CDX2 and Wnt effector TCF7L2 bound near rs16969681, with significantly higher affinity for the risk allele, and CDX2 overexpression in CDX2/GREM1-negative cells caused re-expression of GREM1. rs16969681 influences CRC risk through effects on Wnt-driven GREM1 expression in colorectal tumors.