Determining DNA–Protein Binding Affinities and Specificities from Crude Lysates Using a Combined SILAC/TMT Labeling Strategy

In recent years, quantitative mass spectrometry-based interaction proteomics technology has proven very useful in identifying specific DNA–protein interactions using single pull-downs from crude lysates. Here, we applied a SILAC/TMT-based higher-order multiplexing approach to develop an interaction proteomics workflow called Protein–nucleic acid Affinity and Specificity quantification by MAss spectrometry in Nuclear extracts or PASMAN. In PASMAN, DNA pull-downs using a concentration range of specific and control DNA baits are performed in SILAC-labeled nuclear extracts. MS1-based quantification to determine specific DNA–protein interactions is then combined with sequential TMT-based quantification of fragmented SILAC peptides, allowing the generation of Hill-like curves and determination of apparent binding affinities. We benchmarked PASMAN using the SP/KLF motif and further applied it to gain insights into two CGCG-containing consensus DNA motifs. These motifs are recognized by two BEN domain-containing proteins, BANP and BEND3, which we find to interact with these motifs with distinct affinities. Finally, we profiled the BEND3 proximal proteome, revealing the NuRD complex as the major BEND3 proximal protein complex in vivo. In summary, PASMAN represents, to our knowledge, the first higher-order multiplexing-based interaction proteomics method that can be used to decipher specific DNA–protein interactions and their apparent affinities in various biological and pathological contexts

Determining DNA–Protein Binding Affinities and Specificities from Crude Lysates Using a Combined SILAC/TMT Labeling Strategy

In recent years, quantitative mass spectrometry-based interaction proteomics technology has proven very useful in identifying specific DNA–protein interactions using single pull-downs from crude lysates. Here, we applied a SILAC/TMT-based higher-order multiplexing approach to develop an interaction proteomics workflow called Protein–nucleic acid Affinity and Specificity quantification by MAss spectrometry in Nuclear extracts or PASMAN. In PASMAN, DNA pull-downs using a concentration range of specific and control DNA baits are performed in SILAC-labeled nuclear extracts. MS1-based quantification to determine specific DNA–protein interactions is then combined with sequential TMT-based quantification of fragmented SILAC peptides, allowing the generation of Hill-like curves and determination of apparent binding affinities. We benchmarked PASMAN using the SP/KLF motif and further applied it to gain insights into two CGCG-containing consensus DNA motifs. These motifs are recognized by two BEN domain-containing proteins, BANP and BEND3, which we find to interact with these motifs with distinct affinities. Finally, we profiled the BEND3 proximal proteome, revealing the NuRD complex as the major BEND3 proximal protein complex in vivo. In summary, PASMAN represents, to our knowledge, the first higher-order multiplexing-based interaction proteomics method that can be used to decipher specific DNA–protein interactions and their apparent affinities in various biological and pathological contexts

Determining DNA–Protein Binding Affinities and Specificities from Crude Lysates Using a Combined SILAC/TMT Labeling Strategy

In recent years, quantitative mass spectrometry-based interaction proteomics technology has proven very useful in identifying specific DNA–protein interactions using single pull-downs from crude lysates. Here, we applied a SILAC/TMT-based higher-order multiplexing approach to develop an interaction proteomics workflow called Protein–nucleic acid Affinity and Specificity quantification by MAss spectrometry in Nuclear extracts or PASMAN. In PASMAN, DNA pull-downs using a concentration range of specific and control DNA baits are performed in SILAC-labeled nuclear extracts. MS1-based quantification to determine specific DNA–protein interactions is then combined with sequential TMT-based quantification of fragmented SILAC peptides, allowing the generation of Hill-like curves and determination of apparent binding affinities. We benchmarked PASMAN using the SP/KLF motif and further applied it to gain insights into two CGCG-containing consensus DNA motifs. These motifs are recognized by two BEN domain-containing proteins, BANP and BEND3, which we find to interact with these motifs with distinct affinities. Finally, we profiled the BEND3 proximal proteome, revealing the NuRD complex as the major BEND3 proximal protein complex in vivo. In summary, PASMAN represents, to our knowledge, the first higher-order multiplexing-based interaction proteomics method that can be used to decipher specific DNA–protein interactions and their apparent affinities in various biological and pathological contexts

Supplemental Table 1 from The Pluripotency Regulator PRDM14 Requires Hematopoietic Regulator CBFA2T3 to Initiate Leukemia in Mice

Mass spectrometry results listing LFQs, significance values and gene names.</p

Supplemental Figure 1 from The Pluripotency Regulator PRDM14 Requires Hematopoietic Regulator CBFA2T3 to Initiate Leukemia in Mice

Western blot results of 2.5% input from transfected HA-CBFA2T3 constructs shown at two exposures.</p

Schematic diagram of SNP pull-down.

<p>Synthetic oligonucleotides containing the SNP are phosphorylated, polymerized and subsequently strand-specifically desthiobiotin-labeled. The immobilized DNA fragments are incubated with either light or heavy extract. After removal of unbound proteins, bead fractions are combined and DNA-protein complexes are eluted with biotin. The eluate is precipitated, digested and analyzed by single-run, high resolution, quantitative mass spectrometry. Specific interaction partners result in a ratio different from 1∶1, demonstrating specific enrichment at one variant of the single nucleotide polymorphism.</p

Functional analysis for SNP rs12722508.

<p>A: Immunostaining with an antibody against endogenous RUNX1 validates differential binding between the A- and G-allele of rs12722508, input refers to nuclear extract incubated with either rs12722508 allele. B: mRNA levels are reduced upon esiRNA knock-down with mean and s.d. of triplicates; C: changes in firefly luciferase activity in mock-transfection and upon knock-down of TFAP4, RUNX1, CREB and TFAP4. Knock-down of RUNX1 results in an activation with an allele-specific difference (<i>P</i> = 0.016) demonstrating the functional consequence of differential RUNX1 binding between the two alleles of rs12722508.</p

Multiomics of Colorectal Cancer Organoids Reveals Putative Mediators of Cancer Progression Resulting from SMAD4 Inactivation

The development of metastasis severely reduces the life expectancy of patients with colorectal cancer (CRC). Although loss of SMAD4 is a key event in CRC progression, the resulting changes in biological processes in advanced disease and metastasis are not fully understood. Here, we applied a multiomics approach to a CRC organoid model that faithfully reflects the metastasis-supporting effects of SMAD4 inactivation. We show that loss of SMAD4 results in decreased differentiation and activation of pro-migratory and cell proliferation processes, which is accompanied by the disruption of several key oncogenic pathways, including the TGFβ, WNT, and VEGF pathways. In addition, SMAD4 inactivation leads to increased secretion of proteins that are known to be involved in a variety of pro-metastatic processes. Finally, we show that one of the factors that is specifically secreted by SMAD4-mutant organoidsDKK3reduces the antitumor effects of natural killer cells (NK cells). Altogether, our data provide new insights into the role of SMAD4 perturbation in advanced CRC

Schematic representation of a two-dimensional interaction plot.

<p>While specific outliers are found in the upper left (variant A) or the lower right (variant G) quadrant, most proteins cluster around the origin as they are binding to both variants equally. Contaminants have a SILAC ratios lower than 1 even when labels are switched and thus are grouped in the lower left quadrant.</p

Overview of all SNPs analyzed in this study with T1D disease association.

<p>SNPs which mark their respective haplotype are marked in bold.</p