Dataset Information

Evolution of Modularity, Interactome and Functions of GIV/Girdin (CCDC88A) from Invertebrates to Vertebrates

ABSTRACT: PDZ domains are one of the most abundant protein domains in eukaryotes and frequently found on junction-localized scaffold proteins. Various signaling molecules bind to PDZ proteins via PDZ-binding motifs (PBM) and finetune cellular signaling. Here we describe the presence of a PBM on GIV/Girdin (CCDC88A) that is conserved throughout evolution, from invertebrates to vertebrates, and is generated as a long isoform-variant in humans, which we named GIV-L. Unlike GIV, which lacks PBM and is cytosolic, GIV-L localizes to the cell junctions, and has a unique PDZ-interactome, which impacts GIV-L’s ability to bind and activate trimeric G-protein, Gi through its guanine-nucleotide exchange modulator (GEM) module; the GEM module is found exclusively in vertebrates. Thus, the two functional modules in GIV evolved sequentially: the ability to bind PDZ proteins via the PBM evolved earlier in invertebrates, whereas G-protein binding and activation may have evolved later only among vertebrates. Phenotypic studies in Caco-2 cells revealed that GIV and GIV-L may have antagonistic effects on cell growth, proliferation (cell cycle), and survival. Immunohistochemical analyses in human colon tissues showed that GIV expression increases with a concomitant decrease in GIV-L during cancer initiation. Taken together, these findings reveal how GIV/CCDC88A in humans displays evolutionary flexibility in modularity, which allows the resultant isoforms to play opposing roles either as a tumor suppressor (GIV-L) or as an oncogene (GIV).

INSTRUMENT(S): Orbitrap Fusion Lumos

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Colon

SUBMITTER: Majid Ghassemian

LAB HEAD: Pradipta Ghosh

PROVIDER: PXD022601 | Pride | 2021-09-09

REPOSITORIES: Pride

ACCESS DATA

Dataset's files

Source:

Items per page:

1 - 5 of 12

Publications

A long isoform of GIV/Girdin contains a PDZ-binding module that regulates localization and G-protein binding.

Ear Jason J Abd El-Hafeez Amer Ali AA Roy Suchismita S Ngo Tony T Rajapakse Navin N Choi Julie J Khandelwal Soni S Ghassemian Majid M McCaffrey Luke L Kufareva Irina I Sahoo Debashis D Ghosh Pradipta P

The Journal of biological chemistry 20210101

PDZ domains are one of the most abundant protein domains in eukaryotes and are frequently found on junction-localized scaffold proteins. Various signaling molecules bind to PDZ proteins via PDZ-binding motifs (PBM) and fine-tune cellular signaling. However, how such interaction affects protein function is difficult to predict and must be solved empirically. Here we describe a long isoform of the guanine nucleotide exchange factor GIV/Girdin (CCDC88A) that we named GIV-L, which is conserved throu ...[more]

PMID: 33675748

Similar Datasets

Project description:PDZ (Post-synaptic density, PSD-95)/ Disc large, Dlg/ Zonula occludens, ZO-1) proteins are central in the assembly of multiprotein signalling complexes that are formed in distinctive, specialized cell regions. In these complexes different signals are orchestrated and traduced into quick and efficient responses controlling cell polarization and cell communication. Viral pathogens target host PDZ proteins by expressing viral proteins containing a PDZ binding motif (PBM). SARS-CoV-1 and -2 harbour the protein E, a viroporin with a conserved PBM in its carboxyl-terminal region. SARS-CoV-1 E protein is a pathogenicity factor that in epithelia interacts with the PDZ protein PALS1, which promote disruption of cell junctions. SARS-CoV-2 infects epithelia, but also cells from the innate immune response, including monocytes, dendritic cells, and alveolar macrophages. Identification of targets of SARS-CoV-2 E protein in immune cells might offer valuable clues to understand how SARS-CoV-2 alters immune response. Here we generated a SARS-CoV-2 E protein fused to a GFP-tag at the amino terminal. This recombinant protein was used as bait to identify associated proteins in THP-1 cells, a human monocytic cell line that can be differentiated to macrophages and dendritic cells. Analysis of the GFP-E protein interactome provided 372 proteins that fall into different functional groups. Only eight of these proteins harbor PDZ domains, including the cell polarity protein ZO-2 and the chemoattractant IL-16. Syntenin, a PDZ protein previously identified as interactor of SARS-CoV-1 E protein in epithelia, was also found in our analysis. Little is known about these PDZ proteins in immune cells, so we addressed their expression in monocytes and along the differentiation towards macrophages and dendritic cells. Our findings support the notion that viral targeting of PDZ proteins alters inflammatory response and highlight the importance of identifying the functions of PDZ proteins into maintenance of immune fitness.

Project description:DNA sequence is a major determinant of the binding specificity of transcription factors (TFs) for their genomic targets. However, eukaryotic cells often express, at the same time, TFs with highly similar DNA binding motifs but distinct in vivo targets. Currently, it is not well understood how TFs with seemingly identical DNA motifs achieve unique specificities in vivo. Here, we used custom protein binding microarrays to analyze TF specificity for putative binding sites in their genomic sequence context. Using yeast TFs Cbf1 and Tye7 as our case study, we found that binding sites of these bHLH TFs (i.e., E-boxes) are bound differently in vitro and in vivo, depending on their genomic context. Computational analyses suggest that nucleotides outside E-box binding sites contribute to specificity by influencing the 3D structure of DNA binding sites. Thus, local shape of target sites might play a widespread role in achieving regulatory specificity within TF families. Three protein binding microarray (PBM) experiments of Saccharomyces cerevisiae transcription factors were performed. Briefly, the PBMs involved binding GST-tagged yeast transcription factors Cbf1 and Tye7 to double-stranded 44K Agilent microarrays in order to determine their binding specificity for putative DNA binding sites in native genomic context. Briefly, we represent three categories of 30-bp genomic sequences: 1) ChIP-chip bound probes, 2) ChIP-chip unbound probes, and 3) negative control probes. ChIP-chip bound probes corresponded to genomic regions bound in vivo by Cbf1 or Tye7 (ChIP-chip P < 0.005 in rich medium (YPD) (Harbison et al., Nature 2004, PMID 15343339)) contained at least two consecutive 8-mers with universal PBM E-score > 0.35 (Zhu et al., Genome Research 2009, PMID 19158363). All putative binding sites occurred at the same position within the probes on the array. M-bM-^@M-^\ChIP-chip unboundM-bM-^@M-^] probes corresponded to genomic regions with ChIP-chip P > 0.5 and at least two consecutive 8-mers at a more stringent universal PBM E-score threshold of 0.4. Negative control probes corresponded to S. cerevisiae intergenic regions with a maximum 8-mer E-score < 0.3. We also designed probes that contain, within constant flanking regions, all 10-bp sequences that occur within the M-bM-^@M-^\ChIP-chip boundM-bM-^@M-^] probes and contain the E-box CACGTG, but are flanked by synthetic rather than native genomic sequence. Each DNA sequence represented on the array is present in 4 replicate spots. We report the PBM signal intensity for each spot. The PBM protocol is described in Berger et al., Nature Biotechnology 2006 (PMID 16998473).

Project description:Motivation: The DNA binding specificity of a transcription factor (TF) is typically represented using a position weight matrix (PWM) model, which implicitly assumes that individual bases in a TF binding site contribute independently to the binding affinity, an assumption that does not always hold. For this reason, more complex models of binding specificity have been developed. However, these models have their own caveats: they typically have a large number of parameters, which makes them hard to learn and interpret. Results: We propose novel regression-based models of TF-DNA binding specificity, trained using high resolution in vitro data from custom protein binding microarray (PBM) experiments. Our PBMs are specifically designed to cover a large number of putative DNA binding sites for the TFs of interest (yeast TFs Cbf1 and Tye7, and human TFs c-Myc, Max, and Mad2) in their native genomic context. These high-throughput, quantitative data are well suited for training complex models that take into account not only independent contributions from individual bases, but also contributions from di- and trinucleotides at various positions within or near the binding sites. To ensure that our models remain interpretable, we use feature selection to identify a small number of sequence features that accurately predict TF-DNA binding specificity. To further illustrate the accuracy of our regression models, we show that even in the case of paralogous TF with highly similar PWMs, our new models can distinguish the specificities of individual factors. Thus, our work represents an important step towards better sequence-based models of individual TF-DNA binding specificity. Four protein binding microarray (PBM) experiments of human transcription factors were performed. Briefly, the PBMs involved binding GST-tagged transcription factors c-Myc, Max, and Mad2(Mxi1) to double-stranded 180K Agilent microarrays in order to determine their binding specificity for putative DNA binding sites in native genomic context. Briefly, we represent three categories of 36-bp sequences: 1) bound probes, 2) unbound probes (or negative controls), and 3) test probes. Bound probes corresponded to genomic regions bound in vivo by c-Myc, Max, or Mad2 (ChIP-seq P < 10^(-10) in HeLaS3 or K562 celld (ENCODE)) that contain at least two consecutive 8-mers with universal PBM E-score > 0.4 (Munteanu and Gordan, LNCS 2013). All putative binding sites occurr at the same position within the probes on the array. M-bM-^@M-^\UnboundM-bM-^@M-^] probes corresponded to genomic regions with ChIP-seq P < 10^(-10) and a maximum 8-mer E-score < 0.2. We also designed test probes that contain, within constant flanking regions, all nnCACGTGnn 10-mers and 18 nnnCACGTGnnn 12-mers (where n = A, C, G, or T). Each DNA sequence represented on the array is present in 6 replicate spots. We report the PBM signal intensity for each spot. The PBM protocol is described in Berger et al., Nature Biotechnology 2006 (PMID 16998473).

			Action	DRS
	X1_Plus.raw	Raw
	X1_minus.raw	Raw
	X1minus.csv	Csv
	X1minus.mgf	Mgf
	X1plus.csv	Csv