A systematic screen for protein–lipid interactions in Saccharomyces cerevisiae
Protein–metabolite networks are central to biological systems, but are incompletely understood. Here, we report a screen to catalog protein–lipid interactions in yeast. We used arrays of 56 metabolites to measure lipid‐binding fingerprints of 172 proteins, including 91 with predicted lipid‐binding d...
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| Vydané v: | Molecular systems biology Ročník 6; číslo 1; s. 430 - n/a |
|---|---|
| Hlavní autori: | , , , , , , , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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London
Nature Publishing Group UK
30.11.2010
John Wiley & Sons, Ltd EMBO Press Nature Publishing Group Springer Nature |
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| ISSN: | 1744-4292, 1744-4292 |
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| Abstract | Protein–metabolite networks are central to biological systems, but are incompletely understood. Here, we report a screen to catalog protein–lipid interactions in yeast. We used arrays of 56 metabolites to measure lipid‐binding fingerprints of 172 proteins, including 91 with predicted lipid‐binding domains. We identified 530 protein–lipid associations, the majority of which are novel. To show the data set's biological value, we studied further several novel interactions with sphingolipids, a class of conserved bioactive lipids with an elusive mode of action. Integration of live‐cell imaging suggests new cellular targets for these molecules, including several with pleckstrin homology (PH) domains. Validated interactions with Slm1, a regulator of actin polarization, show that PH domains can have unexpected lipid‐binding specificities and can act as coincidence sensors for both phosphatidylinositol phosphates and phosphorylated sphingolipids.
Synopsis
Deciphering the molecular mechanisms behind cellular processes requires the systematic charting of the multitude of interactions between all cellular components. While protein–protein and protein–DNA networks have been the subject of many systematic surveys, other critically important cellular components, such as lipids, have to date rarely been studied in large‐scale interaction screens. Growing numbers of lipids are known to operate as signaling molecules. The importance of protein–lipid interactions is evident from the variety of protein domains that have evolved to bind particular lipids (Lemmon,
2008
#392) and from the large list of disorders, such as cancer and bipolar disorder, arising from altered protein–lipid interactions. The current understanding of protein–lipid recognition comes from the study of a limited number of lipids, principally PtdInsPs (Zhu
et al
,
2001
#16), and lipid‐binding domains (LBDs) in isolation (Dowler
et al
,
2000
#81; Yu and Lemmon,
2001
#396; Yu
et al
,
2004
#31). For other signaling lipids, such as sphingolipids, intracellular targets and molecular mechanisms are only partially understood (Hannun and Obeid,
2008
#397). The importance of lipids in biological processes and their under‐representation in current biological networks suggest the need for systematic, unbiased biochemical screens.
To systematically study protein–lipid interactions, we developed miniaturized arrays that contained sets of 56 lipids covering the main lipid classes in yeast. We used the arrays to determine the binding profiles of 172 soluble proteins. The selection included proteins that contained one or several predicted LBD that were lipid regulated or enzymes involved in lipid metabolism (Figure
1
). We obtained 530 protein–lipid interactions (accuracy and coverage: 61 and 60%, respectively). More than half were supported by additional experimental evidences obtained from a large validation effort using a variety of biochemical and cell biology approaches, and the integration of a data set of genetic interactions (Figure
1
). As a substantial fraction (45%) of the analyzed proteins were conserved in humans, the protein–lipid data set will have functional implications for higher eukaryotes and thus for human biology.
Overall, 68% of all interactions were novel or unexpected from either protein sequences or known LBDs specificities. We discovered cryptic LBDs that were previously undetected in Ecm25 (a RhoGAP) and Ira2 (a RasGAP). We also identified a set of proteins that bound sphingolipids, a class of bioactive lipids that play important signaling functions in yeast and higher eukaryotes. The exact mode of action for these lipids remains elusive and the data set points to series of new cellular targets. We identified 63 proteins, involved in endocytosis, cell polarity and lipid metabolism that interacted with sphingoid long‐chain bases (LCBs), ceramides or phosphorylated LCBs (Figure
5
).
Despite the importance of sphingolipids in signaling processes, only a few domains, such as START or Saposins, have been reported to specifically bind these lipids in higher eukaryotes, and none of them have been found in yeast. Interestingly, almost 60% of proteins binding to phosphorylated LCBs in our assay also contained a pleckstrin homology (PH) domain and bound PtdInsPs (Figure
5
). This suggests some PH domains might have unanticipated ligands and also have a function in sphingolipid recognition. We showed, using a variety of biochemical and cell‐based assays, that the PH domain of Slm1, a component of the TORC2 signaling pathway (Fadri
et al
,
2005
#429), can bind PtdIns(4,5)P
2
and sphingolipid cooperatively. The structure of Slm1‐PH, which we solved by X‐ray crystallography at 2 Å resolution, suggests the presence of two positively charged binding pockets for anionic lipids. These results indicate that the PH domain of Slm1 might work as a coincidence sensor to integrate both PtdInsP and sphingolipid signaling pathways. This reinforces the emerging notion that cooperative mechanisms have important functions in PH domains functioning (Maffucci and Falasca,
2001
#528). These mechanisms initially described between PtdInsPs and proteins can now be extended to new lipid classes, illustrating the benefit of unbiased and systematic analyses.
This work shows the feasibility and benefits of large‐scale analyses combining biochemical arrays and live‐cell imaging for charting protein–lipid interactions. Accurate representations of biological processes require systematic charting of the physical and functional links between all cellular components. There is a clear need to expand molecular interaction space from proteome‐ to metabolome‐wide efforts and of systematic classifications of bioactive molecules based on their binding profiles. The data provided here represents an excellent resource to enhance the understanding of lipids function in eukaryotic systems.
Lipids are important cellular metabolites, with a wide range of structural and functional diversity. Many operate as signaling molecules. Lipids though have rarely been studied in large‐scale interaction screen; they are poorly represented in current biological networks.
Here, we describe the use of miniaturized lipid–arrays for the large‐scale study of protein–lipid interactions. In yeast, we show general feasibility with a systematic screen implying 172 proteins. We report 530 protein–lipid associations, the majority is novel and several were validated using other techniques.
The screen uncovers numerous insights into lipid function in yeast and equivalent systems in humans. It revealed (i) previously undetected cryptic lipid‐binding domains, (ii) series of new cellular targets for sphingolipids and (iii) new ligands for some PH domains that can cooperatively bind additional lipids and work as coincidence sensor to integrate both phosphatidylinositol phosphates and sphingolipid signaling pathways.
The significant number of biological insights uncovered shows that even major classes of metabolites have been insufficiently studied. This illustrates the general relevance of such systematic screens and calls for further system‐wide analyses. |
|---|---|
| AbstractList | Lipids are important cellular metabolites, with a wide range of structural and functional diversity. Many operate as signaling molecules. Lipids though have rarely been studied in large-scale interaction screen; they are poorly represented in current biological networks.
Here, we describe the use of miniaturized lipid–arrays for the large-scale study of protein–lipid interactions. In yeast, we show general feasibility with a systematic screen implying 172 proteins. We report 530 protein–lipid associations, the majority is novel and several were validated using other techniques.
The screen uncovers numerous insights into lipid function in yeast and equivalent systems in humans. It revealed (i) previously undetected cryptic lipid-binding domains, (ii) series of new cellular targets for sphingolipids and (iii) new ligands for some PH domains that can cooperatively bind additional lipids and work as coincidence sensor to integrate both phosphatidylinositol phosphates and sphingolipid signaling pathways.
The significant number of biological insights uncovered shows that even major classes of metabolites have been insufficiently studied. This illustrates the general relevance of such systematic screens and calls for further system-wide analyses.
Deciphering the molecular mechanisms behind cellular processes requires the systematic charting of the multitude of interactions between all cellular components. While protein–protein and protein–DNA networks have been the subject of many systematic surveys, other critically important cellular components, such as lipids, have to date rarely been studied in large-scale interaction screens. Growing numbers of lipids are known to operate as signaling molecules. The importance of protein–lipid interactions is evident from the variety of protein domains that have evolved to bind particular lipids (Lemmon, 2008 #392) and from the large list of disorders, such as cancer and bipolar disorder, arising from altered protein–lipid interactions. The current understanding of protein–lipid recognition comes from the study of a limited number of lipids, principally PtdInsPs (Zhu et al, 2001 #16), and lipid-binding domains (LBDs) in isolation (Dowler et al, 2000 #81; Yu and Lemmon, 2001 #396; Yu et al, 2004 #31). For other signaling lipids, such as sphingolipids, intracellular targets and molecular mechanisms are only partially understood (Hannun and Obeid, 2008 #397). The importance of lipids in biological processes and their under-representation in current biological networks suggest the need for systematic, unbiased biochemical screens. To systematically study protein–lipid interactions, we developed miniaturized arrays that contained sets of 56 lipids covering the main lipid classes in yeast. We used the arrays to determine the binding profiles of 172 soluble proteins. The selection included proteins that contained one or several predicted LBD that were lipid regulated or enzymes involved in lipid metabolism (Figure 1). We obtained 530 protein–lipid interactions (accuracy and coverage: 61 and 60%, respectively). More than half were supported by additional experimental evidences obtained from a large validation effort using a variety of biochemical and cell biology approaches, and the integration of a data set of genetic interactions (Figure 1). As a substantial fraction (45%) of the analyzed proteins were conserved in humans, the protein–lipid data set will have functional implications for higher eukaryotes and thus for human biology. Overall, 68% of all interactions were novel or unexpected from either protein sequences or known LBDs specificities. We discovered cryptic LBDs that were previously undetected in Ecm25 (a RhoGAP) and Ira2 (a RasGAP). We also identified a set of proteins that bound sphingolipids, a class of bioactive lipids that play important signaling functions in yeast and higher eukaryotes. The exact mode of action for these lipids remains elusive and the data set points to series of new cellular targets. We identified 63 proteins, involved in endocytosis, cell polarity and lipid metabolism that interacted with sphingoid long-chain bases (LCBs), ceramides or phosphorylated LCBs (Figure 5). Despite the importance of sphingolipids in signaling processes, only a few domains, such as START or Saposins, have been reported to specifically bind these lipids in higher eukaryotes, and none of them have been found in yeast. Interestingly, almost 60% of proteins binding to phosphorylated LCBs in our assay also contained a pleckstrin homology (PH) domain and bound PtdInsPs (Figure 5). This suggests some PH domains might have unanticipated ligands and also have a function in sphingolipid recognition. We showed, using a variety of biochemical and cell-based assays, that the PH domain of Slm1, a component of the TORC2 signaling pathway (Fadri et al, 2005 #429), can bind PtdIns(4,5)P2 and sphingolipid cooperatively. The structure of Slm1-PH, which we solved by X-ray crystallography at 2 Å resolution, suggests the presence of two positively charged binding pockets for anionic lipids. These results indicate that the PH domain of Slm1 might work as a coincidence sensor to integrate both PtdInsP and sphingolipid signaling pathways. This reinforces the emerging notion that cooperative mechanisms have important functions in PH domains functioning (Maffucci and Falasca, 2001 #528). These mechanisms initially described between PtdInsPs and proteins can now be extended to new lipid classes, illustrating the benefit of unbiased and systematic analyses. This work shows the feasibility and benefits of large-scale analyses combining biochemical arrays and live-cell imaging for charting protein–lipid interactions. Accurate representations of biological processes require systematic charting of the physical and functional links between all cellular components. There is a clear need to expand molecular interaction space from proteome- to metabolome-wide efforts and of systematic classifications of bioactive molecules based on their binding profiles. The data provided here represents an excellent resource to enhance the understanding of lipids function in eukaryotic systems. Protein–metabolite networks are central to biological systems, but are incompletely understood. Here, we report a screen to catalog protein–lipid interactions in yeast. We used arrays of 56 metabolites to measure lipid-binding fingerprints of 172 proteins, including 91 with predicted lipid-binding domains. We identified 530 protein–lipid associations, the majority of which are novel. To show the data set's biological value, we studied further several novel interactions with sphingolipids, a class of conserved bioactive lipids with an elusive mode of action. Integration of live-cell imaging suggests new cellular targets for these molecules, including several with pleckstrin homology (PH) domains. Validated interactions with Slm1, a regulator of actin polarization, show that PH domains can have unexpected lipid-binding specificities and can act as coincidence sensors for both phosphatidylinositol phosphates and phosphorylated sphingolipids. Protein-metabolite networks are central to biological systems, but are incompletely understood. Here, we report a screen to catalog protein-lipid interactions in yeast. We used arrays of 56 metabolites to measure lipid-binding fingerprints of 172 proteins, including 91 with predicted lipid-binding domains. We identified 530 protein-lipid associations, the majority of which are novel. To show the data set's biological value, we studied further several novel interactions with sphingolipids, a class of conserved bioactive lipids with an elusive mode of action. Integration of live-cell imaging suggests new cellular targets for these molecules, including several with pleckstrin homology (PH) domains. Validated interactions with Slm1, a regulator of actin polarization, show that PH domains can have unexpected lipid-binding specificities and can act as coincidence sensors for both phosphatidylinositol phosphates and phosphorylated sphingolipids. Synopsis Deciphering the molecular mechanisms behind cellular processes requires the systematic charting of the multitude of interactions between all cellular components. While protein-protein and protein-DNA networks have been the subject of many systematic surveys, other critically important cellular components, such as lipids, have to date rarely been studied in large-scale interaction screens. Growing numbers of lipids are known to operate as signaling molecules. The importance of protein-lipid interactions is evident from the variety of protein domains that have evolved to bind particular lipids ( Lemmon, 2008 #392) and from the large list of disorders, such as cancer and bipolar disorder, arising from altered protein-lipid interactions. The current understanding of protein-lipid recognition comes from the study of a limited number of lipids, principally PtdInsPs ( Zhu , 2001 #16), and lipid-binding domains (LBDs) in isolation ( Dowler , 2000 #81; Yu and Lemmon, 2001 #396; Yu , 2004 #31). For other signaling lipids, such as sphingolipids, intracellular targets and molecular mechanisms are only partially understood ( Hannun and Obeid, 2008 #397). The importance of lipids in biological processes and their under-representation in current biological networks suggest the need for systematic, unbiased biochemical screens. To systematically study protein-lipid interactions, we developed miniaturized arrays that contained sets of 56 lipids covering the main lipid classes in yeast. We used the arrays to determine the binding profiles of 172 soluble proteins. The selection included proteins that contained one or several predicted LBD that were lipid regulated or enzymes involved in lipid metabolism (Figure 1). We obtained 530 protein-lipid interactions (accuracy and coverage: 61 and 60%, respectively). More than half were supported by additional experimental evidences obtained from a large validation effort using a variety of biochemical and cell biology approaches, and the integration of a data set of genetic interactions (Figure 1). As a substantial fraction (45%) of the analyzed proteins were conserved in humans, the protein-lipid data set will have functional implications for higher eukaryotes and thus for human biology. Overall, 68% of all interactions were novel or unexpected from either protein sequences or known LBDs specificities. We discovered cryptic LBDs that were previously undetected in Ecm25 (a RhoGAP) and Ira2 (a RasGAP). We also identified a set of proteins that bound sphingolipids, a class of bioactive lipids that play important signaling functions in yeast and higher eukaryotes. The exact mode of action for these lipids remains elusive and the data set points to series of new cellular targets. We identified 63 proteins, involved in endocytosis, cell polarity and lipid metabolism that interacted with sphingoid long-chain bases (LCBs), ceramides or phosphorylated LCBs (Figure 5). Despite the importance of sphingolipids in signaling processes, only a few domains, such as START or Saposins, have been reported to specifically bind these lipids in higher eukaryotes, and none of them have been found in yeast. Interestingly, almost 60% of proteins binding to phosphorylated LCBs in our assay also contained a pleckstrin homology (PH) domain and bound PtdInsPs (Figure 5). This suggests some PH domains might have unanticipated ligands and also have a function in sphingolipid recognition. We showed, using a variety of biochemical and cell-based assays, that the PH domain of Slm1, a component of the TORC2 signaling pathway ( Fadri , 2005 #429), can bind PtdIns(4,5)P sub(2) and sphingolipid cooperatively. The structure of Slm1-PH, which we solved by X-ray crystallography at 2 Aa resolution, suggests the presence of two positively charged binding pockets for anionic lipids. These results indicate that the PH domain of Slm1 might work as a coincidence sensor to integrate both PtdInsP and sphingolipid signaling pathways. This reinforces the emerging notion that cooperative mechanisms have important functions in PH domains functioning ( Maffucci and Falasca, 2001 #528). These mechanisms initially described between PtdInsPs and proteins can now be extended to new lipid classes, illustrating the benefit of unbiased and systematic analyses. This work shows the feasibility and benefits of large-scale analyses combining biochemical arrays and live-cell imaging for charting protein-lipid interactions. Accurate representations of biological processes require systematic charting of the physical and functional links between all cellular components. There is a clear need to expand molecular interaction space from proteome- to metabolome-wide efforts and of systematic classifications of bioactive molecules based on their binding profiles. The data provided here represents an excellent resource to enhance the understanding of lipids function in eukaryotic systems. Protein-metabolite networks are central to biological systems, but are incompletely understood. Here, we report a screen to catalog protein-lipid interactions in yeast. We used arrays of 56 metabolites to measure lipid-binding fingerprints of 172 proteins, including 91 with predicted lipid-binding domains. We identified 530 protein-lipid associations, the majority of which are novel. To show the data set's biological value, we studied further several novel interactions with sphingolipids, a class of conserved bioactive lipids with an elusive mode of action. Integration of live-cell imaging suggests new cellular targets for these molecules, including several with pleckstrin homology (PH) domains. Validated interactions with Slm1, a regulator of actin polarization, show that PH domains can have unexpected lipid-binding specificities and can act as coincidence sensors for both phosphatidylinositol phosphates and phosphorylated sphingolipids. Protein–metabolite networks are central to biological systems, but are incompletely understood. Here, we report a screen to catalog protein–lipid interactions in yeast. We used arrays of 56 metabolites to measure lipid‐binding fingerprints of 172 proteins, including 91 with predicted lipid‐binding domains. We identified 530 protein–lipid associations, the majority of which are novel. To show the data set's biological value, we studied further several novel interactions with sphingolipids, a class of conserved bioactive lipids with an elusive mode of action. Integration of live‐cell imaging suggests new cellular targets for these molecules, including several with pleckstrin homology (PH) domains. Validated interactions with Slm1, a regulator of actin polarization, show that PH domains can have unexpected lipid‐binding specificities and can act as coincidence sensors for both phosphatidylinositol phosphates and phosphorylated sphingolipids. Synopsis Deciphering the molecular mechanisms behind cellular processes requires the systematic charting of the multitude of interactions between all cellular components. While protein–protein and protein–DNA networks have been the subject of many systematic surveys, other critically important cellular components, such as lipids, have to date rarely been studied in large‐scale interaction screens. Growing numbers of lipids are known to operate as signaling molecules. The importance of protein–lipid interactions is evident from the variety of protein domains that have evolved to bind particular lipids (Lemmon, 2008 #392) and from the large list of disorders, such as cancer and bipolar disorder, arising from altered protein–lipid interactions. The current understanding of protein–lipid recognition comes from the study of a limited number of lipids, principally PtdInsPs (Zhu et al, 2001 #16), and lipid‐binding domains (LBDs) in isolation (Dowler et al, 2000 #81; Yu and Lemmon, 2001 #396; Yu et al, 2004 #31). For other signaling lipids, such as sphingolipids, intracellular targets and molecular mechanisms are only partially understood (Hannun and Obeid, 2008 #397). The importance of lipids in biological processes and their under‐representation in current biological networks suggest the need for systematic, unbiased biochemical screens. To systematically study protein–lipid interactions, we developed miniaturized arrays that contained sets of 56 lipids covering the main lipid classes in yeast. We used the arrays to determine the binding profiles of 172 soluble proteins. The selection included proteins that contained one or several predicted LBD that were lipid regulated or enzymes involved in lipid metabolism (Figure 1). We obtained 530 protein–lipid interactions (accuracy and coverage: 61 and 60%, respectively). More than half were supported by additional experimental evidences obtained from a large validation effort using a variety of biochemical and cell biology approaches, and the integration of a data set of genetic interactions (Figure 1). As a substantial fraction (45%) of the analyzed proteins were conserved in humans, the protein–lipid data set will have functional implications for higher eukaryotes and thus for human biology. Overall, 68% of all interactions were novel or unexpected from either protein sequences or known LBDs specificities. We discovered cryptic LBDs that were previously undetected in Ecm25 (a RhoGAP) and Ira2 (a RasGAP). We also identified a set of proteins that bound sphingolipids, a class of bioactive lipids that play important signaling functions in yeast and higher eukaryotes. The exact mode of action for these lipids remains elusive and the data set points to series of new cellular targets. We identified 63 proteins, involved in endocytosis, cell polarity and lipid metabolism that interacted with sphingoid long‐chain bases (LCBs), ceramides or phosphorylated LCBs (Figure 5). Despite the importance of sphingolipids in signaling processes, only a few domains, such as START or Saposins, have been reported to specifically bind these lipids in higher eukaryotes, and none of them have been found in yeast. Interestingly, almost 60% of proteins binding to phosphorylated LCBs in our assay also contained a pleckstrin homology (PH) domain and bound PtdInsPs (Figure 5). This suggests some PH domains might have unanticipated ligands and also have a function in sphingolipid recognition. We showed, using a variety of biochemical and cell‐based assays, that the PH domain of Slm1, a component of the TORC2 signaling pathway (Fadri et al, 2005 #429), can bind PtdIns(4,5)P2 and sphingolipid cooperatively. The structure of Slm1‐PH, which we solved by X‐ray crystallography at 2 Å resolution, suggests the presence of two positively charged binding pockets for anionic lipids. These results indicate that the PH domain of Slm1 might work as a coincidence sensor to integrate both PtdInsP and sphingolipid signaling pathways. This reinforces the emerging notion that cooperative mechanisms have important functions in PH domains functioning (Maffucci and Falasca, 2001 #528). These mechanisms initially described between PtdInsPs and proteins can now be extended to new lipid classes, illustrating the benefit of unbiased and systematic analyses. This work shows the feasibility and benefits of large‐scale analyses combining biochemical arrays and live‐cell imaging for charting protein–lipid interactions. Accurate representations of biological processes require systematic charting of the physical and functional links between all cellular components. There is a clear need to expand molecular interaction space from proteome‐ to metabolome‐wide efforts and of systematic classifications of bioactive molecules based on their binding profiles. The data provided here represents an excellent resource to enhance the understanding of lipids function in eukaryotic systems. Lipids are important cellular metabolites, with a wide range of structural and functional diversity. Many operate as signaling molecules. Lipids though have rarely been studied in large‐scale interaction screen; they are poorly represented in current biological networks. Here, we describe the use of miniaturized lipid–arrays for the large‐scale study of protein–lipid interactions. In yeast, we show general feasibility with a systematic screen implying 172 proteins. We report 530 protein–lipid associations, the majority is novel and several were validated using other techniques. The screen uncovers numerous insights into lipid function in yeast and equivalent systems in humans. It revealed (i) previously undetected cryptic lipid‐binding domains, (ii) series of new cellular targets for sphingolipids and (iii) new ligands for some PH domains that can cooperatively bind additional lipids and work as coincidence sensor to integrate both phosphatidylinositol phosphates and sphingolipid signaling pathways. The significant number of biological insights uncovered shows that even major classes of metabolites have been insufficiently studied. This illustrates the general relevance of such systematic screens and calls for further system‐wide analyses. Protein–metabolite networks are central to biological systems, but are incompletely understood. Here, we report a screen to catalog protein–lipid interactions in yeast. We used arrays of 56 metabolites to measure lipid‐binding fingerprints of 172 proteins, including 91 with predicted lipid‐binding domains. We identified 530 protein–lipid associations, the majority of which are novel. To show the data set's biological value, we studied further several novel interactions with sphingolipids, a class of conserved bioactive lipids with an elusive mode of action. Integration of live‐cell imaging suggests new cellular targets for these molecules, including several with pleckstrin homology (PH) domains. Validated interactions with Slm1, a regulator of actin polarization, show that PH domains can have unexpected lipid‐binding specificities and can act as coincidence sensors for both phosphatidylinositol phosphates and phosphorylated sphingolipids. Deciphering the molecular mechanisms behind cellular processes requires the systematic charting of the multitude of interactions between all cellular components. While protein–protein and protein–DNA networks have been the subject of many systematic surveys, other critically important cellular components, such as lipids, have to date rarely been studied in large‐scale interaction screens. Growing numbers of lipids are known to operate as signaling molecules. The importance of protein–lipid interactions is evident from the variety of protein domains that have evolved to bind particular lipids ( Lemmon, 2008 #392) and from the large list of disorders, such as cancer and bipolar disorder, arising from altered protein–lipid interactions. The current understanding of protein–lipid recognition comes from the study of a limited number of lipids, principally PtdInsPs ( Zhu et al , 2001 #16), and lipid‐binding domains (LBDs) in isolation ( Dowler et al , 2000 #81; Yu and Lemmon, 2001 #396; Yu et al , 2004 #31). For other signaling lipids, such as sphingolipids, intracellular targets and molecular mechanisms are only partially understood ( Hannun and Obeid, 2008 #397). The importance of lipids in biological processes and their under‐representation in current biological networks suggest the need for systematic, unbiased biochemical screens. To systematically study protein–lipid interactions, we developed miniaturized arrays that contained sets of 56 lipids covering the main lipid classes in yeast. We used the arrays to determine the binding profiles of 172 soluble proteins. The selection included proteins that contained one or several predicted LBD that were lipid regulated or enzymes involved in lipid metabolism ( Figure 1 ). We obtained 530 protein–lipid interactions (accuracy and coverage: 61 and 60%, respectively). More than half were supported by additional experimental evidences obtained from a large validation effort using a variety of biochemical and cell biology approaches, and the integration of a data set of genetic interactions ( Figure 1 ). As a substantial fraction (45%) of the analyzed proteins were conserved in humans, the protein–lipid data set will have functional implications for higher eukaryotes and thus for human biology. Overall, 68% of all interactions were novel or unexpected from either protein sequences or known LBDs specificities. We discovered cryptic LBDs that were previously undetected in Ecm25 (a RhoGAP) and Ira2 (a RasGAP). We also identified a set of proteins that bound sphingolipids, a class of bioactive lipids that play important signaling functions in yeast and higher eukaryotes. The exact mode of action for these lipids remains elusive and the data set points to series of new cellular targets. We identified 63 proteins, involved in endocytosis, cell polarity and lipid metabolism that interacted with sphingoid long‐chain bases (LCBs), ceramides or phosphorylated LCBs ( Figure 5 ). Despite the importance of sphingolipids in signaling processes, only a few domains, such as START or Saposins, have been reported to specifically bind these lipids in higher eukaryotes, and none of them have been found in yeast. Interestingly, almost 60% of proteins binding to phosphorylated LCBs in our assay also contained a pleckstrin homology (PH) domain and bound PtdInsPs ( Figure 5 ). This suggests some PH domains might have unanticipated ligands and also have a function in sphingolipid recognition. We showed, using a variety of biochemical and cell‐based assays, that the PH domain of Slm1, a component of the TORC2 signaling pathway ( Fadri et al , 2005 #429), can bind PtdIns(4,5)P 2 and sphingolipid cooperatively. The structure of Slm1‐PH, which we solved by X‐ray crystallography at 2 Å resolution, suggests the presence of two positively charged binding pockets for anionic lipids. These results indicate that the PH domain of Slm1 might work as a coincidence sensor to integrate both PtdInsP and sphingolipid signaling pathways. This reinforces the emerging notion that cooperative mechanisms have important functions in PH domains functioning ( Maffucci and Falasca, 2001 #528). These mechanisms initially described between PtdInsPs and proteins can now be extended to new lipid classes, illustrating the benefit of unbiased and systematic analyses. This work shows the feasibility and benefits of large‐scale analyses combining biochemical arrays and live‐cell imaging for charting protein–lipid interactions. Accurate representations of biological processes require systematic charting of the physical and functional links between all cellular components. There is a clear need to expand molecular interaction space from proteome‐ to metabolome‐wide efforts and of systematic classifications of bioactive molecules based on their binding profiles. The data provided here represents an excellent resource to enhance the understanding of lipids function in eukaryotic systems. Lipids are important cellular metabolites, with a wide range of structural and functional diversity. Many operate as signaling molecules. Lipids though have rarely been studied in large‐scale interaction screen; they are poorly represented in current biological networks. Here, we describe the use of miniaturized lipid–arrays for the large‐scale study of protein–lipid interactions. In yeast, we show general feasibility with a systematic screen implying 172 proteins. We report 530 protein–lipid associations, the majority is novel and several were validated using other techniques. The screen uncovers numerous insights into lipid function in yeast and equivalent systems in humans. It revealed (i) previously undetected cryptic lipid‐binding domains, (ii) series of new cellular targets for sphingolipids and (iii) new ligands for some PH domains that can cooperatively bind additional lipids and work as coincidence sensor to integrate both phosphatidylinositol phosphates and sphingolipid signaling pathways. The significant number of biological insights uncovered shows that even major classes of metabolites have been insufficiently studied. This illustrates the general relevance of such systematic screens and calls for further system‐wide analyses. Abstract Protein–metabolite networks are central to biological systems, but are incompletely understood. Here, we report a screen to catalog protein–lipid interactions in yeast. We used arrays of 56 metabolites to measure lipid‐binding fingerprints of 172 proteins, including 91 with predicted lipid‐binding domains. We identified 530 protein–lipid associations, the majority of which are novel. To show the data set's biological value, we studied further several novel interactions with sphingolipids, a class of conserved bioactive lipids with an elusive mode of action. Integration of live‐cell imaging suggests new cellular targets for these molecules, including several with pleckstrin homology (PH) domains. Validated interactions with Slm1, a regulator of actin polarization, show that PH domains can have unexpected lipid‐binding specificities and can act as coincidence sensors for both phosphatidylinositol phosphates and phosphorylated sphingolipids. Protein-metabolite networks are central to biological systems, but are incompletely understood. Here, we report a screen to catalog protein-lipid interactions in yeast. We used arrays of 56 metabolites to measure lipid-binding fingerprints of 172 proteins, including 91 with predicted lipid-binding domains. We identified 530 protein-lipid associations, the majority of which are novel. To show the data set's biological value, we studied further several novel interactions with sphingolipids, a class of conserved bioactive lipids with an elusive mode of action. Integration of live-cell imaging suggests new cellular targets for these molecules, including several with pleckstrin homology (PH) domains. Validated interactions with Slm1, a regulator of actin polarization, show that PH domains can have unexpected lipid-binding specificities and can act as coincidence sensors for both phosphatidylinositol phosphates and phosphorylated sphingolipids.Protein-metabolite networks are central to biological systems, but are incompletely understood. Here, we report a screen to catalog protein-lipid interactions in yeast. We used arrays of 56 metabolites to measure lipid-binding fingerprints of 172 proteins, including 91 with predicted lipid-binding domains. We identified 530 protein-lipid associations, the majority of which are novel. To show the data set's biological value, we studied further several novel interactions with sphingolipids, a class of conserved bioactive lipids with an elusive mode of action. Integration of live-cell imaging suggests new cellular targets for these molecules, including several with pleckstrin homology (PH) domains. Validated interactions with Slm1, a regulator of actin polarization, show that PH domains can have unexpected lipid-binding specificities and can act as coincidence sensors for both phosphatidylinositol phosphates and phosphorylated sphingolipids. Protein–metabolite networks are central to biological systems, but are incompletely understood. Here, we report a screen to catalog protein–lipid interactions in yeast. We used arrays of 56 metabolites to measure lipid‐binding fingerprints of 172 proteins, including 91 with predicted lipid‐binding domains. We identified 530 protein–lipid associations, the majority of which are novel. To show the data set's biological value, we studied further several novel interactions with sphingolipids, a class of conserved bioactive lipids with an elusive mode of action. Integration of live‐cell imaging suggests new cellular targets for these molecules, including several with pleckstrin homology (PH) domains. Validated interactions with Slm1, a regulator of actin polarization, show that PH domains can have unexpected lipid‐binding specificities and can act as coincidence sensors for both phosphatidylinositol phosphates and phosphorylated sphingolipids. Synopsis Deciphering the molecular mechanisms behind cellular processes requires the systematic charting of the multitude of interactions between all cellular components. While protein–protein and protein–DNA networks have been the subject of many systematic surveys, other critically important cellular components, such as lipids, have to date rarely been studied in large‐scale interaction screens. Growing numbers of lipids are known to operate as signaling molecules. The importance of protein–lipid interactions is evident from the variety of protein domains that have evolved to bind particular lipids (Lemmon, 2008 #392) and from the large list of disorders, such as cancer and bipolar disorder, arising from altered protein–lipid interactions. The current understanding of protein–lipid recognition comes from the study of a limited number of lipids, principally PtdInsPs (Zhu et al , 2001 #16), and lipid‐binding domains (LBDs) in isolation (Dowler et al , 2000 #81; Yu and Lemmon, 2001 #396; Yu et al , 2004 #31). For other signaling lipids, such as sphingolipids, intracellular targets and molecular mechanisms are only partially understood (Hannun and Obeid, 2008 #397). The importance of lipids in biological processes and their under‐representation in current biological networks suggest the need for systematic, unbiased biochemical screens. To systematically study protein–lipid interactions, we developed miniaturized arrays that contained sets of 56 lipids covering the main lipid classes in yeast. We used the arrays to determine the binding profiles of 172 soluble proteins. The selection included proteins that contained one or several predicted LBD that were lipid regulated or enzymes involved in lipid metabolism (Figure 1 ). We obtained 530 protein–lipid interactions (accuracy and coverage: 61 and 60%, respectively). More than half were supported by additional experimental evidences obtained from a large validation effort using a variety of biochemical and cell biology approaches, and the integration of a data set of genetic interactions (Figure 1 ). As a substantial fraction (45%) of the analyzed proteins were conserved in humans, the protein–lipid data set will have functional implications for higher eukaryotes and thus for human biology. Overall, 68% of all interactions were novel or unexpected from either protein sequences or known LBDs specificities. We discovered cryptic LBDs that were previously undetected in Ecm25 (a RhoGAP) and Ira2 (a RasGAP). We also identified a set of proteins that bound sphingolipids, a class of bioactive lipids that play important signaling functions in yeast and higher eukaryotes. The exact mode of action for these lipids remains elusive and the data set points to series of new cellular targets. We identified 63 proteins, involved in endocytosis, cell polarity and lipid metabolism that interacted with sphingoid long‐chain bases (LCBs), ceramides or phosphorylated LCBs (Figure 5 ). Despite the importance of sphingolipids in signaling processes, only a few domains, such as START or Saposins, have been reported to specifically bind these lipids in higher eukaryotes, and none of them have been found in yeast. Interestingly, almost 60% of proteins binding to phosphorylated LCBs in our assay also contained a pleckstrin homology (PH) domain and bound PtdInsPs (Figure 5 ). This suggests some PH domains might have unanticipated ligands and also have a function in sphingolipid recognition. We showed, using a variety of biochemical and cell‐based assays, that the PH domain of Slm1, a component of the TORC2 signaling pathway (Fadri et al , 2005 #429), can bind PtdIns(4,5)P 2 and sphingolipid cooperatively. The structure of Slm1‐PH, which we solved by X‐ray crystallography at 2 Å resolution, suggests the presence of two positively charged binding pockets for anionic lipids. These results indicate that the PH domain of Slm1 might work as a coincidence sensor to integrate both PtdInsP and sphingolipid signaling pathways. This reinforces the emerging notion that cooperative mechanisms have important functions in PH domains functioning (Maffucci and Falasca, 2001 #528). These mechanisms initially described between PtdInsPs and proteins can now be extended to new lipid classes, illustrating the benefit of unbiased and systematic analyses. This work shows the feasibility and benefits of large‐scale analyses combining biochemical arrays and live‐cell imaging for charting protein–lipid interactions. Accurate representations of biological processes require systematic charting of the physical and functional links between all cellular components. There is a clear need to expand molecular interaction space from proteome‐ to metabolome‐wide efforts and of systematic classifications of bioactive molecules based on their binding profiles. The data provided here represents an excellent resource to enhance the understanding of lipids function in eukaryotic systems. Lipids are important cellular metabolites, with a wide range of structural and functional diversity. Many operate as signaling molecules. Lipids though have rarely been studied in large‐scale interaction screen; they are poorly represented in current biological networks. Here, we describe the use of miniaturized lipid–arrays for the large‐scale study of protein–lipid interactions. In yeast, we show general feasibility with a systematic screen implying 172 proteins. We report 530 protein–lipid associations, the majority is novel and several were validated using other techniques. The screen uncovers numerous insights into lipid function in yeast and equivalent systems in humans. It revealed (i) previously undetected cryptic lipid‐binding domains, (ii) series of new cellular targets for sphingolipids and (iii) new ligands for some PH domains that can cooperatively bind additional lipids and work as coincidence sensor to integrate both phosphatidylinositol phosphates and sphingolipid signaling pathways. The significant number of biological insights uncovered shows that even major classes of metabolites have been insufficiently studied. This illustrates the general relevance of such systematic screens and calls for further system‐wide analyses. |
| Author | Kuhn, Michael Müller, Christoph W Betts, Matthew J Bork, Peer Gvozdenovic‐Jeremic, Jelena Gavin, Anne‐Claude Rybin, Vladimir Gallego, Oriol Maeda, Kenji Bonn, Stefan Fernández‐Tornero, Carlos Matetzki, Christian Aguilar‐Gurrieri, Carmen Beltran‐Alvarez, Pedro Kaksonen, Marko Jensen, Lars Juhl Trott, Jamie Russell, Robert B |
| Author_xml | – sequence: 1 givenname: Oriol surname: Gallego fullname: Gallego, Oriol organization: Structural and Computational Biology Unit, European Molecular Biology Laboratory – sequence: 2 givenname: Matthew J surname: Betts fullname: Betts, Matthew J organization: Structural and Computational Biology Unit, European Molecular Biology Laboratory, Cell Networks, University of Heidelberg – sequence: 3 givenname: Jelena surname: Gvozdenovic‐Jeremic fullname: Gvozdenovic‐Jeremic, Jelena organization: Structural and Computational Biology Unit, European Molecular Biology Laboratory – sequence: 4 givenname: Kenji surname: Maeda fullname: Maeda, Kenji organization: Structural and Computational Biology Unit, European Molecular Biology Laboratory – sequence: 5 givenname: Christian surname: Matetzki fullname: Matetzki, Christian organization: Structural and Computational Biology Unit, European Molecular Biology Laboratory – sequence: 6 givenname: Carmen surname: Aguilar‐Gurrieri fullname: Aguilar‐Gurrieri, Carmen organization: Structural and Computational Biology Unit, European Molecular Biology Laboratory – sequence: 7 givenname: Pedro surname: Beltran‐Alvarez fullname: Beltran‐Alvarez, Pedro organization: Structural and Computational Biology Unit, European Molecular Biology Laboratory – sequence: 8 givenname: Stefan surname: Bonn fullname: Bonn, Stefan organization: Structural and Computational Biology Unit, European Molecular Biology Laboratory – sequence: 9 givenname: Carlos surname: Fernández‐Tornero fullname: Fernández‐Tornero, Carlos organization: Structural and Computational Biology Unit, European Molecular Biology Laboratory, Centro de Investigaciones Biológicas‐CSIC – sequence: 10 givenname: Lars Juhl surname: Jensen fullname: Jensen, Lars Juhl organization: Structural and Computational Biology Unit, European Molecular Biology Laboratory, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen – sequence: 11 givenname: Michael surname: Kuhn fullname: Kuhn, Michael organization: Structural and Computational Biology Unit, European Molecular Biology Laboratory, Biotec, TU Dresden – sequence: 12 givenname: Jamie surname: Trott fullname: Trott, Jamie organization: Structural and Computational Biology Unit, European Molecular Biology Laboratory – sequence: 13 givenname: Vladimir surname: Rybin fullname: Rybin, Vladimir organization: Cell Biology and Biophysics Unit, European Molecular Biology Laboratory – sequence: 14 givenname: Christoph W surname: Müller fullname: Müller, Christoph W organization: Structural and Computational Biology Unit, European Molecular Biology Laboratory – sequence: 15 givenname: Peer surname: Bork fullname: Bork, Peer organization: Structural and Computational Biology Unit, European Molecular Biology Laboratory – sequence: 16 givenname: Marko surname: Kaksonen fullname: Kaksonen, Marko organization: Protein Expression and Purification Core Facility, European Molecular Biology Laboratory – sequence: 17 givenname: Robert B surname: Russell fullname: Russell, Robert B organization: Structural and Computational Biology Unit, European Molecular Biology Laboratory, Cell Networks, University of Heidelberg – sequence: 18 givenname: Anne‐Claude surname: Gavin fullname: Gavin, Anne‐Claude email: gavin@embl.de organization: Structural and Computational Biology Unit, European Molecular Biology Laboratory, Structural and Computational Biology Unit, European Molecular Biology Laboratory |
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| DOI | 10.1038/msb.2010.87 |
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| Keywords | pleckstrin homology domains interactome lipid‐array sphingolipids network |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Present address: Biotec, TU Dresden, 01062 Dresden, Germany Present address: Centro de Investigaciones Biológicas-CSIC, 28040 Madrid, Spain Present address: Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen, Denmark Present address: Cell Networks, University of Heidelberg, 69120 Heidelberg, Germany |
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| PublicationTitle | Molecular systems biology |
| PublicationTitleAbbrev | Mol Syst Biol |
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| Snippet | Protein–metabolite networks are central to biological systems, but are incompletely understood. Here, we report a screen to catalog protein–lipid interactions... Protein-metabolite networks are central to biological systems, but are incompletely understood. Here, we report a screen to catalog protein-lipid interactions... Lipids are important cellular metabolites, with a wide range of structural and functional diversity. Many operate as signaling molecules. Lipids though have... Abstract Protein–metabolite networks are central to biological systems, but are incompletely understood. Here, we report a screen to catalog protein–lipid... |
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| SubjectTerms | Actin Algorithms Baking yeast Binding E coli EMBO20 EMBO37 Enzymes Fatty Acid-Binding Proteins - analysis Fatty Acid-Binding Proteins - chemistry Fatty Acid-Binding Proteins - metabolism High-Throughput Screening Assays - methods Homology interactome Kinases Lipid Metabolism - physiology Lipid-Linked Proteins - analysis Lipid-Linked Proteins - chemistry Lipid-Linked Proteins - metabolism Lipids Lipids - analysis lipid‐array Membranes Metabolism Metabolites Metabolome Mode of action Models, Biological network Phosphates Phosphatidylinositol Phosphatidylinositol phosphates Pleckstrin pleckstrin homology domains Protein Array Analysis - methods Protein Binding Protein Interaction Domains and Motifs - physiology Proteins Saccharomyces cerevisiae Saccharomyces cerevisiae - chemistry Saccharomyces cerevisiae - metabolism Saccharomyces cerevisiae Proteins - analysis Saccharomyces cerevisiae Proteins - chemistry Saccharomyces cerevisiae Proteins - metabolism Sphingolipids Validation Studies as Topic Yeast |
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| Title | A systematic screen for protein–lipid interactions in Saccharomyces cerevisiae |
| URI | https://link.springer.com/article/10.1038/msb.2010.87 https://onlinelibrary.wiley.com/doi/abs/10.1038%2Fmsb.2010.87 https://www.ncbi.nlm.nih.gov/pubmed/21119626 https://www.proquest.com/docview/2299165481 https://www.proquest.com/docview/1654671156 https://www.proquest.com/docview/815553596 https://pubmed.ncbi.nlm.nih.gov/PMC3010107 https://doaj.org/article/f43378c8acc249edbcfa2446039a4d6c |
| Volume | 6 |
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