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  1. Author Response:

    Reviewer #1:

    In this study, the authors developed an elegant toolset called HiLITR for identifying the genes that are involved in protein localization to a specific subcellular organelle. The basic strategy is exquisitely designed: Two distinct types of organelle-specific membrane anchored proteins are respectively fused to the TEV protease domain and a transcription factor (TF). Colocalization of the two proteins induces release of the TF by proteolytic cleavage. The TF switches on the expression of a fluorescent protein enabling the amplification of localization signal. The expression levels of fluorescent proteins can be quantified and sorted by FACS.

    In combination with the CRISPRi screening employing a pooled sgRNA library, this strategy turns into a powerful high-throughput platform to discover genes that influence protein localization in various cellular compartments. Applying this method to protein localization in mitochondrial and ER membranes led to an unexpected discovery of the genes, SAE1 (SUMO activating enzyme) as a regulator of the tail-anchored (TA) protein insertion to mitochondrial membranes and EMC10 as an antagonist in the insertion of TA proteins to ER membranes.

    The basic workflow is thoroughly designed and optimized (e.g., the construct design, the choice of targeting sequences, the strategies to filter out false positive hits, FACS analysis, nontargeted and targeted identification of the genes affecting localization, validation of the identified genes, etc.). The triple filtering strategy (i.e., TA screen, SA screen and ER screen) is impressive since this not only enables filtering out false positives but also provides a way to investigate mislocalization or rerouting of TA proteins to ER membranes.

    Overall, this is an excellent study contributing to our understanding of protein localization and mislocalization. The manuscript reasonably well supports the conclusion. Nonetheless, there are several concerns that authors could further address:

    i) It would have been helpful to discuss how this method could be evolved to address more complex problems in protein localization and mislocalization. For example, the current version focuses on single membrane-spanning peptides as a localization signal, but the scientific community would be also interested in the localization problems of membrane proteins with multiple TM segments or larger water-soluble domains. In such case, how could the accessibility issue between TF and protease be overcome?

    The question of using HiLITR to probe targeting mechanisms for multipass transmembrane proteins is a very interesting one. If the protein of interest fails to localize to the same membrane as a single-pass transcription factor, or if topology is inverted in a way that places the protease in the lumen, then either outcome will reduce HiLITR activation. Similarly, a peripheral but non-transmembrane protein would reduce activation of an associated transcription factor if localization were impaired. We have observed that HiLITR can be sensitive to the geometries of the constructs, so it is likely that the targeting domain and linker lengths of the TF construct would need to be refined to enable and optimize activation.

    ii) Although this manuscript majorly focuses on the tool development, more in-depth explanations on the role of the identified genes (SAE1 and EMC10) would have helped readers to appreciate the significance of this work.

    We have added additional discussion on the possible cellular roles of SAE1. We speculate that the effect of SAE1 is likely indirect (i.e., SAE1 is unlikely to be directly interacting with TA proteins). With respect to EMC10, we believe we are the first to impute a functional role for EMC10 in the EMC complex. Recent structural studies have indicated that the EMC samples different states which vary in client protein accommodation. The finding that the EMC is not static hints that it may also be regulated. For several reasons that are complementary to our own findings, EMC10 is a logical lead for this antagonistic regulation. First, EMC10 is dispensable for complex stability and does not genetically cluster with other EMC members, so it has often been regarded as outside of the core complex. Second, the structural studies have indicated that EMC10 is more flexibly associated with the rest of the complex than other EMC subunits. The ability of EMC10 to dissociate from the rest of the complex could be a mechanism for this antagonistic regulation. We have added new text describing these ideas discussion section of the manuscript.

    iii) Signal amplification can be a double-edged dagger since it can magnify small differences more than what is actual. A statement would be needed how authors translate the HiLITR results into the actual effect of an identified component (e.g., HILITR vs Western blotting).

    In general, HiLITR seems to be more sensitive than direct measures of endogenous protein levels, which can be observed in the Western blotting and proteomics data related to SAE1 knockdown and in the Western blotting related to EMC10 knockdown. This is likely a function of the signal amplification of HiLITR, as the reviewer notes, and the use of clonal selection for highly sensitive cell lines. In theory, if there are perturbations that will be known to give specific effect sizes, they could be used to calibrate the HiLITR readout. Otherwise, we would recommend against imputing a specific effect size from HiLITR results. We have added these comments to the revised discussion.

    We do note that some of our data support the idea that, for a given cell line, a larger change in HiLITR activation corresponds to a larger perturbation of protein localization. For example, in Figures S7, S8, and S11, genes with larger effects on the ER HiLITR screen produced larger changes in the mutant protease localization assay (by fluorescence imaging).

    Reviewer #2:

    In this work, Coukos et al. describe the development of a genetic reporter system that involves the use of chimeric, photoactivatable substrate proteins that can be used to monitor the targeting of a tail-anchored (TA) protease to various organelle membranes. The authors present a strategy to couple these sensors with fluorescence activated cell sorting (FACS), deep sequencing, and CRISPRi libraries in order to identify genes that mediate membrane targeting. This study documents extensive optimization efforts and numerous controls to ensure the output of these screens are valid. Furthermore, the results include numerous examples of previously characterized insertases (i.e. core subunits of the ER membrane complex, or EMC) as well as the discovery of two novel genes that play a central role in the targeting of TA proteins to the outer mitochondrial membrane (OMM) or to the endoplasmic reticulum membrane (ERM). In follow up investigations, the authors show that the loss of the SUMO E1 ligase component SAE1 is critical for the targeting of TA proteins to the OMM. Using an array of quantitative cellular assays, the authors then confirm the specificity of the knockdown, and show that the disruption of SUMOylation results in the mis-targeting of endogenous substrates. Using another variation of this assay, the authors also discover that, while the knockdown of core EMC subunits decreases the targeting of TA proteins to the ERM, knocking down EMC10 results in an increase in the targeting of these substrates to the ERM. The authors also verify that this subunit specifically appears to antagonize this insertase activity of the EMC. Overall, this study provides both new tools for pooled genetic screening and identifies novel components of the topogenic machinery in human cells. These results have a clear impact on our understanding of membrane insertion pathways and are likely to influence efforts to develop new screening platforms.

    Strengths:

    This study includes both an impressive number of controls and several counter screens that make this approach both comprehensive and robust. The described approaches are also likely to be somewhat adaptable given the modular architecture of the HiLITR sensor proteins.

    Validation processes both confirm the roles of these genes in each respective process and provide evidence for the accuracy of the results. These efforts along with the detailed methods sections set a high standard for future screens that employ similar approaches.

    The novel roles of the SAE1 and EMC10 subunits suggest new factors that may control the efficiency of OMM and ERM targeting pathways. These findings are sure to inspire a slew of follow up studies centered around the mechanistic roles of these proteins in the context of each pathway.

    Weaknesses:

    Chimeric TA proteases represent an artificial substrate. While these screens clearly pick up the central machinery involved in these pathways, the characterization of such substrates has limited impact on our understanding of how the spectrum of native substrates navigate the partially redundant topogenic pathways within the cell.

    We have added this point to the discussion when describing limitations of our method. We agree that HiLITR screens will be most impactful when there is follow-up on hits by assessing the effect of their KD on endogenous proteins. We have assessed our two key hits, SAE1 and EMC10, in this way.

    The authors characterize two of the more robust and biochemically interesting hits in their follow up studies. Nevertheless, it is unclear how many of the other hits are likely to be relevant due to a lack of biological replicates and to the lack of metrics to describe the precision of the observed effect sizes.

    We performed all three screens in 2 biological replicates, and the whole genome screen was performed in 2 technical replicates. This information was previously only in the methods sections, so we have now also included it in Results. Precision of effect size can be estimated using the CasTLE software and is included for each screen in supplementary table 2. Empirically, we gain confidence from the replication of hits between the whole-genome and TA screens, robust recovery of the TRC pathway from the TA screen (Figure S7) and validation and follow-up on a limited subset of hits (Figure S8).

    The authors' efforts to characterize the effects of SAE1 and EMC10 knockdowns confirm the screening results and show that the activities of these proteins are important for targeting. However, these studies do not establish the mechanistic roles of these proteins within each insertion pathway. This will undoubtedly require additional investigations.

    Yes, we agree and have elaborated on this in the Discussion section.

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  2. Evaluation Summary:

    Here authors present a robust, high-throughput and genome-wide strategy to identify genes that influence protein localization in individual subcellular compartments. The results profile subsets of genes that are involved in localization of tail-anchored proteins to mitochondrial and endoplasmic reticulum membranes and identify several unexpected regulators. This new tool is adaptable to studying various types of protein trafficking processes to shed light on their molecular mechanisms.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1, Reviewer #2 and Reviewer #3 agreed to share their names with the authors.)

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  3. Reviewer #1 (Public Review):

    In this study, the authors developed an elegant toolset called HiLITR for identifying the genes that are involved in protein localization to a specific subcellular organelle. The basic strategy is exquisitely designed: Two distinct types of organelle-specific membrane anchored proteins are respectively fused to the TEV protease domain and a transcription factor (TF). Colocalization of the two proteins induces release of the TF by proteolytic cleavage. The TF switches on the expression of a fluorescent protein enabling the amplification of localization signal. The expression levels of fluorescent proteins can be quantified and sorted by FACS.

    In combination with the CRISPRi screening employing a pooled sgRNA library, this strategy turns into a powerful high-throughput platform to discover genes that influence protein localization in various cellular compartments. Applying this method to protein localization in mitochondrial and ER membranes led to an unexpected discovery of the genes, SAE1 (SUMO activating enzyme) as a regulator of the tail-anchored (TA) protein insertion to mitochondrial membranes and EMC10 as an antagonist in the insertion of TA proteins to ER membranes.

    The basic workflow is thoroughly designed and optimized (e.g., the construct design, the choice of targeting sequences, the strategies to filter out false positive hits, FACS analysis, nontargeted and targeted identification of the genes affecting localization, validation of the identified genes, etc.). The triple filtering strategy (i.e., TA screen, SA screen and ER screen) is impressive since this not only enables filtering out false positives but also provides a way to investigate mislocalization or rerouting of TA proteins to ER membranes.

    Overall, this is an excellent study contributing to our understanding of protein localization and mislocalization. The manuscript reasonably well supports the conclusion. Nonetheless, there are several concerns that authors could further address:

    i) It would have been helpful to discuss how this method could be evolved to address more complex problems in protein localization and mislocalization. For example, the current version focuses on single membrane-spanning peptides as a localization signal, but the scientific community would be also interested in the localization problems of membrane proteins with multiple TM segments or larger water-soluble domains. In such case, how could the accessibility issue between TF and protease be overcome?

    ii) Although this manuscript majorly focuses on the tool development, more in-depth explanations on the role of the identified genes (SAE1 and EMC10) would have helped readers to appreciate the significance of this work.

    iii) Signal amplification can be a double-edged dagger since it can magnify small differences more than what is actual. A statement would be needed how authors translate the HiLITR results into the actual effect of an identified component (e.g., HILITR vs Western blotting).

    Was this evaluation helpful?
  4. Reviewer #2 (Public Review):

    In this work, Coukos et al. describe the development of a genetic reporter system that involves the use of chimeric, photoactivatable substrate proteins that can be used to monitor the targeting of a tail-anchored (TA) protease to various organelle membranes. The authors present a strategy to couple these sensors with fluorescence activated cell sorting (FACS), deep sequencing, and CRISPRi libraries in order to identify genes that mediate membrane targeting. This study documents extensive optimization efforts and numerous controls to ensure the output of these screens are valid. Furthermore, the results include numerous examples of previously characterized insertases (i.e. core subunits of the ER membrane complex, or EMC) as well as the discovery of two novel genes that play a central role in the targeting of TA proteins to the outer mitochondrial membrane (OMM) or to the endoplasmic reticulum membrane (ERM). In follow up investigations, the authors show that the loss of the SUMO E1 ligase component SAE1 is critical for the targeting of TA proteins to the OMM. Using an array of quantitative cellular assays, the authors then confirm the specificity of the knockdown, and show that the disruption of SUMOylation results in the mis-targeting of endogenous substrates. Using another variation of this assay, the authors also discover that, while the knockdown of core EMC subunits decreases the targeting of TA proteins to the ERM, knocking down EMC10 results in an increase in the targeting of these substrates to the ERM. The authors also verify that this subunit specifically appears to antagonize this insertase activity of the EMC. Overall, this study provides both new tools for pooled genetic screening and identifies novel components of the topogenic machinery in human cells. These results have a clear impact on our understanding of membrane insertion pathways and are likely to influence efforts to develop new screening platforms.

    Strengths:

    This study includes both an impressive number of controls and several counter screens that make this approach both comprehensive and robust. The described approaches are also likely to be somewhat adaptable given the modular architecture of the HiLITR sensor proteins.

    Validation processes both confirm the roles of these genes in each respective process and provide evidence for the accuracy of the results. These efforts along with the detailed methods sections set a high standard for future screens that employ similar approaches.

    The novel roles of the SAE1 and EMC10 subunits suggest new factors that may control the efficiency of OMM and ERM targeting pathways. These findings are sure to inspire a slew of follow up studies centered around the mechanistic roles of these proteins in the context of each pathway.

    Weaknesses:

    Chimeric TA proteases represent an artificial substrate. While these screens clearly pick up the central machinery involved in these pathways, the characterization of such substrates has limited impact on our understanding of how the spectrum of native substrates navigate the partially redundant topogenic pathways within the cell.

    The authors characterize two of the more robust and biochemically interesting hits in their follow up studies. Nevertheless, it is unclear how many of the other hits are likely to be relevant due to a lack of biological replicates and to the lack of metrics to describe the precision of the observed effect sizes.

    The authors' efforts to characterize the effects of SAE1 and EMC10 knockdowns confirm the screening results and show that the activities of these proteins are important for targeting. However, these studies do not establish the mechanistic roles of these proteins within each insertion pathway. This will undoubtedly require additional investigations.

    Was this evaluation helpful?
  5. Reviewer #3 (Public Review):

    Pooled sgRNA library-based genetic screen has been a powerful and efficient approach to comprehensively identify essential regulators for key cellular processes. However, the employment of pooled sgRNA library was limited due to the reliance on cell proliferation or FACS. Therefore, it has been difficult to use pooled sgRNA library to screening for genes involved in protein targeting which is not usually related to cell survival or could be easily detected by flow cytometry. To circumvent this problem, the authors developed a smart assay based on location-activated transcription. They engineered the cells with an mCherry gene which could be turned on by a transcription factor. The transcription factor was anchored to the interacellular compartment of interest via a TEV site and LOV sequence, allowing the exposure of the TEV site by blue light activation. In another construct the TEV protease is linked with a targeting sequece to the same compartment. Therefore, of the protease is correctly targeted, it will cleave the TEV site located on the linker of the transcription factor in the presence of blue light, leading to the relocation of the membrane bound transcription factor to the nucleus to trigger mcherry expresss. The assay allows for the translation of the protein location signal to fluorescence production and could be employed in FACS analysis. Using this assay and pooled sgRNA library screening, the authors identified key regulators in tail-anchored protein targeting to the mitochondria and the ER. The study will deepen our insight into how tail-anchored protein targeting is control in the cell. Importantly, the principle of design could be widely applied and broaden the employment of pooled sgRNA library screening in cell biology. The overall concept is novel and the data is properly controlled and support the conclusion reached.

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  6. Excerpt

    Keep it simple! HiLITR is a novel fluorescent reporter converting complex phenotypes into a simple readout suitable for pooled genetic screens.

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