{"id":{"repo_id":"cambridge","oai_identifier":"oai:www.repository.cam.ac.uk:1810/343665"},"canonical_url":"https://search.dev.ndltd.org/etd/cambridge/oai:www.repository.cam.ac.uk:1810/343665","repository":{"repo_id":"cambridge","name":"Cambridge University","base_url":"https://api.repository.cam.ac.uk/server/oai/request"},"display":{"title":"Scalable methods for the discovery of autophagy and disease genes","abstract":"Macroautophagy (henceforth referred to as autophagy) is a major and conserved cellular process in which cells deliver cytoplasmic contents to lysosomes for degradation. As autophagy has been linked to various human diseases, the discovery and characterisation of autophagy genes is of interest for both basic science and medical research. This thesis develops scalable experimental and computational methods for the discovery of autophagy genes and elucidation of their relationships to human disease. To enable scalable genetic screens for autophagy, we develop two high-throughput autophagy assays: (1) an autophagic flux assay, SRAI-LC3B; and (2) an assay for levels of the ATG12-ATG5 conjugate, an important early autophagy marker. SRAI-LC3B can be used to assess autophagic flux via flow cytometry or microscopy and responds robustly to established pharmacological and genetic controls. Additionally, we exploit the optical properties of SRAI-LC3B to develop a novel high-throughput autophagy assay in a human neuronal model. By conducting a targeted screen with the ATG12-ATG5 assay, we identify two chaperone proteins as novel autophagy regulators. These chaperones act together to regulate autophagy at multiple stages, by mechanisms likely to involve stabilising the VPS34 complex and enabling DNM2-mediated autophagosome scission. Computational techniques such as network propagation and machine learning can help to condense large, complex data into testable predictions. We exploit these tools to develop a model that predicts new autophagy genes by analysing systematic datasets. Top predictions were screened using the SRAI-LC3B assay, resulting in eighteen new candidate autophagy genes. Predicted genes were enriched >5-fold in significant screen hits compared to a randomly selected control set. Subsequently, we develop a systematic procedure for process-to-phenotype prediction, which analyses interaction networks to predict diseases that may be associated with autophagy. A targeted screen of top predictions identifies twenty-eight diseases in which disruption to one or more associated genes caused significant changes in autophagy, raising the prospect that dysfunctional autophagy may play a role in some of these diseases.","abstract_html":"Macroautophagy (henceforth referred to as autophagy) is a major and conserved cellular process in which cells deliver cytoplasmic contents to lysosomes for degradation. As autophagy has been linked to various human diseases, the discovery and characterisation of autophagy genes is of interest for both basic science and medical research. This thesis develops scalable experimental and computational methods for the discovery of autophagy genes and elucidation of their relationships to human disease. To enable scalable genetic screens for autophagy, we develop two high-throughput autophagy assays: (1) an autophagic flux assay, SRAI-LC3B; and (2) an assay for levels of the ATG12-ATG5 conjugate, an important early autophagy marker. SRAI-LC3B can be used to assess autophagic flux via flow cytometry or microscopy and responds robustly to established pharmacological and genetic controls. Additionally, we exploit the optical properties of SRAI-LC3B to develop a novel high-throughput autophagy assay in a human neuronal model. By conducting a targeted screen with the ATG12-ATG5 assay, we identify two chaperone proteins as novel autophagy regulators. These chaperones act together to regulate autophagy at multiple stages, by mechanisms likely to involve stabilising the VPS34 complex and enabling DNM2-mediated autophagosome scission. Computational techniques such as network propagation and machine learning can help to condense large, complex data into testable predictions. We exploit these tools to develop a model that predicts new autophagy genes by analysing systematic datasets. Top predictions were screened using the SRAI-LC3B assay, resulting in eighteen new candidate autophagy genes. Predicted genes were enriched >5-fold in significant screen hits compared to a randomly selected control set. Subsequently, we develop a systematic procedure for process-to-phenotype prediction, which analyses interaction networks to predict diseases that may be associated with autophagy. A targeted screen of top predictions identifies twenty-eight diseases in which disruption to one or more associated genes caused significant changes in autophagy, raising the prospect that dysfunctional autophagy may play a role in some of these diseases.","abstract_has_math":false,"creators":["Djajadikerta, Alvin"],"institution":"University of Cambridge","degree_name":"Doctor of Philosophy (PhD)","degree_level":"Doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Rubinsztein, David"],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-08-01","date_published":"2022-08-01","updated_at":"2026-07-22T22:24:08Z","subjects":["autophagy"],"languages":["eng"],"rights":[],"rights_urls":["https://www.rioxx.net/licenses/all-rights-reserved/"],"identifier_entries":[]},"links":{"outbound_url":"https://doi.org/10.17863/CAM.91092","outbound_label":"DOI","outbound_source":"dc:identifier.doi"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Rubinsztein, David"]},{"key":"dc:contributor.sponsor","label":"Sponsor","values":["Mr Charles Allen (and family), Cambridge Australia Scholarships, the Cambridge Trust, the Cambridge Philosophical Society, and the UK Dementia Research Institute (funded by the MRC, Alzheimer’s Research UK, and the Alzheimer’s Society)"]},{"key":"dc:creator","label":"Author","values":["Djajadikerta, Alvin"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2022-08-01"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Cambridge"]},{"key":"dc:relation.isreferencedby.uri","label":"Dc Relation Isreferencedby URI","values":["https://www.repository.cam.ac.uk/handle/1810/343665"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["autophagy"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["https://www.rioxx.net/licenses/all-rights-reserved/"]},{"key":"dc:rights.embargodate","label":"Dc Rights Embargodate","values":["2026-11-25"]},{"key":"dc:rights.embargotype","label":"Dc Rights Embargotype","values":["embargo"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["10.17863/CAM.91092"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/f576e3c3-edc4-4074-b6a2-8e6a870cf5c4/download"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Macroautophagy (henceforth referred to as autophagy) is a major and conserved cellular process in which cells deliver cytoplasmic contents to lysosomes for degradation. As autophagy has been linked to various human diseases, the discovery and characterisation of autophagy genes is of interest for both basic science and medical research. This thesis develops scalable experimental and computational methods for the discovery of autophagy genes and elucidation of their relationships to human disease. To enable scalable genetic screens for autophagy, we develop two high-throughput autophagy assays: (1) an autophagic flux assay, SRAI-LC3B; and (2) an assay for levels of the ATG12-ATG5 conjugate, an important early autophagy marker. SRAI-LC3B can be used to assess autophagic flux via flow cytometry or microscopy and responds robustly to established pharmacological and genetic controls. Additionally, we exploit the optical properties of SRAI-LC3B to develop a novel high-throughput autophagy assay in a human neuronal model. By conducting a targeted screen with the ATG12-ATG5 assay, we identify two chaperone proteins as novel autophagy regulators. These chaperones act together to regulate autophagy at multiple stages, by mechanisms likely to involve stabilising the VPS34 complex and enabling DNM2-mediated autophagosome scission. Computational techniques such as network propagation and machine learning can help to condense large, complex data into testable predictions. We exploit these tools to develop a model that predicts new autophagy genes by analysing systematic datasets. Top predictions were screened using the SRAI-LC3B assay, resulting in eighteen new candidate autophagy genes. Predicted genes were enriched >5-fold in significant screen hits compared to a randomly selected control set. Subsequently, we develop a systematic procedure for process-to-phenotype prediction, which analyses interaction networks to predict diseases that may be associated with autophagy. A targeted screen of top predictions identifies twenty-eight diseases in which disruption to one or more associated genes caused significant changes in autophagy, raising the prospect that dysfunctional autophagy may play a role in some of these diseases."]},{"key":"dc:format.checksum.md5","label":"Dc Format Checksum Md5","values":["8e50722f79da1b3d042bc4b079730fa4"]},{"key":"dc:title","label":"Title","values":["Scalable methods for the discovery of autophagy and disease genes"]}]}],"canonical_facts":{"dc:contributor.advisor":["Rubinsztein, David"],"dc:contributor.sponsor":["Mr Charles Allen (and family), Cambridge Australia Scholarships, the Cambridge Trust, the Cambridge Philosophical Society, and the UK Dementia Research Institute (funded by the MRC, Alzheimer’s Research UK, and the Alzheimer’s Society)"],"dc:creator":["Djajadikerta, Alvin"],"dc:date.issued":["2022-08-01"],"dc:description.abstract":["Macroautophagy (henceforth referred to as autophagy) is a major and conserved cellular process in which cells deliver cytoplasmic contents to lysosomes for degradation. As autophagy has been linked to various human diseases, the discovery and characterisation of autophagy genes is of interest for both basic science and medical research. This thesis develops scalable experimental and computational methods for the discovery of autophagy genes and elucidation of their relationships to human disease. To enable scalable genetic screens for autophagy, we develop two high-throughput autophagy assays: (1) an autophagic flux assay, SRAI-LC3B; and (2) an assay for levels of the ATG12-ATG5 conjugate, an important early autophagy marker. SRAI-LC3B can be used to assess autophagic flux via flow cytometry or microscopy and responds robustly to established pharmacological and genetic controls. Additionally, we exploit the optical properties of SRAI-LC3B to develop a novel high-throughput autophagy assay in a human neuronal model. By conducting a targeted screen with the ATG12-ATG5 assay, we identify two chaperone proteins as novel autophagy regulators. These chaperones act together to regulate autophagy at multiple stages, by mechanisms likely to involve stabilising the VPS34 complex and enabling DNM2-mediated autophagosome scission. Computational techniques such as network propagation and machine learning can help to condense large, complex data into testable predictions. We exploit these tools to develop a model that predicts new autophagy genes by analysing systematic datasets. Top predictions were screened using the SRAI-LC3B assay, resulting in eighteen new candidate autophagy genes. Predicted genes were enriched >5-fold in significant screen hits compared to a randomly selected control set. Subsequently, we develop a systematic procedure for process-to-phenotype prediction, which analyses interaction networks to predict diseases that may be associated with autophagy. A targeted screen of top predictions identifies twenty-eight diseases in which disruption to one or more associated genes caused significant changes in autophagy, raising the prospect that dysfunctional autophagy may play a role in some of these diseases."],"dc:format.checksum.md5":["8e50722f79da1b3d042bc4b079730fa4"],"dc:identifier.doi":["10.17863/CAM.91092"],"dc:identifier.uri":["https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/f576e3c3-edc4-4074-b6a2-8e6a870cf5c4/download"],"dc:language":["eng"],"dc:publisher.institution":["University of Cambridge"],"dc:relation.isreferencedby.uri":["https://www.repository.cam.ac.uk/handle/1810/343665"],"dc:rights":["https://www.rioxx.net/licenses/all-rights-reserved/"],"dc:rights.embargodate":["2026-11-25"],"dc:rights.embargotype":["embargo"],"dc:subject":["autophagy"],"dc:title":["Scalable methods for the discovery of autophagy and disease genes"],"dc:type":["Thesis"],"dc:type.qualificationlevel":["Doctoral"],"dc:type.qualificationname":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-22T22:24:08Z"}