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| The functions of Vti1a in endosomal circulation and the formation of autophagosome. panel (A), it represents the dot of VTI1A on Mutations/variations; the mutations/variations and gene fusions of VTI1A (red dots) in glial cells may be oncogenic. Panel (B), it shows the domains and structures of Vti1a and Vti1b, a Vti protein contains a C-terminal transmembrane domain, a Qb-SNARE motif, and an N-terminal Habc domain; the Habc domain exists 2 mutants (Q29R localized in the loop between Ha and Hb and W79R localized on Hc); there has a low sequence homology between Vti1a and Vti1b. Panel (C), it displays the process of the formation of autophagosome; Vti1a and Vti1b mediates autophagosome maturation. Panel (D), it reveals endosomal circulation; Vti1a forms a SNARE complex with Syntaxin (Stx)10, Stx16, and VAMP3 to mediate the transport of related substances from the endosome to the Golgi apparatus. Vti1a co-immunoprecipitates with VAMP4, Stx6, and Stx16 in early circulating endosomal transport. Vti1b forms a complex with Stx7, Stx8, and VAMP8 and plays a role in late endosomal fusion.
Source publication
Vesicle transport through interaction with t-SNAREs 1A (Vti1a), a member of the N-ethylmaleimide-sensitive factor attachment protein receptor protein family, is involved in cell signaling as a vesicular protein and mediates vesicle trafficking. Vti1a appears to have specific roles in neurons, primarily by regulating upstream neurosecretory events t...
Contexts in source publication
Context 1
... exists a low sequence homology between Vti1a and Vti1b (31-33% homology) (Emperador-Melero et al., 2019). All Vti proteins contain a C-terminal type II transmembrane domain, a Qb-SNARE motif, and an N-terminal triple helix Habc domain (Figure 1; Antonin et al., 2002). The Habc domain of Vti proteins has multiple functions, including the recruitment of tethered proteins and regulators and the correct classification of SNARE proteins ( Gossing et al., 2013). ...
Context 2
... also cooperates with several SNAREs associated with the TGN and endosomes to mediate vesicular trafficking processes (Figure 1; Simonsen et al., 1998;Antonin et al., 2000a;Wendler and Tooze, 2001;Ganley et al., 2008). For example, Vti1a coimmunoprecipitates with VAMP4, Stx6, and Stx16 and assembles into a structurally conserved SNARE complex that mediates early, circulating endosomal transport of Shiga toxin and TGN46 (Simonsen et al., 1998;Wendler and Tooze, 2001). ...
Context 3
... example, mutation of Vti1p in yeast disrupts autophagosome-vacuole fusion ( Ishihara et al., 2001). In mammals, the abnormality of Vti1a and Vti1b affects the formation of autophagosomes (Figure 1; Lu et al., 2013;Chou et al., 2021). Mutations in the CHMP2B gene can cause frontotemporal dementia, the pathogenicity of which is primarily thought to be the result of autophagy-endolysosomal dysfunction (Deng et al., 2022;Roos et al., 2022). ...
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Citations
... VTI (Vacuole Protein Sorting 10-interacting) proteins are a subclass of Qb-SNAREs, including VTI1A and VTI1B, which mediate different steps of endolysosomal trafficking and are necessary for regulated secretion as well [37,38]. VTI1A like SNAP23 is also a component of insulin-sensitive GLUT4-containing vesicles that regulate the GLUT4 trafficking in 3T3-L1 adipocytes [39]. ...
Background: Women with polycystic ovary syndrome (PCOS) have increased odds of concurrent depression, indicating that the relationship between PCOS and depression is more likely to be comorbid. However, the underlying mechanism remains unclear. Here, we aimed to use bioinformatic analysis to screen for the genetic elements shared between PCOS and depression. Methods: Differentially expressed genes (DEGs) were screened out through GEO2R using the PCOS and depression datasets in NCBI. Protein–protein interaction (PPI) network analysis and enrichment analysis were performed to identify the potential hub genes. After verification using other PCOS and depression datasets, the associations between key gene polymorphism and comorbidity were further studied using data from the UK biobank (UKB) database. Results: In this study, three key genes, namely, SNAP23, VTI1A, and PRKAR1A, and their related SNARE interactions in the vesicular transport pathway were identified in the comorbidity of PCOS and depression. The rs112568544 at SNAP23, rs11077579 and rs4458066 at PRKAR1A, and rs10885349 at VTI1A might be the genetic basis of this comorbidity. Conclusions: Our study suggests that the SNAP23, PRKAR1A, and VTI1A genes can directly or indirectly participate in the imbalanced assembly of SNAREs in the pathogenesis of the comorbidity of PCOS and depression. These findings may provide new strategies in diagnosis and therapy for this comorbidity.
Identifying novel autophagy (ATG) associated genes in humans remains an important task for understanding this fundamental physiological process. Machine learning (ML) can highlight potentially “missing pieces” linking core ATG genes with understudied, “dark” genes by mining functional genomic data. Here, a set of 103 (out of 288 genes from the Autophagy Database) was used as training set, based on ATG‐associated terms annotated from 3 secondary sources: GO (gene ontology), Kyoto Encyclopedia of Genes and Genomes pathway, and UniProt keywords, as additional confirmation of their importance in ATG. As negative labels, an OMIM list of genes associated with monogenic diseases was used (after excluding the 288 ATG‐associated genes). Data related to these genes from 17 different sources were compiled and used to derive a trained MetaPath/XGBoost (MPxgb) ML model for distinguishing ATG and non‐ATG genes (10‐fold cross‐validated, 100‐times randomized models, median area under the curve = 0.994 ± 0.008). Sixteen ATG‐relevant variables explained 64% of the total model gain. Overall, 23% of the top 251 predicted genes are annotated in the Autophagy Database, whereas 193 genes (77%) are not. In 2019, we suggested that some of these 193 genes may represent “ATG dark genes.” A literature search in 2022 for those top 20 predicted ATG dark genes found that 9 were subsequently reported as ATG genes during the intervening 3.5 years. A post‐factum evaluation of data leakage (the presence of ATG‐associated terms in the top 40 ML features) confirms that 7 out of these 9 genes and 2 out of 3 other recently validated predictions from the bottom 20 are novel. Those genes with the largest number of ATG features would be most likely to yield valuable experimental insights. Modern high‐throughput testing would be capable of spanning the full 193 ATG genes list reported here. Our analysis demonstrates that ML can guide genomics research to gain a more complete functional and pathway annotation of complex processes.
Key points
–A knowledge‐graph based machine learning model was designed for predicting unknown autophagy genes via mining functional genomic data.
–Literature search validated predicted genes.
–Our machine learning models could be generalized and applied to other genomic libraries to uncover dark genes for various functions.
