iLIR database: A web resource for LIR motif-containing proteins in eukaryotes

<p>Atg8-family proteins are the best-studied proteins of the core autophagic machinery. They are essential for the elongation and closure of the phagophore into a proper autophagosome. Moreover, Atg8-family proteins are associated with the phagophore from the initiation of the autophagic process to, or just prior to, the fusion between autophagosomes with lysosomes. In addition to their implication in autophagosome biogenesis, they are crucial for selective autophagy through their ability to interact with selective autophagy receptor proteins necessary for the specific targeting of substrates for autophagic degradation. In the past few years it has been revealed that Atg8-interacting proteins include not only receptors but also components of the core autophagic machinery, proteins associated with vesicles and their transport, and specific proteins that are selectively degraded by autophagy. Atg8-interacting proteins contain a short linear LC3-interacting region/LC3 recognition sequence/Atg8-interacting motif (LIR/LRS/AIM) motif which is responsible for their interaction with Atg8-family proteins. These proteins are referred to as LIR-containing proteins (LIRCPs). So far, many experimental efforts have been carried out to identify new LIRCPs, leading to the characterization of some of them in the past 10 years. Given the need for the identification of LIRCPs in various organisms, we developed the iLIR database (<a href="https://ilir.warwick.ac.uk" target="_blank">https://ilir.warwick.ac.uk</a>) as a freely available web resource, listing all the putative canonical LIRCPs identified in silico in the proteomes of 8 model organisms using the iLIR server, combined with a Gene Ontology (GO) term analysis. Additionally, a curated text-mining analysis of the literature permitted us to identify novel putative LICRPs in mammals that have not previously been associated with autophagy.</p

Supplemental Dataset 7

Supplemental Data. Walker et al. (2017). Plant Cell 10.1105/tpc.16.00961. Supplemental Dataset 7. Glutathione S-transferase gene family expression is highly variable. All members of the GST family the time series within which differential expression was found are indicated (or not DE), together with the cluster number for that time series. Genes shown on Figure 6 are highlighted in yellow and the time series that has been shown is indicated

Supplemental File 1

CU full network sessio

Supplemental File 6

PR full network sessio

Supplemental Dataset 8

Supplemental Data. Walker et al. (2017). Plant Cell 10.1105/tpc.16.00961. Supplemental Dataset 8. Gene family regulation across cell types and treatments. (A) Summary of gene family regulation: number DE/non-DE, in which timeseries they are regulated and if transcripts are DE in one or both cell types. (B) Lists of the genes in each family together with cluster numbers if differentially expressed in a timeseries

Supplemental File 13

CN backbone network session with shared TFs in middl

Supplemental File 7

CU backbone network sessio

Supplemental Dataset 6

Supplemental Data. Walker et al. (2017). Plant Cell 10.1105/tpc.16.00961. Supplemental Dataset 6. Network statistics and module annotation and characteristics .(A) For each causal regulatory network 8 statistics are shown: number of edges, number of nodes, the node/edge ratio, number of connected TFs, number of targets of those TFs (including TFs that are targets), average targets per TF, the network clustering coefficient and the characteristic path length as given in Cytoscape. (B) Comparison of TF module size and timing response category for CN and PN networks. (C) Network statistics for major modules in each experiment are shown with a cut off of >=10 targets in at least one experiment. The status of each TF in the network in each experiment is shown (DE (0 not DE, 1 DE), indegree, outdegree). The combined outdegree is the sum of the shown out degrees across the six experiments, the number of experiments where outdegree is >7 is also shown. Maximum module size was in part dictated by the number of genes in the network, while the mean number of targets per TF was larger in CU and CN. This indicates that the pericycle and cortex networks have statistically different structures and this was confirmed by analysis of the network connectivity using size independent statistics; specifically there were differing levels of clustering, CN was the least clustered whereas PN had the longest path length while still being highly clustered. This indicates that the cortex is dominated by a few large modules, while the pericycle is more distributed amongst a larger number of modules, in fact extending to very large sizes. (D) Corroborated interactions based on the presence of cis-acting TF family binding sites for networks. Data derived from Franco-Zorrilla et al 2014 [83]. Source contains TFs that interact with a putative regulated gene (Target) and the type of interaction (activation/inhibition) is shown

Supplemental Dataset 3

Supplemental Data. Walker et al. (2017). Plant Cell 10.1105/tpc.16.00961. Supplemental Dataset 3. Phenotypic analysis. (A) Average (ave) and standard error (SE) root trait values measured from 9 day old Col0 seedlings and for the same seedlings after 4 days of N, rhizobial or control/mock treatment with T-test values comparing phenotype values (bold=P<0.01). (B-C) Average (ave) and standard error (SE) root trait values measured from 12 day old wrky15 (B) and nlp8 (C) mutant plants on replete and deplete N with T-test values comparing phenotype values in mutant and silbling WT (bold=P<0.01); LR = lateral root, PR = primary root. (D) Average and SE (n=3 biological replicates) of qPCR measurement, qPCR primer sequences and qPCR product lengths

Supplemental File 9

CR backbone network sessio