Systems biology of the autophagy-lysosomal pathway

Abstract

The mechanisms of the control and activity of the autophagy-lysosomal protein degradation machinery are emerging as an important theme for neurodevelopment and neurodegeneration. However, the underlying regulatory and functional networks of known genes controlling autophagy and lysosomal function and their role in disease are relatively unexplored. We performed a systems biology-based integrative computational analysis to study the interactions between molecular components and to develop models for regulation and function of genes involved in autophagy and lysosomal function. Specifically, we analyzed transcriptional and microRNA-based post-transcriptional regulation of these genes and performed functional enrichment analyses to understand their involvement in nervous system-related diseases and phenotypes. Transcriptional regulatory network analysis showed that binding sites for transcription factors, SREBP1, USF, AP-1 and NFE2, are common among autophagy and lysosomal genes. MicroRNA enrichment analysis revealed miR-130, 98, 124, 204 and 142 as the putative post-transcriptional regulators of the autophagy-lysosomal pathway genes. Pathway enrichment analyses revealed that the mTOR and insulin signaling pathways are important in the regulation of genes involved in autophagy. In addition, we found that glycosaminoglycan and glycosphingolipid pathways also make a major contribution to lysosomal gene regulation. The analysis confirmed the known contribution of the autophagy-lysosomal genes to Alzheimer and Parkinson diseases and also revealed potential involvement in tuberous sclerosis, neuronal ceroidlipofuscinoses, sepsis and lung, liver and prostatic neoplasms. To further probe the impact of autophagy-lysosomal gene deficits on neurologically-linked phenotypes, we also mined the mouse knockout phenotype data for the autophagylysosomal genes and found them to be highly predictive of nervous system dysfunction. Overall this study demonstrates the utility of systems biology-based approaches for understanding the autophagy-lysosomal pathways and gaining additional insights into the potential impact of defects in these complex biological processes.

Figure 1

An overview of functional and physical interactions among autophagy-lysosomal genes and the most common binding sites for transcription factors and microRNA. (A) Venn diagram shows the total of 416 autophagy-lysosomal genes we included in the analysis. We assigned the genes according to their most relevant and best known functions based on Gene Ontology and extensive literature review to four groups that include 38 autophagy genes, 64 lysosomal genes, 161 autophagy regulators and 167 lysosomal regulators. Fourteen of these 416 genes are shared between two groups, as shown in the Venn diagram. (B) Network representation of the autophagy-lysosomal genes showing the physical interactions as well as genes with enriched cis-acting elements for binding of transcription factors, and microRNA regulators that may be important in regulating expression of these genes. The nodes represent genes, cis-elements, or microRNAs, while the edges denote either a protein-protein interaction or a regulatory connection (TFs or microRNAs). In the diagram, the symbol “V$” is placed before the IDs of transcription factor binding sites. The most enriched TFBSs include those recognized by E-box transcription factors SREBP1, USF, AP-1 and NFE2, while the putative microRNA regulators include miR-130, 98, 124, 204 and 142.

Figure 2

A close-up view of the autophagy and lysosomal genes containing cis-acting elements of the E-box transcription factors SREBP1 and USF, AP-1 and NFE2. In the diagram, the symbol “V$” is placed before the IDs of transcription factor binding sites to distinguish them from the autophagy-lysosomal genes. Validation of regulation of autophagy-lysosomal genes by SREBP1. HEK293 cells were transfected by SREBP1 or TFEB cDNA. RNA was harvested by Trizol reagent at 24 hours post-transfection and passed quality control. Quantitative RT-PCR was performed in triplicate. SREBP1 and TFEB mRNA level were increased by 21- and 343-fold, respectively, by real-time RT-PCR assay. The mRNA levels of target genes were plotted in the bar graph. LAMP1, GABARAP, CLN3, CTSS , CTSD, CTNS, ATP6VOC, VPS 18 and PPT1 are upregulated by SREBP1. LAMP1, CLN3, ATP6VOC and PPT1 are upregulated by TFEB. *p < 0.05 by Student t-test compared to control empty vector transfection.

Figure 2

A close-up view of the autophagy and lysosomal genes containing cis-acting elements of the E-box transcription factors SREBP1 and USF, AP-1 and NFE2. In the diagram, the symbol “V$” is placed before the IDs of transcription factor binding sites to distinguish them from the autophagy-lysosomal genes. Validation of regulation of autophagy-lysosomal genes by SREBP1. HEK293 cells were transfected by SREBP1 or TFEB cDNA. RNA was harvested by Trizol reagent at 24 hours post-transfection and passed quality control. Quantitative RT-PCR was performed in triplicate. SREBP1 and TFEB mRNA level were increased by 21- and 343-fold, respectively, by real-time RT-PCR assay. The mRNA levels of target genes were plotted in the bar graph. LAMP1, GABARAP, CLN3, CTSS , CTSD, CTNS, ATP6VOC, VPS 18 and PPT1 are upregulated by SREBP1. LAMP1, CLN3, ATP6VOC and PPT1 are upregulated by TFEB. *p < 0.05 by Student t-test compared to control empty vector transfection.

Figure 3

Autophagy and lysosomal genes contain sequences recognized by miR-130, 98, 124, 204 and 142. Validation of regulation of CAPN1 and CAPN2 by miR-124. Western blot analyses after transfection of control RNA and mir124 antagomir into HEK cells indicated that mir124 antagomir led to increased MAPK14, CAPN1 and CAPN2 protein levels. Western blot analyses after transfection into HEK cells of pCMV-MIR control vector and pCMV-mir-124 vector that expresses miR-124 indicated that miR-124 expression led to decreased MAPK14, CAPN1 and CAPN2 protein levels. Different ECL developing conditions were used in the left and right panels to allow sufficient dynamic range of signal for comparison of both upregulated and downregulated proteins in the two different experiments. β-actin was used as a loading control. n = 6 for each group. DATA = mean ± SEM. *p < 0.05 by Student t-test compared to control.

Figure 3

Autophagy and lysosomal genes contain sequences recognized by miR-130, 98, 124, 204 and 142. Validation of regulation of CAPN1 and CAPN2 by miR-124. Western blot analyses after transfection of control RNA and mir124 antagomir into HEK cells indicated that mir124 antagomir led to increased MAPK14, CAPN1 and CAPN2 protein levels. Western blot analyses after transfection into HEK cells of pCMV-MIR control vector and pCMV-mir-124 vector that expresses miR-124 indicated that miR-124 expression led to decreased MAPK14, CAPN1 and CAPN2 protein levels. Different ECL developing conditions were used in the left and right panels to allow sufficient dynamic range of signal for comparison of both upregulated and downregulated proteins in the two different experiments. β-actin was used as a loading control. n = 6 for each group. DATA = mean ± SEM. *p < 0.05 by Student t-test compared to control.

Figure 4

Unbiased bioinformatics analyses identified that mTOR and insulin signaling pathways play a key role in autophagy regulation, whereas glycosaminoglycan degradation, glycosphingolipid biosynthesis, sphingolipid metabolism, glycosphingolipid biosynthesis and glycan degradation pathways are involved in lysosomal gene regulation.

Figure 5

(A) Unbiased bioinformatics analyses also found that autophagy and lysosomal genes are most implicated in Alzheimer and Parkinson diseases, tuberous sclerosis, neuronal ceroid-lipofuscinoses, sepsis and lung, liver and prostatic neoplasms, implicating a critical role of autophagy-lysosomal pathway in these diseases. Shown are heat maps of relevance of genes and diseases. (B) A summary of the extent that knockout mice deficient of these genes exhibit nervous system phenotypes. Shown are heat maps of relevance of genes and knockout phenotypes.