An integrated multi-omic analysis of iPSC-derived motor neurons from C9ORF72 ALS patients

Abstract

Neurodegenerative diseases present a challenge for systems biology, due to the lack of reliable animal models and the difficulties in obtaining samples from patients at early stages of disease, when interventions might be most effective. Studying induced pluripotent stem cell (iPSC)-derived neurons could overcome these challenges and dramatically accelerate and broaden therapeutic strategies. Here we undertook a network-based multi-omic characterization of iPSC-derived motor neurons from ALS patients carrying genetically dominant hexanucleotide expansions in C9orf72 to gain a deeper understanding of the relationship between DNA, RNA, epigenetics and protein in the same pool of tissue. ALS motor neurons showed the expected C9orf72-related alterations to specific nucleoporins and production of dipeptide repeats. RNA-seq, ATAC-seq and data-independent acquisition mass-spectrometry (DIA-MS) proteomics were then performed on the same motor neuron cultures. Using integrative computational methods that combined all of the omics, we discovered a number of novel dysregulated pathways including biological adhesion and extracellular matrix organization and disruption in other expected pathways such as RNA splicing and nuclear transport. We tested the relevance of these pathways in vivo in a C9orf72 Drosophila model, analyzing the data to determine which pathways were causing disease phenotypes and which were compensatory. We also confirmed that some pathways are altered in late-stage neurodegeneration by analyzing human postmortem C9 cervical spine data. To validate that these key pathways were integral to the C9 signature, we prepared a separate set of C9orf72 and control motor neuron cultures using a different differentiation protocol and applied the same methods. As expected, there were major overall differences between the differentiation protocols, especially at the level of in individual omics data. However, a number of the core dysregulated pathways remained significant using the integrated multiomic analysis. This new method of analyzing patient specific neural cultures allows the generation of disease-related hypotheses with a small number of patient lines which can be tested in larger cohorts of patients.

Integrated transcriptome landscape of ALS identifies genome instability linked to TDP-43 pathology

“…For the integrated analysis, we included 15 datasets comprising 429 iPSMNs, of which 323 were from ALS patients and 106 were from non-ALS controls. ALS iPSMNs carried pathogenic mutations in 10 different genes, including C9orf72 22,23,[25][26][27][28] (n = 60), SOD1 18,19,[21][22][23] (n = 20), FUS 20,23,25,29,30 (n = 14), and TARDBP 23,26,31,32 (n = 10); whilst 208 (64.2%) were from patients without an identifiable ALS mutation, which we refer to here as sporadic ALS (Table 1).…”

“…However, iPSMN cultures are expensive and labour-intensive, and many studies have been limited to three or fewer patients [17][18][19][20][21] . Despite this, there has been a recent expansion of ALS iPSMN biobanks with initiatives such as neuroLINCS 22 and Answer ALS 23 , offering a unique opportunity to identify generalisable motor neuron perturbations across ALS genetic backgrounds.…”

Mutant GGGGCC RNA prevents YY1 from binding to Fuzzy promoter which stimulates Wnt/β-catenin pathway in C9ALS/FTD

“…The DESeq2 method was subsequently used for the analysis of normalized counts to obtain differentially expressed genes. The lists of differentially expressed Article https://doi.org/10.1038/s41467-023-44215-w genes from NeuroLINCS dataset and AnswerALS data portal were downloaded directly from the published paper 72 .…”

A systems-biology approach connects aging mechanisms with Alzheimer’s disease pathogenesis

“…Next, we integrated these multi-species, multi-omic data with a previously published genome-scale screen for tau-mediated neurotoxicity 24 , existing human Alzheimer's disease GWAS hits, proteomics, and metabolomics 10,12,24,33,34 using the Prize-Collecting Steiner Forest algorithm (Figure 1) 35,36 . This approach has been used to identify biological processes in various disease consequences, including Alexander disease, medulloblastoma, Parkinson's disease in Drosophila, amyotrophic lateral sclerosis, and an Appl model of Alzheimer's disease in Drosophila [37][38][39][40][41] .…”