Transcriptional profiling of human brain cortex identifies novel lncRNA-mediated networks dysregulated in amyotrophic lateral sclerosis

Background:

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease where motor neurons die and the muscles they supply are subsequently weakened. Every year this disease affects almost 200,000 people around the world and in the United States alone about 80% of patients die within five years of diagnosis [1, 2, 3]. The cellular mechanisms through which this disease manifests itself are poorly understood. Broadly, ALS has been associated with protein aggregation, cellular stress, oxidative damage, and the disruption of RNA processing in neurons [4].

In this preprint, the researchers argue that ALS affects such a broad range of cellular mechanisms that there must be unknown molecular regulators involved. They highlight recent evidence implying that long non-coding RNAs (lncRNAs) might be involved in ALS pathogenesis. These are a class of regulatory non-coding transcripts, larger than 200bp in length, that interact across different levels of the cell’s regulatory networks [5,6]. With this in mind, the authors explored a public dataset [7] containing single-nuclei RNA sequencing (snRNAseq) data from ALS and pathologically normal (PN) patients and identified several lncRNAs expressed in the brain that could contribute to the neuronal dysregulation seen in ALS patients.

Main Findings:

LncRNAs may regulate distinct cellular processes in several brain cell types

The authors found that among the top 2,000 most variable, differentially expressed genes between ALS and PN samples there were 386 lncRNAs. 11 of these lncRNAs are uniquely expressed in neuronal or brain vascular cells. Because most studies focus on global transcriptomic data analysis, the authors decided to take advantage of the single-nuclei RNA-seq setup and compared lncRNA signatures among 7 cell subtypes.

Interestingly, in ALS patient’ samples, the astrocytes were the only subtype with a substantial decrease in number. Also, their annotations for downregulated genes hinted at RNA regulation impairment related to repressed lncRNAs. Conversely, both microglial and oligodendrocytes in ALS samples had several lncRNAs upregulated. Several of these lncRNAs are shared among different cell types, which indicates a possible shared role for lncRNAs in brain-specific gene modulation and a possible role for lncRNAs in the transcriptional dysregulation observed in ALS.       

LncRNA dysregulation in excitatory neurons could be specific to ALS

The authors next compared the expression levels of lncRNAs between excitatory and inhibitory neurons, also known as neuronal populations. They identified 78 lncRNAs that were differentially expressed between ALS and PN excitatory neurons, with 7 of them being anti-sense and unique to excitatory neurons, thus serving as new biomarkers for this cell type in ALS. Several of the lncRNAs that were identified are upregulated in some cancers, but in all cases these candidates were downregulated in ALS samples. This, topped with the fact that only 10 lncRNAs were found to be differentially expressed in inhibitory cells, hints at the possibility that lncRNA dysregulation in excitatory cells is specific to ALS.

Involvement of lncRNAs in the gene regulatory networks of ALS cell populations

Lastly, the authors decided to further explore regulatory mechanisms relevant to ALS pathogenesis. The authors found five distinct modules (clusters) within the dataset they used, four of which correlate with a specific cell subtype. Each module was enriched for downregulated processes that have previously been associated with ALS pathogenesis. What really stands out is the fact that 14 of the top 100 hub genes (20 top genes of each module) were identified as lncRNAs. Although the majority of these belong to a module that was unconnected from the rest of the network, this indicates that dysregulated cellular and molecular processes across all brain cell subtypes is a feature of ALS.

 

In summary, the researchers were able to show how the transcriptomic profiles of ALS patients show a distinct signal of downregulated regulatory networks across different brain cell subtypes. Though this dysregulation in regulatory networks was not specifically associated with lncRNAs, the high abundance of lncRNAs among the differentially expressed genes hints at a role for this type of RNA molecule in ALS patients.

Why I chose this preprint

I chose this preprint because of the use of (publicly available) DNA expression data to understand differences between diseased and healthy states in human cells. As our RNA-sequencing techniques keep getting better, we have come to realize that our expression patterns and regulatory networks might have much more to do with the cellular state than simply what nucleotide sequences are present in the genome. Moreover, studies have also shown the discrepancies exist in expression and regulation patterns among cell types and among individual cells of the same type. This study uses non-bulk transcriptomic data (single-nuclei RNA-seq) to identify expression variation among brain-cell subtypes between ALS patients and healthy controls. This type of bioinformatic study is essential to further understanding the transcriptomic differences that are relevant to a specific cell’s disease or stress state, as they provide excellent opportunities for identifying key players at the gene and network levels. Like in this case, where lncRNAs were shown to play a role in ALS pathogenesis across several brain cell types. This can serve as the foundation of future studies using model organisms, which could use the data provided by the authors for functional studies of lncRNAs (or other genes) to elucidate their roles in cellular regulation.

Questions for the authors

1. You show in at least one of the cell subtypes that there was a suppression of RNA-related processes (evidenced by the downregulation of related genes) and mention that this might be a feature of ALS in that cell subtype. In many organisms the repression of the RNA-machinery is one of the first transcriptomic responses to stress; do you believe this could be the case in this subtype or in all brain cells in general?
2. Several of the lncRNAs that you encountered were in the anti-sense direction and you mention that this could be relevant in relation to their regulatory role. Could you expand upon this idea, and would your conclusions be different if all were in the sense position instead?
3. You show that there are several lncRNAs that are differentially expressed in ALS samples – do any of these genes have orthologs in model organisms (mouse, worm, fly, yeast) that could be used for functional studies? Are there any other differentially expressed genes that you think are good candidates for testing in model organisms to functionally explore the regulation networks and cellular processes involved in ALS?

References

1. Hardiman, O. et al. Amyotrophic lateral sclerosis. Nat. Rev. Dis. Primers 3, 17071 (2017).
2. Xu, L. et al. Global variation in prevalence and incidence of amyotrophic lateral sclerosis: a systematic review and meta-analysis. J. Neurol. 267, 944–953 (2020).
3. Wolfson, C. et al. Global prevalence and incidence of amyotrophic lateral sclerosis: a systematic review. J. Neurol. 101(6), e613-e623 (2023).
4. Le Gall, L. et al. Molecular and cellular mechanisms affected in ALS. J. Pers. Med. 10(3), 103 (2020).
5. Mattick, J. S. et al. Long non-coding RNAs: definitions, functions, challenges and recommendations. Nat. Rev. Mol. Cell Biol. 1–17 (2023).
6. Humphrey, J. et al. Integrative transcriptomic analysis of the amyotrophic lateral sclerosis spinal cord implicates glial activation and suggests new risk genes. Nat. Neurosci. 26, 150–162 (2023).
7. Pineda, S. S. et al. Single-cell profiling of the human primary motor cortex in ALS and FTLD. bioRxiv 2021.07.07.451374 (2021).

Tags: als, brain, neurodegenerative, rna

Posted on: 16 April 2024 , updated on: 17 April 2024

doi: https://doi.org/10.1242/prelights.37124

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