RNAseq Analyses Identify Tumor Necrosis Factor-Mediated Inflammation as a Major Abnormality in ALS Spinal Cord

Abstract

ALS is a rapidly progressive, devastating neurodegenerative illness of adults that produces disabling weakness and spasticity arising from death of lower and upper motor neurons. No meaningful therapies exist to slow ALS progression, and molecular insights into pathogenesis and progression are sorely needed. In that context, we used high-depth, next generation RNA sequencing (RNAseq, Illumina) to define gene network abnormalities in RNA samples depleted of rRNA and isolated from cervical spinal cord sections of 7 ALS and 8 CTL samples. We aligned >50 million 2X150 bp paired-end sequences/sample to the hg19 human genome and applied three different algorithms (Cuffdiff2, DEseq2, EdgeR) for identification of differentially expressed genes (DEG’s). Ingenuity Pathways Analysis (IPA) and Weighted Gene Co-expression Network Analysis (WGCNA) identified inflammatory processes as significantly elevated in our ALS samples, with tumor necrosis factor (TNF) found to be a major pathway regulator (IPA) and TNFα-induced protein 2 (TNFAIP2) as a major network “hub” gene (WGCNA). Using the oPOSSUM algorithm, we analyzed transcription factors (TF) controlling expression of the nine DEG/hub genes in the ALS samples and identified TF’s involved in inflammation (NFkB, REL, NFkB1) and macrophage function (NR1H2::RXRA heterodimer). Transient expression in human iPSC-derived motor neurons of TNFAIP2 (also a DEG identified by all three algorithms) reduced cell viability and induced caspase 3/7 activation. Using high-density RNAseq, multiple algorithms for DEG identification, and an unsupervised gene co-expression network approach, we identified significant elevation of inflammatory processes in ALS spinal cord with TNF as a major regulatory molecule. Overexpression of the DEG TNFAIP2 in human motor neurons, the population most vulnerable to die in ALS, increased cell death and caspase 3/7 activation. We propose that therapies targeted to reduce inflammatory TNFα signaling may be helpful in ALS patients.

Laser-captured spinal cord motorneurons from ALS subjects show increased gene expression in vacuolar ATPase networks

“…In our prior work using gene expression as a proxy for cell activity, we reported that inflammation, and in particular that mediated by TNF-α, in our cervical spinal cord specimens appeared to be playing an etiologic role [36], but inflammation was not the only process we found to be disturbed. In the present study we isolated with laser capture micro dissection between ~600 and ~1300 anterior motor neurons per sample from the ALS and CTL cervical spinal cord sections available to us.…”

“…Minimum Phred scores were 20 (sequencing accuracy = 99%). Trimmed sequences were aligned against the human hg38 genome using Tophat2/Bowtie2 and quantitated against the hg38 transcriptome using Cufflinks (JPB) as described in [36]. Statistics utilized version 7 of Prism for Mac (www.…”

“…In a recent RNA-sequencing study of ALS cervical spinal cord sections [36], we described an apparent increase in neuroinflammation signaling involving tumor necrosis factor-alpha (TNF-α) as a major upstream regulator. Because whole spinal cord is mostly astrocytes, it was unclear what gene expression changes occur in motor neurons that are selectively vulnerable in ALS.…”

Micro RNA’s (mirna’s) may help explain expression of multiple genes in Alzheimer’s Frontal Cortex

“…(4.7) The AD population was slightly older (but not significantly so) at death (AD = 78 +/-3 years; CTL = 71 +/-4 years; p = 0.19 by t-test). Coding gene RNAseq was carried out as described in our earlier work [20], which provides links to all software algorithms and Unix texts used. Briefly, total RNA samples were treated to remove ribosomal RNA's (Ribo-Free, Illumina) then Illumina multiplex sequencing oligonucleotides were attached.…”

“…We utilized a format similar to that described in our earlier work [20]. Briefly, compressed, paired-end sequencing files in FastQ format for each mRNA sample were downloaded, decompressed and examined in FastQC.…”