James P. Bennett, Jr.1, 2, Paula M. Keeney1,2 and David G. Brohawn2, 3*
1 Neurodegeneration Therapeutics, Inc., Charlottesville, VA
2 Parkinson’s Center, Virginia Commonwealth University, Richmond VA 3 Department of Medical Genetics, Virginia Commonwealth University, Richmond, VA
*Present address: David Brohawn, Ph.D
UCLA Technology Center for Genomics & Bioinformatics
Department of Pathology & Laboratory Medicine
650 Charles E Young Dr.
Los Angeles, CA 90095
Nervous tissues from both humans with neurodegenerative diseases (NDD) and animals with genetic models of human NDD, such as rare monogenic causes of Amyotrophic Lateral Sclerosis (ALS), Alzheimer’s disease (AD) and Parkinson’s disease (PD), show activated microglia, suggesting a potential causal role for inflammation in NDD. We performed paired-end (PE) RNA sequencing (RNAseq) of total RNA’s extracted from frozen sections of cervical spinal cords from ALS and CTL subjects, frontal cortices of AD and CTL subjects, and ventral midbrains of PD and CTL subjects. Trimmed PE reads were aligned against the hg38 human transcriptome using Tophat2/Bowtie2 and quantitated with Cufflinks. PE reads were also aligned using Bowtie2 against genomes from representative species of Toxoplasma gondii and Trichinella sp T6 (parasitic infectious agents), Babesia microtii and Borrelia burgdorferi (tick-vector borne agents), and Treponema denticola and Porphyromonas gingivalis, agents causing chronic gingivitis. Primary aligned reads of each agent in each tissue sample were quantitated with Samtools.
We found small percentages (<0.1%) of transcriptomes aligned with B. microtii, B. burgdorferi, T. denticola and P. gingivalis genomes and larger percentages aligned with T. gondii (0.1-0.2%) and T. sp. 6 (1.0-1.1%) genomes. In AD specimens, but in no others, primary aligned transcriptome percentages, although small, approached significance for being greater in AD compared to CTL samples for B. burgdorferi (p=0.067) and P. gingvalis (p=0.068). Correlation tables of genes’ expressions in all three NDD’s revealed significant correlations among disease-associated microglial (DAM) genes in ALS, AD and PD.
Infectious agents’ transcripts can be detected in RNAseq reads of both NDD and CTL tissues and vary from agent to agent. Expressions of Stage 1 and Stage 2 DAM genes significantly correlated with each other, suggesting the presence of Stages 1 and 2 DAM in our NDD tissue samples. (290 words)
Keywords: Neurodegeneration, microglia, ALS, Alzheimer’s disease, Parkinson’s disease, gene expression
Microglia are CNS-resident immune cells that can serve both beneficial (reduction of immune responses) and detrimental (activation of neurotoxic immune responses) functions (Alexianu et al., 2001; Elliott, 2001; Henkel et al., 2004; Turner et al., 2004; Henkel et al., 2006; Jin et al., 2007; Chiu et al., 2008; Liang et al., 2008; Wang et al., 2009; Corcia et al., 2012; Takata and Kitamura, 2012; Tan et al., 2012; Varnum and Ikezu, 2012; Benarroch, 2013; Blandini, 2013; Carta and Pisanu, 2013; Perry and Teeling, 2013; Rangarajan et al., 2013; Shastri et al., 2013; Siskova and Tremblay, 2013; Streit and Xue, 2013; Zhao et al., 2013; Amor et al., 2014; Amor and Woodroofe, 2014; Baby et al., 2014; Brown and Neher, 2014; Lewis et al., 2014; Perry and Holmes, 2014; Rizzo et al., 2014; Tillement and Papadopoulos, 2014; Brown and Vilalta, 2015; Holtman et al., 2015; Meeker and Williams, 2015; Philips and Rothstein, 2015; Probert, 2015; Schwartz et al., 2015; Shadfar et al., 2015; Tripathy et al., 2015; Ulland et al., 2015; Wang et al., 2015; Yuste et al., 2015; Asiimwe et al., 2016; Beamer et al., 2016; Calsolaro and Edison, 2016; Casas et al., 2016; Chen et al., 2016a; Chen et al., 2016b; Puentes et al., 2016; Ransohoff, 2016; Rothaug et al., 2016; Su et al., 2016; Toledano et al., 2016; Trias et al., 2016; Ulrich and Holtzman, 2016; Wes et al., 2016; Au and Ma, 2017; Bagyinszky et al., 2017; Bickford et al., 2017; Blank and Prinz, 2017; Blaylock, 2017; Bolos et al., 2017; Cerami et al., 2017; Clayton et al., 2017; Collier et al., 2017; Colonna and Butovsky, 2017; Du et al., 2017; Guerriero et al., 2017; Han et al., 2017; Herz et al., 2017; Jay et al., 2017; Joers et al., 2017; Keren-Shaul et al., 2017; Kober and Brett, 2017; Koellhoffer et al., 2017; Labandeira-Garcia et al., 2017; Lall and Baloh, 2017; Lannes et al., 2017; Nissen, 2017; Plaza-Zabala et al., 2017; Roser et al., 2017; Sochocka et al., 2017; Sorce et al., 2017; Spittau, 2017; Thompson and Tsirka, 2017; Tse, 2017; van Horssen et al., 2017; Wolf et al., 2017; Yan et al., 2017; Yang et al., 2017; Aguilera et al., 2018; Baufeld et al., 2018; Bisht et al., 2018; Crisafulli et al., 2018; Deczkowska et al., 2018; Edison and Brooks, 2018; Labzin et al., 2018; Maccioni et al., 2018; Niranjan, 2018; Selles et al., 2018; Solleiro-Villavicencio and Rivas-Arancibia, 2018; Spagnuolo et al., 2018; Taylor et al., 2018).
Because activated microglia can produce known neurotoxic substances, such as tumor necrosis factor alpha (TNF-α) (Mohler et al., 1993; Poloni et al., 2000; Rusiniak et al., 2000; Hensley et al., 2002; Yoshihara et al., 2002; Ma et al., 2003; Micheau and Tschopp, 2003; Park et al., 2003; Zhou et al., 2003; Abel et al., 2004; Tian et al., 2005; Brustolim et al., 2006; Babu et al., 2008; Cereda et al., 2008; Stommel et al., 2009; Walczak, 2011; Saito et al., 2013; Probert, 2015; Brohawn et al., 2016; Bagyinszky et al., 2017; Islam, 2017; Labandeira-Garcia et al., 2017; Sioutas et al., 2017; van Horssen et al., 2017; Bisht et al., 2018) or nitric oxide from induced nitric oxide synthase (NOS2) (Surh et al., 2001; Brustolim et al., 2006; Danzeisen et al., 2006; Jin et al., 2007; Babu et al., 2008; Liang et al., 2008; Lewis et al., 2014; Brown and Vilalta, 2015; Tripathy et al., 2015; Yuste et al., 2015; Asiimwe et al., 2016; Islam, 2017; Tse, 2017), microglial presence has suggested that immune-mediated neurodegeneration may contribute to disease origin and/or progression in human neurodegenerative diseases (NDD) (Liang et al., 2008; Takata and Kitamura, 2012; Tan et al., 2012; Varnum and Ikezu, 2012; Benarroch, 2013; Blandini, 2013; Carta and Pisanu, 2013; Perry and Teeling, 2013; Rangarajan et al., 2013; Shastri et al., 2013; Siskova and Tremblay, 2013; Amor et al., 2014; Amor and Woodroofe, 2014; Baby et al., 2014; Brown and Neher, 2014; Perry and Holmes, 2014; Rizzo et al., 2014; Tillement and Papadopoulos, 2014; Brown and Vilalta, 2015; Holtman et al., 2015; Probert, 2015; Shadfar et al., 2015; Tripathy et al., 2015; Ulland et al., 2015; Wang et al., 2015; Yuste et al., 2015; Asiimwe et al., 2016; Beamer et al., 2016; Calsolaro and Edison, 2016; Chen et al., 2016a; Chen et al., 2016b; Puentes et al., 2016; Ransohoff, 2016; Rothaug et al., 2016; Su et al., 2016; Toledano et al., 2016; Trias et al., 2016; Ulrich and Holtzman, 2016; Wes et al., 2016; Au and Ma, 2017; Bagyinszky et al., 2017; Blank and Prinz, 2017; Blaylock, 2017; Bolos et al., 2017; Cerami et al., 2017; Clayton et al., 2017; Colonna and Butovsky, 2017; Guerriero et al., 2017; Han et al., 2017; Herz et al., 2017; Jay et al., 2017; Joers et al., 2017; Keren-Shaul et al., 2017; Kober and Brett, 2017; Koellhoffer et al., 2017; Labandeira-Garcia et al., 2017; Lall and Baloh, 2017; Lannes et al., 2017; Nissen, 2017; Plaza-Zabala et al., 2017; Roser et al., 2017; Sochocka et al., 2017; Sorce et al., 2017; Spittau, 2017; Thompson and Tsirka, 2017; Tse, 2017; van Horssen et al., 2017; Wolf et al., 2017; Yang et al., 2017; Aguilera et al., 2018; Baufeld et al., 2018; Bisht et al., 2018; Crisafulli et al., 2018; Deczkowska et al., 2018; Edison and Brooks, 2018; Labzin et al., 2018; Maccioni et al., 2018; Niranjan, 2018; Selles et al., 2018; Solleiro-Villavicencio and Rivas-Arancibia, 2018; Spagnuolo et al., 2018; Taylor et al., 2018).
By sorting brain immune cells and carrying out massively parallel RNA sequencing (RNAseq) on these cells over the course of disease progression in the 5X FAD mouse model of human AD, Keren-Shaul (Keren-Shaul et al., 2017), et al, demonstrated the TREM2-independent (“Stage 1”) and subsequent TREM2-dependent (“Stage 2”) emergence of “disease-associated microglia”, or DAM, during clinical and pathological progression. (TREM = “triggering receptor expressed on myeloid cells”)
Such DAM appeared to originate from “homeostatic” microglia (see Keren-Shaul, et al, Figure 6), then due to unknown causes, progressed to Stage 1 DAM by TREM2-independent mechanisms, followed by TREM2-mediated progression into Stage 2 DAM. At Stages 1 and 2, DAM exhibited unique genotypes, consisting mainly of up-regulated genes. Deczkowska and colleagues subsequently reviewed the field of DAM (Deczkowska et al., 2018).
We acquired postmortem samples of CNS tissues and carried out moderate-high density PE RNA sequencing on total RNA to seek systems biology understandings of disease pathogenesis in ALS, AD and PD (Bennett et al., 2016; Brohawn et al., 2016; Bennett and Keeney, 2017; Ladd et al., 2017) (see also http://ndtherapeutics.org/rna-sequencing-of-parkinsons-ventral-midbrain-to-estimate-gene-expression-reveals-marked-heterogeneity-and-gadd45a-as-a-potential-therapeutic-target/).
We now sought to query these data to test the hypothesis that subclinical CNS infections with common agents could be associated with microglial activation and presence of DAM. To do so, we first sought (using the Bowtie2 aligner) to determine if any of the PE RNAseq reads aligned with bacteria or parasite genomes downloaded from the NIH genome site. We then compared the percentages of total PE reads in each disease or CTL sample, represented by reads primary aligned to the infectious agent, with expression of genes associated with Stage 1 or Stage 2 DAM, as reported by Keren-Shaul, et al (Keren-Shaul et al., 2017) and Deczkowska, et al (Deczkowska et al., 2018). We found limited evidence for transcripts of infectious agents, the exception being Trichinella, but extensive correlations among expression of Stage 1 DAM and Stage 2 DAM, particularly in the NDD samples.
In our RNA seq studies we obtained between ~56 and ~172 million PE reads (based on Bowtie2 alignments). From these PE reads, we found wide variation in the number of primary aligned reads. When expressed as % of total PE reads, we observed between ~3.2 X 10-6 (B. microtii) and ~1.1 (Trichinella. sp. 6). These results are summarized in Figures 1 and 2, which show mean % aligned reads in each of the three NDD tissue specimens, expressed as mean +/- SEM. In the case of AD frontal cortex samples, but in no others, we observed a difference between AD and CTL cases for B. burgdorferi and P. gingivalis that approached significance (p=0.067 for B. burgdorferi and p=0.068 for P. gingivalis, both by unpaired t-test). In no other pair did we observe a situation where NDD>CTL for transcript abundance of infectious disease agents. Because all of our total RNA extracts were treated with DNAase and used cDNA’s generated for multiplex RNAseq reads, we are confident that alignments represent NDD tissue transcripts (ie RNA) aligned to infectious agent genomes (ie DNA). We did note a substantial difference in abundance of transcripts aligned to the genome of Trichinella sp. 6 compared to all others examined.
In the second part of our study, we looked for correlations among genes expressed by Stage 1 and Stage 2 disease-associated microglia (DAM), as defined by Keren-Shaul, et al (Keren-Shaul et al., 2017) and discussed by Deczkowska, et al. (Deczkowska et al., 2018). Table 1 shows that the number of alignments for varying infectious organisms in each NDD sample significantly correlated with expression of Stage 1 and/or Stage 2 DAM genes. This trend was particularly notable for Trichinella in PD. Tables 2-4 show the results for significant (p<0.05) correlations of expression among DAM genes in Table 2 (ALS/CTL cervical spinal cord); Table 3 (AD/CTL frontal cortex); and Table 4 (PD/CTL ventral midbrain). Note that there are multiple significant correlations (p<0.05) among Stage 1 and Stage 2 DAM genes, both within each Stage group, and between the two Stage groups. Most notable are the multiple significant correlations among Stage 1 and Stage 2 DAM genes in the PD samples.
By querying ~52 to ~172 million RNA seq paired-end (PE) reads in tissues from the three major adult NDD’s, we found that highly variable numbers of primary alignments could be found for several infectious agents known to affect humans. This is a limited list of infectious agents, and we appreciate that others could have been selected. These agents were selected based on documented infections in humans and availability of genomes. CNS involvement with parasitic infections, in particular, is known for Toxoplasma and Trichinella (Dzikowiec et al., 2017)
Our initial study of this approach yielded both very low frequencies of alignments (for B. burgdorferi, P. gingivalis, and T. denticola) and much higher alignment frequencies (for Trichinella sp. 6). With one exception (AD), we found no evidence that alignment frequencies in a NDD tissue set was greater than in CTL tissues from the same brain region. However, we did find that several infectious agents showed alignments, with FASTQ sequencing files from NDD tissue samples that correlated significantly with Stage 1 and Stage 2 DAM gene expressions. (see Table 1)
By searching for correlations among gene expression (as FPKM from Cufflinks), we found multiple significant (p<0.05) correlations among genes belonging to Stage 1 or Stage 2 disease-associated microglia (DAM), as delineated by Keren-Shaul, et al in 2017 (Keren-Shaul et al., 2017) and discussed later by the same group in 2018 (Deczkowska et al., 2018). That more correlations were found in the PD samples suggests that the levels of DAM (or the gene expressions in DAM) are higher in PD compared to AD or ALS.
There are multiple limitations to our study. These include:
- Use of postmortem materials. RNAseq is always problematic in these tissues, likely due to variable post-mortem intervals, inevitable RNA decay during frozen sectioning, and other unknown variations. We did attempt to use comparable RNA quality specimens, but in our hands these are always less than optimal (compared, for instance, to freshly isolated cells in culture).
- End-stage disease. We do not know the effects of end-stage disease, compared to earlier stages, on any of the variables we examined. For instance, we do not know about potential loss of infectious agents’ transcripts as disease progresses, nor do we know anything about expression of DAM genes over the course of illness in humans (compared to that in mice expressing mutated NDD genes).
- Significance of correlating DAM genes’ expressions. We speculate that correlations of Stage 1 and Stage 2 DAM genes’ expressions relates to the presence of Stage 1 and Stage 2 DAM’s in the NDD tissues we examined, but we do not know that this is the case. Proving this would require immunostaining sections of each NDD case for activated microglia, laser-capture of individual microglia (or identical members of a group) and RNA seq analysis of the captured cells. While approachable, this is a very formidable task that we are logistically incapable of performing.
- Causal relationship(s) of Stage 1/Stage 2 DAM to neurodegeneration in each NDD. We do not presume to ascribe causality of Stage1/Stage 2 DAM presence to the neurodegenerative process represented by the subjects who donated tissues we used. This is particularly of concern since we were not able to define any potential causes for NDD phenotype or DAM gene expression.
In spite of the above limitations, we hope that our findings will stimulate additional investigations into the potential role of DAM in pathogenesis of NDD’s. Lessening of DAM appearance or transition from Stage 1 DAM to Stage 2 DAM (Keren-Shaul et al., 2017) may represent a therapeutic opportunity in NDD. In addition, if our results can be extrapolated to multiple NDD’s, they suggest a common qualitative mechanism that could be therapeutically approached.
Our methods for tissue acquisition, RNA seq analyses and bioinformatics have been described in multiple publications (Brohawn DG; Bennett et al., 2016; Brohawn et al., 2016; Bennett and Keeney, 2017; Ladd et al., 2017). The particular tissue sets for ALS (Bennett et al., 2016; Brohawn et al., 2016; Ladd et al., 2017), AD (Bennett and Keeney, 2017) (http://ndtherapeutics.org/rna-sequencing-reveals-similarities-and-differences-in-gene-expression-in-vulnerable-brain-tissues-of-alzheimers-and-parkinsons-diseases/) and PD (http://ndtherapeutics.org/rna-sequencing-of-parkinsons-ventral-midbrain-to-estimate-gene-expression-reveals-marked-heterogeneity-and-gadd45a-as-a-potential-therapeutic-target/) have been previously described. Briefly, tissues were stored at -80 degrees and blocks dissected from these frozen specimens. Frozen 20-micron tissue sections were placed into Qiazol buffer and stored at -80 degrees until RNA isolation was carried out (miRNeasy, Qiagen). An on-column DNAase step was included for each sample. RNA quality was analyzed by gel electrophoresis. Illumina sequencing libraries were constructed according to manufacturer instructions, either by us (ALS/CTL cervical spinal cord) or by Cofactor Genomics (AD/CTL frontal cortex, PD/CTL ventral midbrain) and PE Illumina sequencing was carried out by Cofactor Genomics, Inc. (https://cofactorgenomics.com/). Compressed PE reads in fastq format were trimmed (Trimmomatic®) and analyzed for expression based on the hg38 human genome by Tophat2/Bowtie2 and quantitated with Cufflinks. In other experiments, trimmed PE reads were aligned using Bowtie2 against genomes of infectious agents, downloaded in FASTA format from the NIH website (https://www.ncbi.nlm.nih.gov/pubmed/) “genome”. Bowtie2-build was used to construct bowtie2 index files for each genome, samtools was used to convert the SAM files to BAM files, and the samtools command samtools view -c -F 260 x.bam was used to quantitate the number of primary aligned reads in each sample for each NDD. All bioinformatics assays were performed “blind” and were based solely on sample number ID (not disease state). All graph constructions, correlations and statistical assays were performed in Prism 7 (www.graphpad.com).
JB, PK and DB designed all studies. PK oversaw tissue acquisition and storage, isolated and assayed RNA’s. DB isolated RNA’s (ALS samples), created and assayed sequencing libraries (ALS) and performed data analysis. JB performed all bioinformatics assays, performed data analysis and wrote the manuscript draft. All authors have reviewed the final manuscript draft and agree with its contents.
All tissues were acquired commercially from National Disease Resource Interchange (http://ndriresource.org; NDRI) (ALS/CTL cervical spinal cords); under the auspices of an IRB-approved collection protocol (most AD/CTL and some PD/CTL), or were declared exempt from IRB oversight (some AD/CTL and some PD/CTL). All sequencing data discussed are the property of Neurodegeneration Therapeutics, Inc. and were acquired with private funds. Untrimmed, compressed (gz) FASTQ sequencing files are available to all legitimate investigators, following request to the Corresponding Author (JPB), completion of a Material Transfer Agreement and provision of either a FTP URL or a memory storage device capable of storing 2TB of data. Trimmed, processed BAM files following Trimmomatic and Tophat2/Bowtie2 analyses are also available upon reasonable request.
Conflicts of Interest
None of the authors has any conflicts of interest with the data reported in this paper.
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