Differential expression of m5C RNA methyltransferase genes NSUN6 and NSUN7 in Alzheimer’s disease and Traumatic Brain Injury

doi:10.21203/rs.3.rs-1525499/v1

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Differential expression of m5C RNA methyltransferase genes NSUN6 and NSUN7 in Alzheimer’s disease and Traumatic Brain Injury

https://doi.org/10.21203/rs.3.rs-1525499/v1

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Epigenetic processes have become increasingly relevant in understanding disease modifying mechanisms. 5-methylcytosine methylation of DNA (5mC) and RNA (m⁵C) have functional transcriptional and RNA translational consequences and are tightly regulated by writers, readers, and erasers effector proteins. To investigate the involvement of 5mC and m⁵C effector proteins contributing to the development of dementia neuropathology, RNA-sequencing data for 32 effector proteins across four brain regions was examined in 51 aged non-affected and 56 Alzheimer’s disease (AD) individuals obtained from the Aging, Dementia and Traumatic Brain Injury (TBI) study. Gene expression profiles were compared between AD and controls, neuropathological Braak and CERAD scores and in individuals with a history of TBI. We found an increase in DNA methylation writers DNMT1, DNMT3A, DNMT3B mRNA and reader UHRF1 mRNA in AD samples across three brain regions. While DNA methyltransferases erasers GADD45B and AICDA showed changes in mRNA abundance within neuropathological load groupings. RNA methylation writers NSUN6 and NSUN7 showed significant expression differences with AD and, along with the reader, ALYREF, differences in expression for neuropathologic ranking. A history of TBI was associated with a significant increase in ZBTB4 (p < 0.01) and decrease in NSUN6 (p < 0.005) mRNA. These findings implicate regulation of protein pathways disrupted in AD and TBI via multiple pre- and post- transcriptional mechanisms including potentially acting upon tRNAs, enhancer RNAs, as well as nuclear-cytoplasmic shuttling and cytoplasmic translational control. The targeting of such processes provide new therapeutic avenues for neurodegenerative brain conditions.

Alzheimer’s disease

5-methylcytosine methylation

NOL1/NOP2/sun domain-containing genes

NSUN6

Traumatic Brain Injury.

Dementia, a progressive decline in neurocognitive ability, is the principal cause of disability in the elderly population [1]. By 2019, the number of people diagnosed with dementia worldwide reached 50 million, incurring a significant socio-economic burden on society [2, 3]. Forms of dementia are caused by progressive and irreversible degeneration of neurons and include Alzheimer’s disease (AD), dementia of Lewy bodies (DLB), and, frontotemporal dementia. Diagnosis of AD by brain tissue histology is made through the identification of intraneuronal aggregation of tau protein known as neurofibrillary tangles (NFT) and the accumulation of insoluble beta amyloid peptide (Aβ) termed senile and neurite plaques [4]. These pathological features progress through stages of severity which correlate to burden of pathology and anatomical localization [5, 6].

Most neurodegenerative dementias have a multifactorial aetiology and are thought to develop due to multiple environmental and genomic factors. One proposed life event risk factor is a history of traumatic brain injury (TBI) which results from head trauma that leads to either focal or diffuse injury [7, 8]. The effect of TBI can be acute or chronic with the latter having a greater link to cognitive decline [9–11]. Neurodegenerative lesions are commonly found in individuals exposed to TBI and are referred to as TBI-related neurodegeneration (TReND) [10, 12, 13]. However, although changes in clinical phenotype, brain activity and histopathological features have been reported in TBI and TReND, less is known about the molecular mechanisms which contribute to long-term neuropathological changes.

Epigenetic processes which regulate transcriptional and translational activity are proposed as mechanisms which mediate environmental influences on brain physiology [14, 15]. Such epigenetic modifications are tissue specific and change during cellular maturation, i.e. during differentiation and aging, and therefore have been implicated in disease processes including dementia pathology [16–18]. A commonly studied epigenetic process, 5-methylcytosine (5mC) DNA methylation, involves the addition of a methyl group to the fifth carbon of the cytosine base and is associated with transcription repression [19]. However, 5mC can be converted into alternative oxidised methylation states known as 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC), and 5-carboxylcytosine (5caC) through Ten-eleven translocation (TET) methylcytosine dioxygenase and Thymine-DNA glycosylase (TDG) mediated active demethylation [20, 21]. Such oxidised states have been shown to be stable and to potentially have functional transcriptional consequences [22–24].

Methylation of RNA species at the same cytosine base known as 5-methylcytosine, m⁵C, also undergoes oxidisation to form 5-hydroxymethylcytosine (hm⁵C), 5-formylcytidine (f⁵C) and 5-carboxycytidine (ca⁵C) [25–28]. m⁵C methylation of mRNA is indicated to increase RNA stability and abundance, regulate nuclear exportation as well as negatively correlating with translation efficiency [29, 30]. In tRNA, rRNA and mt-tRNA, m⁵C modification provides structural stability and improves the accuracy of translation [29, 28]. Similarly, hm⁵C modification enhances mRNA translation whereas f⁵C of mitochondrial tRNA is important for translation of AUA to Methionine [31–33]. As such, the modification of DNA and RNA may have important consequences on cellular localisation of RNAs and regional patterns of gene expression.

DNA and RNA methylation are governed by three families of effector proteins known as writers, readers and erasers. The first family of proteins, methyltransferases, add the methyl groups to nucleotides. Whereas readers proteins bind to methylated DNA/RNA and assist in the formation of protein complexes which influence transcriptional or translational processes. In contrast, erasers are the group of enzymes known as demethylases that generate the oxidised states and take off methyl groups. The expression of these effector proteins across brain regions and within cellular environments will have important consequences for transcription and translation activity.

To investigate the involvement of 5mC and m⁵C methylation processes contributing to the development of dementia neuropathology, we analysed RNA-sequencing data from the Aging, Dementia and Traumatic Brain Injury (TBI) study to characterise RNA expression profiles of 32 DNA and RNA methylation effector protein genes across four brain regions in 51 unaffected individuals and 56 individuals with AD. In a second phase, we examined gene expression within these DNA/RNA methylation genes by grouping the samples by Braak stage and CERAD specific neuropathological staging. To better understand the relationship between TBI and dementia and changes in effector protein abundance, we also explored gene expression profiles in individuals with TBI, or TBI and dementia or unaffected aged controls.

Study samples

Data was obtained from the Aging, Dementia and Traumatic Brain Injury (TBI) study from the Allen Institute for Brain Science (http://aging.brain-map.org/) [34, 35]. The cohort of case and age-matched unaffected control individuals was provided by the Adult Changes in Thought (ACT) study [36]. Information was gathered from 107 individuals consisting of 51 individuals with clinical diagnosis of AD and 56 individuals without AD (controls). Twenty-six of the non-affected individuals had a history of TBI and 30 had no history of TBI, whereas 27 individuals with AD had a history of TBI and 24 had no history of TBI (Supplementary Table 1). Brain tissue was procured by macrodissection from fresh frozen brain with a mean post-mortem Interval (PMI) of 4.6 +/-1.5hrs for cases and 4.7 +/-2hrs for unaffected individuals (Aging, Dementia and TBI Study, 2017b). RNA expression values were obtained for the hippocampus (HIP), the superior temporal gyrus (STG) of the temporal cortex, the inferior parietal cortex (PC), and the white matter (WM) from the parietal cortex (Supplementary Table 1).

Diagnosis criteria for AD established by the National Institute of Ageing and the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) [6], is based on the “ABC” score which consists of the Aβ senile plaque rating, a Braak score and a CERAD staging. The Braak score measures the presence and distribution of NFT through specific brain regions and is classified in stages 0 to VI accordingly. The CERAD rating is a semiquantitative assessment of the presence of Aβ as neuritic plaques (Supplementary Table 2).

Analysis of RNA expression profiles

mRNA expression profiles were examined for the following genes: DNA methylation writers, DNMT1 (NM_001130823), DNMT3A (NM_022552.5), DNMT3B (NM_006892.4); readers, MeCP2 (NM_004992.4), ZBTB4 (NM_001128833.2), ZBTB33 (NM_001184742.2), ZBTB38 (NM_001376113.1), UHRF1 (NM_001048201.3); and erasers, TET1 (NM_030625.3), TET2 (NM_001127208.3), TET3 (NM_001287491.2), TDG (NM_003211.6), MBD4 (NM_001276270.2), AICDA (NM_020661.4), GADD45A (NM_001924.4), GADD45B (NM_015675.4) and GADD45G (NM_006705.4). RNA methylation effector genes examined were: writers, NOP2/NSUN1 (NM_001258308.2), NSUN2 (NM_017755.6), NSUN3 (NM_022072.5), NSUN4 (NM_199044.4), NSUN5 (NM_148956.4), NSUN6 (NM_182543.5), NSUN7 (NM_024677.6), and TRDMT1/DNMT2 (NM_004412.7); readers, ALYREF (NM_005782.4) and YBX1 (NM_004559.5); and erasers, ALKBH1 (NM_006020.3), TET1 (NM_030625.3), TET2 (NM_001127208.3) and TET3 (NM_001287491.2). Expression data were obtained from normalized fragments per kilobase of transcript per million (FPKM) values derived from RNA sequencing and presented as a z-score value of expression.

Individuals were categorised by clinical diagnosis AD versus controls, and grouped by age of death, APOE4 allele carrier status and Braak and CERAD staging in HIP and STG (Supplementary Table 3) and IPL inferior parietal lobe or white matter of the parietal lobe (Supplementary Table 4). For Braak staging, three groups were generated to represent levels of pathology. Group one corresponded to individuals representing low levels of pathology with a Braak stage between 0-II. Group two had a Braak stage between III-IV and had moderate pathology. In the third group, samples had Braak stages between V-VI indicating the highest level of pathology. To analyse by CERAD ranking, samples were divided in two groups with a CERAD score of 0–1 indicating low amyloid load, and a CERAD score of 2–3 representing high amyloid load.

In a first phase, differences between age, sex, APOE4 allele status, Braak and CERAD staging in the groups AD and control was assessed. We then tested for significant correlations between diagnosis status, Braak, CERAD and APOE4 across each of the four brain regions. In a second phase, we compared RNA abundance profiles across the 32 effector proteins in AD and control groups for each brain region. Subsequently, differences in gene expression were examined in individuals classified by Braak and CERAD rankings. In the third arm of the study, we assessed differential expression in individuals with and without a TBI event by divided the cohort into four groups: a control group of aged individuals without dementia and without TBI; a group of individuals with dementia but without TBI; a third group of individuals with TBI but without dementia; and, a fourth group which includes all TBI individuals, i.e. with and without dementia (Supplementary Fig. 1 provides a flow chart of analysis pipeline).

Statistical analysis

Normality of gene expression z-scores was tested by the Shapiro-Wilk test and visual analysis of Q-Q plots. Descriptive statistics was performed presenting the mean with standard deviation (SD) or median with 95% confidence interval (CI). In the first stage of analysis, differences in age between diagnosis status were examined using Mann-Whitney test. Chi-square tests were performed to assess the relationships between diagnosis status and sex, APOE4, Braak and CERAD. Differences in gene expression values of DNA and RNA effector proteins between cases and controls, and between CERAD groups, was evaluated using t-test and Mann-Whitney tests. To identify significant differences in the expression values between Braak groups, one-way ANOVA or Kruskal-Wallis test was performed with post hoc Tukey’s test or Dunn’s test analysis, respectively. All tests were two tailed and in the first two stages of the study, a level of significance of 0.05 was accepted. In the final stage of analysis, the TBI groups comparisons, and owing to multiple corrections, a level of significance of 0.01 was accepted. Data analysis and graphs were generated using IBM SPSS Statistics for Windows, Version 27.0, and GraphPad Prism version 9.1.1 for Windows.

Demographics and neuropathologic load in unaffected and Alzheimer’s groups

We first examined differences in group demographics and pathology scores between unaffected individuals and individuals with AD (Table 1). No significant differences were observed for age, sex and APOE4 allele status between groups. We found a significant difference between AD and control individuals in Braak staging, X² = 14.7, p = 0.0006 and CERAD scores, X² = 7.73, p = 0.005. As expected, AD samples had a higher prevalence of Braak V-VI and of CERAD 2–3 staging, whilst most control individuals presented with a Braak stage of 0-IV and CERAD score of 0–1. This pattern was also consistently observed across the four brain regions with the highest AD case and control group difference for Braak (X² = 17.20, p = 0.0002) and CERAD ranks (X² = 12.39, p = 0.0004) in the hippocampus. As expected, a significant positive correlation between Braak and CERAD scores was apparent across all brain regions (r = 0.62, p < 0.0001) (Supplementary Fig. 2).

Table 1

Demographic characteristics and neuropathological scores of study individuals.
	Total N = 107	Controls N = 56	AD N = 51	Test statistic p value
Age	x̃ (Q₁ - Q₃)	89 (84–92)	89 (86–92)	U = 133
Age	x̃ (Q₁ - Q₃)	89 (84–92)	89 (86–92)	0.55
Sex n (%)	Female Male	21 (47.7) 35 (55.6)	23 (52.3) 28 (44.4)	X² = 0.6 0.43
APOE4 allele n (%)	Carrier	7 (12.5)	13 (25.5)	X² = 5.3 0.07
	No carrier	47 (84.0)	33 (64.7)
	NI	2 (3.5)	5 (9.8)
CERAD scores n (%)	0–1	37 (65.0)	20 (35.0)	X² = 7.7 0.005**
CERAD scores n (%)	2–3	19 (38.0)	31 (62.0)	X² = 7.7 0.005**
Braak stages n (%)	0 - II	22 (39.3)	9 (17.7)	X² = 14.7 0.0006***
	III - IV	26 (46.4)	18 (35.3)
	V - VI	8 (14.3)	24 (47.0)

The Demographic characteristics and neuropathological scores are presented for all individuals and for individuals within the groups no AD and AD. APOE, apolipoprotein E; CERAD, Consortium to Establish a Registry for Alzheimer’s Disease; x̃, median; Q1, lower quartile; Q3, upper quartile; NI, no information; U, Mann-Whitney test; X²,chi-square. **p < 0.01, ***p < 0.001.

Effector proteins expression profiles in unaffected and Alzheimer’s disease individuals

To determine patterns of expression across the brain regions in the normal brain, we first examined the expression profiles of effector proteins in non-affected individuals (Fig. 1). In healthy aged individuals, DNA effector proteins showed varied expression across the four brain regions and generally showed higher expression in the white matter (WM) and lowest expression in the hippocampus (HIP) (Fig. 1A). The superior temporal gyrus (STG) and parietal cortex (PC) were found to have similar gene expression patterns and indicated that the writer, DNMT3B, readers MeCP2 and ZBTB4, and erasers GADD45B and GADD45G had the highest abundance in these regions. In contrast, in the WM of the parietal lobe, the writers DNMT1 and DNMT3A, the reader UHRF1, and the erasers TET1 and TET2 which are involved in both DNA and RNA modification, were found to be the most highly expressed transcripts. These observations suggest that individual effector proteins may have varied roles in DNA methylation processes depending on the brain region and that function maybe dependent on the cell and tissue context.

In the aged individuals with Alzheimer’s disease, DNA effector proteins commonly showed similar tissue-specific patterns in expression to the non-affected samples but showed overall higher abundance of effector proteins in the STG, PC and WM as compared to control individuals (Fig. 1B). The erasers proteins indicated the most varied expression profiles across the four brain tissues and typically showed increased expression in dementia cases across all regions. Quantitative comparisons of DNA effector gene expression between AD and unaffected individuals (Con) in each region revealed significant differences in expression for the writers DNMT1 (AD x̃ = 0.174 [-0.31–0.67], Con − 0.021 [-1.10–0.28], p = 0.010) and DNMT3A (AD x̃ = 0.118 [-0.43–0.69], Con − 0.275 [-0.86–2.30], p = 0.046) in the STG and DNMT3B (AD x̃ = 0.288 [-0.12–0.71], Con 0.013 [-0.61–0.29], p = 0.013) in the WM, and for all higher expression in AD cases compared to controls (Fig. 1C, Supplementary Table 5). Conversely, the DNA reader UHRF1 showed significantly higher abundance in control individuals compared to AD cases (AD x̃ = 0.716 [0.11–1.35], Con 1.140 [0.61–1.93], p = 0.038) in the WM (Fig. 1C). No significant differences in 5mC effector protein abundance were observed in the IPL or HIP.

Like the DNA effector proteins, RNA effector proteins in the healthy brain exhibited overall highest relative expression in the WM tissue (Fig. 1D). However, in contrast to DNA effector proteins, gene expression of effector proteins in the hippocampus, although varied, was moderately high particularly for the writer proteins with the except of NSUN4. YBX1 and TET1 and TET2 were the most abundant reader and eraser proteins respectively in the HIP and WM. Whereas, in the STG and PC, the reader ALYREF, and erasers ALKBH1 and TET3, were more highly expressed again providing evidence for tissue-specific RNA methylation effector protein mechanisms.

RNA effector proteins in the dementia group again showed a similar overall expression profile to healthy controls across the regions (Fig. 1E). However, in the AD group the writer, NSUN6 was found to have significantly lower abundance in two regions, the STG (AD x̃ = -0.42 [-0.87 - -0.17], Con − 0.117 [-0.67–0.44], p = 0.022) and in the WM (AD x̃ = 0.819 [0.18–1.30], Con 1.07 [0.45–1.96], p = 0.031), whereas NSUN7 showed higher abundance in the HIP (AD x̃ = 0.926 [0.41–1.60], Con 0.684 [0.14–0.93], p = 0.023) (Fig. 1F, Supplementary Table 5). No differences in the expression of the writers between affected and non-affected individuals was found in the PC or for any reader or eraser protein transcripts in any of the four brain regions.

Differences in DNA/RNA effector protein expression grouped by Braak and CERAD neuropathological scales

Table 2 presents the relative expression values and associated p values for differences in 5mC DNA and m⁵C RNA methylation effector transcript expression grouped by Braak stages. We observed significantly higher abundance in two DNA eraser protein transcripts GADD45B and AICDA which were associated with differences in Braak staging (Fig. 2A). Here, GADD45B gene expression was significantly lower in the mid-neuropathology scores compared to low-early and high-late neuropathological Braak staging in the hippocampus (H = 6.88, p = 0.03, post hoc between stages 0-II, x̃ = 0.37 and stages III-IV, x̃ = 0.16, p = 0.01) and the superior temporal gyrus (H = 7.01, p = 0.03; post hoc between stages 0-II, x̃ = 0.11, and stages III-IV, x̃ = -0.13, p = 0.008). Similarly, expression of the eraser AICDA in the WM was significantly higher in low early Braak stages compared to both mid and late stages (H = 10.07, p = 0.006; post hoc between stages 0-II, x̃ = 1.01 and stages V-VI; x̃ = -0.25, p = 0.002; post hoc between stages III-IV, x̃ = 0.56, and stages V-VI, x̃ = -0.25, p = 0.03). Conversely, and consistent with differences between healthy aged and AD diagnosis tissue, the reader effector transcript UHRF1 showed significant lower expression in low and mid Braak staging groups compared to Braak V-VI late stages in the STG (H = 7.88, p = 0.02; post hoc between stages 0-II, x̃ = -0.44, and stages V-VI, x̃ = -0.16, p = 0.019; post hoc between stages III-IV, x̃ = -0.45, and stages V-VI, x̃ = -0.16, p = 0.01).

Table 2

**Significant differences in expression of 5mC DNA and m**⁵**C RNA methylation effector proteins between Braak stages**
		Braak stages
		0-II	III-IV	V-VI		All groups			Post hoc
		0-II	III-IV	V-VI		All groups		0-II vs III-IV			0-II vs V-VI	III-IV vs V-VI
		x̃ (95% CI)			Statistic (H)		p value		p value
DNA effector proteins
Hippocampus	GADD45B	0.37 (0.66)	0.16 (0.24)	0.30 (0.53)	6.88		0.03*	0.01*		0.38		0.10
Superior Temporal Gyrus	GADD45B	0.11 (0.42)	-0.13 (-0.02)	0.00 (0.23)	7.01		0.03*	0.008**		0.20		0.21
Superior Temporal Gyrus	UHRF1	-0.44 (-0.36)	-0.45 (-0.32)	-0.16 (0.30)	7.88		0.02*	0.96		0.019*		0.01*
White Matter	AICDA	1.01 (1.41)	0.56 (0.91)	-0.25 (0.50)	10.07		0.006**	0.22		0.002**		0.03*
RNA effector proteins
Hippocampus	NSUN6	-0.56^† (0.70)	-0.14^† (0.73)	-0.67^† (0.83)	4.67^†		0.01*	0.03*		0.60		0.005**
	NSUN7	0.34^† (0.70)	0.96^† (1.11)	0.96^† (1.05)	3.79^†		0.03*	0.01*		0.02*		0.98
	ALYREF	0.45 (0.84)	0.25 (0.57)	-0.08 (0.21)	6.23		0.04*	0.24		0.01*		0.14
Parietal Cortex	ALYREF	-0.09 (0.42)	-0.04 (0.32)	-0.59 (-0.29)	8.36		0.02*	0.37		0.006**		0.03*

The median (x̃), 95% CI values per Braak group, test statistic and p value for comparisions across all groups and per post hoc comparison are presented. † denotes mean, standard deviation and F statistic are presented for these values. *p < 0.05, **p < 0.01.

Differences in levels of expression of the RNA methylation effector transcripts NSUN6, NSUN7 and ALYREF were also observed across the Braak staging groups (Table 2, Fig. 2B). In the hippocampus, NSUN6 showed higher expression in the mid neuropathological load, Braak stages III-IV (F = 4.67, p = 0.01, post hoc between stages 0-II, x̅ = -0.56 and stages III-IV, x̅ = -0.14, p = 0.03; post hoc between stages III-IV, x̅ = -0.14, and stages V-VI, x̅ = -0.67, p = 0.005). Whereas NSUN7 showed higher expression in mid and later Braak stages (F = 3.79, p = 0.03, post hoc between stages 0-II, x̅ = -0.34 and stages III-IV, x̅ = 0.96, p = 0.01; post hoc between stages 0 - II, x̅ = -0.34, and stages V-VI, x̅ = 0.96, p = 0.02). This finding is consistent with a higher relative expression of NSUN7 in CERAD stages 2–3 compared to CERAD stages 0–1 (Fig. 2C), p = 0.0295 (CERAD 0–1, x̃ = 0.502, IQR = 0.009–1.052; CERAD 2–3, x̃ = 0.879, IQR = 0.570–1.528). In contrast, ALYREF showed significantly low abundance in late Braak stages V-VI in both the hippocampus (H = 6.23, p = 0.04; post hoc between stages 0-II, x̃ = -0.45, and stages V-VI, x̃ = -0.08, p = 0.01) and the parietal cortex (H = 8.36, p = 0.02; post hoc between stages 0-II, x̃ = -0.09, and stages V-VI, x̃ = -0.59, p = 0.006; post hoc between stages III-IV, x̃ = -0.04, and stages V-VI, x̃ = -0.59, p = 0.03) (Figure 2B).

In the final stage of the study, expression of 5mC DNA and m⁵C RNA methylation effector proteins was investigated in unaffected individuals and in individuals with and without TBI and AD (Supplementary Fig. 1). Consistent with our previous findings, we found that the DNA writers DNMT1 and DNMT3B were significantly lower in abundance in the unaffected control group of individuals when compared to individuals with AD but with no history of TBI (DNMT1 Con x̃ = -0.37 [-1.30–0.15], AD no TBI x̃ = 0.19 [-0.23–0.68]), p < 0.01 in the STG; DNMT3B Con x̃= -0.04 [-0.70–0.26], AD no TBI x̃= 0.39 [0.07–0.80]) p < 0.01 in the WM (Fig. 3A, Supplementary Table 6). However, one DNA effector protein, the reader ZBTB4, showed a change in RNA abundance in TBI diagnosis groups only, and was observed to be less abundant in individuals of the control group (x̃ = 0.25 [-0.03–0.52]) compared to individuals with TBI and no AD (x̃ = 0.60 [0.36–1.05]) p < 0.01 and compared with all individuals with a diagnosis of TBI irrespective of whether they do or do not have AD (x̃ = 0.60 [0.33–0.95]) p < 0.01 (Fig. 3A, Supplementary Table 6).

Finally, similar to the AD and Braak scoring analysis, the RNA methylation writer NSUN6, exhibited the most significant difference in TBI groups. In the STG, we observed a significantly lower expression of NSUN6 in the group of individuals with TBI and no AD (x̃ = -0.34 [-0.72–0.18]) and in all individuals with TBI (x̃ = -0.59 [-1.10 - -0.05]), compared to the control group (x̃ = -0.12 [-0.67–0.44]), p < 0.01 and p < 0.0001 respectively (Fig. 3B), (Supplementary Table 7). These findings suggest that decreased expression of NSUN6 in TBI is not driven by a dementia phenotype.

Epigenetic processes have become increasingly relevant in understanding disease modifying mechanisms [37–39]. In the current study, we observed significant increased expression in the DNA writer proteins DNMT1, DNMT3A and DNMT3B but a decrease in UHRF1 expression in individuals with AD. DNMT writer proteins interact with chromatin and histone modification enzymes to stabilise the methylation machinery complex and direct the addition of methyl groups onto DNA [40]. The UHRF1 DNA reader binds the writer DNMT1 to chromatin substrate sites and when such direct interaction is disrupted, global 5mC hypomethylation results [41–43]. Of relevance, the functional consequences of mutations within DNMT1, DNMT3B, and DNMT3A, which cause monogenic human diseases and rare cancers e.g. DNMT1 complex disorders (HSAN1E) [44, 45]; DNMT3B (ICF syndrome) [46, 47]; DNMT3A (Tatton Brown-Rahman syndrome) [48, 49]; are proposed to be dependent on mutation type and the extent they cause a reduction in gene function or expression, i.e. the degree of hypomorphism. As a result, DNMT complex activity can be either weakly, moderately or strongly disrupted and can cause localised changes or more global widespread changes in DNA methylation [50–53]. Therefore, our findings of differential expression of DNMT1, DNMT3, DNMT3B and UHRF1 in AD could lead to alterations in complex binding which is consistent with causing changes in DNA 5mC methylation sites. However, whether disruption of the writer complexes effect widespread oxidised DNA methylation states and the process of demethylation which influences increased transcriptional activity, remain to be investigated.

Expression of DNA demethylation eraser proteins, GADD45B and AICDA, was also found to be significantly decreased with pathology progression. GADD45B is involved in cellular stress through modulation of unoxidized DNA 5mC methylation [54] and prolonged Gadd45b expression in mouse brain deregulates expression of genes related to heterochromatin maintenance, DNA methylation, or Parkinson’s disease [55]. Furthermore, knock-out of Gadd45b in mouse embryonic stem cells leads to 5mC hypermethylation and 5fC oxidised DNA methylation enrichment in thymine DNA glycosylase-deficient cells [56]. A reduction in GADD45B in differentiated myoblasts has also been shown to inhibit mitochondrial biogenesis [57], an interesting finding given that 5fC DNA methylation is localised to mitochondria in human brain tissue (Perez Grovas-Saltijeral and Knight, personal communication) and mitochondrial dysfunction has been strongly implicated in the onset of AD [58]. In contrast, AICDA a single-stranded DNA-specific cytidine deaminase has a key role in acquired and innate immunity [59–61]. Similarly, ZBTB4 a DNA reader which showed significant changes in expression in the TBI-specific cohorts, is associated with Immunodeficiency-Centromeric Instability-Facial Anomalies Syndrome [62, 63]. As yet, there is a lack of evidence suggesting either AICDA or ZBTB4 contribute directly to neurodegenerative processes, although immune system processes are affiliated with both AD and TBI development [64, 65]. Future functional studies of AICDA or ZBTB4 in neuronal and microglial cells may provide new insight into DNA regulatory control after brain trauma and in dementia pathology.

RNA methylation effector proteins and are known to target various types of RNA species. For example, m⁵C occurs on mRNA, tRNA, rRNA and mt-RNA and alternative non-coding RNA species such as vault RNA (vtRNA) and circular RNA (circRNA) [66–68]. As a consequence, disruption to methylated bases could have multiple effects on cellular processes. Of the seven NOL1/NOP2/sun domain (NSUN) family of m⁵C writer proteins, we found significant differences in expression in NSUN6 and NSUN7 within disease/pathology groups. NSUN7 although classified as an m⁵C RNA methylation writer, its status in the regulation of different types of RNA, and cellular function in brain, is still relatively unknown. It is suggested to target enhancer RNAs, i.e. long non coding RNAs transcribed from the DNA enhancer regions [69], and mutations within NSUN7 give rise to low sperm motility and male infertility.

In contrast, NSUN6, is known to methylate tRNA^Thr and tRNA^Cys tRNA molecules in human cells [70] and was recently discovered, together with NSUN2, to determine most mRNA transcription-wide m⁵C sites [71]. Furthermore, NSUN6 is associate with what has been termed ‘type II’ m⁵C mRNA sites which contain a downstream UCCA motif and are predicted to be located in the loops of putative hairpin structures [72, 71, 73]. Like NSUN6, type II m⁵C mRNAs are commonly found in the cytoplasm of cells and in NSUN6 knock out cells, type II modification is associated with a modest overall increase in translation efficiency although translational efficiency is dependent on genic location [71]. Of interest, within several regions of the human brain, a circular RNA transcript is predicted to be encoded within NSUN6 coding regions [74, 75]. As circular RNAs can regulate mRNA through binding to RNA binding proteins such as RNA methylation effector proteins and, have been shown to be abnormally expressed in Alzheimer’s disease brain [76], this highlights the potential for a more complex transcriptional regulatory system involving NSUN6 underlying AD pathology.

Finally, the eraser ALYREF, also known as THOC4, showed expression differences with Braak pathology. ALYREF has been described as the main regulator of m⁵C-modified mRNA export out of the nucleus and mutations within transcription and export (TREX) complex proteins involved in the export process cause syndromic forms of intellectual disabilities [77–79]. Furthermore, the recognition and hence nuclear-cytoplasmic shuttling of specific transcripts by ALYREF is reduced with the knockdown of NSUN2 [80, 30]. Together, these observations suggest that both NSUN6 and ALYREF require NSUN2 for their function in the distribution of m⁵C mRNA sites as well as of m⁵C mRNAs transport into the cytoplasm and that tissue specific changes in m⁵C writer abundance may have cell-type dependent consequences on protein translation and protein complexes within cytoplasmic sites such as at synapses. One limitation of this study is that changes in expression was neither cell type-specific or explored subcellular localisation, an issue which needs to be addressed in future studies. However, our findings provide novel evidence of epitranscptional control involved in AD and TBI and with pharmacological targeting of DNA and RNA methyltransferases and methylation pathways currently underway for forms of cancers, new therapeutic avenues for dementia may advance.

Ethical approval

The study used data from the Allen Institute for Brain Science, University of Washington Medicine, and Kaiser Permanente Washington Health Research Institute (2016); Aging, Dementia, and TBI Study; Adult Changes in Thought study [dataset]. Ethics for this study was reviewed and approved by Institutional Review Boards at Kaiser Permanente Washington and the University of Washington [35].

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Funding

The project was funded by Nottingham University and the Neuroscience Support Group Charity, UK. A Perez Grovas-Saltijeral was supported by a CONACYT PhD scholarship.

Competing Interests: The authors have no relevant financial or non-financial interests to disclose. The authors have no competing interests to declare that are relevant to the content of this article.

Author Contributions: H M Knight and A Perez Grovas-Saltijeral designed the study. A Perez Grovas-Saltijeral performed the data analysis. H M Knight, A Perez Grovas-Saltijeral and A.P. Rajkumar wrote the manuscript. All authors read and approved the final manuscript.

Data Availability: The dataset analysed during the current study are publicly available from the Allen Institute for Brain Science, University of Washington Medicine, and Kaiser Permanente Washington Health Research Institute (2016). Aging, Dementia, and TBI Study [dataset]. Available from aging.brain-map.org. RRID:SCR_014554 | Primary publication: Miller J. A., et al. (2017). Neuropathological and transcriptomic characteristics of the aged brain. eLife, 2017;6:e31126. https://doi.org/10.7554/eLife.31126.

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Differential expression of m5C RNA methyltransferase genes NSUN6 and NSUN7 in Alzheimer’s disease and Traumatic Brain Injury

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Abstract

Figures

Introduction

Methods

Study samples

Analysis of RNA expression profiles

Statistical analysis

Results

Demographics and neuropathologic load in unaffected and Alzheimer’s groups

Effector proteins expression profiles in unaffected and Alzheimer’s disease individuals

Differences in DNA/RNA effector protein expression grouped by Braak and CERAD neuropathological scales

Discussion

Declarations

References

Supplementary Files

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