Conceived and designed the experiments: MJ KJ. Performed the experiments: MJ KJ. Analyzed the data: NAT MRW MJ. Wrote the paper: NAT MRW MJ.
The authors have declared that no competing interests exist.
Recent studies strongly indicate that aberrations in the control of gene expression might contribute to the initiation and progression of Alzheimer's disease (AD). In particular, alternative splicing has been suggested to play a role in spontaneous cases of AD. Previous transcriptome profiling of AD models and patient samples using microarrays delivered conflicting results. This study provides, for the first time, transcriptomic analysis for distinct regions of the AD brain using RNA-Seq next-generation sequencing technology. Illumina RNA-Seq analysis was used to survey transcriptome profiles from total brain, frontal and temporal lobe of healthy and AD post-mortem tissue. We quantified gene expression levels, splicing isoforms and alternative transcript start sites. Gene Ontology term enrichment analysis revealed an overrepresentation of genes associated with a neuron's cytological structure and synapse function in AD brain samples. Analysis of the temporal lobe with the Cufflinks tool revealed that transcriptional isoforms of the apolipoprotein E gene,
Alzheimer's disease (AD) is the most common cause of dementia in the human population; it mainly affects individuals over the age of 60, and one's risk of developing it increases steadily with age
The transcriptome reflects cellular activity within a tissue at a given point in time. Genome-wide expression studies, which are not influenced by deductive assumptions, provide an unbiased approach for investigating the pathogenesis of complex diseases like AD. Transcriptome analyses have been performed using transgenic animals models of AD and patient-derived cell lines
RNA-Seq analyzes complementary DNA (cDNA) by means of highly efficient, next-generation DNA sequencing methods and subsequent mapping of short sequence fragments (reads) onto the reference genome. That this new technology makes it possible to identify exons and introns, mapping their boundaries and the 5′ and 3′ ends of genes, in turn makes it possible to understand the complexity of eukaryotic transcriptomes comprehensively. Moreover, RNA-Seq enables identification of transcription initiation sites (TSSs) and new splicing variants, and it permits of a precise quantitative determination of exon and splicing isoform expression
Some recent reports, which systematically compare microarrays and next-generation sequencing, have clearly proven the superiority of the latter, both with respect to low frequency of false positive signals and high reproducibility of the method
In the present study, we performed a comparative gene expression analysis of normal human brain tissue and tissue affected by Alzheimer's disease, using the RNA-Seq technique. Along with samples from whole normal and AD brains, mRNA samples from two different brain regions, namely the frontal and temporal lobes, were analyzed. We found significant differences in gene isoform expression levels, alternated use of promoters and transcription start sites between normal and AD brain tissue.
Total RNA from post-mortem human brains was obtained from Ambion (Austin, USA) and Capital Biosciences (Rockville, USA).
Condition | Sample | Gender | Age (years) | Source |
|
Total brain | 13 male;10 female | 23–86 (x˜≈68.3) | Ambion |
Frontal lobe | 5 male | 22–29 (x˜≈26.4) | Capital Biosciences | |
Temporal lobe | 5 male | 23–29 (x˜≈26.0) | Capital Biosciences | |
Total brain | 1 male | 87 | Capital Biosciences | |
Frontal lobe | 1 male | 87 | Capital Biosciences | |
Temporal lobe | 1 male | 80 | Capital Biosciences |
For the mRNA-Seq sample preparation, the Illumina standard kit was used according to the manufacturer's protocol. Briefly, 10 µg of each total RNA sample was used for polyA mRNA selection using streptavidin-coated magnetic beads, followed by thermal mRNA fragmentation. The fragmented mRNA was subjected to cDNA synthesis using reverse transcriptase (SuperScript II) and random primers. The cDNA was further converted into double stranded cDNA and, after an end repair process (Klenow fragment, T4 polynucleotide kinase and T4 polymerase), was finally ligated to Illumina paired end (PE) adaptors. Size selection was performed using a 2% agarose gel, generating cDNA libraries ranging in size from 200–250 bp. Finally, the libraries were enriched using 15 cycles of PCR and purified by the QIAquick PCR purification kit (Qiagen). The enriched libraries were diluted with Elution Buffer to a final concentration of 10 nM. Each library was run at a concentration of 7 pM on one Genome Analyzer (GAII) lane using 36 bp sequencing. Six samples were analyzed in this manner, taken from frontal, temporal and total brain tissue of both AD and healthy brains.
RNA-Seq reads were obtained using Bustard (Illumina Pipeline version 1.3). Reads were quality-filtered using the standard Illumina process, and a 0 (no) or 1 (yes) was used to define whether a read passed filtering or not. Six sequence files were generated in FASTQ format (sequence read plus quality information in Phred format); each file corresponded to the brain tissue from which the RNA originated. The median number of reads per sequence file (corresponding to one lane on the flow cell) was 14,974,824. The sequence data have been submitted to the NCBI Short Read Archive with accession number SRA027308.2.
Reads were then processed and aligned to the UCSC
The aligned read files were processed by Cufflinks v0.8.0
Once all short read sequences were assembled with Cufflinks, the output.GTF files were sent to Cuffcompare along with a reference.GTF annotation file downloaded from the Ensembl database (Homo_sapiens.GRCh37.55.gtf;
Cuffcompare produces a combined.GTF file which is passed to Cuffdiff along with the original alignment (.SAM) files produced by TopHat. Cuffdiff then re-estimates the abundance of transcripts listed in the.GTF file using alignments from the.SAM file, and concurrently tests for differential expression. The expression testing is done at the level of transcripts, primary transcripts and genes. By tracking changes in the relative abundance of transcripts with a common transcription start site, Cuffdiff can identify changes in splicing. Relative promoter use within a single gene is also monitored by following the abundance changes of primary transcripts from that gene. We used Cuffdiff to perform three pairwise comparisons of expression, splicing and promoter use between normal and diseased samples from temporal, frontal and total brain regions.
To identify which allele of APOE was present in the frontal, temporal lobe and total brain AD samples, the genotype of SNPs rs429358 and rs7412 were determined using the Integrated Genome Viewer.
Mapping results were visualized using both the University of California, Santa Cruz (UCSC) genome browser
The Database for Annotation, Visualization and Integrated Discovery (DAVID) v6.7 is a set of web-based functional annotation tools
TopHat and Bowtie were installed and run on a SGI Altix 4700 64-bit shared memory machine with 1 TB RAM, 128 Dual-Core CPUs of 1.6 GHz. Cufflinks was run on a desktop computer with 4 GB RAM.
During the amplification step of sequence generation, the Illumina GAII produces clusters of identical sequence fragments. The number of these clusters is reported, as is the percentage that pass quality filtering by the Illumina image analysis software. Across all 6 samples, between 192,093 and 211,779 raw clusters were generated. Between 67.6% and 74.1% of these clusters passed filtering; these values are within the acceptable range recommended by Illumina. The total number of reads produced for each brain sample ranged from 13,442,077 to 15,772,947, with a median of 14,974,824 (
Total brain N |
Total brain AD |
Temp lobe N | Temp lobe AD | Front lobe N | Front lobe AD | |
|
13,442,077 | 14,720,816 | 15,256,752 | 14,227,702 | 15,772,947 | 15,228,832 |
|
0.05% | 0.04% | 0.02% | 0.04% | 0.03% | 0.04% |
|
91.85% | 92.42% | 92.40% | 90.41% | 91.46% | 90.96% |
TopHat allows up to two mismatches when mapping reads to a reference genome. The number of reads removed due to poor quality and the number of reads mapping uniquely to the reference genome are both expressed as percentages of the total number of reads.
Normal brain samples.
Alzheimer's disease brain samples.
To investigate the level and uniformity of the read coverage against the human genome, we plotted mapped reads of the normal temporal lobe sample along the human chromosome 1 (
The RNASeq read density along the length of the chromosome is shown. The coverage values are measured along intervals of the genome. These intervals vary in size from 1 bp to 10 Mbp depending on how variable the read density is for a particular genomic location. Each bar represents log2 of the frequency reads plotted against chromosome coordinates.
After mapping the RNA-Seq reads to the reference genome with TopHat, transcripts were assembled and their relative abundances calculated using Cufflinks. The summation of FPKM values for every transcript associated with a particular gene gives the expression (abundance) measurement for that gene, in FPKM. Cufflinks uses the Cuffdiff algorithm to calculate differential expression at both the gene and transcript levels. Differential gene expression (DGE) for total brain, frontal and temporal lobes was calculated using the ratio of AD versus normal FPKM values for every gene. The DGE ratios were tested for statistical significance as described recently
The range of DGE ratios observed was −26.20 to 26.24 for frontal lobe, −183 to 13.27 for temporal lobe and −350 to 36.63 for total brain. These three ranges for DGE ratios were all statistically significant. The expression ratios in AD versus normal were skewed towards down-regulation. This is potentially due to the lower overall levels of transcriptional activity present in AD vs. normal brain following significant loss of neuronal tissue in the former. The top 10 up- and down-regulated genes in total, frontal and temporal AD brain regions are listed in
Gene | Description | Chromosome | FPKM N | FPKM AD | Fold change | p-value | Ensembl Gene ID |
|
immunoglobulin heavy constant alpha 1 | chr14 | 0.234092 | 5.275364 | 22.53543051 | 0.00018499 | ENSG00000211895 |
|
not annotated | chr6 | 1.87539 | 14.272193 | 7.610253334 | 8.76E-009 | not annotated |
|
phosphate cytidylyltransferase 1, choline, alpha | chr3 | 0.413637 | 3.021956 | 7.305816453 | 0.00801203 | ENSG00000161217 |
|
solute carrier family 7 (cationic amino acid transporter, y+ system), member 9 | chr19 | 0.705822 | 4.834326 | 6.849214108 | 0.0105864 | ENSG00000021488 |
|
RAD54-like (S. cerevisiae) | chr1 | 0.436495 | 2.391719 | 5.479373189 | 0.0259394 | ENSG00000085999 |
|
2′,5′-oligoadenylate synthetase 1, 40/46kDa | chr12 | 3.82773 | 20.973536 | 5.479366622 | 4.89E-008 | ENSG00000089127 |
|
mitochondrial translational initiation factor 2 | chr2 | 3.75753 | 16.176999 | 4.305221515 | 7.00E-007 | ENSG00000085760 |
|
stabilin 1 | chr3 | 0.729626 | 2.887364 | 3.9573206 | 0.0317452 | ENSG00000010327 |
|
CD22 molecule | chr19 | 9.83818 | 36.883742 | 3.749041184 | 0 | ENSG00000012124 |
|
not annotated | chr2 | 9.4161 | 32.907895 | 3.494854027 | 8.88E-016 | not annotated |
|
reelin | chr7 | 19.4443 | 0.055404 | −350.9548047 | 2.22E-016 | ENSG00000189056 |
|
ankyrin 1, erythrocytic | chr8 | 13.7202 | 0.086115 | −159.3241596 | 8.88E-013 | ENSG00000029534 |
|
glutamate receptor, metabotropic 4 | chr6 | 29.2203 | 0.392424 | −74.46104214 | 0 | ENSG00000124493 |
|
glutamate receptor, metabotropic 1 | chr6 | 7.96543 | 0.142632 | −55.84602333 | 1.76E-008 | ENSG00000152822 |
|
transferrin receptor (p90, CD71) | chr3 | 9.17108 | 0.180114 | −50.91819625 | 3.81E-008 | ENSG00000072274 |
|
D-amino-acid oxidase | chr12 | 10.0459 | 0.20387 | −49.27600922 | 4.99E-008 | ENSG00000110887 |
|
actin binding LIM protein 1 | chr10 | 19.2058 | 0.39862 | −48.1807235 | 3.21E-011 | ENSG00000099204 |
|
KIAA0802 | chr18 | 14.4233 | 0.387405 | −37.23054684 | 4.61E-007 | ENSG00000168502 |
|
mediator complex subunit 13-like | chr12 | 7.77748 | 0.210969 | −36.86551105 | 7.40E-010 | ENSG00000123066 |
|
integrin, beta 8 | chr7 | 7.38908 | 0.20143 | −36.68311572 | 5.17E-007 | ENSG00000105855 |
Differential gene expression for total brain was calculated using the ratio of AD versus normal (N) FPKM values for every gene identified as expressed by Cufflinks. The genes were ranked on their fold changes and the ten with the highest or lowest fold changes are shown here.
Gene | Description | Chromosome | FPKM N | FPKM AD | Fold change | p-value | Ensembl Gene ID |
|
phosphoenolpyruvate carboxykinase 1 (soluble) | chr20 | 0.121441 | 3.186619 | 26.24005896 | 5.87E-006 | ENSG00000124253 |
|
CD163 molecule | chr12 | 0.264139 | 4.435869 | 16.79369196 | 0.000108665 | ENSG00000177575 |
|
Bac clone | chr16 | 0.295506 | 3.913347 | 13.24286817 | 0.0128902 | not annotated |
|
nuclear protein, transcriptional regulator, 1 | chr16 | 7.93458 | 94.488709 | 11.90847014 | 0 | ENSG00000176046 |
|
glycerophosphodiester phosphodiesterase domain containing 3 | chr16 | 0.262915 | 3.104517 | 11.80806344 | 2.23E-006 | ENSG00000102886 |
|
stabilin 1 | chr3 | 0.407594 | 4.278127 | 10.49604999 | 0.00152394 | ENSG00000010327 |
|
Mov10, Moloney leukemia virus 10, homolog (mouse) | chr1 | 0.584517 | 5.521605 | 9.446440394 | 0.00022429 | ENSG00000155363 |
|
mixed lineage kinase domain-like | chr16 | 0.268134 | 2.53291 | 9.4464335 | 0.00258816 | ENSG00000168404 |
|
lymphocyte antigen 6 complex, locus G5C | chr6 | 0.275959 | 2.462004 | 8.921629662 | 0.00341639 | ENSG00000111971 |
|
inositol 1,4,5-triphosphate receptor, type 3 | chr6 | 0.27173 | 2.281668 | 8.396820373 | 0.00455198 | ENSG00000096433 |
|
slit homolog 1 (Drosophila) | chr10 | 9.63131 | 0.367602 | −26.20037432 | 5.71E-006 | ENSG00000187122 |
|
protein tyrosine phosphatase, receptor type, O | chr12 | 8.77741 | 0.368512 | −23.8185188 | 1.10E-005 | ENSG00000151490 |
|
lipin 2 | chr18 | 7.50745 | 0.335313 | −22.38937948 | 1.67E-005 | ENSG00000101577 |
|
attractin | chr20 | 7.2984 | 0.333062 | −21.91303721 | 1.92E-005 | ENSG00000088812 |
neuroblastoma amplified sequence | chr2 | 6.54659 | 0.327206 | −20.00754876 | 3.48E-005 | ENSG00000151779 | |
|
G protein-coupled receptor 107 | chr9 | 7.31149 | 0.383708 | −19.05482815 | 4.75E-005 | ENSG00000148358 |
|
acyl-CoA oxidase 1, palmitoyl | chr17 | 8.28587 | 0.442214 | −18.73724034 | 7.37E-007 | ENSG00000161533 |
|
ER degradation enhancer, mannosidase alpha-like 3 | chr1 | 5.64477 | 0.303834 | −18.57846719 | 5.57E-005 | ENSG00000116406 |
|
ATPase, aminophospholipid transporter (APLT), class I, type 8A, member 1 | chr4 | 7.66187 | 0.412407 | −18.57841889 | 5.57E-005 | ENSG00000124406 |
|
von Willebrand factor | chr12 | 6.0129 | 0.32365 | −18.5784026 | 5.57E-005 | ENSG00000110799 |
Differential gene expression for frontal lobe was calculated using the ratio of AD versus normal (N) FPKM values for every gene identified as expressed by Cufflinks. The genes were ranked on their fold changes and the ten with the highest or lowest fold changes are shown here.
Gene | Description | Chromosome | FPKM N | FPKM AD | Fold change | p-value | Ensembl ID |
|
Bac clone – not annotated | chr2 | 0.28593 | 3.793698 | 13.26792572 | 0.0129943 | not annotated |
|
metallothionein 1G | chr16 | 15.1637 | 148.115649 | 9.767777587 | 0 | ENSG00000125144 |
|
S100 calcium binding protein A4 | chr1 | 3.0191 | 23.552175 | 7.801058262 | 4.44E-016 | ENSG00000196154 |
|
desmin | chr2 | 4.23774 | 31.344441 | 7.396499313 | 0 | ENSG00000175084 |
|
UPF0608 protein C19orf42 Precursor | chr19 | 0.626087 | 4.153445 | 6.633974192 | 0.0132315 | ENSG00000214046 |
|
mitochondrial poly(A) polymerase | chr10 | 1.87181 | 12.003598 | 6.412829294 | 0.000124237 | ENSG00000107951 |
|
non-metastatic cells 3, protein expressed in | chr16 | 9.40287 | 45.776586 | 4.86836317 | 0 | ENSG00000103024 |
|
kinesin family member 1C | chr17 | 39.0482 | 180.483489 | 4.622069366 | 0 | ENSG00000129250 |
|
mitogen-activated protein kinase kinase kinase kinase 4 | chr2 | 5.65184 | 24.058735 | 4.256796902 | 7.85E-009 | ENSG00000071054 |
|
transforming growth factor, beta 3 | chr14 | 4.62642 | 17.951668 | 3.880250388 | 0 | ENSG00000119699 |
|
microtubule associatedmonoxygenase, calponin and LIM domain containing 2 | chr11 | 43.9961 | 0.240419 | −182.9976 | 0 | ENSG00000133816 |
|
dynein, cytoplasmic 1, intermediate chain 1 | chr7 | 51.4985 | 0.292 | −176.364726 | 2.96E-013 | ENSG00000158560 |
|
rabphilin 3A homolog (mouse) | chr12 | 42.8148 | 0.271284 | −157.8227982 | 9.50E-013 | ENSG00000089169 |
|
Ras protein-specific guanine nucleotide-releasing factor 1 | chr15 | 29.1194 | 0.19051 | −152.8497192 | 1.32E-012 | ENSG00000058335 |
|
ATPase, Ca++ transporting, plasma membrane 1 | chr12 | 27.8105 | 0.195853 | −141.9968037 | 2.82E-012 | ENSG00000070961 |
|
ELMO/CED-12 domain containing 1 | chr11 | 25.7148 | 0.185023 | −138.9816401 | 0 | ENSG00000110675 |
|
NEL-like 2 (chicken) | chr12 | 48.256 | 0.356889 | −135.2129093 | 4.64E-012 | ENSG00000184613 |
|
phosphodiesterase 2A, cGMP-stimulated | chr11 | 33.2491 | 0.250937 | −132.4997908 | 5.69E-012 | ENSG00000186642 |
|
calcium/calmodulin-dependent protein kinase kinase 2, beta | chr12 | 44.693 | 0.352967 | −126.6209022 | 8.97E-012 | ENSG00000110931 |
|
intercellular adhesion molecule 5, telencephalin | chr19 | 20.7834 | 0.170851 | −121.6463468 | 1.34E-011 | ENSG00000105376 |
Differential gene expression for temporal lobe was calculated using the ratio of AD versus normal (N) FPKM values for every gene identified as expressed by Cufflinks. The genes were ranked on their fold changes and the ten with the highest or lowest fold changes are shown here.
When comparing the top 30 most over- and under-expressed genes in AD across the 3 brain samples (
In the top 30 over- and under-expressed genes in AD between the 3 brain samples, there are a number of genes without annotation, described either as putative or novel transcripts in the Ensembl database. RP11-552E20.3 and AC018730.1 are up-regulated in AD total brain (7.61 FC, p = 8.76×10−9 and 3.49 FC, p = 8.88×10−16, respectively), AC074289.4 is up-regulated in AD temporal lobe (13.27 FC, p = 0.01) and RP4-697K14.12 is up-regulated in AD frontal lobe (5.77 FC, p = 0.02). None of these putative or novel transcripts is described as protein coding by Ensembl.
There is some concordance between gene expression differences found with RNA-Seq and those reported in previous microarray studies on Alzheimer's disease
The NCBI web-based functional annotation tool DAVID v 6.7 (Database for Annotation, Visualization and Integrated Discovery) was used to investigate functional associations of gene expression changes seen in AD brain
There is a high degree of overlap between the top ten most enriched clusters (
Interestingly, the frontal lobe is different from the other samples in that it shows greater changes in genes associated with brain-specific biological processes. These are regulation of synaptic transmission (rank 9), neurotransmitter transport (11), response to metal ion (13), metal ion transport (15), regulation of synaptic plasticity (18), negative regulation of neuron apoptosis (19) and axon transport (20). By contrast, the brain-specific categories apparent in the temporal lobe are axon transport (rank 14) and neurotransmitter transport (18), and cerebellum development (12) is implicated for the total brain.
Genes known to be involved in programmed cell death were enriched in the frontal lobe of AD brain (rank 10) and an induction of apoptosis is present in both frontal and temporal lobes (rank 16 and 12, respectively). An over-representation of apoptosis-related genes clearly indicates the ongoing process of neurodegeneration and associated cell loss. The top 20 DAVID functional clusters for total, frontal and temporal brain regions can be seen in
A key feature of RNA Seq is its ability to identify alternative splicing of transcripts. It also has an advantage over microarray-based methods of detection in its ability to identify novel transcripts. Accordingly, we next investigated the splicing status of all genes and whether genes show differential splicing patterns between normal and diseased tissues.
TopHat builds a database of potential splice junctions by identifying the splice donor and acceptor sites (GT-AG) for each region of a gene with high coverage of short mRNA reads. TopHat then compares the previously unmapped reads against this database of putative junctions. Regions of genes with a high coverage are also screened for internal junction sites. One of the advantages of identifying potential exons without using predefined annotation information is the capability to highlight splicing in unannotated regions of the genome.
A range of 52,438 to 54,808 splice junctions was predicted for normal brain (
Total brain N |
Total brain AD |
Temp lobe N | Temp lobe AD | Front lobe N | Front lobe AD | |
|
13,442,077 | 14,720,816 | 15,256,752 | 14,227,702 | 15,772,947 | 15,228,832 |
|
54,458 | 29,012 | 52,438 | 17,265 | 54,808 | 38,647 |
|
2.14% | 0.94% | 2.10% | 0.47% | 2.20% | 1.28% |
RNA-Seq data were mapped to the UCSC Human genome build 19. The number of splice junctions predicted by TopHat is shown, as well as the percentage of the total number of reads.
Normal brain samples.
Alzheimer's disease brain samples.
Using the Cuffdiff algorithm to calculate differential expression at the transcript level allowed discovery of which transcripts are common, differentially expressed or present/absent between normal and AD brain tissue.
Frontal, temporal and total brain specimens showed a large proportion of transcripts at similar expression levels between normal and AD tissue (
Venn diagram showing the number of differentially expressed transcripts between AD and normal tissue samples across total brain, temporal and frontal lobe. The number of transcripts unique to AD and normal tissues is shown in universe area outside the circles. The numbers of transcripts up-regulated by more than two-fold in AD tissue are indicated in the dark grey circle, while the numbers up-regulated by more than two-fold in normal tissue are highlighted in the light grey circle. The intersection of the two circles refers to number of transcripts which are expressed in both AD and normal tissues but which are less than two-fold different in expression level.
Further analysis revealed a considerable portion of transcripts that were unique to either AD or normal brains. AD brain tissue showed between 19,578 and 28,407 (10.7–14.5%) unique transcripts compared to the corresponding normal tissue. Larger numbers of transcripts were seen to be unique to normal tissue, for which between 46,672 to 68,025 transcripts were observed (23.9% to 37.5%).
To detect transcriptional regulation, RNA-Seq data can be analyzed with Cufflinks. This identifies how many transcription start sites (TSS) are used in each gene and groups transcripts from that gene by their TSS. Each TSS is thus associated with a primary transcript. Cufflinks compares ratios of grouped transcripts between normal and AD tissue to detect alternative promoter usage. Cufflinks also identifies post-transcriptional regulation by looking for changes in relative abundances of mRNAs spliced from the same primary transcript between normal and AD tissue, which it detects as alternative splicing. In this way, Cufflinks discriminates between transcriptional and post-transcriptional processing
Cufflinks analysis of the transcriptome from total brain, temporal and frontal lobe samples revealed that numerous genes are controlled by different promoters in normal and AD tissue (
Gene | Description | p-value |
|
||
|
calnexin | 0 |
|
DnaJ (Hsp40) homolog, subfamily C, member 5 | 5.64E-006 |
|
meningioma expressed antigen 5 (hyaluronidase) | 0 |
|
transmembrane protein 66 | 1.16E-009 |
|
WD repeat domain 92 | 0 |
|
||
|
ArfGAP with coiled-coil, ankyrin repeat and PH domains 3 | 2.24E-005 |
|
arginine and glutamate rich 1 | 6.43E-007 |
|
chromodomain helicase DNA binding protein 3 | 0 |
|
kinesin family member 5A | 2.35E-013 |
|
leukocyte receptor cluster (LRC) member 8 | 0 |
|
mitogen-activated protein kinase 3 | 0 |
|
nuclear receptor subfamily 1, group D, member 1 | 0 |
|
phosphodiesterase 1B, calmodulin-dependent | 0 |
|
phosphatidylinositol-5-phosphate 4-kinase, type II, beta | 2.22E-016 |
|
rabphilin 3A homolog (mouse) | 0 |
|
WD repeat domain 47 | 0 |
|
||
|
apolipoprotein E | 1.92E-006 |
|
kinesin family member 5A | 0 |
|
protein phosphatase 2A activator, regulatory subunit 4 | 7.18E-007 |
Genes identified by Cufflinks as exhibiting statistically significant alternative promoter usage between normal and AD tissue. Results are shown for total brain, frontal and temporal lobe tissue.
We also investigated whether splicing patterns for transcripts sharing the same transcription start site (TSS) differ between normal and AD brain tissue (
Gene | Description | p-value |
|
||
|
calmodulin 3 (phosphorylase kinase, delta) | 1.11E-016 |
|
calnexin | 0 |
|
DnaJ (Hsp40) homolog, subfamily C, member 5 | 1.38E-008 |
|
meningioma expressed antigen 5 (hyaluronidase) | 0 |
|
||
|
ArfGAP with coiled-coil, ankyrin repeat and PH domains 3 | 0 |
|
adaptor-related protein complex 2, beta 1 subunit | 1.07E-010 |
|
atrophin 1 | 2.34E-008 |
|
beta-2-microglobulin | 6.68E-004 |
|
chromodomain helicase DNA binding protein 3 | 3.16E-005 |
|
C-terminal binding protein 1 | 1.06E-009 |
|
EF-hand domain family, member D2 | 2.66E-007 |
|
leukocyte receptor cluster (LRC) member 8 | 0 |
|
mitogen-activated protein kinase 3 | 0 |
|
nuclear receptor subfamily 1, group D, member 1 | 3.73E-007 |
|
NudC domain containing 3 | 2.11E-004 |
|
phosphodiesterase 1B, calmodulin-dependent | 3.42E-004 |
|
rhomboid domain containing 2 | 0 |
|
septin 5 | 4.44E-016 |
|
WD repeat domain 47 | 6.65E-009 |
|
||
|
apolipoprotein E | 1.56E-010 |
|
kinesin family member 5A | 2.22E-016 |
|
PDZ domain containing 4 | 9.39E-005 |
|
spectrin, beta, non-erythrocytic 1 | 8.47E-007 |
Gene names for transcripts identified by Cufflinks as exhibiting statistically significant alternative splicing between normal and AD tissue. Results are shown for total brain, frontal and temporal lobe tissue. Alternative splicing is detected between transcripts, which share the same transcription start site (TSS).
Apolipoprotein E gene (
RNA-Seq read mapping to the UCSC reference genome (hg19) of the gene
(
In addition to a switch in promoter usage in the normal and AD temporal lobe, significant alternative splicing between the two isoforms is seen under the control of TSS A (p = 1.46×10−10). The abundance of isoform
A comparison of
To identify which allele of
Our study provides the first comprehensive insight into the transcriptome of brain tissue affected by Alzheimer's disease. Using a whole transcriptome sequencing technique (RNA-Seq), we were able to identify the levels of differentially expressed genes and establish genes with alternative promoter usage and splicing patterns that changed in association with neurodegeneration. Moreover, comparative analysis of samples derived from different brain regions produced an increased molecular resolution for our analysis. This revealed that the frontal and temporal lobes of AD brains not only differed in the quantitative composition of the genes expressed but also showed lobe-specific alternations in transcript assembly.
For whole transcriptome sequencing, we used an Illumina Genome Analyser II with 36 bp sequence reads length. We obtained ∼14×106 sequence reads per sample, which has been previously reported to deliver sufficient sequence coverage for transcriptome profiling
Cufflinks analysis of gene isoform expression levels, alternative splicing and alternative promoter usage revealed significant differences in transcriptome profiles between frontal and temporal lobe of the AD brain. These variations might reflect temporal and spatial differences in the progression of AD neuropathology across the aging brain. Widespread neuronal loss and a presence of the intraneuronal neurofibrillary tangles (NFTs) and the extracellular neuritic or senile plaques (NPs) are key features of the AD neuropathology. The main components of NPs are peptides of varying length collectively described as beta-amyloid whereas NFTs are mainly composed of paired helical filaments of a hyperphosphorylated form of the microtubule-associated protein tau (MAPT)
The tissue-specific enrichment for gene ontology processes suggest region-specific, sequential progression of brain tissue neurodegeneration, with the temporal lobe being affected earlier than the frontal part of the cortex
Many of the changes we observed in gene expression between normal and AD brains were similar to those reported previously. However, some differences were noted. This lack of concordance among our RNA-Seq transcriptome data set and previously reported gene expression profiles is likely to stem from inherent limitations in microarray systems. For example, background levels of hybridization (i.e. hybridization to a probe that occurs irrespective of the corresponding transcript's expression level) limit the accuracy of microarray expression measurements, particularly for transcripts present at low abundance. Furthermore, probes differ considerably in their hybridization properties
Moreover, validation techniques such as quantitative PCR (qPCR)
Regarding quantification of gene expression, Cufflinks analysis of RNA-Seq data allowed us to dissect expression of individual genes into quantification of particular mRNA isoforms contributing to the final cumulative value of gene expression. To our knowledge, this is the first report where quantitative information about particular splice variants at a genome-wide scale has been generated for different anatomical segments of normal and AD brains. Thus, our study creates a useful data set supplementing previous microarray-generated information, which lacked isoform-specific resolution of gene expression
Despite the magnitude of the
Top 30 up and top 30 down regulated genes in AD total brain. Differential gene expression for total brain was calculated using the ratio of AD versus normal FPKM values for every gene identified as expressed by Cufflinks. The genes were ranked on this ratio (fold change), and those with the 30 highest and 30 lowest fold change values are shown here.
(XLSX)
Top 30 up and top 30 down regulated genes in AD frontal lobe. Differential gene expression for frontal lobe was calculated using the ratio of AD versus normal FPKM values for every gene identified as expressed by Cufflinks. The genes were ranked on this ratio (fold change), and those with the 30 highest and 30 lowest fold change values are shown here.
(XLSX)
Top 30 up and top 30 down regulated genes in AD temporal lobe. Differential gene expression for temporal lobe was calculated using the ratio of AD versus normal FPKM values for every gene identified as expressed by Cufflinks. The genes were ranked on this ratio (fold change), and those with the 30 highest and 30 lowest fold change values are shown here.
(XLSX)
Top 20 Clusters from functional enrichment analysis using the DAVID tool for total brain. The NCBI tool, DAVID, was used to investigate functional associations of gene expression changes seen in AD total brain. There were 1071 genes that were more than two-fold over- or under-expressed in AD relative to normal total brain and these were analysed by the functional clustering tool. Gene Ontology Biological Process was selected as the annotation category for clustering. Once the tool has identified enriched ontologies for a particular gene list, it creates annotation clusters with those that have a statistically significant overlap in terms of their constituent genes. The top 20 annotation clusters are shown in this table.
(XLSX)
Top 20 Clusters from functional enrichment analysis using the DAVID tool for frontal lobe. The NCBI tool, DAVID, was used to investigate functional associations of gene expression changes seen in AD frontal lobe. There were 944 genes that were more than two-fold over- or under-expressed in AD relative to normal frontal lobe and these were analysed by the functional clustering tool. Gene Ontology Biological Process was selected as the annotation category for clustering. Once the tool has identified enriched ontologies for a particular gene list, it creates annotation clusters with those that have a statistically significant overlap in terms of their constituent genes. The top 20 annotation clusters are shown in this table.
(XLSX)
Top 20 Clusters from functional enrichment analysis using the DAVID tool for temporal lobe. The NCBI tool, DAVID, was used to investigate functional associations of gene expression changes seen in AD temporal lobe. There were 1416 genes that were more than two-fold over- or under-expressed in AD relative to normal temporal lobe and these were analysed by the functional clustering tool. Gene Ontology Biological Process was selected as the annotation category for clustering. Once the tool has identified enriched ontologies for a particular gene list, it creates annotation clusters with those that have a statistically significant overlap in terms of their constituent genes. The top 20 annotation clusters are shown in this table.
(XLSX)