Stage IIIA rectal cancer has distinctive oncological features, including limited depth of intestinal wall invasion and early regional lymph node metastasis. We aim to compare survival outcomes and clinicopathological features for stage IIIA rectal cancer with those for stage II rectal cancer.We analyzed patients with stage II or stage IIIA rectal cancer treated with surgery without receiving preoperative radiotherapy based on data from the US Surveillance, Epidemiology, and End Results (SEER) database between 1988 and 2003. Survival curves were plotted using the Kaplan-Meier method. Multivariate Cox proportional analyses were utilized to analyze independent prognostic factors for cancer-specific survival (CSS).We included 12,036 rectal cancer patients  from the SEER database. Patients with stage IIIA rectal cancer had smaller tumor size than patients with stage II rectal cancer. A multivariate analysis suggested that compared with patients with stage IIIA rectal cancer, patients with stage II disease were more likely to have more unfavorable CSS . When stage II rectal cancer was further analyzed as stage IIA, IIB and IIC rectal cancer, the multivariate analysis indicated that compared with patients with stage IIIA rectal cancer, patients with stage IIA rectal cancer , stage IIB rectal cancer  and stage IIC rectal cancer  were also more likely to exhibit more unfavorable CSS.Patients with stage IIIA rectal cancer had more favorable survival outcomes and smaller tumor size compared with patients with stage II rectal cancer. Rectal cancer is one of the most commonly diagnosed malignancies worldwide, with approximately 40 thousand new cases predicted to occur in the United States in 2016 . In the Stage IIIA rectal cancer is defined as tumors that invade submucosa with metastasis in 1-6 regional lymph nodes (T1N1–N2a) and muscularis propria with involvement of 1-3 regional lymph nodes (T2N1) . PatientMultimodality therapy, i.e., preoperative chemoradiotherapy (CRT) followed by curative surgery, has been established as the standard treatment for patients with locally advanced rectal cancer during the last decade , 12. PrePatients with stage IIIA colorectal cancer are reported to have a favorable prognosis; however, comparisons of survival outcomes and clinicopathological features between stage II and stage IIIA rectal cancer have rarely been performed. Here, we conduct an analysis that compares the survival and clinicopathological differences between patients with stage II and stage IIIA rectal cancer based on patient records in the US Surveillance, Epidemiology, and End Results (SEER) database.We included 12,036 rectal cancer patients  in the analysis. In total, 3,891 (32.3%) rectal cancer-specific deaths were identified. The median follow-up duration was 88 months . Clinicopathological characteristics of the stage II and stage IIIA rectal cancer patients are shown in Table Kaplan-Meier analysis suggested that patients with stage IIIA rectal cancer had a more favorable CSS compared with patients with stage II disease  and early regional lymph node metastasis, patients with stage IIIA rectal cancer may theoretically exhibit conflicting survival outcomes. On one hand, the disease possesses a relatively limited depth of intestinal wall invasion and metastasis is confined to regional lymph nodes around the primary tumor. Disease in this stage should be easily resected with curative intent and is generally regarded as exhibiting a favorable prognosis. On the other hand, early metastasis to regional lymph nodes with a limited intestinal wall invasion may suggest the aggressive nature of these lesions, which may result in a decreased prognosis . Actuallen bloc resection of the primary lesion should be easier to be accomplished than for a T3/T4 tumor with larger tumor size  to achieve negative margins status. In addition, surgical proficiency of the operation on locally advanced rectal cancer may vary among different surgeons, leading to a greater rate of positive margin in stage II rectal cancer than in stage IIIA disease [The results from our study suggested that patients with stage IIIA rectal cancer had more favorable CSS than patients with stage II rectal cancer. However, the hazard ratio (1.113) between stage IIA and stage IIIA rectal cancer is quite small, therefore, the clinical significance of survival paradox between stage IIA and stage IIIA rectal cancer may be limited. In surgical procedures of colorectal cancer, every effort should be done to achieve a negative margin. Chu et al. indicate disease . The posThe obvious defect of the current AJCC cancer staging system in colorectal cancer is the relatively over-estimated weighting of node metastasis (N stage) , 21. In According to the National Comprehensive Cancer Network (NCCN) guidelines for rectal cancer, preoperative CRT has been recommended as a crucial therapeutic strategy for locally advanced rectal cancer. Growing evidence suggests that patients with locally advanced rectal cancer benefit from preoperative CRT in terms of pCR, local disease control and sphincter preservation in comparison with surgery alone or postoperative CRT , 26–28. To the best of our knowledge, this is the first comprehensive study to reveal favorable prognosis in and distinctive clinicopathological features of patients with stage IIIA rectal cancer. We used the SEER database to ensure a large sample size, and to be specific, our study included of 12,036 total patients, which guaranteed our findings would have adequate power. However, there are still several limitations to our study. One remarkable limitation of the SEER database is that it does not include record of some important patient- and disease-related variables, including lymphovascular or perineural invasion, comorbidities, intestinal obstruction or penetration, circumferential resection margin, and data on adjuvant chemotherapy. These clinicopathological data may be valuable additions to the current study. Another limitation of our study is that only 34.0% rectal cancer patients had adequate lymph node harvest (≥12), for the reason that patients extracted from the SEER database were diagnosed between 1988 and 2003. The definition of tumor deposits (TDs) has changed in the 5th, 6th, and 7th edition of TNM staging system  revealedIn conclusion, our results provide the first evidence that patients with stage IIIA disease had more favorable survival outcomes and smaller tumor size compared with patients with stage II rectal cancer. Our findings on the favorable prognosis in and distinctive clinicopathological features of patients with stage IIIA rectal cancer are expected to be validated in other institutions.The SEER database contains 18 cancer registries covering 26% of the U.S. population and collects and supplies cancer incidence and survival data. We extracted demographic and pathological data for invasive rectal cancer patients between January 1988 and December 2003 from the SEER database (April 2013 release). Patients meeting the following criteria were included in the current analysis: (1) adenocarcinoma, mucinous adenocarcinoma or signet-ring carcinoma of the rectum; (2) known invasion depth and lymph node status; (3) AJCC stage II or stage IIIA; (4) rectal cancer surgically resected with pathology specimen; (5) pathologically confirmed diagnosis; and (6) known survival time and cause of death. Patients were excluded if (1) they received preoperative radiotherapy, (2) they underwent only local tumor excision, (3) their rectal cancer was diagnosed by death certificate or autopsy, or (4) there were other concurrent malignancies. The Fudan University Shanghai Cancer Center Ethical Committee and Institutional Review Board reviewed and approved this study protocol.Records on the following clinicopathological variables were extracted from the SEER database: gender; race; age at and year of diagnosis; primary tumor site; histological type; American Joint Committee on Cancer (AJCC) TNM stage; number of lymph nodes harvested, with 12 as the cutoff value; number of metastatic lymph nodes; depth of intestinal wall invasion; tumor size; grade of differentiation; radiation sequence with surgery; number of primaries; follow-up duration; and SEER classification of cause-specific death. All patients were restaged based on the AJCC cancer stages (7th edition), in which stage IIA was defined as T3N0M0, stage IIB as T4aN0M0, stage IIC as T4bN0M0 and stage IIIA as T1-2N1M0 or T1N2aM0 in rectal cancer. Cancer-specific survival (CSS) from the time of diagnosis to the time of rectal cancer-specific death was the primary outcome. Patients who died from other causes or who were alive at the last follow-up were censored.Stage II and stage IIIA rectal cancer patient data were summarized using cross-tabulation, and distributions were compared using chi-squared tests. Survival curves were plotted using the Kaplan-Meier method. The log-rank test was used for univariate analysis and variables with a P value less than 0.1 were entered into the multivariate analysis. Multivariate Cox regression analyses were utilized to generate adjusted hazard ratios (HR) and their corresponding 95% confidence intervals (CIs). Tumor size was analyzed as a continuous variable in the multivariate Cox regression analyses. A two-sided P value of less than 0.05 was accepted as statistically significant. All statistical analyses were performed using SPSS statistical program version 20.0 .


Obtaining high quality RNA from complex biological tissues, such as the brain, is needed for establishing high-fidelity cell-type specific transcriptomes. Although combining genetic labeling techniques with laser capture microdissection (LCM) is generally sufficient, concerns over RNA degradation and limited yields call into question results of many sequencing studies. Here we set out to address both of these issues by: (1) developing a fluorescence-assisted LCM protocol that yields high quality RNA from fresh-frozen tissues; and (2) determining a suitable RNA-Seq library generation method for limited amounts of RNA . The latter focused on comparing commercially available kits able to produce libraries of sufficient concentration and complexity while limiting PCR amplification biases. We find that high quality RNA  of sufficient concentration can be isolated from laser-captured material from thinly-sectioned tissues when digestion time and temperature are minimized. Furthermore, we found that library generation approaches that retain ribosomal RNA (rRNA) through cDNA library generation required fewer cycles of PCR, minimizing bias in the resulting libraries. Lastly, end stage depletion of rRNA prior to sequencing enriches for target RNAs, thereby increasing read depth and level of gene detection while decreasing sequencing costs. Here we describe our protocol for generating robust RNA-Seq libraries from laser-captured tissue and demonstrate that with this method, we obtain samples with RNA quality superior to the current standard in the LCM field, and show that low-input RNA-Seq kits that minimize PCR bias produce high fidelity sequencing metrics with less variability compared to current practices. Transcriptome profiling of minute samples is critical for understanding complex tissues. Given the unstable nature of RNA, current methods have been unable to ensure that high quality RNA is obtained for subsequent sequencing applications. Here we compared commercially available low-input RNA isolation kits to evaluate RNA quality and low-input RNA-Seq library kits to evaluate the robustness of low-input library generation approaches. We find that high quality RNA can be extracted in sufficient quantity from thinly-sectioned laser-captured tissues when minimal digestion time and temperature are used. Furthermore, we found that library generation approaches that retain rRNA through cDNA library generation required fewer cycles of PCR, which minimized bias in the libraries and produced more uniform gene coverage with less variability than current practices. Combined, our methods enable high quality and robust transcriptome profiling from minute tissue samples.Laser capture microdissection (LCM) is a microscopy-based technique developed to isolate select cell populations from complex, heterogeneous tissue. Using a thermoplastic polymer film that can be focally activated by infrared (IR) or ultraviolet (UV) lasers, discrete regions of tissue can be rapidly procured while retaining cellular morphology and location useful for identifying certain cell populations  housed under a 12:12 light/dark cycle with access to food and water RRID:MMRRC_033018-UCD) and bred for at least 10 generations onto C57bl6/n background. Mice were deeply anesthetized with Fatal Plus (50 mg/kg) before decapitation and swift removal of the brain (<2 min). Brains were bisected in the sagittal plane and individually flash frozen in 22 × 22 mm disposable cryomold  filled with Tissue-Tek Optimal Cutting Temperature  compound and then submerged in isopentane cooled to −20°C in a dry ice and ethanol slurry. For midbrain sections: adult C57bI6 mice were treated with a single systemic injection of LPS (5 mg/kg) or saline at 2 months old and a body weight of roughly 22–24 g for an unrelated study. After 10 months, brains were removed and immediately embedded in 4557 standard cryomold  with OCT on dry ice. For liver sections: liver tissues were harvested from male C57bI6 mice  and embedded as above for midbrain sections. Frozen tissue blocks were stored at −80°C until processing.For hippocampal sections: brains were harvested from adult male Amigo2-EGFP transgenic mice  born from separate litters. The Amigo2-EGFP line was acquired from GENSAT  that was pre-set to –18°C and allowed to equilibrate for at least 10 min prior to cutting. To avoid contamination across samples, a clean paint brush was used instead of the roll plate. In order to accommodate five sections per slide, the frozen OCT blocks were trimmed on all sides using a razor blade, leaving 3–4 mm of OCT surrounding the tissue for handling with the paint brush. Eight micron thick sections were cut and mounted on either RNase-free Superfrost Plus glass slides (FISHER) for use with the PixCell instrument, or MicroDissect polyethylene terephthalate (PET) membrane single frame slides  for use with the CellCut instrument. Prior to LCM, a few sections were stained with cresyl violet to verify coordinates  using the 1st edition Watson and Paxinos . EGFP flFive serial 8-micron thick sections from Amigo2-EGFP mouse brains were lined up in the cryostat and simultaneously mounted onto the slide . Approximately, 10 sections (2 slides) were harvested on one CapSure HS LCM cap per region for a maximum microdissection time of 30 min. Immediately after LCM, the cap was placed on the ExtracSure device in a CapSure HS Alignment Tray. Lysis was performed using either the PicoPure or QIAGEN kits as described below. Subsequent slides were processed until the entire target region was harvested.Slides were removed one at a time from xylene and air-dried in a fume hood for 5 min before capturing the target region using a PixCell IIe LCM instrument (Arcturus/Molecular Devices) equipped with an IR laser and an inverted epifluorescence microscope. The CapSure HS LCM caps  were used for collecting each region separately. For the microdissection of dorsal hippocampal CA2, approximately 20 slides (five sections per slide) were generated and captured over 2 days from the left hemisphere of each mouse  equipped with an UV laser and an inverted epifluorescence microscope. PET slides were inverted and placed onto a glass slide so that the tissue section was sandwiched between the membrane and glass slide. Each target region was collected using 0.5 ml MicroDissect caps  as described for the PixCell instrument. Immediately after LCM, lysis was performed using either the PicoPure or QIAGEN kits as described below. Subsequent slides were processed until the entire target region was captured.® Micro kit  was added directly on to the cap. A new RNase-free 0.5 ml microcentrifuge tube was placed onto the CapSure ExtracSure assembly and the set-up incubated at room temperature for 5 min. The microcentrifuge tube was spun with the CapSureExtracSure assembly at 800× g for 2 min to collect the cell extract into the microcentrifuge tube. Cell extracts were immediately frozen on dry ice. Lysate samples were stored at −80°C until RNA isolation. For RNA isolation, samples were thawed at room temperature and samples from one region were pooled into one lysate in an RNase-free 1.5 ml microcentrifuge tube. One volume of freshly prepared RNase-free 70% ethanol was added to the lysate and total RNA purification was performed by following RNeasy® Micro user guide . RNase-free DNase set  was utilized to remove genomic DNA that can interfere with downstream applications. One sample was processed at a time to limit RNA degradation.Ten microliter RLT lysis buffer with ß-ME from the RNeasyg for 2 min to collect cell extract into the microcentrifuge tube. Cell extracts were immediately frozen on dry ice. Lysate samples were stored at −80°C until RNA isolation. For RNA isolation, conditioning Buffer  was added to the PicoPure purification spin column and incubated for 5 min at room temperature. The purification spin column was then spun in the provided collection tube at 16,000× g for 1 min. Microdissected samples from one region were thawed and pooled into one lysate in an RNase-free 1.5 ml microcentrifuge tube and mixed with one volume 70% ethanol (supplied). The lysates were loaded onto the spin column and total RNA purification was performed by following PicoPure® RNA isolation kit user guide , including on-column DNase-treatment . One sample region was processed at a time to limit RNA degradation.Ten microliter XB extraction buffer from the Picopure RNA Isolation kit  was added into the buffer well. An RNase-free 0.5 ml microcentrifuge tube was placed onto the CapSureExtracSure assembly and incubated for 30 min at 42°C. After incubation, the CapSureExtracSure assembly with the microcentrifuge tube was spun at 800× RRID:SCR_002798). Normality  and variances were tested prior to two-way analysis of variance (ANOVA) analyses .Total RNA samples were analyzed for RIN and concentration using the 2100 Agilent Bioanalyzer instrument and RNA 6000 Pico assays . Statistical tests comparing RNA quality and yield were performed using Graphpad Prism (v7) software  were depleted of rRNA sequences using the Clontech RiboGone mammalian kit according to manufacturer’s instructions. Note that we also tested the Illumina/Epicenter Ribo-Zero rRNA Removal Kit for mouse using the Clontech product note for truly low-input RNA samples but were unsuccessful in obtaining sufficient yield after the protocol. Thus, we prepared stranded RNA-Seq libraries from the RiboGone depleted RNA samples using the Clontech SMARTer Stranded RNA-Seq Kit according to manufacturer’s instructions using 20 cycles of PCR to amplify the final library. Twenty cycles of PCR were required achieve sufficient library concentration for quantitation and sequencing.N = 3 mice. The RNA was extracted using the QIAGEN kit. We found no differences in any parameters tested . To directly compare across kits, however, we used 20 cycles of PCR. Further, the cDNA samples used for the kit comparison were not fragmented using the Covaris S series ultrasonicator, whereas the three CA2 LCM samples were fragmented by ultrasonication. This step is recommended to reduce the amount of rRNA mapped reads and to ensure equal 5′ to 3′ read distribution. Lastly, Covaris fragmentation is recommended if sequencing on the Illumina NextSeq500 system because it is optimal for libraries less than 400 nucleotides (nt). Because smaller sized libraries are recommended for the NextSeq500 instrument, fewer total reads were acquired for the NuGEN samples (average library ~500 nt) than for the Clontech samples (average library ~200 nt) during multiplexed sequencing (see below).Stranded RNA-Seq libraries were prepared using 3.0 ng total RNA per sample (max input 5 μl) with the NuGEN Ovation RNA-Seq Systems 1–16 for Model Organisms (mouse) according to manufacturer’s instructions. In this kit, rRNA is depleted prior to library amplification instead of prior to cDNA synthesis. This yield saving step allows for only 16 cycles of PCR to be sufficient when amplifying the final library. We compared 16, 18 and 20 cycles of PCR using LCM CA2 RNA samples from a separate cohort of Libraries were analyzed for size and concentration using the 2100 Agilent Bioanalyzer instrument and the High Sensitivity DNA assay . Libraries prepared from the Clontech SMARTer and NuGEN Ovation kits were four-plexed  or three-plexed (3 CA2 LCM samples) and run on a mid-level NextSeq500 lane acquiring 100 bp paired-end reads. The data are available on Gene Expression Omnibus (GEO) #GSE95257. All code used for generating the data is available upon request.Reads were trimmed using Sickle . FASTQ files were run through identical pipelines as described above with the following changes; the GTF file was derived from vM12 GENCODE gene models, the Hipposeq dataset was aligned with STAR using single-end and unstranded parameters, and no genes were excluded from analyses. Approximately 22 million reads were mapped from the Farris dataset  and 32 million reads from the Hipposeq dataset . The correlation plot consists of the mean per dataset from 14,045 genes with at least one count in all replicates of both datasets. The MA plots were centered by the mean of each dataset and one representative sample from each dataset is shown. The coverage profiles and heatmaps were generated as described above.Comparisons with Hipposeq  captured from various mouse brain and liver samples Table  using twp < 0.0001, CellCut vs. PixCell F = 114.6, two-way ANOVA). This was true when using either extraction kit, however, we note that our PixCell PicoPure samples consisted solely of samples captured from brain tissue and our CellCut QIAGEN samples were entirely from liver tissue  = 732.5, two-way ANOVA). This was true across numerous experimental and biological replicates . The difference in RNA quality can clearly be seen in the representative bioanalyzer gels and electropherograms in Figures p = 0.0925, F = 4.845, two-way ANOVA). Note that the anatomical area of the CA2 region is smaller than the anatomical area of the CA1 region , use random hexamer primers to generate cDNA  and maintain strandedness. The kits differ in their methods to remove rRNA, with the Clontech kit removing rRNA prior to cDNA synthesis and the NuGEN kit depleting rRNA prior to library amplification. The kits also differ in their chemistry and length of protocol, with the Clontech kit taking less than 1 day and the NuGEN kit requiring 2 days to generate libraries for sequencing.We were able to successfully generate cDNA libraries from both kits using 3 ng total RNA input Figure . AdditioThe fact that about half as many reads were acquired with the NuGEN samples can likely be attributed to the fact that we omitted the cDNA shearing step that would have resulted in NuGEN cDNA libraries within the preferred range (<400 bp) of the NEXTseq instrument (Clontech cDNA libraries are ~200 bp). The lack of shearing also resulted in a slight 5′ bias in read coverage for NuGEN samples Figure . HoweverLastly, likely due to the differences in the rRNA depletion methods, the NuGEN kit generated libraries of suitable concentration for sequencing with fewer PCR cycles than the Clontech kit (16 cycles vs. 20 cycles). This is important because fewer PCR cycles minimizes the amount of amplification bias in the resulting libraries providing a more faithful representation of the starting material. Using the RiboGone rRNA depletion kit prior to cDNA synthesis in the Clontech kit likely results in a loss of yield that requires an increased number of PCR cycles to achieve sufficient cDNA concentration for sequencing. Note that Clontech has since introduced a library generation kit  that depletes rRNA prior to library amplification similar to the NuGEN kit but with SMARTer chemistry. As of yet we have not compared performance of this new kit with those tested here.N = 3 mice) to a commendable recent RNA-Seq study that used manual sorting of fluorescently-labeled mouse hippocampal neurons, including area CA2 (N = 3 mice), from microdissected slices for RNA-Seq  vs. CellCut Hippocampus/Midbrain PicoPure RIN mean = 6.65 (N = 11), unpaired, one-tailed Mann Whitney t-test), indicating that, at least for brain with the methods tested here, the IR-instrument produced a modest but statistically significant increase in RNA quality. Additional experiments varying only the LCM instrument are needed to test whether this is also the case for liver. Furthermore, we found the effect of LCM instrument on RNA integrity was minimal  compared to the impact of RNA isolation kit ; the interaction effect was essentially additive (IR QIAGEN-UV PicoPure RIN mean difference = 2.76). The major difference between the QIAGEN and PicoPure RNA isolation kits is the tissue lysis step. QIAGEN incubates the LCM tissue for 5 min at room temp whereas PicoPure incubates the tissue for 30 min at 42°C. In our system, the shorter incubation time in the QIAGEN kit did not significantly affect the RNA yield/area, suggesting that longer lysis incubation times negatively affect RNA quality without much gain in RNA yield. Interestingly, when comparing previous LCM articles that used either PicoPure -tagging , suggesting that both methods reliably detect the CA2 transcriptome. However, our replicates had less variability, which when compared to other samples should produce a greater number of differentially expressed genes. The lower variability seen in our dataset could be attributable to differences in the RNA extraction methods (QIAGEN vs. PicoPure), library generation methods  and/or sequence format (paired-end vs. single-end). Furthermore, our dataset had more uniform 5′-3′ coverage than the Hipposeq dataset. When adding that our libraries were strand-specific and sequenced with paired-end reads, it suggests our methods would be particularly suitable for quantification of isoform-specific expression.The cross-correlation analysis between our CA2 RNA-Seq and the Hipposeq CA2 neuron RNA-Seq data demonstrated a high degree of correlation (Our improvements yield high-quality RNA from minute tissues that can generate highly reproducible transcriptome data. Thus, we believe our optimized methods for producing RNA-Seq data from minute tissue samples will greatly enhance the robustness of RNA-Seq datasets in the field.SF, YW, JMW and SMD designed research. SF and YW performed research. SF and JMW analyzed data. SF and SMD wrote the article.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Chromatin is structurally involved in the transcriptional regulation of all genes. While the nucleosome positioning at RNA polymerase II (pol II) promoters has been extensively studied, less is known about the chromatin structure at pol III promoters in human cells. We use a high-resolution analysis to show substantial differences in chromatin structure of pol II and pol III promoters, and between subtypes of pol III genes. Notably, the nucleosome depleted region at the transcription start site of pol III genes extends past the termination sequences, resulting in nucleosome free gene bodies. The +1 nucleosome is located further downstream than at pol II genes and furthermore displays weak positioning. The variable position of the +1 location is seen not only within individual cell populations and between cell types, but also between different pol III promoter subtypes, suggesting that the +1 nucleosome may be involved in the transcriptional regulation of pol III genes. We find that expression and DNA methylation patterns correlate with distinct accessibility patterns, where DNA methylation associates with the silencing and inaccessibility at promoters. Taken together, this study provides the first high-resolution map of nucleosome positioning and occupancy at human pol III promoters at specific loci and genome wide. Transcription in eukaryotes is carried out by multiple DNA-dependent RNA polymerases, pol I, pol II and pol III. Pol I transcribes ribosomal RNAs, pol II transcribes mainly protein-encoding genes, while pol III transcribes genes encoding short , non-coding RNAs12The nucleosome is the fundamental unit of chromatin, consisting of 146 bp DNA wrapped around a histone core6789Early studies showing that pol III genes were constitutively expressed and devoid of nucleosomes contributed to the notion that chromatin did not regulate pol III genes9814151617181920172324202627Here, we generated the first high resolution map of nucleosome positioning at pol III promoters in human cells to examine the differences and similarities between pol II and III promoters, as well as within pol III promoter subtypes.We employed the NOMe-seq assay developed by our group to generate a genome wide map of chromatin accessibility in K562 cells to study the chromatin landscape of active promoters. Freshly isolated nuclei were treated with M.CviPI, an enzyme that methylates cytosines within GpC dinucleotides that are not protected by nucleosomes or tightly bound proteinsActive tRNA gene promoters, which are Type II genes that contain internal promoter elements, exhibit an NDR with a well-positioned −1 nucleosome (centered at −150 bp) and a +1 nucleosome (centered at +220 bp), as well as upstream/downstream nucleosome phasing. This phasing becomes less apparent past the −2/+2 nucleosomes . This po30The NDR structure and nucleosome positioning patterns at non-tRNAs are distinct from that in the tRNA group , where tUnlike yeast, where all tRNA promoters are occupied and expressed2627Our data shows that the coding regions of pol III genes are devoid of nucleosomes, which is in accordance with previous studies20303738Moreover, in general, differences in gene size is a prominent characteristic between pol II and III genes, where the majority of pol III genes are only a few hundred bases long, compared to several thousands for a typical pol II gene. Hence, transcript size could be an important determinant of the nucleosome positioning, and potentially explain some of the positioning differences we observe between active pol II and III genes. To examine this further, we analyzed accessibility specifically at pol II genes <100 bp in length, in order to mimic the size of pol III genes . Here, iTaken together, the data suggests that the distinctive features between active pol II and pol III promoters are 1) the location and occupancy of the +1 nucleosome and 2) depletion of nucleosomes of active pol III gene promoters and bodies. While further validation is required, this data indicates that similar mechanisms govern positioning at pol II and III genes, and that promoter element usage and gene length also are important determinants of nucleosome positioning. In addition, the chromatin structures at the tRNA and non-tRNA groups differ markedly, also indicating that the promoter element composition may affect positioning patterns.To verify the genome wide observations, and further examine the differences in nucleosome positioning in pol III promoter subtypes, we extended the study to encompass three additional human cell lines  and performed a modified, high resolution NOMe-seq analysis at specific loci representing Type I/II  promoters and Type III  pol III promoters. The modified NOMe-seq analysis combines M.CviPI and M.SssI enzymes, the latter of which methylates cytosines in CpG dinucleotides that are not endogenously methylatedWe first examined the nucleosome positioning at tRNA-Leu, a canonical Type II gene with internal promoter elements. We observed an NDR ranging from −100 to >+200 bp that extends across the internal promoter elements in all cell types . The dis172330We next examined the structure of the non-tRNA promoter of RNU6-1, which is a Type III gene containing external promoter elements . ConsistTaken together, these data extend our genome wide observations and provide the first detailed map of nucleosome occupancy and positioning across multiple human cell lines at specific pol III promoter subtypes at the level of individual DNA molecules. Our data confirms that the nucleosome positioning and occupancy patterns appear highly dependent on the location of the underlying promoter sequences  that may orchestrate the nucleosomal arrangements as well as the binding of the transcriptional machinery.We next examined if the positioning of nucleosomes around the pol III promoter could be correlated with expression patterns. Due to the repetitive nature and high sequence similarities of many pol III genes, expression analysis at individual genes is challenging. We therefore examined the expression and promoter accessibility around vtRNA1-1, vtRNA1-3 and nc886 (previously annotated as vtRNA2-1) genes, where we previously have documented unique expression primers424445We observe differential expression of vtRNA1-1 and vtRNA1-3 in the four human cell lines . Across We next examined chromatin accessibility patterns at the vtRNA promoters  varies wWe cannot examine expression at the level of individual DNA molecules using the NOMe-seq method, but must infer patterns as exemplified above. However, if a locus contains endogenous promoter DNA methylation, it is empirically known that the region is silenced. nc886 is a pol III transcript which exhibits monoallelic methylation patterns4348Collectively, our data at vtRNA and nc886 genes indicate that expression and methylation patterns at pol III genes correlate with nucleosome occupancy profiles around regulatory elements. While anecdotal, this suggests that the chromatin environment actively regulates pol III gene expression and future experiments will aim to uncover these interactions.Pol III transcripts play a central role in governing translational output and cellular function. Therefore, gaining a basic understanding of the chromatin structure at pol III genes is important in addressing the differences in nucleosome positioning, occupancy and NDR structures of pol III promoter subtypes. Previous reports, which investigated global histone modification profiles suggested that active pol III genes were nucleosome depleted20c, TFIIS))2024262750515253In contrast to pol II genes that have a highly positioned +1 nucleosome centered at +125 bp, the +1 nucleosome at active pol III genes displays a different structure, and is generally located further downstream of the TSS. We provide preliminary data which indicates that short pol II genes adopt similar positioning patterns to pol III, indicating that gene length may be in important regulator of positioning, irrespective of polymerase, and that the mechanistic properties of nucleosome positioning may be shared. This is in line with the recent observations that highlight a great degree of transcriptional sharing  immediately after isolation of the nuclei prior to M.CviPI treatment.Treatment of nuclei with M.CviPI was performed as previously described2, 1, 5 ul SAM and 50 units M.SssI (NEB) were added. The sample was incubated for an additional 10 minutes at 37 °C before the reaction was stopped . The DNA was treated with proteinase K (NEB) overnight at 55 °C and DNA was purified using standard phenol chloroform extraction and then bisulfite converted (Zymo Research).Due to low GpC content of certain regions, we further modified this protocol to combine both M.CviPI and M.SssI enzymes in order to increase resolution of endogenously unmethylated sites. Briefly, for the combination treatment, freshly isolated nuclei were incubated with 300 units of M.CviPI, 5 ul 10X enzyme buffer (NEB), 1, 5 ul SAM (NEB), and 45 ul 1 M sucrose for 10 minutes at 37 °C. Next, 10 mM MgClFor loci-specific analysis, PCR was performed using primers that do not contain CpG or GpC sites, followed by gel purification (Qiagen), TA cloning (Invitrogen), plasmid amplification using Templiphi (Illustra) and Sanger sequencing. Pink bars denote regions of continuous inaccessibility of ≥146 bp, hence indicating the presence of a nucleosome. Regions of continuous inaccessibility >25 bp, but <146 bp around the TSS or TTS are indicated as footprints by orange bars. Prior to utilizing the modified protocol, the endogenous methylation status of the region was analyzed in each cell type to ensure an unmethylated state  and 4. PGenome wide NOMe-seq libraries were prepared using 2–3 μg of M.CviPI-treated DNA as previously described and sequenced on HiSeq 250029http://genome.ucsc.edu/ENCODE/); all data used in this study is past the 9-months moratorium. Z-score for each ChIP-seq data was calculated as previously describedThe library was sequenced on Illumina Hiseq 2500, with 75PE reads. 819106996 reads were sequenced, and 757776380 reads were mapped (93% mapping rate), and used to call for methylation. The bisulfite inconversion rate was 0.9%. NOMe-seq sequencing reads were aligned to the hg19 genome using BSMAP (Xi 2009). Methylation levels of CpG and GpC dinucleotides were determined using Bis-SNP and previously described parameters63RNA was extracted using the miRNeasy Mini Kit (Qiagen) with on-column DNase digestion according to the manufacturer’s instructions. RNA was reverse transcribed using SuperScript III reverse transcriptase (Invitrogen) with Oligo-dT and random hexamers according to the manufacturer’s instructions.RT-qPCR was performed using custom TaqMan probes (Applied Biosystems) for vtRNA and nc886 transcripts, GAPDH was quantified using SYBR green (Roche) on the LC480 machine (Roche). Primer sequences are listed in NOMe-seq data generated in this study have been deposited to the NCBI Gene Expression Omnibus (GEO) under the accession number GSE64929.How to cite this article: Helbo, A. S. et al. Nucleosome Positioning and NDR Structure at RNA Polymerase III Promoters. Sci. Rep.7, 41947; doi: 10.1038/srep41947 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Mitochondria are the organelles responsible for energy metabolism and have a direct impact on neuronal function and survival. Mitochondrial abnormalities have been well characterized in Alzheimer Disease (AD). It is believed that mitochondrial fragmentation, due to impaired fission and fusion balance, likely causes mitochondrial dysfunction that underlies many aspects of neurodegenerative changes in AD. Mitochondrial fission and fusion proteins play a major role in maintaining the health and function of these important organelles. Mitofusion 2 (Mfn2) is one such protein that regulates mitochondrial fusion in which mutations lead to the neurological disease.To examine whether and how impaired mitochondrial fission/fusion balance causes neurodegeneration in AD, we developed a transgenic mouse model using the CAMKII promoter to knockout neuronal Mfn2 in the hippocampus and cortex, areas significantly affected in AD.Electron micrographs of neurons from these mice show swollen mitochondria with cristae damage and mitochondria membrane abnormalities. Over time the Mfn2 cKO model demonstrates a progression of neurodegeneration via mitochondrial morphological changes, oxidative stress response, inflammatory changes, and loss of MAP2 in dendrites, leading to severe and selective neuronal death. In this model, hippocampal CA1 neurons were affected earlier and resulted in nearly total loss, while in the cortex, progressive neuronal death was associated with decreased cortical size.Overall, our findings indicate that impaired mitochondrial fission and fusion balance can cause many of the neurodegenerative changes and eventual neuron loss that characterize AD in the hippocampus and cortex which makes it a potential target for treatment strategies for AD.The online version of this article (10.1186/s13024-018-0238-8) contains supplementary material, which is available to authorized users. Alzheimer Disease (AD) is a multifactorial, age-related neurodegenerative disease of the elderly that leads to progressive memory loss, impairments in behavior, language, visual-spatial skills and ultimately death. The disease is characterized by a progressive neuronal loss and accumulation of extracellular senile plaques composed of amyloid beta (Aß) and intracellular neurofibrillary tangles (NFT) composed of hyperphosphorylated tau in selective brain areas such as hippocampus and cortex . AccordiSeveral decades of intensive research have revealed that multiple cellular changes have been implicated during the course of AD including widespread oxidative damage, extensive neuroinflammation, and aberrant cytoskeletal alteration among which mitochondrial dysfunction is an early prominent feature in susceptible neurons in the brain from AD patients and models of AD, and likely plays a critical role in the pathogenesis of AD , 3, 4. IDrosophila models [Mitochondria are dynamic organelles that continuously fuse with each other to form larger tubular networks and divide into smaller structures, a process regulated by the balance of fission/fusion machinery mainly involving several large GTPases: mitochondrial fission is regulated by cytosolic protein DLP1 which translocates to mitochondria during fission while mitochondrial fusion is regulated by mitofusin 1 and 2 on the outer mitochondrial membrane and OPA1 on the inner mitochondrial membrane . The dela models , 16. Abna models –19. Fibra models , 21. Inda models , 22, 23.Mfn2tm3Dcc/J mice (Mfn2loxP). PCR of genomic DNA was used for routine genotyping of offspring to detect the presence of the CAMKCre gene and the Mfn2loxP gene. We chose these mice since the Cre-recombinase was shown to be expressed in the forebrain and hippocampus, highest in the CA1 layer. Mice were housed under standard conditions and all animal studies were performed following an approved protocol through the Case Western Reserve University IACUC board. At various ages, mice were perfused with saline and brain tissue collected and bisected with one half frozen  and the other half fixed in 10% buffered formalin. For this constitutive expression study, Mfn2 cKO mice from ages 4, 8, 12, 18, 28, and 78 weeks (at least n = 3 per age group) and age-matched control mice  were collected.Experimental mice were generated by crossing B6.Cg-Tg(Camk2a-cre)T29-1Stl/J mice (Jackson Laboratory) with B6.129(Cg)-To confirm recombination of the floxed gene, DNA was extracted from frozen samples of cortex, hippocampus, cerebellum and brainstem using the Wizard genomic DNA kit. PCR was performed using primers designed to amplify a 240 bp band only after Cre recombination has occurred.For Western blot analysis frozen dissected hippocampal and cortical tissues were individually homogenized in 10X volume Cell lysis buffer  with added protease and phosphatase inhibitors (Roche) and centrifuged at 14,000 g for 20 min in a refrigerated microcentrifuge. Protein concentration of the supernatant was determined using BCA protein assay. Proteins, 10 μg per lane (specified in figure legends), were resolved with SDS-PAGE and transferred to Immobilon (Millipore). Blots were blocked in 10% nonfat milk for 1 h and probed with primary antibodies overnight. After washing 5X in TBS-tween, HRP-conjugated secondary antibodies  were applied for 1 h, rinsed again and bands detected using ECL (Santa Cruz or Millipore). Loading control was anti-actin (Millipore). Bands were quantified using QuantityOne (Biorad) or ImageJ.2O2. Antigen retrieval using citrate buffer and pressure cooking (Biocare) was performed for most IHC experiments. After blocking for 30 min in 10% normal goat serum (NGS), primary antibodies were applied and incubated overnight at 4 °C. Species specific secondary antibodies and PAP complexes were applied and after 3 changes of tris buffer, the sections were developed with DAB (Dako) and slides were rinsed in dH2O, dehydrated and coverslipped with Permount. Antibodies used were directed against Cre-recombinase (Millipore), GFAP (Invitrogen), MAP2 (Millipore), NeuN (Millipore), COXI (Molecular Probes), HO-1 (Enzo), iba1 (Wako), AT8 (Thermo Fisher), and mitochondria complex cocktail (Abcam).Formalin fixed brain samples were embedded in paraffin and 6 μm sections were cut using a microtome and placed on coated slides. Immunohistochemistry was performed using the peroxidase anti peroxidase method as previously described . BrieflyTo detect protein carbonyls, the dinitrophenol binding -assay was also performed. After tissue sections were rehydrated, 50 ul of DNP solution was applied  and incubated for 15 min at 37 °C. The sections were rinsed with changes of acetic acid until no yellow color could be further removed. After rinsing thoroughly with water and then with TBS, sections were blocked and DNP detected with rabbit antibody to DNP . For western blot analysis, samples were first denatured and then treated with DNP solution following manufacturer’s instructions (Millipore), sample buffer directly added and the entire sample run on the gel.TUNEL method was used to label cells undergoing apoptosis following manufacturer’s instructions (Roche). After TUNEL method, the sections were stained with DapI and mounted with Fluoromount (SouthernBiotech) and FITC-labelled apoptotic cells were imaged on a Zeiss Axiophot.Images were acquired on a Axiophot with a axiocam (Zeiss). Quantification was performed on these images using Axiovision software and either the number of cells stained per area, or density of stained structures was determined using Axiocam image analysis software. To measure hippocampal size, H&E stained sections from brain areas collected from Bregma 1.08–1.8, representing the complete hippocampus and similar ventricle presentation were imaged and measured in a blinded fashion. To measure total cortical size, sections from similar levels were imaged at the same magnification, the cortical area cropped using Photoshop and the area was quantified.2).For electron microscopic analysis, brain samples from control and KO mice were collected and fixed as previously described following brain dissection . Brain sThe real-time measurement of oxygen consumption rate (OCR) in synaptic mitochondria in synaptosomes was performed using the Seahorse XF24 Analyzer , according to the manufacturer’s instructions. If needed, ATP synthase inhibitor oligomycin (1 μM), uncoupler FCCP (4 μM) and complex I inhibitors antimycin A (1 μM) and rotenone (1 μM) were injected sequentially. After measurement, cells and synaptosomes were lysed and OCR data was normalized by total protein as previously described [loxP/loxP) with CaMKII-Cre mice, in which the Cre recombinase is expressed in the forebrain and result in specific ablation of Mfn2 in selective neurons in the hippocampus and cortex, brain areas that are heavily afflicted in AD. Genomic DNA PCR analysis confirmed Cre-mediated recombination and excision of floxed Mfn2 . Western blot revealed that Mfn2 protein levels were significantly reduced in both the hippocampus and cortex of the homozygous Mfn2 cKO mice, compared to control mice  (not shown) and the oldest littermate control mice examined  , swollen mitochondria with significantly enlarged size  and almost complete round shape (aspect ratio close to 1) demonstrated almost complete loss of cristae, suggesting severe damage to mitochondria, a phenotype that was also observed in older DLP1 KO mice . MitochoThe most striking change in the Mfn2 cKO mice is the apparent neurodegeneration in the hippocampal area at 18 weeks of age where near 90% of the CA1 hippocampal neurons were wiped out. While this extreme loss of neurons was not apparent in individual fields of the cortical area, taken together with the progressive shrinkage of the entire cortex with age, significant cortical neuron loss was apparent at 18 weeks of age when around 25% neurons were lost. Indeed, TUNEL assay did reveal that these neurons die by apoptosis in both the hippocampus and cortex at 18 weeks of age. Significant cortical neuronal loss continues to progress and reached around 50% at 28 weeks of age. It was noted that disabling mitochondrial function could produce the same pathological changes. For example, malonate treatment, which inhibits succinic dehydrogenase, resulted in strikingly similar hippocampal neurodegeneration . Given tInterestingly, Mfn2 ablation induced neurodegeneration in the hippocampus and cortex is preceded by a series of pathological events in temporal order that may suggest a causal relationship between these events. Increased oxidative stress was observed in the Mfn2 cKO mice at the age of 8 weeks. It must be noted that oxidative stress is also a prominent and early feature of AD . For exaIn addition to mitochondrial fragmentation and ultrastructural damage, we found an abnormal distribution of mitochondria, especially in the neuronal process, in the pyramidal neurons of Mfn2 cKO mice months before neurodegeneration. Loss of Mfn2 leads to decreased number of mitochondria in the axon and dendritic processes. In this regard, it is important to note that such an abnormal distribution of mitochondria was also noted in the brain of AD patients  and AD aAs we demonstrated in this study, CA1 neurons are susceptible to Mfn2 loss. However, these neurons are resistant to DLP1 loss . It is oOverall, in this study we demonstrated that Mfn2-ablation induced mitochondrial fragmentation led to neurodegeneration through mitochondrial dysfunction and increased oxidative stress and a series of event in a strict temporal order in mice and all these pathological changes are also characteristics seen in AD during the course of disease. These results do not necessarily suggest that the pathological events occur at a similar temporal order in the AD brain, however, they suggest that disrupted mitochondrial dynamics and mitochondrial dysfunction could contribute to these pathological events in AD.


With the results we i) classified patients based on their ex vivo drug response profile; ii) identified and matched potential drug candidates to recurrent cytogenetic alterations; and iii) correlated ex vivo drug sensitivity to patient outcome. Based on their drug sensitivity profiles, MM patients were stratified into four distinct subgroups with varied survival outcomes. Patients with progressive disease and poor survival clustered in a drug response group exhibiting high sensitivity to signal transduction inhibitors. Del(17p) positive samples were resistant to most drugs tested with the exception of histone deacetylase and BCL2 inhibitors. Samples positive for t were highly sensitive to immunomodulatory drugs, proteasome inhibitors and several targeted drugs. Three patients treated based on the ex vivo results showed good response to the selected treatments. Our results demonstrate that ex vivo drug testing may potentially be applied to optimize treatment selection and achieve therapeutic benefit for relapsed/refractory MM.Novel agents have increased survival of multiple myeloma (MM) patients, however high-risk and relapsed/refractory patients remain challenging to treat and their outcome is poor. To identify novel therapies and aid treatment selection for MM, we assessed the  While sderstood . Prior iderstood , genomicderstood –10. A reThe myeloma genome contains complex cytogenetic alterations that affect both the number and structure of chromosomes . Recurreex vivo drug sensitivity and resistance profiling of 50 MM patient samples to 308 drugs and use the overall drug response profile to classify patients. We compared the responses to the patient cytogenetics to identify potential drug candidates for each karyotype and identify novel treatment strategies for relapsed/refractory (RR) MM. Results were used to tailor treatment for three patients. Our studies indicate that an individualized approach by functional, ex vivo drug testing may be effectively applied to MM to provide additional information to guide treatment selection and potentially improve therapeutic benefit.Increasing genomic complexity during disease progression leads to the activation of multiple signaling pathways that are known to contribute to treatment resistance, which can potentially be targeted using signal transduction inhibitors in combination with approved drugs. However, it is extremely important to identify the responding patients in advance to apply those drugs to maximize benefit. Here, we describe n = 8) to the drugs and subtracting the mean DSS of the controls from the DSS of the tumor sample [n = 16) showed selective sensitivity to several signal transduction inhibitors including those targeting IGF1R-PI3K-mTOR, HDAC, MEK, CDK and HSP90  responded to many of the same drugs, the sensitivities were more moderate, in particular to MEK, HDAC and HSP90 inhibitors. In addition, group II samples lacked sensitivity to rapalogs. In contrast, group III samples (n = 18) were relatively insensitive to many targeted therapies and exhibited diminished response to most drugs compared to healthy controls. The most striking response was observed for group IV (n = 3). Although the number of samples comprising this group was small, the samples were distinctively resistant to almost all drugs tested with the exception of bryostatin1 and the pan-BCL2 inhibitor navitoclax . DSS is a modified form of the area under the curve (AUC) calculation that integrates multiple dose response parameters for each drug, while a selective drug sensitivity score (sDSS) was calculated by taking into account the sensitivity of healthy BM control samples  using samples from nine patients. To assess the value of the drug sensitivity results in predicting patient outcome, we compared time to next therapy (TTNT) among the four different chemosensitivity groups. While most of the samples analyzed came from relapsed/refractory patients with expected poor outcome, TTNT differed between the four groups Figure . Interesn = 8/13), but were also present in group III (n = 3/18) and group I (n = 2/16)  and IV (n = 2/3) , t, t and 1q gain. We investigated if there was correlation between drug response profiles and karyotypes. Samples from patients with t predominantly clustered in group II  Figure , while s) Figure . SamplesDel(17p) patient samples tended to be very resistant with some exceptions. For example, del(17p) along with other HR patient samples exhibited good sensitivity to panobinostat Figure . The delFGFR3 was highly expressed in these samples  cells were less sensitive to navitoclax, but were highly sensitive to pomalidomide Figure . The t4. In addi samples .ex vivo sensitivity to pomalidomide  positive relapsed patients showing extremely good e Figure  were tree Figure . Oral cyethasone  suggesteEx vivo drug testing showed that the cells were sensitive to bortezomib, modestly sensitive to immunomodulatory drugs, but insensitive to dexamethasone  [NRAS and NF1, with the NRAS mutation and a REL mutation enriched in the post-temsirolimus treatment sample. In addition, a mutation to DNA-Damage-Inducible Transcript 4 (DDIT4), a repressor of mTORC1, was also detected in both samples [NRAS and NF1 may have also contributed to the sensitivity to MEK inhibitors .For a t patient, drug testing analysis at the time of relapse showed exceptional sensitivity to rapalogs  Figure . Prior tethasone . Based o cycles) , 25. The samples –28. Thesex vivo drug response profiles. Surprisingly, correlating the groups to patient outcome showed that patients comprising the most highly sensitive group (group I) had the shortest survival. As inferred from susceptibility to numerous signal transduction inhibitors, the activation of multiple signaling pathways in the malignant cells may drive the disease of these patients and potentially be the cause of relapse and acquired resistance to approved therapies. Noticeably, many group I samples were from standard-risk patients at relapse and end stage disease. While the samples exhibited sensitivity to many current therapies, multiple escape routes could circumvent blockade on growth. These patients could potentially benefit from treatment strategies based on signal transduction inhibitors combined with other standard therapies.Considering both genetics and clinical outcome, multiple myeloma presents extreme inter- and intra-individual heterogeneity  resultinex vivo testing before treatment can potentially be exploited for patients who are more refractory to currently used therapies and may benefit from tailored treatment, while molecular profiling will be helpful to understand changes induced by treatment and identify indicators of response.Among standard of care drugs, there was considerable variation in response to glucocorticoids. Dexamethasone, methylprednisolone and prednisolone showed similar patterns in efficacy with clearly responsive and non-responsive samples. Further analysis of mutation and gene expression profiles is needed to identify potential indicators of response, such as mutations to the glucocorticoid receptor or specific gene expression signatures , 30. EmeFGFR3 and MMSET [FGFR3 in the t samples, the cells were not sensitive to FGFR inhibitors . Efficacy of FGFR3 inhibitors has been explored in MM and other indications with FGFR1-3 amplifications or somatic aberrations [MMSET encodes the histone methyltransferase NSD2 [MMSET is associated with a global increase in dimethylation of lysine 36 on histone H3 (H3K36me2) and simultaneous decrease in lysine 27 trimethylation on histone H3 (H3K27me3), which is a repressive histone modification [By correlating standard cytogenetic markers with drug response profiles, we evaluated if any genetically driven treatment options were available, particularly for high-risk patients. Cells with t were sensitive to proteasome inhibitors and immunomodulatory drugs. This is supported by findings from a clinical trial suggesting that high-risk patients with t are likely to benefit from receiving these drugs as frontline treatment . The t aspirates were collected from 16 diagnostic and 27 relapse patients and 8 healthy donors. Patient characteristics are detailed in n ≥ 100) were used for interphase fluorescence in situ hybridization (FISH) following the guidelines of the European Myeloma Network 2012 [Plasma cells were selected by immunomagnetic bead enrichment of CD138+ cells . Selected cells (ork 2012 . FISH prn = 141) and investigational compounds (n = 167) targeting multiple signaling networks and molecular targets  with a PHERAstar® microplate reader  to measure luminescence. The mean viability of untreated cells at day three was 124 ± 10.40%. The data was normalized to negative (DMSO only) and positive control wells (containing 100 μM benzethonium chloride).CD138+ cells were enriched using the EasySep™ Human CD138 Positive Selection kit  from the mononuclear cell fraction of BM aspirates following gradient separation . Drug sensitivity and resistance testing (DSRT) was performed based on methods described previously . CD138+  targets . In brien = 1000) using a bootstrapping method with Pvclust R-package [Output from the plate reader was used as input for Dotmatics software  to generate dose response curves of individual drugs. A four parameter  logistic regression was applied to fit the dose response curves. Curve fitting parameters were further used to quantitate drug responses with a drug sensitivity score (DSS) as described previously . DSS is -package .7 and 10×107 2×100 bp paired-end reads were sequenced for the skin germline control and CD138+ cells, respectively. Somatic mutations were identified and annotated as described earlier [Genomic DNA was isolated from a skin biopsy and CD138+ cells using the DNeasy Blood & Tissue kit or AllPrep DNA/RNA/miRNA Universal kit . Exome capture was performed using the SureSelect Clinical Research Exome kit or the SureSelect Human All Exon V5 kit . Sequencing was performed on HiSeq 1500 and 2500 instruments . For the skin germline control 4 × 10 earlier . The canp-value under 0.05.Statistical analyses were performed using GraphPad Prism version 5.0 for Mac OS,  and SPSS version 23.0 Software . One-way ANOVA followed by Tukey’s multiple comparisons test was performed to test variances among different groups in the scatterplots. Univariate survival analysis was carried out using the Kaplan-Meier method with log-rank test and survival curves presented. Multivariate survival analysis was performed with the Cox proportional hazard model with DSRT group, age, gender, paraprotein subtype, HR cytogenetics and clinical treatment sensitivity used as predictors. Variable selection was performed with the forward stepwise method. Results from multivariate survival analysis were presented with hazard ratios and 95% confidence intervals. In this study, the statistical significance level was a


Previous genome-wide association studies (GWAS), conducted by our group and others, have identified loci that harbor risk variants for neurodegenerative diseases, including Alzheimer's disease (AD). Human disease variants are enriched for polymorphisms that affect gene expression, including some that are known to associate with expression changes in the brain. Postulating that many variants confer risk to neurodegenerative disease via transcriptional regulatory mechanisms, we have analyzed gene expression levels in the brain tissue of subjects with AD and related diseases. Herein, we describe our collective datasets comprised of GWAS data from 2,099 subjects; microarray gene expression data from 773 brain samples, 186 of which also have RNAseq; and an independent cohort of 556 brain samples with RNAseq. We expect that these datasets, which are available to all qualified researchers, will enable investigators to explore and identify transcriptional mechanisms contributing to neurodegenerative diseases. Despite this progress, a comprehensive understanding of the molecular mechanisms underlying these complex conditions remains elusive. This is partly due to the inability of the disease GWAS approach to identify the actual disease gene and the functional disease risk variants. We10 and others12 utilized combined gene expression GWAS (eGWAS) and disease GWAS to identify loci which harbor regulatory variants that confer disease risk and to nominate the actual disease genes at these loci. The underlying premise of these studies is that genetic variants that modulate expression levels of genes, which encode critical members of disease molecular pathways, will also influence disease risk13. If this is correct, then there should be significant overlap between disease GWAS and eGWAS variants, especially if assessed in the disease-relevant tissue. Indeed, in an eGWAS of brain tissue from subjects with AD and non-AD, comprised largely of other neurodegenerative diagnoses, we identified significant enrichment for disease GWAS variants for AD and other diseases10. We14–18 and others19–22 determined that many of the risk variants for AD and other neurodegenerative diseases influence brain levels of genes that are nearby in the genome. These studies implicate the genes that are likely to be involved in disease pathways, nominate regulatory variants as the functional disease risk factors and provide testable hypotheses for their downstream effects.In the past decade GWAS identified risk loci for human diseases, including AD23 utilize microarray-based gene or exon arrays. Despite the versatility, cost-effectiveness and large-scale utility, this approach has limitations, including restricted dynamic range, lack of probes for all known gene isoforms and confinement of assays to known transcripts. RNA sequencing (RNAseq) provides an attractive alternative that can surpass these limitations and provide much more in-depth information about the human transcriptome in a high-throughput manner24. To expand our prior work on the human transcriptome based on microarray approaches and to evaluate gene/exon/isoform levels in a comparative fashion between AD and other neurodegenerative diseases, we have generated RNAseq data on brain samples from both a subset of the subjects that underwent microarray transcriptome studies18 and also an independent cohort. These datasets will be of utility in performing expression quantitative trait loci (eQTL), expression profiling and network analyses to facilitate interpretation of genetic associations and further understanding of disease-mediated changes in transcriptional regulation.Most large-scale gene expression studies in human brains published to date25 from Sage Bionetworks as part of a series of datasets developed in support of the AMP-AD Target Identification and Preclinical Validation Project. The AMP-AD consortium includes six academic teams that will be generating genomic data from human brain or blood samples collected from more than 10 cohorts. Datasets are hosted in a common environment with standardized meta-data and annotations to facilitate cross-cohort query, access, and analysis. Each dataset provides a unique perspective on AD; therefore, datasets differ in types, generation protocols, and underlying patient characteristics. Together, this collection represents to date the most comprehensive collection of human genomic data in the field and, as such, it will be invaluable to a broad set of researchers.The present report is a description of the large-scale human genetic, and both microarray- and RNAseq-based transcriptome datasets we generated. The datasets described in this report have been made available to the research community through the Accelerating Medicines Partnership in Alzheimer’s Disease (AMP-AD) Knowledge Portal (Data Citation 1). The portal is hosted in the Synapse software platform1 (Mayo LOAD GWAS) on 2,099 subjects (Data Citation 2); (2) Mayo eGWAS10 on 773 samples from the cerebellum (CER) and temporal cortex (TCX) brain regions from a subset of Mayo LOAD GWAS participants ; (3) Mayo Pilot RNAseq18 generated on a subset of 186 TCX samples from the Mayo eGWAS (Data Citation 5); (4) Mayo RNAseq on an independent cohort of 556 TCX26 (Data Citation 6) and CER (Data Citation 7) samples from subjects with AD, progressive supranuclear palsy (PSP), pathologic aging and elderly controls without neurodegenerative diseases. This report provides a comprehensive understanding of these cohorts, a detailed description of subjects, samples, data generation, and quality control (QC) as well as instructions to access these rich datasets by the scientific community.The datasets described herein include the following: (1) late-onset AD GWASMayo LOAD GWAS1 (Data Citation 2) and microarray-based whole transcriptome data from the Mayo eGWAS10 . Next-generation RNA-sequencing (RNAseq) data from a subset of the patients from the Mayo Clinic eGWAS, referred to as the ‘Mayo Pilot RNAseq’ (Data Citation 5), was published in part18. A non-overlapping cohort with RNAseq-based transcriptome data named ‘Mayo RNAseq’  has also been published in part26. For a comprehensive description of the overall repository, the data from the published studies are also described herein, albeit in an abbreviated fashion. These four study cohorts will be referred to by their names as mentioned above, preceded by letters A-D 28. Age for the clinically diagnosed LOAD cases is defined as age at first diagnosis of AD, since age at onset is not always available. Age at entry into the study is used for the clinically diagnosed controls. Age at death is utilized for the cases and controls in the postmortem Mayo Clinic Brain Bank series, given that for this cohort, age at clinical diagnosis/ evaluation is not always available. Illumina Hap300 microarray genotypes from the subjects in these three case-control series were utilized to conduct a GWAS of LOAD risk1.The characteristics of the cohort for this study (Data Citation 2) were previously described in detailication1 . Subject4.0 ref. , while c10. All subjects in the Mayo eGWAS  are a subset of the Mayo Clinic Brain Bank series from the Mayo LOAD GWAS (Data Citation 2) (29. Transcriptome measurements were obtained from TCX of 202 AD subjects and from CER of 197 AD (n=107); Lewy body disease ; corticobasal degeneration ; frontotemporal lobar degeneration ; multiple system atrophy , vascular dementia ; other (n=10). There were 177 non-AD subjects with CER transcriptome measurements that had the following neuropathologies: PSP (n=98); LBD (n=23); CBD (n=22); FTLD (n=15); MSA (n=7); VaD (n=4); other (n=8). Eighty-five percent of the subjects in the TCX cohort overlapped with those in the CER cohort. Demographics for the Mayo eGWAS subjects and samples, including RNA quality as assessed by RNA Integrity Numbers (RIN) are shown in This cohort was previously described in detailation 2) . The Mayf 197 AD . This st26. PSP is a primary tauopathy characterized neuropathologically by neurofibrillary tangles (NFT) and tau-positive glial lesions30; and often presents clinically as a parkinsonian disorder. All PSP subjects were diagnosed neuropathologically by a single neuropathologist (DWD). For this study, only TCX samples were assessed (All subjects in the Mayo Pilot RNAseq study (Data Citation 5) are a subset of the Mayo eGWAS , and are therefore also participants of the Mayo Clinic Brain Bank series that was included in the Mayo LOAD GWAS (Data Citation 2) . The diaassessed .26 (Data Citation 6) and 278 CER (Data Citation 7) samples. Two hundred thirty-eight subjects had both CER and TCX RNAseq and the rest had either CER or TCX RNAseq measurements based on tissue availability. CER samples were from the following diagnostic categories: 86 AD, 84 PSP, 28 pathologic aging and 80 controls without neurodegenerative diagnoses. TCX samples had the following diagnostic groups: 84 AD, 84 PSP, 30 pathologic aging and 80 controls. Control subjects each had Braak28 NFT stage of 3.0 or less, CERAD31 neuritic and cortical plaque densities of 0 (none) or 1 (sparse) and lacked any of the following pathologic diagnoses: AD, Parkinson’s disease (PD), DLB, VaD, PSP, motor neuron disease (MND), CBD, Pick’s disease (PiD), Huntington’s disease (HD), FTLD, hippocampal sclerosis (HipScl) or dementia lacking distinctive histology (DLDH). Subjects with pathologic aging also lacked the above diagnoses and had Braak NFT stage of 3.0 or less, but had CERAD neuritic and cortical plaque densities of 2 or more. None of the pathologic aging subjects had a clinical diagnosis of dementia or mild cognitive impairment. Given the presence of amyloid plaques, but not tangles and the absence of dementia, pathologic aging is considered to be either a prodrome of AD or a condition, in which there is resistance to the development of NFT and/or dementia32.The subjects from this cohort are non-overlapping with the cohorts described above. The Mayo RNAseq cohort was utilized to generate RNAseq-based whole transcriptome data from 278 TCXWithin the Mayo RNAseq cohort , all AD and PSP subjects were from the Mayo Clinic Brain Bank, and all pathologic aging subjects were obtained from the Banner Sun Health Institute. Thirty-four control CER and 31 control TCX samples were from the Mayo Clinic Brain Bank, and the remaining control tissue was from the Banner Sun Health Institute. All subjects were North American Caucasians. All but control subjects, had ages at death ≥60, and a more relaxed lower age cutoff of ≥50 was applied for normal controls to achieve sample sizes similar to that of AD and PSP subjects. No upper age limit was imposed on this cohort, however when subjects had ages at death of ≥90, their ages were recorded as ‘90_or_above’ and shown as ‘90’ in APOE4 positive subjects in the AD group, followed by pathologic aging, then PSP and control subjects. AD and pathologic aging subjects had greater female sex frequency (57%), followed by controls (49%), then PSP subjects (39%). RIN for all samples were selected to be ≥5.0. Pathologic aging and control samples had slightly lower RINs than AD and PSP samples, due to limitations in availability of samples in these former diagnostic categories.1. For the antemortem Mayo Clinic Jacksonville and Mayo Clinic Rochester series, whole blood samples were collected in 10 ml EDTA tubes followed by DNA extraction using AutoGenFlex STAR instrument (AutoGen), whereas cerebellar tissue was used for DNA extraction from the postmortem Mayo Clinic Brain Bank series using the Wizard Genomic DNA purification kit (Promega). Given limited amounts of DNA from samples in the Mayo Clinic Rochester series and Mayo Clinic Brain Bank series, whole genome amplification (WGA) was applied using the Illustra GenomiPhi V2 DNA Amplification Kit , in four 5 ml reactions that utilized 5–15 ng genomic DNA as a template. Subsequent to the pooling of these reaction products, WGA DNA was subjected to quality control (QC) using SNP genotyping as previously described.For the Mayo LOAD GWAS (A) (Data Citation 2), DNA samples were collected and processed as previously described10 (B)  and Mayo Pilot RNAseq18 (C) (Data Citation 5) were previously described. Total RNA was extracted from frozen brain samples using the Ambion RNAqueous kit  according to the manufacturer’s instructions. Brain samples for the Mayo RNAseq (D)  study underwent RNA extractions via the Trizol/chloroform/ethanol method, followed by DNase and Cleanup of RNA using Qiagen RNeasy Mini Kit and Qiagen RNase -Free DNase Set. The quantity and quality of all RNA samples were determined by the Agilent 2100 Bioanalyzer using the Agilent RNA 6000 Nano Chip . Samples had to have an RNA Integrity Number (RIN) ≥5.0 for inclusion in either study  according to the manufacturer’s protocols. Two samples were genotyped per chip for 318,237 SNPs across the genome. Genotype calls were made using the auto-calling algorithm in Illumina’s BeadStudio 2.0 software.The genotype data for the Mayo LOAD GWAS (A) (Data Citation 2) was generated using HumanHap300-Duo Genotyping BeadChips10. Probe annotations were done based on NCBI RefSeq, Build 36.2. The RNA samples were randomized across the chips and plates using a stratified approach to ensure balance with respect to diagnosis, age, gender, RIN and APOE genotype. Raw probe mRNA expression data were exported from GenomeStudio software (Illumina Inc.) and preprocessed for background correction, variance stabilizing transformation, quantile normalization and probe filtering using the lumi package of BioConductor33.For the Mayo eGWAS study (B) , transcript levels were measured using the Whole Genome DASL assay  as previously described26 and CER samples  underwent RNAseq at different times. These samples were randomized across flowcells, taking into account age at death, sex, RIN, Braak stage and diagnosis. The TruSeq RNA Sample Prep Kit  was used for library preparation from all samples. The library concentration and size distribution was determined on an Agilent Bioanalyzer DNA 1000 chip. All samples were run in triplicates using barcoding (3 samples per flowcell lane). For Mayo Pilot RNAseq (C) (Data Citation 5) samples, 50 base-pair, paired-end sequencing was done, whereas Mayo RNAseq (D)  samples underwent 101 bp, paired-end sequencing.Samples for both Mayo Pilot RNAseq (C) (Data Citation 5) and Mayo RNAseq (D)  studies were randomized prior to transfer to the Mayo Clinic MGF Gene Expression Core for library preparation and then the Sequencing Core for RNA sequencing. Mayo Pilot RNAseq (C) (Data Citation 5) AD and PSP samples were randomized across flowcells, taking into account age at death, sex and RIN. These samples underwent library preparation and sequencing at different times and therefore should be considered as separate datasets. Likewise, Mayo RNAseq (D) of TCX34 and converted to binary ped (.bed) and map (.bim) files, which are deposited together with PLINK format fam and covariate files (DOI and descriptions for each these files are provided in Mayo LOAD GWAS (A) (Data Citation 2) genotypes from Illumina BeadStudio 2.0 software were utilized to generate lgen, map and fam files that were imported into PLINK10 and described above. The Mayo eGWAS ‘eSNP Results’ (Data Citation 4) are the eQTL results from the test of association between the Mayo LOAD GWAS (Data Citation 2) genotypes and the WG-DASL gene expression measures analyzed by multivariable linear regression using an additive model in PLINK34, as published previously10 , age at death, sex, PCR plate, RIN and adjusted RIN squared (RIN-RINmean)2. Analyses were limited to SNP-probe pairs that were in-cis, defined as +/−100 kb of the targeted gene according to NCBI Build 36. The ADs and non–ADs were analyzed both separately and jointly. The joint analyses included diagnosis as an additional covariate . Results of analyses for both the genotyped SNPs as well as genotypes imputed to HapMap2 reference are provided. HapMap2 imputations were done as described10. The eGWAS results were previously made available through the NIAGADS repository (https://www.niagads.org/datasets/ng00025).The Mayo eGWAS WG-DASL microarray expression dataset from TCX and CER (B) includes covariates and probe expression levels (Data Citation 3), which are preprocessed as published18 (Data Citation 5), Mayo RNAseq TCX26 and CER data  were processed using the same analytic pipeline. Read alignments were done using the SNAPR software35, an RNA sequence aligner based on SNAP, using GRCh38 reference and Ensembl v77 gene models. Outputs include per-sample gene and transcript counts, which are merged into a single file per data type (gene or transcript) that contains data for all samples across all genes/transcripts  normalization method to calculate counts per million (CPM). These normalized counts are saved for both gene and transcript levels  (Data Citation 2) QC methods were previously published10 (B)  data was generated as follows: We annotated probes for presence of genetic variants by comparing their positions according to NCBI RefSeq, Build 36.3 to those of all variants within dbSNP131 and identified the list of probes that have ≥1 variants within their sequence. We depict this information in the files for the Mayo eGWAS, ‘eSNP Results’ (Data Citation 4)  samples were also run on each PCR plate as part of QC. The expression phenotypes include results from only one of the replicate subjects selected randomly and exclude UHR results. It should be noted that 3 AD and 9 non-AD subjects for TCX, and 4 AD subjects for CER, do not have associated GWAS genotypes as they did not pass≥1 GWAS QC parameter described above.The Mayo eGWAS18 (C) (Data Citation 5) data principal components analysis (PCA) identified 2 outliers in the AD and 4 in the PSP cohort. The covariates for these subjects were set to missing (=NA) in the respective covariate files (DOI and descriptions for these files are provide in 26 samples (Data Citation 6) but 2 such subjects in the CER analyses (Data Citation 7). The covariate data in the relevant CER files for these two subjects were set to missing. We likewise assessed the RNASeq data for sex discrepancies based on Y chromosome gene expression and documented sex and identified 2 subjects with mis-matched sex for both TCX and CER, plus a third subject in the CER cohort. These were also set to missing in the covariate files. At the time of this publication, the Mayo RNAseq subjects did not have GWAS genotypes deposited on Synapse.For the Mayo Pilot RNAseqwww.niagads.org).The data described herein is available for use by the research community and has been deposited in the AMP-AD Knowledge Portal . 25 for backend support, providing users with both web-based and programmatic access to data files. All data files in the portal are annotated using a standard vocabulary to enable users to search for relevant content across the AMP-AD datasets using programmatic queries. Data is stored in a cloud based manner hosted by Amazon web services (AWS), which enables user to execute cloud-based compute. Detailed descriptions including data processing, QC metrics, and assay and cohort specific variables are provided for each file as applicable.The AMP-AD Knowledge Portal hosts data derived from multiple cohorts that were generated as part of or used in support of the AMP-AD Target Identification and Preclinical Validation project . The portal uses the Synapse software platformAccess for the data described herein is controlled in a manner set forth by the institutional review board (IRB) at the Mayo Clinic. All data use terms include: (1) maintenance of data in a secure and confidential manner, (2) respect for the privacy of study participants, (3) citation of the data contributors in any publications resulting from data use, and (4) informing data contributors of resultant publications. Specific data use terms are provided for each dataset  under the header ‘Terms of use’; users must register for a Synapse account and provide electronic agreement to these terms prior to accessing the study files. Access to the Mayo LOAD GWAS data (A)  requires a data use certificate (doi:10.7303/syn2954402.2). User approvals are managed by the Synapse Access and Compliance Team (ACT).Data on the AMP-AD Knowledge Portal are annotated with a common dictionary of terms (doi:10.7303/syn5478487.2) to enable querying of the data using the Synapse analytical clients . Fields, their allowable values specific to the datasets described herein and the dictionary of annotations are shown in How to cite this article: Allen, M et al. Human whole genome genotype and transcriptome data for Alzheimer's and other neurodegenerative diseases. Sci. Data 3:160089 doi: 10.1038/sdata.2016.89 (2016).