Identification of early gene expression changes in primary cultured neurons treated with topoisomerase I poisons.

Topoisomerase 1 (TOP1) poisons like camptothecin (CPT) are currently used in cancer chemotherapy but these compounds can have damaging, off-target effects on neurons leading to cognitive, sensory and motor deficits. To understand the molecular basis for the enhanced sensitivity of neurons to CPT, we examined the effects of compounds that inhibit TOP1-CPT, actinomycin D (ActD) and β-lapachone (β-Lap)-on primary cultured rat motor (MN) and cortical (CN) neurons as well as fibroblasts. Neuronal cells expressed higher levels of Top1 mRNA than fibroblasts but transcript levels are reduced in all cell types after treatment with CPT. Microarray analysis was performed to identify differentially regulated transcripts in MNs in response to a brief exposure to CPT. Pathway analysis of the differentially expressed transcripts revealed activation of ERK and JNK signaling cascades in CPT-treated MNs. Immediate-early genes like Fos, Egr-1 and Gadd45b were upregulated in CPT-treated MNs. Fos mRNA levels were elevated in all cell types treated with CPT; Egr-1, Gadd45b and Dyrk3 transcript levels, however, increased in CPT-treated MNs and CNs but decreased in CPT-treated fibroblasts. These transcripts may represent new targets for the development of therapeutic agents that mitigate the off-target effects of chemotherapy on the nervous system

Transcriptome profiling of spinal muscular atrophy motor neurons derived from mouse embryonic stem cells.

Proximal spinal muscular atrophy (SMA) is an early onset, autosomal recessive motor neuron disease caused by loss of or mutation in SMN1 (survival motor neuron 1). Despite understanding the genetic basis underlying this disease, it is still not known why motor neurons (MNs) are selectively affected by the loss of the ubiquitously expressed SMN protein. Using a mouse embryonic stem cell (mESC) model for severe SMA, the RNA transcript profiles (transcriptomes) between control and severe SMA (SMN2+/+;mSmn-/-) mESC-derived MNs were compared in this study using massively parallel RNA sequencing (RNA-Seq). The MN differentiation efficiencies between control and severe SMA mESCs were similar. RNA-Seq analysis identified 3,094 upregulated and 6,964 downregulated transcripts in SMA mESC-derived MNs when compared against control cells. Pathway and network analysis of the differentially expressed RNA transcripts showed that pluripotency and cell proliferation transcripts were significantly increased in SMA MNs while transcripts related to neuronal development and activity were reduced. The differential expression of selected transcripts such as Crabp1, Crabp2 and Nkx2.2 was validated in a second mESC model for SMA as well as in the spinal cords of low copy SMN2 severe SMA mice. Furthermore, the levels of these selected transcripts were restored in high copy SMN2 rescue mouse spinal cords when compared against low copy SMN2 severe SMA mice. These findings suggest that SMN deficiency affects processes critical for normal development and maintenance of MNs

Evaluation of the Orally Bioavailable 4-Phenylbutyrate-Tethered Trichostatin a Analogue AR42 in Models of Spinal Muscular Atrophy

Proximal spinal muscular atrophy (SMA) is a leading genetic cause for infant death in the world and results from the selective loss of motor neurons in the spinal cord. SMA is a consequence of low levels of SMN protein and small molecules that can increase SMN expression are of considerable interest as potential therapeutics. Previous studies have shown that both 4-phenylbutyrate (4PBA) and trichostatin A (TSA) increase SMN expression in dermal fibroblasts derived from SMA patients. AR42 is a 4PBA-tethered TSA derivative that is a very potent histone deacetylase inhibitor. SMA patient fibroblasts were treated with either AR42, AR19 (a related analogue), 4PBA, TSA or vehicle for 5 days and then immunostained for SMN localization. AR42 as well as 4PBA and TSA increased the number of SMN-positive nuclear gems in a dose-dependent manner while AR19 did not show marked changes in gem numbers. While gem number was increased in AR42-treated SMA fibroblasts, there were no significant changes in FL-SMN mRNA or SMN protein. The neuroprotective effect of this compound was then assessed in SMNΔ7 SMA (SMN2+/+;SMNΔ7+/+;mSmn−/−) mice. Oral administration of AR42 prior to disease onset increased the average lifespan of SMNΔ7 SMA mice by ~ 27% (20.1 ± 1.6 days for AR42-treated mice vs. 15.8 ± 0.4 days for vehicle-treated mice). AR42 treatment also improved motor function in these mice. AR42 treatment inhibited histone deacetylase (HDAC) activity in treated spinal cord although it did not affect SMN protein expression in these mice. AKT and GSK3β phosphorylation were both significantly increased in SMNΔ7 SMA mouse spinal cords. In conclusion, presymptomatic administration of the HDAC inhibitor AR42 ameliorates the disease phenotype in SMNΔ7 SMA mice in a SMN-independent manner possibly by increasing AKT neuroprotective signaling

A framework for human microbiome research

A variety of microbial communities and their genes (the microbiome) exist throughout the human body, with fundamental roles in human health and disease. The National Institutes of Health (NIH)-funded Human Microbiome Project Consortium has established a population-scale framework to develop metagenomic protocols, resulting in a broad range of quality-controlled resources and data including standardized methods for creating, processing and interpreting distinct types of high-throughput metagenomic data available to the scientific community. Here we present resources from a population of 242 healthy adults sampled at 15 or 18 body sites up to three times, which have generated 5,177 microbial taxonomic profiles from 16S ribosomal RNA genes and over 3.5 terabases of metagenomic sequence so far. In parallel, approximately 800 reference strains isolated from the human body have been sequenced. Collectively, these data represent the largest resource describing the abundance and variety of the human microbiome, while providing a framework for current and future studies

Structure, function and diversity of the healthy human microbiome

Author Posting. © The Authors, 2012. This article is posted here by permission of Nature Publishing Group. The definitive version was published in Nature 486 (2012): 207-214, doi:10.1038/nature11234.Studies of the human microbiome have revealed that even healthy individuals differ remarkably in the microbes that occupy habitats such as the gut, skin and vagina. Much of this diversity remains unexplained, although diet, environment, host genetics and early microbial exposure have all been implicated. Accordingly, to characterize the ecology of human-associated microbial communities, the Human Microbiome Project has analysed the largest cohort and set of distinct, clinically relevant body habitats so far. We found the diversity and abundance of each habitat’s signature microbes to vary widely even among healthy subjects, with strong niche specialization both within and among individuals. The project encountered an estimated 81–99% of the genera, enzyme families and community configurations occupied by the healthy Western microbiome. Metagenomic carriage of metabolic pathways was stable among individuals despite variation in community structure, and ethnic/racial background proved to be one of the strongest associations of both pathways and microbes with clinical metadata. These results thus delineate the range of structural and functional configurations normal in the microbial communities of a healthy population, enabling future characterization of the epidemiology, ecology and translational applications of the human microbiome.This research was supported in part by National Institutes of Health grants U54HG004969 to B.W.B.; U54HG003273 to R.A.G.; U54HG004973 to R.A.G., S.K.H. and J.F.P.; U54HG003067 to E.S.Lander; U54AI084844 to K.E.N.; N01AI30071 to R.L.Strausberg; U54HG004968 to G.M.W.; U01HG004866 to O.R.W.; U54HG003079 to R.K.W.; R01HG005969 to C.H.; R01HG004872 to R.K.; R01HG004885 to M.P.; R01HG005975 to P.D.S.; R01HG004908 to Y.Y.; R01HG004900 to M.K.Cho and P. Sankar; R01HG005171 to D.E.H.; R01HG004853 to A.L.M.; R01HG004856 to R.R.; R01HG004877 to R.R.S. and R.F.; R01HG005172 to P. Spicer.; R01HG004857 to M.P.; R01HG004906 to T.M.S.; R21HG005811 to E.A.V.; M.J.B. was supported by UH2AR057506; G.A.B. was supported by UH2AI083263 and UH3AI083263 (G.A.B., C. N. Cornelissen, L. K. Eaves and J. F. Strauss); S.M.H. was supported by UH3DK083993 (V. B. Young, E. B. Chang, F. Meyer, T. M. S., M. L. Sogin, J. M. Tiedje); K.P.R. was supported by UH2DK083990 (J. V.); J.A.S. and H.H.K. were supported by UH2AR057504 and UH3AR057504 (J.A.S.); DP2OD001500 to K.M.A.; N01HG62088 to the Coriell Institute for Medical Research; U01DE016937 to F.E.D.; S.K.H. was supported by RC1DE0202098 and R01DE021574 (S.K.H. and H. Li); J.I. was supported by R21CA139193 (J.I. and D. S. Michaud); K.P.L. was supported by P30DE020751 (D. J. Smith); Army Research Office grant W911NF-11-1-0473 to C.H.; National Science Foundation grants NSF DBI-1053486 to C.H. and NSF IIS-0812111 to M.P.; The Office of Science of the US Department of Energy under Contract No. DE-AC02-05CH11231 for P.S. C.; LANL Laboratory-Directed Research and Development grant 20100034DR and the US Defense Threat Reduction Agency grants B104153I and B084531I to P.S.C.; Research Foundation - Flanders (FWO) grant to K.F. and J.Raes; R.K. is an HHMI Early Career Scientist; Gordon&BettyMoore Foundation funding and institutional funding fromthe J. David Gladstone Institutes to K.S.P.; A.M.S. was supported by fellowships provided by the Rackham Graduate School and the NIH Molecular Mechanisms in Microbial Pathogenesis Training Grant T32AI007528; a Crohn’s and Colitis Foundation of Canada Grant in Aid of Research to E.A.V.; 2010 IBM Faculty Award to K.C.W.; analysis of the HMPdata was performed using National Energy Research Scientific Computing resources, the BluBioU Computational Resource at Rice University

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder

Analytic and clinical validity of thyroid nodule mutational profiling using droplet digital polymerase chain reaction

Abstract Background Recent guidelines for the management of thyroid nodules incorporate mutation testing as an adjunct for surgical decision-making, however current tests are costly with limited accuracy. Droplet digital PCR (ddPCR) is an ultrasensitive method of nucleic acid detection that is particularly useful for identifying gene mutations. This study aimed to assess the analytic and clinical validity of RAS and BRAF ddPCR mutational testing as a diagnostic tool for thyroid fine needle aspirate biopsy (FNAB). Methods Patients with thyroid nodules meeting indication for FNAB were prospectively enrolled from March 2015 to September 2017. In addition to clinical protocol, an additional FNAB was obtained for ddPCR. Optimized ddPCR probes were used to detect mutations including HRASG12 V, HRASQ61K, HRASQ61R, NRASQ61R, NRASQ61K and BRAFV600E. The diagnostic performance of BRAF and RAS mutations was assessed individually or in combination with Bethesda classification against final surgical pathology. Results A total of 208 patients underwent FNAB and mutational testing with the following Bethesda cytologic classification: 26.9% non-diagnostic, 55.2% benign, 5.3% FLUS/AUS, 2.9% FN/SPN, 2.4% SFM and 7.2% malignant. Adequate RNA was obtained from 91.3% (190) FNABs from which mutations were identified in 21.1% of HRAS, 11.5% of NRAS and 7.4% of BRAF. Malignant cytology or BRAFV600E was 100% specific for malignancy. Combining cytology with ddPCR BRAF600E mutations testing increased the sensitivity of Bethesda classification from 41.7 to 75%. Combined BRAFV600E and Bethesda results had a positive predictive value (PPV) of 100% and negative predictive value (NPV) of 89.7% for thyroid malignancy in our cohort. Conclusions DdPCR offers a novel and ultrasensitive method of detecting RAS and BRAF mutations from thyroid FNABs. BRAFV600E mutation testing by ddPCR may serve as a useful adjunct to increase sensitivity and specificity of thyroid FNAB

RNA-Seq reads mapped to NCBI mouse genome build 37 using TopHat.

<p>RNA-Seq reads mapped to NCBI mouse genome build 37 using TopHat.</p

Upstream regulator analysis of differentially expressed transcripts between mESC-derived Hb9 control and A2 SMA MNs.

<p>IPA of significantly modified upstream (<b>A</b>) transcriptional regulator, (<b>B</b>) endogenous signaling and (<b>C</b>) drug pathways in upregulated or downregulated transcripts in A2 SMA MNs compared to Hb9 control MNs. Significant upstream regulators were identified as those having an activation z-score greater than or equal to 2.0 for activated regulators or less than or equal to −2.0 for inhibited regulators.</p

Validation of differentially expressed transcripts determined by RNA-Seq using qRT-PCR.

<p>The levels of <i>Crabp1</i>, <i>Crabp2</i>, <i>Isl1</i>, <i>Nkx2.2</i>, <i>Pla2g1b</i>, <i>Smn1</i> and <i>Vim</i> transcripts were measured in total RNAs from control and SMA mESC-derived MNs. (<b>A</b>) The magnitude of change (log₂(fold change)) of selected transcripts in A2 SMA MNs relative to Hb9 control MNs (n = 3/genotype) as determined by RNA-Seq (black bars) or qRT-PCR (grey bars). (<b>B</b>) The magnitude of change of selected transcripts in HB9:eGFP-labeled (black bars) or unlabeled (grey bars) SMA MNs (A2 and E2 cells, respectively) relative to control MNs (Hb9 and C4 cells, respectively; n = 3/genotype).</p