A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles.
TL;DR: The expanded CMap is reported, made possible by a new, low-cost, high-throughput reduced representation expression profiling method that is shown to be highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts.
About: This article is published in Cell.The article was published on 2017-11-30 and is currently open access. It has received 1943 citations till now.
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TL;DR: Evidence is provided that DHA treatment activates critical pathways regulating neuroplasticity, which may contribute to enhanced neuronal cell viability and neuronal connectivity in MDD and other brain disorders.
14 citations
TL;DR: The meta-analysis results are consistent with a concept of aging based on critical dynamics of molecular level variables (e.g., gene expression), and support the view of aging as arising from dynamic instability of a single (critical) mode.
Abstract: To detect overlap or convergence among the diverse genetic pathways that can extend lifespan, we collected a dataset of 60 C.elegans age-dependent transcriptomes by RNA-seq technique for worm strains with vastly different lifespans. We selected four exceptionally long-lived mutants and three examples of the most successful life-extending RNAi treatments (which increased mean lifespan by 35% rather than 120% as reported). We used the dataset augmented with publicly available gene expression datasets to produce a transcriptomic signature of biological age. We introduced a transcriptomic measure of biological age and observed that its dependence on chronological age is modulated by a single parameter, the rate of aging.
We hypothesized that the scaling revealed in the gene expression kinetics underlies the recently observed scaling of the survival curves in C.elegans, and the stochasticity in gene expressions leads to deceleration of mortality with age, reaching a plateau at advanced ages. Using experimental survival data, we confirm that the plateau mortality agrees closely with the estimate of Gompertz exponent at the cross-over age near the mean lifespan. The genes associated with aging in our data are enriched with the targets of transcription factors such as DAF-16, ELT-2, ELT-6, NHR-10, ZTF-9, NHR-86, and miRNAs including miR-57, -59, and -244, which is in agreement with previous studies. Overall, our meta-analysis results are consistent with a concept of aging based on critical dynamics of molecular level variables (e.g., gene expression), and support our view of aging as arising from dynamic instability of a single (critical) mode.
13 citations
TL;DR: An approach to generate a multidimensional biomarker panel for mechanistic de-risking and prediction of kidney toxicity in in vitro for new therapeutic candidates and chemical entities is reported.
13 citations
TL;DR: In this article, the effects and possible mechanisms of palmatine-mediated neuroprotection were investigated in transgenic Caenorhabditis elegans models containing human Aβ1-42.
Abstract: Palmatine is a naturally occurring isoquinoline alkaloid that has been reported to display neuroprotective effects against amyloid-β- (Aβ-) induced neurotoxicity. However, the mechanisms underlying the neuroprotective activities of palmatine remain poorly characterized in vivo. We employed transgenic Caenorhabditis elegans models containing human Aβ1-42 to investigate the effects and possible mechanisms of palmatine-mediated neuroprotection. Treatment with palmatine significantly delayed the paralytic process and reduced the elevated reactive oxygen species levels in Aβ-transgenic C. elegans. In addition, it increased oxidative stress resistance without affecting the lifespan of wild-type C. elegans. Pathway analysis suggested that the differentially expressed genes were related mainly to aging, detoxification, and lipid metabolism. Real-time PCR indicated that resistance-related genes such as sod-3 and shsp were significantly upregulated, while the lipid metabolism-related gene fat-5 was downregulated. Further studies demonstrated that the inhibitory effects of palmatine on Aβ toxicity were attributable to the free radical-scavenging capacity and that the upregulated expression of resistance-related genes, especially shsp, whose expression was regulated by HSF-1, played crucial roles in protecting cells from Aβ-induced toxicity. The research showed that there were significantly fewer Aβ deposits in transgenic CL2006 nematodes treated with palmatine than in control nematodes. In addition, our study found that Aβ-induced toxicity was accompanied by dysregulation of lipid metabolism, leading to excessive fat accumulation in Aβ-transgenic CL4176 nematodes. The alleviation of lipid disorder by palmatine should be attributed not only to the reduction in fat synthesis but also to the inhibition of Aβ aggregation and toxicity, which jointly maintained metabolic homeostasis. This study provides new insights into the in vivo neuroprotective effects of palmatine against Aβ aggregation and toxicity and provides valuable targets for the prevention and treatment of AD.
13 citations
TL;DR: In this paper, the Cancer Genome Atlas (TCGA) RNA-sequencing data were utilized to identify differentially expressed genes (DEGs) between tumor and normal samples, and the DEGs were then applied to construct a co-expression and mined using structure network analysis.
Abstract: Prostate cancer (PCa) is one of the most common malignancies for males, but very little is known about its pathogenesis. This study aimed to identify novel biomarkers associated with PCa prognosis and elucidate the underlying molecular mechanism. First, The Cancer Genome Atlas (TCGA) RNA-sequencing data were utilized to identify differentially expressed genes (DEGs) between tumor and normal samples. The DEGs were then applied to construct a co-expression and mined using structure network analysis. The magenta module that was highly related to the Gleason score (r = 0.46, p = 3e-26) and tumor stage (r = 0.38, p = 2e-17) was screened. Subsequently, all genes of the magenta module underwent function annotation. From the key module, CCNA2, CKAP2L, NCAPG, and NUSAP1 were chosen as the four candidate genes. Finally, internal (TCGA) and external data sets (GSE32571, GSE70770, and GSE141551) were combined to validate and predict the value of real hub genes. The results show that the above genes are up-regulated in PCa samples, and higher expression levels show significant association with higher Gleason scores and tumor T stage. Moreover, receiver operating characteristic curve and survival analysis validate the excellent value of hub genes in PCa progression and prognosis. In addition, the protein levels of these four genes also remain higher in tumor tissues when compared with normal tissues. Gene set enrichment analysis and gene set variation analysis for a single gene reveal the close relation with cell proliferation. Meanwhile, 11 small molecular drugs that have the potential to treat PCa were also screened. In conclusion, our research identified four potential prognostic genes and several candidate molecular drugs for treating PCa.
13 citations
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TL;DR: The Gene Set Enrichment Analysis (GSEA) method as discussed by the authors focuses on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation.
Abstract: Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
34,830 citations
Journal Article•
TL;DR: A new technique called t-SNE that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map, a variation of Stochastic Neighbor Embedding that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map.
Abstract: We present a new technique called “t-SNE” that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map. The technique is a variation of Stochastic Neighbor Embedding (Hinton and Roweis, 2002) that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map. t-SNE is better than existing techniques at creating a single map that reveals structure at many different scales. This is particularly important for high-dimensional data that lie on several different, but related, low-dimensional manifolds, such as images of objects from multiple classes seen from multiple viewpoints. For visualizing the structure of very large datasets, we show how t-SNE can use random walks on neighborhood graphs to allow the implicit structure of all of the data to influence the way in which a subset of the data is displayed. We illustrate the performance of t-SNE on a wide variety of datasets and compare it with many other non-parametric visualization techniques, including Sammon mapping, Isomap, and Locally Linear Embedding. The visualizations produced by t-SNE are significantly better than those produced by the other techniques on almost all of the datasets.
30,124 citations
TL;DR: The Gene Expression Omnibus (GEO) project was initiated in response to the growing demand for a public repository for high-throughput gene expression data and provides a flexible and open design that facilitates submission, storage and retrieval of heterogeneous data sets from high-power gene expression and genomic hybridization experiments.
Abstract: The Gene Expression Omnibus (GEO) project was initiated in response to the growing demand for a public repository for high-throughput gene expression data. GEO provides a flexible and open design that facilitates submission, storage and retrieval of heterogeneous data sets from high-throughput gene expression and genomic hybridization experiments. GEO is not intended to replace in house gene expression databases that benefit from coherent data sets, and which are constructed to facilitate a particular analytic method, but rather complement these by acting as a tertiary, central data distribution hub. The three central data entities of GEO are platforms, samples and series, and were designed with gene expression and genomic hybridization experiments in mind. A platform is, essentially, a list of probes that define what set of molecules may be detected. A sample describes the set of molecules that are being probed and references a single platform used to generate its molecular abundance data. A series organizes samples into the meaningful data sets which make up an experiment. The GEO repository is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo.
10,968 citations
TL;DR: How BLAT was optimized is described, which is more accurate and 500 times faster than popular existing tools for mRNA/DNA alignments and 50 times faster for protein alignments at sensitivity settings typically used when comparing vertebrate sequences.
Abstract: Analyzing vertebrate genomes requires rapid mRNA/DNA and cross-species protein alignments A new tool, BLAT, is more accurate and 500 times faster than popular existing tools for mRNA/DNA alignments and 50 times faster for protein alignments at sensitivity settings typically used when comparing vertebrate sequences BLAT's speed stems from an index of all nonoverlapping K-mers in the genome This index fits inside the RAM of inexpensive computers, and need only be computed once for each genome assembly BLAT has several major stages It uses the index to find regions in the genome likely to be homologous to the query sequence It performs an alignment between homologous regions It stitches together these aligned regions (often exons) into larger alignments (typically genes) Finally, BLAT revisits small internal exons possibly missed at the first stage and adjusts large gap boundaries that have canonical splice sites where feasible This paper describes how BLAT was optimized Effects on speed and sensitivity are explored for various K-mer sizes, mismatch schemes, and number of required index matches BLAT is compared with other alignment programs on various test sets and then used in several genome-wide applications http://genomeucscedu hosts a web-based BLAT server for the human genome
8,326 citations
TL;DR: This paper proposed parametric and non-parametric empirical Bayes frameworks for adjusting data for batch effects that is robust to outliers in small sample sizes and performs comparable to existing methods for large samples.
Abstract: SUMMARY Non-biological experimental variation or “batch effects” are commonly observed across multiple batches of microarray experiments, often rendering the task of combining data from these batches difficult. The ability to combine microarray data sets is advantageous to researchers to increase statistical power to detect biological phenomena from studies where logistical considerations restrict sample size or in studies that require the sequential hybridization of arrays. In general, it is inappropriate to combine data sets without adjusting for batch effects. Methods have been proposed to filter batch effects from data, but these are often complicated and require large batch sizes (>25) to implement. Because the majority of microarray studies are conducted using much smaller sample sizes, existing methods are not sufficient. We propose parametric and non-parametric empirical Bayes frameworks for adjusting data for batch effects that is robust to outliers in small sample sizes and performs comparable to existing methods for large samples. We illustrate our methods using two example data sets and show that our methods are justifiable, easy to apply, and useful in practice. Software for our method is freely available at: http://biosun1.harvard.edu/complab/batch/.
6,319 citations