Michael Snyder

Bio: Michael Snyder is an academic researcher from Stanford University. The author has contributed to research in topics: Gene & Genome. The author has an hindex of 169, co-authored 840 publications receiving 130225 citations. Previous affiliations of Michael Snyder include Wyss Institute for Biologically Inspired Engineering & Public Health Research Institute.

Quality-control mechanisms targeting translationally stalled and C-terminally extended poly(GR) associated with ALS/FTD.

Abstract: Maintaining the fidelity of nascent peptide chain (NP) synthesis is essential for proteome integrity and cellular health. Ribosome-associated quality control (RQC) serves to resolve stalled translation, during which untemplated Ala/Thr residues are added C terminally to stalled peptide, as shown during C-terminal Ala and Thr addition (CAT-tailing) in yeast. The mechanism and biological effects of CAT-tailing–like activity in metazoans remain unclear. Here we show that CAT–tailing-like modification of poly(GR), a dipeptide repeat derived from amyotrophic lateral sclerosis with frontotemporal dementia (ALS/FTD)-associated GGGGCC (G4C2) repeat expansion in C9ORF72, contributes to disease. We find that poly(GR) can act as a mitochondria-targeting signal, causing some poly(GR) to be cotranslationally imported into mitochondria. However, poly(GR) translation on mitochondrial surface is frequently stalled, triggering RQC and CAT-tailing–like C-terminal extension (CTE). CTE promotes poly(GR) stabilization, aggregation, and toxicity. Our genetic studies in Drosophila uncovered an important role of the mitochondrial protease YME1L in clearing poly(GR), revealing mitochondria as major sites of poly(GR) metabolism. Moreover, the mitochondria-associated noncanonical Notch signaling pathway impinges on the RQC machinery to restrain poly(GR) accumulation, at least in part through the AKT/VCP axis. The conserved actions of YME1L and noncanonical Notch signaling in animal models and patient cells support their fundamental involvement in ALS/FTD.

Discovery of stress responsive DNA regulatory motifs in Arabidopsis.

Abstract: The discovery of DNA regulatory motifs in the sequenced genomes using computational methods remains challenging. Here, we present MotifIndexer - a comprehensive strategy for de novo identification of DNA regulatory motifs at a genome level. Using word-counting methods, we indexed the existence of every 8-mer oligo composed of bases A, C, G, T, r, y, s, w, m, k, n or 12-mer oligo composed of A, C, G, T, n, in the promoters of all predicted genes of Arabidopsis thaliana genome and of selected stress-induced co-expressed genes. From this analysis, we identified number of over-represented motifs. Among these, major critical motifs were identified using a position filter. We used a model based on uniform distribution and the z-scores derived from this model to describe position bias. Interestingly, many motifs showed position bias towards the transcription start site. We extended this model to show biased distribution of motifs in the genomes of both A. thaliana and rice. We also used MotifIndexer to identify conserved motifs in co-expressed gene groups from two Arabidopsis species, A. thaliana and A. lyrata. This new comparative genomics method does not depend on alignments of homologous gene promoter sequences.

Rapid mapping of antigenic coding regions and constructing insertion mutations in yeast genes by mini-Tn10 "transplason" mutagenesis

Abstract: A "transplason" mutagenesis procedure was developed for the dual purposes of low resolution mapping of antigenic coding regions (using transposons) and constructing insertion mutations in yeast genes (by transplacement). Mini-Tn10 transposon derivatives containing both Escherichia coli and yeast selectable markers have been constructed. These elements are used to mutagenize lambda gt11 clones that express foreign antigens in E. coli. The transposition events are first selected in E. coli, and the effect of these insertions on antigen expression is used to locate the antigenic coding regions on the cloned DNA. Insertion mutations located within a desired yeast sequence are then substituted for the genomic copies by one-step gene transplacement. This provides a powerful method for rapidly mapping antigenic coding sequences of cloned genes and inactivating these genes in yeast to help determine their function. Several examples using this technique are presented.

The conserved histone deacetylase Rpd3 and its DNA binding subunit Ume6 control dynamic transcript architecture during mitotic growth and meiotic development

Abstract: It was recently reported that the sizes of many mRNAs change when budding yeast cells exit mitosis and enter the meiotic differentiation pathway. These differences were attributed to length variations of their untranslated regions. The function of UTRs in protein translation is well established. However, the mechanism controlling the expression of distinct transcript isoforms during mitotic growth and meiotic development is unknown. In this study, we order developmentally regulated transcript isoforms according to their expression at specific stages during meiosis and gametogenesis, as compared to vegetative growth and starvation. We employ regulatory motif prediction, in vivo protein-DNA binding assays, genetic analyses and monitoring of epigenetic amino acid modification patterns to identify a novel role for Rpd3 and Ume6, two components of a histone deacetylase complex already known to repress early meiosis-specific genes in dividing cells, in mitotic repression of meiosis-specific transcript isoforms. Our findings classify developmental stage-specific early, middle and late meiotic transcript isoforms, and they point to a novel HDAC-dependent control mechanism for flexible transcript architecture during cell growth and differentiation. Since Rpd3 is highly conserved and ubiquitously expressed in many tissues, our results are likely relevant for development and disease in higher eukaryotes.

A limited set of transcriptional programs define major cell types

Abstract: We have produced RNA sequencing data for 53 primary cells from different locations in the human body. The clustering of these primary cells reveals that most cells in the human body share a few broad transcriptional programs, which define five major cell types: epithelial, endothelial, mesenchymal, neural, and blood cells. These act as basic components of many tissues and organs. Based on gene expression, these cell types redefine the basic histological types by which tissues have been traditionally classified. We identified genes whose expression is specific to these cell types, and from these genes, we estimated the contribution of the major cell types to the composition of human tissues. We found this cellular composition to be a characteristic signature of tissues and to reflect tissue morphological heterogeneity and histology. We identified changes in cellular composition in different tissues associated with age and sex, and found that departures from the normal cellular composition correlate with histological phenotypes associated with disease.

STAR: ultrafast universal RNA-seq aligner

Abstract: Motivation Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. Results To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. Availability and implementation STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.

Ultrafast and memory-efficient alignment of short DNA sequences to the human genome

Abstract: Bowtie is an ultrafast, memory-efficient alignment program for aligning short DNA sequence reads to large genomes. For the human genome, Burrows-Wheeler indexing allows Bowtie to align more than 25 million reads per CPU hour with a memory footprint of approximately 1.3 gigabytes. Bowtie extends previous Burrows-Wheeler techniques with a novel quality-aware backtracking algorithm that permits mismatches. Multiple processor cores can be used simultaneously to achieve even greater alignment speeds. Bowtie is open source http://bowtie.cbcb.umd.edu.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

Abstract: RNA-Seq is revolutionizing the way transcript abundances are measured. A key challenge in transcript quantification from RNA-Seq data is the handling of reads that map to multiple genes or isoforms. This issue is particularly important for quantification with de novo transcriptome assemblies in the absence of sequenced genomes, as it is difficult to determine which transcripts are isoforms of the same gene. A second significant issue is the design of RNA-Seq experiments, in terms of the number of reads, read length, and whether reads come from one or both ends of cDNA fragments. We present RSEM, an user-friendly software package for quantifying gene and isoform abundances from single-end or paired-end RNA-Seq data. RSEM outputs abundance estimates, 95% credibility intervals, and visualization files and can also simulate RNA-Seq data. In contrast to other existing tools, the software does not require a reference genome. Thus, in combination with a de novo transcriptome assembler, RSEM enables accurate transcript quantification for species without sequenced genomes. On simulated and real data sets, RSEM has superior or comparable performance to quantification methods that rely on a reference genome. Taking advantage of RSEM's ability to effectively use ambiguously-mapping reads, we show that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads. On the other hand, estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired-end reads, depending on the number of possible splice forms for each gene. RSEM is an accurate and user-friendly software tool for quantifying transcript abundances from RNA-Seq data. As it does not rely on the existence of a reference genome, it is particularly useful for quantification with de novo transcriptome assemblies. In addition, RSEM has enabled valuable guidance for cost-efficient design of quantification experiments with RNA-Seq, which is currently relatively expensive.

An integrated encyclopedia of DNA elements in the human genome

Abstract: The human genome encodes the blueprint of life, but the function of the vast majority of its nearly three billion bases is unknown. The Encyclopedia of DNA Elements (ENCODE) project has systematically mapped regions of transcription, transcription factor association, chromatin structure and histone modification. These data enabled us to assign biochemical functions for 80% of the genome, in particular outside of the well-studied protein-coding regions. Many discovered candidate regulatory elements are physically associated with one another and with expressed genes, providing new insights into the mechanisms of gene regulation. The newly identified elements also show a statistical correspondence to sequence variants linked to human disease, and can thereby guide interpretation of this variation. Overall, the project provides new insights into the organization and regulation of our genes and genome, and is an expansive resource of functional annotations for biomedical research.