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.

Microbial Synergy via an Ethanol-Triggered Pathway

Abstract: We have discovered a microbial interaction between yeast, bacteria, and nematodes. Upon coculturing, Saccharomyces cerevisiae stimulated the growth of several species of Acinetobacter, including, A. baumannii, A. haemolyticus, A. johnsonii, and A. radioresistens, as well as several natural isolates of Acinetobacter. This enhanced growth was due to a diffusible factor that was shown to be ethanol by chemical assays and evaluation of strains lacking ADH1, ADH3, and ADH5, as all three genes are involved in ethanol production by yeast. This effect is specific to ethanol: methanol, butanol, and dimethyl sulfoxide were unable to stimulate growth to any appreciable level. Low doses of ethanol not only stimulated growth to a higher cell density but also served as a signaling molecule: in the presence of ethanol, Acinetobacter species were able to withstand the toxic effects of salt, indicating that ethanol alters cell physiology. Furthermore, ethanol-fed A. baumannii displayed increased pathogenicity when confronted with a predator, Caenorhabditis elegans. Our results are consistent with the concept that ethanol can serve as a signaling molecule which can affect bacterial physiology and survival.

Defective sphingosine-1-phosphate receptor 1 (S1P1) phosphorylation exacerbates TH17-mediated autoimmune neuroinflammation

Abstract: Sphingosine 1-phosphate (S1P) signaling regulates lymphocyte egress from lymphoid organs into systemic circulation. The sphingosine phosphate receptor 1 (S1P1) agonist FTY-720 (Gilenya) arrests immune trafficking and prevents multiple sclerosis (MS) relapses. However, alternative mechanisms of S1P-S1P1 signaling have been reported. Phosphoproteomic analysis of MS brain lesions revealed S1P1 phosphorylation on S351, a residue crucial for receptor internalization. Mutant mice harboring an S1pr1 gene encoding phosphorylation-deficient receptors (S1P1(S5A)) developed severe experimental autoimmune encephalomyelitis (EAE) due to autoimmunity mediated by interleukin 17 (IL-17)-producing helper T cells (TH17 cells) in the peripheral immune and nervous system. S1P1 directly activated the Jak-STAT3 signal-transduction pathway via IL-6. Impaired S1P1 phosphorylation enhances TH17 polarization and exacerbates autoimmune neuroinflammation. These mechanisms may be pathogenic in MS.

iPSC-derived cardiomyocytes reveal abnormal TGF-β signalling in left ventricular non-compaction cardiomyopathy.

Abstract: Left ventricular non-compaction (LVNC) is the third most prevalent cardiomyopathy in children and its pathogenesis has been associated with the developmental defect of the embryonic myocardium. We show that patient-specific induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) generated from LVNC patients carrying a mutation in the cardiac transcription factor TBX20 recapitulate a key aspect of the pathological phenotype at the single-cell level and this was associated with perturbed transforming growth factor beta (TGF-β) signalling. LVNC iPSC-CMs have decreased proliferative capacity due to abnormal activation of TGF-β signalling. TBX20 regulates the expression of TGF-β signalling modifiers including one known to be a genetic cause of LVNC, PRDM16, and genome editing of PRDM16 caused proliferation defects in iPSC-CMs. Inhibition of TGF-β signalling and genome correction of the TBX20 mutation were sufficient to reverse the disease phenotype. Our study demonstrates that iPSC-CMs are a useful tool for the exploration of pathological mechanisms underlying poorly understood cardiomyopathies including LVNC.

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.