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.

Cell-free DNA (cfDNA) and exosome profiling from a year-long human spaceflight reveals circulating biomarkers

Abstract: The health impact of prolonged space flight on the human body is not well understood. Liquid biopsies based on cell-free DNA (cfDNA) or exosome analysis provide a noninvasive approach to monitor the dynamics of genomic, epigenomic and proteomic biomarkers, and the occurrence of DNA damage, physiological stress, and immune responses. To study the molecular consequences of spaceflight we profiled cfDNA isolated from plasma of an astronaut (TW) during a year-long mission on the International Space Station (ISS), sampling before, during, and after spaceflight, and compared the results to cfDNA profiling of the subject’s identical twin (HR) who remained on Earth, as well as healthy donors. We characterized cfDNA concentration and fragment size, and the positioning of nucleosomes on cfDNA, observing a significant increase in the proportion of cell-free mitochondrial DNA inflight, suggesting that cf-mtDNA is a potential biomarker for space flight-associated stress, and that this result was robust to ambient transit from the International Space Station (ISS). Analysis of exosomes isolated from post-flight plasma revealed a 30-fold increase in circulating exosomes and distinct exosomal protein cargo, including brain-derived peptides, in TW compared to HR and all known controls. This study provides the first longitudinal analysis of astronaut cfDNA during spaceflight, as well as the first exosome profiles, and highlights cf-mtDNA levels as a potential biomarker for physiological stress or immune system responses related to microgravity, radiation exposure, and other unique environmental conditions on the ISS.

A metadata framework for interoperating heterogeneous genome data using XML.

Abstract: The rapid advances in the Human Genome Project and genomic technologies have produced massive amounts of data populated in a large number of network-accessible databases. These technological advances and the associated data can have a great impact on biomedicine and healthcare. To answer many of the biologically or medically important questions, researchers often need to integrate data from a number of independent but related genome databases. One common practice is to download data sets (text files) from various genome Web sites and process them by some local programs. One main problem with this approach is that these programs are written on a case-by-case basis because the data sets involved are heterogeneous in structure. To address this problem, we define metadata that maps these heterogeneously structured files into a common eXtensible Markup Language (XML) structure to facilitate data interoperation. We illustrate this approach by interoperating two sets of essential yeast genes that are stored in two yeast genome databases (MIPS and YPD).

Design and Methods of the Validating Injury to the Renal Transplant Using Urinary Signatures (VIRTUUS) Study in Children.

Abstract: Lack of noninvasive diagnostic and prognostic biomarkers to reliably detect early allograft injury poses a major hindrance to long-term allograft survival in pediatric kidney transplant recipients. Methods Validating Injury to the Renal Transplant Using Urinary Signatures Children's Study, a North American multicenter prospective cohort study of pediatric kidney transplant recipients, aims to validate urinary cell mRNA and metabolite profiles that were diagnostic and prognostic of acute cellular rejection (ACR) and BK virus nephropathy (BKVN) in adult kidney transplant recipients in Clinical Trials in Organ Transplantation-4. Specifically, we are investigating: (1) whether a urinary cell mRNA 3-gene signature (18S-normalized CD3e, CXCL10 mRNA, and 18S ribosomal RNA) discriminates biopsies with versus without ACR, (2) whether a combined metabolite profile with the 3-gene signature increases sensitivity and specificity of diagnosis and prognostication of ACR, and (3) whether BKV-VP1 mRNA levels in urinary cells are diagnostic of BKVN and prognostic for allograft failure. Results To date, 204 subjects are enrolled, with 1405 urine samples, including 144 biopsy-associated samples. Among 424 urine samples processed for mRNA, the median A260:280 ratio (RNA purity) was 1.91, comparable with Clinical Trials in Organ Transplantation-4 (median 1.82). The quality control failure rate was 10%. Preliminary results from urine supernatant showed that our metabolomics platform successfully captured a broad array of metabolites. Clustering of pool samples and overlay of samples from various batches demonstrated platform robustness. No study site effect was noted. Conclusions Multicenter efforts to ascertain urinary biomarkers in pediatric kidney transplant recipients are feasible with high-quality control. Further study will inform whether these signatures are discriminatory and predictive for rejection and infection.

Gut microbiota analyses of Saudi populations for type 2 diabetes-related phenotypes reveals significant association

Abstract: Large-scale gut microbiome sequencing has revealed key links between microbiome dysfunction and metabolic diseases such as T2D. To date, these efforts have largely focused on Western populations, with few studies assessing T2D microbiota associations in Middle Eastern communities where T2D prevalence is now over 20%. We analyzed the composition of stool 16S rRNA from 461 T2D and 119 non-T2Dparticipants from the Eastern Province of Saudi Arabia. We quantified the abundance of microbial communities to examine any significant differences between subpopulations of samples based on diabetes status and glucose level. We observed overall positive enrichment within diabetics compared to healthy individuals and amongst diabetic participants; those with high glucose levels exhibited slightly more positive enrichment compared to those at lower risk of fasting hyperglycemia. In particular, the genus Firmicutes was upregulated in diabetic participants compared to non-diabetic participants, and T2D was associated with an elevated Firmicutes/Bacteroidetes ratio, consistent with previous findings. Based on diabetes status and glucose levels of Saudi participants, relatively stable differences in stool composition were perceived by differential abundance and alpha diversity measures.

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.