[Show abstract][Hide abstract] ABSTRACT: Rationale: Sepsis is a leading cause of morbidity and mortality. Currently, early diagnosis and the progression of the disease are difficult to make. The integration of metabolomic and transcriptomic data in a primate model of sepsis may provide a novel molecular signature of clinical sepsis. Objectives: Develop a biomarker panel to characterize sepsis in primates and ascertain its relevance to early diagnosis and progression of human sepsis. Methods: Intravenous inoculation of Macaca fascicularis with Escherichia coli produced mild to severe sepsis, lung injury and death. Plasma samples were obtained before and after one, three, and five days of E. coli challenge and at the time of euthanasia. At necropsy, blood, lung, kidney and spleen samples were collected. An integrative analysis of the metabolomic and transcriptomic datasets was performed to identify a panel of sepsis biomarkers. Measurements and Main Results: The extent of E. coli invasion, respiratory distress, lethargy and mortality was dependent on the bacterial dose. Metabolomic and transcriptomic changes characterized severe infections and death and indicated impaired mitochondrial, peroxisomal and liver functions. Analysis of the pulmonary transcriptome and plasma metabolome suggested impaired fatty acid catabolism regulated by peroxisome-proliferator activated receptor signaling. A representative 4-metabolite model effectively diagnosed sepsis in primates (AUC 0.966) and in two human sepsis cohorts (AUC=0.78 and 0.82). Conclusion: A model of sepsis based on reciprocal metabolomic and transcriptomic data was developed in primates and validated in two human patient cohorts. It is anticipated that the identified parameters will facilitate early diagnosis and management of sepsis.
American Journal of Respiratory and Critical Care Medicine 07/2014; 190(4). DOI:10.1164/rccm.201404-0624OC · 13.00 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: High-throughput DNA sequencing technologies, coupled with advanced bioinformatics tools, have enabled rapid advances in microbial ecology and our understanding of the human microbiome. QIIME (Quantitative Insights Into Microbial Ecology) is an open-source bioinformatics software package designed for microbial community analysis based on DNA sequence data, which provides a single analysis framework for analysis of raw sequence data through publication-quality statistical analyses and interactive visualizations. In this chapter, we demonstrate the use of the QIIME pipeline to analyze microbial communities obtained from several sites on the bodies of transgenic and wild-type mice, as assessed using 16S rRNA gene sequences generated on the Illumina MiSeq platform. We present our recommended pipeline for performing microbial community analysis and provide guidelines for making critical choices in the process. We present examples of some of the types of analyses that are enabled by QIIME and discuss how other tools, such as phyloseq and R, can be applied to expand upon these analyses.
Methods in enzymology 09/2013; 531:371-444. DOI:10.1016/B978-0-12-407863-5.00019-8 · 2.09 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: DNA sequencing continues to decrease in cost with the Illumina HiSeq2000 generating up to 600 Gb of paired-end 100 base reads in a ten-day run. Here we present a protocol for community amplicon sequencing on the HiSeq2000 and MiSeq Illumina platforms, and apply that protocol to sequence 24 microbial communities from host-associated and free-living environments. A critical question as more sequencing platforms become available is whether biological conclusions derived on one platform are consistent with what would be derived on a different platform. We show that the protocol developed for these instruments successfully recaptures known biological results, and additionally that biological conclusions are consistent across sequencing platforms (the HiSeq2000 versus the MiSeq) and across the sequenced regions of amplicons.Keywords: illumine; barcoded sequencing; QIIME
The ISME Journal 03/2012; 6(8):1621-1624. DOI:10.1038/ismej.2012.8 · 9.30 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Meiosis is a critical process in the reproduction and life cycle of flowering plants in which homologous chromosomes pair, synapse, recombine and segregate. Understanding meiosis will not only advance our knowledge of the mechanisms of genetic recombination, but also has substantial applications in crop improvement. Despite the tremendous progress in the past decade in other model organisms (e.g., Saccharomyces cerevisiae and Drosophila melanogaster), the global identification of meiotic genes in flowering plants has remained a challenge due to the lack of efficient methods to collect pure meiocytes for analyzing the temporal and spatial gene expression patterns during meiosis, and for the sensitive identification and quantitation of novel genes.
A high-throughput approach to identify meiosis-specific genes by combining isolated meiocytes, RNA-Seq, bioinformatic and statistical analysis pipelines was developed. By analyzing the studied genes that have a meiosis function, a pipeline for identifying meiosis-specific genes has been defined. More than 1,000 genes that are specifically or preferentially expressed in meiocytes have been identified as candidate meiosis-specific genes. A group of 55 genes that have mitochondrial genome origins and a significant number of transposable element (TE) genes (1,036) were also found to have up-regulated expression levels in meiocytes.
These findings advance our understanding of meiotic genes, gene expression and regulation, especially the transcript profiles of MGI genes and TE genes, and provide a framework for functional analysis of genes in meiosis.
[Show abstract][Hide abstract] ABSTRACT: Whole transcriptome sequencing (RNA-seq) technologies were used to profile gene expression in dissected Arabidopsis stage 5-7 anthers and isolated male meiocytes. Previously, we reported the discovery of over 1,000 coding genes that were specifically expressed in meiocytes, some 50 genes on a large mitochondrial genome insertion on chromosome-2 pericentromeric region, and 1036 transposable element genes were up-regulated in isolated male meiocytes in comparison to the anthers. Here we present the secondary data analysis, focusing on genes that were preferentially expressed in anthers, which are likely up-regulated in anther wall cells. To assess differential expression between isolated male meiocytes and stage 5-7 anthers undergoing meiosis, we performed a negative binomial test in DESeq on the 25,881 genes expressed at a minimum of 5 reads per million in at least one sample; we identified 490 genes differentially expressed (adjusted p-value of 0.001) that had 4-fold greater expression in anthers versus meiocytes, 2 of which were only expressed in anthers. 88 genes (18%) encode unknown proteins; 27 genes are annotated coding transcription factors. In addition, well-studied anther-wall specific genes such as DYT1 (At4G21330) and ATA1 (At3G42960) are presented in this gene list. The results have advanced our understanding on the transcriptome landscapes of anthers that undergo meiosis.
International Plant and Animal Genome Conference XX 2012;