Deep functional analysis of synII, a 770-kilobase synthetic yeast chromosome.

Here, we report the successful design, construction, and characterization of a 770-kilobase synthetic yeast chromosome II (synII). Our study incorporates characterization at multiple levels-including phenomics, transcriptomics, proteomics, chromosome segregation, and replication analysis-to provide a thorough and comprehensive analysis of a synthetic chromosome. Our Trans-Omics analyses reveal a modest but potentially relevant pervasive up-regulation of translational machinery observed in synII, mainly caused by the deletion of 13 transfer RNAs. By both complementation assays and SCRaMbLE (synthetic chromosome rearrangement and modification by loxP-mediated evolution), we targeted and debugged the origin of a growth defect at 37°C in glycerol medium, which is related to misregulation of the high-osmolarity glycerol response. Despite the subtle differences, the synII strain shows highly consistent biological processes comparable to the native strain.

Omics-based interpretation of synergism in a soil-derived cellulose-degrading microbial community.

Reaching a comprehensive understanding of how nature solves the problem of degrading recalcitrant biomass may eventually allow development of more efficient biorefining processes. Here we interpret genomic and proteomic information generated from a cellulolytic microbial consortium (termed F1RT) enriched from soil. Analyses of reconstructed bacterial draft genomes from all seven uncultured phylotypes in F1RT indicate that its constituent microbes cooperate in both cellulose-degrading and other important metabolic processes. Support for cellulolytic inter-species cooperation came from the discovery of F1RT microbes that encode and express complimentary enzymatic inventories that include both extracellular cellulosomes and secreted free-enzyme systems. Metabolic reconstruction of the seven F1RT phylotypes predicted a wider genomic rationale as to how this particular community functions as well as possible reasons as to why biomass conversion in nature relies on a structured and cooperative microbial community.

Expansion of the ion library for mining SWATH-MS data through fractionation proteomics.

The strategy of sequential window acquisition of all theoretical fragment ion spectra (SWATH) is emerging in the field of label-free proteomics. A critical consideration for the processing of SWATH data is the quality of the ion library (or mass spectrometric reference map). As the availability of open spectral libraries that can be used to process SWATH data is limited, most users currently create their libraries in-house. Herein, we propose an approach to construct an expanded ion library using the data-dependent acquisition (DDA) data generated by fractionation proteomics. We identified three critical elements for achieving a satisfactory ion library during the iterative process of our ion library expansion, including a correction of the retention times (RTs) gained from fractionation proteomics, appropriate integrations of the fractionated proteomics into an ion library, and assessments of the impact of the expanded ion libraries to data mining in SWATH. Using a bacterial lysate as an evaluation material, we employed sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) to fractionate the lysate proteins and constructed the expanded ion library using the fractionation proteomics data. Compared with the ion library built from the unfractionated proteomics, approximately 20% more peptides were extracted from the expanded ion library. The extracted peptides, moreover, were acceptable for further quantitative analysis.

Identification of novel biomarkers for sepsis prognosis via urinary proteomic analysis using iTRAQ labeling and 2D-LC-MS/MS.

OBJECTIVES: Sepsis is the major cause of death for critically ill patients. Recent progress in proteomics permits a thorough characterization of the mechanisms associated with critical illness. The purpose of this study was to screen potential biomarkers for early prognostic assessment of patients with sepsis. METHODS: For the discovery stage, 30 sepsis patients with different prognoses were selected. Urinary proteins were identified using isobaric tags for relative and absolute quantitation (iTRAQ) coupled with LC-MS/MS. Mass spec instrument analysis were performed with Mascot software and the International Protein Index (IPI); bioinformatic analyses were used by the algorithm of set and the Gene Ontology (GO) Database. For the verification stage, the study involved another 54 sepsis-hospitalized patients, with equal numbers of patients in survivor and non-survivor groups based on 28-day survival. Differentially expressed proteins were verified by Western Blot. RESULTS: A total of 232 unique proteins were identified. Proteins that were differentially expressed were further analyzed based on the pathophysiology of sepsis and biomathematics. For sepsis prognosis, five proteins were significantly up-regulated: selenium binding protein-1, heparan sulfate proteoglycan-2, alpha-1-B glycoprotein, haptoglobin, and lipocalin; two proteins were significantly down-regulated: lysosome-associated membrane proteins-1 and dipeptidyl peptidase-4. Based on gene ontology clustering, these proteins were associated with the biological processes of lipid homeostasis, cartilage development, iron ion transport, and certain metabolic processes. Urinary LAMP-1 was down-regulated, consistent with the Western Blot validation. CONCLUSION: This study provides the proteomic analysis of urine to identify prognostic biomarkers of sepsis. The seven identified proteins provide insight into the mechanism of sepsis. Low urinary LAMP-1 levels may be useful for early prognostic assessment of sepsis. TRIAL REGISTRATION: ClinicalTrial.gov NCT01493492.

Urinary proteomics analysis for sepsis biomarkers with iTRAQ labeling and two-dimensional liquid chromatography-tandem mass spectrometry.

BACKGROUND: Proteomics has only recently been applied to the field of critical care research. Sepsis is a major factor contributing to intensive care unit admissions and deaths. The purpose of this study was to screen potential urinary biomarkers for sepsis using A proteomics approach. METHODS: Fifteen sepsis and 15 systemic inflammatory response syndrome patients were involved in this study. Urinary proteins were identified by isobaric tag for relative and absolute quantitation coupled with liquid chromatography-tandem mass spectrometry. Mass spectroscopy analysis was performed with the Mascot software and the International Protein Index. Bioinformatics analyses were performed using the hierarchy cluster analysis, the STRING software, the Gene Ontology, and the Kyoto Encyclopedia of Genes and Genome database. RESULTS: One hundred thirty proteins were identified, and 34 differentially expressed proteins were selected (fold change, >1.5). On the basis of the Gene Ontology and the Kyoto Encyclopedia of Genes and Genome database, these 34 proteins were identified to be involved in inflammation, immunity, and structural or cytoskeletal processes. Five proteins were selected by a protein-protein interaction network for sepsis differentiation: cadherin 1, haptoglobin, complement 3, alpha-1-antitrypsin, and ceruloplasmin. CONCLUSION: Urinary proteomics may represent a suitable approach for sepsis-related research. The detection of urinary biomarkers is expected to become a noninvasive and acceptable method, which facilitates the close surveillance of diseases and reduces medical costs. LEVELS OF EVIDENCE: Diagnostic study, level IV.

Qualitative and quantitative expression status of the human chromosome 20 genes in cancer tissues and the representative cell lines.

Under the guidance of the Chromosome-centric Human Proteome Project (C-HPP), (1, 2) we conducted a systematic survey of the expression status of genes located at human chromosome 20 (Chr.20) in three cancer tissues, gastric, colon, and liver carcinoma, and their representative cell lines. We have globally profiled proteomes in these samples with combined technology of LC-MS/MS and acquired the corresponding mRNA information upon RNA-seq and RNAchip. In total, 323 unique proteins were identified, covering 60% of the coding genes (323/547) in Chr.20. With regards to qualitative information of proteomics, we overall evaluated the correlation of the identified Chr.20 proteins with target genes of transcription factors or of microRNA, conserved genes and cancer-related genes. As for quantitative information, the expression abundances of Chr.20 genes were found to be almost consistent in both tissues and cell lines of mRNA in all individual chromosome regions, whereas those of Chr.20 proteins in cells are different from tissues, especially in the region of 20q13.33. Furthermore, the abundances of Chr.20 proteins were hierarchically evaluated according to tissue- or cancer-related distribution. The analysis revealed several cancer-related proteins in Chr.20 are tissue- or cell-type dependent. With integration of all the acquired data, for the first time we established a solid database of the Chr.20 proteome.

An improvement of shotgun proteomics analysis by adding next-generation sequencing transcriptome data in orange.

BACKGROUND: Shotgun proteomics data analysis usually relies on database search. Because commonly employed protein sequence databases of most species do not contain sufficient protein information, the application of shotgun proteomics to the research of protein sequence profile remains a big challenge, especially to the species whose genome has not been sequenced yet. METHODOLOGY/PRINCIPAL FINDINGS: In this paper, we present a workflow with integrated database to partly address this problem. First, we downloaded the homologous species database. Next, we identified the transcriptome of the sample, created a protein sequence database based on the transcriptome data, and integtrated it with homologous species database. Lastly, we developed a workflow for identifying peptides simultaneously from shotgun proteomics data. CONCLUSIONS/SIGNIFICANCE: We used datasets from orange leaves samples to demonstrate our workflow. The results showed that the integrated database had great advantage on orange shotgun proteomics data analysis compared to the homologous species database, an 18.5% increase in number of proteins identification.