Search

Microbial depolymerization of plant cell walls contributes to global carbon balance and is a critical component of renewable energy. The genomes of lignocellulose degrading microorganisms encode diverse classes of carbohydrate modifying enzymes, although currently there is a paucity of knowledge on the role of these proteins in vivo. We report the comprehensive analysis of the cellulose degradation system in the saprophytic bacterium Cellvibrio japonicus. Gene expression profiling of C. japonicus demonstrated that three of the 12 predicted β-1,4 endoglucanases (cel5A, cel5B, and cel45A) and the sole predicted cellobiohydrolase (cel6A) showed elevated expression during growth on cellulose. Targeted gene disruptions of all 13 predicted cellulase genes showed that only cel5B and cel6A were required for optimal growth on cellulose. Our analysis also identified three additional genes required for cellulose degradation: lpmo10B encodes a lytic polysaccharide monooxygenase (LPMO), while cbp2D and cbp2E encode proteins containing carbohydrate binding modules and predicted cytochrome domains for electron transfer. CjLPMO10B oxidized cellulose and Cbp2D demonstrated spectral properties consistent with redox function. Collectively, this report provides insight into the biological role of LPMOs and redox proteins in cellulose utilization and suggests that C. japonicus utilizes a combination of hydrolytic and oxidative cleavage mechanisms to degrade cellulose.

Liquid chromatography-mass spectrometry (LC-MS)-based proteomics is becoming an increasingly important tool in characterizing the abundance of proteins in biological samples of various types and across conditions. Effects of disease or drug treatments on protein abundance are of particular interest for the characterization of biological processes and the identification of biomarkers. Although state-of-the-art instrumentation is available to make high-quality measurements and commercially available software is available to process the data, the complexity of the technology and data presents challenges for bioinformaticians and statisticians. Here, we describe a pipeline for the analysis of quantitative LC-MS data. Key components of this pipeline include experimental design (sample pooling, blocking, and randomization) as well as deconvolution and alignment of mass chromatograms to generate a matrix of molecular abundance profiles. An important challenge in LC-MS-based quantitation is to be able to accurately identify and assign abundance measurements to members of protein families. To address this issue, we implement a novel statistical method for inferring the relative abundance of related members of protein families from tryptic peptide intensities. This pipeline has been used to analyze quantitative LC-MS data from multiple biomarker discovery projects. We illustrate our pipeline here with examples from two of these studies, and show that the pipeline constitutes a complete workable framework for LC-MS-based differential quantitation. Supplementary material is available at http://iec01.mie.utoronto.ca/~thodoros/Bukhman/.

The ability to quantitatively compare protein levels across different regions of the brain to identify disease mechanisms remains a fundamental research challenge. It requires both a robust method to efficiently isolate proteins from small amounts of tissue and a differential technique that provides a sensitive and comprehensive analysis of these proteins. Here, we describe a proteomic approach for the quantitative mapping of membrane proteins between mouse fore- and hindbrain regions. The approach focuses primarily on a recently developed method for the fractionation of membranes and on-membrane protein digestion, but incorporates off-line SCX-fractionation of the peptide mixture and nano-LC-MS/MS analysis using an LTQ-FT-ICR instrument as part of the analytical method. Comparison of mass spectral peak intensities between samples, mapping of peaks to peptides and protein sequences, and statistical analysis were performed using in-house differential analysis software (DAS). In total, 1213 proteins were identified and 967 were quantified; 81% of the identified proteins were known membrane proteins and 38% of the protein sequences were predicted to contain transmembrane helices. Although this paper focuses primarily on characterizing the efficiency of this purification method from a typical sample set, for many of the quantified proteins such as glutamate receptors, GABA receptors, calcium channel subunits, and ATPases, the observed ratios of protein abundance were in good agreement with the known mRNA expression levels and/or intensities of immunostaining in rostral and caudal regions of murine brain. This suggests that the approach would be well-suited for incorporation in more rigorous, larger scale quantitative analysis designed to achieve biological significance.

Motivation: Information about a particular protein or protein family is usually distributed among multiple databases and often in more than one entry in each database. Retrieval and organization of this information can be a laborious task. This task is complicated even further by the existence of alternative terms for the same concept. Results: The PDB, SWISS-PROT, ENZYME, and CATH databases have been imported into a combined relational database, BioMolQuest. A powerful search engine has been built using this database as a back end. The search engine achieves significant improvements in query performance by automatically utilizing cross-references between the legacy databases. The results of the queries are presented in an organized, hierarchical way. Availability: http://bioinformatics.danforthcenter.org/yury/public/home.html. Unavailable for commercial users. Contact: skolnick@danforthcenter.org * To whom correspondence should be addressed.

The highly conserved ANP32 proteins are proposed to function in a broad array of physiological activities through molecular mechanisms as diverse as phosphatase inhibition, chromatin regulation, caspase activation, and intracellular transport. On the basis of previous analyses of mice bearing targeted mutations of Anp32a or Anp32e , there has been speculation that all ANP32 proteins play redundant roles and are dispensable for normal development. However, more recent work has suggested that ANP32B may in fact have functions that are not shared by other ANP32 family members. Here we report that ANP32B expression is associated with a poor prognosis in human breast cancer, consistent with the increased levels of Anp32b mRNA present in proliferating wild-type (WT) murine embryonic fibroblasts and stimulated WT B and T lymphocytes. Moreover, we show that, contrary to previous assumptions, Anp32b is very important for murine embryogenesis. In a mixed genetic background, ANP32B-deficient mice displayed a partially penetrant perinatal lethality that became fully penetrant in a pure C57BL/6 background. Surviving ANP32B-deficient mice showed reduced viability due to variable defects in various organ systems. Study of compound mutants lacking ANP32A, ANP32B, and/or ANP32E revealed previously hidden roles for ANP32A in mouse development that became apparent only in the complete absence of ANP32B. Our data demonstrate a hierarchy of importance for the mammalian Anp32 genes, with Anp32b being the most critical for normal development.

The gene expression profiles of human postmortem parietal and prefrontal cortex samples of normal controls and patients with bipolar disease, or human neuroblastoma flat (NBFL) cells treated with the mood-stabilizing drug, valproate, were used to compare the performance of Affymetrix oligonucleotide U133A GeneChips ® and Agilent Human 1 cDNA microarrays. Among those genes represented on both platforms, the oligo array identified 26–53% more differentially expressed genes compared to the cDNA array in the three experiments, when identical fold change and t -test criteria were applied. The increased sensitivity was primarily the result of more robust fold changes measured by the oligonucleotide system. Essentially all gene changes overlapping between the two platforms were co-directional, and ranged from 4 to 19% depending upon the amount of biological variability within and between the comparison groups. Q-PCR validation rates were virtually identical for the two platforms, with 23–24% validation in the prefrontal cortex experiment, and 56% for both platforms in the cell culture experiment. Validated genes included dopa decarboxylase, dopamine beta-hydroxylase, and dihydropyrimidinase-related protein 3, which were decreased in NBFL cells exposed to valproate, and spinocerebellar ataxia 7, which was increased in bipolar disease. The modest overlap but similar validation rates show that each microarray system identifies a unique set of differentially expressed genes, and thus the greatest information is obtained from the use of both platforms.