Profiling gene expression using onto-express

Gene expression profiles obtained through microarray or data mining analyses often exist as vast data strings. To interpret the biology of these genetic profiles, investigators must analyze this data in the context of other information such as the biological, biochemical, or molecular function of the translated proteins. This is particularly challenging for a human analyst because large quantities of less than relevant data often bury such information. To address this need we implemented an automated routine, called Onto-Express (http://vortex.cs.wayne.edu:8080), to systematically translate genetic fingerprints into functional profiles. Using strings of accession or cluster identification numbers, Onto-Express searches the public databases and returns tables that correlate expression profiles with the cytogenetic locations, biochemical and molecular functions, biological processes, cellular components, and cellular roles of the translated proteins. The profiles created by Onto-Express fundamentally increase the value of gene expression analyses by facilitating the translation of quantitative value sets to records that contain biological implications.     

Profiling of cytokine expression by biotin-labeled-based protein arrays

Global analysis of protein expression holds great promise in basic research and patient care. Previously we demonstrated that multiple cytokines could be detected simultaneously using an enzyme-linked immunosorbent assay protein array system with high sensitivity and specificity. In this paper, we described a biotin-labeled-based protein array system to detect multiple cytokines simultaneously from biological samples. In this new approach, proteins from a variety of biological sources are labeled with biotin. The biotin-labeled proteins are then incubated with antibody chips. Targeted proteins are captured by the array antibodies spotted on the antibody chips. The presence of targeted proteins is detected using Cy3- or Cy5-conjugated streptavidin and signals are imaged by laser scanner. The system also can be easily adapted to a two-color binding assay, allowing measurement of the levels of proteins in a test sample with respect to a reference sample at the same chip. To demonstrate its potential applications, we applied this technology to profile human cytokines, chemokines, growth factors, angiogenic factors and proteases in estrogen receptor (ER)+ and ER- cells. These results suggest that biotin-labeled-based antibody chip technology can provide a practical and powerful means of profiling hundreds or thousands of proteins for research and clinical purposes.     

Selecting targets for therapeutic validation through differential protein expression using chromatography-mass spectrometry

The identification of potential targets for therapeutic intervention can be accomplished on a systematic basis by a variety of techniques that include quantitative analysis of gene-specific mRNA levels and expressed proteins in normal and diseased cells. Differences in the expression levels of nucleic acid and protein gene products could suggest protein drug targets that are directly causative of disease, or reveal biochemical pathways that could be modulated by therapeutic molecules. Any effort based on mRNA or protein expression level comparisons could be confounded by a number of factors: level in steady-state may not be correlated with actual encoded protein levels; differentially expressed protein levels might be a result of disease process, and not causative of the process, and therapeutic intervention based on such a difference will be unproductive and the differential expression of mRNA or protein may be the result of biological variation unrelated to the disease process under study. In order to address these possibly confounding factors, it is necessary to validate potential targets by establishing their firm association with disease, and their minimal distribution in non-diseased tissues of any type. This requirement suggests that emphasis on true and reproducible quantitation of protein expression levels in a variety of samples will be an effective and highly efficient method of generating drug targets with a high degree of utility. To achieve this aim, we have established an industrial-scale proteomics-based discovery platform consisting of cell biology, protein chemistry, and mass spectrometry technical groups together with bioinformatics groups. The analytical method used for quantitation employs isotope labeling for differential analysis (ICATTM, Applied Biosystems, Inc.). With this technique, tryptic peptides are generated from labeled proteins that have been specifically captured from various subcellular locations or protein families. The resulting peptides are identified and quantified by mass spectrometry. To evaluate this approach on a large-scale, we have applied it to a study of continuous cell lines derived from human pancreatic adenocarcinomas. We have been able to establish processes for target discovery for small molecule drug targets as well as therapeutic antibody target identification for cell surface proteins. In addition, we have developed a process for identification of serum markers of this disease based upon standardized fractionation procedures. The results of these analyses will be presented together with the some of the issues from both the wet and dry (computational) lab that need to be addressed in such an undertaking.     

Proteomic analysis of differential protein expression in atherosclerosis

Although recent studies have shown that several pro-inflammatory proteins can be used as biomarkers for atherosclerosis, the mechanism of atherogenesis is unclear and little information is available regarding proteins involved in development of the disease. Atherosclerotic tissue samples were collected from patients in order to identify the proteins involved in atherogenesis. The protein expression profile of atherosclerosis patients was analysed using two-dimensional electrophoresis-based proteomics. Thirty-nine proteins were detected that were differentially expressed in the atherosclerotic aorta compared with the normal aorta. Twenty-seven of these proteins were identified in the MS-FIT database. They are involved in a number of biological processes, including calcium-mediated processes, migration of vascular smooth muscle cells, matrix metalloproteinase activation and regulation of pro-inflammatory cytokines. Confirmation of differential protein expression was performed by Western blot analysis. Potential applications of the results include the identification and characterization of signalling pathways involved in atherogenesis, and further exploration of the role of selected identified proteins in atherosclerosis.     

Proteomic analysis of differential protein expression in atherosclerosis

Although recent studies have shown that several pro-inflammatory proteins can be used as biomarkers for atherosclerosis, the mechanism of atherogenesis is unclear and little information is available regarding proteins involved in development of the disease. Atherosclerotic tissue samples were collected from patients in order to identify the proteins involved in atherogenesis. The protein expression profile of atherosclerosis patients was analysed using two-dimensional electrophoresis-based proteomics. Thirty-nine proteins were detected that were differentially expressed in the atherosclerotic aorta compared with the normal aorta. Twenty-seven of these proteins were identified in the MS-FIT database. They are involved in a number of biological processes, including calcium-mediated processes, migration of vascular smooth muscle cells, matrix metalloproteinase activation and regulation of pro-inflammatory cytokines. Confirmation of differential protein expression was performed by Western blot analysis. Potential applications of the results include the identification and characterization of signalling pathways involved in atherogenesis, and further exploration of the role of selected identified proteins in atherosclerosis.     

Profiling antibiotic resistance in Escherichia coli strains displaying differential antibiotic susceptibilities using Raman spectroscopy

The rapid identification of antibiotic resistant bacteria is important for public health. In the environment, bacteria are exposed to sub-inhibitory antibiotic concentrations which has implications in the generation of multi-drug resistant strains. To better understand these issues, Raman spectroscopy was employed coupled with partial least squares-discriminant analysis to profile Escherichia coli strains treated with sub-inhibitory concentrations of antibiotics. Clear differences were observed between cells treated with bacteriostatic (tetracycline and rifampicin) and bactericidal (ampicillin, ciprofloxacin, and ceftriaxone) antibiotics for 6 hr: First, atomic force microscopy revealed that bactericidal antibiotics cause extensive cell elongation whereas short filaments are observed with bacteriostatic antibiotics. Second, Raman spectral analysis revealed that bactericidal antibiotics lower nucleic acid to protein (I₈₁₂/I₈₃₀) and nucleic acid to lipid ratios (I₁₄₈₃/I₁₄₅₂) whereas the opposite is seen with bacteriostatic antibiotics. Third, the protein to lipid ratio (I₂₉₃₆/I₂₈₈₅ and I₂₉₃₆/I₂₈₅₀) is a Raman stress signature common to both the classes. These signatures were validated using two mutants, Δlon and ΔacrB, that exhibit relatively high and low resistance towards antibiotics, respectively. In addition, these spectral markers correlated with the emergence of phenotypic antibiotic resistance. Overall, this study demonstrates the efficacy of Raman spectroscopy to identify resistance in bacteria to sub-lethal concentrations of antibiotics.     

Profiling microRNA expression using sensitive cDNA probes and filter arrays

MicroRNAs (miRNAs) are small noncoding RNAs (approximately 22 nucleotides) that have recently emerged as important regulators of gene expression in both plants and animals. With few exceptions, however, the target genes and the expression levels of most miRNAs are unknown. Here we show that direct random-primed cDNA synthesis on either chemically synthesized small RNAs (21-22 nucleotides) or gel-purified mature miRNAs from human cells can produce specific and sensitive full-length cDNA probes. Using oligonucleotide filter arrays, we demonstrate that the internally labeled cDNA probes are sensitive for detecting differential miRNA expression between untreated and O-tetradecanoylphorbol-13-acetate (TPA)-treated HL60 cells. The present study should facilitate a high-throughput analysis of miRNA expression between samples.     

Profiling the expression of mitogen-induced T-cell proteins by using multi-membrane dot-blotting

High throughput technologies are standard methods for analysis of the proteome. Multi-layer multi-well plate dot-blotting system (MLDot) technology is a high-throughput dot blotting system that provides a simple, cost-effective approach for protein expression profiling in multiple samples. In contrast to traditional dot blot, MLDot uses a layered stack of thin, sieve-like membranes in place of a single nitrocellulose membrane. Therefore, up to 10 membranes can be prepared from the samples arrayed in a single 96-well plate. We describe the ability of MLDot to detect the predicted changes in protein expression following multiple mitogen treatment of T-cells. We compare the levels of the phopshorylated forms of CREB, Jun, and Akt in Jurkat T-cells as detected by MLDot to those measured by a gel-based assay. We also describe the ability of MLDot to detect differences in the levels of phosphorylated Akt in Jurkat cells as compared to primary lymphocytes.     

Profiling microRNA expression in Arabidopsis pollen using microRNA array and real-time PCR

Background

Arachis hypogaea (peanut) is an important crop worldwide, being mostly used for edible oil production, direct consumption and animal feed. Cultivated peanut is an allotetraploid species with two different genome components, A and B. Genetic linkage maps can greatly assist molecular breeding and genomic studies. However, the development of linkage maps for A. hypogaea is difficult because it has very low levels of polymorphism. This can be overcome by the utilization of wild species of Arachis, which present the A- and B-genomes in the diploid state, and show high levels of genetic variability.

Results

In this work, we constructed a B-genome linkage map, which will complement the previously published map for the A-genome of Arachis, and produced an entire framework for the tetraploid genome. This map is based on an F₂ population of 93 individuals obtained from the cross between the diploid A. ipaënsis (K30076) and the closely related A. magna (K30097), the former species being the most probable B genome donor to cultivated peanut. In spite of being classified as different species, the parents showed high crossability and relatively low polymorphism (22.3%), compared to other interspecific crosses. The map has 10 linkage groups, with 149 loci spanning a total map distance of 1,294 cM. The microsatellite markers utilized, developed for other Arachis species, showed high transferability (81.7%). Segregation distortion was 21.5%. This B-genome map was compared to the A-genome map using 51 common markers, revealing a high degree of synteny between both genomes.

Conclusion

The development of genetic maps for Arachis diploid wild species with A- and B-genomes effectively provides a genetic map for the tetraploid cultivated peanut in two separate diploid components and is a significant advance towards the construction of a transferable reference map for Arachis. Additionally, we were able to identify affinities of some Arachis linkage groups with Medicago truncatula, which will allow the transfer of information from the nearly-complete genome sequences of this model legume to the peanut crop.     

Profiling conserved microRNA expression in recombinant CHO cell lines using Illumina sequencing

MicroRNAs (miRNAs) are small, non-coding RNAs that regulate multiple aspects of cell physiology. The differential expression of conserved miRNAs in two Chinese hamster ovary (CHO) cell lines producing recombinant proteins was examined relative to the CHO-K1 cell line. A total of 190 conserved CHO miRNAs were identified through homology with known human and rodent miRNAs. More than 80% of these miRNAs showed differential expression in recombinant CHO cell lines. The small RNA sequencing data were analyzed in context of the CHO-K1 genome to examine miRNA organization and develop sequence-specific miRNA resources for CHO cells. The identification and characterization of CHO miRNAs will facilitate the use of miRNA tools in cell line engineering efforts to improve product yield and quality.     

Analysis of differential protein expression in Acidithiobacillus ferrooxidans grown under different energy resources respectively using SELDI-ProteinChip technologies

Surface-enhanced laser desorption/ionization (SELDI)-time of flight is an affinity-based mass spectrometric method in which proteins of interest are selectively absorbed to a chemically modified surface on a chip, which allows proteomic analysis with limited material requirements. This characteristic makes it a valuable technique for microbiologists handling problematic samples, such as low cell number cultures. In this study, we explored differential-expressed proteome of Acidithiobacillus ferrooxidans cultivated with Fe(2+) and elemental sulfur separately by adopting the protein biochip SELDI approach. The cell lysates of A. ferrooxidans were applied onto Ciphergen ProteinChip WCX2, SAX2 and IMAC-Cu arrays. Proteins bound to the chips were analyzed on a ProteinChip Reader Model PBS II. A summary of the molecular masses of the differentially regulated proteins found on WCX2, IMAC-Cu and SAX2 was obtained and 28 differentially expressed proteins were found on the molecular weight range of 5.0 to 25 kDa.     

Proteomic analysis of differential protein expression in platelets of septic patients

Sepsis is one of the major health problems all over the world. Early diagnostic of sepsis is an attractive strategy to decrease the mortality of septic patients. However, an effective biomarker that fulfills all the necessary requirements for the accurate characterization of sepsis is still unavailable until now. In this study, the 2-DE technique followed by mass spectrometry and a database search was used for searching and identifying the differential expressed proteins in platelets between septic patients and paired healthy controls. Platelet 2-DE profiles of septic patients and paired healthy controls with high resolution and reproducibility were obtained. Differential platelet 2-DE profiles between septic patients and paired healthy controls were established. Differential protein spots between normal healthy volunteers and septic patients from platelet 2-DE profiles were identified by 2-DE followed with mass spectrometry and a database search. Five proteins with increased expression were identified between septic patients and healthy controls from platelet samples. These up-expressed proteins were EF-hand calcium-binding domain-containing protein 7, actin, interleukin-1β, glycoprotein IX, and glycoprotein IIB. Sepsis induces a complex regulation of platelet protein changes. Our study highlights the important role of these differential expressed proteins in sepsis, which deserve further research as potential candidates for therapeutic strategies. Furthermore, our research is beneficial for the future developments of sepsis diagnosis and therapy.     

Detecting differential and correlated protein expression in label-free shotgun proteomics

Recent studies have revealed a relationship between protein abundance and sampling statistics, such as sequence coverage, peptide count, and spectral count, in label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS) shotgun proteomics. The use of sampling statistics offers a promising method of measuring relative protein abundance and detecting differentially expressed or coexpressed proteins. We performed a systematic analysis of various approaches to quantifying differential protein expression in eukaryotic Saccharomyces cerevisiae and prokaryotic Rhodopseudomonas palustris label-free LC-MS/MS data. First, we showed that, among three sampling statistics, the spectral count has the highest technical reproducibility, followed by the less-reproducible peptide count and relatively nonreproducible sequence coverage. Second, we used spectral count statistics to measure differential protein expression in pairwise experiments using five statistical tests: Fisher's exact test, G-test, AC test, t-test, and LPE test. Given the S. cerevisiae data set with spiked proteins as a benchmark and the false positive rate as a metric, our evaluation suggested that the Fisher's exact test, G-test, and AC test can be used when the number of replications is limited (one or two), whereas the t-test is useful with three or more replicates available. Third, we generalized the G-test to increase the sensitivity of detecting differential protein expression under multiple experimental conditions. Out of 1622 identified R. palustris proteins in the LC-MS/MS experiment, the generalized G-test detected 1119 differentially expressed proteins under six growth conditions. Finally, we studied correlated expression of these 1119 proteins by analyzing pairwise expression correlations and by delineating protein clusters according to expression patterns. Through pairwise expression correlation analysis, we demonstrated that proteins co-located in the same operon were much more strongly coexpressed than those from different operons. Combining cluster analysis with existing protein functional annotations, we identified six protein clusters with known biological significance. In summary, the proposed generalized G-test using spectral count sampling statistics is a viable methodology for robust quantification of relative protein abundance and for sensitive detection of biologically significant differential protein expression under multiple experimental conditions in label-free shotgun proteomics.