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PubMed2026

Integrative proteomics and bioinformatics pipelines for PTM profiling.

Post-translational modifications (PTMs) regulate protein function across all life forms and allow plants to respond rapidly to biotic and abiotic stress. Over 450 PTM types have been described across organisms, of which 23-33 have been experimentally confirmed in plants, including phosphorylation, acetylation, methylation, glycosylation, ubiquitination, and sumoylation. These modifications are highly dynamic and often reversible, and frequently act in combination, or "crosstalk," to fine-tune cellular processes. Advances in high-resolution mass spectrometry and large-scale genome sequencing continue to expand the catalogue of known PTM sites, while machine learning and deep learning approaches increasingly support prediction of PTM site localization and function. Unlike broader surveys of plant PTMs, this review focuses specifically on O-phosphorylation and Lys-N(ε)-acetylation, the two best-characterized and most extensively crosstalking PTMs in plants, and integrates four perspectives: the historical development of proteomic and bioinformatics approaches to these modifications; current mass spectrometry-based workflows and enrichment strategies; the bioinformatics tools and databases available for their analysis; and the technical and species-related challenges, particularly in non-model plants, that currently limit their study. We close by outlining priority directions for future research, including multi-omics integration, AI-based prediction, and the translation of PTM knowledge into crop stress resilience and breeding applications.

Protein Processing, Post-Translational↗

PubMed2005

Serum protein fingerprinting coupled with artificial neural network distinguishes glioma from healthy population or brain benign tumor.

To screen and evaluate protein biomarkers for the detection of gliomas (Astrocytoma grade I-IV) from healthy individuals and gliomas from brain benign tumors by using surface enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF-MS) coupled with an artificial neural network (ANN) algorithm. SELDI-TOF-MS protein fingerprinting of serum from 105 brain tumor patients and healthy individuals, included 28 patients with glioma (Astrocytoma I-IV), 37 patients with brain benign tumor, and 40 age-matched healthy individuals. Two thirds of the total samples of every compared pair as training set were used to set up discriminating patterns, and one third of total samples of every compared pair as test set were used to cross-validate; simultaneously, discriminate-cluster analysis derived SPSS 10.0 software was used to compare Astrocytoma grade I-II with grade III-IV ones. An accuracy of 95.7%, sensitivity of 88.9%, specificity of 100%, positive predictive value of 90% and negative predictive value of 100% were obtained in a blinded test set comparing gliomas patients with healthy individuals; an accuracy of 86.4%, sensitivity of 88.9%, specificity of 84.6%, positive predictive value of 90% and negative predictive value of 85.7% were obtained when patient's gliomas was compared with benign brain tumor. Total accuracy of 85.7%, accuracy of grade I-II Astrocytoma was 86.7%, accuracy of III-IV Astrocytoma was 84.6% were obtained when grade I-II Astrocytoma was compared with grade III-IV ones (discriminant analysis). SELDI-TOF-MS combined with bioinformatics tools, could greatly facilitate the discovery of better biomarkers. The high sensitivity and specificity achieved by the use of selected biomarkers showed great potential application for the discrimination of gliomas patients from healthy individuals and gliomas from brain benign tumors.

Adult↗

PubMed2004

Discovering genes: the use of microarrays and laser capture microdissection in pain research.

The DNA microarray is a powerful, high throughput technique for assessing gene expression on a system-wide genomic scale. It has great potential in pain research for determining the network of gene regulation in different pain conditions, and also for producing detailed gene expression maps in anatomical areas that process nociceptive stimuli. However, for the potential of this high throughput technology to be realised in pain research, microarrays need to be combined with other technologies. Laser capture microdissection is capable of isolating small populations of homogenous cells, allowing distinct areas involved in nociceptive processing to be examined. In combination with sophisticated PCR-based amplification protocols this technique provides sufficient amounts of messenger RNA (mRNA) for application to microarrays. Aside from the technological issues, a difficult task in any microarray study is the analysis of the resulting enormous data set to reveal the key genes, whose regulation is central to the phenotypic changes observed. For this to be achieved, the methods of data analysis, pattern searching and feature recognition, and bioinformatics have to be properly deployed all within the context of an appropriate statistical design. These issues are especially relevant to pain research where interindividual and interpopulation variation is likely to be high, and where polymorphisms can greatly affect nociceptive sensitivity and susceptibility to pain conditions. Methods for assessing the function of new candidate genes identified in microarray screening experiments are also discussed.

Animals↗

PubMed2002

[GeneChip system from a bioinformatical point of view].

GeneChip (Affymetrix, Inc., USA) employs a specific method for spotting DNA probes on chips, which is different from any other DNA chips, and can complete the whole process from sample preparation to data construction and analysis. The GeneChip system can be applied to both gene expression analysis and genomic mutation analysis, which would play an important role in human genome analysis in the future. Techniques for data construction ("wet" experimental techniques), which are the major components in the GeneChip system, are generally established as routine work in the first screening process in most laboratories worldwide. The most important point would be how we exchange experimental data produced by researchers and gene/genome information available both on the public and the commercial bases so that we reduce useful information on gene expression. Recently, the center of the research has been shifting to computing technology for data processing ("bioinformatics"). This article separately deals with gene expression analysis and genomic analysis, with emphasis on bioinformatics. We describe the data on gene expression screening, the gene targeting process, the analysis of genomic DNA mutations using the P53 probe array, and the HuSNP mapping assays, by presenting our experimental examples.

Computational Biology↗

PubMed2006

Analysis of gene expression using cDNA microarrays.

A DNA microarray consists of an orderly arrangement of DNA, cDNA, or oligonucleotide probes that represent individual genes of an organism. Today, microarrays are the most important tools used to analyze gene transcription on a large scale. Investigations reach from finding key genes in whole genomes to looking at overall patterns of gene expression. This provides a better understanding of the architecture of genetic regulatory networks and helps to identify those genes in the genome that are differentially expressed, e.g., during cellular programs, in tumors or as the result of a treatment. The use of different fluorescent dyes allows mRNAs from two different sources (tissues, strains, or differently treated cells) to be labeled in different colors, mixed, and hybridized to the same array, which results in competitive binding of the target to the arrayed sequences. After hybridization and washing, the slide is scanned using two different wavelengths corresponding to the dyes used, and the intensity of the same spot in both channels is compared. This results in a measurement of the ratio of transcript levels for each gene represented on the array. In order to find significantly regulated genes, the resulting data are analyzed by different bioinformatic tools such as R and significance analysis of microarrays (SAM).

Animals↗

PubMed2004

Expression Profiler: next generation--an online platform for analysis of microarray data.

Expression Profiler (EP, http://www.ebi.ac.uk/expressionprofiler) is a web-based platform for microarray gene expression and other functional genomics-related data analysis. The new architecture, Expression Profiler: next generation (EP:NG), modularizes the original design and allows individual analysis-task-related components to be developed by different groups and yet still seamlessly to work together and share the same user interface look and feel. Data analysis components for gene expression data preprocessing, missing value imputation, filtering, clustering methods, visualization, significant gene finding, between group analysis and other statistical components are available from the EBI (European Bioinformatics Institute) web site. The web-based design of Expression Profiler supports data sharing and collaborative analysis in a secure environment. Developed tools are integrated with the microarray gene expression database ArrayExpress and form the exploratory analytical front-end to those data. EP:NG is an open-source project, encouraging broad distribution and further extensions from the scientific community.

Gene Expression Profiling↗

PubMed2004

Identification of novel Myc target genes with a potential role in lymphomagenesis.

The c-Myc transcription factor regulates a wide set of genes involved in processes such as proliferation, differentiation and apoptosis. Therefore, altered expression of Myc leads to deregulation of a large number of target genes and, as a consequence, to tumorigenesis. For understanding Myc-induced transformation, identification of these target genes is essential. In this study, we searched for Myc target genes involved in lymphomagenesis using different mouse T and B cell lymphoma cell lines transformed by a conditional Myc-allele. Target genes obtained by microarray experiments were further subjected to a kinetic analysis of mRNA expression upon Myc inactivation/reactivation, bioinformatic examination of Myc binding sites and chromatin immunoprecipitation. This approach allowed us to define targets whose activation is a direct consequence of Myc binding. Among the 38 novel Myc targets, we identified several genes implicated in the tumor development. These genes are not only relevant for mouse lymphomas because we observed their upregulation in human lymphomas as well. Our findings further the understanding of Myc-induced lymphomagenesis and help toward developing more efficient antitumor strategies.

Animals↗

PubMed2003

A method to improve selection of molecular targets by circumventing the ADME pharmacokinetic system utilizing PharmArray DNA microarrays.

DNA microarrays may be used to identify potential molecular targets for drug discovery. Yet, DNA microarray experiments provide massive amounts of data. To limit the choice of potential molecular targets, it may be desirable to eliminate genes coincidentally up-regulated in tissues implicated in absorption, distribution, metabolism, and excretion (ADME) pharmacokinetics. DNA microarray experiments were performed to demonstrate a gene-exclusion approach using as an example RNA samples of neural origin, i.e., a human neuroblastoma cell line (SK-N-SH) and brain tissue, as the intended hypothetical site(s) of drug action. Biomarkers were identified using PharmArray DNA microarrays. The lists of neuroblastoma and neural biomarkers were constrained by limiting selection to the subset of genes that were not highly expressed in three transformed cell lines from liver, colon, and kidney (HepG2, Caco-2, and 786-O, respectively) that are routinely used as representatives of the ADME system during in vitro pharmacology and toxicology experiments. Principal component analysis methods with likelihood ratio-related bioinformatic tools were utilized to identify robust potential biomarker genes for the three ADME-related cell lines, neuroblastoma, and normal brain. Biomarkers of each sample were identified and selected genes were validated by qRT-PCR. Hundreds of biomarkers of the three ADME-related cell types, representing hepatocytes, kidney epithelium, and gastrointestinal tract, may now be used as a valuable database to restrict selection of biomarkers as potential molecular targets from the intended samples (e.g., neuroblastoma in this work). In addition to biomarker discovery per se, this demonstration suggests that our model method may be viable to help restrict gene lists during selection of potential molecular targets for subsequent drug discovery.

Adult↗

PubMed2003

Genome-wide loss of heterozygosity analysis from laser capture microdissected prostate cancer using single nucleotide polymorphic allele (SNP) arrays and a novel bioinformatics platform dChipSNP.

Oligonucleotide arrays that detect single nucleotide polymorphisms were used to generate genome-wide loss of heterozygosity (LOH) maps from laser capture microdissected paraffin-embedded samples using as little as 5 ng of DNA. The allele detection rate from such samples was comparable with that obtained with standard amounts of DNA prepared from frozen tissues. A novel informatics platform, dChipSNP, was used to automate the definition of statistically valid regions of LOH, assign LOH genotypes to prostate cancer samples, and organize by hierarchical clustering prostate cancers based on the pattern of LOH. This organizational strategy revealed apparently distinct genetic subsets of prostate cancer.

Alleles↗

PubMed2000

Metabolic flux balance analysis and the in silico analysis of Escherichia coli K-12 gene deletions.

BACKGROUND: Genome sequencing and bioinformatics are producing detailed lists of the molecular components contained in many prokaryotic organisms. From this 'parts catalogue' of a microbial cell, in silico representations of integrated metabolic functions can be constructed and analyzed using flux balance analysis (FBA). FBA is particularly well-suited to study metabolic networks based on genomic, biochemical, and strain specific information. RESULTS: Herein, we have utilized FBA to interpret and analyze the metabolic capabilities of Escherichia coli. We have computationally mapped the metabolic capabilities of E. coli using FBA and examined the optimal utilization of the E. coli metabolic pathways as a function of environmental variables. We have used an in silico analysis to identify seven gene products of central metabolism (glycolysis, pentose phosphate pathway, TCA cycle, electron transport system) essential for aerobic growth of E. coli on glucose minimal media, and 15 gene products essential for anaerobic growth on glucose minimal media. The in silico tpi-, zwf, and pta- mutant strains were examined in more detail by mapping the capabilities of these in silico isogenic strains. CONCLUSIONS: We found that computational models of E. coli metabolism based on physicochemical constraints can be used to interpret mutant behavior. These in silica results lead to a further understanding of the complex genotype-phenotype relation.

Computational Biology↗

PubMed2002

[Hidden Markov model used in protein sequence analysis].

Hidden Markov model (HMM) used in the research of protein is a new field of bioinformatics. Nowadays large amount of data about protein sequences and structures have been obtained. Traditional methods of protein analysis are no longer used. Biologists have updated their research methods with computer technology and statistics, which can deal with large amount of data. HMM can be used to distinguish protein sequence with the same characteristics. A family of protein from SCOP database was selected, through which a HMM model representing the family was obtained, and then the model was utilized to analyze protein sequences. Results indicate that HMM can express particular family of protein, and recognize the given protein sequences of the family from many sequences.

Algorithms↗

PubMed2006

Transforming omics data into context: bioinformatics on genomics and proteomics raw data.

Differential gene expression analysis and proteomics have exerted significant impact on the elucidation of concerted cellular processes, as simultaneous measurement of hundreds to thousands of individual objects on the level of RNA and protein ensembles became technically feasible. The availability of such data sets has promised a profound understanding of phenomena on an aggregate level, expressed as the phenotypic response (observables) of cells, e.g., in the presence of drugs, or characterization of cells and tissue displaying distinct patho-physiological states. However, the step of transforming these data into context, i.e., linking distinct expression or abundance patterns with phenotypic observables - and furthermore enabling a sound biological interpretation on the level of reaction networks and concerted pathways, is still a major shortcoming. This finding is certainly based on the enormous complexity embedded in cellular reaction networks, but a variety of computational approaches have been developed over the last few years to overcome these issues. This review provides an overview on computational procedures for analysis of genomic and proteomic data introducing a sequential analysis workflow: Explorative statistics for deriving a first, from the purely statistical viewpoint, relevant candidate gene/protein list, followed by co-regulation and network analysis to biologically expand this core list toward functional networks and pathways. The review on these procedures is complemented by example applications tailored at identification of disease-associated proteins. Optimization of computational procedures involved, in conjunction with the continuous increase in additional biological data, clearly has the potential of boosting our understanding of processes on a cell-wide level.

Animals↗

PubMed2006

Biophysical characterisation reveals structural disorder in the nucleolar protein, Dribble.

Dribble (DBE) is an essential protein in Drosophila that belongs to the evolutionarily conserved Krr1p protein family. Proteins in this family are localised in the cell nucleolus and are important for the processing of ribosomal RNAs. However, little is known about their structural and biophysical properties. We have expressed and purified full-length DBE protein from Escherichia coli. Consistent with the native role of DBE in RNA processing, recombinant DBE was shown to bind RNA homo-polymers in vitro. By bioinformatics, size-exclusion chromatography, equilibrium sedimentation analysis, controlled proteolysis, and a variety of spectroscopic techniques, we have found that DBE is a monomeric protein in solution containing both alpha- and beta-structures. Moreover, the structure of DBE is expanded and significantly disordered (approximately 45% disordered). Natively disordered proteins are thought to provide a disproportionately large surface area and structural plasticity for nucleic acid binding. We therefore propose that the presence of structural disorder is an important feature of DBE that facilitates the protein to interact with RNAs in the nucleolus.

Amino Acid Sequence↗

PubMed2005

Guidelines for the routine application of the peptide hits technique.

A set of guidelines has been developed for using the peptide hits technique (PHT) as a semi-quantitative screening tool for the identification of proteins that change in abundance in a complex mixture. The dataset that formed the basis for these experiments was created using a cell lysate derived from the yeast Saccharomyces cerevisiae, spiked at various levels with serum albumin (BSA), and analyzed by LC/MS/MS and SEQUEST. Knowing that the level of only one protein (BSA) actually changed in the mixture allowed for the development and refinement of the necessary bioinformatics and statistical analyses, e.g., principal component analysis (PCA), normalization, and analysis of variation (ANOVA). As expected, the number of BSA peptide hits changed in proportion to the amount of BSA added to the sample. PCA was able to clearly distinguish between the spiked samples and the untreated sample, indicating that PCA may be able to classify samples, e.g., healthy versus diseased, in future experiments. The use of an endogenous "housekeeping" protein was found to be superior to the use of total hits for data normalization prior to analysis. An ANOVA based model readily identified BSA as a protein of interest, that is, one likely to be changing from amongst the background proteins, indicating that an ANOVA model may be able to identify individual proteins in target or biomarker discovery experiments. General guidelines based on these combined observations are set forth for future analyses and the rapid screening for candidate proteins of interest.

Analysis of Variance↗

PubMed2024

Mapping Start Codons of Small Open Reading Frames by N-Terminomics Approach.

sORF-encoded peptides (SEPs) refer to proteins encoded by small open reading frames (sORFs) with a length of less than 100 amino acids, which play an important role in various life activities. Analysis of known SEPs showed that using non-canonical initiation codons of SEPs was more common. However, the current analysis of SEP sequences mainly relies on bioinformatics prediction, and most of them use AUG as the start site, which may not be completely correct for SEPs. Chemical labeling was used to systematically analyze the N-terminal sequences of SEPs to accurately define the start sites of SEPs. By comparison, we found that dimethylation and guanidinylation are more efficient than acetylation. The ACN precipitation and heating precipitation performed better in SEP enrichment. As an N-terminal peptide enrichment material, Hexadhexaldehyde was superior to CNBr-activated agarose and NHS-activated agarose. Combining these methods, we identified 128 SEPs with 131 N-terminal sequences. Among them, two-thirds are novel N-terminal sequences, and most of them start from the 11-31st amino acids of the original sequence. Partial novel N-termini were produced by proteolysis or signal peptide removal. Some SEPs' transcription start sites were corrected to be non-AUG start codons. One novel start codon was validated using GFP-tag vectors. These results demonstrated that the chemical labeling approaches would be beneficial for identifying the start codons of sORFs and the real N-terminal of their encoded peptides, which helps better understand the characterization of SEPs.

Open Reading Frames↗

PubMed2006

Mass spectrometric analysis of N- and O-glycosylation of tissues and cells.

Mass spectrometry has been at the forefront of glycobiological analysis for more than two decades. Recent advances in technology, coupled with increased global appreciation of the importance of the glycome in solving biological problems, have laid the foundations for an unprecedented increase in output. Under the auspices of major international consortia, significant progress is being made towards the development of integrated databases of specific cell and tissue N- and O-glycan profiles. Tools for the automation of both sample handling and data analysis are enabling a more concerted approach to glycan structural analysis. When combined with complementary transcriptomics and bioinformatics analyses, these tools will facilitate a new systems glycobiology approach to research.

Animals↗

PubMed2002

Phenomics: fiction or the future?

The ease with which genetic mutations can be induced in or introduced into mammalian organisms, such as the mouse, has created a significant need for phenotypic analysis. Developments in computer technology, instrumentation and bioinformatics, as well as in numerous neuroscience disciplines, will help to meet the demands set by the molecular revolution. As a result, the field of 'phenomics' is being born. This will integrate multidisciplinary research, with the goal of understanding the complex phenotypic consequences of genetic mutations at the level of the organism. This paper focuses on one of the disciplines that show promising developments, behavioral science.

Animals↗

PubMedDate not supplied

"Plasmo2D": an ancillary proteomic tool to aid identification of proteins from Plasmodium falciparum.

Bioinformatics tools to aid gene and protein sequence analysis have become an integral part of biology in the post-genomic era. Release of the Plasmodium falciparum genome sequence has allowed biologists to define the gene and the predicted protein content as well as their sequences in the parasite. Using pI and molecular weight as characteristics unique to each protein, we have developed a bioinformatics tool to aid identification of proteins from Plasmodium falciparum. The tool makes use of a Virtual 2-DE generated by plotting all of the proteins from the Plasmodium database on a pI versus molecular weight scale. Proteins are identified by comparing the position of migration of desired protein spots from an experimental 2-DE and that on a virtual 2-DE. The procedure has been automated in the form of user-friendly software called "Plasmo2D". The tool can be downloaded from http://144.16.89.25/Plasmo2D.zip.

Animals↗