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Analysis of differentially expressed parasite genes and proteins using transcriptomics and proteomics.

At any particular point in time, the full complement of transcribed RNAs and relevant proteins of a cell are known as the transcriptome and proteome, respectively. The composition of these two populations changes throughout the life cycle of a parasite or in response to environmental factors, such as drug treatments. Comparing the changes in the composition of the transcriptome and proteome between different life-cycle stages or in the same stage but under different conditions can be of particular interest, as it can allow the identification of potentially important differentially expressed genes and proteins. Combining the analysis of both the transcriptome and proteome in tandem allows changes in RNA transcripts to be followed right through to changes in the level of protein expression. The protocols in this chapter describe methods for analyzing the transcriptome, by using suppression subtraction hybridization to construct subtracted complementary DNA libraries, and the proteome, by using two-dimensional sodium dodecyl sulfate-polyacrylamide gel electrophoresis. These two methods are then integrated to allow the global changes in RNA and protein expression to be examined. The protocols have been adapted for working on parasites and contain extensive notes.

Animals↗

PROTICdb: a web-based application to store, track, query, and compare plant proteome data.

PROTICdb is a web-based application, mainly designed to store and analyze plant proteome data obtained by two-dimensional polyacrylamide gel electrophoresis (2-D PAGE) and mass spectrometry (MS). The purposes of PROTICdb are (i) to store, track, and query information related to proteomic experiments, i.e., from tissue sampling to protein identification and quantitative measurements, and (ii) to integrate information from the user's own expertise and other sources into a knowledge base, used to support data interpretation (e.g., for the determination of allelic variants or products of post-translational modifications). Data insertion into the relational database of PROTICdb is achieved either by uploading outputs of image analysis and MS identification software, or by filling web forms. 2-D PAGE annotated maps can be displayed, queried, and compared through a graphical interface. Links to external databases are also available. Quantitative data can be easily exported in a tabulated format for statistical analyses. PROTICdb is based on the Oracle or the PostgreSQL Database Management System and is freely available upon request at the following URL: http://moulon.inra.fr/ bioinfo/PROTICdb.

Computer Graphics↗

From XML to RDF: how semantic web technologies will change the design of 'omic' standards.

With the ongoing rapid increase in both volume and diversity of 'omic' data (genomics, transcriptomics, proteomics, and others), the development and adoption of data standards is of paramount importance to realize the promise of systems biology. A recent trend in data standard development has been to use extensible markup language (XML) as the preferred mechanism to define data representations. But as illustrated here with a few examples from proteomics data, the syntactic and document-centric XML cannot achieve the level of interoperability required by the highly dynamic and integrated bioinformatics applications. In the present article, we discuss why semantic web technologies, as recommended by the World Wide Web consortium (W3C), expand current data standard technology for biological data representation and management.

Algorithms↗

Analytical Considerations for the Development of Plate-Based Proteomics Platforms Using Isobaric Labeling.

Mass spectrometer-based proteomics platforms have great potential to rapidly advance our systematic understanding of complex biological problems, enable drug discovery, decipher drug mechanisms of action, and discover novel biomarkers. As the demand for processing large sets of samples in an automatic manner is constantly increasing, the integration of automation platforms (nanoliter dispensers, liquid handlers, etc.) has become a routinary configuration paired with liquid chromatography-mass spectrometers. The functional integration of all of those instruments into a single unit is what we call a plate-based high-throughput proteomics platform (HT proteomics). The readout of the platform is the quantitative proteome data at the protein or peptide level. In this work, we developed a plate-based HT proteomics standard that we called the HT-sKO. The HT-sKO allows the evaluation of accuracy and the estimation of the relative limit of quantification when the target proteins vary up to 60-fold in abundance. The HT-sKO utilizes nonhuman recombinant proteins that can be spiked into the samples, allowing for sample acquisition and HT proteomics platform evaluation at the same time. We also showed the foundational role of a robust acquisition strategy for developing a stable HT proteomics platform and the value of using a tube-based method as an informant assay on data quality expectations for the platform. Using this new standard, we demonstrated that the intra- and inter-plate variance is around 4-6% for the protein level or around 10% for the peptide-level readout. We also showed that the HT-sKO standard is compatible with whole-proteome, phospho-proteome, and reactive cysteine profiling platforms.

Proteomics↗

Fungi to the rescue: recent advances, mechanistic insights and omics-based perspectives in heavy metal mycoremediation.

Heavy metal (HM) contamination arising from rapid industrialization poses critical threats to global ecosystem integrity and public health. Conventional physicochemical approaches are limited by high costs, incomplete removal, and toxic waste generation, necessitating sustainable alternatives. Mycoremediation, which harnesses the remarkable, diverse capacities of fungi to tolerate and mitigate HM stress through sophisticated biological mechanisms, has emerged as a promising and sustainable approach to address HM pollution. This review examines the sources and ecotoxicological impacts of HM pollution, alongside the intracellular and extracellular mechanisms underlying fungal tolerance and removal, including biosorption, precipitation, membrane transport, antioxidant defense, chelation, bioaccumulation, and biotransformation. It further synthesizes fungal-based bioremediation strategies, while examining how metagenomic, metatranscriptomic, transcriptomic, proteomic, and metabolomic approaches are advancing understanding of fungal community structure and active detoxification pathways. This work uniquely integrates community- and isolate-level multi-omics data, explicitly bridges mechanistic understanding with omics-driven insights, and extends this into translational roadmap for applied bioremediation.

Biodegradation, Environmental↗

Advances in proteomic technologies.

Proteomics is a rapidly emerging set of key technologies that are being used to identify proteins and map their interactions in a cellular context. With the sequencing of the human genome, the scope of proteomics has shifted from protein identification and characterization to include protein structure, function and protein-protein interactions. Technologies used in proteomic research include two-dimensional gel electrophoresis, mass spectrometry, yeast two-hybrids screens, and computational prediction programs. While some of these technologies have been in use for a long time, they are currently being applied to study physiology and cellular processes in high-throughput formats. It is the high-throughput approach that defines and characterizes modern proteomics. In this review, we discuss the current status of these experimental and computational technologies relevant to the three major aspects of proteomics-characterization of proteomes, identification of proteins, and determination of protein function. We also briefly discuss the development of new proteomic technologies that are based on recent advances in analytical and biochemical techniques, engineering, microfabrication, and computational prowess. The integration of these advances with established technologies is invaluable for the drive toward a comprehensive understanding of protein structure and function in the cellular milieu.

Electrophoresis, Gel, Two-Dimensional↗

"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↗

Multiplex proteomic approaches to sepsis research: case studies employing new technologies.

Sepsis is a multifactorial disease that provides unique challenges to the critical care physician. Diagnosis is hampered by the lack of a quantitative in vitro diagnostic test, instead, it relies on a series of clinical measures. The complex nature of the disease, with involvement of several physiologic systems, suggests a need to simultaneously monitor many clinical parameters. Novel proteomic technologies now exist that enable the multiplex measurement of multiple protein analytes from the same sample. Integration of these analytical measures with patient clinical data may provide the foundation for a better understanding of disease diagnosis, disease progression and the selection of optimal therapeutic regimen. The future challenge is the translation of these multiplex approaches from investigative research to clinical diagnostics for the greatest impact on patient treatment decisions.

Animals↗

The Karyote physico-chemical genomic, proteomic, metabolic cell modeling system.

Modeling approaches to the dynamics of a living cell are presented that are strongly based on its underlying physical and chemical processes and its hierarchical spatio-temporal organization. Through the inclusion of a broad spectrum of processes and a rigorous analysis of the multiple scale nature of cellular dynamics, we are attempting to advance cell modeling and its applications. The presentation focuses on our cell modeling system, which integrates data archiving and quantitative physico-chemical modeling and information theory to provide a seamless approach to the modeling/data analysis endeavor. Thereby the rapidly growing mess of genomic, proteomic, metabolic, and cell physiological data can be automatically used to develop and calibrate a predictive cell model. The discussion focuses on the Karyote cell modeling system and an introduction to the CellX and VirusX models. The Karyote software system integrates three elements: (1) a model-building and data archiving module that allows one to define a cell type to be modeled through its reaction network, structure, and transport processes as well as to choose the surrounding medium and other parameters of the phenomenon to be modeled; (2) a genomic, proteomic, metabolic cell simulator that solves the equations of metabolic reaction, transcription/translation polymerization and the exchange of molecules between parts of the cell and with the surrounding medium; and (3) an information theory module (ITM) that automates model calibration and development, and integrates a variety of data types with the cell dynamic computations. In Karyote, reactions may be fast (equilibrated) or slow (finite rate), and the special effects of enzymes and other minority species yielding steady-state cycles of arbitrary complexities are accounted for. These features of the dynamics are handled via rigorous multiple scale analysis. A user interface allows for an automated generation and solution of the equations of multiple timescale, compartmented dynamics. Karyote is based on a fixed intracellular structure. However, cell response to changes in the host medium, damage, development or transformation to abnormality can involve dramatic changes in intracellular structure. As this changes the nature of the cellular dynamics, a new model, CellX, is being developed based on the spatial distribution of concentration and other variables. This allows CellX to capture the self-organizing character of cellular behavior. The self-assembly of organelles, viruses, and other subcellular bodies is being addressed in a second new model, VirusX, that integrates molecular mechanics and continuum theory. VirusX is designed to study the influence of a host medium on viral self-assembly, structural stability, infection of a single cell, and transmission of disease.

Animals↗

Advancing proteomic discovery through optimized multi-stage scoring and deep learning-enhanced open search.

MOTIVATION: Protein search engines are essential for interpreting mass spectrometry data into biological insight. Current tools often face limitations in sensitivity when analyzing complex modern datasets, and lack a unified framework that effectively integrates deep learning features for both restricted and open searches, especially for scenarios aimed at discovering unknown modifications. RESULTS: We present pFind+, a high-performance search engine for data-dependent acquisition (DDA) proteomics, extending pFind. It introduces an enhanced raw scoring that delivers substantially improved pre-filtering ability, while recovering most of the computational overhead through a tailored acceleration strategy. Coupled with an enhanced rescoring framework that effectively integrates deep learning features, pFind+ uniquely supports high-sensitivity, DL-enhanced open search, enabling comprehensive PTM discovery while incorporating hardware-aware inference optimizations for practical deployment. Evaluations across diverse datasets demonstrate its superior sensitivity, with gains of 12.7%-29.3% (average 17.9%) in restricted search and 8.0%-38.4% (average 25.8%) in open search over the best existing tools.

Deep Learning↗

Molecular constituents of the postsynaptic density fraction revealed by proteomic analysis using multidimensional liquid chromatography-tandem mass spectrometry.

Protein constituents of the postsynaptic density (PSD) fraction were analysed using an integrated liquid chromatography (LC)-based protein identification system, which was constructed by coupling microscale two-dimensional liquid chromatography (2DLC) with electrospray ionization (ESI) tandem mass spectrometry (MS/MS) and an automated data analysis system. The PSD fraction prepared from rat forebrain was solubilized in 6 m guanidium hydrochloride, and the proteins were digested with trypsin after S-carbamoylmethylation under reducing conditions. The tryptic peptide mixture was then analysed with the 2DLC-MS/MS system in a data-dependent mode, and the resultant spectral data were automatically processed to search a genome sequence database for protein identification. In triplicate analyses, the system allowed assignments of 5264 peptides, which could finally be attributed to 492 proteins. The PSD contained various proteins involved in signalling transduction, including receptors, ion channel proteins, protein kinases and phosphatases, G-protein and related proteins, scaffold proteins, and adaptor proteins. Structural proteins, including membrane proteins involved in cell adhesion and cell-cell interaction, proteins involved in endocytosis, motor proteins, and cytoskeletal proteins were also abundant. These results provide basic data on a major protein set associated with the PSD and a basis for future functional studies of this important neural machinery.

Animals↗

The dynamic range of protein expression: a challenge for proteomic research.

Proteomic research, for its part, is benefiting enormously from the last decade of genomic research as we now have archived, annotated and audited sequence databases to correlate and query experimental data. While the two-dimensional electrophoresis (2-DE) gels are still a central part of proteomics, we reflect on the possibilities and realities of the current 2-DE technology with regard to displaying and analysing proteomes. Limitations of analysing whole cell/tissue lysates by 2-DE alone are discussed, and we investigate whether extremely narrow p/ranges (1 pH unit/25 cm) provide a solution to display comprehensive protein expression profiles. We are confronted with a challenging task: the dynamic range of protein expression. We believe that most of the existing technology is capable of displaying many more proteins than is currently achievable by integrating existing and new techniques to prefractionate samples prior to 2-DE display or analysis. The availability of a "proteomics toolbox", consisting of defined reagents, methods, and equipment, would assist a comprehensive analysis of defined biological systems.

Chemical Fractionation↗

[Beyond the genome to the proteome].

The sequence of the human genome has been decoded and a post-genome era is now beginning. This is the start of the protein/peptide era. Working in the fundamental research division of a pharmaceutical company and being engaged in studies on the research and development of new drugs based upon genomic information, it was clear to me that genomic information was useful, significant and indispensable in driving forward to new drug discovery. However, it is also true that we need more than that. I have faced multiple barriers in pursuing genomic information alone. There are numerous dead-end stories in genome-based new drug discovery and in many cases, those hurdles are very hard to get over. What approach can be a breakthrough of this bottleneck? I believe that one answer is research into proteins. The proteome describes all the proteins within an organism. The proteome analysis research field promises a bright future for the discovery of new drugs, diagnosis and therapeutics. I believe the mass spectrometer(MS) will be a key instrument in this research. The contribution of mass spectrometry was recently recognized in the Nobel Prize for Chemistry 2002, for 'the discovery of Soft Laser Desorption/Ionization' awarded to Mr. Koichi Tanaka. There is no doubt that the MS will play an essential role in accelerating proteome analysis. However, the present proteome analysis technology has not yet reached a sufficient scientific level. Further improvements of both hardware and software are necessary. For the hardware, improvements in sensitivity, accuracy and high-throughput, in addition to equipment for ultra-micro analysis applied to the analysis of the proteome should be addressed. Regarding software, we need to develop new chemistries for proteome analyses and propose original, user-friendly proteome analysis methods. In this review, starting with a briefing on genome-based drug discovery, I will discuss a new concept for proteome analysis based on mass spectrometry and new methodologies. This 'next generation' platform may provide a solution for the development of an integrated system that will contribute to our understanding of clinical information/bioinformatics, therapeutics, diagnosis and even drug discovery.

Amides↗

From genomics via proteomics to cellular physiology of the Gram-positive model organism Bacillus subtilis.

Complementing proteomic technologies enable an unbiased view of cellular adaptation and thus may provide a new understanding of cellular physiology, particularly for microorganisms because a major fraction of their proteome is accessible to currently available technology. In combination with transcriptional profiling expression proteomics provides access to interesting candidate genes and proteins that will then need to be validated and supplemented by traditional physiological, biochemical and genetic approaches. After a description of the current status of the technology, we display the potential of microbial proteomics using the model organism Bacillus subtilis as example. Starting from a proteome map a proteomic view of the metabolism will be provided. Furthermore, we demonstrate that proteomics complemented by transcriptomics is also useful for the study of stress and starvation responses and that integration of these data will lead to a comprehensive understanding of the adaptational network of bacterial cells. Thus, B. subtilis constitutes a highly versatile and tractable model organism for the study of generic stress responses and the expertise that has been gained can easily be transferred to the study of the cellular physiology of related Gram-positive pathogens and their pathophysiology.

Bacillus subtilis↗

Pathway mapping tools for analysis of high content data.

The complexity of human biology requires a systems approach that uses computational approaches to integrate different data types. Systems biology encompasses the complete biological system of metabolic and signaling pathways, which can be assessed by measuring global gene expression, protein content, metabolic profiles, and individual genetic, clinical, and phenotypic data. High content screening assays can also be used to generate systems biology knowledge. In this review, we will summarize the pathway databases and describe biological network tools used predominantly with this genomics, proteomics, and metabolomics data but which are equally as applicable for high content screening data analysis. We describe in detail the integrated data-mining tools applicable to building biological networks developed by GeneGo, namely, MetaCore and MetaDrug.

Computational Biology↗

Strategies for the physiome project.

The physiome is the quantitative description of the functioning organism in normal and pathophysiological states. The human physiome can be regarded as the virtual human. It is built upon the morphome, the quantitative description of anatomical structure, chemical and biochemical composition, and material properties of an intact organism, including its genome, proteome, cell, tissue, and organ structures up to those of the whole intact being. The Physiome Project is a multicentric integrated program to design, develop, implement, test and document, archive and disseminate quantitative information, and integrative models of the functional behavior of molecules, organelles, cells, tissues, organs, and intact organisms from bacteria to man. A fundamental and major feature of the project is the databasing of experimental observations for retrieval and evaluation. Technologies allowing many groups to work together are being rapidly developed. Internet II will facilitate this immensely. When problems are huge and complex, a particular working group can be expert in only a small part of the overall project. The strategies to be worked out must therefore include how to pull models composed of many submodules together even when the expertise in each is scattered amongst diverse institutions. The technologies of bioinformatics will contribute greatly to this effort. Developing and implementing code for large-scale systems has many problems. Most of the submodules are complex, requiring consideration of spatial and temporal events and processes. Submodules have to be linked to one another in a way that preserves mass balance and gives an accurate representation of variables in nonlinear complex biochemical networks with many signaling and controlling pathways. Microcompartmentalization vitiates the use of simplified model structures. The stiffness of the systems of equations is computationally costly. Faster computation is needed when using models as thinking tools and for iterative data analysis. Perhaps the most serious problem is the current lack of definitive information on kinetics and dynamics of systems, due in part to the almost total lack of databased observations, but also because, though we are nearly drowning in new information being published each day, either the information required for the modeling cannot be found or has never been obtained. "Simple" things like tissue composition, material properties, and mechanical behavior of cells and tissues are not generally available. The development of comprehensive models of biological systems is a key to pharmaceutics and drug design, for the models will become gradually better predictors of the results of interventions, both genomic and pharmaceutic. Good models will be useful in predicting the side effects and long term effects of drugs and toxins, and when the models are really good, to predict where genomic intervention will be effective and where the multiple redundancies in our biological systems will render a proposed intervention useless. The Physiome Project will provide the integrating scientific basis for the Genes to Health initiative, and make physiological genomics a reality applicable to whole organisms, from bacteria to man.

Computer Simulation↗

Proteomics in neuropsychiatric disorders.

Assessing human cerebrospinal fluid (CSF) provides a practical way to conduct longitudinal molecular analyses of changes during the course of neurological disease. Integrated and parallel analyses of neurotransmitters, neuropeptides and proteins in CSF may reveal better insights into complex interaction of numerous cell types in the central nervous system (CNS) at an unprecedented level of complexity and detail. Intricate molecular fingerprints of CSF proteins may pinpoint multiple underlying pathogenic mechanisms as well as an acute and a chronic CNS disease component. Some of these changes may be mapped to altered protein expression patterns in clinically relevant cell populations with a causative or diagnostic disease link. A CNS proteome database of primary human CNS tissues may avoid ambiguities of experimental models and accelerate pre- and clinical development of more specific diagnostic and prognostic disease markers and new selective therapeutics.

Biomarkers↗

An experimental study of chromatographic dynamics in open and packed non-cylindrical conduits.

A systematic investigation of the influence of the perimeter shape of open and particle packed fused silica capillaries on chromatographic properties such as resistance to flow and dispersion of solutes propelled through these channels has been conducted. Verifications of these uncommon experiments with existing theoretical treatments are presented and the insights transferred to a novel polymer chip design with integrated facilities for complex separations. A comparison of the chromatographic performance of a real life proteomics sample on this chip with a capillary column of "similar" dimensions is presented.

Chromatography, High Pressure Liquid↗