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PubMed2006

JVirGel 2.0: computational prediction of proteomes separated via two-dimensional gel electrophoresis under consideration of membrane and secreted proteins.

MOTIVATION: After the publication of JVirGel 1.0 in 2003 we got many requests and suggestions from the proteomics community to further improve the performance of the software and to add additional useful new features. RESULTS: The integration of the PrediSi algorithm for the prediction of signal peptides for the Sec-dependent protein export into JVirGel 2.0 allows the exclusion of most exported preproteins from calculated proteomic maps and provides the basis for the calculation of Sec-based secretomes. A tool for the identification of transmembrane helices carrying proteins (JCaMelix) and the prediction of the corresponding membrane proteome was added. Finally, in order to directly compare experimental and calculated proteome data, a function to overlay and evaluate predicted and experimental two-dimensional gels was included. AVAILABILITY: JVirGel 2.0 is freely available as precompiled package for the installation on Windows or Linux operating systems. Furthermore, there is a completely platform-independent Java version available for download. Additionally, we provide a Java Server Pages based version of JVirGel 2.0 which can be operated in nearly all web browsers. All versions are accessible at http://www.jvirgel.de

Algorithms↗

PubMed2005

The next wave in metabolome analysis.

The metabolome of a cell represents the amplification and integration of signals from other functional genomic levels, such as the transcriptome and the proteome. Although this makes metabolomics a useful tool for the high-throughput analysis of phenotypes, the lack of a direct connection to the genome makes it difficult to interpret metabolomic data. Nevertheless, functional genomics has produced examples of the use of metabolomics to elucidate the phenotypes of otherwise silent mutations. Despite several successes, we believe that future metabolomic studies must focus on the accurate measurement of the concentrations of unambiguously identified metabolites. The research community must develop databases of metabolite concentrations in cells that are grown in several well-defined conditions if metabolomic data are to be integrated meaningfully with data from the other levels of functional-genomic analysis and to make a significant contribution to systems biology.

Animals↗

PubMed2006

Identification of disease markers in human cerebrospinal fluid using lipidomic and proteomic methods.

Lipids comprise the bulk of the dry mass of the brain. In addition to providing structural integrity to membranes, insulation to cells and acting as a source of energy, lipids can be rapidly converted to mediators of inflammation or to signaling molecules that control molecular and cellular events in the brain. The advent of soft ionization procedures such as electrospray ionization (ESI) and atmospheric pressure chemical ionization (APCI) have made it possible for compositional studies of the diverse lipid structures that are present in brain. These include phospholipids, ceramides, sphingomyelin, cerebrosides, cholesterol and their oxidized derivatives. Lipid analyses have delineated metabolic defects in disease conditions including mental retardation, Parkinson's Disease (PD), schizophrenia, Alzheimer's Disease (AD), depression, brain development, and ischemic stroke. In this review, we examine the structure of the major lipid classes in the brain, describe methods used for their characterization, and evaluate their role in neurological diseases. The potential utility of characterizing lipid markers in the brain, with specific emphasis on disease mechanisms, will be discussed. Additionally, we describe several proteomic strategies for characterizing lipid-metabolizing proteins in human cerebrospinal fluid (CSF). These proteins may be potential therapeutic targets since they transport lipids required for neuronal growth or convert lipids into molecules that control brain physiology. Combining lipidomics and proteomics will enhance existing knowledge of disease pathology and increase the likelihood of discovering specific markers and biochemical mechanisms of brain diseases.

Biomarkers↗

PubMed2006

LEGER: knowledge database and visualization tool for comparative genomics of pathogenic and non-pathogenic Listeria species.

Listeria species are ubiquitous in the environment and often contaminate foods because they grow under conditions used for food preservation. Listeria monocytogenes, the human and animal pathogen, causes Listeriosis, an infection with a high mortality rate in risk groups such as immune-compromised individuals. Furthermore, L.monocytogenes is a model organism for the study of intracellular bacterial pathogens. The publication of its genome sequence and that of the non-pathogenic species Listeria innocua initiated numerous comparative studies and efforts to sequence all species comprising the genus. The Proteome database LEGER (http://leger2.gbf.de/cgi-bin/expLeger.pl) was developed to support functional genome analyses by combining information obtained by applying bioinformatics methods and from public databases to improve the original annotations. LEGER offers three unique key features: (i) it is the first comprehensive information system focusing on the functional assignment of genes and proteins; (ii) integrated visualization tools, KEGG pathway and Genome Viewer, alleviate the functional exploration of complex data; and (iii) LEGER presents results of systematic post-genome studies, thus facilitating analyses combining computational and experimental results. Moreover, LEGER provides an unpublished membrane proteome analysis of L.innocua and in total visualizes experimentally validated information about the subcellular localizations of 789 different listerial proteins.

Bacterial Proteins↗

PubMed2026

Unlocking the Full Potential of Spatial Omics in Plants: Practical Challenges, Solutions, and a Path Forward.

Spatial omics technologies are providing new opportunities for plant biology by enabling molecular profiling within structurally intact tissues, revealing spatially organised cell states, developmental gradients, and regulatory interactions. While spatial transcriptomics has driven early advances, the field is rapidly expanding toward integrated spatial multi-omics by combining single-cell and spatial transcriptomic, epigenomic, proteomic, and metabolomic data. These approaches offer new opportunities to study development, physiology, and plant biotic and abiotic interactions in spatially preserved cellular contexts. However, despite rapid adoption, the field remains constrained by plant-specific challenges when applying technologies largely developed for animal systems. Compared with animal systems, plant tissues pose additional challenges due to rigid cell walls, and diverse chemistries, complicating sample preparation, cell and subcellular segmentation, signal detection, and data integration. As a result, many studies rely on bespoke protocols and analysis pipelines that are often difficult to reproduce or generalise. Here, we provide a practical, solution-oriented synthesis of current bottlenecks across experimental and computational pipelines, highlight emerging strategies to overcome these limitations, and propose a roadmap for community-driven protocol sharing, benchmarking, and integration across spatial and multi-omics modalities. Addressing these challenges will be essential to establish spatial omics as a routine and scalable tool for plant biology.

Journal Article↗

PubMed2026

Toward large-scale mass spectrometry-based omics for clinical applications.

INTRODUCTION: As healthcare advances toward personalized medicine, mass spectrometry-based research is advancing our understanding of cellular biology and disease states, and translating these findings into clinical applications. This review highlights recent advances in methodology and technology that demonstrate the capabilities of mass spectrometry-based proteomics, lipidomics, and metabolomics in clinical practice. AREAS COVERED: The ability to directly analyze functional molecules with mass spectrometry uncovers crucial clinical information. Each data modality (proteins, lipids, and metabolites) provides essential insight into healthy and disease states. As technology advances, integrating data from different modalities unlocks new possibilities for clinical research. To gain the most from this multi-omic data, unsupervised integration methods can provide detailed insights into complex biological processes. As the field applies this knowledge, healthcare could experience significant leaps in the near future. This review examines recent advancements in mass spectrometry-based proteomics, lipidomics, and metabolomics, focusing on how improvements in sample preparation, automation, and multi-omics data integration are making large-scale clinical studies more accessible. EXPERT OPINION: Recent technical and methodological advancements in mass spectrometry analysis have propelled healthcare toward a tipping point, shifting from traditional RNA- and DNA-based research to downstream analysis of protein, lipid, and metabolite effectors.

Humans↗

PubMed2004

Reconstruction and functional characterization of the human mitochondrial metabolic network based on proteomic and biochemical data.

Diverse datasets including genomic, proteomic, isotopomer, and DNA sequence variation are becoming available for human mitochondria. Thus there is a need to integrate these data within an in silico modeling framework where mitochondrial biology and related disorders can be studied and analyzed. This paper reports a reconstruction and characterization of the human mitochondrial metabolic network based on proteomic and biochemical data. The 189 reactions included in this reconstruction are both elementally and charge-balanced and are assigned to their respective cellular compartments (mitochondrial, cytosol, or extracellular). The capabilities of the reconstructed network to fulfill three metabolic functions (ATP production, heme synthesis, and mixed phospholipid synthesis) were determined. Network-based analysis of the mitochondrial energy conversion process showed that the overall ATP yield per glucose is 31.5. Network flexibility, characterized by allowable variation in reaction fluxes, was evaluated using flux variability analysis and analysis of all of the possible optimal flux distributions. Results showed that the network has high flexibility for the biosynthesis of heme and phospholipids but modest flexibility for maximal ATP production. A subset of all of the optimal network flux distributions, computed with respect to the three metabolic functions individually, was found to be highly correlated, suggesting that this set may contain physiological meaningful fluxes. Examinations of optimal flux distributions also identified correlated reaction sets that form functional modules in the network.

Amino Acids↗

PubMed2006

Proteomic analysis of acute promyelocytic leukemia: PML-RARalpha leads to decreased phosphorylation of OP18 at serine 63.

In the present study, we employed 2-DE to characterize the effect of the acute promyelocytic leukemia (APL)-specific PML-RARalpha fusion protein on the proteome. Differentially expressed proteins, a number of which are related to the cell cycle function, including oncoprotein18 (OP18), heat shock protein70, glucose-regulated protein75, and peptidyl-prolyl isomerase, were identified by MS. Subsequent bioinformatic pathway discovery revealed an integrated network constituting SMARCB1, MYC, and TP53-regulated pathways. The data from the DNA microarray and proteomic experiments demonstrated the correlation between the translocation and higher expression of OP18 at mRNA and protein levels. Transient cotransfection assay revealed that PML-RARalpha is a potent activator of OP18 promoter and this transcriptional activation is retinoic acid sensitive. PML-RARalpha induction also leads to decreased phosphorylation on Ser63 residue of OP18, which is okadaic acid sensitive suggesting the involvement of a phosphatase pathway. Overexpression of a constitutively phosphorylated Ser63 mutant of OP18 in PML-RARalpha expressing APL patient, PR9, and NB4 cells led to a G2/M-phase arrest in contrast to a phosphorylation-deficient Ser63 mutant and untransfected control. Taken together, our results demonstrate the significance of decreased Ser63 phosphorylation of OP18 in PML-RARalpha-mediated effects on cell cycle.

Cell Line↗

PubMed2006

One hundred years of high-throughput Drosophila research.

From the beginning, Drosophila was a high-throughput model organism. Unbiased and genome-wide efforts ranging from Morgan's search for spontaneous mutations and subsequent saturating loss-of-function and gain-of-function screens up to more recent techniques such as microarrays, proteomics and cellular assays have been and will continue to be the backbone of Drosophila research. Integrating these large datasets is one of the next challenges. However, once achieved, a plethora of information far exceeding the information content of the singular experiments will be revealed. Several high-throughput techniques and experimental strategies highlighting the unbiased and integrative nature of Drosophila research during the last century will be discussed.

Animals↗

PubMed2026

Insights into Genome Ejection by a Therapeutic phiKMV-like Bacteriophage.

Ar-KM is a phiKMV-like therapeutic bacteriophage used in clinical candidate phage therapy cocktails to treat lung infections caused by P. aeruginosa. Here, we present an integrative structural atlas of Ar-KM proteins using cryo-EM, proteomics, and bioinformatics. From a single purified Ar-KM preparation, we identified three distinct populations: mature DNA-filled virions, open-nozzle particles with ejection proteins extending from the tail, and closed-nozzle empty particles. Near-atomic-resolution reconstructions of all three states enabled us to build atomic models for eleven structural proteins. The mature virion revealed the pre-ejection conformation of three ejection proteins, gp41, gp42, and gp43, homologous to coliphage T7's gp14, gp15, and gp16, respectively. Unlike T7, peptidoglycan hydrolase activity associated with the ejectosome resides in the gp15-like periplasmic tunnel protein gp42, whereas in T7 the lysozyme-like domain is located at the N-terminus of gp16, underscoring the structural plasticity and evolutionary mosaicity of ejection proteins. We further identified a short α-helical factor, gp34, present in eight copies at the mismatched interface between the portal barrel and gp41. Gp34 forms a cage within the nozzle, acting as a molecular wedge that stabilizes the open conformation and permits gp41 to assemble into a hexameric channel during ejection. Evolutionarily, gp34 appears to be an ortholog of the essential gene gp7.3 in phage T7 and is conserved across sequenced phiKMV-like phages. We propose that this protein functions as an ejection protein assembly factor, stabilizing the open nozzle during infection and allowing the coordinated exit of ejection proteins and their assembly into a DNA-ejectosome.

Pseudomonas phages↗

PubMed2003

Protein structural class identification directly from NMR spectra using averaged chemical shifts.

Knowledge of the three-dimensional structure of proteins is integral to understanding their functions, and a necessity in the era of proteomics. A wide range of computational methods is employed to estimate the secondary, tertiary, and quaternary structures of proteins. Comprehensive experimental methods, on the other hand, are limited to nuclear magnetic resonance (NMR) and X-ray crystallography. The full characterization of individual structures, using either of these techniques, is extremely time intensive. The demands of high throughput proteomics necessitate the development of new, faster experimental methods for providing structural information. As a first step toward such a method, we explore the possibility of determining the structural classes of proteins directly from their NMR spectra, prior to resonance assignment, using averaged chemical shifts. This is achieved by correlating NMR-based information with empirical structure-based information available in widely used electronic databases. The results are analyzed statistically for their significance. The robustness of the method as a structure predictor is probed by applying it to a set of proteins of unknown structure. Our results show that this NMR-based method can be used as a low-resolution tool for protein structural class identification.

Algorithms↗

PubMed2006

Animal models that elucidate basic principles of the developmental origins of adult diseases.

Human epidemiological and animal laboratory studies show that suboptimal environments in the womb and during early neonatal life alter development and predispose the individual to lifelong health problems. The concept of the developmental origins of adult diseases has become well accepted because of the compelling animal studies that have precisely defined the outcomes of specific exposures such as nutrient restriction, overfeeding during pregnancy, maternal stress, and exogenously administered glucocorticoids. This review focuses on the use of animal models to evaluate exposures, mechanisms, and outcomes involved in developmental programming of hypertension, diabetes, obesity, and altered pituitary-adrenal function in offspring in later life. Ten principles of developmental programming are described as fundamental, regardless of the exposure during development and the physiological system involved in the altered outcome. The 10 principles are discussed in the context of the physiological systems involved and the animal model studies that have been conducted to evaluate exposures, mechanisms, and outcomes. For example, the fetus responds to challenges such as hypoxia and nutrient restriction in ways that help to ensure its survival, but this "developmental plasticity" may have long-term consequences that may not be beneficial in adult life. To understand developmental programming, which represents the interaction of nature and nurture, it is necessary to integrate whole animal systems physiology, in vitro cellular biology, and genomic and proteomic approaches, and to use animal models that are carefully characterized and appropriate for the questions under study. Animal models play an important role in this evaluation because they permit combined in vivo and in vitro study at different critical time windows during the exposure and the ensuing developmental responses.

Animals↗

PubMed2003

Trace element biology: the knowledge base and its application for the nutrition of individuals and populations.

Impressive strides are being made in the understanding of trace element metabolism and function. This is underscored by the many contributions in these proceedings. However, not so impressive are: i) the precise recognition of mild trace element deficiencies and how to establish their functional consequences, possibly confounded by concurrent trace element inadequacies, are difficult to assess, ii) approaches to the quantitative determination of requirements for trace elements remain unsatisfactory and archaic, in so many ways, iii) our understanding of the extent of the biological basis for the variation in requirements among apparently similar individuals is poor, and iv) much needs to be learned about the quantitative extent to which genetic, epigenetic and dietary factors interact to determine the nutritional phenotype. Some ideas are presented as to how we might embrace, in the context of a reconstructive approach, the exciting new knowledge and related techniques emerging during the postgenome era and develop new paradigms for assessing trace element needs and status, and for establishing effective nutrient intake under different conditions of complex genotype-environment interactions. Metabolites are functional cellular entities and I also urge a vigorous application of metabolomics and of metabolic profiling that is closely linked with genomics, proteomics, trace element kinetics and system analysis, as components of the new integrative paradigm. We need to understand the system and its strategy, not only the molecular details of its component parts and its individual controls. An interdisciplinary research and teaching enterprise will be necessary to best achieve this aim. All of this is related to our common goal to promote, through expanded biological knowledge and its effective application, the enhanced role of trace elements for human well-being.

Animals↗

PubMed2001

Functional genomic research of alpha 1-adrenoceptors.

The Human Genome Project is now almost completed, and we are about to move into the post-genome sequence era of functional genomics. The advent of genome science has markedly changed the way life science research including pharmacological study is conducted; thus, systematic and integrated 'genome-wide' survey is feasible. The stream of 'Genome-->Transcriptome--> Proteomics' is logical and, in each aspect, approaches for functional genomics are now pursued at a high pace. We have recently developed a standardized technical platform (in various levels, such as transcription, cell and whole animal levels, etc.), and applied these techniques to the study of functional genomics of G-protein-coupled receptors, particularly alpha1-adrenoceptors as a model. Combining the genome information and technology, future pharmacological studies would become the genome-based search and research.

Animals↗

PubMed2006

Honing the in silico toolkit for detecting protein disorder.

Not all proteins form well defined three-dimensional structures in their native states. Some amino-acid sequences appear to strongly favour the disordered state, whereas some can apparently transition between disordered and ordered states under the influence of changes in the biological environment, thereby playing an important role in processes such as signalling. Although important biologically, for the structural biologist disordered regions of proteins can be disastrous even preventing successful structure determination. The accurate prediction of disorder is therefore important, not least for directing the design of expression constructs so as to maximize the chances of successful structure determination. Such design criteria have become integral to the construct-design strategies of laboratories within the Structural Proteomics In Europe (SPINE) consortium. This paper assesses the current state of the art in disorder prediction in terms of prediction reliability and considers how best to use these methods to guide construct design. Finally, it presents a brief discussion as to how methods of prediction might be improved in the future.

Algorithms↗

PubMed2005

Comparative study of apoptosis-related gene loci in human, mouse and rat genomes.

Many genes are involved in mammalian cell apoptosis pathway. These apoptosis genes often contain characteristic functional domains, and can be classified into at least 15 functional groups, according to previous reports. Using an integrated bioinformatics platform for motif or domain search from three public mammalian proteomes (International Protein Index database for human, mouse, and rat), we systematically cataloged all of the proteins involved in mammalian apoptosis pathway. By localizing those proteins onto the genomes, we obtained a gene locus centric apoptosis gene catalog for human, mouse and rat. Further phylogenetic analysis showed that most of the apoptosis related gene loci are conserved among these three mammals. Interestingly, about one-third of apoptosis gene loci form gene clusters on mammal chromosomes, and exist in the three species, which indicated that mammalian apoptosis gene orders are also conserved. In addition, some tandem duplicated gene loci were revealed by comparing gene loci clusters in the three species. All data produced in this work were stored in a relational database and may be viewed at http://pcas.cbi.pku.edu.cn/database/apd.php.

Animals↗

PubMed2026

Ten quick tips for spatial transcriptomics analysis.

Spatial transcriptomics (ST) enables genome-wide gene expression profiling while retaining spatial context within tissue sections. Since the foundational work by Ståhl et al. in 2016, the field has expanded rapidly, with diverse platforms now spanning sequencing-based (e.g., Visium, Visium HD, Slide-seq, Stereo-seq, and Seq-Scope) and imaging-based (e.g., MERFISH, Xenium, and CosMx SMI) approaches. The breadth of platforms, data structures, and computational tools, however, can be daunting for newcomers. Here, we present ten quick tips spanning the entire ST research workflow: whether ST suits a given biological question, how to select a platform aligned with study objectives, how to understand and process ST data, and which software tools to employ for analysis and visualization. We further discuss interpreting spatial patterns in biological context, integrating complementary modalities such as single-cell RNA sequencing and spatial proteomics, and leveraging public datasets and sharing results. Finally, we highlight current limitations of ST, particularly the challenge of reconstructing three-dimensional tissue architecture from serial tissue sections. This review provides biologists, bioinformaticians, and clinician-scientists with a concise, platform-neutral roadmap for incorporating ST into research, from experimental design to biological discovery.

Spatial Transcriptomics↗

PubMed2024

Digital pathology and spatial omics in steatohepatitis: Clinical applications and discovery potentials.

Steatohepatitis with diverse etiologies is the most common histological manifestation in patients with liver disease. However, there are currently no specific histopathological features pathognomonic for metabolic dysfunction-associated steatotic liver disease, alcohol-associated liver disease, or metabolic dysfunction-associated steatotic liver disease with increased alcohol intake. Digitizing traditional pathology slides has created an emerging field of digital pathology, allowing for easier access, storage, sharing, and analysis of whole-slide images. Artificial intelligence (AI) algorithms have been developed for whole-slide images to enhance the accuracy and speed of the histological interpretation of steatohepatitis and are currently employed in biomarker development. Spatial biology is a novel field that enables investigators to map gene and protein expression within a specific region of interest on liver histological sections, examine disease heterogeneity within tissues, and understand the relationship between molecular changes and distinct tissue morphology. Here, we review the utility of digital pathology (using linear and nonlinear microscopy) augmented with AI analysis to improve the accuracy of histological interpretation. We will also discuss the spatial omics landscape with special emphasis on the strengths and limitations of established spatial transcriptomics and proteomics technologies and their application in steatohepatitis. We then highlight the power of multimodal integration of digital pathology augmented by machine learning (ML)algorithms with spatial biology. The review concludes with a discussion of the current gaps in knowledge, the limitations and premises of these tools and technologies, and the areas of future research.

Humans↗