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PubMed2006

OMICS-driven biomarker discovery in nutrition and health.

While traditional nutrition research has dealt with providing nutrients to nourish populations, it nowadays focuses on improving health of individuals through diet. Modern nutritional research is aiming at health promotion and disease prevention and on performance improvement. As a consequence of these ambitious objectives, the disciplines "nutrigenetics" and "nutrigenomics" have evolved. Nutrigenetics asks the question how individual genetic disposition, manifesting as single nucleotide polymorphisms, copy-number polymorphisms and epigenetic phenomena, affects susceptibility to diet. Nutrigenomics addresses the inverse relationship, that is how diet influences gene transcription, protein expression and metabolism. A major methodological challenge and first pre-requisite of nutrigenomics is integrating genomics (gene analysis), transcriptomics (gene expression analysis), proteomics (protein expression analysis) and metabonomics (metabolite profiling) to define a "healthy" phenotype. The long-term deliverable of nutrigenomics is personalised nutrition for maintenance of individual health and prevention of disease. Transcriptomics serves to put proteomic and metabolomic markers into a larger biological perspective and is suitable for a first "round of discovery" in regulatory networks. Metabonomics is a diagnostic tool for metabolic classification of individuals. The great asset of this platform is the quantitative, non-invasive analysis of easily accessible human body fluids like urine, blood and saliva. This feature also holds true to some extent for proteomics, with the constraint that proteomics is more complex in terms of absolute number, chemical properties and dynamic range of compounds present. Apart from addressing the most complex "-ome", proteomics represents the only platform that delivers not only markers for disposition and efficacy but also targets of intervention. The Omics disciplines applied in the context of nutrition and health have the potential to deliver biomarkers for health and comfort, reveal early indicators for disease disposition, assist in differentiating dietary responders from non-responders, and, last but not least, discover bioactive, beneficial food components. This paper reviews the state-of-the-art of the three Omics platforms, discusses their implication in nutrigenomics and elaborates on applications in nutrition and health such as digestive health, allergy, diabetes and obesity, nutritional intervention and nutrient bioavailability. Proteomic developments, applications and potential in the field of nutrition have been specifically addressed in another review issued by our group.

Animals↗

PubMed2026

Ancient DNA as a temporal lens: reconstructing evolution, migration, and disease dynamics.

Ancient DNA (aDNA) has transformed evolutionary biology and anthropology by providing direct, chronologically validated genetic evidence over millennia. This review synthesizes significant findings from the paleogenomic era (2010-2025), demonstrating how ancient DNA has resolved persistent debates across four interconnected themes: (i) human migration and admixture, revealing complex population transitions from archaic hominins to Holocene expansions; (ii) adaptation, tracking allele frequency changes during domestication and selection; (iii) pathogen history, clarifying the origins of pandemics and the evolution of microbiomes; and (iv) ecosystem dynamics, identifying extinction causes through sedimentary DNA and conservation genomics. We contend that scientific rigor and ethical stewardship are crucial for accurate conclusions, given ancient DNA study requires the destructive collection of culturally significant remains. This review argues that continued advancement will depend on the integration of genomic data with archaeological, isotopic, and proteomic evidence, and highlights the necessity for equitable involvement with descendant communities. By conceptualizing the past as a continuum of dynamic processes rather than static events, ancient DNA provides a revised historical narrative and insights relevant to contemporary concerns in conservation, health, and social justice.

Evolution↗

PubMed2000

Using bioinformatics in gene and drug discovery.

Bioinformatics has, out of necessity, become a key aspect of drug discovery in the genomic revolution, contributing to both target discovery and target validation. The author describes the role that bioinformatics has played and will continue to play in response to the waves of genome-wide data sources that have become available to the industry, including expressed sequence tags, microbial genome sequences, model organism sequences, polymorphisms, gene expression data and proteomics. However, these knowledge sources must be intelligently integrated.

Journal Article↗

PubMed2002

Progress in bioinformatics and the importance of being earnest.

In silico biology has gathered momentum as, worldwide, scientists have united in a common quest to sequence, store and analyse complete genomes. This year, a pivotal achievement of this cooperative endeavour was realised in the release of a public draft of the human genome, and with it the promises to improve our understanding of diverse aspects of biology and to yield a healthier future with safe personalized medicines. Key to these goals will be the need to elucidate and characterise the genes and gene products encoded not just in the human genome, but in many genomes. These tasks are underpinned by the concepts and processes of genome and gene/protein evolution, regulation of gene expression, mechanisms of protein folding, the manifestation of protein function, and so on, all of which must be understood in the context of complex, dynamic biological systems. Our use of computers to model such concepts and systems must be placed in the context of the current limits of our understanding of them:- it is important to recognise, for example, that we don't have a common understanding either of what constitutes a gene or a protein function; we can't invariably say that a particular sequence or fold has arisen via divergent or convergent evolution; and we don't fully understand the rules of protein folding. Accepting what we can't do in silico is essential in appreciating what we can do. Without this understanding, it is easy to be misled, as notions of what particular computational approaches can achieve are sometimes rather optimistic. There are valuable lessons to be learned here from the field of Artificial Intelligence, principal among which is the realisation that capturing and representing complex knowledge is time consuming, expensive and hard. Thus, we argue here that if bioinformatics is to tackle biological complexity in earnest, it would be wise to absorb the experience distilled from decades of artificial intelligence research, and to approach the road ahead with caution, rigour and pragmatism.

Artificial Intelligence↗

PubMed2026

Long-read proteogenomic atlas of human neuronal differentiation reveals isoform diversity informing neurodevelopmental risk mechanisms.

RNA splicing shapes neuronal identity and disease risk, yet current maps lack the developmental resolution and depth to resolve this complexity. Here, we integrate deep long-read RNA sequencing and proteomics in induced pluripotent stem cell-derived cortical neurons to generate a high-resolution proteogenomic atlas of human neuron development. We identify 182,371 mRNA isoforms (over half previously unknown) and provide direct peptide evidence for the translation of hundreds of novel protein-coding sequences. Population genetics demonstrates that variants affecting novel exons and splice sites are under negative selection, underscoring the potential significance of these isoforms. During neuronal maturation, we observe that autism risk genes undergo dynamic isoform switching, including microexon inclusion and intron retention, that remodel key protein domains and regulatory regions. Furthermore, we uncover widespread, long-range coordination between alternative transcript processing events, including transcription start sites, exon splicing, and polyadenylation. Finally, our atlas enables variant reinterpretation in autism, highlighting the value of an isoform-centric view for interpreting pathogenic variation in neurodevelopment.

Humans↗

PubMed2006

Engineering challenges of BioNEMS: the integration of microfluidics, micro- and nanodevices, models and external control for systems biology.

Systems biology, i.e. quantitative, postgenomic, postproteomic, dynamic, multiscale physiology, addresses in an integrative, quantitative manner the shockwave of genetic and proteomic information using computer models that may eventually have 10(6) dynamic variables with non-linear interactions. Historically, single biological measurements are made over minutes, suggesting the challenge of specifying 10(6) model parameters. Except for fluorescence and micro-electrode recordings, most cellular measurements have inadequate bandwidth to discern the time course of critical intracellular biochemical events. Micro-array expression profiles of thousands of genes cannot determine quantitative dynamic cellular signalling and metabolic variables. Major gaps must be bridged between the computational vision and experimental reality. The analysis of cellular signalling dynamics and control requires, first, micro- and nano-instruments that measure simultaneously multiple extracellular and intracellular variables with sufficient bandwidth; secondly, the ability to open existing internal control and signalling loops; thirdly, external BioMEMS micro-actuators that provide high bandwidth feedback and externally addressable intracellular nano-actuators; and, fourthly, real-time, closed-loop, single-cell control algorithms. The unravelling of the nested and coupled nature of cellular control loops requires simultaneous recording of multiple single-cell signatures. Externally controlled nano-actuators, needed to effect changes in the biochemical, mechanical and electrical environment both outside and inside the cell, will provide a major impetus for nanoscience.

Animals↗

PubMed2003

An oriented peptide array library (OPAL) strategy to study protein-protein interactions.

One of the major questions in signal transduction is how the specificities of protein-protein interactions determine the assembly of distinct signaling complexes in response to stimuli. Several peptide library methods have been developed and widely used to study protein-protein interactions. These approaches primarily rely on peptide or DNA sequencing to identify the peptide or consensus motif for binding and may prove too costly or difficult to accommodate high throughput applications. We report here an oriented peptide array library (OPAL) approach that should facilitate high throughput proteomic analysis of protein-protein interactions. OPAL integrates the principles of both the oriented peptide libraries and array technologies. Hundreds of pools of oriented peptide libraries are synthesized as amino acid scan arrays. We demonstrate that these arrays can be used to map the specificities of a variety of interactions, including antibodies, protein domains such Src homology 2 domains, and protein kinases.

Animals↗

PubMed2003

From annotated genomes to metabolic flux models and kinetic parameter fitting.

Significant advances in system-level modeling of cellular behavior can be achieved based on constraints derived from genomic information and on optimality hypotheses. For steady-state models of metabolic networks, mass conservation and reaction stoichiometry impose linear constraints on metabolic fluxes. Different objectives, such as maximization of growth rate or minimization of flux distance from a reference state, can be tested in different organisms and conditions. In particular, we have suggested that the metabolic properties of mutant bacterial strains are best described by an algorithm that performs a minimization of metabolic adjustment (MOMA) upon gene deletion. The increasing availability of many annotated genomes paves the way for a systematic application of these flux balance methods to a large variety of organisms. However, such a high throughput goal crucially depends on our capacity to build metabolic flux models in a fully automated fashion. Here we describe a pipeline for generating models from annotated genomes and discuss the current obstacles to full automation. In addition, we propose a framework for the integration of flux modeling results and high throughput proteomic data, which can potentially help in the inference of whole-cell kinetic parameters.

Algorithms↗

PubMed2005

Focusing of gene expression as the basis of stem cell differentiation.

In a prior report (Stem Cells Dev 14(4):354-366, 2005), we employed two-dimensional gel electrophoresis followed by advanced proteomics and the Database for Annotation, Visualization and Integrated Discovery (DAVID) to compare the protein expression profiles of mesenchymal stem cells to that of fully differentiated osteoblasts. These data were reported to advance technical approaches to define the basis of differentiation, but also led us to suggest that osteogenic differentiation of stem cells may result from the focusing of gene expression in functional clusters (e.g., calcium-regulated signaling proteins or adherence proteins) rather than simply from the induced expression of new genes, as many have assumed. Here, we have employed these analytical techniques to compare protein expression by mesenchymal stem cells directly with that of cells derived from them after induced osteogenic differentiation. Our results support the concept of gene focusing as the basis of differentiation. Specifically, induced differentiation results in a decrease in the number of mesenchymal cell markers and calcium-mediated signaling molecules expressed by their differentiated progeny. This effect was seen in parallel to increased expression of specific extracellular matrix (ECM) molecules and their receptors. These results strongly imply that changes in the ECM have a direct impact on stem cell differentiation, and that osteogenic differentiation of stem cells directed by matrix clues results from focusing of the expression of genes involved in Ca2+-dependent signaling pathways.

Calcium Signaling↗

PubMed2001

Individual metabolism should guide agriculture toward foods for improved health and nutrition.

Genomics and bioinformatics have the vast potential to identify genes that cause disease by investigating whole-genome databases. Comparison of an individual's geno-type with a genomic database will allow the prescription of drugs to be tailored to an individual's genotype. This same bioinformatic approach, applied to the study of human metabolites, has the potential to identify and validate targets to improve personalized nutritional health and thus serve to define the added value for the next generation of foods and crops. Advances in high-throughput analytic chemistry and computing technologies make the creation of a vast database of metabolites possible for several subsets of metabolites, including lipids and organic acids. In creating integrative databases of metabolites for bioinformatic investigation, the current concept of measuring single biomarkers must be expanded to 3 dimensions to 1) include a highly comprehensive set of metabolite measurements (a profile) by multiparallel analyses, 2) measure the metabolic profile of individuals over time rather than simply in the fasted state, and 3) integrate these metabolic profiles with genomic, expression, and proteomic databases. Application of the knowledge of individual metabolism will revolutionize the ability of nutrition to deliver health benefits through food in the same way that knowledge of genomics will revolutionize individual treatment of dis-ease with pharmaceuticals.

Biomarkers↗

PubMed2005

A query language for biological networks.

MOTIVATION: Many areas of modern biology are concerned with the management, storage, visualization, comparison and analysis of networks, but no appropriate query language for such complex data structures yet exists. RESULTS: We have designed and implemented the pathway query language (PQL) for querying large protein interaction or pathway databases. PQL is based on a simple graph data model with extensions reflecting properties of biological objects. Queries match subgraphs in the database based on node properties and paths between nodes. The syntax is easy to learn for anybody familiar with SQL. As an important feature, a query may require a certain structure in the database to exist but return a different subgraph. We have tested PQL queries on networks of up to 16,000 nodes and found it to scale very well. AVAILABILITY: The code is available on request from the author.

Computational Biology↗

PubMed2005

The European Bioinformatics Institute's data resources: towards systems biology.

Genomic and post-genomic biological research has provided fine-grain insights into the molecular processes of life, but also threatens to drown biomedical researchers in data. Moreover, as new high-throughput technologies are developed, the types of data that are gathered en masse are diversifying. The need to collect, store and curate all this information in ways that allow its efficient retrieval and exploitation is greater than ever. The European Bioinformatics Institute's (EBI's) databases and tools have evolved to meet the changing needs of molecular biologists: since we last wrote about our services in the 2003 issue of Nucleic Acids Research, we have launched new databases covering protein-protein interactions (IntAct), pathways (Reactome) and small molecules (ChEBI). Our existing core databases have continued to evolve to meet the changing needs of biomedical researchers, and we have developed new data-access tools that help biologists to move intuitively through the different data types, thereby helping them to put the parts together to understand biology at the systems level. The EBI's data resources are all available on our website at http://www.ebi.ac.uk.

Computational Biology↗

PubMed2006

Clinical assessment of endothelial dysfunction: combine and rule.

PURPOSE OF REVIEW: To briefly survey the literature for the past 2-3 years on the subject of diagnostic and prognostic markers of endothelial cell dysfunction. RECENT FINDINGS: Established and emerging techniques to detect endothelial cell dysfunction are divided into two large families: (1) functional and structural markers of endothelial cell dysfunction, and (2) surrogate markers of endothelial cell dysfunction. The first category includes high-resolution Doppler studies of conduit arteries, laser Doppler flowmetry of cutaneous microvasculature, and measurements of arterial stiffness and intima: media thickness. The second category of tests provides brief descriptions of detection of oxidative stress, inflammation, coagulation abnormalities, circulating endothelial cells and microparticles. I also focus on integration of various techniques and some novel genomic, proteomic and metabolomic tests. SUMMARY: This snapshot of existing and emerging technologies diagnosing endothelial cell dysfunction is intended to provide the foundation for the future (a) development of personalized noninvasive screening of endothelial function, and (b) delineation of preclinical diagnostic biomarkers heralding cardiovascular complications.

Biomarkers↗

PubMed2025

Physiological and metabolic responses of Zymomonas mobilis to lignocellulosic hydrolysate.

Zymomonas mobilis is a promising biocatalyst for the sustainable conversion of lignocellulosic sugars into biofuels and bioproducts, yet its response to lignocellulosic hydrolysates remains poorly understood. Here, we investigate the physiological response of Z. mobilis to ammonia fiber expansion (AFEX)-pretreated switchgrass hydrolysate using a systems-level approach integrating LC-MS/MS-based lipidomics and shotgun proteomics. Growth on hydrolysate induced substantial shifts in fatty acid and membrane phospholipid composition, alongside broad proteomic remodeling. Notably, Z. mobilis exhibited a stress response characterized by the upregulation of heat shock proteins and efflux transporters and the downregulation of cell motility proteins. Unexpectedly, hydrolysate exposure also led to a robust upregulation of the Entner-Doudoroff pathway, the ethanol fermentation pathway, and other central carbon metabolism enzymes, indicating a substantial cellular investment potentially driven by additional nutrient availability in hydrolysate. These findings provide new insights into the metabolic adaptations of Z. mobilis to lignocellulosic hydrolysates, informing strategies to enhance its biofuel production capabilities.IMPORTANCEBiomass pretreatment processes release fermentable sugars from lignocellulosic biomass, but they also generate inhibitors that can impact microbial metabolism. This study provides a systems-level evaluation of how Zymomonas mobilis responds to hydrolysate stress, revealing distinct physiological and lipid membrane remodeling responses. While some stress responses overlap with those induced by ethanol and isobutanol toxicity, both valuable biofuels, hydrolysate exposure elicits unique metabolic shifts. These findings offer valuable insights for engineering Z. mobilis strains with improved tolerance and performance for efficient bioconversion of lignocellulosic hydrolysates into biofuels and bioproducts.

Zymomonas↗

PubMed2026

Necroptosis in alveolar epithelium orchestrates lung ischemia-reperfusion injury: a multi-omics study.

BACKGROUND: Lung ischemia-reperfusion injury (LIRI) is a leading cause of early morbidity and mortality following lung transplantation and other cardiopulmonary procedures. It is characterized by acute sterile inflammation driven by regulated cell death (RCD). While various RCD modalities, including apoptosis, necroptosis, pyroptosis, and ferroptosis, have been implicated in lung injury, their relative contributions and distinct activation patterns in LIRI remain poorly defined. METHODS: We employed an integrated multi-omics approach combining transcriptomics and proteomics with histological and functional validations in a murine hilar clamping model of LIRI. Key findings were further corroborated using single-cell RNA sequencing (scRNA-seq) data from human lung transplant recipients. The functional role of necroptosis was validated using pharmacological inhibitors (Nec-1, GSK'872) and Mlkl-deficient (Mlkl-/-) mice. RESULTS: LIRI triggered acute, time-dependent lung injury peaking within 24 h of reperfusion. Although transcriptomic profiling suggested broad activation of multiple RCD pathways, proteomic and biochemical analyses revealed a distinct landscape in our experimental setting: markers of apoptosis, pyroptosis, and ferroptosis were either downregulated or showed no significant positive correlation with injury severity and inflammatory peaks. In contrast, the necroptotic pathway emerged as a highly activated modality. Specifically, necroptosis, marked by phosphorylated RIPK1, RIPK3, and MLKL, was localized primarily in alveolar epithelial cells, correlated strongly with cytokine release and histological lung injury, and preceded the inflammatory response. Pharmacological inhibition or genetic ablation of necroptosis significantly attenuated tissue damage and inflammation. This pronounced necroptotic signature appeared distinct from the broad multi-pathway activation observed in lipopolysaccharide (LPS)-induced lung injury. Translational analysis of human scRNA-seq data further confirmed the selective upregulation of necroptosis signatures in alveolar type 2 (AT2) cells following lung transplantation. CONCLUSION: Our multi-omics analysis identifies necroptosis, particularly in alveolar epithelial cells, as a critical driver of sterile inflammation and tissue injury in the early phase of LIRI. Targeting alveolar epithelial necroptosis may represent a precise and promising therapeutic strategy for lung transplantation and ischemia-reperfusion-associated pulmonary disorders.

Animals↗

PubMed2006

The promise of personalized medicine: a conversation with Michael Svinte. Interview by Michael Millenson.

In early 2004, IBM combined its Healthcare unit, which focused on the technology needs of providers, with its Life Sciences unit, which catered to research scientists. Out of that union was born an "emerging business opportunity" called information-based medicine, in which IBM has already invested tens of millions in the expectation of reaping billions of dollars in revenues. Michael Svinte describes his mission as providing the information technology infrastructure that will enable technologies such as proteomics and molecular imaging to progress from the bench to the bedside, thereby resulting in predictive and personalized health care.

Academic Medical Centers↗

PubMed2005

Modeling and designing a proteomics application on PROTEUS.

OBJECTIVES: Biomedical applications, such as analysis and management of mass spectrometry proteomics experiments, involve heterogeneous platforms and knowledge, massive data sets, and complex algorithms. Main requirements of such applications are semantic modeling of the experiments and data analysis, as well as high performance computational platforms. In this paper we propose a software platform allowing to model and execute biomedical applications on the Grid. METHODS: Computational Grids offer the required computational power, whereas ontologies and workflow help to face the heterogeneity of biomedical applications. In this paper we propose the use of domain ontologies and workflow techniques for modeling biomedical applications, whereas Grid middleware is responsible for high performance execution. As a case study, the modeling of a proteomics experiment is discussed. RESULTS: The main result is the design and first use of PROTEUS, a Grid-based problem-solving environment for biomedical and bioinformatics applications. CONCLUSION: To manage the complexity of biomedical experiments, ontologies help to model applications and to identify appropriate data and algorithms, workflow techniques allow to combine the elements of such applications in a systematic way. Finally, translation of workflow into execution plans allows the exploitation of the computational power of Grids. Along this direction, in this paper we present PROTEUS discussing a real case study in the proteomics domain.

Algorithms↗

PubMed2002

Functional genomics of the social amoebae, Dictyostelium discoideum.

Dictyostelium discoideum is one of the simplest organisms to form a multicellular structure, and it offers several advantages as a model. In order to understand the genetic basis of the multicellular development, a comprehensive analysis of cDNAs is being performed. To date, about 75,000 ESTs have been collected at different stages of development. They have been assembled into about 6,400 independent sequences that represent 70-80% of all of the expected genes in D. discoideum. The results are available on the Internet. In addition to structural analyses, functional analyses of the temporal and spatial expression patterns and gene targeting are being carried out. Furthermore, there are plans to combine the information that is obtained from the cDNA, Genome, and Proteome Projects, as well as the published results, into an integrated database, DictyBase.

Animals↗