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In conclusion, array-based technologies have emerged that contribute to profiling tissues at the genomic, transcriptomic and proteomic levels. Analytical tools are needed to mine the vast amount of data generated. Ultimately the molecular analysis of cancer at a genome and proteome scale will allow better classification of disease and tailored individualized therapy for individual patients.
Biosensors are devices that combine a biochemical recognition/binding element (ligand) with a signal conversion unit (transducer). Biosensors are already used for several clinical applications, for example for electrochemical measurement of blood glucose concentrations. Application of biosensors in cancer clinical testing has several potential advantages over other clinical analysis methods including increased assay speed and flexibility, capability for multi-target analyses, automation, reduced costs of diagnostic testing and a potential to bring molecular diagnostic assays to community health care systems and to underserved populations. They have the potential for facilitating Point of Care Testing (POCT), where state-of-the-art molecular analysis is carried out without requiring a state-of-the-art laboratory. However, not many biosensors have been developed for cancer-related testing. One major challenge in harnessing the potential of biosensors is that cancer is a very complex set of diseases. Tumors vary widely in etiology and pathogenesis. Oncologists rely heavily on histological characterization of tumors and a few biomarkers that have demonstrated clinical utility to aid in patient management decisions. New genomic and proteomic molecular tools are being used to profile tumors and produce "molecular signatures." These signatures include genetic and epigenetic signatures, changes in gene expression, protein profiles and post-translational modifications of proteins. These molecular signatures provide new opportunities for utilizing biosensors. Biosensors have enormous potential to deliver the promise of new molecular diagnostic strategies to patients. This article describes some of the basic elements of cancer biology and cancer biomarkers relevant for the development of biosensors for cancer clinical testing, along with the challenges in using this approach.
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International consensus has been reached on the principles regarding evaluation of the food safety of genetically modified plants. The concept of substantial equivalence has been developed as part of a safety evaluation framework, based on the idea that existing foods can serve as a basis for comparing the properties of genetically modified foods with the appropriate counterpart. Application of the concept is not a safety assessment per se, but helps to identify similarities and differences between the existing food and the new product, which are then subject to further toxicological investigation. Substantial equivalence is a starting point in the safety evaluation, rather than an endpoint of the assessment. Consensus on practical application of the principle should be further elaborated. Experiences with the safety testing of newly inserted proteins and of whole genetically modified foods are reviewed, and limitations of current test methodologies are discussed. The development and validation of new profiling methods such as DNA microarray technology, proteomics, and metabolomics for the identification and characterization of unintended effects, which may occur as a result of the genetic modification, is recommended. The assessment of the allergenicity of newly inserted proteins and of marker genes is discussed. An issue that will gain importance in the near future is that of post-marketing surveillance of the foods derived from genetically modified crops. It is concluded, among others that, that application of the principle of substantial equivalence has proven adequate, and that no alternative adequate safety assessment strategies are available.
Structural coloration, a physical phenomenon observed in many living organisms, may arise from the interference of light with highly organized surface nanostructures. In some seaweeds, these nanostructures consist of cuticular lamellae in the outer part of the extracellular matrix (ECM) of the epidermis. However, the chemical composition of seaweed cuticles is poorly understood and the molecular components of lamellae remain unidentified. Here, we use integrated genomic, transcriptomic, proteomic, and metabolomic approaches together with analytical profiling of carbohydrates to determine the composition of the multilayered cuticle in the red alga Chondrus crispus and assess its evolutionary conservation. The structural assembly reveals common features with the ECM of animals. The carbohydrate fraction includes a complex mixture of carrageenans and glycosaminoglycan-like compositions. A major von Willebrand factor A domain protein, Lamellae Cohesive Protein, plays a critical role in protein-protein interactions and binding to sulfated polysaccharides. We have further identified the major proteins of the algal cuticle, providing a framework for addressing the evolutionary origins of the cuticle and raising important questions regarding its role, particularly across the red algal life cycle marked by major structural differences in its ECM.
In previous studies with cystic fibrosis (CF) IB3-1 lung epithelial cells in culture, we identified 194 unique high abundance proteins by conventional two-dimensional gel electrophoresis and mass spectrometry (Pollard, H. B., Ji, X.-D., Jozwik, C. J., and Jacobowitz, D. M. (2005) High abundance protein profiling of cystic fibrosis lung epithelial cells. Proteomics 5, 2210-2226). In the present work we compared the IB3-1 cells with IB3-1/S9 daughter cells repaired by gene transfer with AAV-(wild type)CFTR. We report that gene transfer resulted in significant changes in silver stain intensity of only 20 of the 194 proteins. However, simultaneous measurement of de novo biosynthetic rates with [(35)S]methionine of all 194 proteins in both cell types resulted in the identification of an additional 31 CF-specific proteins. Of the 51 proteins identified by this hybrid approach, only six proteins changed similarly in both the mass and kinetics categories. This kinetic portion of the high abundance CF proteome, hidden from direct analysis of abundance, included proteins from transcription and signaling pathways such as NFkappaB, chaperones such as HSC70, cytoskeletal proteins, and others. Connectivity analysis indicated that approximately 30% of the 51-member hybrid high abundance CF proteome interacts with the NFkappaB signaling pathway. In conclusion, measurement of biosynthetic rates on a global scale can be used to identify disease-specific differences within the high abundance cystic fibrosis proteome. Most of these kinetically defined proteins are unaffected in expression level when using conventional silver stain analysis. We anticipate that this novel hybrid approach to discovery of the high abundance CF proteome will find general application to other proteomic problems in biology and medicine.
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.
BACKGROUND: Natural microbial communities are extremely complex and dynamic systems in terms of their population structure and functions. However, little is known about the in situ functions of the microbial communities. RESULTS: This study describes the application of proteomic approaches (metaproteomics) to observe expressed protein profiles of natural microbial communities (metaproteomes). The technique was validated using a constructed community and subsequently used to analyze Chesapeake Bay microbial community (0.2 to 3.0 microm) metaproteomes. Chesapeake Bay metaproteomes contained proteins from pI 4-8 with apparent molecular masses between 10-80 kDa. Replicated middle Bay metaproteomes shared approximately 92% of all detected spots, but only shared 30% and 70% of common protein spots with upper and lower Bay metaproteomes. MALDI-TOF analysis of highly expressed proteins produced no significant matches to known proteins. Three Chesapeake Bay proteins were tentatively identified by LC-MS/MS sequencing coupled with MS-BLAST searching. The proteins identified were of marine microbial origin and correlated with abundant Chesapeake Bay microbial lineages, Bacteroides and alpha-proteobacteria. CONCLUSION: Our results represent the first metaproteomic study of aquatic microbial assemblages and demonstrate the potential of metaproteomic approaches to link metagenomic data, taxonomic diversity, functional diversity and biological processes in natural environments.
The availability of a draft sequence for the human genome will revolutionise research into airway disease. This review deals with two of the most important areas impinging on the treatment of patients: pharmacogenetics and pharmacogenomics. Considerable inter-individual variation exists at the DNA level in targets for medication, and variability in response to treatment may, in part, be determined by this genetic variation. Increased knowledge about the human genome might also permit the identification of novel therapeutic targets by expression profiling at the RNA (genomics) or protein (proteomics) level. This review describes recent advances in pharmacogenetics and pharmacogenomics with regard to airway disease.
Human immunodeficiency virus type 1 (HIV-1)-associated dementia is a neuroinflammatory brain disorder that is fueled by viral infection and immune activation of brain mononuclear phagocytes (MP; macrophages and microglia). MP serve as a reservoir for persistent viral infection, a vehicle for viral dissemination throughout the brain, and a major source of neurotoxic products that when produced in abundance, affect neuronal function. Such neurotoxic substances secreted by MP lead to clinical neurological impairment (cognitive, behavior, and motor abnormalities), which occurs usually years after the initial viral infection. How HIV-1 evades the immune function characteristic for MP as a first line of defense, including phagocytosis and intracellular killing, is not well understood despite more than two decades of study. In this report, we review the complex role(s) played by MP in the neuropathogenesis of HIV-1 infection. The clinical manifestations, pathology and pathogenesis, and treatment options are discussed in relationship to innate and adaptive immunity. Particular emphasis is given to the diversity of MP functions and how it may affect the disease process and manifestations. New insights into disease mechanisms are provided by advances in enhanced magnetic resonance imaging and proteomics to identify cell movement and genetic profiles of disease. New therapeutic strategies are discussed based on current knowledge of HIV-1-associated dementia pathogenesis.
This study, designed and conducted as part of the International Life Sciences Institute working group on the Application of Genomics and Proteomics, examined the changes in the expression profile of genes associated with the administration of three different nephrotoxicants--cisplatin, gentamicin, and puromycin--to assess the usefulness of microarrays in the understanding of mechanism(s) of nephrotoxicity. Male Sprague-Dawley rats were treated with daily doses of puromycin (5-20 mg/kg/day for 21 days), gentamicin (2-240 mg/kg/day for 7 days), or a single dose of cisplatin (0.1-5 mg/kg). Groups of rats were sacrificed at various times after administration of these compounds for standard clinical chemistry, urine analysis, and histological evaluation of the kidney. RNA was extracted from the kidney for microarray analysis. Principal component analysis and gene expression-based clustering of compound effects confirmed sample separation based on dose, time, and degree of renal toxicity. In addition, analysis of the profile components revealed some novel changes in the expression of genes that appeared to be associated with injury in specific portions of the nephron and reflected the mechanism of action of these various nephrotoxicants. For example, although puromycin is thought to specifically promote injury of the podocytes in the glomerulus, the changes in gene expression after chronic exposure of this compound suggested a pattern similar to the known proximal tubular nephrotoxicants cisplatin and gentamicin; this prediction was confirmed histologically. We conclude that renal gene expression profiling coupled with analysis of classical end points affords promising opportunities to reveal potential new mechanistic markers of renal toxicity.
Gene-based molecular diagnostics is changing the practice of medicine and will continue to do so for the foreseeable future. The major underlying principle of these diagnostic tests is the use of specific nucleic acid sequences as surrogates; amplification of the surrogate markers enables the detection of pathogens or disease-related gene mutations. Gene targets can be amplified by target-, probe- or signal-based methods. Combined use of nucleic acid amplification and enzyme-linked immunosorbent assay with methods such as immuno-polymerase-chain reaction allows us to detect protein at femtogram (10(15) g) levels. A variety of choices are available for the detection of amplified amplicons with the fluorophore-linked nanoparticles as the most sensitive markers. The unique advantages of using covalently-linked nanoparticles include the detection of single molecules, the ability to enrich molecules of interest with unprecedented detection sensitivity (up to zeptogram levels, 10(21) g) and the flexibility of multiple functionalization. Automation appears to be the current trend for high-volume molecular testing of infectious diseases. Molecular profiling of various diseases using genomic or proteomic approaches opens up a molecule wonderland with promise and emergence of new molecular testing that will likely impact the practice of medicine to a greater degree in the future. The future of molecular-based testing and the journey toward personalized testing will be discussed.
MOTIVATION: Advances in single-cell sequencing have enabled multiomics profiling at unprecedented resolution; however, mass spectrometry-based single-cell proteomics (scMS) remains inherently destructive, precluding simultaneous transcriptomic capture. Unlike antibody-based methods such as CITE-seq, which permit paired profiling but are restricted to targeted protein panels, scMS provides unbiased, genome-scale coverage of the intracellular proteome yet necessitates post hoc integration of unpaired datasets. This diagonal integration challenge, where transcriptomes and proteomes are measured in separate cells lacking shared anchors, remains underserved by existing reviews, which focus predominantly on vertical integration strategies enabled by non-destructive assays. RESULTS: We survey the complete computational pipeline for constructing mechanistic proteogenomic networks from unpaired single-cell data, covering: (i) unimodal network inference such as knowledge-based approaches, probabilistic graphical models, temporal directionality inference, and generative and foundation model strategies that establish the transcriptomic scaffold; (ii) cross-modal integration architectures such as network propagation, graph neural networks (scMRDR, scmFormer, scCotag), and consensus frameworks designed explicitly for the unpaired proteomics setting; and (iii) benchmarking paradigms spanning network reconstruction (BEELINE, GRETA, CausalBench) and multi-task integration evaluation (scMultiBench, SCMMIB), with guidance on metric selection under network sparsity and class imbalance. We identify three principal axes of future development: generative proteomic translation from transcriptomic precursors, inductive prior embedding in next-generation architectures, and perturbation-based causal benchmarking. AVAILABILITY AND IMPLEMENTATION: This is a review article; no novel software is distributed. A curated benchmark resource table, methods starter guide, and per-method bottleneck annotations are provided in the Supplementary Material.
Chronic stress can trigger several pathologies including mood disorders for which no clear diagnostic molecular markers have been established yet. Attractive biomarker sources are extracellular vesicles (EVs). Evs are released by cells in health and disease and contain genetic material, proteins and lipids characteristic of the cell state. Here we show that Evs recovered from the blood of animals exposed to a repeated interrupted stress protocol (RIS) have a different protein profile compared to those obtained from control animals. Proteomic analysis indicated that proteins differentially present in bulk serum Evs from stressed animals were implicated in metabolic and inflammatory pathways and several of them were previously related to psychiatric disorders. Interestingly, these serum Evs carry brain-enriched proteins including the stress-responsive neuronal protein M6a. Then, we used an in-utero electroporation strategy to selectively overexpress M6a-GFP in brain neurons and found that M6a-GFP could also be detected in bulk serum Evs suggesting a neuronal origin. Finally, to determine if these Evs could have functional consequences, we administered Evs from control and RIS animals intranasally to naïve mice. Animals receiving stress EVs showed changes in behavior and brain M6a levels similar to those observed in physically stressed animals. Such changes could therefore be attributed, or at least in part, to EV protein transfer. Altogether these findings show that EVs may participate in stress signaling and propose proteins carried by EVs as a valuable source of biomarkers for stress-induced diseases.
BACKGROUND & OBJECTIVE: Small cell lung cancer (SCLC) is particularly aggressive, and characterized by rapid growth and early metastasis. At present, there is no data concerning SCLC two-dimensional polyacrylamide gel electrophoresis (2-DE) reference map,and its protein profiles in public databases. This study was to establish a well-resolved, reproducible 2-DE map of proteome in SCLC cell line NCI-H446, and analyze its protein profiles. METHODS: Two-DE was applied to separate the total proteins of NCI-H446 cells, which were then silver-stained in the gel. Well-separated protein spots were selected from the gel by ImageMaster 2D analysis system. Matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF-MS), peptide map fingerprinting (PMF),and database searching were used to identify the protein spots. RESULTS: Clear,well-resolved, reproducible 2-DE patterns of proteome in NCI-H446 cells were obtained. The average protein spots of 3 gels were 1506+/-74; and 1412+/-56 spots were matched with an average matching rate of 93.4%. The average deviation of spot position was (0.96+/-0.27) mm in IEF direction, and (1.24+/-0.41) mm in SDS-PAGE direction indicating relatively good reproducibility of the protein spots. Fifty-eight proteins were identified, certain proteins were products of oncogenes, and others were involved in cell cycle regulation, and signal transduction. CONCLUSIONS: A reference map of NCI-H446 cells was established,certain proteins were identified by MALDI-TOF-MS and PMF. These data will be useful for establishing human SCLC proteome database.
Embryonic stem (ES) cells are totipotent stem cells, which can differentiate into various kinds of cell types, including neurons. They are widely used as a model system for investigating mechanisms of differentiation events during early mouse development. In this study, proteomic techniques were used to approach the protein profile associated with the early-stage differentiation of ES cells into neuronal cells induced by all-trans retinoic acid (ATRA) in vitro. In comparison of the protein profile of parent ES cells with that of ES-derived neural-committed cells, which was induced by ATRA for four days, 24 differentially displayed protein spots were selected from two-dimensional electrophoresis (2-DE) gels for further protein identification by pepide mass fingerprinting (PMF). Nine proteins were known to being involved in the process of neural differentiation and/or neural survival. Of those, alpha-3/alpha-7 tubulin and vimentin were down-regulated, while cytokeratin 8, cytokeratin 18, G1/S-special cyclin D2, follistatin-related protein, NEL protein, platelet-activating factor acetylhydrolase IB alpha-subunit, and thioredoxin peroxidase 2 were upregulated during differentiation of ES cells to neural cells. Additionally, other 12 protein (five upregulated and seven downregulated) spots associated with ES cell differentiation into neuronal cells were not matched to known proteins so far, implicating that they might be novel proteins. The results above indicated that the molecular mechanisms of differentiation of ES cells to neural cells in vitro might be similar to those of other neural systems in vitro and identified that proteomic analysis is an effective strategy to comprehensively unravel the regulatory network of differentiation.
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.
Elephant endotheliotropic herpesvirus hemorrhagic disease (EEHV-HD) is a rapidly fatal syndrome of juvenile Asian elephants, but the host-response programs distinguishing progression from survival and the underlying pathophysiology remain poorly defined. The serum proteome of 62 Asian elephants (Elephas maximus) was profiled using a multi-contrast design stratified by age, clinical status, and infection history; protein abundance was analyzed by empirical Bayes linear modeling and Gene Ontology enrichment with semantic similarity reduction. Clinically affected elephants showed enrichment of inflammatory and stress-associated processes-including cytokine signaling and chromatin remodeling-with suppression of type I interferon signaling and homeostatic functions, whereas asymptomatic exposed elephants showed enrichment of metabolic pathways, including fatty acid and pyruvate metabolism, vesicle-mediated transport, and protein quality control. Disease-versus-exposure comparisons distinguished a progression program (inflammatory escalation with loss of proteostasis and cell adhesion) from a resilience program (preserved metabolic and cellular homeostasis); juvenile susceptibility was further associated with impaired lipid and calcium regulation and disrupted intracellular transport. Collectively, these patterns support a pathology-centered model in which fatal EEHV-HD reflects endothelial injury coupled with maladaptive inflammation and metabolic failure. Protein-level interpretation identified candidate drivers of inflammatory amplification, endothelial barrier disruption, coagulation imbalance, and resilience-including JAK1, IL1RL2, IFI44, KCNJ15, MSN, HECW2, ITPR3, MFN2, AKT1, BMPER, and DROSHA-providing a mechanistic bridge between serum proteomic changes and the vascular lesions, thrombocytopenia, DIC-like coagulopathy, edema, and hemorrhage of EEHV-HD. These findings nominate candidate proteomic signatures for future diagnostic and risk-stratification studies; longitudinal individual-level validation is required before clinical application. Because diagnostic screening identified all PCR-positive sick cases as EEHV1A and pooled group-level serum profiles were analyzed, these signatures should be interpreted as exploratory host-response programs specifically reflecting acute EEHV1A disease requiring individual-level validation.