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PubMed2005

Information integration in molecular bioscience.

Integrating information in the molecular biosciences involves more than the cross-referencing of sequences or structures. Experimental protocols, results of computational analyses, annotations and links to relevant literature form integral parts of this information, and impart meaning to sequence or structure. In this review, we examine some existing approaches to integrating information in the molecular biosciences. We consider not only technical issues concerning the integration of heterogeneous data sources and the corresponding semantic implications, but also the integration of analytical results. Within the broad range of strategies for integration of data and information, we distinguish between platforms and developments. We discuss two current platforms and six current developments, and identify what we believe to be their strengths and limitations. We identify key unsolved problems in integrating information in the molecular biosciences, and discuss possible strategies for addressing them including semantic integration using ontologies, XML as a data model, and graphical user interfaces as integrative environments.

Animals↗
PubMed2005

Integrated software suite for magnetocardiographic data analysis--a proposal based on an interactive programming environment.

OBJECTIVES: This paper describes an integrated software suite (ISS) for the processing of magnetocardiographic (MCG) recordings obtained with super-conducting multi-channel systems having different characteristics. We aimed to develop a highly flexible suite including toolboxes for current MCG applications, organized consistently with an open architecture that allows function integrations and upgrades with minimal modifications; the suite was designed for the compliance not only of physicists and engineers but also of physicians, who have a different professional profile and are accustomed to retrieve information in different ways. METHODS: The MCG-ISS was designed to work with all common graphical user interface operative systems. MATLAB was chosen as the interactive programming environment (IPE), and the software was developed to achieve usability, interactivity, reliability, modularity, expansibility, interoperability, adaptability and graphics style tailoring. Three users, already experienced in MCG data analysis, have intensively tested MCG-ISS for six months. A great amount of MCG data on normal subjects and patients was used to assess software performances in terms of user compliance and confidence and total analysis time. RESULTS: The proposed suite is an all-in-one analysis tool that succeeded in speeding MCG data analysis up to about 55% with respect to standard reference routines; it consequently enhanced analysis performance and user compliance. CONCLUSIONS: Those results, together with the MCG-ISS advantage of being independent on the acquisition system, suggest that software suites like the proposed one could uphold a wider diffusion of MCG as a diagnostic tool in the clinical setting.

Diagnosis, Computer-Assisted↗
PubMed2006

Integrated analysis of transcriptomic and proteomic data of Desulfovibrio vulgaris: zero-inflated Poisson regression models to predict abundance of undetected proteins.

MOTIVATION: Integrated analysis of global scale transcriptomic and proteomic data can provide important insights into the metabolic mechanisms underlying complex biological systems. However, because the relationship between protein abundance and mRNA expression level is complicated by many cellular and physical processes, sophisticated statistical models need to be developed to capture their relationship. RESULTS: In this study, we describe a novel data-driven statistical model to integrate whole-genome microarray and proteomic data collected from Desulfovibrio vulgaris grown under three different conditions. Based on the Poisson distribution pattern of proteomic data and the fact that a large number of proteins were undetected (excess zeros), zero-inflated Poisson (ZIP)-based models were proposed to define the correlation pattern between mRNA and protein abundance. In addition, by assuming that there is a probability mass at zero representing unexpressed genes and expressed proteins that were undetected owing to technical limitations, a Potential ZIP model was established. Two significant improvements introduced by this approach are (1) the predicted protein abundance level values for experimentally detected proteins are corrected by considering their mRNA levels and (2) protein abundance values can be predicted for undetected proteins (in the case of this study, approximately 83% of the proteins in the D.vulgaris genome) for better biological interpretation. We demonstrated the use of these statistical models by comparatively analyzing proteomic and microarray results from D.vulgaris grown on lactate-based versus formate-based media. These models correctly predicted increased expression of Ech hydrogenase and decreased expression of Coo hydrogenase for D.vulgaris grown on formate.

Adenosine Triphosphate↗
PubMed1995

A new extrapolation method from animals to man: application to a metabolized compound, mofarotene.

Allometric scaling (a technique which uses data obtained in laboratory animals to predict human pharmacokinetics) works well for drugs that are cleared intact, but is less successful with extensively metabolised compounds. This paper describes a new method to improve the accuracy of such projections, by integrating metabolic data obtained in vitro (e.g. with liver microsomes or hepatocytes) into these calculations. The approach was used prospectively, to predict the clearance of mofarotene (Ro 40-8757) in humans from in vivo kinetic data obtained in mouse, rat and dog. This compound was selected to illustrate this approach because it is exclusively eliminated through metabolism. Without the metabolic correction or using empirical correcting factors, the values predicted for man were 2.7 and 0.6 ml/min/kg. This fell outside the range subsequently obtained in healthy volunteers dosed orally with 300 mg of mofarotene (7.5 +/- 4.0 ml/min/kg, n = 12). However, inclusion of the microsomal or hepatocyte data gave values of 5.1 and 4.2 ml/min/kg, respectively, illustrating that the integration of in vitro metabolic data improves the accuracy of kinetic extrapolations. In contrast to the existing empirical techniques, this approach offers a rational basis to predict clearance of metabolized compounds in human.

Animals↗
PubMed2005

Time series of metals in mosses and their correlation with selected sampling site-specific and ecoregional characteristics in Germany.

GOAL, SCOPE AND BACKGROUND: The UNECE Heavy Metals in Mosses Surveys provide exposure data (Predicted Environmental Concentrations--PEC) for ecotoxicological risk assessments by measuring the accumulation of several metals in naturally growing mosses throughout Europe. Germany took part in the monitoring campaigns 1990, 1995 and 2000. The article deals with the description and application of the extensive methodical design developed to investigate the following three hypotheses: 1. The metal accumulation in mosses measured at up to 1000 sites may be geostatistically extrapolated from the sampling sites to ecoregions in order to transform the site-specific PEC values to surface PEC values. 2. The metal specific measurement values may be aggregated to metals integrating accumulation indices. 3. The ecoregional situation as well as sampling site-specific topographical features such as altitude, slope gradient or direction influence the accumulation of metals in mosses. METHODS: The methodical design integrates several data sources as well as statistical analysis and GIS techniques: The site-specific data on metal accumulation are geostatistically transformed to valid surface data on metal accumulation. The sampling site-specific measurement data and the estimated surface data on the accumulation of As, Cd, Cr, Cu, Fe, Hg, Ni, Pb, Sb, Ti, V and Zn are aggregated to integrative metal accumulation indices by means of percentile statistics. The metal-specific estimated data and the metals integrating accumulation indices are correlated with site-specific data on altitude, slope gradient and direction as well as with the ecoregional conditions derived from a multivariate ecoregionalisation. The mean multi-metal accumulation index for the whole of Germany over the ten year period from 1990 to 2000 was related to the accumulation indices within each of the ecoregions and each of the monitoring campaigns 1990, 1995 and 2000. In addition to this ecoregionalisation of the temporal trends of metal accumulation, the hot spots of accumulation are mapped. RESULTS AND DISCUSSION: The Heavy Metals in Mosses Survey 2000 reveals low metal concentrations in most European countries. In Germany, all metals decreased about 22% (Cu) to 64% (Pb) from 1990 to 2000. Mapping concentrations as dot maps deliver the most unbiased and detailed picture of the spatial structure of the metal accumulation in mosses. This information, detailed with respect to metal species and sampling site, is spatially generalized by means of geostatistical estimation. According to the cross-validation, the precision of the geostatistical estimation is quite good so that the extrapolation does not bias the spatial structure, but helps to clarify it. By percentile statistics, an ordinally scaled multi-metal accumulation index is calculated and spatially differentiated over time in terms of ecoregions which were calculated by means of Classification and Regression Trees (CART). The integrative statistical analysis reveals declinations of up to 80% of the multi-metal accumulation in some of Germany's ecoregions from 1990 to 2000. CONCLUSION: The monitoring of heavy metal bioaccumulation by means of mosses is an effective and cheap method for the analysis of the environmental concentrations of metals accumulated in terrestrial ecosystems. Geostatistics, percentile statistics, cross-tabulations and ecoregionalisation serve well to clarify the spatial and temporal trends in the large data sets coming out of the UNECE Heavy Metals in Mosses Surveys: By combining these statistical methods and integrating them into a geographical information system (GIS), they allow to detect and map the spatial-temporal trends of metal accumulation, to calculate metal-integrating accumulation indices, to describe temporal trends of metal accumulation within ecoregions, and to detect and map hot spots. RECOMMENDATION AND PERSPECTIVE: The spatial and temporal trends of metal accumulation in mosses should be linked with deposition data and data on the vitality of forest ecosystems, as well as with data on human health. Statistical valid interspecies calibration is needed. The integrated methodology of data evaluation presented in the article at hand should be implemented in the future UNECE Heavy Metals in Mosses Surveys. The hot spot areas should be investigated with special care in the 2005 survey to prove if the PEC values of the metals exceed the Predicted no Effect Concentrations (PNEC values). In addition to the metals, the 2005 survey should monitor the nitrogen accumulation in mosses.

Bryophyta↗
PubMed2002

Assessing sources of variability in microarray gene expression data.

Experiments using microarrays abound in genomic research, yet one factor remains in question. Without replication, how much stock can we put into the findings of microarray experiments? In addition, there is a growing desire to integrate microarray data with other molecular databases. To accomplish this in a scientifically acceptable manner, we must be able to measure the validity and quality of microarray data. Otherwise, it would be the weakest link in any integration process. Validating and evaluating the quality of data requires the ability to determine the reproducibility of results. Data obtained from a microarray experiment designed as a feasibility test provided a unique opportunity to partition and quantify several sources of variation that are likely to be present in most microarray experiments. We use this opportunity to discuss the origins of variability observed in microarray experiments and provide some suggestions for how to minimize or avoid them when designing an experiment.

Adolescent↗
PubMed2007

Technology insight: will systems pathology replace the pathologist?

By using systems pathology, it might be possible to provide a predictive, personalized therapeutic recommendation for patients with prostate cancer. Systems pathology integrates quantitative data and information from many sources to generate a reliable prediction of the expected natural course of the disease and response to different therapeutic options. In other words, through the integration of relatively large data sets and the use of knowledge engineering, systems pathology aims at predicting the future behavior of tumors and their interaction with the host. In this Review, we introduce the methods used in systems pathology and summarize a recent study providing the first evidence of a concept for this strategy. The results show that systems pathology can provide a personalized prediction of the risk of recurrence after prostatectomy for cancer.

Humans↗
PubMed2005

The implications of electronic health record for personalized medicine.

The emerging concept of an electronic health record (EHR) targeted at a patient centric, cross-institutional and longitudinal information entity (possibly spanning the individuals lifetime) has great promise for personalized medicine. In fact, it is probably the only vehicle through which we may truly realize the personalization of medicine beyond population-based genetic profiles that are expected to become part of medication and treatment indications in the near future. The new EHR standards include mechanisms that integrate clinical data with genomic testing results obtained through applying research-type procedures, such as full DNA sequencing, to an individual patient. Although the most optimal process for the utilization of integrated clinical-genomic data in the EHR framework is still unclear, the new Health Level Seven (HL7) Clinical Genomics Draft Standard for Trial Use suggests using the 'encapsulate & bubble-up' approach, which includes two main phases: the encapsulation of raw genomic data and bubbling-up the most clinically significant portions of that data, while associating it with clinical phenotypes residing in the individual's EHR.

Computational Biology↗
PubMed1992

Mutations on free and integrated hepatitis B virus DNA in a hepatocellular carcinoma: footprints of homologous recombination.

Hepatitis B virus nucleotide sequences derived from a hepatocellular carcinoma with free and multiply integrated viral DNAs were determined. Based on a comparison within the X-gene region, cloned free viral DNA previously had been attributed to two distinct groups of preC minus genomes. The comparison of the complete sequence identified one of the genome equivalents as a recombinant between members of these groups. Four different integrated viral DNA elements were cloned and analysed. Similarity to either one of two DNAs representing the two groups of free viral DNA on one hand and the presence of certain mutations only on integrated DNA on the other hand, allowed to recognize distinct segments within the integrants. The data suggest a contribution of different but related genotypes to contiguous stretches of integrated viral DNA via homologous recombination. On this basis an evolutionary relationship between free and integrated DNAs of the preC and the preC minus genotype could be recognized when short sequence segments were compared. The observed coexistence on a given integrated DNA of segments homologous to free viral DNA and of segments homologous to another integrated DNA is consistent with (1) a long lasting association of individual genotypes with dividing cells and (2) multiple integration events being the result of a series of steps not separated by a long time span.

Base Sequence↗
PubMed2025

Integrating multiscale mathematical modeling and multidimensional data reveals the effects of epigenetic instability on acquired drug resistance in cancer.

Biological and dynamic mechanisms by which Drug-tolerant persister (DTP) cells contribute to the development of acquired drug resistance have not been fully elucidated. Here, by integrating multidimensional data from drug-treated PC9 cells, we developed a novel multiscale mathematical model from an evolutionary perspective that encompasses epigenetic and cellular population dynamics. By coupling stochastic simulation with quantitative analysis, we identified epigenetic instability as the most prominent kinetic feature related to the emergence of DTP cell subpopulations and the effectiveness of intermittent treatment. Moreover, we revealed the optimal schedule for intermittent treatment, including the optimal area for therapeutic time and drug holidays. By leveraging single-cell RNA-seq data characterizing the drug tolerance of lung cancer, we validated the predictions made by our model and further revealed previously unrecognized biological features of DTP cells, such as cell autophagy and migration, as well as new biomarker genes of therapeutic tolerance. Our work not only provides a paradigm for the integration of multiscale mathematical models with newly emerging genomics data but also improves our understanding of the crucial roles of DTP cells and offers guidance for developing new intermittent treatment strategies against acquired drug resistance in cancer.

Drug Resistance, Neoplasm↗
PubMed2006

Protein-protein interactions in the mammalian brain.

Recent genome-wide high-throughput (HTS) analyses of protein-protein interactions (PPIs) provide molecular-based information to uncover functions of cells and tissues, such as those of the mammalian brain. However, the HTS PPI data contain much false-negatives and false-positives, which should be primarily addressed in experiments. Integrating PPI data sets with other genome-wide data, such as expression profiles and phenotype data sets, provides novel biological insights. Such integration analysis is valuable for addressing the complexity of the mammalian brain. Discovery of novel interactions followed by a detailed analysis is a successful approach to uncover the function of proteins. For example, extensive PPI screens for parkin, a hereditary Parkinson's disease gene, elucidated the function of parkin as an E3 ubiquitin ligase, with localization and activity regulated by contact with its interaction partners, uncovering at least a part of the molecular pathogenesis of Parkinson's disease.

Animals↗
PubMed2004

The coupled dipole model: an integrated model for multiple MEG/EEG data sets.

Often MEG/EEG is measured in a few slightly different conditions to investigate the functionality of the human brain. This kind of data sets show similarities, though are different for each condition. When solving the inverse problem (IP), performing the source localization, one encounters the problem that this IP is ill-posed: constraints are necessary to solve and stabilize the solution to the IP. Moreover, a substantial amount of data is needed to avoid a signal to noise ratio (SNR) that is too poor for source localizations. In the case of similar conditions, this common information can be exploited by analyzing the data sets simultaneously. The here proposed coupled dipole model (CDM) provides an integrated method in which these similarities between conditions are used to solve and stabilize the inverse problem. The coupled dipole model is applicable when data sets contain common sources or common source time functions. The coupled dipole model uses a set of common sources and a set of common source time functions (STFs) to model all conditions in one single model. The data of each condition are mathematically described as a linear combination of these common spatial and common temporal components. This linear combination is specified in a coupling matrix for each data set. The coupled dipole model was applied in two simulation studies and in one experimental study. The simulations show that the errors in the estimated spatial and temporal parameters decrease compared to the standard separate analyses. A decrease in position error of a factor of 10 was shown for the localization of two nearby sources. In the experimental application, the coupled dipole model was shown to be necessary to obtain a plausible solution in at least 3 of 15 conditions investigated. Moreover, using the CDM, a direct comparison between parameters in different conditions is possible, whereas in separate models, the scaling of the amplitude parameters varies in general from data set to data set.

Algorithms↗
PubMed1994

Characteristics of 60Co gamma-ray SPR (scatter-primary ratio), SF (scatter factor), beta (dose-kerma ratio), and dmax (depth of maximum dose).

For 60Co gamma-rays, using zero-area tissue-maximum ratio (TMR) and revised scatter-maximum ratio (SMR) data, we investigate how the scatter-primary ratio (SPR), the scatter factor (SF), and the dose-kerma ratio (beta) change with field size and depth. We also investigate how the depth of maximum absorbed dose (dmax) changes with field size in three ways: the first uses zero-area primary plus scatter absorbed dose data, the second uses integrated primary absorbed dose data, and the third uses integrated primary plus scatter absorbed dose data. The investigated characteristics are also compared with reported ones.

Algorithms↗
PubMed2026

An oxidative stress - and immunotherapy-related six-gene signature defines immune subtypes and predicts prognosis and immunotherapy response in hepatocellular carcinoma.

BACKGROUND: Oxidative stress and the tumor immune microenvironment jointly shape hepatocellular carcinoma (HCC) progression and response to immunotherapy, yet integrated biomarkers linking these processes are lacking. METHODS: Transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets were used to identify oxidative stress- and immunotherapyrelated differentially expressed genes (OSIRDEGs). Functional enrichment, weighted gene co-expression network analysis (WGCNA) and LASSO-Cox regression were used to construct a prognostic signature. Consensus clustering, TIDE, CIBERSORT and ssGSEA characterized immune phenotypes. Somatic mutation, copy-number and drug-response data were integrated to assess genomic alterations and drug sensitivity. Expression of model genes was validated by qRT-PCR and western blotting in HCC cell lines. RESULTS: We identified 24 OSIRDEGs enriched in cell-cycle and mitotic pathways. WGCNA intersection yielded 18 module genes, from which a six-gene signature (BUB1B, CDKN2A, CENPE, HMMR, PTTG1, SPP1) was derived. The signature robustly stratified patients into high- and low-risk groups with significantly different progression-free and disease-free survival in both TCGA-LIHC and GSE14520. Based on signature expression, two molecular subtypes were defined, exhibiting distinct survival, immune landscapes and predicted immunotherapy responsiveness. Model genes harbored recurrent alterations and showed significant correlations with anticancer agents. All six genes were upregulated at mRNA and protein levels in metastatic HCC cell lines versus normal hepatocytes. CONCLUSIONS: We systematically explored the landscape of OSIRDEGs in HCC, and proposed a validated six-gene signature that refines prognostic stratification, delineates immunerelevant HCC subtypes and highlights candidate biomarkers for therapeutic selection and mechanistic investigation.

Humans↗
PubMed1983

A re-evaluation of the 'saturated scatter' method for estimating the energy imparted to patients during diagnostic radiology examinations.

Central-axis depth dose data for diagnostic qualities (1-4 mm Al HVL) have been used to calculate the total energy imparted to patients or phantoms during X-ray examinations. A 'saturated scatter' method has been used and allowance is made for the finite dimensions of the patient by using PDD data for large but finite field sizes. For the purpose of integration, PDD data have been represented as a function of depth by two exponential components. Data in the form of energy imparted per unit central-axis surface dose and per unit surface X-ray field are tabulated as functions of kVp, first HVL, phantom thickness and the field size of the original PDD data. Comparison with energy fluence calculations of total energy imparted made by other workers indicates good agreement under similar phantom and X-ray quality conditions, although it is noted that anatomical variations between patients will in practice lead to uncertainties in the total energy imparted.

Humans↗
PubMed2026

Pan-cancer characterization of HMGA1 reveals its oncogenic role in tumor microenvironment and stemness: functional validation in pancreatic cancer migration and invasion.

BACKGROUND: HMGA1 is a chromatin-associated oncogenic factor implicated in tumor progression, epithelial-mesenchymal transition (EMT), stemness, and metastasis. However, its pan-cancer expression and prognostic patterns, epigenetic activation, and relationship with malignant-cell stemness/plasticity and tumor microenvironment (TME) remodeling in pancreatic adenocarcinoma (PAAD) remain incompletely defined. This study aimed to systematically characterize HMGA1 across cancers and clarify its clinical and biological relevance in PAAD. METHODS: Pan-cancer transcriptomic, clinical, genetic, methylation, immune, and stemness data were integrated from multiple public databases. PAAD single-cell RNA sequencing data were analyzed to localize HMGA1 expression, infer malignant-cell pseudotime, calculate a stemness module score, and assess ligand-receptor communication using CellChat. Public HMGA1-knockdown RNA sequencing data were reanalyzed to evaluate transcriptional remodeling. The Cancer Genome Atlas (TCGA)-PAAD expression and methylation data were used to assess TME-remodeling, immune-suppression, stemness/plasticity, cytokine/chemokine, checkpoint, and promoter-methylation features. HMGA1 expression and function were further examined using immunohistochemistry (IHC), quantitative real-time polymerase chain reaction, Western blotting, wound-healing assays, and Transwell migration and invasion assays. RESULTS: HMGA1 was upregulated in most tumor types, and high expression was associated with unfavorable survival in multiple cancers, including PAAD. In PAAD, HMGA1 was enriched in malignant epithelial cells and positively correlated with pseudotime (Spearman's rho =0.594), while the stemness module score increased along pseudotime (rho =0.748). HMGA1-high malignant cells showed markedly stronger CellChat-inferred outgoing communication, predominantly involving extracellular matrix (ECM)-receptor, adhesion-related, and selected immunomodulatory ligand-receptor axes. HMGA1 knockdown was associated with broad remodeling of EMT, TGF-β, Hedgehog, IL6/JAK/STAT3, KRAS, and cancer stem cell/stemness-related programs rather than uniform suppression of these programs. HMGA1 promoter methylation was inversely correlated with HMGA1 expression (rho =-0.633) and the TME-remodeling score (rho =-0.347). HMGA1 was associated with selected mediators, including PPIA, PLAU, ANXA1, LGALS9, TGFB1, CD276, and CD47, but not with a generalized checkpoint-high phenotype. Functionally, HMGA1 knockdown significantly reduced pancreatic cancer (PC) cell migration and invasion. CONCLUSIONS: These findings support an association-based model in which promoter hypomethylation-associated HMGA1 activation is linked to malignant epithelial stemness/plasticity, ECM/adhesion-dominant TME remodeling, selected immunomodulatory programs, and aggressive PAAD phenotypes. Further mechanistic and clinical validation is required before HMGA1 can be used for therapeutic stratification or immunotherapy-response prediction.

HMGA1↗
PubMed2004

An in silico study of energy metabolism in cardiac excitation-contraction coupling.

The heart produces and uses ATP at a high rate. Each step involved in ATP metabolism has been extensively studied. However, functional coupling between ATP production and membrane excitation-contraction coupling, which is the main ATP consumption process, is not yet fully understood because of complicated interactions and the lack of quantitative data obtained in vivo. Computer simulation is a powerful tool for integrating experimental data and for solving their complicated interactions. To investigate the mechanisms underlying cardiac excitation-contraction-energy metabolism coupling, we have developed a computer model of cardiac excitation-contraction coupling (Kyoto model) that includes the major processes of ATP production, such as oxidative phosphorylation that was originally developed for skeletal muscle by Korzeniewski and Zoladz [Biophys Chem 92: 17-34, 2001], creatine kinase, and adenylate kinase. In this review, we briefly summarize cardiac energy metabolism and discuss the regulation of mitochondrial ATP synthesis, using the Kyoto model.

Adenosine Triphosphate↗
PubMed2001

Physical and biological studies with protons and HZE particles in a NASA supported research center in radiation health.

NASA has established and supports a specialized center for research and training (NSCORT) to specifically address the potential deleterious effects of HZE particles on human health. The NSCORT in radiation health is a joint effort between Lawrence Berkeley National Laboratory (LBNL) and Colorado State University (CSU). The overall scope of research encompasses a broad range of subjects from microdosimetric studies to cellular and tissue responses to initial damage produced by highly energetic protons and heavy charged particles of the type found in galactic cosmic rays (GCR) spectrum. The objectives of the microdosimetry studies are to determine the response of Tissue Equivalent Proportional Counter (TEPC) to cosmic rays using ground based accelerators. This includes evaluation of energy loss due to the escape of high-energy delta rays and increased energy deposition due to the enhanced delta ray production in the wall of the detector. In this report major results are presented for 56Fe at 1000, 740, 600 and 400 MeV/nucleon. An assessment of DNA repair and early development of related chromosomal changes is extremely important to our overall understanding of enhanced biological effectiveness of high LET particle radiation. Results are presented with respect to the fidelity of the rejoining of double strand breaks and the implications of misrejoining. The relationship between molecular and cytogenetic measurements is presented by studying damage processing in highly heterochromatic supernumerary (correction of sypernumerary) X chromosomes and the active X-chromosome. One of the important consequences of cell's inability to handle DNA damage can be evaluated through mutation studies. Part of our goal is the assessment of potential radioprotectors to reduce the mutation yield following HZE exposures, and some promising results are presented on one compound. A second goal is the integration of DNA repair and mutation studies. Results are presented on a direct comparison of initial double strand breaks induction, the time course and fidelity of double strand break rejoining, cell killing and mutation induction in the same human model system. In order to understand the carcinogenic potential of protons and HZE particles, the role of damaged microenvironment in this process must be understood. In this project it has been postulated that radiation affects the microenvironment, which then modifies cell interactions in a manner conducive to neoplastic progression. Both TGF-beta and FGF-2 are important components of microenvironment. A recent result on the assessment of the role of FGF-2 and its cross-talk with TGF-beta as a function of radiation quality is presented. Theoretical modeling has so far played a central role in analyzing and integrating experimental data on repair and mutation studies and predicting new phenomena. The integrated NSCORT program also provides a broad training experience for students and postdoctoral fellows in space radiation health.

Animals↗