To Örebro University

Lipids have many important biological roles, such as energy storage sources, structural components of plasma membranes and as intermediates in metabolic and signaling pathways. Lipid metabolism is under tight homeostatic control, exhibiting spatial and dynamic complexity at multiple levels. Consequently, lipid-related disturbances play important roles in the pathogenesis of most of the common diseases. Lipidomics, defined as the study of lipidomes in biological systems, has emerged as a rapidly-growing field. Due to the chemical and functional diversity of lipids, the application of a systems biology approach is essential if one is to address lipid functionality at different physiological levels. In parallel with analytical advances to measure lipids in biological matrices, the field of computational lipidomics has been rapidly advancing, enabling modeling of lipidomes in their pathway, spatial and dynamic contexts. This review focuses on recent progress in systems biology approaches to study lipids in health and disease, with specific emphasis on methodological advances and biomedical applications.

We propose a network structure-based model for heterosis, and investigate it relying on metabolite profiles from Arabidopsis. A simple feed-forward two-layer network model (the Steinbuch matrix) is used in our conceptual approach. It allows for directly relating structural network properties with biological function. Interpreting heterosis as increased adaptability, our model predicts that the biological networks involved show increasing connectivity of regulatory interactions. A detailed analysis of metabolite profile data reveals that the increasing-connectivity prediction is true for graphical Gaussian models in our data from early development. This mirrors properties of observed heterotic Arabidopsis phenotypes. Furthermore, the model predicts a limit for increasing hybrid vigor with increasing heterozygosity--a known phenomenon in the literature.

To contribute to a further insight into heterosis we applied an integrative analysis to a systems biological network approach and a quantitative genetics analysis towards biomass heterosis in early Arabidopsis thaliana development. The study was performed on the parental accessions C24 and Col-0 and the reciprocal crosses. In an over-representation analysis it was tested if the overlap between the resulting gene lists of the two approaches is significantly larger than expected by chance. Top ranked genes in the results list of the systems biological analysis were significantly over-represented in the heterotic QTL candidate regions for either hybrid as well as regarding mid-parent and best-parent heterosis. This suggests that not only a few but rather several genes that influence biomass heterosis are located within each heterotic QTL region. Furthermore, the overlapping resulting genes of the two integrated approaches were particularly enriched in biomass related pathways. A chromosome-wise over-representation analysis gave rise to the hypothesis that chromosomes number 2 and 4 probably carry a majority of the genes involved in biomass heterosis in the early development of Arabidopsis thaliana.

Heterosis is a well-known phenomenon but the underlying molecular mechanisms are not yet established. To contribute to the understanding of heterosis at the molecular level, we analyzed genome-wide gene expression profile data of Arabidopsis thaliana in a systems biological approach. We used partial correlations to estimate the global interaction structure of regulatory networks. Our hypothesis states that heterosis comes with an increased number of partial correlations which we interpret as increased numbers of regulatory interactions leading to enlarged adaptability of the hybrids. This hypothesis is true for mid-parent heterosis for our dataset of gene expression in two homozygous parental lines and their reciprocal crosses. For the case of best-parent heterosis just one hybrid is significant regarding our hypothesis based on a resampling analysis. Summarizing, both metabolome and gene expression level of our illustrative dataset support our proposal of a systems biological approach towards a molecular basis of heterosis.

Genome-wide platelet transcriptomics is increasingly used to uncover new aspects of platelet biology and as a diagnostic and prognostic tool. Nevertheless, platelet isolation methods for transcriptomic studies are not standardized, introducing challenges for cross-study comparisons, data integration, and replication. In this prospective multicenter study, called "Standardizing Platelet Transcriptomics for Discovery, Diagnostics, and Therapeutics in the Thrombosis and Hemostasis Community (STRIDE)" by the ISTH SSCs, we assessed how three of the most commonly used platelet isolation protocols influence metrics from next-generation bulk RNA sequencing and functional assays. Compared with washing alone, more stringent removal of leukocytes by anti-CD45 beads or PALLTM filters resulted in a sufficient quantity of RNA for next-generation sequencing and similar quality of RNA sequencing metrics. Importantly, stringent removal of leukocytes resulted in the lower relative expression of known leukocyte-specific genes and the higher relative expression of known platelet-specific genes. The results were consistent across enrolling sites, suggesting the techniques are transferrable and reproducible. Moreover, all three isolation techniques did not influence basal platelet reactivity, but agonist-induced integrin αIIbβ3 activation is reduced by anti-CD45 bead isolation compared to washing alone. In conclusion, the isolation technique chosen influences genome-wide transcriptional and functional assays in platelets. These results should help the research community make informed choices about platelet isolation techniques in their own platelet studies.

To cope with oxidative stress, the metabolic network of plant cells must be reconfigured either to bypass damaged enzymes or to support adaptive responses. To characterize the dynamics of metabolic change during oxidative stress, heterotrophic Arabidopsis (Arabidopsis thaliana) cells were treated with menadione and changes in metabolite abundance and (13)C-labeling kinetics were quantified in a time series of samples taken over a 6 h period. Oxidative stress had a profound effect on the central metabolic pathways with extensive metabolic inhibition radiating from the tricarboxylic acid cycle and including large sectors of amino acid metabolism. Sequential accumulation of metabolites in specific pathways indicated a subsequent backing up of glycolysis and a diversion of carbon into the oxidative pentose phosphate pathway. Microarray analysis revealed a coordinated transcriptomic response that represents an emergency coping strategy allowing the cell to survive the metabolic hiatus. Rather than attempt to replace inhibited enzymes, transcripts encoding these enzymes are in fact down-regulated while an antioxidant defense response is mounted. In addition, a major switch from anabolic to catabolic metabolism is signaled. Metabolism is also reconfigured to bypass damaged steps (e.g. induction of an external NADH dehydrogenase of the mitochondrial respiratory chain). The overall metabolic response of Arabidopsis cells to oxidative stress is remarkably similar to the superoxide and hydrogen peroxide stimulons of bacteria and yeast (Saccharomyces cerevisiae), suggesting that the stress regulatory and signaling pathways of plants and microbes may share common elements.

INTRODUCTION BACKGROUND TO METABOLOMICS: Metabolomics is the comprehensive study of the metabolome, the repertoire of biochemicals (or small molecules) present in cells, tissues, and body fluids. The study of metabolism at the global or "-omics" level is a rapidly growing field that has the potential to have a profound impact upon medical practice. At the center of metabolomics, is the concept that a person's metabolic state provides a close representation of that individual's overall health status. This metabolic state reflects what has been encoded by the genome, and modified by diet, environmental factors, and the gut microbiome. The metabolic profile provides a quantifiable readout of biochemical state from normal physiology to diverse pathophysiologies in a manner that is often not obvious from gene expression analyses. Today, clinicians capture only a very small part of the information contained in the metabolome, as they routinely measure only a narrow set of blood chemistry analytes to assess health and disease states. Examples include measuring glucose to monitor diabetes, measuring cholesterol and high density lipoprotein/low density lipoprotein ratio to assess cardiovascular health, BUN and creatinine for renal disorders, and measuring a panel of metabolites to diagnose potential inborn errors of metabolism in neonates.

OBJECTIVES OF WHITE PAPER—EXPECTED TREATMENT OUTCOMES AND METABOLOMICS ENABLING TOOL FOR PRECISION MEDICINE: We anticipate that the narrow range of chemical analyses in current use by the medical community today will be replaced in the future by analyses that reveal a far more comprehensive metabolic signature. This signature is expected to describe global biochemical aberrations that reflect patterns of variance in states of wellness, more accurately describe specific diseases and their progression, and greatly aid in differential diagnosis. Such future metabolic signatures will: (1) provide predictive, prognostic, diagnostic, and surrogate markers of diverse disease states; (2) inform on underlying molecular mechanisms of diseases; (3) allow for sub-classification of diseases, and stratification of patients based on metabolic pathways impacted; (4) reveal biomarkers for drug response phenotypes, providing an effective means to predict variation in a subject's response to treatment (pharmacometabolomics); (5) define a metabotype for each specific genotype, offering a functional read-out for genetic variants: (6) provide a means to monitor response and recurrence of diseases, such as cancers: (7) describe the molecular landscape in human performance applications and extreme environments. Importantly, sophisticated metabolomic analytical platforms and informatics tools have recently been developed that make it possible to measure thousands of metabolites in blood, other body fluids, and tissues. Such tools also enable more robust analysis of response to treatment. New insights have been gained about mechanisms of diseases, including neuropsychiatric disorders, cardiovascular disease, cancers, diabetes and a range of pathologies. A series of ground breaking studies supported by National Institute of Health (NIH) through the Pharmacometabolomics Research Network and its partnership with the Pharmacogenomics Research Network illustrate how a patient's metabotype at baseline, prior to treatment, during treatment, and post-treatment, can inform about treatment outcomes and variations in responsiveness to drugs (e.g., statins, antidepressants, antihypertensives and antiplatelet therapies). These studies along with several others also exemplify how metabolomics data can complement and inform genetic data in defining ethnic, sex, and gender basis for variation in responses to treatment, which illustrates how pharmacometabolomics and pharmacogenomics are complementary and powerful tools for precision medicine.

CONCLUSIONS KEY SCIENTIFIC CONCEPTS AND RECOMMENDATIONS FOR PRECISION MEDICINE: Our metabolomics community believes that inclusion of metabolomics data in precision medicine initiatives is timely and will provide an extremely valuable layer of data that compliments and informs other data obtained by these important initiatives. Our Metabolomics Society, through its "Precision Medicine and Pharmacometabolomics Task Group", with input from our metabolomics community at large, has developed this White Paper where we discuss the value and approaches for including metabolomics data in large precision medicine initiatives. This White Paper offers recommendations for the selection of state of-the-art metabolomics platforms and approaches that offer the widest biochemical coverage, considers critical sample collection and preservation, as well as standardization of measurements, among other important topics. We anticipate that our metabolomics community will have representation in large precision medicine initiatives to provide input with regard to sample acquisition/preservation, selection of optimal omics technologies, and key issues regarding data collection, interpretation, and dissemination. We strongly recommend the collection and biobanking of samples for precision medicine initiatives that will take into consideration needs for large-scale metabolic phenotyping studies.

Background: During the past ten years many quantitative trait loci (QTL) affecting mastitis incidence and mastitis related traits like somatic cell score (SCS) were identified in cattle. However, little is known about the molecular architecture of QTL affecting mastitis susceptibility and the underlying physiological mechanisms and genes causing mastitis susceptibility. Here, a genome-wide expression analysis was conducted to analyze molecular mechanisms of mastitis susceptibility that are affected by a specific QTL for SCS on Bos taurus autosome 18 (BTA18). Thereby, some first insights were sought into the genetically determined mechanisms of mammary gland epithelial cells influencing the course of infection.

Methods: Primary bovine mammary gland epithelial cells (pbMEC) were sampled from the udder parenchyma of cows selected for high and low mastitis susceptibility by applying a marker-assisted selection strategy considering QTL and molecular marker information of a confirmed QTL for SCS in the telomeric region of BTA18. The cells were cultured and subsequently inoculated with heat-inactivated mastitis pathogens Escherichia coli and Staphylococcus aureus, respectively. After 1, 6 and 24 h, the cells were harvested and analyzed using the microarray expression chip technology to identify differences in mRNA expression profiles attributed to genetic predisposition, inoculation and cell culture.

Results: Comparative analysis of co-expression profiles clearly showed a faster and stronger response after pathogen challenge in pbMEC from less susceptible animals that inherited the favorable QTL allele 'Q' than in pbMEC from more susceptible animals that inherited the unfavorable QTL allele 'q'. Furthermore, the results highlighted RELB as a functional and positional candidate gene and related non-canonical Nf-kappaB signaling as a functional mechanism affected by the QTL. However, in both groups, inoculation resulted in up-regulation of genes associated with the Ingenuity pathways 'dendritic cell maturation' and 'acute phase response signaling', whereas cell culture affected biological processes involved in 'cellular development'.

Conclusions: The results indicate that the complex expression profiling of pathogen challenged pbMEC sampled from cows inheriting alternative QTL alleles is suitable to study genetically determined molecular mechanisms of mastitis susceptibility in mammary epithelial cells in vitro and to highlight the most likely functional pathways and candidate genes underlying the QTL effect.

Integrative (or systems biology) is a new approach to analyzing biological entities as integrated systems of genetic, genomic, protein, metabolite, cellular, and pathway events that are in flux and interdependent. Here, we demonstrate the application of intregrative biological analysis to a mammalian disease model, the apolipoprotein E3-Leiden (APO*E3) transgenic mouse. Mice selected for the study were fed a normal chow diet and sacrificed at 9 weeks of age-conditions under which they develop only mild type I and II atherosclerotic lesions. Hepatic mRNA expression analysis showed a 25% decrease in APO A1 and a 43% increase in liver fatty acid binding protein expression between transgenic and wild type control mice, while there was no change in PPAR-alpha expression. On-line high performance liquid chromatography-mass spectrometry quantitative profiling of tryptic digests of soluble liver proteins and liver lipids, coupled with principle component analysis, enabled rapid identification of early protein and metabolite markers of disease pathology. These included a 44% increase in L-FABP in transgenic animals compared to controls, as well as an increase in triglycerides and select bioactive lysophosphatidylcholine species. A correlation analysis of identified genes, proteins, and lipids was used to construct an interaction network. Taken together, these results indicate that integrative biology is a powerful tool for rapid identification of early markers and key components of pathophysiologic processes, and constitute the first application of this approach to a mammalian system.

The development of microarray technology allows the simultaneous measurement of the expression of many thousands of genes. The information gained offers an unprecedented opportunity to fully characterize biological processes. However, this challenge will only be successful if new tools for the efficient integration and interpretation of large datasets are available. One of these tools, pathway analysis, involves looking for consistent but subtle changes in gene expression by incorporating either pathway or functional annotations. We review several methods of pathway analysis and compare the performance of three, the binomial distribution, z scores, and gene set enrichment analysis, on two microarray datasets. Pathway analysis is a promising tool to identify the mechanisms that underlie diseases, adaptive physiological compensatory responses and new avenues for investigation.

Systems biology aims at creating mathematical models, i.e., computational reconstructions of biological systems and processes that will result in a new level of understanding-the elucidation of the basic and presumably conserved "design" and "engineering" principles of biomolecular systems. Thus, systems biology will move biology from a phenomenological to a predictive science. Mathematical modeling of biological networks and processes has already greatly improved our understanding of many cellular processes. However, given the massive amount of qualitative and quantitative data currently produced and number of burning questions in health care and biotechnology needed to be solved is still in its early phases. The field requires novel approaches for abstraction, for modeling bioprocesses that follow different biochemical and biophysical rules, and for combining different modules into larger models that still allow realistic simulation with the computational power available today. We have identified and discussed currently most prominent problems in systems biology: (1) how to bridge different scales of modeling abstraction, (2) how to bridge the gap between topological and mechanistic modeling, and (3) how to bridge the wet and dry laboratory gap. The future success of systems biology largely depends on bridging the recognized gaps. 

Systems Biology is an approach to biology and medicine that has the potential to lead to a better understanding of how biological properties emerge from the interaction of genes, proteins, molecules, cells and organisms. The approach aims at elucidating how these interactions govern biological function by employing experimental data, mathematical models and computational simulations. As Systems Biology is inherently multidisciplinary, education within this field meets numerous hurdles including departmental barriers, availability of all required expertise locally, appropriate teaching material and example curricula. As university education at the Bachelor's level is traditionally built upon disciplinary degrees, we believe that the most effective way to implement education in Systems Biology would be at the Master's level, as it offers a more flexible framework. Our team of experts and active performers of Systems Biology education suggest here (i) a definition of the skills that students should acquire within a Master's programme in Systems Biology, (ii) a possible basic educational curriculum with flexibility to adjust to different application areas and local research strengths, (iii) a description of possible career paths for students who undergo such an education, (iv) conditions that should improve the recruitment of students to such programmes and (v) mechanisms for collaboration and excellence spreading among education professionals. With the growing interest of industry in applying Systems Biology approaches in their fields, a concerted action between academia and industry is needed to build this expertise. Here we present a reflection of the European situation and expertise, where most of the challenges we discuss are universal, anticipating that our suggestions will be useful internationally. We believe that one of the overriding goals of any Systems Biology education should be a student's ability to phrase and communicate research questions in such a manner that they can be solved by the integration of experiments and modelling, as well as to communicate and collaborate productively across different experimental and theoretical disciplines in research and development. 

We have studied the relative contribution of inversions, transpositions, deletions, and nucleotide substitutions to the evolution of Chlamydia trachomatis and Chlamydia pneumoniae. The minimal number of rearrangement events required for converting the gene order structure of one genome into that of the other was estimated to 59 6 events, including 13% inversions, 38% short inversions, and 49% transpositions. In contrast to previous findings, no examples of horizontal gene transfer subsequent to species divergence were identified, nor any evidence for an excessive number of tandem gene duplications. A statistical model was used to compare nucleotide frequencies for a set of genes uniquely present in one species to a set of orthologous genes present in both species. The two data sets were not significantly different, which is indicative of a low frequency of horizontal gene transfer events. This is based on the assumption that a foreign gene of different nucleotide content will not have become completely ameliorated, as verified by simulations of the amelioration rate at twofold and fourfold degenerate codon sites. The frequencies of nucleotide substitutions at twofold and fourfold degenerate sites, deletions, inversions, and translocations were estimated to 1.42, 0.62, 0,18, 0.01, and 0.01 per site, respectively.

Motivation: The evolutionary distance inferred from gene order comparisons of related bacteria is dependent on the model. Therefore, it is highly important to establish reliable assumptions before inferring its magnitude.

Results: We investigate the patterns of dotplots between species of bacteria with the purpose of model selection in gene order problems. We find several categories of data which can be explained by carefully weighing the contributions of reversals, transpositions, symmetrical reversals, single gene transpositions, and single gene reversals. We also derive method of moments distance estimates for some previously uncomputed cases, such as symmetrical reversals, single gene reversals and their combinations, as well as the single gene transpositions edit distance.

Multitiered quantitative analysis of biological systems is rapidly becoming the desired approach to study hierarchical functional interactions between proteins and metabolites. We describe here a novel systematic approach to analyze organisms with complex metabolic regulatory networks. By using precise analytical methods to measure biochemical constituents and their relative abundance in whole plasma of transgenic ApoE*3-Leiden mice and an isogenic wild-type control group, simultaneous snapshots of metabolic and protein states were obtained. Novel data processing and multivariate analysis tools such as Impurity Resolution Software (IMPRESS) and Windows-based linear fit program (WINLIN) were used to compare protein and metabolic profiles in parallel. Canonical correlations of the resulting data show quantitative relationships between heterogeneous components in the TG animals. These results, obtained solely from whole plasma analysis allowed us, in a rapid manner, to corroborate previous findings as well as find new events pertaining to dominant and peripheral events in lipoprotein metabolism of a genetically modified mammalian organism in relation to ApoE3, a key mediator of lipoprotein metabolism.

At the present time, omics experiments are commonly used in wet lab practice to identify leads involved in interesting phenotypes. These omics experiments often result in unstructured gene lists, the interpretation of which in terms of pathways or the mode of action is challenging. To aid in the interpretation of such gene lists, we developed PheNetic, a decision theoretic method that exploits publicly available information, captured in a comprehensive interaction network to obtain a mechanistic view of the listed genes. PheNetic selects from an interaction network the sub-networks highlighted by these gene lists. We applied PheNetic to an Escherichia coli interaction network to reanalyse a previously published KO compendium, assessing gene expression of 27 E. coli knock-out mutants under mild acidic conditions. Being able to unveil previously described mechanisms involved in acid resistance demonstrated both the performance of our method and the added value of our integrated E. coli network.

In clonal systems, interpreting driver genes in terms of molecular networks helps understanding how these drivers elicit an adaptive phenotype. Obtaining such a network-based understanding depends on the correct identification of driver genes. In clonal systems, independent evolved lines can acquire a similar adaptive phenotype by affecting the same molecular pathways, a phenomenon referred to as parallelism at the molecular pathway level. This implies that successful driver identification depends on interpreting mutated genes in terms of molecular networks. Driver identification and obtaining a network-based understanding of the adaptive phenotype are thus confounded problems that ideally should be solved simultaneously. In this study, a network-based eQTL method is presented that solves both the driver identification and the network-based interpretation problem. As input the method uses coupled genotype-expression phenotype data (eQTL data) of independently evolved lines with similar adaptive phenotypes and an organism-specific genome-wide interaction network. The search for mutational consistency at pathway level is defined as a subnetwork inference problem, which consists of inferring a subnetwork from the genome-wide interaction network that best connects the genes containing mutations to differentially expressed genes. Based on their connectivity with the differentially expressed genes, mutated genes are prioritized as driver genes. Based on semisynthetic data and two publicly available data sets, we illustrate the potential of the network-based eQTL method to prioritize driver genes and to gain insights in the molecular mechanisms underlying an adaptive phenotype. The method is available at http://bioinformatics.intec.ugent.be/phenetic_eqtl/index.html.

Understanding the molecular basis of plant performance under water-limiting conditions will help to breed crop plants with a lower water demand. We investigated the physiological and gene expression response of drought-tolerant (IR57311 and LC-93-4) and drought-sensitive (Nipponbare and Taipei 309) rice (Oryza sativa L.) cultivars to 18 days of drought stress in climate chamber experiments. Drought stressed plants grew significantly slower than the controls. Gene expression profiles were measured in leaf samples with the 20 K NSF oligonucleotide microarray. A linear model was fitted to the data to identify genes that were significantly regulated under drought stress. In all drought stressed cultivars, 245 genes were significantly repressed and 413 genes induced. Genes differing in their expression pattern under drought stress between tolerant and sensitive cultivars were identified by the genotype x environment (G x E) interaction term. More genes were significantly drought regulated in the sensitive than in the tolerant cultivars. Localizing all expressed genes on the rice genome map, we checked which genes with a significant G x E interaction co-localized with published quantitative trait loci regions for drought tolerance. These genes are more likely to be important for drought tolerance in an agricultural environment. To identify the metabolic processes with a significant G x E effect, we adapted the analysis software MapMan for rice. We found a drought stress induced shift toward senescence related degradation processes that was more pronounced in the sensitive than in the tolerant cultivars. In spite of higher growth rates and water use, more photosynthesis related genes were down-regulated in the tolerant than in the sensitive cultivars.

A curated Web-based user-friendly sequence typing tool based on antimicrobial resistance determinants in Neisseria gonorrhoeae was developed and is publicly accessible (https://ngstar.canada.ca). The N. gonorrhoeae Sequence Typing for Antimicrobial Resistance (NG-STAR) molecular typing scheme uses the DNA sequences of 7 genes (penA, mtrR, porB, ponA, gyrA, parC, and 23S rRNA) associated with resistance to β-lactam antimicrobials, macrolides, or fluoroquinolones. NG-STAR uses the entire penA sequence, combining the historical nomenclature for penA types I to XXXVIII with novel nucleotide sequence designations; the full mtrR sequence and a portion of its promoter region; portions of ponA, porB, gyrA, and parC; and 23S rRNA sequences. NG-STAR grouped 768 isolates into 139 sequence types (STs) (n = 660) consisting of 29 clonal complexes (CCs) having a maximum of a single-locus variation, and 76 NG-STAR STs (n = 109) were identified as unrelated singletons. NG-STAR had a high Simpson's diversity index value of 96.5% (95% confidence interval [CI] = 0.959 to 0.969). The most common STs were NG-STAR ST-90 (n = 100; 13.0%), ST-42 and ST-91 (n = 45; 5.9%), ST-64 (n = 44; 5.72%), and ST-139 (n = 42; 5.5%). Decreased susceptibility to azithromycin was associated with NG-STAR ST-58, ST-61, ST-64, ST-79, ST-91, and ST-139 (n = 156; 92.3%); decreased susceptibility to cephalosporins was associated with NG-STAR ST-90, ST-91, and ST-97 (n = 162; 94.2%); and ciprofloxacin resistance was associated with NG-STAR ST-26, ST-90, ST-91, ST-97, ST-150, and ST-158 (n = 196; 98.0%). All isolates of NG-STAR ST-42, ST-43, ST-63, ST-81, and ST-160 (n = 106) were susceptible to all four antimicrobials. The standardization of nomenclature associated with antimicrobial resistance determinants through an internationally available database will facilitate the monitoring of the global dissemination of antimicrobial-resistant N. gonorrhoeae strains.

MOTIVATION: Coexpression networks have recently emerged as a novel holistic approach to microarray data analysis and interpretation. Choosing an appropriate cutoff threshold, above which a gene-gene interaction is considered as relevant, is a critical task in most network-centric applications, especially when two or more networks are being compared.

RESULTS: We demonstrate that the performance of traditional approaches, which are based on a pre-defined cutoff or significance level, can vary drastically depending on the type of data and application. Therefore, we introduce a systematic procedure for estimating a cutoff threshold of coexpression networks directly from their topological properties. Both synthetic and real datasets show clear benefits of our data-driven approach under various practical circumstances. In particular, the procedure provides a robust estimate of individual degree distributions, even from multiple microarray studies performed with different array platforms or experimental designs, which can be used to discriminate the corresponding phenotypes. Application to human T helper cell differentiation process provides useful insights into the components and interactions controlling this process, many of which would have remained unidentified on the basis of expression change alone. Moreover, several human-mouse orthologs showed conserved topological changes in both systems, suggesting their potential importance in the differentiation process.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Together with the widely used Affymetrix microarrays, the recently introduced Illumina platform has become a cost-effective alternative for genome-wide studies. To efficiently use data from both array platforms, there is a pressing need for methods that allow systematic integration of multiple datasets, especially when the number of samples is small. To address these needs, we introduce a meta-analytic procedure for combining Affymetrix and Illumina data in the context of detecting differentially expressed genes between the platforms. We first investigate the effect of different expression change estimation procedures within the platforms on the agreement of the most differentially expressed genes. Using the best estimation methods, we then show the benefits of the integrative analysis in producing reproducible results across bootstrap samples. In particular, we demonstrate its biological relevance in identifying small but consistent changes during T helper 2 cell differentiation.

Lipidomics is a rapidly-growing field which focuses on global characterization of lipids at molecular and systems levels. As small changes in the concentrations of lipids may have important physiological consequences, much attention in the field has recently been paid to more accurate quantitation and identification of lipids. Community-wide efforts have been initiated, aiming to develop best practices for lipidomic analyses and reporting of lipidomic data. Nevertheless, current approaches for comprehensive analysis of lipidomes have some inherent challenges and limitations. Additionally, there is, currently, limited knowledge concerning the impacts of various external and internal exposures on lipid levels. In this review, we discuss the recent progress in lipidomics analysis, with a primary focus on analytical approaches, as well as on the different sources of variation in quantifying lipid levels, both technical and biological.

Antimicrobial resistance (AMR) in Neisseria gonorrhoeae is common, compromising gonorrhoea treatment internationally. Rapid characterisation of AMR strains could ensure appropriate and personalised treatment, and support identification and investigation of gonorrhoea outbreaks in nearly real-time. Whole-genome sequencing is ideal for investigation of emergence and dissemination of AMR determinants, predicting AMR, in the gonococcal population and spread of AMR strains in the human population. The novel, rapid and revolutionary long-read sequencer MinION is a small hand-held device that generates bacterial genomes within one day. However, accuracy of MinION reads has been suboptimal for many objectives and the MinION has not been evaluated for gonococci. In this first MinION study for gonococci, we show that MinION-derived sequences analysed with existing open-access, web-based sequence analysis tools are not sufficiently accurate to identify key gonococcal AMR determinants. Nevertheless, using an in house-developed CLC Genomics Workbench including de novo assembly and optimised BLAST algorithms, we show that 2D ONT-derived sequences can be used for accurate prediction of decreased susceptibility or resistance to recommended antimicrobials in gonococcal isolates. We also show that the 2D ONT-derived sequences are useful for rapid phylogenomic-based molecular epidemiological investigations, and, in hybrid assemblies with Illumina sequences, for producing contiguous assemblies and finished reference genomes.

MOTIVATION: Integration of heterogeneous data in life sciences is a growing and recognized challenge. The problem is not only to enable the study of such data within the context of a biological question but also more fundamentally, how to represent the available knowledge and make it accessible for mining.

RESULTS: Our integration approach is based on the premise that relationships between biological entities can be represented as a complex network. The context dependency is achieved by a judicious use of distance measures on these networks. The biological entities and the distances between them are mapped for the purpose of visualization into the lower dimensional space using the Sammon's mapping. The system implementation is based on a multi-tier architecture using a native XML database and a software tool for querying and visualizing complex biological networks. The functionality of our system is demonstrated with two examples: (1) A multiple pathway retrieval, in which, given a pathway name, the system finds all the relationships related to the query by checking available metabolic pathway, transcriptional, signaling, protein-protein interaction and ontology annotation resources and (2) A protein neighborhood search, in which given a protein name, the system finds all its connected entities within a specified depth. These two examples show that our system is able to conceptually traverse different databases to produce testable hypotheses and lead towards answers to complex biological questions.

The emergence of systems biology necessitates development of platforms to organise and interpret plentitude of biological data. We present a system to integrate data across multiple bioinformatics databases and enable mining across various conceptual levels of biological information. The results are represented as complex networks. Context dependent mining of these networks is achieved by use of distances. Our approach is demonstrated with three applications: full metabolic network retrieval with network topology study, exploration of properties and relationships of a set of selected proteins, and combined visualisation and exploration of gene expression data with related pathways and ontologies.

In response to environmental challenges, biological systems respond with dynamic adaptive changes in order to maintain the functionality of the system. Such adaptations may lead to cumulative stress over time, possibly leading to global failure of the system. When studying such systems responses, it is therefore important to understand them in system-wide and dynamic context. Here we hypothesize that dynamic changes in the topology of functional modules of integrated biological networks reflect their activity under specific environmental challenges. We introduce topological enrichment analysis of functional subnetworks (TEAFS), a method for the analysis of integrated molecular profile and interactome data, which we validated by comprehensive metabolomic analysis of dynamic yeast response under oxidative stress. TEAFS identified activation of multiple stress response related mechanisms, such as lipid metabolism and phospholipid biosynthesis. We identified, among others, a fatty acid elongase IFA38 as a hub protein which was absent at all time points under oxidative stress conditions. The deletion mutant of the IFA38 encoding gene is known for the accumulation of ceramides. By applying a comprehensive metabolomic analysis, we confirmed the increased concentrations over time of ceramides and palmitic acid, a precursor of de novo ceramide biosynthesis. Our results imply that the connectivity of the system is being dynamically modulated in response to oxidative stress, progressively leading to the accumulation of (lipo)toxic lipids such as ceramides. Studies of local network topology dynamics can be used to investigate as well as predict the activity of biological processes and the system's responses to environmental challenges and interventions.

Global gene expression studies using microarray technology are widely employed to identify biological processes which are influenced by a treatment e.g. a specific diet. Affected processes are characterized by a significant enrichment of differentially expressed genes (functional annotation analysis). However, different choices of statistical thresholds to select candidates for differential expression will alter the resulting candidates list. This study was conducted to investigate the effect of applying a False Discovery Rate (FDR) correction and different fold change thresholds in statistical analysis of microarray data on diet-affected biological processes based on a significantly increased proportion of differentially expressed genes. In a model feeding experiment with rats fed genetically modified food additives, animals received a supplement of either lyophilized inactivated recombinant VP60 baculovirus (rBV-VP60) or lyophilized inactivated wild type baculovirus (wtBV). Comparative expression profiling was done in spleen, liver and small intestine mucosa. We demonstrated the extent to which threshold choice can affect the biological processes identified as significantly regulated and thus the conclusion drawn from the microarray data. In our study, the combined application of a moderate fold change threshold (FC≥1.5) and a stringent FDR threshold (q≤0.05) exhibited high reliability of biological processes identified as differentially regulated. The application of a stringent FDR threshold of q≤0.05 seems to be an essential prerequisite to reduce considerably the number of false positives. Microarray results of selected differentially expressed molecules were validated successfully by using real-time RT-PCR.

Background: MicroRNAs (miRNAs) are approximately 22 nucleotide long, non-coding RNAs that regulate gene expression by binding to the 3'-untranslated region of target mRNAs and also a variety of cellular processes. It has recently been established that dysregulation of miRNA expression can be detected in the majority of human cancers. A variety of high-throughput screening methods has been developed to identify dysregulated miRNA species in tumours. For retrospective clinical studies formalin-fixed, paraffin-embedded (FFPE) tissue is the most widely used material.

Materials and methods: The miRNA expression profiles of freshly frozen (CRYO) and FFPE tissues of seven tonsil and four liver samples were compared, using a qPCR-based assay, profiling 157 miRNA species.

Results: The significance of miRNA-profiles was barely influenced by FFPE treatment in both tissues and the variance induced by FFPE treatment was much smaller than the variance caused by biologically based differential expression.

Conclusion: FFPE material is well suited for miRNA profiling.

The more we learn about the immune response against tuberculosis (TB) and particularly about the features which distinguish protective immunity, disease susceptibility and pathology, the better we can define biomarkers which correlate with these different stages of infection. The most widely used biomarker in TB, which without a doubt is an important component of protective immunity, is IFNgamma secreted by antigen-specific CD4 T-cells. However, the complexity of the immune response against TB makes it more than likely that additional biomarkers are required for a reliable correlate of protection. As a corollary, we assume that a set of biomarkers will be required, termed a biosignature.

Objectives: Microarray analysis requires standardized specimens and evaluation procedures to achieve acceptable results. A major limitation of this method is caused by heterogeneity in the cellular composition of tissue specimens, which frequently confounds data analysis. We introduce a linear model to deconfound gene expression data from tissue heterogeneity for genes exclusively expressed by a single cell type.

Methods: Gene expression data are deconfounded from tissue heterogeneity effects by analyzing them using an appropriate linear regression model. In our illustrating data set tissue heterogeneity is being measured using flow cytometry. Gene expression data are determined in parallel by real time quantitative polymerase chain reaction (qPCR) and microarray analyses. Verification of deconfounding is enabled using protein quantification for the respective marker genes.

Results: For our illustrating dataset, quantification of cell type proportions for peripheral blood mononuclear cells (PBMC) from tuberculosis patients and controls revealed differences in B cell and monocyte proportions between both study groups, and thus heterogeneity for the tissue under investigation. Gene expression analyses reflected these differences in celltype distribution. Fitting an appropriate linear model allowed us to deconfound measured transcriptome levels from tissue heterogeneity effects. In the case of monocytes, additional differential expression on the single cell level could be proposed. Protein quantification verified these deconfounded results.

Conclusions: Deconfounding of transcriptome analyses for cellular heterogeneity greatly improves interpretability, and hence the validity of transcriptome profiling results.

Infection with Mycobacterium tuberculosis is controlled by an efficacious immune response in about 90% of infected individuals who do not develop disease. Although essential mediators of protection, e.g., interferon-gamma, have been identified, these factors are insufficient to predict the outcome of M. tuberculosis infection. As a first step to determine additional biomarkers, we compared gene expression profiles of peripheral blood mononuclear cells from tuberculosis patients and M. tuberculosis-infected healthy donors by microarray analysis. Differentially expressed candidate genes were predominantly derived from monocytes and comprised molecules involved in the antimicrobial defense, inflammation, chemotaxis, and intracellular trafficking. We verified differential expression for alpha-defensin 1, alpha-defensin 4, lactoferrin, Fcgamma receptor 1A (cluster of differentiation 64 [CD64]), bactericidal permeability-increasing protein, and formyl peptide receptor 1 by quantitative polymerase chain reaction analysis. Moreover, we identified increased protein expression of CD64 on monocytes from tuberculosis patients. Candidate biomarkers were then assessed for optimal study group discrimination. Using a linear discriminant analysis, a minimal group of genes comprising lactoferrin, CD64, and the Ras-associated GTPase 33A was sufficient for classification of (1) tuberculosis patients, (2) M. tuberculosis-infected healthy donors, and (3) noninfected healthy donors.

Ras-associated small GTPases (Rabs) are specific regulators of intracellular vesicle trafficking. Interference with host cell vesicular transport is a hallmark of many intracellular pathogens, including the notable example Mycobacterium tuberculosis. We performed, by quantitative polymerase chain reaction, gene-expression analyses for selected Rab molecules in peripheral-blood mononuclear cells from patients with tuberculosis (TB) and healthy control subjects, to identify candidate genes that are critically involved in the host immune response. Comparison revealed significant differences in the expression of genes for Rab13, Rab24, and Rab33A. Rab33A gene expression was down-regulated in patients with TB and was predominantly expressed in CD8+ T cells. We excluded possible influences of differences in T cell percentages between the 2 study groups, demonstrating that Rab33A gene expression changes on the single-cell level. In vitro, Rab33A RNA expression was induced in T cells on activation and by dendritic cells infected with M. tuberculosis. Our findings identify Rab33A as a T cell regulatory molecule in TB and suggest its involvement in disease processes.

Aberrant metabolism is a hallmark of cancer, but whole metabolomic flux measurements remain scarce. To bridge this gap, we developed a novel metabolic phenotypic analysis (MPA) method that infers metabolic phenotypes based on the integration of transcriptomics or proteomics data within a human genome-scale metabolic model. MPA was applied to conduct the first genome-scale study of breast cancer metabolism based on the gene expression of a large cohort of clinical samples. The modeling correctly predicted cell lines' growth rates, tumor lipid levels, and amino acid biomarkers, outperforming extant metabolic modeling methods. Experimental validation was obtained in vitro. The analysis revealed a subtype-independent "go or grow" dichotomy in breast cancer, where proliferation rates decrease as tumors evolve metastatic capability. MPA also identified a stoichiometric tradeoff that links the observed reduction in proliferation rates to the growing need to detoxify reactive oxygen species. Finally, a fundamental stoichiometric tradeoff between serine and glutamine metabolism was found, presenting a novel hallmark of estrogen receptor (ER)(+) versus ER(-) tumor metabolism. Together, our findings greatly extend insights into core metabolic aberrations and their impact in breast cancer.

UNLABELLED: We present metabolite pathway enrichment analysis (MPEA) for the visualization and biological interpretation of metabolite data at the system level. Our tool follows the concept of gene set enrichment analysis (GSEA) and tests whether metabolites involved in some predefined pathway occur towards the top (or bottom) of a ranked query compound list. In particular, MPEA is designed to handle many-to-many relationships that may occur between the query compounds and metabolite annotations. For a demonstration, we analysed metabolite profiles of 14 twin pairs with differing body weights. MPEA found significant pathways from data that had no significant individual query compounds, its results were congruent with those discovered from transcriptomics data and it detected more pathways than the competing metabolic pathway method did.

AVAILABILITY: The web server and source code of MPEA are available at http://ekhidna.biocenter.helsinki.fi/poxo/mpea/.

Modern analytical technologies afford comprehensive and quantitative investigation of a multitude of different metabolites. Typical metabolomic experiments can therefore produce large amounts of data. Handling such complex datasets is an important step that has big impact on extent and quality at which the metabolite identification and quantification can be made, and thus on the ultimate biological interpretation of results. Increasing interest in metabolomics thus led to resurgence of interest in related data processing. A wide variety of methods and software tools have been developed for metabolomics during recent years, and this trend is likely to continue. In this paper we overview the key steps of metabolomic data processing and focus on reviewing recent literature related to this topic, particularly on methods for handling data from liquid chromatography mass spectrometry (LC-MS) experiments.

BACKGROUND: Liquid chromatography coupled to mass spectrometry (LC/MS) has been widely used in proteomics and metabolomics research. In this context, the technology has been increasingly used for differential profiling, i.e. broad screening of biomolecular components across multiple samples in order to elucidate the observed phenotypes and discover biomarkers. One of the major challenges in this domain remains development of better solutions for processing of LC/MS data.

RESULTS: We present a software package MZmine that enables differential LC/MS analysis of metabolomics data. This software is a toolbox containing methods for all data processing stages preceding differential analysis: spectral filtering, peak detection, alignment and normalization. Specifically, we developed and implemented a new recursive peak search algorithm and a secondary peak picking method for improving already aligned results, as well as a normalization tool that uses multiple internal standards. Visualization tools enable comparative viewing of data across multiple samples. Peak lists can be exported into other data analysis programs. The toolbox has already been utilized in a wide range of applications. We demonstrate its utility on an example of metabolic profiling of Catharanthus roseus cell cultures.

CONCLUSION: The software is freely available under the GNU General Public License and it can be obtained from the project web page at: http://mzmine.sourceforge.net/.

BACKGROUND: Improvements in mass spectrometry (MS) technologies coupled with bioinformatics developments have allowed considerable advancement in the measurement and interpretation of lipidomics data in recent years. Since research areas employing lipidomics are rapidly increasing, there is a great need for bioinformatic tools that capture and utilize the complexity of the data. Currently, the diversity and complexity within the lipidome is often concealed by summing over or averaging individual lipids up to (sub)class-based descriptors, losing valuable information about biological function and interactions with other distinct lipids molecules, proteins and/or metabolites.

AIM OF REVIEW: To address this gap in knowledge, novel bioinformatics methods are needed to improve identification, quantification, integration and interpretation of lipidomics data. The purpose of this mini-review is to summarize exemplary methods to explore the complexity of the lipidome.

KEY SCIENTIFIC CONCEPTS OF REVIEW: Here we describe six approaches that capture three core focus areas for lipidomics: (1) lipidome annotation including a resolvable database identifier, (2) interpretation via pathway- and enrichment-based methods, and (3) understanding complex interactions to emphasize specific steps in the analytical process and highlight challenges in analyses associated with the complexity of lipidome data.

Various studies aiming to elucidate the role of the gut microbiome-metabolome co-axis in health and disease have primarily focused on water-soluble polar metabolites, whilst non-polar microbial lipids have received less attention. The concept of microbiota-dependent lipid biotransformation is over a century old. However, only recently, several studies have shown how microbial lipids alter intestinal and circulating lipid concentrations in the host, thus impacting human lipid homeostasis. There is emerging evidence that gut microbial communities play a particularly significant role in the regulation of host cholesterol and sphingolipid homeostasis. Here, we review and discuss recent research focusing on microbe-host-lipid co-metabolism. We also discuss the interplay of human gut microbiota and molecular lipids entering host systemic circulation, and its role in health and disease.

High physical activity/aerobic fitness predicts low morbidity and mortality. Our aim was to identify the most up-regulated gene sets related to long-term physical activity vs. inactivity in skeletal muscle and adipose tissues and to obtain further information about their link with cardio-metabolic risk factors. We studied ten same-sex twin pairs (age range 50-74 years) who had been discordant for leisure-time physical activity for 30 years. The examinations included biopsies from m. vastus lateralis and abdominal subcutaneous adipose tissue. RNA was analyzed with the genome-wide Illumina Human WG-6 v3.0 Expression BeadChip. For pathway analysis we used Gene Set Enrichment Analysis utilizing active vs. inactive co-twin gene expression ratios. Our findings showed that among the physically active members of twin pairs, as compared to their inactive co-twins, gene expression in the muscle tissue samples was chronically up-regulated for the central pathways related to energy metabolism, including oxidative phosphorylation, lipid metabolism and supportive metabolic pathways. Up-regulation of these pathways was associated in particular with aerobic fitness and high HDL cholesterol levels. In fat tissue we found physical activity-associated increases in the expression of polyunsaturated fatty acid metabolism and branched-chain amino acid degradation gene sets both of which associated with decreased 'high-risk' ectopic body fat and plasma glucose levels. Consistent with other findings, plasma lipidomics analysis showed up-regulation of the triacylglycerols containing the polyunsaturated fatty acids. Our findings identified skeletal muscle and fat tissue pathways which are associated with the long-term physical activity and reduced cardio-metabolic disease risk, including increased aerobic fitness. In particular, improved skeletal muscle oxidative energy and lipid metabolism as well as changes in adipocyte function and redistribution of body fat are associated with reduced cardio-metabolic risk.

Recent clinical evidence suggests important role of lipid and amino acid metabolism in early pre-autoimmune stages of type 1 diabetes pathogenesis. We study the molecular paths associated with the incidence of insulitis and type 1 diabetes in the Non-Obese Diabetic (NOD) mouse model using available gene expression data from the pancreatic tissue from young pre-diabetic mice. We apply a graph-theoretic approach by using a modified color coding algorithm to detect optimal molecular paths associated with specific phenotypes in an integrated biological network encompassing heterogeneous interaction data types. In agreement with our recent clinical findings, we identified a path downregulated in early insulitis involving dihydroxyacetone phosphate acyltransferase (DHAPAT), a key regulator of ether phospholipid synthesis. The pathway involving serine/threonine-protein phosphatase (PP2A), an upstream regulator of lipid metabolism and insulin secretion, was found upregulated in early insulitis. Our findings provide further evidence for an important role of lipid metabolism in early stages of type 1 diabetes pathogenesis, as well as suggest that such dysregulation of lipids and related increased oxidative stress can be tracked to beta cells.

BACKGROUND: Saccharomyces cerevisiae is able to adapt to a wide range of external oxygen conditions. Previously, oxygen-dependent phenotypes have been studied individually at the transcriptional, metabolite, and flux level. However, the regulation of cell phenotype occurs across the different levels of cell function. Integrative analysis of data from multiple levels of cell function in the context of a network of several known biochemical interaction types could enable identification of active regulatory paths not limited to a single level of cell function.

RESULTS: The graph theoretical method called Enriched Molecular Path detection (EMPath) was extended to enable integrative utilization of transcription and flux data. The utility of the method was demonstrated by detecting paths associated with phenotype differences of S. cerevisiae under three different conditions of oxygen provision: 20.9%, 2.8% and 0.5%. The detection of molecular paths was performed in an integrated genome-scale metabolic and protein-protein interaction network.

CONCLUSIONS: The molecular paths associated with the phenotype differences of S. cerevisiae under conditions of different oxygen provisions revealed paths of molecular interactions that could potentially mediate information transfer between processes that respond to the particular oxygen availabilities.

We have developed a system called megNet for integrating and visualizing heterogeneous biological data in order to enable modeling biological phenomena using a systems approach. Herein we describe megNet, including a recently developed user interface for visualizing biological networks in three dimensions and a web user interface for taking input parameters from the user, and an in-house text mining system that utilizes an existing knowledge base. We demonstrate the software with a case study in which we integrate lipidomics data acquired in-house with interaction data from external databases, and then find novel interactions that could possibly explain our previous associations between biological data and medical images. The flexibility of megNet assures that the tool can be applied in diverse applications, from target discovery in medical applications to metabolic engineering in industrial biotechnology.

Bartonella henselae is present in a wide range of wild and domestic feline hosts and causes cat-scratch disease and bacillary angiomatosis in humans. We have estimated here the gene content of Bartonella koehlerae, a novel species isolated from cats that was recently identified as an agent of human endocarditis. The investigation was accomplished by comparative genomic hybridization (CGH) to a microarray constructed from the sequenced 1.93-Mb genome of B. henselae. Control hybridizations of labeled DNA from the human pathogen Bartonella quintana with a reduced genome of 1.58 Mb were performed to evaluate the accuracy of the array for genes with known levels of sequence divergence. Genome size estimates of B. koehlerae by pulsed-field gel electrophoresis matched that calculated by the CGH, indicating a genome of 1.7 to 1.8 Mb with few unique genes. As in B. quintana, sequences in the prophage and the genomic islands were reported absent in B. koehlerae. In addition, sequence variability was recorded in the chromosome II-like region, where B. koehlerae showed an intermediate retention pattern of both coding and noncoding sequences. Although most of the genes missing in B. koehlerae are also absent from B. quintana, its phylogenetic placement near B. henselae suggests independent deletion events, indicating that host specificity is not solely attributed to genes in the genomic islands. Rather, the results underscore the instability of the genomic islands even within bacterial populations adapted to the same host-vector system, as in the case of B. henselae and B. koehlerae.

Cats are the natural host for Bartonella henselae, an opportunistic human pathogen and the agent of cat scratch disease. Here, we have analyzed the natural variation in gene content and genome structure of 38 Bartonella henselae strains isolated from cats and humans by comparative genome hybridizations to microarrays and probe hybridizations to pulsed-field gel electrophoresis (PFGE) blots. The variation in gene content was modest and confined to the prophage and the genomic islands, whereas the PFGE analyses indicated extensive rearrangements across the terminus of replication with breakpoints in areas of the genomic islands. We observed no difference in gene content or structure between feline and human strains. Rather, the results suggest multiple sources of human infection from feline B. henselae strains of diverse genotypes. Additionally, the microarray hybridizations revealed DNA amplification in some strains in the so-called chromosome II-like region. The amplified segments were centered at a position corresponding to a putative phage replication initiation site and increased in size with the duration of cultivation. We hypothesize that the variable gene pool in the B. henselae population plays an important role in the establishment of long-term persistent infection in the natural host by promoting antigenic variation and escape from the host immune response.

Fluid volume changes during hemodialysis was monitored by continuous whole body impedance measurements. The fluid changes recorded using this method was compared to fluid volume changes measured in plasma water (PV) using125I-albumin, and extracellular volume (ECV) using51Cr-EDTA before and after treatment, and total body water (TBW) changes reflected by continuous bed scale monitoring. Changes in impedance correlated to TBW changes, r=0.80, p<0.001, while correlations to changes in ECV and PV were: r=0.57 and r=0.55, respectively, p<0.05. Alterations in body fluid volumes recorded with whole body impedance is best correlated to total body water changes. The use of continuous whole body impedance monitoring has been shown to offer a simple non-invasive method for recording total body water changes during hemodialysis.