Module-detection approaches for the integration of multilevel omics data highlight the comprehensive response of Aspergillus fumigatus to caspofungin

Background

Omics data provide deep insights into overall biological processes of organisms. However, integration of data from different molecular levels such as transcriptomics and proteomics, still remains challenging. Analyzing lists of differentially abundant molecules from diverse molecular levels often results in a small overlap mainly due to different regulatory mechanisms, temporal scales, and/or inherent properties of measurement methods. Module-detecting algorithms identifying sets of closely related proteins from protein-protein interaction networks (PPINs) are promising approaches for a better data integration.

Results

Here, we made use of transcriptome, proteome and secretome data from the human pathogenic fungus Aspergillus fumigatus challenged with the antifungal drug caspofungin. Caspofungin targets the fungal cell wall which leads to a compensatory stress response. We analyzed the omics data using two different approaches: First, we applied a simple, classical approach by comparing lists of differentially expressed genes (DEGs), differentially synthesized proteins (DSyPs) and differentially secreted proteins (DSePs); second, we used a recently published module-detecting approach, ModuleDiscoverer, to identify regulatory modules from PPINs in conjunction with the experimental data. Our results demonstrate that regulatory modules show a notably higher overlap between the different molecular levels and time points than the classical approach. The additional structural information provided by regulatory modules allows for topological analyses. As a result, we detected a significant association of omics data with distinct biological processes such as regulation of kinase activity, transport mechanisms or amino acid metabolism. We also found a previously unreported increased production of the secondary metabolite fumagillin by A. fumigatus upon exposure to caspofungin. Furthermore, a topology-based analysis of potential key factors contributing to drug-caused side effects identified the highly conserved protein polyubiquitin as a central regulator. Interestingly, polyubiquitin UbiD neither belonged to the groups of DEGs, DSyPs nor DSePs but most likely strongly influenced their levels.

Conclusion

Module-detecting approaches support the effective integration of multilevel omics data and provide a deep insight into complex biological relationships connecting these levels. They facilitate the identification of potential key players in the organism’s stress response which cannot be detected by commonly used approaches comparing lists of differentially abundant molecules.

Electronic supplementary material

The online version of this article (10.1186/s12918-018-0620-8) contains supplementary material, which is available to authorized users.

Prediction of Cardiovascular Risk in Hemodialysis Patients by Data Mining

Objectives: The objective of this work was to contribute to the development, validation and application of data mining methods for prediction in decision support systems in medicine. The particular focus was on the prediction of cardiovascular risk factors in hemodialysis patients, specifically the interventricular septum (IVS) thickness of the heart of individual patients as an important quantitative indicator to diagnose left ventricular hypertrophy. The work was based on data from 63 long-term hemodialysis patients of the KfH Dialysis Centre in Jena, Germany.

Methods: The approach applied is based on data mining methods and involves four major steps: data based clustering, cluster based rule extraction, rulebase construction and cluster and rule based prediction. The methods employed include crisp and fuzzy algorithms. At each step, logical and medical validation of results was carried out. Different sets of randomly selected patient data were used to train, test and optimize the clusterbases and rulebases for prediction.

Results: Using the best clusterbase/rulebase combination designed, the IVS thickness cluster (‘small’ or ‘large’) was predicted correctly for 30 of the 35 patients with known IVS values in the training data set; no patient was predicted incorrectly and 5 were parity predicted. For the test data set, 4 of the 6 patients with known IVS values were predicted correctly, no patient incorrectly and 2 parity. These results did not substantially differ from those obtained using the second best clusterbase/rulebase combination which was finally recommended for use based on further performance criteria. The prediction of the IVS thickness clusters of the 22 patients with unknown IVS values also yielded good results that were (and could only be) validated by a medical individual risk assessment of these patients.

Conclusions: The approach applied proved successful for the cluster and rule based prediction of a quantitative variable, such as IVS thickness, for individual patients from other variables relevant to the problem. The results obtained demonstrate the high potential of the approach and the methods developed and validated to support decision-making in hemodialysis and other fields of medicine by individual risk prediction.

Conserved Senescence Associated Genes and Pathways in Primary Human Fibroblasts Detected by RNA-Seq

Cellular senescence correlates with changes in the transcriptome. To obtain a complete view on senescence-associated transcription networks and pathways, we assessed by deep RNA sequencing the transcriptomes of five of the most commonly used laboratory strains of human fibroblasts during their transition into senescence. In a number of cases, we verified the RNA-seq data by real-time PCR. By determining cellular protein levels we observed that the age-related expression of most but not all genes is regulated at the transcriptional level. We found that 78% of the age-affected differentially expressed genes were commonly regulated in the same direction (either up- or down-regulated) in all five fibroblast strains, indicating a strong conservation of age-associated changes in the transcriptome. KEGG pathway analyses confirmed up-regulation of the senescence-associated secretory phenotype and down-regulation of DNA synthesis/repair and most cell cycle pathways common in all five cell strains. Newly identified senescence-induced pathways include up-regulation of endocytotic/phagocytic pathways and down-regulation of the mRNA metabolism and the mRNA splicing pathways. Our results provide an unprecedented comprehensive and deep view into the individual and common transcriptome and pathway changes during the transition into of senescence of five human fibroblast cell strains.

A theoretical study of lipid accumulation in the liver-implications for nonalcoholic fatty liver disease

A hallmark of the nonalcoholic fatty liver disease is the accumulation of lipids. We developed a mathematical model of the hepatic lipid dynamics to simulate the fate of fatty acids in hepatocytes. Our model involves fatty acid uptake, lipid oxidation, and lipid export. It takes into account that storage of triacylglycerol within hepatocytes leads to cell enlargement reducing the sinusoids radius and impairing hepatic microcirculation. Thus oxygen supply is reduced, which impairs lipid oxidation. The analysis of our model revealed a bistable behavior (two stable steady states) of the system, in agreement with histological observations showing distinct areas of lipid accumulation in lobules. The first (healthy) state is characterized by intact lipid oxidation and a low amount of stored lipids. The second state in our model may correspond to the steatotic cell; it is marked by a high amount of stored lipids and a reduced lipid oxidation caused by impaired oxygen supply. Our model stresses the role of insufficient oxygen supply for the development of steatosis. We discuss implications of our results in regard to the experimental design aimed at exploring lipid metabolism reactions under steatotic conditions. Moreover, the model helps to understand the reversibility of lipid accumulation and predicts the reversible switch to show hysteresis. The system can switch from the steatotic state back to the healthy state by reduction of fatty acid uptake below the threshold at which steatosis started. The reversibility corresponds to the observation that caloric restriction can reduce the lipid content in the liver.

Network analysis of transcriptional regulation in response to intramuscular interferon-β-1a multiple sclerosis treatment

Interferon-β (IFN-β) is one of the major drugs for multiple sclerosis (MS) treatment. The purpose of this study was to characterize the transcriptional effects induced by intramuscular IFN-β-1a therapy in patients with relapsing-remitting form of MS. By using Affymetrix DNA microarrays, we obtained genome-wide expression profiles of peripheral blood mononuclear cells of 24 MS patients within the first 4 weeks of IFN-β administration. We identified 121 genes that were significantly up- or downregulated compared with baseline, with stronger changed expression at 1 week after start of therapy. Eleven transcription factor-binding sites (TFBS) are overrepresented in the regulatory regions of these genes, including those of IFN regulatory factors and NF-κB. We then applied TFBS-integrating least angle regression, a novel integrative algorithm for deriving gene regulatory networks from gene expression data and TFBS information, to reconstruct the underlying network of molecular interactions. An NF-κB-centered sub-network of genes was highly expressed in patients with IFN-β-related side effects. Expression alterations were confirmed by real-time PCR and literature mining was applied to evaluate network inference accuracy.

Mass spectrometric and peptide chip epitope analysis on the RA33 autoantigen with sera from rheumatoid arthritis patients

As the potential of epitope chips for routine application in diagnostics relies on the careful selection of peptides, reliable epitope mapping results are of utmost interest to the medical community. Mass spectrometric epitope mapping in combination with peptide chip analysis showed that autoantibodies from patients who suffered from rheumatoid arthritis (RA) were directed against distinct surface structures on the full-length human autoantigen RA33 as well as against partial sequences. Using the combined mass spectrometric epitope extraction and peptide chip analysis approach, four sequence motifs on RA33 emerged as immuno-positive, showing that epitopes were not randomly distributed on the entire RA33 amino acid sequence. A sequential epitope motif ((245)GYGGG(249)) was determined on the C-terminal part of RA33 which matched with the Western blot patient screening results using the full-length protein and, thus, was regarded as a disease-associated epitope. Other epitope motifs were found on N-terminal partial sequences ((59)RSRGFGF(65), (111)KKLFVG(116)) and again on the C-terminal part ((266)NQQPSNYG(273)) of RA33. As recognition of these latter three motifs was also recorded by peptide chip analysis using control samples which were negative in the Western blot screening, these latter motifs were regarded as "cryptic epitopes". Knowledge of disease-associated epitopes is crucial for improving the design of a customized epitope peptide chip for RA and mass spectrometric epitope mapping pivotally assisted with selecting the most informative peptide(s) to be used for future diagnostic purposes.

Dynamics of amino acid metabolism of primary human liver cells in 3D bioreactors

The kinetics of 18 amino acids, ammonia (NH3) and urea (UREA) in 18 liver cell bioreactor runs were analyzed and simulated by a two-compartment model consisting of a system of 42 differential equations. The model parameters, most of them representing enzymatic activities, were identified and their values discussed with respect to the different liver cell bioreactor performance levels. The nitrogen balance based model was used as a tool to quantify the variability of runs and to describe different kinetic patterns of the amino acid metabolism, in particular with respect to glutamate (GLU) and aspartate (ASP).

Prediction of cardiovascular risk in hemodialysis patients by data mining

OBJECTIVES

The objective of this work was to contribute to the development, validation and application of data mining methods for prediction in decision support systems in medicine. The particular focus was on the prediction of cardiovascular risk factors in hemodialysis patients, specifically the interventricular septum (IVS) thickness of the heart of individual patients as an important quantitative indicator to diagnose left ventricular hypertrophy. The work was based on data from 63 long-term hemodialysis patients of the KfH Dialysis Centre in Jena, Germany.

METHODS

The approach applied is based on data mining methods and involves four major steps: data based clustering, cluster based rule extraction, rulebase construction and cluster and rule based prediction. The methods employed include crisp and fuzzy algorithms. At each step, logical and medical validation of results was carried out. Different sets of randomly selected patient data were used to train, test and optimize the clusterbases and rulebases for prediction.

RESULTS

Using the best clusterbase/rulebase combination designed, the IVS thickness cluster ('small' or 'large') was predicted correctly for 30 of the 35 patients with known IVS values in the training data set; no patient was predicted incorrectly and 5 were parity predicted. For the test data set, 4 of the 6 patients with known IVS values were predicted correctly, no patient incorrectly and 2 parity. These results did not substantially differ from those obtained using the second best clusterbase/rulebase combination which was finally recommended for use based on further performance criteria. The prediction of the IVS thickness clusters of the 22 patients with unknown IVS values also yielded good results that were (and could only be) validated by a medical individual risk assessment of these patients.

CONCLUSIONS

The approach applied proved successful for the cluster and rule based prediction of a quantitative variable, such as IVS thickness, for individual patients from other variables relevant to the problem. The results obtained demonstrate the high potential of the approach and the methods developed and validated to support decision-making in hemodialysis and other fields of medicine by individual risk prediction.

Data and knowledge based experimental design for fermentation process optimization

A novel method for the sequential experimental design in order to optimize fed-batch fermentations was applied to a hyaluronidase fermentation by Streptococcus agalactiae. A Lambda-optimal design was introduced to minimize the model parameter estimation error and to maximize the performance of the fermentation process. The method employs hybrid models that contain mechanistic, fuzzy and neural network components.

High-cell-density cultivation of microorganisms

High-cell-density cultivation (HCDC) is required to improve microbial biomass and product formation substantially. An overview of HCDC is given for microorganisms including bacteria, archae and eukarya (yeasts). Problems encountered by HCDC and their possible solutions are discussed. Improvements of strains, different types of bioreactors and cultivation strategies for successful HCDC are described. Stirred-tank reactors with and without cell retention, a dialysis-membrane reactor, a gas-lift reactor and a membrane cyclone reactor used for HCDC are outlined. Recently modified traditional feeding strategies and new ones are included, in particular those for unlimited growth to very dense cultures. Emphasis is placed on robust fermentation control because of the growing industrial interest in this field. Therefore, developments in the application of multivariate statistical control, artificial neural networks, fuzzy control and knowledge-based supervision (expert systems) are summarized. Recent advances using Escherichia coli--the pioneer organism for HCDC--are outlined.

Analysis and simulation of complex interactions during dynamic microfiltration of Escherichia coli suspensions

Microfiltration is an important unit operation in downstream processing. However, due to the influence of membrane fouling, prediction of the filtration performance for biological suspensions is difficult. This paper describes a modeling approach that allows a comprehensive description of filtration performance. On the basis of experimental data and linguistic information, a specific artificial neural network was developed that predicts the process behavior within a certain range of parameters. This approach allows us to analyze influences of fermentation on filtration. By using extensive simulations, the interactions of 17 parameters were examined and the fouling causes determined. The model was developed for cell harvesting of Escherichia coli through a shear-enhanced module. The method can be applied to any cross-flow filtration process.

Influence of steroid hormones on pS2/BCEI gene expression in xenografted colon tumors

The biological function of the hormone inducible human pS2/BCEI gene, cloned from the breast cancer cell line MCF-7, still remains unknown. Our aim was to determine the in vivo influence of steroid hormones on pS2 expression and tumour growth in colorectal tumours. We transplanted aliquots of human colon tumours into male and female nude mice and studied tumour growth and expression of the pS2/BCEI gene by immunostaining and mRNA analysis. Our results show that the sex of the host and therefore the hormonal background does not play a dominant role in pS2 expression and growth of the xenografts.

Dynamic model of the pathogenesis of Mengo virus infection in mice

A mathematical model of the pathogenesis of experimental Mengo virus infection in mice has been developed and fitted using kinetic data of both virus multiplication in different organs and mortality. The behaviour of the model proved to be bistable. In contrast to the widely accepted hypothesis that an acutely virus-infected host dies when virus replication has attained a critical level in the main target organ, the present results showed the following: the maximum virus titre in brain, the main target organ, has been reached already 24 hr post infection (p.i.) but the animals began to die since 60 hr. Hence, it was postulated and confirmed by a good model fit to the experimental data that the so-called AUC (area under the curve) of the virus multiplication kinetics may be a critical quantity. From this finding a hypothesis was deduced assuming that in the presence of high amounts of the virus the antiviral effect of IFN wanes with time. Since this process accounts for death, it may be a potential target of antiviral therapy.

Two-pool model analysis of data in hemodialysis by means of programmable pocket calculator TI 59

Four parameters of a two-pool model are evaluated by an iterative method using the explicit solutions of the linear differential equations. For this it was presumed that the residual renal clearance is sufficiently small. Five data pairs of measured plasma concentrations ci for the time points ti (i = 0 to 4), as well as the dialyzer and residual renal clearances (KD and KR), must be given and put in the calculator. A sample run is shown for urea kinetics. The parameter estimation takes about 10 min. The program is suitable to assist in the individualization of dialysis therapy.

Model aided dynamic process analysis and optimization for the nourseothricin fermentation

The relation between product formation and growth kinetics could be characterized by two facts: the specific product formation rate depends on the ageing of the population and on the specific growth rate. These relation was formulated and quantified by a mathematical model, which was fitted to experimental data of a representative fermentation run und used to predict an optimal fermentation mode. In the result of this discussion cyclic fed batch fermentation was found to be optimal.

[Multiphasic growth of microorganisms: modeling and computer simulation of linear growth phases]

A phenomenological and a more causal model are developed for the multiphasic discontinuous growth. The first model distinguished between the lag-phase, the exponential phase, the transient phase from the exponential to the linear phase, the linear phase, the transient phase from the linear to the stationary phase, and the stationary phase. The parameters are rate constants, critical values of biomass, and time constants. The parameters are estimated for experimental data of growth of Candida lipolytica under limitation of thiamine (Müller et al. 1978). These data are fitted also by a more causal model. This second model is in agreement with Monod's idea that a linear growth phase may be due to an enzyme or enzyme system which has a constant activity. In the analysed case of limitation of thiamine the constancy of the dehydrogenase activity is caused by a constant level of the coenzymethiaminepyrophosphate. Thus, when such a thiamine requiring enzymatic step becomes to the "bottle-neck", bacterial culture switches over from exponential to linear growth. The end of linear phase is discussed more hypothetically by the high cooperativity of activity of dehydrogenases and the existence of a mimimal specific growth rate. The results of modeling and parameter estimation are compared with experimental data of C. lipolytica. These two models are able to interpret the growth kinetic of these multiphasic growth.