Biomedical subjects

Arno Lukas

Publications and source records attributed to Arno Lukas.

7 recordsLinked to original sources

PubMed2007

Gene expression and biomarkers in renal transplant ischemia reperfusion injury.

The incidence of postischemic acute renal allograft failure (ARF) occurs in roughly 25% of cadaveric donor kidney recipients. This high rate remained virtually unchanged over the last decades despite modification in recipient management and modern immunosuppressive strategies. It has recently been shown that among other reasons, the systemic inflammation in the brain death cadaveric organ donor contributes to subsequent ARF in the recipient. This review focuses on the consequences of ischemia and reperfusion on the cellular level and offers potential solutions for the reduction of ARF. Genome-wide gene expression analysis together with sophisticated biostatistical analysis made it possible to identify several candidate gene products and proteins that may act as specific and sensitive biomarker for renal inflammation and ischemia. These markers may be very helpful in the clinical management of patients with a high a priori risk of subsequent ARF such as recipients of marginal donor kidneys. Ongoing clinical trials will evaluate whether immunosuppression of the cadaveric organ donor before organ harvest will have the potential to reduce inflammation in the transplant kidney and subsequently lead to a reduction in the rate of ARF.

Animals↗

PubMed2006

Transforming omics data into context: bioinformatics on genomics and proteomics raw data.

Differential gene expression analysis and proteomics have exerted significant impact on the elucidation of concerted cellular processes, as simultaneous measurement of hundreds to thousands of individual objects on the level of RNA and protein ensembles became technically feasible. The availability of such data sets has promised a profound understanding of phenomena on an aggregate level, expressed as the phenotypic response (observables) of cells, e.g., in the presence of drugs, or characterization of cells and tissue displaying distinct patho-physiological states. However, the step of transforming these data into context, i.e., linking distinct expression or abundance patterns with phenotypic observables - and furthermore enabling a sound biological interpretation on the level of reaction networks and concerted pathways, is still a major shortcoming. This finding is certainly based on the enormous complexity embedded in cellular reaction networks, but a variety of computational approaches have been developed over the last few years to overcome these issues. This review provides an overview on computational procedures for analysis of genomic and proteomic data introducing a sequential analysis workflow: Explorative statistics for deriving a first, from the purely statistical viewpoint, relevant candidate gene/protein list, followed by co-regulation and network analysis to biologically expand this core list toward functional networks and pathways. The review on these procedures is complemented by example applications tailored at identification of disease-associated proteins. Optimization of computational procedures involved, in conjunction with the continuous increase in additional biological data, clearly has the potential of boosting our understanding of processes on a cell-wide level.

Animals↗

PubMed2005

Detection of coregulation in differential gene expression profiles.

Genomics and proteomics approaches generate distinct gene expression and protein profiles, listing individual genes embedded in broad functional terms as gene ontologies. However, interpretation of gene profiles in a regulatory and functional context remains a major issue. Elucidation of regulatory mechanisms at the gene expression level via analysis of promoter regions is a prominent procedure to decipher such gene regulatory networks. We propose a novel genetic algorithm (GA) to extract joint promoter modules in a set of coexpressed genes as resulting from differential gene expression experiments. Algorithm design has focused on the following constraints: (I) identification of the major promoter modules, which are (II) characterized by a maximum number of joint motifs and (III) are found in a maximum number of coexpressed genes. The capability of the GA in detecting multiple modules was evaluated on various test data sets, analyzing the impact of the number of motifs per promoter module, the number of genes associated with a module, as well as the total number of distinct promoter modules encoded in a sequence set. In addition to the test data sets, the GA was evaluated on two biological examples, namely a muscle-specific data set and the upstream sequences of the beta-actin gene (ACTB) derived from different species, complemented by a comparison to alternative promoter module identification routines.

Actins↗

PubMed2003

Body mass index is the main risk factor for arterial hypertension in young subjects without major comorbidity.

BACKGROUND: Analytical statistics revealed a variety of risk factors for hypertension, but the complex interplay between different factors remains to be determined by more powerful statistical techniques. METHODS: Analytical as well as new, explorative statistical methods such as natural segmentation (k-means) and predictive modelling algorithms (C4.5) were used to classify the interactions of the individual risk factors for arterial hypertension in a large cohort of subjects. Fifty-five attributes (subject base, sociodemographic, medical history, laboratory data) were obtained from each of the 3547 participants of a community-based health survey. The study subjects, mean age of 41 years, were free of major comorbidity. RESULTS: Twenty-five percent of the subjects had at least stage 1 hypertension. No clear linear dependency of risk factors with the diagnosis hypertension could be derived by the analytical statistics. In particular, the mutual amplification of different risk factors towards hypertension could not be revealed by these techniques. Explorative analytics however, uncovered body mass index (BMI) as the main single risk factor associated with hypertension. High predictive accuracy was achieved when combinations of certain risk factors including male gender and age were used. CONCLUSIONS: In summary, the survey of risk factors for hypertension using explorative analytics yielded high increases for the correct prediction of arterial hypertension. In this cohort, BMI was the single strongest parameter associated with arterial hypertension.

Adult↗

PubMed2003

The influence of comorbidity on the effect of levofloxacin treatment success of ambulatory respiratory tract infections.

The influence of patient relevant parameters such as age, comorbidity, or duration of disease on the treatment success of levofloxacin for community-acquired respiratory tract infections (CARTI) has not been thoroughly elucidated. We therefore conducted a prospective cohort study of 9831 patients with CARTI in a clinical practice setting. The patients received 500 mg of levofloxacin once a day over a mean of seven days. Twenty-two attributes per patient were recorded before treatment initiation and after seven to fourteen days after start of treatment. Descriptive and explorative statistics such as the k-means and C4.5 algorithms were used to analyze the dataset. The overall success rate of levofloxacin therapy for CARTI was over 98%, side effects occurred in 1.6% of patients. Descriptive analysis revealed a weak correlation between parameters which significantly influence the course of disease, such as the number of comorbidities, the duration of infection before levofloxacin start, or the severity of symptoms and the treatment success. Explorative statistics yielded similar results. Two homogenous clusters, holding 34 and 45% of patients respectively, yielded the number of comorbid conditions and the duration of infection as main attributes negatively influencing treatment success. We therefore conclude, that the number of co-morbid conditions and the duration of infection before start of treatment as the strongest negative predictors for treatment success.

Adolescent↗

PubMed2002

Discrete simulation of regulatory homo- and heterodimerization in the apoptosis effector phase.

MOTIVATION: Quantitative simulation of molecular reaction networks is among the most promising approaches towards an understanding of complex biochemical pathways. Numerous qualitative as well as quantitative data from diverse experimental settings, in particular from genomics and proteomics, have to be contextually linked to convert static data into dynamic functionality. RESULTS: This paper presents the Lattice Molecular Automaton, a Cellular Automaton-based simulation tool, capable of representing complex molecular dynamics at different levels of granularity. A data structure concept represents molecular units, whose dynamics, embedded on a 2D grid, is defined via detailed intermolecular interaction profiles. The data structures hold diverse information as molecular type, potential, as well as kinetic energy states, which allows a precise representation of intracellular reaction networks. The molecular dynamics is performed via local computation of individual molecular states on the lattice, which, in conjunction with discretized space and time, enables excellent scalability of this simulation concept. This paper finally gives Lattice Molecular Automaton simulation results on key elements of apoptosis, the cell death cascade, in particular focusing on the regulatory function of homo- and heterodimerization of members of the Bcl-2 protein family in the apoptosis effector phase. The regulatory proteins Bcl2, Bax, and Bak constitute a diffusion-driven molecular switch with inherent damping of apoptosis induction, thereby controlling the apoptosis reaction cascade under noisy, external apoptosis inducing conditions.

Apoptosis↗

PubMed2002

Effects of microinjection of synthetic Bcl-2 domain peptides on apoptosis of renal tubular epithelial cells.

Bcl-2 protein family members are among the key regulators of the apoptosis effector phase. Therefore, we investigated the ability of synthetic peptides derived from proteins of the Bcl-2 family, namely, the NH2-terminal region of Bcl-2 (Bcl2_syn), a central domain of Bax (Bax_syn), and a central domain of Bak (Bak_syn) to interfere with the apoptotic process in LLC-PK1 cells. Apoptosis was induced by tacrolimus or lipopolysaccharide treatment, and microinjection of Bcl2_syn into stimulated LLC-PK1 cells significantly reduced the percentage of apoptotic cells detected within 4 h after the treatment. Microinjection of Bax_syn or Bax_syn, in contrast, induced apoptosis in otherwise untreated LLC-PK1 cells during the same period of time. A random sequence control peptide (Control_syn), which served as a negative control, as well as FITC-labeled dextran, which was coinjected in all experiments for visualization, were ineffective in either preventing or inducing apoptosis. These results suggest that synthetic peptides mimicking the functional domains of proteins of the Bcl-2 family are capable of regulating apoptosis when microinjected into LLC-PK1 cells in vivo. Analogs to these regulatory peptides could therefore provide valuable lead compounds in the therapeutical context.

Animals↗