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Institute
Poster presentation: The mammalian pineal organ is a peripheral oscillator, depending on afferent information from the so-called master clock in the suprachiasmatic nuclei of the hypothalamus. One of the best studied outputs of the pineal gland is the small and hydrophobic molecule melatonin. In all vertebrates, melatonin is synthesized rhythmically with high levels at night, signalling the body the duration of the dark period. Changes or disruptions of melatonin rhythms in humans are related to a number of pathophysiological disorders, like Alzheimer's disease, seasonal affective disorder or the Smith-Magenis-Syndrome. To use melatonin in preventive or curative interferences with the human circadian system, a complete understanding of the generation of the rhythmic melatonin signal in the human pineal gland is essential. Melatonin biosynthesis is best studied in the rodent pineal gland, where the activity of the penultimate and rate-limiting enzyme, the arylalkylamine N-acetyltransferase (AA-NAT), is regulated on the transcriptional level, whereas the regulatory role of the ultimate enzymatic step, achieved by the hydroxyindole O-methyltransferase (HIOMT), is still under debate. In rodents, Aa-nat mRNA is about 100-fold elevated during the night in response to adrenergic stimulation of the cAMP-signalling pathway, with AA-NAT protein levels closely following this dynamics. In contrast, in all ungulates studied so far (cow, sheep), a post-transcriptional regulation of the AA-NAT is central to determine rhythmic melatonin synthesis. AA-NAT mRNA levels are constantly elevated, and lead to a constitutive up-regulation of AA-NAT protein, which is, however, rapidly degraded via proteasomal proteolysis during the day. AA-NAT proteolysis is only terminated upon the nocturnal increase in cAMP levels. Similar to ungulates, a post-transcriptional control of this enzyme seems evident in the pineal gland of the primate Macaca mulatta. Studies on the molecular basis of melatonin synthesis in the human being are sparse and almost exclusively based on phenomenological data, derived from non-invasive investigations. Yet the molecular mechanisms underlying the generation of the hormonal message of darkness can currently only be deciphered using autoptic material. We therefore analyzed in human post-mortem pineal tissue Aa-nat and Hiomt mRNA levels, AA-NAT and HIOMT enzyme activity, and melatonin levels for the first time simultaneously within tissue samples of the same specimen. Here presented data show the feasibility of this approach. Our results depict a clear diurnal rhythm in AA-NAT activity and melatonin content, despite constant values for Aa-nat and Hiomt mRNA, and for HIOMT activity. Notably, the here elevated AA-NAT activity during the dusk period does not correspond to a simultaneous elevation in melatonin content. It is currently unclear whether this finding may suggest a more important role of the ultimate enzyme in melatonin synthesis, the HIOMT, for rate-limiting the melatonin rhythm, as reported recently for the rodent pineal gland. Thus, our data support for the first time experimentally that post-transcriptional mechanisms are responsible for the generation of rhythmic melatonin synthesis in the human pineal gland.
Background: Signal transduction pathways are important cellular processes to maintain the cell’s integrity. Their imbalance can cause severe pathologies. As signal transduction pathways feature complex regulations, they form intertwined networks. Mathematical models aim to capture their regulatory logic and allow an unbiased analysis of robustness and vulnerability of the signaling network. Pathway detection is yet a challenge for the analysis of signaling networks in the field of systems biology. A rigorous mathematical formalism is lacking to identify all possible signal flows in a network model.
Results: In this paper, we introduce the concept of Manatee invariants for the analysis of signal transduction networks. We present an algorithm for the characterization of the combinatorial diversity of signal flows, e.g., from signal reception to cellular response. We demonstrate the concept for a small model of the TNFR1-mediated NF- κB signaling pathway. Manatee invariants reveal all possible signal flows in the network. Further, we show the application of Manatee invariants for in silico knockout experiments. Here, we illustrate the biological relevance of the concept.
Conclusions: The proposed mathematical framework reveals the entire variety of signal flows in models of signaling systems, including cyclic regulations. Thereby, Manatee invariants allow for the analysis of robustness and vulnerability of signaling networks. The application to further analyses such as for in silico knockout was shown. The new framework of Manatee invariants contributes to an advanced examination of signaling systems.
Background: Microarray analysis represents a powerful way to test scientific hypotheses on the functionality of cells. The measurements consider the whole genome, and the large number of generated data requires sophisticated analysis. To date, no gold-standard for the analysis of microarray images has been established. Due to the lack of a standard approach there is a strong need to identify new processing algorithms.
Methods: We propose a novel approach based on hyperbolic partial differential equations (PDEs) for unsupervised spot segmentation. Prior to segmentation, morphological operations were applied for the identification of co-localized groups of spots. A grid alignment was performed to determine the borderlines between rows and columns of spots. PDEs were applied to detect the inflection points within each column and row; vertical and horizontal luminance profiles were evolved respectively. The inflection points of the profiles determined borderlines that confined a spot within adapted rectangular areas. A subsequent k-means clustering determined the pixels of each individual spot and its local background.
Results: We evaluated the approach for a data set of microarray images taken from the Stanford Microarray Database (SMD). The data set is based on two studies on global gene expression profiles of Arabidopsis Thaliana. We computed values for spot intensity, regression ratio, and coefficient of determination. For spots with irregular contours and inner holes, we found intensity values that were significantly different from those determined by the GenePix Pro microarray analysis software. We determined the set of differentially expressed genes from our intensities and identified more activated genes than were predicted by the GenePix software.
Conclusions: Our method represents a worthwhile alternative and complement to standard approaches used in industry and academy. We highlight the importance of our spot segmentation approach, which identified supplementary important genes, to better explains the molecular mechanisms that are activated in a defense responses to virus and pathogen infection.
Objectives: To analyze the performance of radiological assessment categories and quantitative computational analysis of apparent diffusion coefficient (ADC) maps using variant machine learning algorithms to differentiate clinically significant versus insignificant prostate cancer (PCa). Methods: Retrospectively, 73 patients were included in the study. The patients (mean age, 66.3 ± 7.6 years) were examined with multiparametric MRI (mpMRI) prior to radical prostatectomy (n = 33) or targeted biopsy (n = 40). The index lesion was annotated in MRI ADC and the equivalent histologic slides according to the highest Gleason Grade Group (GrG). Volumes of interest (VOIs) were determined for each lesion and normal-appearing peripheral zone. VOIs were processed by radiomic analysis. For the classification of lesions according to their clinical significance (GrG ≥ 3), principal component (PC) analysis, univariate analysis (UA) with consecutive support vector machines, neural networks, and random forest analysis were performed. Results: PC analysis discriminated between benign and malignant prostate tissue. PC evaluation yielded no stratification of PCa lesions according to their clinical significance, but UA revealed differences in clinical assessment categories and radiomic features. We trained three classification models with fifteen feature subsets. We identified a subset of shape features which improved the diagnostic accuracy of the clinical assessment categories (maximum increase in diagnostic accuracy ΔAUC = + 0.05, p < 0.001) while also identifying combinations of features and models which reduced overall accuracy. Conclusions: The impact of radiomic features to differentiate PCa lesions according to their clinical significance remains controversial. It depends on feature selection and the employed machine learning algorithms. It can result in improvement or reduction of diagnostic performance.
Purpose: To stratify differences in visual semantic and quantitative imaging features in intensive care patients with nonspecific mastoid effusions versus patients with acute mastoiditis (AM) requiring surgical treatment. Methods: We included 48 patients (male, 28; female, 20; mean age, 59.5 ± 18.1 years) with mastoid opacification (AM, n = 24; control, n = 24) who underwent clinically indicated cerebral CT between 12/2007 and 07/2018 in this retrospective study. Semantic features described the extend and asymmetry of mastoid and middle-ear cavity opacification and complications like erosive changes. Minimum, maximum and mean Hounsfield unit (HU) values were obtained as quantitative features. We analyzed the features employing univariate testing. Results: Compared to intensive care patients, AM patients revealed asymmetric mastoid or middle-ear cavity opacification (likelihood-ratio (LR) < 0.001). Applying a dedicated threshold of the extent of opacification, AM patients reached significance levels of LR = 0.042 and 0.002 for mastoid and middle-ear cavity opacification. AM cases showed higher maximum and mean HU values (p = 0.009, p = 0.024). Conclusions: We revealed that the extent and asymmetry of mastoid and middle-ear cavity opacification differs significantly between AM patients and intensive care patients. Multicenter research is needed to expand our cohort and possibly pave the way to build a non-invasive predictive model for AM in the future.
Purpose: To identify transjugular intrahepatic portosystemic shunt (TIPS) thrombosis in abdominal CT scans applying quantitative image analysis.
Materials and methods: We retrospectively screened 184 patients to include 20 patients (male, 8; female, 12; mean age, 60.7 ± 8.87 years) with (case, n = 10) and without (control, n = 10) in-TIPS thrombosis who underwent clinically indicated contrast-enhanced and unenhanced abdominal CT followed by conventional TIPS-angiography between 08/2014 and 06/2020. First, images were scored visually. Second, region of interest (ROI) based quantitative measurements of CT attenuation were performed in the inferior vena cava (IVC), portal vein and in four TIPS locations. Minimum, maximum and average Hounsfield unit (HU) values were used as absolute and relative quantitative features. We analyzed the features with univariate testing.
Results: Subjective scores identified in-TIPS thrombosis in contrast-enhanced scans with an accuracy of 0.667 – 0.833. Patients with in-TIPS thrombosis had significantly lower average (p < 0.001), minimum (p < 0.001) and maximum HU (p = 0.043) in contrast-enhanced images. The in-TIPS / IVC ratio in contrast-enhanced images was significantly lower in patients with in-TIPS thrombosis (p < 0.001). No significant differences were found for unenhanced images. Analyzing the visually most suspicious ROI with consecutive calculation of its ratio to the IVC, all patients with a ratio < 1 suffered from in-TIPS thrombosis (p < 0.001, sensitivity and specificity = 100%).
Conclusion: Quantitative analysis of abdominal CT scans facilitates the stratification of in-TIPS thrombosis. In contrast-enhanced scans, an in-TIPS / IVC ratio < 1 could non-invasively stratify all patients with in-TIPS thrombosis.
Our purpose was to analyze the robustness and reproducibility of magnetic resonance imaging (MRI) radiomic features. We constructed a multi-object fruit phantom to perform MRI acquisition as scan-rescan using a 3 Tesla MRI scanner. We applied T2-weighted (T2w) half-Fourier acquisition single-shot turbo spin-echo (HASTE), T2w turbo spin-echo (TSE), T2w fluid-attenuated inversion recovery (FLAIR), T2 map and T1-weighted (T1w) TSE. Images were resampled to isotropic voxels. Fruits were segmented. The workflow was repeated by a second reader and the first reader after a pause of one month. We applied PyRadiomics to extract 107 radiomic features per fruit and sequence from seven feature classes. We calculated concordance correlation coefficients (CCC) and dynamic range (DR) to obtain measurements of feature robustness. Intraclass correlation coefficient (ICC) was calculated to assess intra- and inter-observer reproducibility. We calculated Gini scores to test the pairwise discriminative power specific for the features and MRI sequences. We depict Bland Altmann plots of features with top discriminative power (Mann–Whitney U test). Shape features were the most robust feature class. T2 map was the most robust imaging technique (robust features (rf), n = 84). HASTE sequence led to the least amount of rf (n = 20). Intra-observer ICC was excellent (≥ 0.75) for nearly all features (max–min; 99.1–97.2%). Deterioration of ICC values was seen in the inter-observer analyses (max–min; 88.7–81.1%). Complete robustness across all sequences was found for 8 features. Shape features and T2 map yielded the highest pairwise discriminative performance. Radiomics validity depends on the MRI sequence and feature class. T2 map seems to be the most promising imaging technique with the highest feature robustness, high intra-/inter-observer reproducibility and most promising discriminative power.
In pathology, tissue images are evaluated using a light microscope, relying on the expertise and experience of pathologists. There is a great need for computational methods to quantify and standardize histological observations. Computational quantification methods become more and more essential to evaluate tissue images. In particular, the distribution of tumor cells and their microenvironment are of special interest. Here, we systematically investigated tumor cell properties and their spatial neighborhood relations by a new application of statistical analysis to whole slide images of Hodgkin lymphoma, a tumor arising in lymph nodes, and inflammation of lymph nodes called lymphadenitis. We considered properties of more than 400, 000 immunohistochemically stained, CD30-positive cells in 35 whole slide images of tissue sections from subtypes of the classical Hodgkin lymphoma, nodular sclerosis and mixed cellularity, as well as from lymphadenitis. We found that cells of specific morphology exhibited significant favored and unfavored spatial neighborhood relations of cells in dependence of their morphology. This information is important to evaluate differences between Hodgkin lymph nodes infiltrated by tumor cells (Hodgkin lymphoma) and inflamed lymph nodes, concerning the neighborhood relations of cells and the sizes of cells. The quantification of neighborhood relations revealed new insights of relations of CD30-positive cells in different diagnosis cases. The approach is general and can easily be applied to whole slide image analysis of other tumor types.
Functional modules of metabolic networks are essential for understanding the metabolism of an organism as a whole. With the vast amount of experimental data and the construction of complex and large-scale, often genome-wide, models, the computer-aided identification of functional modules becomes more and more important. Since steady states play a key role in biology, many methods have been developed in that context, for example, elementary flux modes, extreme pathways, transition invariants and place invariants. Metabolic networks can be studied also from the point of view of graph theory, and algorithms for graph decomposition have been applied for the identification of functional modules. A prominent and currently intensively discussed field of methods in graph theory addresses the Q-modularity. In this paper, we recall known concepts of module detection based on the steady-state assumption, focusing on transition-invariants (elementary modes) and their computation as minimal solutions of systems of Diophantine equations. We present the Fourier-Motzkin algorithm in detail. Afterwards, we introduce the Q-modularity as an example for a useful non-steady-state method and its application to metabolic networks. To illustrate and discuss the concepts of invariants and Q-modularity, we apply a part of the central carbon metabolism in potato tubers (Solanum tuberosum) as running example. The intention of the paper is to give a compact presentation of known steady-state concepts from a graph-theoretical viewpoint in the context of network decomposition and reduction and to introduce the application of Q-modularity to metabolic Petri net models.
Abstract: The hallmarks of Alzheimer’s disease (AD) are characterized by cognitive decline and behavioral changes. The most prominent brain region affected by the progression of AD is the hippocampal formation. The pathogenesis involves a successive loss of hippocampal neurons accompanied by a decline in learning and memory consolidation mainly attributed to an accumulation of senile plaques. The amyloid precursor protein (APP) has been identified as precursor of Aβ-peptides, the main constituents of senile plaques. Until now, little is known about the physiological function of APP within the central nervous system. The allocation of APP to the proteome of the highly dynamic presynaptic active zone (PAZ) highlights APP as a yet unknown player in neuronal communication and signaling. In this study, we analyze the impact of APP deletion on the hippocampal PAZ proteome. The native hippocampal PAZ derived from APP mouse mutants (APP-KOs and NexCreAPP/APLP2-cDKOs) was isolated by subcellular fractionation and immunopurification. Subsequently, an isobaric labeling was performed using TMT6 for protein identification and quantification by high-resolution mass spectrometry. We combine bioinformatics tools and biochemical approaches to address the proteomics dataset and to understand the role of individual proteins. The impact of APP deletion on the hippocampal PAZ proteome was visualized by creating protein-protein interaction (PPI) networks that incorporated APP into the synaptic vesicle cycle, cytoskeletal organization, and calcium-homeostasis. The combination of subcellular fractionation, immunopurification, proteomic analysis, and bioinformatics allowed us to identify APP as structural and functional regulator in a context-sensitive manner within the hippocampal active zone network.
Author Summary: More than 20 years ago, the amyloid precursor protein (APP) was identified as the precursor protein of the Aβ peptide, the main component of senile plaques in brains affected by Alzheimer’s disease. However, little is known about the physiological function of amyloid precursor protein. Allocating APP to the proteome of the structurally and functionally dynamic presynaptic active zone highlights APP as a hitherto unknown player within the presynaptic network. The hippocampus is the most prominent brain region for learning and memory consolidation, and a vulnerable target for neurodegenerative disease, e. g. Alzheimer’s disease. Therefore, our experimental design is focused on the hippocampal neurotransmitter release site. Currently, the underlying mechanism of how APP acts within presynaptic networks is still elusive. Within the scope of this research article, we constructed a network of APP within the presynaptic active zone and how deletion of APP affects these individual networks. We combine bioinformatics tools and biochemical approaches to address the dataset provided by proteomics. Furthermore, we could unravel that APP executes regulatory functions within the synaptic vesicle cycle, cytoskeletal rearrangements and Ca2+-homeostasis. Taken together, our findings offer a new perspective on the physiological function of APP in the central nervous system and may provide a molecular link to the pathogenesis of Alzheimer’s disease.