A systems biology approach to define mechanisms, phenotypes, and drivers in PanNETs with a personalized perspective.

Pancreatic neuroendocrine tumors (PanNETs) are a rare tumor entity with largely unpredictable progression and increasing incidence in developed countries. Molecular pathways involved in PanNETs development are still not elucidated, and specific biomarkers are missing. Moreover, the heterogeneity of PanNETs makes their treatment challenging and most approved targeted therapeutic options for PanNETs lack objective responses. Here, we applied a systems biology approach integrating dynamic modeling strategies, foreign classifier tailored approaches, and patient expression profiles to predict PanNETs progression as well as resistance mechanisms to clinically approved treatments such as the mammalian target of rapamycin complex 1 (mTORC1) inhibitors. We set up a model able to represent frequently reported PanNETs drivers in patient cohorts, such as Menin-1 (MEN1), Death domain associated protein (DAXX), Tuberous Sclerosis (TSC), as well as wild-type tumors. Model-based simulations suggested drivers of cancer progression as both first and second hits after MEN1 loss. In addition, we could predict the benefit of mTORC1 inhibitors on differentially mutated cohorts and hypothesize resistance mechanisms. Our approach sheds light on a more personalized prediction and treatment of PanNET mutant phenotypes.

Unsupervised domain adaptation for the detection of cardiomegaly in cross-domain chest X-ray images.

In recent years, several deep learning approaches have been successfully applied in the field of medical image analysis. More specifically, different deep neural network architectures have been proposed and assessed for the detection of various pathologies based on chest X-ray images. While the performed assessments have shown very promising results, most of them consist in training and evaluating the performance of the proposed approaches on a single data set. However, the generalization of such models is quite limited in a cross-domain setting, since a significant performance degradation can be observed when these models are evaluated on data sets stemming from different medical centers or recorded under different protocols. The performance degradation is mostly caused by the domain shift between the training set and the evaluation set. To alleviate this problem, different unsupervised domain adaptation approaches are proposed and evaluated in the current work, for the detection of cardiomegaly based on chest X-ray images, in a cross-domain setting. The proposed approaches generate domain invariant feature representations by adapting the parameters of a model optimized on a large set of labeled samples, to a set of unlabeled images stemming from a different data set. The performed evaluation points to the effectiveness of the proposed approaches, since the adapted models outperform optimized models which are directly applied to the evaluation sets without any form of domain adaptation.

Efficient cross-validation traversals in feature subset selection.

Sparse and robust classification models have the potential for revealing common predictive patterns that not only allow for categorizing objects into classes but also for generating mechanistic hypotheses. Identifying a small and informative subset of features is their main ingredient. However, the exponential search space of feature subsets and the heuristic nature of selection algorithms limit the coverage of these analyses, even for low-dimensional datasets. We present methods for reducing the computational complexity of feature selection criteria allowing for higher efficiency and coverage of screenings. We achieve this by reducing the preparation costs of high-dimensional subsets [Formula: see text] to those of one-dimensional ones [Formula: see text]. Our methods are based on a tight interaction between a parallelizable cross-validation traversal strategy and distance-based classification algorithms and can be used with any product distance or kernel. We evaluate the traversal strategy exemplarily in exhaustive feature subset selection experiments (perfect coverage). Its runtime, fitness landscape, and predictive performance are analyzed on publicly available datasets. Even in low-dimensional settings, we achieve approximately a 15-fold increase in exhaustively generating distance matrices for feature combinations bringing a new level of evaluations into reach.

NADH Fluorescence Lifetime Imaging Microscopy Reveals Selective Mitochondrial Dysfunction in Neurons Overexpressing Alzheimer's Disease-Related Proteins.

Alzheimer's disease (AD), the most prevalent form of dementia, affects globally more than 30 million people suffering from cognitive deficits and neuropsychiatric symptoms. Substantial evidence for the involvement of mitochondrial dysfunction in the development and/or progression of AD has been shown in addition to the pathological hallmarks amyloid beta (Aß) and tau. Still, the selective vulnerability and associated selective mitochondrial dysfunction cannot even be resolved to date. We aimed at optically quantifying mitochondrial function on a single-cell level in primary hippocampal neuron models of AD, unraveling differential involvement of cell and mitochondrial populations in amyloid precursor protein (APP)-associated mitochondrial dysfunction. NADH lifetime imaging is a highly sensitive marker-free method with high spatial resolution. However, deciphering cellular bioenergetics of complex cells like primary neurons has still not succeeded yet. To achieve this, we combined highly sensitive NADH lifetime imaging with respiratory inhibitor treatment, allowing characterization of mitochondrial function down to even the subcellular level in primary neurons. Measuring NADH lifetime of the same neuron before and after respiratory treatment reveals the metabolic delta, which can be taken as a surrogate for cellular redox capacity. Correlating NADH lifetime delta with overexpression strength of Aß-related proteins on the single-cell level, we could verify the important role of intracellular Aß-mediated mitochondrial toxicity. Subcellularly, we could demonstrate a higher respiration in neuronal somata in general than dendrites, but a similar impairment of somatic and dendritic mitochondria in our AD models. This illustrates the power of NADH lifetime imaging in revealing mitochondrial function on a single and even subcellular level and its potential to shed light into bioenergetic alterations in neuropsychiatric diseases and beyond.

Capturing dynamic relevance in Boolean networks using graph theoretical measures.

MOTIVATION: Interaction graphs are able to describe regulatory dependencies between compounds without capturing dynamics. In contrast, mathematical models that are based on interaction graphs allow to investigate the dynamics of biological systems. However, since dynamic complexity of these models grows exponentially with their size, exhaustive analyses of the dynamics and consequently screening all possible interventions eventually becomes infeasible. Thus, we designed an approach to identify dynamically relevant compounds based on the static network topology. RESULTS: Here, we present a method only based on static properties to identify dynamically influencing nodes. Coupling vertex betweenness and determinative power, we could capture relevant nodes for changing dynamics with an accuracy of 75% in a set of 35 published logical models. Further analyses of the selected compounds' connectivity unravelled a new class of not highly connected nodes with high impact on the networks' dynamics, which we call gatekeepers. We validated our method's working concept on logical models, which can be readily scaled up to complex interaction networks, where dynamic analyses are not even feasible. AVAILABILITY AND IMPLEMENTATION: Code is freely available at https://github.com/sysbio-bioinf/BNStatic. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Predicting disease progression in behavioral variant frontotemporal dementia.

INTRODUCTION: The behavioral variant of frontotemporal dementia (bvFTD) is a rare neurodegenerative disease. Reliable predictors of disease progression have not been sufficiently identified. We investigated multivariate magnetic resonance imaging (MRI) biomarker profiles for their predictive value of individual decline. METHODS: One hundred five bvFTD patients were recruited from the German frontotemporal lobar degeneration (FTLD) consortium study. After defining two groups ("fast progressors" vs. "slow progressors"), we investigated the predictive value of MR brain volumes for disease progression rates performing exhaustive screenings with multivariate classification models. RESULTS: We identified areas that predict disease progression rate within 1 year. Prediction measures revealed an overall accuracy of 80% across our 50 top classification models. Especially the pallidum, middle temporal gyrus, inferior frontal gyrus, cingulate gyrus, middle orbitofrontal gyrus, and insula occurred in these models. DISCUSSION: Based on the revealed marker combinations an individual prognosis seems to be feasible. This might be used in clinical studies on an individualized progression model.

Constraining classifiers in molecular analysis: invariance and robustness.

Analysing molecular profiles requires the selection of classification models that can cope with the high dimensionality and variability of these data. Also, improper reference point choice and scaling pose additional challenges. Often model selection is somewhat guided by ad hoc simulations rather than by sophisticated considerations on the properties of a categorization model. Here, we derive and report four linked linear concept classes/models with distinct invariance properties for high-dimensional molecular classification. We can further show that these concept classes also form a half-order of complexity classes in terms of Vapnik-Chervonenkis dimensions, which also implies increased generalization abilities. We implemented support vector machines with these properties. Surprisingly, we were able to attain comparable or even superior generalization abilities to the standard linear one on the 27 investigated RNA-Seq and microarray datasets. Our results indicate that a priori chosen invariant models can replace ad hoc robustness analysis by interpretable and theoretically guaranteed properties in molecular categorization.

Assessing phenotype order in molecular data.

Biological entities are key elements of biomedical research. Their definition and their relationships are important in areas such as phylogenetic reconstruction, developmental processes or tumor evolution. Hypotheses about relationships like phenotype order are often postulated based on prior knowledge or belief. Evidence on a molecular level is typically unknown and whether total orders are reflected in the molecular measurements is unclear or not assessed. In this work we propose a method that allows a fast and exhaustive screening for total orders in large datasets. We utilise ordinal classifier cascades to identify discriminable molecular representations of the phenotypes. These classifiers are constrained by an order hypothesis and are highly sensitive to incorrect assumptions. Two new error bounds, which are introduced and theoretically proven, lead to a substantial speed-up and allow the application to large collections of many phenotypes. In our experiments we show that by exhaustively evaluating all possible candidate orders, we are able to identify phenotype orders that best coincide with the high-dimensional molecular profiles.

Semantic Multi-Classifier Systems Identify Predictive Processes in Heart Failure Models across Species.

Genetic model organisms have the potential of removing blind spots from the underlying gene regulatory networks of human diseases. Allowing analyses under experimental conditions they complement the insights gained from observational data. An inevitable requirement for a successful trans-species transfer is an abstract but precise high-level characterization of experimental findings. In this work, we provide a large-scale analysis of seven weak contractility/heart failure genotypes of the model organism zebrafish which all share a weak contractility phenotype. In supervised classification experiments, we screen for discriminative patterns that distinguish between observable phenotypes (homozygous mutant individuals) as well as wild-type (homozygous wild-types) and carriers (heterozygous individuals). As the method of choice we use semantic multi-classifier systems, a knowledge-based approach which constructs hypotheses from a predefined vocabulary of high-level terms (e.g., Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways or Gene Ontology (GO) terms). Evaluating these models leads to a compact description of the underlying processes and guides the screening for new molecular markers of heart failure. Furthermore, we were able to independently corroborate the identified processes in Wistar rats.

3D Network exploration and visualisation for lifespan data.

BACKGROUND: The Ageing Factor Database AgeFactDB contains a large number of lifespan observations for ageing-related factors like genes, chemical compounds, and other factors such as dietary restriction in different organisms. These data provide quantitative information on the effect of ageing factors from genetic interventions or manipulations of lifespan. Analysis strategies beyond common static database queries are highly desirable for the inspection of complex relationships between AgeFactDB data sets. 3D visualisation can be extremely valuable for advanced data exploration. RESULTS: Different types of networks and visualisation strategies are proposed, ranging from basic networks of individual ageing factors for a single species to complex multi-species networks. The augmentation of lifespan observation networks by annotation nodes, like gene ontology terms, is shown to facilitate and speed up data analysis. We developed a new Javascript 3D network viewer JANet that provides the proposed visualisation strategies and has a customised interface for AgeFactDB data. It enables the analysis of gene lists in combination with AgeFactDB data and the interactive visualisation of the results. CONCLUSION: Interactive 3D network visualisation allows to supplement complex database queries by a visually guided exploration process. The JANet interface allows gaining deeper insights into lifespan data patterns not accessible by common database queries alone. These concepts can be utilised in many other research fields.

Loss of the novel Vcp (valosin containing protein) interactor Washc4 interferes with autophagy-mediated proteostasis in striated muscle and leads to myopathy in vivo.

VCP/p97 (valosin containing protein) is a key regulator of cellular proteostasis. It orchestrates protein turnover and quality control in vivo, processes fundamental for proper cell function. In humans, mutations in VCP lead to severe myo- and neuro-degenerative disorders such as inclusion body myopathy with Paget disease of the bone and frontotemporal dementia (IBMPFD), amyotrophic lateral sclerosis (ALS) or and hereditary spastic paraplegia (HSP). We analyzed here the in vivo role of Vcp and its novel interactor Washc4/Swip (WASH complex subunit 4) in the vertebrate model zebrafish (Danio rerio). We found that targeted inactivation of either Vcp or Washc4, led to progressive impairment of cardiac and skeletal muscle function, structure and cytoarchitecture without interfering with the differentiation of both organ systems. Notably, loss of Vcp resulted in compromised protein degradation via the proteasome and the macroautophagy/autophagy machinery, whereas Washc4 deficiency did not affect the function of the ubiquitin-proteasome system (UPS) but caused ER stress and interfered with autophagy function in vivo. In summary, our findings provide novel insights into the in vivo functions of Vcp and its novel interactor Washc4 and their particular and distinct roles during proteostasis in striated muscle cells.

A Boolean network of the crosstalk between IGF and Wnt signaling in aging satellite cells.

Aging is a complex biological process, which determines the life span of an organism. Insulin-like growth factor (IGF) and Wnt signaling pathways govern the process of aging. Both pathways share common downstream targets that allow competitive crosstalk between these branches. Of note, a shift from IGF to Wnt signaling has been observed during aging of satellite cells. Biological regulatory networks necessary to recreate aging have not yet been discovered. Here, we established a mathematical in silico model that robustly recapitulates the crosstalk between IGF and Wnt signaling. Strikingly, it predicts critical nodes following a shift from IGF to Wnt signaling. These findings indicate that this shift might cause age-related diseases.

sAPPß and sAPPα increase structural complexity and E/I input ratio in primary hippocampal neurons and alter Ca2+ homeostasis and CREB1-signaling.

One major pathophysiological hallmark of Alzheimer's disease (AD) is senile plaques composed of amyloid ß (Aß). In the amyloidogenic pathway, cleavage of the amyloid precursor protein (APP) is shifted towards Aß production and soluble APPß (sAPPß) levels. Aß is known to impair synaptic function; however, much less is known about the physiological functions of sAPPß. The neurotrophic properties of sAPPα, derived from the non-amyloidogenic pathway of APP cleavage, are well-established, whereas only a few, conflicting studies on sAPPß exist. The intracellular pathways of sAPPß are largely unknown. Since sAPPß is generated alongside Aß by ß-secretase (BACE1) cleavage, we tested the hypothesis that sAPPß effects differ from sAPPα effects as a neurotrophic factor. We therefore performed a head-to-head comparison of both mammalian recombinant peptides in developing primary hippocampal neurons (PHN). We found that sAPPα significantly increases axon length (p = 0.0002) and that both sAPPα and sAPPß increase neurite number (p < 0.0001) of PHN at 7 days in culture (DIV7) but not at DIV4. Moreover, both sAPPα- and sAPPß-treated neurons showed a higher neuritic complexity in Sholl analysis. The number of glutamatergic synapses (p < 0.0001), as well as layer thickness of postsynaptic densities (PSDs), were significantly increased, and GABAergic synapses decreased upon sAPP overexpression in PHN. Furthermore, we showed that sAPPα enhances ERK and CREB1 phosphorylation upon glutamate stimulation at DIV7, but not DIV4 or DIV14. These neurotrophic effects are further associated with increased glutamate sensitivity and CREB1-signaling. Finally, we found that sAPPα levels are significantly reduced in brain homogenates of AD patients compared to control subjects. Taken together, our data indicate critical stage-dependent roles of sAPPs in the developing glutamatergic system in vitro, which might help to understand deleterious consequences of altered APP shedding in AD patients, beyond Aß pathophysiology.

Tissue-, sex-, and age-specific DNA methylation of rat glucocorticoid receptor gene promoter and insulin-like growth factor 2 imprinting control region.

Tissue-, sex-, and age-specific epigenetic modifications such as DNA methylation are largely unknown. Changes in DNA methylation of the glucocorticoid receptor gene (NR3C1) and imprinting control region (ICR) of IGF2 and H19 genes during the lifespan are particularly interesting since these genes are susceptible to epigenetic modifications by prenatal stress or malnutrition. They are important regulators of development and aging. Methylation changes of NR3C1 affect glucocorticoid receptor expression, which is associated with stress sensitivity and stress-related diseases predominantly occurring during aging. Methylation changes of IGF2/H19 affect growth trajectory and nutrient use with risk of metabolic syndrome. Using a locus-specific approach, we characterized DNA methylation patterns of different Nr3c1 promoters and Igf2/H19 ICR in seven tissues of rats at 3, 9, and 24 mo of age. We found a complex pattern of locus-, tissue-, sex-, and age-specific DNA methylation. Tissue-specific methylation was most prominent at the shores of the Nr3c1 CpG island (CGI). Sex-specific differences in methylation peaked at 9 mo. During aging, Nr3c1 predominantly displayed hypomethylation mainly in females and at shores, whereas hypermethylation occurred within the CGI. Igf2/H19 ICR exhibited age-related hypomethylation occurring mainly in males. Methylation patterns of Nr3c1 in the skin correlated with those in the cortex, hippocampus, and hypothalamus. Skin may serve as proxy for methylation changes in central parts of the hypothalamic-pituitary-adrenal axis and hence for vulnerability to stress- and age-associated diseases. Thus, we provide in-depth insight into the complex DNA methylation changes of rat Nr3c1 and Igf2/H19 during aging that are tissue and sex specific.

Comparative gene-expression profiling of the large cell variant of gastrointestinal marginal-zone B-cell lymphoma.

Gastrointestinal (g.i.) large cell lymphoma is currently regarded as diffuse large B-cell lymphoma (DLBCL) despite a more favorable clinical outcome compared to other DLBCL. Cluster analyses on a transcriptome signature of NF-κB target genes of 30 g.i. marginal zone B-cell lymphomas (MZBL; 8 g.i. MZBL, 22 large cell MZBL - among them 9 with coexisting small cell component) and 6 DLBCL (3 activated B-cell like (ABC), 3 germinal center-like (GCB)) reveals a distinct pattern. The distinctiveness of large cell MZBL samples is further confirmed by a cohort of 270 available B-cell lymphoma and B-cell in silico profiles. Of the NF-κB genes analyzed, c-REL was overexpressed in g.i. MZBL. c-REL amplification was limited to 6/22 large cell MZBL including the large cell component of 2/9 composite small cell/large cell lymphomas, and c-Rel protein expression was found in the large cell compartment of composite lymphomas. Classification experiments on DLBCL and large cell MZBL profiles support the concept that the large cell MZBL is a distinct type of B-cell lymphoma.

Reduced cGMP levels in CSF of AD patients correlate with severity of dementia and current depression.

BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disorder, primarily affecting memory. That disorder is thought to be a consequence of neuronal network disturbances and synapse loss. Decline in cognitive function is associated with a high burden of neuropsychiatric symptoms (NPSs) such as depression. The cyclic nucleotides cyclic adenosine-3',5'-monophosphate (cAMP) and cyclic guanosine-3',5'-monophosphate (cGMP) are essential second messengers that play a crucial role in memory processing as well as synaptic plasticity and are potential therapeutic targets. Biomarkers that are able to monitor potential treatment effects and that reflect the underlying pathology are of crucial interest. METHODS: In this study, we measured cGMP and cAMP in cerebrospinal fluid (CSF) in a cohort of 133 subjects including 68 AD patients and 65 control subjects. To address the association with disease progression we correlated cognitive status with cyclic nucleotide levels. Because a high burden of NPSs is associated with decrease in cognitive function, we performed an exhaustive evaluation of AD-relevant marker combinations in a depressive subgroup. RESULTS: We show that cGMP, but not cAMP, levels in the CSF of AD patients are significantly reduced compared with the control group. Reduced cGMP levels in AD patients correlate with memory impairment based on Mini-Mental State Examination score (r = 0.17, p = 0.048) and tau as a marker of neurodegeneration (r = -0.28, p = 0.001). Moreover, we were able to show that AD patients suffering from current depression show reduced cGMP levels (p = 0.07) and exhibit a higher degree of cognitive impairment than non-depressed AD patients. CONCLUSION: These results provide further evidence for an involvement of cGMP in AD pathogenesis and accompanying co-morbidities, and may contribute to elucidating synaptic plasticity alterations during disease progression.

Combined microRNA and mRNA microfluidic TaqMan array cards for the diagnosis of malignancy of multiple types of pancreatico-biliary tumors in fine-needle aspiration material.

Pancreatic ductal adenocarcinoma (PDAC) continues to carry the lowest survival rates among all solid tumors. A marked resistance against available therapies, late clinical presentation and insufficient means for early diagnosis contribute to the dismal prognosis. Novel biomarkers are thus required to aid treatment decisions and improve patient outcomes. We describe here a multi-omics molecular platform that allows for the first time to simultaneously analyze miRNA and mRNA expression patterns from minimal amounts of biopsy material on a single microfluidic TaqMan Array card. Expression profiles were generated from 113 prospectively collected fine needle aspiration biopsies (FNAB) from patients undergoing surgery for suspect masses in the pancreas. Molecular classifiers were constructed using support vector machines, and rigorously evaluated for diagnostic performance using 10×10fold cross validation. The final combined miRNA/mRNA classifier demonstrated a sensitivity of 91.7%, a specificity of 94.5%, and an overall diagnostic accuracy of 93.0% for the differentiation between PDAC and benign pancreatic masses, clearly outperfoming miRNA-only classifiers. The classification algorithm also performed very well in the diagnosis of other types of solid tumors (acinar cell carcinomas, ampullary cancer and distal bile duct carcinomas), but was less suited for the diagnostic analysis of cystic lesions. We thus demonstrate that simultaneous analysis of miRNA and mRNA biomarkers from FNAB samples using multi-omics TaqMan Array cards is suitable to differentiate suspect solid pancreatic masses with high precision.

Genetic Factors of the Disease Course After Sepsis: Rare Deleterious Variants Are Predictive.

Sepsis is a life-threatening organ dysfunction caused by dysregulated host response to infection. For its clinical course, host genetic factors are important and rare genomic variants are suspected to contribute. We sequenced the exomes of 59 Greek and 15 German patients with bacterial sepsis divided into two groups with extremely different disease courses. Variant analysis was focusing on rare deleterious single nucleotide variants (SNVs). We identified significant differences in the number of rare deleterious SNVs per patient between the ethnic groups. Classification experiments based on the data of the Greek patients allowed discrimination between the disease courses with estimated sensitivity and specificity>75%. By application of the trained model to the German patients we observed comparable discriminatory properties despite lower population-specific rare SNV load. Furthermore, rare SNVs in genes of cell signaling and innate immunity related pathways were identified as classifiers discriminating between the sepsis courses. Sepsis patients with favorable disease course after sepsis, even in the case of unfavorable preconditions, seem to be affected more often by rare deleterious SNVs in cell signaling and innate immunity related pathways, suggesting a protective role of impairments in these processes against a poor disease course.

BiTrinA--multiscale binarization and trinarization with quality analysis.

MOTIVATION: When processing gene expression profiles or other biological data, it is often required to assign measurements to distinct categories (e.g. 'high' and 'low' and possibly 'intermediate'). Subsequent analyses strongly depend on the results of this quantization. Poor quantization will have potentially misleading effects on further investigations. We propose the BiTrinA package that integrates different multiscale algorithms for binarization and for trinarization of one-dimensional data with methods for quality assessment and visualization of the results. By identifying measurements that show large variations over different time points or conditions, this quality assessment can determine candidates that are related to the specific experimental setting. AVAILABILITY AND IMPLEMENTATION: BiTrinA is freely available on CRAN. CONTACT: hans.kestler@leibniz-fli.de or hans.kestler@uni-ulm.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.