Guided Docking as a Data Generation Approach Facilitates Structure-Based Machine Learning on Kinases.

Drug discovery pipelines nowadays rely on machine learning models to explore and evaluate large chemical spaces. While including 3D structural information is considered beneficial, structural models are hindered by the availability of protein-ligand complex structures. Exemplified for kinase drug discovery, we address this issue by generating kinase-ligand complex data using template docking for the kinase compound subset of available ChEMBL assay data. To evaluate the benefit of the created complex data, we use it to train a structure-based E(3)-invariant graph neural network. Our evaluation shows that binding affinities can be predicted with significantly higher precision by models that take synthetic binding poses into account compared to ligand- or drug-target interaction models alone.

[Resilient Children: Evaluation of a Programme to Promote Resilience in Kindergarten and Elementary School]. / Resiliente Kinder: Evaluation eines Programms zur Förderung der Resilienz in der Elementar- und Primarpädagogik.

In the project "Resilient Children", a resilience promotion program for kindergartens and elementary schools was directly applied and evaluated during the COVID-19-crisis.The aim of the study was to strengthen the three sources of resilience according to Grotberg (1995) I HAVE, I AM and I CAN through targeted exercises and resilience-promoting communication (transfer to everyday life). Additionally, gender differences with regard to the effect of the programme were addressed. "Resilient Children" was evaluated at the impact level (pre-post design) and process level. Eight kindergartens and three elementary schools with 125 children participated. A total of 122 teachers and 70 parents provided information about the children. The results at the impact level showed that from the parent and teacher perspective, and from the self-perspective (children), the three sources of resilience were significantly strengthened. With regard to gender differences, the results from the perspective of teachers and parents showed that girls were characterised by greater changes than boys. Compared to the girls, the physical andmental well-being of the boys improved fromthe parents' point of view. The results of the process evaluation revealed a high level of motivation and enthusiasm for participation in the programme on the part of participating children and teachers. The success of "Resilient Children" depends on the identification of the teachers with the program.

Yeast Svf1 binds ceramides and contributes to sphingolipid metabolism at the ER cis-Golgi interface.

Ceramides are essential precursors of complex sphingolipids and act as potent signaling molecules. Ceramides are synthesized in the endoplasmic reticulum (ER) and receive their head-groups in the Golgi apparatus, yielding complex sphingolipids (SPs). Transport of ceramides between the ER and the Golgi is executed by the essential ceramide transport protein (CERT) in mammalian cells. However, yeast cells lack a CERT homolog, and the mechanism of ER to Golgi ceramide transport remains largely elusive. Here, we identified a role for yeast Svf1 in ceramide transport between the ER and the Golgi. Svf1 is dynamically targeted to membranes via an N-terminal amphipathic helix (AH). Svf1 binds ceramide via a hydrophobic binding pocket that is located in between two lipocalin domains. We showed that Svf1 membrane-targeting is important to maintain flux of ceramides into complex SPs. Together, our results show that Svf1 is a ceramide binding protein that contributes to sphingolipid metabolism at Golgi compartments.

Human primary muscle stem cells regenerate injured urethral sphincter in athymic rats.

BACKGROUND: The aim of the study was to demonstrate the efficacy of human muscle stem cells (MuSCs) isolated using innovative technology in restoring internal urinary sphincter function in a preclinical animal model. METHODS: Colonies of pure human MuSCs were obtained from muscle biopsy specimens. Athymic rats were subjected to internal urethral sphincter damage by electrocauterization. Five days after injury, 2 × 105 muscle stem cells or medium as control were injected into the area of sphincter damage (n = 5 in each group). Peak bladder pressure and rise in pressure were chosen as outcome measures. To repeatedly obtain the necessary pressure values, telemetry sensors had been implanted into the rat bladders 10 days prior to injury. RESULTS: There was a highly significant improvement in the ability to build up peak pressure as well as a pressure rise in animals that had received muscle stem cells as compared to control (p = 0.007) 3 weeks after the cells had been injected. Only minimal histologic evidence of scarring was observed in treated rats. CONCLUSION: Primary human muscle stem cells obtained using innovative technology functionally restore internal urethral sphincter function after injury. Translation into use in clinical settings is foreseeable.

A comprehensive approach for genome-wide efficiency profiling of DNA modifying enzymes.

A precise understanding of DNA methylation dynamics is of great importance for a variety of biological processes including cellular reprogramming and differentiation. To date, complex integration of multiple and distinct genome-wide datasets is required to realize this task. We present GwEEP (genome-wide epigenetic efficiency profiling) a versatile approach to infer dynamic efficiencies of DNA modifying enzymes. GwEEP relies on genome-wide hairpin datasets, which are translated by a hidden Markov model into quantitative enzyme efficiencies with reported confidence around the estimates. GwEEP predicts de novo and maintenance methylation efficiencies of Dnmts and furthermore the hydroxylation efficiency of Tets. Its design also allows capturing further oxidation processes given available data. We show that GwEEP predicts accurately the epigenetic changes of ESCs following a Serum-to-2i shift and applied to Tet TKO cells confirms the hypothesized mutual interference between Dnmts and Tets.

Myogenic Cell Transplantation in Genetic and Acquired Diseases of Skeletal Muscle.

This article will review myogenic cell transplantation for congenital and acquired diseases of skeletal muscle. There are already a number of excellent reviews on this topic, but they are mostly focused on a specific disease, muscular dystrophies and in particular Duchenne Muscular Dystrophy. There are also recent reviews on cell transplantation for inflammatory myopathies, volumetric muscle loss (VML) (this usually with biomaterials), sarcopenia and sphincter incontinence, mainly urinary but also fecal. We believe it would be useful at this stage, to compare the same strategy as adopted in all these different diseases, in order to outline similarities and differences in cell source, pre-clinical models, administration route, and outcome measures. This in turn may help to understand which common or disease-specific problems have so far limited clinical success of cell transplantation in this area, especially when compared to other fields, such as epithelial cell transplantation. We also hope that this may be useful to people outside the field to get a comprehensive view in a single review. As for any cell transplantation procedure, the choice between autologous and heterologous cells is dictated by a number of criteria, such as cell availability, possibility of in vitro expansion to reach the number required, need for genetic correction for many but not necessarily all muscular dystrophies, and immune reaction, mainly to a heterologous, even if HLA-matched cells and, to a minor extent, to the therapeutic gene product, a possible antigen for the patient. Finally, induced pluripotent stem cell derivatives, that have entered clinical experimentation for other diseases, may in the future offer a bank of immune-privileged cells, available for all patients and after a genetic correction for muscular dystrophies and other myopathies.

Heterogeneity matters: Contact structure and individual variation shape epidemic dynamics.

In the recent COVID-19 pandemic, mathematical modeling constitutes an important tool to evaluate the prospective effectiveness of non-pharmaceutical interventions (NPIs) and to guide policy-making. Most research is, however, centered around characterizing the epidemic based on point estimates like the average infectiousness or the average number of contacts. In this work, we use stochastic simulations to investigate the consequences of a population's heterogeneity regarding connectivity and individual viral load levels. Therefore, we translate a COVID-19 ODE model to a stochastic multi-agent system. We use contact networks to model complex interaction structures and a probabilistic infection rate to model individual viral load variation. We observe a large dependency of the dispersion and dynamical evolution on the population's heterogeneity that is not adequately captured by point estimates, for instance, used in ODE models. In particular, models that assume the same clinical and transmission parameters may lead to different conclusions, depending on different types of heterogeneity in the population. For instance, the existence of hubs in the contact network leads to an initial increase of dispersion and the effective reproduction number, but to a lower herd immunity threshold (HIT) compared to homogeneous populations or a population where the heterogeneity stems solely from individual infectivity variations.

Cytoplasmic LXR expression is an independent marker of poor prognosis for patients with early stage primary breast cancer.

PURPOSE: The aim of this study was to investigate the expression of liver X receptors α/ß (LXR) in primary breast cancer (BC) tissues and to analyze its correlations with clinicopathological parameters including patient survival. METHODS: In a well-characterized cohort of 305 primary BC, subcellular distribution of LXR was evaluated by immunohistochemistry. Correlations with clinicopathological characteristics as well as with patient outcome were analyzed. RESULTS: LXR was frequently localized in both nuclei and cytoplasms of BC cells, with stronger staining in nuclei. Total and nuclear LXR expression was positively correlated with ER and PR status. Overall survival analysis demonstrated that cytoplasmic LXR was significantly correlated with poor survival and appeared as an independent marker of poor prognosis, in stage I but not in stage II-III tumors CONCLUSION: Altogether, these data suggest that cytoplasmic LXR could be defined as a prognostic marker in early stage primary BC.

Analytic solutions for stochastic hybrid models of gene regulatory networks.

Discrete-state stochastic models are a popular approach to describe the inherent stochasticity of gene expression in single cells. The analysis of such models is hindered by the fact that the underlying discrete state space is extremely large. Therefore hybrid models, in which protein counts are replaced by average protein concentrations, have become a popular alternative. The evolution of the corresponding probability density functions is given by a coupled system of hyperbolic PDEs. This system has Markovian nature but its hyperbolic structure makes it difficult to apply standard functional analytical methods. We are able to prove convergence towards the stationary solution and determine such equilibrium explicitly by combining abstract methods from the theory of positive operators and elementary ideas from potential analysis.

Efficient simulation of non-Markovian dynamics on complex networks.

We study continuous-time multi-agent models, where agents interact according to a network topology. At any point in time, each agent occupies a specific local node state. Agents change their state at random through interactions with neighboring agents. The time until a transition happens can follow an arbitrary probability density. Stochastic (Monte-Carlo) simulations are often the preferred-sometimes the only feasible-approach to study the complex emerging dynamical patterns of such systems. However, each simulation run comes with high computational costs mostly due to updating the instantaneous rates of interconnected agents after each transition. This work proposes a stochastic rejection-based, event-driven simulation algorithm that scales extremely well with the size and connectivity of the underlying contact network and produces statistically correct samples. We demonstrate the effectiveness of our method on different information spreading models.

Cytoplasmic PPARγ is a marker of poor prognosis in patients with Cox-1 negative primary breast cancers.

BACKGROUND: The aim of this study was to investigate the expression of the nuclear receptor PPARγ, together with that of the cyclooxygenases Cox-1 and Cox-2, in breast cancer (BC) tissues and to correlate the data with several clinicobiological parameters including patient survival. METHODS: In a well characterized cohort of 308 primary BC, PPARγ, Cox-1 and Cox-2 cytoplasmic and nuclear expression were evaluated by immunohistochemistry. Correlations with clinicopathological and aggressiveness features were analyzed, as well as survival using Kaplan-Meier analysis. RESULTS: PPARγ was expressed in almost 58% of the samples with a predominant cytoplasmic location. Cox-1 and Cox-2 were exclusively cytoplasmic. Cytoplasmic PPARγ was inversely correlated with nuclear PPARγ and ER expression, but positively with Cox-1, Cox-2, and other high-risk markers of BC, e.g. HER2, CD133, and N-cadherin. Overall survival analysis demonstrated that cytoplasmic PPARγ had a strong correlation with poor survival in the whole cohort, and even stronger in the subgroup of patients with no Cox-1 expression where cytoplasmic PPARγ expression appeared as an independent marker of poor prognosis. In support of this cross-talk between PPARγ and Cox-1, we found that Cox-1 became a marker of good prognosis only when cytoplasmic PPARγ was expressed at high levels. CONCLUSION: Altogether, these data suggest that the relative expression of cytoplasmic PPARγ and Cox-1 may play an important role in oncogenesis and could be defined as a potential prognosis marker to identify specific high risk BC subgroups.

Cytoplasmic and Nuclear Forms of Thyroid Hormone Receptor ß1 Are Inversely Associated with Survival in Primary Breast Cancer.

The aim of this study was to investigate the expression of thyroid hormone receptor ß1 (THRß1) by immunohistochemistry in breast cancer (BC) tissues and to correlate the results with clinico-biological parameters. In a well-characterized cohort of 274 primary BC patients, THRß1 was widely expressed with a predominant nuclear location, although cytoplasmic staining was also frequently observed. Both nuclear and cytoplasmic THRß1 were correlated with high-risk BC markers such as human epidermal growth factor receptor 2 (HER2), Ki67 (also known as MKI67), prominin-1 (CD133), and N-cadherin. Overall survival analysis demonstrated that cytoplasmic THRß1 was correlated with favourable survival (p = 0.015), whereas nuclear THRß1 had a statistically significant correlation with poor outcome (p = 0.038). Interestingly, in our cohort, nuclear and cytoplasmic THRß1 appeared to be independent markers either for poor (p = 0.0004) or for good (p = 0.048) prognosis, respectively. Altogether, these data indicate that the subcellular expression of THRß1 may play an important role in oncogenesis. Moreover, the expression of nuclear THRß1 is a negative outcome marker, which may help to identify high-risk BC subgroups.

Hidden Markov Modelling Reveals Neighborhood Dependence of Dnmt3a and 3b Activity.

DNA methylation is an epigenetic mark whose important role in development has been widely recognized. This epigenetic modification results in heritable information not encoded by the DNA sequence. The underlying mechanisms controlling DNA methylation are only partly understood. Several mechanistic models of enzyme activities responsible for DNA methylation have been proposed. Here, we extend existing Hidden Markov Models (HMMs) for DNA methylation by describing the occurrence of spatial methylation patterns over time and propose several models with different neighborhood dependences. Furthermore, we investigate correlations between the neighborhood dependence and other genomic information. We perform numerical analysis of the HMMs applied to comprehensive hairpin and non-hairpin bisulfite sequencing measurements and accurately predict wild-type data. We find evidence that the activities of Dnmt3a and Dnmt3b responsible for de novo methylation depend on 5' (left) but not on 3' (right) neighboring CpGs in a sequencing string.

Stochastic hybrid models of gene regulatory networks - A PDE approach.

A widely used approach to describe the dynamics of gene regulatory networks is based on the chemical master equation, which considers probability distributions over all possible combinations of molecular counts. The analysis of such models is extremely challenging due to their large discrete state space. We therefore propose a hybrid approximation approach based on a system of partial differential equations, where we assume a continuous-deterministic evolution for the protein counts. We discuss efficient analysis methods for both modeling approaches and compare their performance. We show that the hybrid approach yields accurate results for sufficiently large molecule counts, while reducing the computational effort from one ordinary differential equation for each state to one partial differential equation for each mode of the system. Furthermore, we give an analytical steady-state solution of the hybrid model for the case of a self-regulatory gene.

Simulating the Large-Scale Erosion of Genomic Privacy Over Time.

The dramatically decreasing costs of DNA sequencing have triggered more than a million humans to have their genotypes sequenced. Moreover, these individuals increasingly make their genomic data publicly available, thereby creating privacy threats for themselves and their relatives because of their DNA similarities. More generally, an entity that gains access to a significant fraction of sequenced genotypes might be able to infer even the genomes of unsequenced individuals. In this paper, we propose a simulation-based model for quantifying the impact of continuously sequencing and publicizing personal genomic data on a population's genomic privacy. Our simulation probabilistically models data sharing and takes into account events such as migration and interracial mating. We exemplarily instantiate our simulation with a sample population of 1,000 individuals and evaluate the privacy under multiple settings over 6,000 genomic variants and a subset of phenotype-related variants. Our findings demonstrate that an increasing sharing rate in the future entails a substantial negative effect on the privacy of all older generations. Moreover, we find that mixed populations face a less severe erosion of privacy over time than more homogeneous populations. Finally, we demonstrate that genomic-data sharing can be much more detrimental for the privacy of the phenotype-related variants.

Moment-Based Parameter Estimation for Stochastic Reaction Networks in Equilibrium.

Calibrating parameters is a crucial problem within quantitative modeling approaches to reaction networks. Existing methods for stochastic models rely either on statistical sampling or can only be applied to small systems. Here, we present an inference procedure for stochastic models in equilibrium that is based on a moment matching scheme with optimal weighting and that can be used with high-throughput data like the one collected by flow cytometry. Our method does not require an approximation of the underlying equilibrium probability distribution and, if reaction rate constants have to be learned, the optimal values can be computed by solving a linear system of equations. We discuss important practical issues such as the selection of the moments and evaluate the effectiveness of the proposed approach on three case studies.

Two are better than one: HPoxBS - hairpin oxidative bisulfite sequencing.

The controlled and stepwise oxidation of 5mC to 5hmC, 5fC and 5caC by Tet enzymes is influencing the chemical and biological properties of cytosine. Besides direct effects on gene regulation, oxidised forms influence the dynamics of demethylation and re-methylation processes. So far, no combined methods exist which allow to precisely determine the strand specific localisation of cytosine modifications along with their CpG symmetric distribution. Here we describe a comprehensive protocol combining conventional hairpin bisulfite with oxidative bisulfite sequencing (HPoxBS) to determine the strand specific distribution of 5mC and 5hmC at base resolution. We apply this method to analyse the contribution of local oxidative effects on DNA demethylation in mouse ES cells. Our method includes the HPoxBS workflow and subsequent data analysis using our developed software tools. Besides a precise estimation and display of strand specific 5mC and 5hmC levels at base resolution we apply the data to predict region specific activities of Dnmt and Tet enzymes. Our experimental and computational workflow provides a precise double strand display of 5mC and 5hmC modifications at single base resolution. Based on our data we predict region specific Tet and Dnmt enzyme efficiencies shaping the distinct locus levels and patterns of 5hmC and 5mC.

EP3 (prostaglandin E2 receptor 3) expression is a prognostic factor for progression-free and overall survival in sporadic breast cancer.

BACKGROUND: In various cancers, overexpression of cyclooxygenase (COX)-2 and elevated prostaglandin (PG) E2 synthesis have been associated with tumor development and progression. The potential of COX-2 inhibitors in cancer prevention and treatment has been shown repeatedly; however, their clinical use is limited due to toxicity. PGE2 signals via EP receptors 1-4, whose functions are analyzed in current research in search for targeted anti-PG therapies. EP2 and EP4 rather promote tumorigenesis, while the role of EP3, especially in breast cancer, is not yet clear and both pro- and anti-tumorigenic effects have been described. Our study evaluates EP3 receptor expression in sporadic breast cancer and its association with clinicopathological parameters, progression-free and overall survival. METHODS: Two hundred eighty-nine sporadic breast cancer samples without primary distant metastasis were immunohistochemically analyzed for EP3 receptor expression. Tissue was stained with primary anti-EP3-antibodies. Immunoreactivity was quantified by the immunoreactivity-score (IRS); samples with an IRS ≥ 2 scored as EP3 positive. Chi-squared and Mann-Whitney-U test were used for comparison of data; Kaplan-Meier estimates and Cox-regression were used for survival analyses. RESULTS: EP3 receptor was expressed in 205 of 289 samples analyzed (70.9%). EP3 receptor expression was not associated with clinicopathological parameters (e. g. tumor size, hormone receptors, lymph node status). Kaplan-Meier estimates showed a significant association of EP3 positivity with improved progression-free survival (p = 0.002) and improved overall survival (p = 0.001) after up to 10 years. Cox regression analysis confirmed EP3 positivity as a significant prognostic factor even when other known prognosticators were accounted for. CONCLUSIONS: In sporadic breast cancer, EP3 receptor expression is not significantly associated with clinicopathological parameters but is a significant prognostic factor for improved progression-free and overall survival. However, the functional aspects of EP3 receptor in breast cancer and the way how EP3 may oppose the pro-tumorigenic effects of PGE2 elevation and COX-2 overexpression are not fully understood so far. Further studies aiming at identification of the factors regulated by EP3 are necessary to evaluate the possibility of targeting EP3 in future anti-tumor therapy in breast cancer.

Lumping of degree-based mean-field and pair-approximation equations for multistate contact processes.

Contact processes form a large and highly interesting class of dynamic processes on networks, including epidemic and information-spreading networks. While devising stochastic models of such processes is relatively easy, analyzing them is very challenging from a computational point of view, particularly for large networks appearing in real applications. One strategy to reduce the complexity of their analysis is to rely on approximations, often in terms of a set of differential equations capturing the evolution of a random node, distinguishing nodes with different topological contexts (i.e., different degrees of different neighborhoods), such as degree-based mean-field (DBMF), approximate-master-equation (AME), or pair-approximation (PA) approaches. The number of differential equations so obtained is typically proportional to the maximum degree k_{max} of the network, which is much smaller than the size of the master equation of the underlying stochastic model, yet numerically solving these equations can still be problematic for large k_{max}. In this paper, we consider AME and PA, extended to cope with multiple local states, and we provide an aggregation procedure that clusters together nodes having similar degrees, treating those in the same cluster as indistinguishable, thus reducing the number of equations while preserving an accurate description of global observables of interest. We also provide an automatic way to build such equations and to identify a small number of degree clusters that give accurate results. The method is tested on several case studies, where it shows a high level of compression and a reduction of computational time of several orders of magnitude for large networks, with minimal loss in accuracy.