The Digital Bee Brain: Integrating and Managing Neurons in a Common 3D Reference System

The honeybee standard brain (HSB) serves as an interactive tool for relating morphologies of bee brain neurons and provides a reference system for functional and bibliographical properties (http://www.neurobiologie.fu-berlin.de/beebrain/). The ultimate goal is to document not only the morphological network properties of neurons collected from separate brains, but also to establish a graphical user interface for a neuron-related data base. Here, we review the current methods and protocols used to incorporate neuronal reconstructions into the HSB. Our registration protocol consists of two separate steps applied to imaging data from two-channel confocal microscopy scans: (1) The reconstruction of the neuron, facilitated by an automatic extraction of the neuron's skeleton based on threshold segmentation, and (2) the semi-automatic 3D segmentation of the neuropils and their registration with the HSB. The integration of neurons in the HSB is performed by applying the transformation computed in step (2) to the reconstructed neurons of step (1). The most critical issue of this protocol in terms of user interaction time – the segmentation process – is drastically improved by the use of a model-based segmentation process. Furthermore, the underlying statistical shape models (SSM) allow the visualization and analysis of characteristic variations in large sets of bee brain data. The anatomy of neural networks composed of multiple neurons that are registered into the HSB are visualized by depicting the 3D reconstructions together with semantic information with the objective to integrate data from multiple sources (electrophysiology, imaging, immunocytochemistry, molecular biology). Ultimately, this will allow the user to specify cell types and retrieve their morphologies along with physiological characterizations

Simultaneous deletion of Tet1 and Tet3 increases transcriptome variability in early embryogenesis

Dioxygenases of the TET (Ten-Eleven Translocation) family produce oxidized methylcytosines, intermediates in DNA demethylation, as well as new epigenetic marks. Here we show data suggesting that TET proteins maintain the consistency of gene transcription. Embryos lacking Tet1 and Tet3 (Tet1/3 DKO) displayed a strong loss of 5-hydroxymethylcytosine (5hmC) and a concurrent increase in 5-methylcytosine (5mC) at the eight-cell stage. Single cells from eight-cell embryos and individual embryonic day 3.5 blastocysts showed unexpectedly variable gene expression compared with controls, and this variability correlated in blastocysts with variably increased 5mC/5hmC in gene bodies and repetitive elements. Despite the variability, genes encoding regulators of cholesterol biosynthesis were reproducibly down-regulated in Tet1/3 DKO blastocysts, resulting in a characteristic phenotype of holoprosencephaly in the few embryos that survived to later stages. Thus, TET enzymes and DNA cytosine modifications could directly or indirectly modulate transcriptional noise, resulting in the selective susceptibility of certain intracellular pathways to regulation by TET proteins.J.K. was supported by a postdoctoral fellowship from the Jane Coffin Childs Memorial Fund for Medical Research. W.A.P. was supported by the National Science Foundation predoctoral graduate research fellowship while this work was being performed, and subsequently by a postdoctoral fellowship from the Jane Coffin Childs Memorial Fund for Medical Research. L.C. was the recipient of a Feodor-Lynen fellowship from the Alexander von Humboldt foundation. M.L. is supported by the Max Planck Society within its International Max Planck Research School for Computational Biology and Scientific Computing program (IMPRS-CBSC). A.T. was the recipient of an Irvington postdoctoral fellowship from the Cancer Research Institute. This work was supported by NIH R01 Grants AI044432 and HD065812 (to A.R.) and a Director’s New Innovator Award (DP2-OD-008646-01) (to S.K.).This is the author accepted manuscript. The final version is available from The National Academy of Sciences via http://dx.doi.org/10.1073/pnas.151051011

Mental Health, ART Adherence, and Viral Suppression Among Adolescents and Adults Living with HIV in South Africa: A Cohort Study.

We followed adolescents and adults living with HIV aged older than 15 years who enrolled in a South African private-sector HIV programme to examine adherence and viral non-suppression (viral load > 400 copies/mL) of participants with (20,743, 38%) and without (33,635, 62%) mental health diagnoses. Mental health diagnoses were associated with unfavourable adherence patterns. The risk of viral non-suppression was higher among patients with organic mental disorders [adjusted risk ratio (aRR) 1.55, 95% confidence interval (CI) 1.22-1.96], substance use disorders (aRR 1.53, 95% CI 1.19-1.97), serious mental disorders (aRR 1.30, 95% CI 1.09-1.54), and depression (aRR 1.19, 95% CI 1.10-1.28) when compared with patients without mental health diagnoses. The risk of viral non-suppression was also higher among males, adolescents (15-19 years), and young adults (20-24 years). Our study highlights the need for psychosocial interventions to improve HIV treatment outcomes-particularly of adolescents and young adults-and supports strengthening mental health services in HIV treatment programmes

Therapeutic targeting of ependymoma as informed by oncogenic enhancer profiling

Genomic sequencing has driven precision-based oncology therapy; however, the genetic drivers of many malignancies remain unknown or non-targetable, so alternative approaches to the identification of therapeutic leads are necessary. Ependymomas are chemotherapy-resistant brain tumours, which, despite genomic sequencing, lack effective molecular targets. Intracranial ependymomas are segregated on the basis of anatomical location (supratentorial region or posterior fossa) and further divided into distinct molecular subgroups that reflect differences in the age of onset, gender predominance and response to therapy1,2,3. The most common and aggressive subgroup, posterior fossa ependymoma group A (PF-EPN-A), occurs in young children and appears to lack recurrent somatic mutations2. Conversely, posterior fossa ependymoma group B (PF-EPN-B) tumours display frequent large-scale copy number gains and losses but have favourable clinical outcomes1,3. More than 70% of supratentorial ependymomas are defined by highly recurrent gene fusions in the NF-κB subunit gene RELA (ST-EPN-RELA), and a smaller number involve fusion of the gene encoding the transcriptional activator YAP1 (ST-EPN-YAP1)1,3,4. Subependymomas, a distinct histologic variant, can also be found within the supratetorial and posterior fossa compartments, and account for the majority of tumours in the molecular subgroups ST-EPN-SE and PF-EPN-SE. Here we describe mapping of active chromatin landscapes in 42 primary ependymomas in two non-overlapping primary ependymoma cohorts, with the goal of identifying essential super-enhancer-associated genes on which tumour cells depend. Enhancer regions revealed putative oncogenes, molecular targets and pathways; inhibition of these targets with small molecule inhibitors or short hairpin RNA diminished the proliferation of patient-derived neurospheres and increased survival in mouse models of ependymomas. Through profiling of transcriptional enhancers, our study provides a framework for target and drug discovery in other cancers that lack known genetic drivers and are therefore difficult to treat.This work was supported by an Alex's Lemonade Stand Young Investigator Award (S.C.M.), The CIHR Banting Fellowship (S.C.M.), The Cancer Prevention Research Institute of Texas (S.C.M., RR170023), Sibylle Assmus Award for Neurooncology (K.W.P.), the DKFZ-MOST (Ministry of Science, Technology & Space, Israel) program in cancer research (H.W.), James S. McDonnell Foundation (J.N.R.) and NIH grants: CA154130 (J.N.R.), R01 CA169117 (J.N.R.), R01 CA171652 (J.N.R.), R01 NS087913 (J.N.R.) and R01 NS089272 (J.N.R.). R.C.G. is supported by NIH grants T32GM00725 and F30CA217065. M.D.T. is supported by The Garron Family Chair in Childhood Cancer Research, and grants from the Pediatric Brain Tumour Foundation, Grand Challenge Award from CureSearch for Children’s Cancer, the National Institutes of Health (R01CA148699, R01CA159859), The Terry Fox Research Institute and Brainchild. M.D.T. is also supported by a Stand Up To Cancer St. Baldrick’s Pediatric Dream Team Translational Research Grant (SU2C-AACR-DT1113)

Crowdsourced mapping of unexplored target space of kinase inhibitors

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome. The IDG-DREAM Challenge carried out crowdsourced benchmarking of predictive algorithms for kinase inhibitor activities on unpublished data. This study provides a resource to compare emerging algorithms and prioritize new kinase activities to accelerate drug discovery and repurposing efforts

Systematic assessment of long-read RNA-seq methods for transcript identification and quantification

The Long-read RNA-Seq Genome Annotation Assessment Project (LRGASP) Consortium was formed to evaluate the effectiveness of long-read approaches for transcriptome analysis. The consortium generated over 427 million long-read sequences from cDNA and direct RNA datasets, encompassing human, mouse, and manatee species, using different protocols and sequencing platforms. These data were utilized by developers to address challenges in transcript isoform detection and quantification, as well as de novo transcript isoform identification. The study revealed that libraries with longer, more accurate sequences produce more accurate transcripts than those with increased read depth, whereas greater read depth improved quantification accuracy. In well-annotated genomes, tools based on reference sequences demonstrated the best performance. When aiming to detect rare and novel transcripts or when using reference-free approaches, incorporating additional orthogonal data and replicate samples are advised. This collaborative study offers a benchmark for current practices and provides direction for future method development in transcriptome analysis

Computergestützte Analyse genomweiter Methylierungsanreicherungsexperimente

1) INTRODUCTION 1.1) Motivation 1.2) Research Objective 1.3) Thesis Outline 2) BIOLOGICAL BACKGROUND : EPIGENETICS 2.1) Epigenetics in Development 2.2) Epigenetics in Disease 2.3) Chromatin structure 2.4) Histone Modification 2.5) DNA Methylation 3) FUNDAMENTAL PRINCIPLES 3.1) Approaching the Epigenome: Experimental Principles 3.2) Modeling Read Enrichment: Statistical Principles 3.3) Benchmark Dataset 4) ESTIMATING ABSOLUTE METHYLATION VALUES 4.1) Coupling Factor Scaling 4.2) Relation of Methylation Level and Enrichment 4.3) Modeling the Read Coverage 4.4) Estimating Local C P G Density 4.5) Estimating Abundance of Background Reads 4.6) Assessing Sample Specific Enrichment 4.7) Fitting Enrichment Profiles 4.8) Statistical Model of the Absolute Methylation Level 4.9) Bayesian Estimators for the Absolute Methylation Level 4.10) Assessment of Methylation Level Estimation Methods 4.11) Conclusion 5) DIFFERENTIAL METHYLATION ANALYSIS 5.1) Wilcoxon’s Rank Sum Test 5.2) Generalized Linear Model Likelihood Ratio Test 5.3) Comparison of Statistical Tests 5.4) Conclusion 6) IMPLEMENTATION 6.1) MEDIPS package 6.2) QSEA package 6.3) Conclusion 7) APPLICATIONS 7.1) DNA-Methylome Analysis of Mouse Intestinal Adenoma 7.2) QSEA – Modeling genome-wide DNA methylation enrichment 7.3) Predicting Therapy Resistance in NSCLC by Epigenomic Profiling 7.4) Epigenomic Analysis of Immune Cells from Asthma Patients 8) DISCUSSION BIBLIOGRAPHY APPENDICES MATHEMATICAL PROOFS CURRICULUM VITAE PUBLICATIONS SUMMARY ZUSAMMENFASSUNGEnrichment of methylated DNA followed by sequencing offers a reasonable compromise between experimental cost and genomic coverage, allowing genome- wide DNA methylation to be assessed for large numbers of samples, which is a common requirement for clinical studies. However, the computational analysis of these experiments is complex, and depends on specific normalization and statistical approaches. Furthermore, quantification of the enrichment signals in terms of absolute levels of methylation requires specific transformation. In this dissertation, I introduce specific computational methods for the individual steps of the analysis workflow. I assess the impact of sequencing library size, alterations in DNA copy number and CpG density on the local enrichment, and present a suitable normalization procedure. As the central part of the workflow, I developed a statistical model for the enrichment read counts, which is deployed in the Bayesian estimation of absolute levels of methylation. The model involves experimental parameters, such as sample specific enrichment characteristics. Accounting for different levels of prior knowledge, I suggest several calibration strategies for the model's parameters, which use either additional data or certain general assumptions. The transformation to absolute methylation levels greatly enhances interpretability and facilitates comparison with other methylation assays. By comparing the results with bisulfite sequencing validation data, I demonstrate the accuracy of the transformation, as well as the improvement over existing alternative methods. A common objective of methylome analysis is the detection of differentially methylated regions between groups of samples. I compare different statistical approaches for this task and discuss the inherent properties. I thereby identify likelihood ratio tests of nested generalized linear models to be well suited in terms of reliability and efficiency. The methods are implemented in two different R/bioconductor packages, MEDIPS and QSEA, which are easy to use and provide comprehensive functionality for the analysis of enrichment based experiments. All functions are documented and demonstrated by runnable examples, as well as detailed tutorials for specific practically relevant use cases. By presenting four representative studies published in peer-reviewed journals, I demonstrate the applicability and the versatility of the introduced methods. Taken together, this dissertation provides new computational methods for the analysis of enrichment based methylation experiments; these methods enhance the interpretability and reliability of the results from these experiments.Hochdurchsatzsequenzierung von angereicherter methylierter DNS erlaubt genomweite Methylierungsmessung zu relativ günstigen Kosten, wodurch die Analyse von zahlreichen Proben, zum Beispiel für klinische Studien, ermöglicht wird. Die computergestützte statistische Auswertung dieser Experimente ist jedoch komplex, und bedarf spezieller Normalisierungsmethoden und Schätzverfahren. In dieser Dissertation stelle ich spezifische computergestützte Methoden für die einzelnen Analyseschritte der Auswertung vor. Ich untersuche den Einfluss von Sequenziertiefe, Amplifikationen oder Deletationen der DNS, sowie der Häufigkeit von CpGs auf die Anreicherung der entsprechenden genomischen Region, und führe ein geeignetes Normalisierungsverfahren ein. Als zentralen Analyseschritt rekonstruiere ich das absolute Methylierungsniveau aus der relativen Anreicherung mittels Bayes'schen Schätzern. Hierfür habe ich ein statistisches Modell der angereicherten sequenzierten DNS-Fragmente entwickelt. Abhängig vom Vorwissen über die Proben schlage ich verschiedene Kalibrierungsstrategien für die probenspezifischen Anreicherungsparameter des Modells vor, basierend auf zusätzlichen Daten oder allgemeinen Annahmen. Die Umwandlung in absolute Methylierungswerte erhöht die Interpretierbarkeit erheblich und erleichtert den Vergleich mit anderen Methylierungsexperimenten. Durch Vergleich der Ergebnisse mit Bisulfit-Sequenzierung Validierungsdaten zeige ich die Schätzgenauigkeit des Verfahrens sowie die Verbesserung gegenüber bestehender alternativer Methoden. Ein häufiges Ziel der Methylomanalyse ist der Nachweis von differentiell methylierten Regionen zwischen Probengruppen. Ich vergleiche verschiedene statistische Ansätze für diesen Schritt und zeige diesbezüglich die Eignung von Likelihood-Quotienten-Tests geschachtelter generalisierter linearer Modelle hinsichtlich Zuverlässigkeit und Effizienz. Die vorgestellten Methoden sind in zwei R / Bioconductor-Paketen implementiert, MEDIPS und QSEA. Die Pakete sind einfach zu bedienen bieten umfassende Funktionalität. Alle Funktionen sind dokumentiert und werden mittels anschaulichen Beispiele, sowie ausführlichen Tutorials zu spezifischen praktisch relevanten Anwendungsfällen veranschaulicht. Vier vorgestellte repräsentative Studien, welche in wissenschaftlichen Fachzeitschriften veröffentlicht wurden, demonstrieren die praktische Anwendbarkeit und die Vielseitigkeit der eingeführten Methoden. Zusammengefasst bietet diese Dissertation neue computergestützte Methoden zur Analyse anreicherungsbasierter Methylierungsexperimente, welche sowohl die Interpretierbarkeit als auch die Zuverlässigkeit der Ergebnisse solcher Experimente erhöhen

Matthias Mettler : Digital Health Trendradar

Im Digital Health Talk mit Stefan Lienhard ist heute der Unternehmensberater Matthias Mettler zu Gast. Als BWLer konzentriert er sich in seiner täglichen Arbeit auf das Gesundheitswesen. Mit seiner Leidenschaft für Digital Health ist er Mitgründer der Plattform www.health-trends.ch. Wie man systematisch neue Technologien, Themen und Trends findet, die für das Spital interessant sein könnten und vor allem wie man diese unter Abwägung aller Risiken und Chancen in das Gesundheitsunternehmen implementiert, diskutieren Alfred, Stefan und Matthias in dieser Folge