Approximating multivariate posterior distribution functions from Monte Carlo samples for sequential Bayesian inference

An important feature of Bayesian statistics is the opportunity to do sequential inference: the posterior distribution obtained after seeing a dataset can be used as prior for a second inference. However, when Monte Carlo sampling methods are used for inference, we only have a set of samples from the posterior distribution. To do sequential inference, we then either have to evaluate the second posterior at only these locations and reweight the samples accordingly, or we can estimate a functional description of the posterior probability distribution from the samples and use that as prior for the second inference. Here, we investigated to what extent we can obtain an accurate joint posterior from two datasets if the inference is done sequentially rather than jointly, under the condition that each inference step is done using Monte Carlo sampling. To test this, we evaluated the accuracy of kernel density estimates, Gaussian mixtures, vine copulas and Gaussian processes in approximating posterior distributions, and then tested whether these approximations can be used in sequential inference. In low dimensionality, Gaussian processes are more accurate, whereas in higher dimensionality Gaussian mixtures or vine copulas perform better. In our test cases, posterior approximations are preferable over direct sample reweighting, although joint inference is still preferable over sequential inference. Since the performance is case-specific, we provide an R package mvdens with a unified interface for the density approximation methods

Discrimination against Turkish minorities in Germany and the Netherlands: field experimental evidence on the effect of diagnostic information on labour market outcomes

Previous studies have found that the labour market outcomes of Turkish minorities are slightly better in Germany than in the Netherlands. In this paper we test one of the explanations: differences in ethnic discrimination in hiring. We use a harmonised field experiment to test whether discrimination against job candidates of Turkish origin (age 23-25) varies across Germany and the Netherlands, while holding individual characteristics of job seekers constant. We find that, compared to majority candidates, job candidates of Turkish origin are on average eleven percentage points less likely to receive a positive call-back. Moreover, we find that discrimination against Turkish minorities is significantly higher in the Netherlands than in Germany. In Germany, job candidates of Turkish origin are five percentage points less likely to receive a call-back than equally qualified majority candidates, whereas in the Netherlands this ethnic gap is fifteen percentage points. However, the presented evidence does not support the often-mentioned argument that the amount of diagnostic information in application materials explains why discrimination against Turkish minorities is lower in Germany. Overall, adding diagnostic information has little effect on the relative employment chances of job applicants of Turkish origin, both in Germany and the Netherlands

Feedforward phase noise compensation

Clock systems with phase noise compensation are provided herein. In certain implementations, a clock system includes a phase noise detector for detecting a phase noise of a clock signal, and an adjustable delay circuit for generating an adjusted clock signal based on delaying the clock signal with a controllable delay. Additionally, the phase noise detector generates an error signal indicated the phase noise of the clock signal, and controls the delay of the adjustable delay circuit with the error signal over time to thereby compensate the clock signal for phase noise. Thus, the adjusted clock signal has reduced phase noise compared to the clock signal

Inferring single-cell protein levels and cell cycle behavior in heterogeneous cell populations

Individual cells in a genetically identical population can show highly variable behavior. Single-cell measurements allow us to study this variability, but the available measurement techniques have limitations: live-cell microscopy is typically restricted to one or a few molecular markers, while techniques that simultaneously measure large numbers of molecular markers are destructive and cannot be used to follow cells over time. To help overcome these limitations, we present here scMeMo (single cell Mechanistic Modeler): a mechanistic modeling framework that can leverage diverse sets of measurements in order to infer unobserved variables in heterogeneous single cells. We used this framework to construct a model describing cell cycle progression in human cells, and show that it can predict the levels of several proteins in individual cells, based on live-cell microscopy measurements of only one marker and information learned from other experiments. The framework incorporates an uncertainty calibration step that makes the posterior distributions robust against partial model misspecification. Our modeling framework can be used to integrate information from separate experiments with diverse readouts, and to infer single cell variables that may be difficult to measure directly

nature biotechnology VOLUME

To better understand the molecular mechanisms and genetic basis of human disease, we systematically examine relationships between 3,949 genes, 62,663 mutations and 3,453 associated disorders by generating a three-dimensional, structurally resolved human interactome. This network consists of 4,222 high-quality binary protein-protein interactions with their atomic-resolution interfaces. We find that in-frame mutations (missense point mutations and in-frame insertions and deletions) are enriched on the interaction interfaces of proteins associated with the corresponding disorders, and that the disease specificity for different mutations of the same gene can be explained by their location within an interface. We also predict 292 candidate genes for 694 unknown disease-to-gene associations with proposed molecular mechanism hypotheses. This work indicates that knowledge of how in-frame disease mutations alter specific interactions is critical to understanding pathogenesis. Structurally resolved interaction networks should be valuable tools for interpreting the wealth of data being generated by large-scale structural genomics and disease association studies. Over the past few decades, a tremendous amount of resources and effort have been invested in mapping human disease loci genetically and later physically 1 . Since the completion of the human genome sequence, especially with advances in genome-wide association studies and ongoing cancer genome sequencing projects, an impressive list of disease-associated genes and their mutations have been produced 2 . However, it has rarely been possible to translate this wealth of information on individual mutations and their association with disease into biological or therapeutic insights 3 . Most of the drugs approved by the US Food and Drug Administration today are palliative 4 -they merely treat symptoms, rather than targeting specific genes or pathways responsible, even if associated genes are known. One main reason for this lack of success is the complex genotype-tophenotype relationships among diseases and their associated genes and mutations. In particular, (i) the same gene can be associated with multiple disorders (gene pleiotropy); and (ii) mutations in any one of many genes can cause the same clinical disorder (locus heterogeneity). For example, mutations in TP53 are linked to 32 clinically distinguishable forms of cancer and cancer-related disorders, whereas mutations in any of at least 12 different genes can lead to long QT syndrome. With the publication of several large-scale protein-protein interaction networks in human 5-8 , researchers have recently begun to use complex cellular networks to explore these genotype-to-phenotype relationships 2,9 , on the basis that many proteins function by interacting with other proteins. However, most analyses model proteins as graph-theoretical nodes, ignoring the structural details of individual proteins and the spatial constraints of their interactions. Here, we investigate on a large-scale the underlying molecular mechanisms for the complex genotype-to-phenotype relationships by integrating three-dimensional (3D) atomic-level protein structure information with high-quality large-scale protein-protein interaction data. Within the framework of this structurally resolved protein interactome, we examine the relationships among human diseases and their associated genes and mutations. RESULTS Structurally resolved protein interactome for human disease We first combined 12,577 reliable literature-curated binary interactions filtered from six widely used databases 10-15 (Online Methods) and 8,173 well-verified, high-throughput, yeast two-hybrid (Y2H) interactions Next, we structurally resolved the interfaces of these interactions using a homology modeling approach 16 . We used both iPfam 17 and 3did 18 to identify the interfaces of two interacting proteins by mapping them to known atomic-resolution 3D structures of interactions in the Protein Data Bank (PDB) Finally, to compile a comprehensive list of disease-associated genes and their mutations, we combined information from both Online Mendelian Inheritance in Man (OMIM

Reconstruction of the origin and dispersal of the worldwide dominant Hepatitis B Virus subgenotype D1

Funding Information: N.S.T. and P.L. were supported by the European Union Seventh Framework Programme [FP7/2007-2013] under Grant Agreement number 278433-PREDEMICS. The research leading to these results has received funding from the European Research Council under the European Union's Horizon 2020 research and innovation programme (grant agreement no. 725422 - ReservoirDOCS). MT is a PhD fellow at the Research Foundation Flanders (FWO, Belgium, grant number 1S47118N). A.-C.P.-P. was supported by European Funds through grant 'Bio-Molecular and Epidemiological Surveillance of HIV Transmitted Drug Resistance, Hepatitis Co- Infections and Ongoing Transmission Patterns in Europe' (BEST HOPE) (project funded through HIVERA: Harmonizing Integrating Vitalizing European Research on HIV/Aids, grant 249697); by Fundação para a Cieñcia e Tecnologia for funds to GHTMUID/ Multi/04413/2013; by the Migrant HIV project (financed by FCT: PTDC/DTP-EPI/7066/2014; and by Gilead Ǵenese HIVLatePresenters. B.V. was supported by a postdoctoral grant (12U7121N) of the FWO (Fonds Wetenschappelijk Onderzoek - Vlaanderen). G.B. acknowledges support from the Interne Fondsen KU Leuven/ Internal Funds KU Leuven under grant agreement C14/18/094 and the Research Foundation - Flanders ('Fonds voor Wetenschappelijk Onderzoek - Vlaanderen', G0E1420N, G098321N). This work was supported by the Bijzonder Onderzoeksfonds KU Leuven (BOF) No. OT/14/115. This work was supported by public grants. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Publisher Copyright: © 2022 The Author(s).Hepatitis B is a potentially life-threatening liver infection caused by the hepatitis B virus (HBV). HBV-D1 is the dominant subgenotype in the Mediterranean basin, Eastern Europe, and Asia. However, little is currently known about its evolutionary history and spatio-temporal dynamics. We use Bayesian phylodynamic inference to investigate the temporal history of HBV-D1, for which we calibrate the molecular clock using ancient sequences, and reconstruct the viral global spatial dynamics based, for the first time, on full-length publicly available HBV-D1 genomes from a wide range of sampling dates. We pinpoint the origin of HBV subgenotype D1 before the current era (BCE) in Turkey/Anatolia. The spatial reconstructions reveal global viral transmission with a high degree of mixing. By combining modern-day and ancient sequences, we ensure sufficient temporal signal in HBV-D1 data to enable Bayesian phylodynamic inference using a molecular clock for time calibration. Our results shed light on the worldwide HBV-D1 epidemics and suggest that this originally Middle Eastern virus significantly affects more distant countries, such as those in mainland Europe.publishersversionpublishe

Extensive preclinical validation of combined RMC-4550 and LY3214996 supports clinical investigation for KRAS mutant pancreatic cancer

Over 90% of pancreatic cancers present mutations in KRAS, one of the most common oncogenic drivers overall. Currently, most KRAS mutant isoforms cannot be targeted directly. Moreover, targeting single RAS downstream effectors induces adaptive resistance mechanisms. We report here on the combined inhibition of SHP2, upstream of KRAS, using the allosteric inhibitor RMC-4550 and of ERK, downstream of KRAS, using LY3214996. This combination shows synergistic anti-cancer activity in vitro, superior disruption of the MAPK pathway, and increased apoptosis induction compared with single-agent treatments. In vivo, we demonstrate good tolerability and efficacy of the combination, with significant tumor regression in multiple pancreatic ductal adenocarcinoma (PDAC) mouse models. Finally, we show evidence that 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) can be used to assess early drug responses in animal models. Based on these results, we will investigate this drug combination in the SHP2 and ERK inhibition in pancreatic cancer (SHERPA; ClinicalTrials.gov: NCT04916236) clinical trial, enrolling patients with KRAS-mutant PDAC.This work was funded by the American Association for Cancer Research, Lustgarten Foundation, and Stand Up to Cancer as a Pancreatic Cancer Collective New Therapies Challenge grant (grant no. SU2C-AACR-PCC-01-18)