Biology subjects

Loos, R.

Publications and source records attributed to Loos, R..

9 recordsLinked to original sources

bioRxiv2023

In vitro lymph node-mimicking 3D model displays long-term T cell-dependent CLL proliferation and survival

Chronic lymphocytic leukemia (CLL) cells are highly dependent on microenvironmental cells and signals. The lymph node (LN) is the critical site of in vivo CLL proliferation and development of resistance to both chemotherapy and targeted agents. We present a new model that incorporates key aspects of the CLL LN which enables investigation of CLL cells in the context of a protective niche. We describe a 3D in vitro culture system utilizing ultra-low attachment (ULA) plates to create spheroids of CLL cells derived from peripheral blood (PB). Starting from CLL:T cell ratios as observed in LN samples, CLL activation was induced by either direct stimulation and/or indirectly via T cells. Compared to 2D cultures, 3D cultures promoted CLL proliferation in a T cell-dependent manner, and enabled expansion for up to 7 weeks, including the formation of follicle-like structures after several weeks of culture. Addition of LN-derived stromal cells further enhanced the proliferative capacity. This model enables high-throughput drug screening, of which we describe response to Btk inhibition, venetoclax resistance, and T cell-mediated cytotoxicity as examples. In summary, we present the first LN-mimicking in vitro 3D culture for primary CLL, which enables readouts such as real-time drug screens, kinetic growth assays and spatial localization. This is the first in vitro CLL system that allows testing of response and resistance to venetoclax and Btk inhibitors in the context of the tumor microenvironment, thereby opening up new possibilities for clinically useful applications.

cancer biology↗

bioRxiv2022

Multi-ancestry GWAS of major depression aids locus discovery, fine-mapping, gene prioritisation, and causal inference

Most genome-wide association studies (GWAS) of major depression (MD) have been conducted in samples of European ancestry. Here we report a multi-ancestry GWAS of MD, adding data from 21 studies with 88,316 MD cases and 902,757 controls to previously reported data from individuals of European ancestry. This includes samples of African (36% of effective sample size), East Asian (26%) and South Asian (6%) ancestry and Hispanic/Latinx participants (32%). The multi-ancestry GWAS identified 190 significantly associated loci, 53 of them novel. For previously reported loci from GWAS in European ancestry the power-adjusted transferability ratio was 0.6 in the Hispanic/Latinx group and 0.3 in each of the other groups. Fine-mapping benefited from additional sample diversity: the number of credible sets with [≤]5 variants increased from 3 to 12. A transcriptome-wide association study identified 354 significantly associated genes, 205 of them novel. Mendelian Randomisation showed a bidirectional relationship with BMI exclusively in samples of European ancestry. This first multi-ancestry GWAS of MD demonstrates the importance of large diverse samples for the identification of target genes and putative mechanisms.

genetics↗

bioRxiv2022

Improving the assessment of deep learning models in the context of drug-target interaction prediction

AO_SCPLOWBSTRACTC_SCPLOWMachine Learning techniques have been widely adopted to predict drug-target interactions, a central area of research in early drug discovery. These techniques have shown promising results on various benchmarks although they tend to suffer from poor generalization. This is typically related to very sparse and nonuniform datasets available, which limits the applicability domain of machine learning techniques. Moreover, widespread approaches to split datasets (into training and test sets) treat a drug-target interaction as an independent entities, when in reality the drug and target involved may take part in other interactions, breaking apart the assumption of independence. We observe that this leads to overly optimistic test results and poor generalization of out-of-distribution samples for various state-of-the-art sequence-based machine learning models for drug-target prediction. We show that previous approaches to reduce bias in binding datasets focus on drug or target information only and, thus, lead to similar pitfalls. Finally, we propose a minimum viable solution to evaluate the generalization capability of a machine learning model based on the systematic separation of test samples with respect to drugs and targets in the training set, thus discerning the three out-of-distribution scenarios seen at test time: (1) drug or (2) target present in the training set, or (3) neither.

bioinformatics↗

bioRxiv2021

Quantitative systems pharmacology modeling of avadomide-induced neutropenia enables virtual clinical dose and schedule finding studies

Avadomide is a cereblon E3 ligase modulator and a potent antitumor and immunomodulatory agent. Avadomide trials are challenged by neutropenia as a major adverse event and a dose-limiting toxicity. Intermittent dosing schedules supported by preclinical data provide a strategy to reduce frequency and severity of neutropenia, however the identification of optimal dosing schedules remains a clinical challenge. Quantitative Systems Pharmacology (QSP) modeling offers opportunities for virtual screening of efficacy and toxicity levels produced by alternative dose and schedule regimens, thereby supporting decision-making in translational drug development. We formulated a QSP model to capture the mechanism of avadomide-induced neutropenia, which involves cereblon-mediated degradation of transcription factor Ikaros, resulting in a maturation block of the neutrophil lineage. The neutropenia model was integrated with avadomide-specific pharmacokinetic and pharmacodynamic models to capture dose-dependent effects. Additionally, we generated a disease-specific virtual patient population to represent the variability in patient characteristics and response to treatment observed for a diffuse large B-cell lymphoma trial cohort. Model utility was demonstrated by simulating avadomide effect in the virtual population for various dosing schedules and determining the incidence of high-grade neutropenia, its duration, and the probability of recovery to low grade-neutropenia.

pharmacology and toxicology↗

bioRxiv2020

A maternal effect regulates global DNA methylation patterns

Variation in DNA methylation between individuals has been shown to be influenced by both genetic and environmental factors. However, the relative impact of genetic and non-genetic factors on DNA methylation patterns across the mammalian genome has not been systematically studied. We performed whole-genome methylation analysis in two inbred mouse strains, revealing striking differences in the global distribution of DNA methylation. Although global methylation patterns were indistinguishable for most genomic features, a significant increase in the number of unmethylated CpG-island promoters and first exons was observed between strains. Experiments using F1 reciprocal hybrid strains demonstrated that the genotype of the mother dictated global DNA methylation patterns. Cross-fostering experiments ruled out a postnatal maternal effect on these differences and suggested that they were driven by a prenatal maternal effect, possibly via differential deposition of maternal gene products into the oocyte or uterine environment. These data demonstrate that maternal effects have a major impact on global DNA methylation patterns.

genomics↗

bioRxiv2018

The role of glycaemic, lipid, blood pressure and obesity risk factors as mediators of the effect of height on Coronary Artery Disease and Type 2 Diabetes Mellitus: A Mendelian Randomisation Study.

We investigated by Mendelian Randomisation (MR) analyses the causal effect of adult height on coronary artery disease (CAD) (23,755 cases, 425,339 controls) and type 2 diabetes (T2D) (29,427 cases, 416,908 controls) in UK Biobank. We then examined whether such effects are mediated via known cardiometabolic risk factors including body mass index (BMI), glycaemic traits, lipid levels and blood pressure. We further cross-checked our findings by two-sample MR and Multivariate MR analyses with publicly available summary statistics data. One standard deviation (SD) higher genetically determined height (~6.5 cm) was causally associated with a 14% decrease in CAD risk (OR= 0.86, 95% CI= 0.790.94). This causal association remained significant after performing sensitivity analyses relaxing pleiotropy assumptions. The causal effect of height on CAD risk appeared to be independent of risk factors such as lipid levels, blood pressure and BMI; association was reduced by only 1-3% after adjustment for potential mediators. We observed no direct causal effect of height on T2D risk, once its effect on BMI was taken into account (OR= 0.99, 95% CI= 0.96-1.02), suggesting that BMI acts as a mediator.

genetics↗

bioRxiv2017

Comparative analysis of neutrophil and monocyte epigenomes

Neutrophils and monocytes provide a first line of defense against infections as part of the innate immune system. Here we report the integrated analysis of transcriptomic and epigenetic landscapes for circulating monocytes and neutrophils with the aim to enable downstream interpretation and functional validation of key regulatory elements in health and disease. We collected RNA-seq data, ChIP-seq of six histone modifications and of DNA methylation by bisulfite sequencing at base pair resolution from up to 6 individuals per cell type. Chromatin segmentation analyses suggested that monocytes have a higher number of cell-specific enhancer regions (4-fold) compared to neutrophils. This highly plastic epigenome is likely indicative of the greater differentiation potential of monocytes into macrophages, dendritic cells and osteoclasts. In contrast, most of the neutrophil-specific features tend to be characterized by repressed chromatin, reflective of their status as terminally differentiated cells. Enhancers were the regions where most of differences in DNA methylation between cells were observed, with monocyte-specific enhancers being generally hypomethylated. Monocytes show a substantially higher gene expression levels than neutrophils, in line with epigenomic analysis revealing that gene more active elements in monocytes. Our analyses suggest that the overexpression of c-Myc in monocytes and its binding to monocyte-specific enhancers could be an important contributor to these differences. Altogether, our study provides a comprehensive epigenetic chart of chromatin states in primary human neutrophils and monocytes, thus providing a valuable resource for studying the regulation of the human innate immune system.

genomics↗

bioRxiv2017

Comparison of RNA-seq and Microarray Platforms for Splice Event Detection using a Cross-Platform Algorithm

RNA-seq is a reference technology for determining alternative splicing at genome-wide level. Exon arrays remain widely used for the analysis of gene expression, but show poor validation rate with regard to splicing events. Commercial arrays that include probes within exon junctions have been developed in order to overcome this problem.\n\nWe compare the performance of RNA-seq (Illumina HiSeq) and junction arrays (Affymetrix Human Transcriptome array) for the analysis of transcript splicing events. Three different breast cancer cell lines were treated with CX-4945, a drug that severely affects splicing. To enable a direct comparison of the two platforms, we adapted EventPointer, an algorithm that detects and labels alternative splicing events using junction arrays, to work also on RNA-seq data. Common results and discrepancies between the technologies were validated and/or resolved by over 200 PCR experiments.\n\nAs might be expected, RNA-seq appears superior in cases where the technologies disagree, and is able to discover novel splicing events beyond the limitations of physical probe-sets. We observe a high degree of coherence between the two technologies, however, with correlation of EventPointer results over 0.90. Through decimation, the detection power of the junction arrays is equivalent to RNA-seq with up to 60 million reads. Our results suggest, therefore, that exon-junction arrays are a viable alternative to RNA-seq for detection of alternative splicing events when focusing on well-described transcriptional regions.

bioinformatics↗

bioRxiv2017

The Nucleosome Remodelling and Deacetylation complex restricts Mediator access to enhancers to control transcription

A number of different chromatin remodelling complexes in mammalian cells are implicated in the control of gene expression. The genetic requirements for many such complex components have been described, and the biochemical activities of complex components characterised in vitro, yet the molecular mechanisms by which these biochemical activities impact transcriptional regulation in vivo remain ill-defined. Using an inducible system with fine temporal resolution, we show that the Nucleosome Remodelling and Deacetylation (NuRD) complex directly regulates chromatin architecture at enhancer regions in ES cells, in turn influencing the activity of RNA polymerase II via Mediator. Through this mechanism NuRD restricts Mediator access to enhancer chromatin during lineage commitment, thereby enabling appropriate transcriptional regulation. In contrast, acetylation levels of histone H3 lysine 27 are not immediately impacted by NuRD activity, correlating with transcriptional response only after expression levels have changed. These findings provide a detailed, molecular picture of genome-wide modulation of lineage-specific transcription by an abundant chromatin remodelling complex.

molecular biology↗