The outbreak of SARS-CoV-2 (2019-nCoV) virus has highlighted the need for fast and efficacious vaccine development. Stimulation of a proper immune response that leads to protection is highly ...dependent on presentation of epitopes to circulating T-cells via the HLA complex. SARS-CoV-2 is a large RNA virus and testing of all of its overlapping peptides in vitro to deconvolute an immune response is not feasible. Therefore HLA-binding prediction tools are often used to narrow down the number of peptides to test. We tested NetMHC suite tools' predictions by using an in vitro peptide-MHC stability assay. We assessed 777 peptides that were predicted to be good binders across 11 MHC alleles in a complex-stability assay and tested a selection of 19 epitope-HLA-binding prediction tools against the assay. In this investigation of potential SARS-CoV-2 epitopes we found that current prediction tools vary in performance when assessing binding stability, and they are highly dependent on the MHC allele in question. Designing a COVID-19 vaccine where only a few epitope targets are included is therefore a very challenging task. Here, we present 174 SARS-CoV-2 epitopes with high prediction binding scores, validated to bind stably to 11 HLA alleles. Our findings may contribute to the design of an efficacious vaccine against COVID-19.
Full text
Available for:
IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Cancer sequencing studies have implicated regulators of pre-mRNA splicing as important disease determinants in acute myeloid leukemia (AML), but the underlying mechanisms have remained elusive. We ...hypothesized that "non-mutated" splicing regulators may also play a role in AML biology and therefore conducted an in vivo shRNA screen in a mouse model of CEBPA mutant AML. This has led to the identification of the splicing regulator RBM25 as a novel tumor suppressor. In multiple human leukemic cell lines, knockdown of RBM25 promotes proliferation and decreases apoptosis. Mechanistically, we show that RBM25 controls the splicing of key genes, including those encoding the apoptotic regulator BCL-X and the MYC inhibitor BIN1. This mechanism is also operative in human AML patients where low RBM25 levels are associated with high MYC activity and poor outcome. Thus, we demonstrate that RBM25 acts as a regulator of MYC activity and sensitizes cells to increased MYC levels.
Enhancers control the correct temporal and cell-type-specific activation of gene expression in multicellular eukaryotes. Knowing their properties, regulatory activity and targets is crucial to ...understand the regulation of differentiation and homeostasis. Here we use the FANTOM5 panel of samples, covering the majority of human tissues and cell types, to produce an atlas of active, in vivo-transcribed enhancers. We show that enhancers share properties with CpG-poor messenger RNA promoters but produce bidirectional, exosome-sensitive, relatively short unspliced RNAs, the generation of which is strongly related to enhancer activity. The atlas is used to compare regulatory programs between different cells at unprecedented depth, to identify disease-associated regulatory single nucleotide polymorphisms, and to classify cell-type-specific and ubiquitous enhancers. We further explore the utility of enhancer redundancy, which explains gene expression strength rather than expression patterns. The online FANTOM5 enhancer atlas represents a unique resource for studies on cell-type-specific enhancers and gene regulation.
Full text
Available for:
DOBA, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Unsupervised machine learning methods (deep learning) have shown their usefulness with noisy single cell mRNA-sequencing data (scRNA-seq), where the models generalize well, despite the zero-inflation ...of the data. A class of neural networks, namely autoencoders, has been useful for denoising of single cell data, imputation of missing values and dimensionality reduction.
Here, we present a striking feature with the potential to greatly increase the usability of autoencoders: With specialized training, the autoencoder is not only able to generalize over the data, but also to tease apart biologically meaningful modules, which we found encoded in the representation layer of the network. Our model can, from scRNA-seq data, delineate biological meaningful modules that govern a dataset, as well as give information as to which modules are active in each single cell. Importantly, most of these modules can be explained by known biological functions, as provided by the Hallmark gene sets.
We discover that tailored training of an autoencoder makes it possible to deconvolute biological modules inherent in the data, without any assumptions. By comparisons with gene signatures of canonical pathways we see that the modules are directly interpretable. The scope of this discovery has important implications, as it makes it possible to outline the drivers behind a given effect of a cell. In comparison with other dimensionality reduction methods, or supervised models for classification, our approach has the benefit of both handling well the zero-inflated nature of scRNA-seq, and validating that the model captures relevant information, by establishing a link between input and decoded data. In perspective, our model in combination with clustering methods is able to provide information about which subtype a given single cell belongs to, as well as which biological functions determine that membership.
Full text
Available for:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Single cell mRNA sequencing technologies have transformed our understanding of cellular heterogeneity and identity. For sensitive discovery or clinical marker estimation where high transcript capture ...per cell is needed only plate-based techniques currently offer sufficient resolution.
Here, we present a performance evaluation of four different plate-based scRNA-seq protocols. Our evaluation is aimed towards applications taxing high gene detection sensitivity, reproducibility between samples, and minimum hands-on time, as is required, for example, in clinical use. We included two commercial kits, NEBNext® Single Cell/ Low Input RNA Library Prep Kit (NEB®), SMART-seq® HT kit (Takara®), and the non-commercial protocols Genome & Transcriptome sequencing (G&T) and SMART-seq3 (SS3). G&T delivered the highest detection of genes per single cell. SS3 presented the highest gene detection per single cell at the lowest price. Takara® kit presented similar high gene detection per single cell, and high reproducibility between samples, but at the absolute highest price. NEB® delivered a lower detection of genes but remains an alternative to more expensive commercial kits.
For the tested kits we found that ease-of-use came at higher prices. Takara can be selected for its ease-of-use to analyse a few samples, but we recommend the cheaper G&T-seq or SS3 for laboratories where a substantial sample flow can be expected.
Full text
Available for:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The nuclear receptor binding SET domain protein 1 (NSD1) is recurrently mutated in human cancers including acute leukemia. We show that NSD1 knockdown alters erythroid clonogenic growth of human CD34
...hematopoietic cells. Ablation of Nsd1 in the hematopoietic system of mice induces a transplantable erythroleukemia. In vitro differentiation of Nsd1
erythroblasts is majorly impaired despite abundant expression of GATA1, the transcriptional master regulator of erythropoiesis, and associated with an impaired activation of GATA1-induced targets. Retroviral expression of wildtype NSD1, but not a catalytically-inactive NSD1
SET-domain mutant induces terminal maturation of Nsd1
erythroblasts. Despite similar GATA1 protein levels, exogenous NSD1 but not NSD
significantly increases the occupancy of GATA1 at target genes and their expression. Notably, exogenous NSD1 reduces the association of GATA1 with the co-repressor SKI, and knockdown of SKI induces differentiation of Nsd1
erythroblasts. Collectively, we identify the NSD1 methyltransferase as a regulator of GATA1-controlled erythroid differentiation and leukemogenesis.
Abstract
BloodSpot is a specialised database integrating gene expression data from acute myeloid leukaemia (AML) patients related to blood cell development and maturation. The database and interface ...has helped numerous researchers and clinicians to quickly get an overview of gene expression patterns in healthy and malignant haematopoiesis. Here, we present an update to our framework that includes protein expression data of sorted single cells. With this update we also introduce datasets broadly spanning age groups, which many users have requested, with particular interest for researchers studying paediatric leukaemias. The backend of the database has been rewritten and migrated to a cloud-based environment to accommodate the growth, and provide a better user-experience for our many international users. Users can now enjoy faster transfer speeds and a more responsive interface. In conclusion, the continuing popularity of the database and emergence of new data modalities has prompted us to rewrite and futureproof the back-end, including paediatric centric views, as well as single cell protein data, allowing us to keep the database updated and relevant for the years to come. The database is freely available at www.bloodspot.eu.
Graphical Abstract
Graphical Abstract
Gene expression profiling has been used extensively to characterize cancer, identify novel subtypes, and improve patient stratification. However, it has largely failed to identify transcriptional ...programs that differ between cancer and corresponding normal cells and has not been efficient in identifying expression changes fundamental to disease etiology. Here we present a method that facilitates the comparison of any cancer sample to its nearest normal cellular counterpart, using acute myeloid leukemia (AML) as a model. We first generated a gene expression-based landscape of the normal hematopoietic hierarchy, using expression profiles from normal stem/progenitor cells, and next mapped the AML patient samples to this landscape. This allowed us to identify the closest normal counterpart of individual AML samples and determine gene expression changes between cancer and normal. We find the cancer vs normal method (CvN method) to be superior to conventional methods in stratifying AML patients with aberrant karyotype and in identifying common aberrant transcriptional programs with potential importance for AML etiology. Moreover, the CvN method uncovered a novel poor-outcome subtype of normal-karyotype AML, which allowed for the generation of a highly prognostic survival signature. Collectively, our CvN method holds great potential as a tool for the analysis of gene expression profiles of cancer patients.
•This study describes a method for the comparison of gene expression data of any type of cancer cells with their corresponding normal cells.•Our analyses reveal novel disease entities, identify common deregulated transcriptional networks, and predict survival.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Abstract
Understanding the mRNA life cycle requires information about the dynamics and macromolecular composition and stoichiometry of mRNPs. Fluorescence correlation and cross-correlation ...spectroscopy (FCS and FCCS) are appealing technologies to study these macromolecular structures because they have single molecule sensitivity and readily provide information about their molecular composition and dynamics. Here, we demonstrate how FCS can be exploited to study cytoplasmic mRNPs with high accuracy and reproducibility in cell lysates. Cellular lysates not only recapitulate data from live cells but provide improved readings and allow investigation of single mRNP analysis under particular conditions or following enzymatic treatments. Moreover, FCCS employing minute amounts of cells closely corroborated previously reported RNA dependent interactions and provided estimates of the relative overlap between factors in the mRNPs, thus depicting their heterogeneity. The described lysate-based FCS and FCCS analysis may not only complement current biochemical approaches but also provide novel opportunities for the quantitative analysis of the molecular composition and dynamics of single mRNPs.
Graphical Abstract
Graphical Abstract
The use of cell lysates in combination with Fluorescence Correlation and Cross-correlation Spectroscopy allows a precise analysis and quantification of RNA-binding protein diffusion, stoichiometry and heterogeneity in mRNPs.
Urokinase plasminogen activator receptor (uPAR) encoded by the
gene is known as a clinical marker for cell invasiveness in glioblastoma multiforme (GBM). It is additionally implicated in various ...processes, including angiogenesis and inflammation within the tumor microenvironment. However, there has not been a comprehensive study that depicts the overall functions and molecular cooperators of
with respect to intra-tumoral subtypes of GBM. Using single-cell RNA sequencing data from 37 GBM patients, we identified
as a marker gene for two distinct subtypes in GBM. One subtype is featured by inflammatory activities and the other subtype is marked by ECM remodeling processes. Using the whole-transcriptome data from single cells, we are able to uncover the molecular cooperators of
for both subtypes without presuming biological pathways. Two protein networks comprise the molecular context of
, with each of the two subtypes characterized by a different dominant network. We concluded that targeting
directly influences the mechanisms represented by these two protein networks, regardless of the subtype of the targeted cell.
Full text
Available for:
IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
PDF
