RNA-seq analysis of Lgr6(+) stem cells and identification of an Lgr6 isoform

Dermatology-oncolog

Discourse Semantics for the Analysis of Change in Language

This paper purports to elaborate and address several issues which lie at the intersection of computational linguistics and psychology. The first issue addressed is that of the interaction between discourse and semantics by virtue of empirical linguistic and psychotherapeutic evidence. This paper then gives a formal account of the knowledge representation and reasoning processes involved in the construction of an XML knowledge base for use in the sematic analysis of psychotherapeutic transcripts. Computational methods for the automatic mark-up and inference of the psychotherapeutic phenomena under investigation are detailed in order to further develop intuitions behind a particular pragmatic theory of language known as the Metamodel. The work presented here ultimately aims to produce a sustainable system for the evaluation of the effectiveness of any given psychotherapeutic technique. The possibility exists for such a system to recognise successful therapeutic mechanisms and further still, to infer new ones, or suggest improvements, or offer novel explanations as to the success or failure of the therapy itself. The work discussed here stems from research in computational linguistics, psychotherapy, and philosophy. The corpus used is a culmination of client transcripts taken before, during, and after therapy. The particular therapeutic technique used here is known as the Metamodel (Bandler and Grinder, 1975). The Metamodel was originally proffered as a method of language analysis suitable for use by practitioners of any psychotherapeutic technique. It theorises that speech utterances are related to a clients deep structure through three primary mechanisms, namely generalisation, deletion, and distortion. Previous hand tagging of our data has proven support for such claims. It is our aim to automate the identification and reasoning process. The issues and processes involved in the automation of such tagging are discussed here. Architectural and philosophical issues relating syntax (or grammar), semantics (Larson and Segal, 1995), and pragmatics (Grice, 1989; Searle, 1969) are raised. Discourse Representation Theory (Kamp, 1981; Asher and Lascarides, 1995) is discussed and used here in order to infer discourse relations.Hosted by the Scholarly Text and Imaging Service (SETIS), the University of Sydney Library, and the Research Institute for Humanities and Social Sciences (RIHSS), the University of Sydney

CACTUS: integrating clonal architecture with genomic clustering and transcriptome profiling of single tumor cells

Background: Drawing genotype-to-phenotype maps in tumors is of paramount importance for understanding tumor heterogeneity. Assignment of single cells to their tumor clones of origin can be approached by matching the genotypes of the clones to the mutations found in RNA sequencing of the cells. The confidence of the cell-to-clone mapping can be increased by accounting for additional measurements. Follicular lymphoma, a malignancy of mature B cells that continuously acquire mutations in parallel in the exome and in B cell receptor loci, presents a unique opportunity to join exome-derived mutations with B cell receptor sequences as independent sources of evidence for clonal evolution.Methods: Here, we propose CACTUS, a probabilistic model that leverages the information from an independent genomic clustering of cells and exploits the scarce single cell RNA sequencing data to map single cells to given imperfect genotypes of tumor clones.Results: We apply CACTUS to two follicular lymphoma patient samples, integrating three measurements: whole exome, single-cell RNA, and B cell receptor sequencing. CACTUS outperforms a predecessor model by confidently assigning cells and B cell receptor-based clusters to the tumor clones.Conclusions: The integration of independent measurements increases model certainty and is the key to improving model performance in the challenging task of charting the genotype-to-phenotype maps in tumors. CACTUS opens the avenue to study the functional implications of tumor heterogeneity, and origins of resistance to targeted therapies. CACTUS is written in R and source code, along with all supporting files, are available on GitHub (https://github.com/LUMC/CACTUS).Development and application of statistical models for medical scientific researc

Optimized whole genome association scanning for discovery of HLA class I-restricted minor histocompatibility antigens

Patients undergoing allogeneic stem cell transplantation as treatment for hematological diseases face the risk of Graft-versus-Host Disease as well as relapse. Graft-versus-Host Disease and the favorable Graft-versus-Leukemia effect are mediated by donor T cells recognizing polymorphic peptides, which are presented on the cell surface by HLA molecules and result from single nucleotide polymorphism alleles that are disparate between patient and donor. Identification of polymorphic HLA-binding peptides, designated minor histocompatibility antigens, has been a laborious procedure, and the number and scope for broad clinical use of these antigens therefore remain limited. Here, we present an optimized whole genome association approach for discovery of HLA class I minor histocompatibility antigens. T cell clones isolated from patients who responded to donor lymphocyte infusions after HLA-matched allogeneic stem cell transplantation were tested against a panel of 191 EBV-transformed B cells, which have been sequenced by the 1000 Genomes Project and selected for expression of seven common HLA class I alleles (HLA-A*01:01, A*02:01, A*03:01, B*07:02, B*08:01, C*07:01, and C*07:02). By including all polymorphisms with minor allele frequencies above 0.01, we demonstrated that the new approach allows direct discovery of minor histocompatibility antigens as exemplified by seven new antigens in eight different HLA class I alleles including one antigen in HLA-A*24:02 and HLA-A*23:01, for which the method has not been originally designed. Our new whole genome association strategy is expected to rapidly augment the repertoire of HLA class I-restricted minor histocompatibility antigens that will become available for donor selection and clinical use to predict, follow or manipulate Graft-versus-Leukemia effect and Graft-versus-Host Disease after allogeneic stem cell transplantation.Development and application of statistical models for medical scientific researc

Hippocampal glucocorticoid target genes associated with enhancement of memory consolidation

Glucocorticoids enhance memory consolidation of emotionally arousing events via largely unknown molecular mechanisms. This glucocorticoid effect on the consolidation process also requires central noradrenergic neurotransmission. The intracellular pathways of these two stress mediators converge on two transcription factors: the glucocorticoid receptor (GR) and phosphorylated cAMP response element-binding protein (pCREB). We therefore investigated, in male rats, whether glucocorticoid effects on memory are associated with genomic interactions between the GR and pCREB in the hippocampus. In a two-by-two design, object exploration training or no training was combined with post-training administration of a memory-enhancing dose of corticosterone or vehicle. Genomic effects were studied by chromatin immunoprecipitation followed by sequencing (ChIP-seq) of GR and pCREB 45 min after training and transcriptome analysis after 3 hr. Corticosterone administration induced differential GR DNA-binding and regulation of target genes within the hippocampus, largely independent of training. Training alone did not result in long-term memory nor did it affect GR or pCREB DNA-binding and gene expression. No strong evidence was found for an interaction between GR and pCREB. Combination of the GR DNA-binding and transcriptome data identified a set of novel, likely direct, GR target genes that are candidate mediators of corticosterone effects on memory consolidation. Cell-specific expression of the identified target genes using single-cell expression data suggests that the effects of corticosterone reflect in part non-neuronal cells. Together, our data identified new GR targets associated with memory consolidation that reflect effects in both neuronal and non-neuronal cells.Development and application of statistical models for medical scientific researc

NKG2A is a late immune checkpoint on CD8 T cells and marks repeated stimulation and cell division

The surface inhibitory receptor NKG2A forms heterodimers with the invariant CD94 chain and is expressed on a subset of activated CD8 T cells. As antibodies to block NKG2A are currently tested in several efficacy trials for different tumor indications, it is important to characterize the NKG2A(+) CD8 T cell population in the context of other inhibitory receptors. Here we used a well-controlled culture system to study the kinetics of inhibitory receptor expression. Naive mouse CD8 T cells were synchronously and repeatedly activated by artificial antigen presenting cells in the presence of the homeostatic cytokine IL-7. The results revealed NKG2A as a late inhibitory receptor, expressed after repeated cognate antigen stimulations. In contrast, the expression of PD-1, TIGIT and LAG-3 was rapidly induced, hours after first contact and subsequently down regulated during each resting phase. This late, but stable expression kinetics of NKG2A was most similar to that of TIM-3 and CD39. Importantly, single-cell transcriptomics of human tumor-infiltrating lymphocytes (TILs) showed indeed that these receptors were often coexpressed by the same CD8 T cell cluster. Furthermore, NKG2A expression was associated with cell division and was promoted by TGF-beta in vitro, although TGF-beta signaling was not necessary in a mouse tumor model in vivo. In summary, our data show that PD-1 reflects recent TCR triggering, but that NKG2A is induced after repeated antigen stimulations and represents a late inhibitory receptor. Together with TIM-3 and CD39, NKG2A might thus mark actively dividing tumor-specific TILs.Experimental cancer immunology and therap

Transcriptional profiling and biomarker identification reveal tissue specific effects of expanded ataxin-3 in a spinocerebellar ataxia type 3 mouse model

Molecular Epidemiolog

Cancer-associated fibroblasts are key determinants of cancer cell Invasion in the earliest stage of colorectal cancer

BACKGROUND & AIMS: Improving clinical management of early stage colorectal cancers (T1CRCs) requires a better understanding of their underlying biology. Accumulating evidence shows that cancer-associated fibroblasts (CAFs) are important determinants of tumor progression in advanced colorectal cancer (CRC), but their role in the initial stages of CRC tumorigenesis is unknown. Therefore, we investigated the contribution of T1CAFs to early CRC progression. METHODS: Primary T1CAFs and patient-matched normal fibroblasts (NFs) were isolated from endoscopic biopsy specimens of histologically confirmed T1CRCs and normal mucosa, respectively. The impact of T1CAFs and NFs on tumor behavior was studied using 3-dimensional co-culture systems with primary T1CRC organoids and extracellular matrix (ECM) remodeling assays. Whole-transcriptome sequencing and gene silencing were used to pinpoint mediators of T1CAF functions. RESULTS: In 3-dimensional multicellular cultures, matrix invasion of T1CRC organoids was induced by T1CAFs, but not by matched NFs. Enhanced T1CRC invasion was accompanied by T1CAF-induced ECM remodeling and up-regulation of CD44 in epithelial cells. RNA sequencing of 10 NF-T1CAF pairs revealed 404 differentially expressed genes, with significant enrichment for ECM-related pathways in T1CAFs. Cathepsin H, a cysteine-type protease that was specifically up-regulated in T1CAFs but not in fibroblasts from premalignant lesions or advanced CRCs, was identified as a key factor driving matrix remodeling by T1CAFs. Finally, we showed high abundance of cathepsin H-expressing T1CAFs at the invasive front of primary T1CRC sections. CONCLUSIONS: Already in the earliest stage of CRC, cancer cell invasion is promoted by CAFs via direct interactions with epithelial cancer cells and stage-specific, cathepsin H-dependent ECM remodeling. RNA sequencing data of the 10 NF-T1CAF pairs can be found under GEO accession number GSE200660.Cellular mechanisms in basic and clinical gastroenterology and hepatolog

Comprehensive diagnostics of acute myeloid leukemia by whole transcriptome RNA sequencing

Acute myeloid leukemia (AML) is caused by genetic aberrations that also govern the prognosis of patients and guide risk-adapted and targeted therapy. Genetic aberrations in AML are structurally diverse and currently detected by different diagnostic assays. This study sought to establish whole transcriptome RNA sequencing as single, comprehensive, and flexible platform for AML diagnostics. We developed HAMLET (Human AML Expedited Transcriptomics) as bioinformatics pipeline for simultaneous detection of fusion genes, small variants, tandem duplications, and gene expression with all information assembled in an annotated, user-friendly output file. Whole transcriptome RNA sequencing was performed on 100 AML cases and HAMLET results were validated by reference assays and targeted resequencing. The data showed that HAMLET accurately detected all fusion genes and overexpression of EVI1 irrespective of 3q26 aberrations. In addition, small variants in 13 genes that are often mutated in AML were called with 99.2% sensitivity and 100% specificity, and tandem duplications in FLT3 and KMT2A were detected by a novel algorithm based on soft-clipped reads with 100% sensitivity and 97.1% specificity. In conclusion, HAMLET has the potential to provide accurate comprehensive diagnostic information relevant for AML classification, risk assessment and targeted therapy on a single technology platform

Controlling bias and inflation in epigenome- and transcriptome-wide association studies using the empirical null distribution

We show that epigenome- and transcriptome-wide association studies (EWAS and TWAS) are prone to significant inflation and bias of test statistics, an unrecognized phenomenon introducing spurious findings if left unaddressed. Neither GWAS-based methodology nor state-of-the-art confounder adjustment methods completely remove bias and inflation. We propose a Bayesian method to control bias and inflation in EWAS and TWAS based on estimation of the empirical null distribution. Using simulations and real data, we demonstrate that our method maximizes power while properly controlling the false positive rate. We illustrate the utility of our method in large-scale EWAS and TWAS meta-analyses of age and smoking