71 research outputs found
Projection of High-Dimensional Genome-Wide Expression on SOM Transcriptome Landscapes
The self-organizing maps portraying has been proven to be a powerful approach for
analysis of transcriptomic, genomic, epigenetic, single-cell, and pathway-level data as well as for
âmulti-omicâ integrative analyses. However, the SOM method has a major disadvantage: it requires
the retraining of the entire dataset once a new sample is added, which can be resource- and timedemanding.
It also shifts the gene landscape, thus complicating the interpretation and comparison
of results. To overcome this issue, we have developed two approaches of transfer learning that
allow for extending SOM space with new samples, meanwhile preserving its intrinsic structure. The
extension SOM (exSOM) approach is based on adding secondary data to the existing SOM space by
âmeta-gene adaptationâ, while supervised SOM portrayal (supSOM) adds support vector machine
regression model on top of the original SOM algorithm to âpredictâ the portrait of a new sample.
Both methods have been shown to accurately combine existing and new data. With simulated data,
exSOM outperforms supSOM for accuracy, while supSOM significantly reduces the computing time
and outperforms exSOM for this parameter. Analysis of real datasets demonstrated the validity of
the projection methods with independent datasets mapped on existing SOM space. Moreover, both
methods well handle the projection of samples with new characteristics that were not present in
training datasets
Melanoma Single-Cell Biology in Experimental and Clinical Settings
Cellular heterogeneity is regarded as a major factor for treatment response and resistance in a variety of malignant tumors, including malignant melanoma. More recent developments of single-cell sequencing technology provided deeper insights into this phenomenon. Single-cell data were used to identify prognostic subtypes of melanoma tumors, with a special emphasis on immune cells and fibroblasts in the tumor microenvironment. Moreover, treatment resistance to checkpoint inhibitor therapy has been shown to be associated with a set of differentially expressed immune cell signatures unraveling new targetable intracellular signaling pathways. Characterization of T cell states under checkpoint inhibitor treatment showed that exhausted CD8+ T cell types in melanoma lesions still have a high proliferative index. Other studies identified treatment resistance mechanisms to targeted treatment against the mutated BRAF serine/threonine protein kinase including repression of the melanoma differentiation gene microphthalmia-associated transcription factor (MITF) and induction of AXL receptor tyrosine kinase. Interestingly, treatment resistance mechanisms not only included selection processes of pre-existing subclones but also transition between different states of gene expression. Taken together, single-cell technology has provided deeper insights into melanoma biology and has put forward our understanding of the role of tumor heterogeneity and transcriptional plasticity, which may impact on innovative clinical trial designs and experimental approaches
Integrated Multi-Omics Maps of Lower-Grade Gliomas
Multi-omics high-throughput technologies produce data sets which are not restricted to
only one but consist of multiple omics modalities, often as patient-matched tumour specimens. The
integrative analysis of these omics modalities is essential to obtain a holistic view on the otherwise
fragmented information hidden in this data. We present an intuitive method enabling the combined
analysis of multi-omics data based on self-organizing maps machine learning. It âportraysâ the
expression, methylation and copy number variations (CNV) landscapes of each tumour using the
same gene-centred coordinate system. It enables the visual evaluation and direct comparison of the
different omics layers on a personalized basis. We applied this combined molecular portrayal to lower
grade gliomas, a heterogeneous brain tumour entity. It classifies into a series of molecular subtypes
defined by genetic key lesions, which associate with large-scale effects on DNA methylation and
gene expression, and in final consequence, drive with cell fate decisions towards oligodendroglioma-,
astrocytoma- and glioblastoma-like cancer cell lineages with different prognoses. Consensus modes of
concerted changes of expression, methylation and CNV are governed by the degree of co-regulation
within and between the omics layers. The method is not restricted to the triple-omics data used here.
The similarity landscapes reflect partly independent effects of genetic lesions and DNA methylation
with consequences for cancer hallmark characteristics such as proliferation, inflammation and blocked
differentiation in a subtype specific fashion. It can be extended to integrate other omics features such
as genetic mutation, protein expression data as well as extracting prognostic markers
Homozygosity in any HLA locus is a risk factor for specific antibody production: the taboo concept 2.0
ObjectiveIn a cooperative study of the University Hospital Leipzig, University of Leipzig, and the CharitĂ© Berlin on kidney transplant patients, we analysed the occurrence of HLA-specific antibodies with respect to the HLA setup of the patients. We aimed at the definition of specific HLA antigens towards which the patients produced these antibodies.MethodsPatients were typed for the relevant HLA determinants using mainly the next-generation technology. Antibody screening was performed by the state-of-the-art multiplex-based technology using microspheres coupled with the respective HLA alleles of HLA class I and II determinants.ResultsPatients homozygous for HLA-A*02, HLA-A*03, HLA-A*24, HLA-B*07, HLA-B*18, HLA-B*35, HLA-B*44, HLA-C*03, HLA-C*04, and HLA-C*07 in the class I group and HLA-DRB1*01, HLA-DRB1*03, HLA-DRB1*07, HLA-DRB1*15, HLA-DQA1*01, HLA-DQA1*05, HLA-DQB1*02, HLA-DQB1*03(7), HLA-DQB1*06, HLA-DPA1*01, and HLA-DPB1*04 in the class II group were found to have a significant higher antibody production compared to the heterozygous ones. In general, all HLA determinants are affected. Remarkably, HLA-A*24 homozygous patients can produce antibodies towards all HLA-A determinants, while HLA-B*18 homozygous ones make antibodies towards all HLA-B and selected HLA-A and C antigens, and are associated with an elevation of HLA-DRB1, parts of DQB1 and DPB1 alleles. Homozygosity for the HLA class II HLA-DRB1*01, and HLA-DRB1*15 seems to increase the risk for antibody responses against most of the HLA class I antigens (HLA-A, HLA-B, and HLA-C) in contrast to HLA-DQB1*03(7) where a lower risk towards few HLA-A and HLA-B alleles is found. The widely observed differential antibody response is therefore to be accounted to the patientâs HLA type.ConclusionHomozygous patients are at risk of producing HLA-specific antibodies hampering the outcome of transplantation. Including this information on the allocation procedure might reduce antibody-mediated immune reactivity and prevent graft loss in a patient at risk, increasing the life span of the transplanted organ
The Evolving Faces of the SARS-CoV-2 Genome
Surveillance of the evolving SARS-CoV-2 genome combined with epidemiological monitoring and emerging vaccination became paramount tasks to control the pandemic which is rapidly changing in time and space. Genomic surveillance must combine generation and sharing sequence data with appropriate bioinformatics monitoring and analysis methods. We applied molecular portrayal using self-organizing maps machine learning (SOM portrayal) to characterize the diversity of the virus genomes, their mutual relatedness and development since the beginning of the pandemic. The genetic landscape obtained visualizes the relevant mutations in a lineage-specific fashion and provides developmental paths in genetic state space from early lineages towards the variants of concern alpha, beta, gamma and delta. The different genes of the virus have specific footprints in the landscape reflecting their biological impact. SOM portrayal provides a novel option for âbioinformatics surveillanceâ of the pandemic, with strong odds regarding visualization, intuitive perception and âpersonalizationâ of the mutational patterns of the virus genomes
Footprints of Sepsis Framed Within Community Acquired Pneumonia in the Blood Transcriptome
We analyzed the blood transcriptome of sepsis framed within community-acquired pneumonia (CAP) and characterized its molecular and cellular heterogeneity in terms of functional modules of co-regulated genes with impact for the underlying pathophysiological mechanisms. Our results showed that CAP severity is associated with immune suppression owing to T-cell exhaustion and HLA and chemokine receptor deactivation, endotoxin tolerance, macrophage polarization, and metabolic conversion from oxidative phosphorylation to glycolysis. We also found footprints of hostâs response to viruses and bacteria, altered levels of mRNA from erythrocytes and platelets indicating coagulopathy that parallel severity of sepsis and survival. Finally, our data demonstrated chromatin re-modeling associated with extensive transcriptional deregulation of chromatin modifying enzymes, which suggests the extensive changes of DNA methylation with potential impact for marker selection and functional characterization. Based on the molecular footprints identified, we propose a novel stratification of CAP cases into six groups differing in the transcriptomic scores of CAP severity, interferon response, and erythrocyte mRNA expression with impact for prognosis. Our analysis increases the resolution of transcriptomic footprints of CAP and reveals opportunities for selecting sets of transcriptomic markers with impact for translation of omics research in terms of patient stratification schemes and sets of signature genes
Transcriptome-Guided Drug Repositioning
Drug repositioning can save considerable time and resources and significantly speed up
the drug development process. The increasing availability of drug action and disease-associated
transcriptome data makes it an attractive source for repositioning studies. Here, we have developed a
transcriptome-guided approach for drug/biologics repositioning based on multi-layer self-organizing
maps (ml-SOM). It allows for analyzing multiple transcriptome datasets by segmenting them into
layers of drug action- and disease-associated transcriptome data. A comparison of expression changes
in clusters of functionally related genes across the layers identifies âdrug targetâ spots in disease layers
and evaluates the repositioning possibility of a drug. The repositioning potential for two approved
biologics drugs (infliximab and brodalumab) confirmed the drugsâ action for approved diseases
(ulcerative colitis and Crohnâs disease for infliximab and psoriasis for brodalumab). We showed
the potential efficacy of infliximab for the treatment of sarcoidosis, but not chronic obstructive
pulmonary disease (COPD). Brodalumab failed to affect dysregulated functional gene clusters in
Crohnâs disease (CD) and systemic juvenile idiopathic arthritis (SJIA), clearly indicating that it may
not be effective in the treatment of these diseases. In conclusion, ml-SOM offers a novel approach for
transcriptome-guided drug repositioning that could be particularly useful for biologics drugs
High-Resolution Cartography of the Transcriptome and Methylome Landscapes of Diffuse Gliomas
Molecular mechanisms of lower-grade (IIâIII) diffuse gliomas (LGG) are still poorly understood, mainly because of their heterogeneity. They split into astrocytoma- (IDH-A) and oligodendroglioma-like (IDH-O) tumors both carrying mutations(s) at the isocitrate dehydrogenase (IDH) gene and into IDH wild type (IDH-wt) gliomas of glioblastoma resemblance. We generated detailed maps of the transcriptomes and DNA methylomes, revealing that cell functions divided into three major archetypic hallmarks: (i) increased proliferation in IDH-wt and, to a lesser degree, IDH-O; (ii) increased inflammation in IDH-A and IDH-wt; and (iii) the loss of synaptic transmission in all subtypes. Immunogenic properties of IDH-A are diverse, partly resembling signatures observed in grade IV mesenchymal glioblastomas or in grade I pilocytic astrocytomas. We analyzed details of coregulation between gene expression and DNA methylation and of the immunogenic micro-environment presumably driving tumor development and treatment resistance. Our transcriptome and methylome maps support personalized, case-by-case views to decipher the heterogeneity of glioma states in terms of data portraits. Thereby, molecular cartography provides a graphical coordinate system that links gene-level information with glioma subtypes, their phenotypes, and clinical context
Deciphering the Transcriptomic Heterogeneity of Duodenal Coeliac Disease Biopsies
Coeliac disease (CD) is a clinically heterogeneous autoimmune disease with variable presentation
and progression triggered by gluten intake. Molecular or genetic factors contribute to disease
heterogeneity, but the reasons for different outcomes are poorly understood. Transcriptome studies
of tissue biopsies from CD patients are scarce. Here, we present a high-resolution analysis of the
transcriptomes extracted from duodenal biopsies of 24 children and adolescents with active CD and
21 individuals without CD but with intestinal afflictions as controls. The transcriptomes of CD patients
divide into three groupsâa mixed group presenting the control cases, and CD-low and CD-high
groups referring to lower and higher levels of CD severity. Persistence of symptoms was weakly
associated with subgroup, but the highest marsh stages were present in subgroup CD-high, together
with the highest cell cycle rates as an indicator of virtually complete villous atrophy. Considerable
variation in inflammation-level between subgroups was further deciphered into immune cell types
using cell type de-convolution. Self-organizing maps portrayal was applied to provide high-resolution
landscapes of the CD-transcriptome. We find asymmetric patterns of miRNA and long non-coding
RNA and discuss the effect of epigenetic regulation. Expression of genes involved in interferon
gamma signaling represent suitable markers to distinguish CD from non-CD cases. Multiple pathways
overlay in CD biopsies in different ways, giving rise to heterogeneous transcriptional patterns,
which potentially provide information about etiology and the course of the disease
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