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1.
Front Immunol. 2018 Jul 17;9:1620. doi: 10.3389/fimmu.2018.01620. eCollection 2018.

Footprints of Sepsis Framed Within Community Acquired Pneumonia in the Blood Transcriptome.

Author information

1
Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Leipzig, Germany.
2
Group of Bioinformatics, Institute of Molecular Biology, National Academy of Sciences, Yerevan, Armenia.

Abstract

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 host's 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.

KEYWORDS:

blood disturbances; community-acquired pneumonia severity; epigenetics; immune suppression; infections; molecular subtypes; prognostic impact

2.
Oncogene. 2018 Jul 11. doi: 10.1038/s41388-018-0385-y. [Epub ahead of print]

RNA-seq analysis identifies different transcriptomic types and developmental trajectories of primary melanomas.

Author information

1
Department of Dermatology, Venereology and Allergology, University of Leipzig, Philipp-Rosenthal-Str. 23-25, 04103, Leipzig, Germany. Manfred.kunz@medizin.uni-leipzig.de.
2
Interdisciplinary Centre for Bioinformatics, University of Leipzig, Härtelstrasse 16-18, 04107, Leipzig, Germany.
3
Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, 04103, Leipzig, Germany.
4
Bioinformatics Group, Faculty for Mathematics and Computer Science, University of Leipzig, Härtelstrasse 16-18, 04107, Leipzig, Germany.
5
Department of Dermatology, Venereology and Allergology, University of Leipzig, Philipp-Rosenthal-Str. 23-25, 04103, Leipzig, Germany.
6
Department of Dermatology and Allergy, University of Bonn, Sigmund-Freud-Strasse 25, 53127, Bonn, Germany.
7
Department of Dermatology, University of Magdeburg, Leipziger Strasse 44, 39120, Magdeburg, Germany.
8
Department of Dermatology, Venereology and Allergology, University of Köln, 50937 Köln, Germany.
9
Skin Cancer Unit, German Cancer Research Center (DKFZ), Heidelberg, and Department of Dermatology, Venereology and Allergology, University Medical Center Mannheim, Ruprecht-Karl University of Heidelberg, 68167 Mannheim, Germany.
10
Department of Dermatology, Fachklinik Hornheide, Dorbaumstrasse 300, 48157, Münster, Germany.
11
Unit for RNA Biology, Department of Clinical Chemistry and Clinical Pharmacology, University of Bonn, 53127 Bonn, Germany.
12
Department of Dermatology, University Hospital Essen, West German Cancer Center, University Duisburg-Essen and the German Cancer Consortium (DKTK), University of Duisburg-Essen, 45122 Essen, Germany.
13
Department of Physiological Chemistry, University of Würzburg, Biozentrum, Am Hubland, 97074, Würzburg, Germany.
14
Comprehensive Cancer Center Mainfranken, University Clinic Würzburg, 97080, Würzburg, Germany.
15
Institute of Biochemistry, Emil-Fischer Zentrum, University of Erlangen, Fahrstraße 17, 91054, Erlangen, Germany.
16
Hagler Institute for Advanced Study and Department of Biology, Texas A&M University, College Station, TX, 77843-3572, USA.

Abstract

Recent studies revealed trajectories of mutational events in early melanomagenesis, but the accompanying changes in gene expression are far less understood. Therefore, we performed a comprehensive RNA-seq analysis of laser-microdissected melanocytic nevi (n = 23) and primary melanoma samples (n = 57) and characterized the molecular mechanisms of early melanoma development. Using self-organizing maps, unsupervised clustering, and analysis of pseudotime (PT) dynamics to identify evolutionary trajectories, we describe here two transcriptomic types of melanocytic nevi (N1 and N2) and primary melanomas (M1 and M2). N1/M1 lesions are characterized by pigmentation-type and MITF gene signatures, and a high prevalence of NRAS mutations in M1 melanomas. N2/M2 lesions are characterized by inflammatory-type and AXL gene signatures with an equal distribution of wild-type and mutated BRAF and low prevalence of NRAS mutations in M2 melanomas. Interestingly, N1 nevi and M1 melanomas and N2 nevi and M2 melanomas, respectively, cluster together, but there is no clustering in a stage-dependent manner. Transcriptional signatures of M1 melanomas harbor signatures of BRAF/MEK inhibitor resistance and M2 melanomas harbor signatures of anti-PD-1 antibody treatment resistance. Pseudotime dynamics of nevus and melanoma samples are suggestive for a switch-like immune-escape mechanism in melanoma development with downregulation of immune genes paralleled by an increasing expression of a cell cycle signature in late-stage melanomas. Taken together, the transcriptome analysis identifies gene signatures and mechanisms underlying development of melanoma in early and late stages with relevance for diagnostics and therapy.

3.
Epigenomics. 2018 Jun;10(6):745-764. doi: 10.2217/epi-2017-0140. Epub 2018 Jun 11.

Combined SOM-portrayal of gene expression and DNA methylation landscapes disentangles modes of epigenetic regulation in glioblastoma.

Author information

1
Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Härtelstr. 16-18, 04107 Leipzig, Germany.

Abstract

AIM:

We present here a novel method that enables unraveling the interplay between gene expression and DNA methylation in complex diseases such as cancer.

MATERIALS & METHODS:

The method is based on self-organizing maps and allows for analysis of data landscapes from 'governed by methylation' to 'governed by expression'.

RESULTS:

We identified regulatory modules of coexpressed and comethylated genes in high-grade gliomas: two modes are governed by genes hypermethylated and underexpressed in IDH-mutated cases, while two other modes reflect immune and stromal signatures in the classical and mesenchymal subtypes. A fifth mode with proneural characteristics comprises genes of repressed and poised chromatin states active in healthy brain. Two additional modes enrich genes either in active or repressed chromatin states.

CONCLUSION:

The method disentangles the interplay between gene expression and methylation. It has the potential to integrate also mutation and copy number data and to apply to large sample cohorts.

KEYWORDS:

cancer heterogeneity; gene regulation; integrative bioinformatics; machine learning; molecular subtypes; transcriptome and methylome

4.
Nature. 2018 Jul;559(7714):E10. doi: 10.1038/s41586-018-0167-2.

Author Correction: The landscape of genomic alterations across childhood cancers.

Author information

1
Hopp-Children's Cancer Center at the NCT Heidelberg (KiTZ), Heidelberg, Germany.
2
Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
3
German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.
4
Department of Pediatric Oncology, Hematology & Immunology, Heidelberg University Hospital, Heidelberg, Germany.
5
European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany.
6
The Finsen Laboratory, Rigshospitalet, Biotech Research and Innovation Centre (BRIC), Copenhagen University, Copenhagen, Denmark.
7
Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
8
Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA.
9
Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
10
Department of Bioinformatics and Functional Genomics, Institute of Pharmacy and Molecular Biotechnology, Heidelberg University and BioQuant Center, 69120, Heidelberg, Germany.
11
Klinikum Stuttgart - Olgahospital, Zentrum für Kinder-, Jugend- und Frauenmedizin, Pädiatrie, Stuttgart, Germany.
12
Department of Pediatric Oncology, Hematology & Clinical Immunology, University Children's Hospital, Heinrich Heine University, Düsseldorf, Germany.
13
Department of Pediatric Oncology/Hematology, Charité-Universitätsmedizin Berlin, Berlin, Germany.
14
Institute for Experimental Cancer Research in Pediatrics, University Hospital Frankfurt, Frankfurt am Main, Germany.
15
Theodor-Boveri-Institute/Biocenter, Developmental Biochemistry, and Comprehensive Cancer Center Mainfranken, Wuerzburg University, Würzburg, Germany.
16
Department of Pediatric Surgery, Research Laboratories, Dr. von Hauner Children's Hospital, Ludwig Maximilians University Munich, Munich, Germany.
17
Bone Tumor Reference Center at the Institute of Pathology, University Hospital Basel and University of Basel, Basel, Switzerland.
18
Children's Cancer Research Centre and Department of Pediatrics, Klinikum rechts der Isar, Technische Universität München, Munich, Germany.
19
Division of Pediatric Hematology and Oncology, University Medical Center Aachen, Aachen, Germany.
20
Department of Human Genetics, University Hospital Essen, Essen, Germany.
21
Division of Pediatric Hematology and Oncology, Department of Pediatrics, University Medical Center Freiburg, Freiburg, Germany.
22
Department of Pediatric Oncology, Klinikum Kassel, Kassel, Germany.
23
Institute of Human Genetics, University of Ulm & University Hospital of Ulm, Ulm, Germany.
24
Division of Neuroblastoma Genomics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
25
Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands.
26
Innovative Therapies for Children with Cancer Consortium and Department of Clinical Research, Gustave Roussy, Université Paris-Saclay, Villejuif, France.
27
Pediatric Hematology and Oncology, University Hospital Münster, Muenster, Germany.
28
Pediatric Hematology and Oncology, Hannover Medical School, Hannover, Germany.
29
Clinical Cooperation Unit Pediatric Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
30
Center for Individualized Pediatric Oncology (ZIPO) and Brain Tumors, University Hospital and German Cancer Research Center (DKFZ), Heidelberg, Germany.
31
Division of Pediatric Hematology and Oncology, University Medical Center Göttingen, Göttingen, Germany.
32
Pediatric Oncology & Hematology, Pediatrics III, University Hospital of Essen, Essen, Germany.
33
Department of Pediatric Hematology and Oncology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
34
Swabian Children's Cancer Center, Children's Hospital, Klinikum Augsburg, Augsburg, Germany.
35
Genomics and Proteomics Core Facility, High Throughput Sequencing Unit, German Cancer Research Center (DKFZ), Heidelberg, Germany.
36
Hospital for Children and Adolescents, University Hospital Frankfurt, Frankfurt, Germany.
37
University Hospital Cologne, Klinik und Poliklinik für Kinder- und Jugendmedizin, Cologne, Germany.
38
Department of Oncogenomics, Academic Medical Center, Amsterdam, The Netherlands.
39
Division of Neurosurgery, Center for Childhood Cancer Research, Department of Biomedical and Health Informatics and Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
40
Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA.
41
Division of Oncology, Center for Childhood Cancer Research, Department of Biomedical and Health Informatics and Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, USA.
42
Division of Molecular Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
43
Institute of Computer Science, Freie Universität Berlin, Berlin, Germany.
44
Institute of Medical Genetics and Human Genetics, Charité University Hospital, Berlin, Germany.
45
Bioinformatics and Omics Data Analysis, German Cancer Research Center (DKFZ), Heidelberg, Germany.
46
Hopp-Children's Cancer Center at the NCT Heidelberg (KiTZ), Heidelberg, Germany. s.pfister@dkfz.de.
47
Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany. s.pfister@dkfz.de.
48
German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany. s.pfister@dkfz.de.
49
Department of Pediatric Oncology, Hematology & Immunology, Heidelberg University Hospital, Heidelberg, Germany. s.pfister@dkfz.de.

Abstract

In this Article, author Benedikt Brors was erroneously associated with affiliation number '8' (Department of Developmental Neurobiology, St Jude Children's Research Hospital, Memphis, Tennessee, USA); the author's two other affiliations (affiliations '3' and '7', both at the German Cancer Research Center (DKFZ)) were correct. This has been corrected online.

5.
Biology (Basel). 2018 Apr 3;7(2). pii: E23. doi: 10.3390/biology7020023.

Pseudotime Dynamics in Melanoma Single-Cell Transcriptomes Reveals Different Mechanisms of Tumor Progression.

Author information

1
Interdisciplinary Center for Bioinformatics, University of Leipzig, 04107 Leipzig, Germany. wirth@izbi.uni-leipzig.de.
2
Interdisciplinary Center for Bioinformatics, University of Leipzig, 04107 Leipzig, Germany. binder@izbi.uni-leipzig.de.
3
Interdisciplinary Center for Bioinformatics, University of Leipzig, 04107 Leipzig, Germany. willscher@izbi.uni-leipzig.de.
4
Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology Leipzig, 04103 Leipzig, Germany. tobias_gerber@eva.mpg.de.
5
Department of Dermatology, Venereology and Allergology, University of Leipzig, 04103 Leipzig, Germany. manfred.kunz@medizin.uni-leipzig.de.

Abstract

Single-cell transcriptomics has been used for analysis of heterogeneous populations of cells during developmental processes and for analysis of tumor cell heterogeneity. More recently, analysis of pseudotime (PT) dynamics of heterogeneous cell populations has been established as a powerful concept to study developmental processes. Here we perform PT analysis of 3 melanoma short-term cultures with different genetic backgrounds to study specific and concordant properties of PT dynamics of selected cellular programs with impact on melanoma progression. Overall, in our setting of melanoma cells PT dynamics towards higher tumor malignancy appears to be largely driven by cell cycle genes. Single cells of all three short-term cultures show a bipolar expression of microphthalmia-associated transcription factor (MITF) and AXL receptor tyrosine kinase (AXL) signatures. Furthermore, opposing gene expression changes are observed for genes regulated by epigenetic mechanisms suggesting epigenetic reprogramming during melanoma progression. The three melanoma short-term cultures show common themes of PT dynamics such as a stromal signature at initiation, bipolar expression of the MITF/AXL signature and opposing regulation of poised and activated promoters. Differences are observed at the late stage of PT dynamics with high, low or intermediate MITF and anticorrelated AXL signatures. These findings may help to identify targets for interference at different stages of tumor progression.

KEYWORDS:

gene signatures; melanoma; pseudotime; single-cell transcriptomics; tumor progression

Conflict of interest statement

The authors declare no conflict of interest.

6.
Nature. 2018 Mar 15;555(7696):321-327. doi: 10.1038/nature25480. Epub 2018 Feb 28.

The landscape of genomic alterations across childhood cancers.

Author information

1
Hopp-Children's Cancer Center at the NCT Heidelberg (KiTZ), Heidelberg, Germany.
2
Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
3
German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.
4
Department of Pediatric Oncology, Hematology & Immunology, Heidelberg University Hospital, Heidelberg, Germany.
5
European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany.
6
The Finsen Laboratory, Rigshospitalet, Biotech Research and Innovation Centre (BRIC), Copenhagen University, Copenhagen, Denmark.
7
Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
8
Department of Developmental Neurobiology, St Jude Children's Research Hospital, Memphis, Tennessee, USA.
9
Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
10
Department of Bioinformatics and Functional Genomics, Institute of Pharmacy and Molecular Biotechnology, Heidelberg University and BioQuant Center, 69120, Heidelberg, Germany.
11
Klinikum Stuttgart - Olgahospital, Zentrum für Kinder-, Jugend- und Frauenmedizin, Pädiatrie, Stuttgart, Germany.
12
Department of Pediatric Oncology, Hematology & Clinical Immunology, University Children's Hospital, Heinrich Heine University, Düsseldorf, Germany.
13
Department of Pediatric Oncology/Hematology, Charité-Universitätsmedizin Berlin, Berlin, Germany.
14
Institute for Experimental Cancer Research in Pediatrics, University Hospital Frankfurt, Frankfurt am Main, Germany.
15
Theodor-Boveri-Institute/Biocenter, Developmental Biochemistry, and Comprehensive Cancer Center Mainfranken, University of Würzburg, Würzburg, Germany.
16
Department of Pediatric Surgery, Research Laboratories, Dr von Hauner Children's Hospital, Ludwig Maximilians University Munich, Munich, Germany.
17
Bone Tumor Reference Center at the Institute of Pathology, University Hospital Basel and University of Basel, Basel, Switzerland.
18
Children's Cancer Research Centre and Department of Pediatrics, Klinikum rechts der Isar, Technische Universität München, Munich, Germany.
19
Division of Pediatric Hematology and Oncology, University Medical Center Aachen, Aachen, Germany.
20
Department of Human Genetics, University Hospital Essen, Essen, Germany.
21
Division of Pediatric Hematology and Oncology, Department of Pediatrics, University Medical Center Freiburg, Freiburg, Germany.
22
Department of Pediatric Oncology, Klinikum Kassel, Kassel, Germany.
23
Institute of Human Genetics, University of Ulm & University Hospital of Ulm, Ulm, Germany.
24
Division of Neuroblastoma Genomics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
25
Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands.
26
Innovative Therapies for Children with Cancer Consortium and Department of Clinical Research, Gustave Roussy, Université Paris-Saclay, Villejuif, France.
27
Pediatric Hematology and Oncology, University Hospital Münster, Münster, Germany.
28
Pediatric Hematology and Oncology, Hannover Medical School, Hannover, Germany.
29
Clinical Cooperation Unit Pediatric Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
30
Center for Individualized Pediatric Oncology (ZIPO) and Brain Tumors, University Hospital and German Cancer Research Center (DKFZ), Heidelberg, Germany.
31
Division of Pediatric Hematology and Oncology, University Medical Center Göttingen, Göttingen, Germany.
32
Pediatric Oncology & Hematology, Pediatrics III, University Hospital of Essen, Essen, Germany.
33
Department of Pediatric Hematology and Oncology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
34
Swabian Children's Cancer Center, Children's Hospital, Klinikum Augsburg, Augsburg, Germany.
35
Genomics and Proteomics Core Facility, High Throughput Sequencing Unit, German Cancer Research Center (DKFZ), Heidelberg, Germany.
36
Hospital for Children and Adolescents, University Hospital Frankfurt, Frankfurt, Germany.
37
University Hospital Cologne, Klinik und Poliklinik für Kinder- und Jugendmedizin, Cologne, Germany.
38
Department of Oncogenomics, Academic Medical Center, Amsterdam, The Netherlands.
39
Division of Neurosurgery, Center for Childhood Cancer Research, Department of Biomedical and Health Informatics and Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
40
Department of Computational Biology, St Jude Children's Research Hospital, Memphis, Tennessee, USA.
41
Division of Oncology, Center for Childhood Cancer Research, Department of Biomedical and Health Informatics and Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, USA.
42
Division of Molecular Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
43
Institute of Computer Science, Freie Universität Berlin, Berlin, Germany.
44
Institute of Medical Genetics and Human Genetics, Charité University Hospital, Berlin, Germany.
45
Bioinformatics and Omics Data Analysis, German Cancer Research Center (DKFZ), Heidelberg, Germany.

Abstract

Pan-cancer analyses that examine commonalities and differences among various cancer types have emerged as a powerful way to obtain novel insights into cancer biology. Here we present a comprehensive analysis of genetic alterations in a pan-cancer cohort including 961 tumours from children, adolescents, and young adults, comprising 24 distinct molecular types of cancer. Using a standardized workflow, we identified marked differences in terms of mutation frequency and significantly mutated genes in comparison to previously analysed adult cancers. Genetic alterations in 149 putative cancer driver genes separate the tumours into two classes: small mutation and structural/copy-number variant (correlating with germline variants). Structural variants, hyperdiploidy, and chromothripsis are linked to TP53 mutation status and mutational signatures. Our data suggest that 7-8% of the children in this cohort carry an unambiguous predisposing germline variant and that nearly 50% of paediatric neoplasms harbour a potentially druggable event, which is highly relevant for the design of future clinical trials.

Comment in

7.
PLoS One. 2017 Nov 3;12(11):e0187572. doi: 10.1371/journal.pone.0187572. eCollection 2017.

Autoimmunity and autoinflammation: A systems view on signaling pathway dysregulation profiles.

Author information

1
Bioinformatics Group, Institute of Molecular Biology, National Academy of Sciences RA, Yerevan, Armenia.
2
Department of Bioinformatics and Bioengineering, Russian-Armenian University, Yerevan, Armenia.
3
Zaven and Sonia Akian College of Science and Engineering, American University of Armenia, Yerevan, Armenia.
4
Group of Immune Response Regulation, Institute of Molecular Biology, National Academy of Sciences RA, Yerevan, Armenia.
5
Interdisciplinary Centre for Bioinformatics, University of Leipzig, Leipzig, Germany.
6
Department of Parasitology and Insect Vectors, Institut Pasteur, Paris, France.

Abstract

INTRODUCTION:

Autoinflammatory and autoimmune disorders are characterized by aberrant changes in innate and adaptive immunity that may lead from an initial inflammatory state to an organ specific damage. These disorders possess heterogeneity in terms of affected organs and clinical phenotypes. However, despite the differences in etiology and phenotypic variations, they share genetic associations, treatment responses and clinical manifestations. The mechanisms involved in their initiation and development remain poorly understood, however the existence of some clear similarities between autoimmune and autoinflammatory disorders indicates variable degrees of interaction between immune-related mechanisms.

METHODS:

Our study aims at contributing to a holistic, pathway-centered view on the inflammatory condition of autoimmune and autoinflammatory diseases. We have evaluated similarities and specificities of pathway activity changes in twelve autoimmune and autoinflammatory disorders by performing meta-analysis of publicly available gene expression datasets generated from peripheral blood mononuclear cells, using a bioinformatics pipeline that integrates Self Organizing Maps and Pathway Signal Flow algorithms along with KEGG pathway topologies.

RESULTS AND CONCLUSIONS:

The results reveal that clinically divergent disease groups share common pathway perturbation profiles. We identified pathways, similarly perturbed in all the studied diseases, such as PI3K-Akt, Toll-like receptor, and NF-kappa B signaling, that serve as integrators of signals guiding immune cell polarization, migration, growth, survival and differentiation. Further, two clusters of diseases were identified based on specifically dysregulated pathways: one gathering mostly autoimmune and the other mainly autoinflammatory diseases. Cluster separation was driven not only by apparent involvement of pathways implicated in adaptive immunity in one case, and inflammation in the other, but also by processes not explicitly related to immune response, but rather representing various events related to the formation of specific pathophysiological environment. Thus, our data suggest that while all of the studied diseases are affected by activation of common inflammatory processes, disease-specific variations in their relative balance are also identified.

PMID:
29099860
PMCID:
PMC5669448
DOI:
10.1371/journal.pone.0187572
[Indexed for MEDLINE]
Free PMC Article
Icon for Public Library of Science Icon for PubMed Central
8.
PLoS One. 2017 Oct 20;12(10):e0186881. doi: 10.1371/journal.pone.0186881. eCollection 2017.

Body typing of children and adolescents using 3D-body scanning.

Author information

1
Interdisciplinary Centre for Bioinformatics, Leipzig University, Härtelstraße 16-18, Leipzig, Germany.
2
LIFE, Leipzig Research Center for Civilization Diseases; Leipzig University, Philipp-Rosenthal-Straße 27, Leipzig, Germany.
3
Hospital for Children and Adolescents, Centre for Pediatric Research; Leipzig University, Liebigstraße 20a, Leipzig, Germany.
4
Institute for Medical Informatics, Statistics and Epidemiology, Leipzig University, Härtelstraße 16-18, Leipzig, Germany.

Abstract

Three-dimensional (3D-) body scanning of children and adolescents allows the detailed study of physiological development in terms of anthropometrical alterations which potentially provide early onset markers for obesity. Here, we present a systematic analysis of body scanning data of 2,700 urban children and adolescents in the age range between 5 and 18 years with the special aim to stratify the participants into distinct body shape types and to describe their change upon development. In a first step, we extracted a set of eight representative meta-measures from the data. Each of them collects a related group of anthropometrical features and changes specifically upon aging. In a second step we defined seven body types by clustering the meta-measures of all participants. These body types describe the body shapes in terms of three weight (lower, normal and overweight) and three age (young, medium and older) categories. For younger children (age of 5-10 years) we found a common 'early childhood body shape' which splits into three weight-dependent types for older children, with one or two years delay for boys. Our study shows that the concept of body types provides a reliable option for the anthropometric characterization of developing and aging populations.

PMID:
29053732
PMCID:
PMC5650166
DOI:
10.1371/journal.pone.0186881
[Indexed for MEDLINE]
Free PMC Article
Icon for Public Library of Science Icon for PubMed Central
9.
Sci Rep. 2017 Sep 28;7(1):12369. doi: 10.1038/s41598-017-12393-5.

IRS1 DNA promoter methylation and expression in human adipose tissue are related to fat distribution and metabolic traits.

Author information

1
IFB Adiposity Diseases, University of Leipzig, Leipzig, Germany.
2
Department of Clinical and Molecular Biology and Akershus University Hospital, University of Oslo, Lørenskog, Norway.
3
Interdisciplinary Centre for Bioinformatics, University of Leipzig, Leipzig, Germany.
4
Department of Computer Science, University of Leipzig, Leipzig, Germany.
5
Department of Clinical Sciences, Skåne University Hospital, Malmö, Sweden.
6
Department of Medicine, University of Leipzig, Leipzig, Germany.
7
Department of Surgery, University of Leipzig, Leipzig, Germany.
8
Clinic of Visceral Surgery, Städtisches Klinikum Karlsruhe, Karlsruhe, Germany.
9
Municipal Clinic Dresden-Neustadt, Dresden, Germany.
10
IFB Adiposity Diseases, University of Leipzig, Leipzig, Germany. yvonne.bottcher@medisin.uio.no.
11
University of Oslo, Institute of Clinical Medicine, Department of Clinical and Molecular Biology; Akershus University Hospital, 1478, Lørenskog, Norway. yvonne.bottcher@medisin.uio.no.

Abstract

The SNP variant rs2943650 near IRS1 gene locus was previously associated with decreased body fat and IRS1 gene expression as well as an adverse metabolic profile in humans. Here, we hypothesize that these effects may be mediated by an interplay with epigenetic alterations. We measured IRS1 promoter DNA methylation and mRNA expression in paired human subcutaneous and omental visceral adipose tissue samples (SAT and OVAT) from 146 and 41 individuals, respectively. Genotyping of rs2943650 was performed in all individuals (N = 146). We observed a significantly higher IRS1 promoter DNA methylation in OVAT compared to SAT (N = 146, P = 8.0 × 10-6), while expression levels show the opposite effect direction (N = 41, P = 0.011). OVAT and SAT methylation correlated negatively with IRS1 gene expression in obese subjects (N = 16, P = 0.007 and P = 0.010). The major T-allele is related to increased DNA methylation in OVAT (N = 146, P = 0.019). Finally, DNA methylation and gene expression in OVAT correlated with anthropometric traits (waist- circumference waist-to-hip ratio) and parameters of glucose metabolism in obese individuals. Our data suggest that the association between rs2943650 near the IRS1 gene locus with clinically relevant variables may at least be modulated by changes in DNA methylation that translates into altered IRS1 gene expression.

10.
J Pathol. 2017 Oct;243(2):242-254. doi: 10.1002/path.4948. Epub 2017 Sep 5.

Genomic and transcriptomic heterogeneity of colorectal tumours arising in Lynch syndrome.

Author information

1
Interdisciplinary Centre for Bioinformatics, Leipzig University, Leipzig, Germany.
2
Institute of Pathology, Centre for Integrated Oncology, University Hospital Cologne, Cologne, Germany.
3
Translational Epigenomics, University Hospital Cologne, Cologne, Germany.
4
Max Planck Institute for Molecular Genetics, Berlin, Germany.
5
Inserm, U1110, Institut de Recherche sur les Maladies Virales et Hépatiques, Strasbourg, France.
6
Université de Strasbourg, Strasbourg, France.
7
Group of Bioinformatics, Institute of Molecular Biology, National Academy of Sciences, Yerevan, Armenia.
8
Institute for Medical Informatics, Statistics and Epidemiology, Leipzig University, Leipzig, Germany.
9
Department of Hereditary Tumour Syndromes, Surgical Centre, HELIOS Clinic, University Witten/Herdecke, Wuppertal, Germany.
10
Institute of Human Genetics, University Hospital Bonn, Centre for Hereditary Tumour Syndromes, University of Bonn, Bonn, Germany.
11
Department of Internal Medicine I, University Hospital Bonn, Centre for Hereditary Tumour Syndromes, University of Bonn, Bonn, Germany.
12
Department of Medicine, Haematology and Oncology, Ruhr-University of Bochum, Knappschaftskrankenhaus, Bochum, Germany.
13
Institute of Human Genetics and Anthropology, Heinrich-Heine University, Düsseldorf, Germany.
14
Department of Applied Tumour Biology, Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany.
15
Clinical Cooperation Unit of Applied Tumour Biology, DKFZ (German Cancer Research Centre) Heidelberg, Germany.
16
Molecular Medicine Partnership Unit, University Hospital Heidelberg and EMBL Heidelberg, Heidelberg, Germany.

Abstract

Colorectal cancer (CRC) arising in Lynch syndrome (LS) comprises tumours with constitutional mutations in DNA mismatch repair genes. There is still a lack of whole-genome and transcriptome studies of LS-CRC to address questions about similarities and differences in mutation and gene expression characteristics between LS-CRC and sporadic CRC, about the molecular heterogeneity of LS-CRC, and about specific mechanisms of LS-CRC genesis linked to dysfunctional mismatch repair in LS colonic mucosa and the possible role of immune editing. Here, we provide a first molecular characterization of LS tumours and of matched tumour-distant reference colonic mucosa based on whole-genome DNA-sequencing and RNA-sequencing analyses. Our data support two subgroups of LS-CRCs, G1 and G2, whereby G1 tumours show a higher number of somatic mutations, a higher amount of microsatellite slippage, and a different mutation spectrum. The gene expression phenotypes support this difference. Reference mucosa of G1 shows a strong immune response associated with the expression of HLA and immune checkpoint genes and the invasion of CD4+ T cells. Such an immune response is not observed in LS tumours, G2 reference and normal (non-Lynch) mucosa, and sporadic CRC. We hypothesize that G1 tumours are edited for escape from a highly immunogenic microenvironment via loss of HLA presentation and T-cell exhaustion. In contrast, G2 tumours seem to develop in a less immunogenic microenvironment where tumour-promoting inflammation parallels tumourigenesis. Larger studies on non-neoplastic mucosa tissue of mutation carriers are required to better understand the early phases of emerging tumours. Copyright © 2017 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

KEYWORDS:

hereditary cancer; immune editing; mismatch repair; tumour heterogeneity

PMID:
28727142
DOI:
10.1002/path.4948
[Indexed for MEDLINE]
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11.
Nature. 2017 Jun 22;546(7659):533-538. doi: 10.1038/nature22796. Epub 2017 Jun 14.

Multilineage communication regulates human liver bud development from pluripotency.

Author information

1
Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, Leipzig 04103, Germany.
2
Department of Regenerative Medicine, Yokohama City University Graduate School of Medicine, 3-9 Fukuura, Kanazawa-ku, Yokohama, Kanagawa 236-0004, Japan.
3
Interdisciplinary Centre for Bioinformatics, Leipzig University, 16 Härtelstrasse, Leipzig 04107, Germany.
4
Department of Hepatobiliary and Transplantation Surgery, University Hospital of Leipzig, Liebigstrasse 20, Leipzig 04103, Germany.
5
Saxonian Incubator for Clinical Translation (SIKT), University of Leipzig, 55 Philipp-Rosenthal-Strasse, Leipzig 04103, Germany.
6
Max Planck Institute of Molecular Cell Biology and Genetics, 108 Pfotenhauerstrasse, Dresden 01307, Germany.
7
Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, 3333 Burnet Avenue, Cincinnati, Ohio 45229-3039, USA.

Abstract

Conventional two-dimensional differentiation from pluripotency fails to recapitulate cell interactions occurring during organogenesis. Three-dimensional organoids generate complex organ-like tissues; however, it is unclear how heterotypic interactions affect lineage identity. Here we use single-cell RNA sequencing to reconstruct hepatocyte-like lineage progression from pluripotency in two-dimensional culture. We then derive three-dimensional liver bud organoids by reconstituting hepatic, stromal, and endothelial interactions, and deconstruct heterogeneity during liver bud development. We find that liver bud hepatoblasts diverge from the two-dimensional lineage, and express epithelial migration signatures characteristic of organ budding. We benchmark three-dimensional liver buds against fetal and adult human liver single-cell RNA sequencing data, and find a striking correspondence between the three-dimensional liver bud and fetal liver cells. We use a receptor-ligand pairing analysis and a high-throughput inhibitor assay to interrogate signalling in liver buds, and show that vascular endothelial growth factor (VEGF) crosstalk potentiates endothelial network formation and hepatoblast differentiation. Our molecular dissection reveals interlineage communication regulating organoid development, and illuminates previously inaccessible aspects of human liver development.

PMID:
28614297
DOI:
10.1038/nature22796
[Indexed for MEDLINE]
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12.
Mol Metab. 2016 Nov 16;6(1):86-100. doi: 10.1016/j.molmet.2016.11.003. eCollection 2017 Jan.

Genome-wide DNA promoter methylation and transcriptome analysis in human adipose tissue unravels novel candidate genes for obesity.

Author information

1
IFB Adiposity Diseases, University of Leipzig, Leipzig, 04103, Germany.
2
Interdisciplinary Centre for Bioinformatics, University of Leipzig, Leipzig, 04103, Germany.
3
IFB Adiposity Diseases, University of Leipzig, Leipzig, 04103, Germany; Bioinformatics Group, Department of Computer Science, University of Leipzig, 04107, Leipzig, Germany.
4
Molecular Biology Laboratory, Istituto Auxologico Italiano IRCCS, Milan, 20149, Italy.
5
Epigenetics and Diabetes Unit, Department of Clinical Sciences, Lund University Diabetes Centre, Scania University Hospital, 205 02, Malmoe, Sweden.
6
Department of Medicine, University of Leipzig, Leipzig, 04103, Germany.
7
IFB Adiposity Diseases, University of Leipzig, Leipzig, 04103, Germany; Department of Surgery, University of Leipzig, Leipzig, 04103, Germany.
8
Städtisches Klinikum Karlsruhe, Clinic of Visceral Surgery, Karlsruhe, 76133, Germany.
9
Municipal Clinic Dresden-Neustadt, Dresden, 01129, Germany.
10
IFB Adiposity Diseases, University of Leipzig, Leipzig, 04103, Germany; Department of Medicine, University of Leipzig, Leipzig, 04103, Germany.
11
IFB Adiposity Diseases, University of Leipzig, Leipzig, 04103, Germany; Department of Medicine, University of Leipzig, Leipzig, 04103, Germany. Electronic address: matthias.blueher@medizin.uni-leipzig.de.
12
IFB Adiposity Diseases, University of Leipzig, Leipzig, 04103, Germany. Electronic address: yvonne.boettcher@medizin.uni-leipzig.de.

Abstract

OBJECTIVE/METHODS:

DNA methylation plays an important role in obesity and related metabolic complications. We examined genome-wide DNA promoter methylation along with mRNA profiles in paired samples of human subcutaneous adipose tissue (SAT) and omental visceral adipose tissue (OVAT) from non-obese vs. obese individuals.

RESULTS:

We identified negatively correlated methylation and expression of several obesity-associated genes in our discovery dataset and in silico replicated ETV6 in two independent cohorts. Further, we identified six adipose tissue depot-specific genes (HAND2, HOXC6, PPARG, SORBS2, CD36, and CLDN1). The effects were further supported in additional independent cohorts. Our top hits might play a role in adipogenesis and differentiation, obesity, lipid metabolism, and adipose tissue expandability. Finally, we show that in vitro methylation of SORBS2 directly represses gene expression.

CONCLUSIONS:

Taken together, our data show distinct tissue specific epigenetic alterations which associate with obesity.

KEYWORDS:

DNA methylation; Epigenetic mechanisms; Human adipose tissue depots; Obesity-related co-morbidities; mRNA expression

13.
Oncotarget. 2017 Jan 3;8(1):846-862. doi: 10.18632/oncotarget.13666.

Mapping heterogeneity in patient-derived melanoma cultures by single-cell RNA-seq.

Author information

1
Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology Leipzig, 04103 Leipzig, Germany.
2
Interdisciplinary Center for Bioinformatics, University of Leipzig, 04107 Leipzig, Germany.
3
Department of Dermatology, Venereology and Allergology, University Hospital Essen, 45147 Essen, Germany.
4
Department of Physiological Chemistry, University of Würzburg, Biozentrum, Am Hubland, 97074 Würzburg, Germany.
5
Comprehensive Cancer Center Mainfranken, University Clinic Würzburg, 97080 Würzburg, Germany.
6
Institute for Advanced Study, 3572 Texas A&M University, College Station, Texas 77843-3572, USA.
7
Department of Dermatology, Venereology and Allergology, University of Leipzig, 04103 Leipzig, Germany.

Abstract

Recent technological advances in single-cell genomics make it possible to analyze cellular heterogeneity of tumor samples. Here, we applied single-cell RNA-seq to measure the transcriptomes of 307 single cells cultured from three biopsies of three different patients with a BRAF/NRAS wild type, BRAF mutant/NRAS wild type and BRAF wild type/NRAS mutant melanoma metastasis, respectively. Analysis based on self-organizing maps identified sub-populations defined by multiple gene expression modules involved in proliferation, oxidative phosphorylation, pigmentation and cellular stroma. Gene expression modules had prognostic relevance when compared with gene expression data from published melanoma samples and patient survival data. We surveyed kinome expression patterns across sub-populations of the BRAF/NRAS wild type sample and found that CDK4 and CDK2 were consistently highly expressed in the majority of cells, suggesting that these kinases might be involved in melanoma progression. Treatment of cells with the CDK4 inhibitor palbociclib restricted cell proliferation to a similar, and in some cases greater, extent than MAPK inhibitors. Finally, we identified a low abundant sub-population in this sample that highly expressed a module containing ABC transporter ABCB5, surface markers CD271 and CD133, and multiple aldehyde dehydrogenases (ALDHs). Patient-derived cultures of the BRAF mutant/NRAS wild type and BRAF wild type/NRAS mutant metastases showed more homogeneous single-cell gene expression patterns with gene expression modules for proliferation and ABC transporters. Taken together, our results describe an intertumor and intratumor heterogeneity in melanoma short-term cultures which might be relevant for patient survival, and suggest promising targets for new treatment approaches in melanoma therapy.

KEYWORDS:

melanoma; single cell transcriptome sequencing; stem cells

PMID:
27903987
PMCID:
PMC5352202
DOI:
10.18632/oncotarget.13666
[Indexed for MEDLINE]
Free PMC Article
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14.
Stem Cells. 2017 Mar;35(3):694-704. doi: 10.1002/stem.2514. Epub 2016 Nov 8.

Bistable Epigenetic States Explain Age-Dependent Decline in Mesenchymal Stem Cell Heterogeneity.

Author information

1
INSERM U972, University Paris 11, Hôpital Paul Brousse, Villejuif, France.
2
Faculty of Biology, Mouloud Mammeri University, Tizi-ouzou, Algeria.
3
Interdisciplinary Center for Bioinformatics, University Leipzig, Germany.
4
LIFE: Leipzig Research Center for Civilization Diseases, University Leipzig, Germany.
5
Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany.
6
IBPS Laboratory of Developmental Biology, University Pierre & Marie Curie, Paris, France.

Abstract

The molecular mechanisms by which heterogeneity, a major characteristic of stem cells, is achieved are yet unclear. We here study the expression of the membrane stem cell antigen-1 (Sca-1) in mouse bone marrow mesenchymal stem cell (MSC) clones. We show that subpopulations with varying Sca-1 expression profiles regenerate the Sca-1 profile of the mother population within a few days. However, after extensive replication in vitro, the expression profiles shift to lower values and the regeneration time increases. Study of the promoter of Ly6a unravels that the expression level of Sca-1 is related to the promoter occupancy by the activating histone mark H3K4me3. We demonstrate that these findings can be consistently explained by a computational model that considers positive feedback between promoter H3K4me3 modification and gene transcription. This feedback implicates bistable epigenetic states which the cells occupy with an age-dependent frequency due to persistent histone (de-)modification. Our results provide evidence that MSC heterogeneity, and presumably that of other stem cells, is associated with bistable epigenetic states and suggest that MSCs are subject to permanent state fluctuations. Stem Cells 2017;35:694-704.

KEYWORDS:

Aging; Epigenetics; FACS; Mesenchymal stem cells; Methylation

PMID:
27734598
PMCID:
PMC5347872
DOI:
10.1002/stem.2514
[Indexed for MEDLINE]
Free PMC Article
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15.
PLoS One. 2016 Jul 28;11(7):e0159887. doi: 10.1371/journal.pone.0159887. eCollection 2016.

Novel Anthropometry Based on 3D-Bodyscans Applied to a Large Population Based Cohort.

Author information

1
Interdisciplinary Centre for Bioinformatics, Leipzig University, Härtelstraße 16 - 18, 04107 Leipzig, Germany.
2
LIFE, Leipzig Research Center for Civilization Diseases; Leipzig University, Philipp-Rosenthal-Straße 27, 04103 Leipzig, Germany.
3
Institute for Medical Informatics, Statistics and Epidemiology, Leipzig University, Härtelstraße 16 - 18, 04107 Leipzig, Germany.

Abstract

Three-dimensional (3D) whole body scanners are increasingly used as precise measuring tools for the rapid quantification of anthropometric measures in epidemiological studies. We analyzed 3D whole body scanning data of nearly 10,000 participants of a cohort collected from the adult population of Leipzig, one of the largest cities in Eastern Germany. We present a novel approach for the systematic analysis of this data which aims at identifying distinguishable clusters of body shapes called body types. In the first step, our method aggregates body measures provided by the scanner into meta-measures, each representing one relevant dimension of the body shape. In a next step, we stratified the cohort into body types and assessed their stability and dependence on the size of the underlying cohort. Using self-organizing maps (SOM) we identified thirteen robust meta-measures and fifteen body types comprising between 1 and 18 percent of the total cohort size. Thirteen of them are virtually gender specific (six for women and seven for men) and thus reflect most abundant body shapes of women and men. Two body types include both women and men, and describe androgynous body shapes that lack typical gender specific features. The body types disentangle a large variability of body shapes enabling distinctions which go beyond the traditional indices such as body mass index, the waist-to-height ratio, the waist-to-hip ratio and the mortality-hazard ABSI-index. In a next step, we will link the identified body types with disease predispositions to study how size and shape of the human body impact health and disease.

PMID:
27467550
PMCID:
PMC4965021
DOI:
10.1371/journal.pone.0159887
[Indexed for MEDLINE]
Free PMC Article
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16.
Nat Commun. 2016 Jun 24;7:11807. doi: 10.1038/ncomms11807.

MYC/MIZ1-dependent gene repression inversely coordinates the circadian clock with cell cycle and proliferation.

Author information

1
Heidelberg University, Biochemistry Center, Im Neuenheimer Feld 328, D-69120 Heidelberg, Germany.
2
Division Theoretical Bioinformatics (B080), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, D-69120 Heidelberg, Germany.
3
Department for Bioinformatics and Functional Genomics, Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant, Heidelberg University, Im Neuenheimer Feld 364, D-69120 Heidelberg, Germany.

Abstract

The circadian clock and the cell cycle are major cellular systems that organize global physiology in temporal fashion. It seems conceivable that the potentially conflicting programs are coordinated. We show here that overexpression of MYC in U2OS cells attenuates the clock and conversely promotes cell proliferation while downregulation of MYC strengthens the clock and reduces proliferation. Inhibition of the circadian clock is crucially dependent on the formation of repressive complexes of MYC with MIZ1 and subsequent downregulation of the core clock genes BMAL1 (ARNTL), CLOCK and NPAS2. We show furthermore that BMAL1 expression levels correlate inversely with MYC levels in 102 human lymphomas. Our data suggest that MYC acts as a master coordinator that inversely modulates the impact of cell cycle and circadian clock on gene expression.

17.
Neuro Oncol. 2016 Dec;18(12):1610-1621. Epub 2016 Jun 10.

Limited role for transforming growth factor-β pathway activation-mediated escape from VEGF inhibition in murine glioma models.

Author information

1
Laboratory of Molecular Neuro-Oncology, Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland (D.M., M.W., E.S.S., K.S., G.T., H.S.); Center for Neuroscience, University of Zurich, Zurich, Switzerland (M.W., G.T.); Interdisciplinary Center for Bioinformatics, University of Leipzig, Leipzig, Germany (E.W., H.B.); Institute of Neuropathology, Heinrich Heine University, Düsseldorf, Germany (G.R.); German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ) Heidelberg, partner site, Essen/Düsseldorf, Germany (G.R.).

Abstract

BACKGROUND:

The vascular endothelial growth factor (VEGF) and transforming growth factor (TGF)-β pathways regulate key biological features of glioblastoma. Here we explore whether the TGF-β pathway, which promotes angiogenesis, invasiveness, and immunosuppression, acts as an escape pathway from VEGF inhibition.

METHODS:

The role of the TGF-β pathway in escape from VEGF inhibition was assessed in vitro and in vivo and by gene expression profiling in syngeneic mouse glioma models.

RESULTS:

We found that TGF-β is an upstream regulator of VEGF, whereas VEGF pathway activity does not alter the TGF-β pathway in vitro. In vivo, single-agent activity was observed for the VEGF antibody B20-4.1.1 in 3 and for the TGF-β receptor 1 antagonist LY2157299 in 2 of 4 models. Reduction of tumor volume and blood vessel density, but not induction of hypoxia, correlated with benefit from B20-4.1.1. Reduction of phosphorylated (p)SMAD2 by LY2157299 was seen in all models but did not predict survival. Resistance to B20 was associated with anti-angiogenesis escape pathway gene expression, whereas resistance to LY2157299 was associated with different immune response gene signatures in SMA-497 and GL-261 on transcriptomic profiling. The combination of B20 with LY2157299 was ineffective in SMA-497 but provided prolongation of survival in GL-261, associated with early suppression of pSMAD2 in tumor and host immune cells, prolonged suppression of angiogenesis, and delayed accumulation of tumor infiltrating microglia/macrophages.

CONCLUSIONS:

Our study highlights the biological heterogeneity of murine glioma models and illustrates that cotargeting of the VEGF and TGF-β pathways might lead to improved tumor control only in subsets of glioblastoma.

KEYWORDS:

TGF-β; VEGF; coinhibition; escape mechanism; glioblastoma

PMID:
27286797
DOI:
10.1093/neuonc/now112
[Indexed for MEDLINE]
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18.
Front Genet. 2016 May 6;7:79. doi: 10.3389/fgene.2016.00079. eCollection 2016.

Cartography of Pathway Signal Perturbations Identifies Distinct Molecular Pathomechanisms in Malignant and Chronic Lung Diseases.

Author information

1
Group of Bioinformatics, Institute of Molecular Biology, National Academy of SciencesYerevan, Armenia; College of Science and Engineering, American University of ArmeniaYerevan, Armenia.
2
Laboratory of Immunogenomics, Department of Pathological Physiology, Faculty of Medicine and Dentistry, Institute of Molecular and Translational Medicine, Palacky University Olomouc Olomouc, Czech Republic.
3
Interdisciplinary Centre for Bioinformatics, University of Leipzig Leipzig, Germany.

Abstract

Lung diseases are described by a wide variety of developmental mechanisms and clinical manifestations. Accurate classification and diagnosis of lung diseases are the bases for development of effective treatments. While extensive studies are conducted toward characterization of various lung diseases at molecular level, no systematic approach has been developed so far. Here we have applied a methodology for pathway-centered mining of high throughput gene expression data to describe a wide range of lung diseases in the light of shared and specific pathway activity profiles. We have applied an algorithm combining a Pathway Signal Flow (PSF) algorithm for estimation of pathway activity deregulation states in lung diseases and malignancies, and a Self Organizing Maps algorithm for classification and clustering of the pathway activity profiles. The analysis results allowed clearly distinguish between cancer and non-cancer lung diseases. Lung cancers were characterized by pathways implicated in cell proliferation, metabolism, while non-malignant lung diseases were characterized by deregulations in pathways involved in immune/inflammatory response and fibrotic tissue remodeling. In contrast to lung malignancies, chronic lung diseases had relatively heterogeneous pathway deregulation profiles. We identified three groups of interstitial lung diseases and showed that the development of characteristic pathological processes, such as fibrosis, can be initiated by deregulations in different signaling pathways. In conclusion, this paper describes the pathobiology of lung diseases from systems viewpoint using pathway centered high-dimensional data mining approach. Our results contribute largely to current understanding of pathological events in lung cancers and non-malignant lung diseases. Moreover, this paper provides new insight into molecular mechanisms of a number of interstitial lung diseases that have been studied to a lesser extent.

KEYWORDS:

biological pathways; chronic lung diseases; high-throughput gene expression; lung cancers; pathway signal flow; self-organizing maps

20.
J Cell Biol. 2015 Dec 7;211(5):1057-75. doi: 10.1083/jcb.201404147.

A keratin scaffold regulates epidermal barrier formation, mitochondrial lipid composition, and activity.

Author information

1
Translational Centre for Regenerative Medicine Leipzig, University of Leipzig, 04103 Leipzig, Germany Institute of Biology, Division of Cell and Developmental Biology, University of Leipzig, 04103 Leipzig, Germany.
2
Department of Environmental Toxicology, University of California, Davis, Davis, CA 95616.
3
Center for Physiology and Pathophysiology, Institute for Vegetative Physiology, University of Cologne, 50931 Cologne, Germany.
4
Department of Dermatology, University of Colorado, Denver, CO 80045 Charles C. Gates Center for Regenerative Medicine and Stem Cell Biology, University of Colorado, Denver, CO 80045.
5
Institute of Molecular and Cellular Anatomy, Rheinisch-Westfälische Technische Hochschule Aachen University, 52074 Aachen, Germany.
6
Center for Physiology and Pathophysiology, Institute for Vegetative Physiology, University of Cologne, 50931 Cologne, Germany Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases, Medical Faculty, University of Cologne, 50931 Cologne, Germany Center for Molecular Medicine Cologne, 50931 Cologne, Germany.
7
Center for Biotechnology and Biomedicine, 04103 Leipzig, Germany.
8
Interdisciplinary Centre for Bioinformatics, University of Leipzig, 04107 Leipzig, Germany.
9
Translational Centre for Regenerative Medicine Leipzig, University of Leipzig, 04103 Leipzig, Germany Institute of Biology, Division of Cell and Developmental Biology, University of Leipzig, 04103 Leipzig, Germany thomas.magin@uni-leipzig.de.

Abstract

Keratin intermediate filaments (KIFs) protect the epidermis against mechanical force, support strong adhesion, help barrier formation, and regulate growth. The mechanisms by which type I and II keratins contribute to these functions remain incompletely understood. Here, we report that mice lacking all type I or type II keratins display severe barrier defects and fragile skin, leading to perinatal mortality with full penetrance. Comparative proteomics of cornified envelopes (CEs) from prenatal KtyI(-/-) and KtyII(-/-)(K8) mice demonstrates that absence of KIF causes dysregulation of many CE constituents, including downregulation of desmoglein 1. Despite persistence of loricrin expression and upregulation of many Nrf2 targets, including CE components Sprr2d and Sprr2h, extensive barrier defects persist, identifying keratins as essential CE scaffolds. Furthermore, we show that KIFs control mitochondrial lipid composition and activity in a cell-intrinsic manner. Therefore, our study explains the complexity of keratinopathies accompanied by barrier disorders by linking keratin scaffolds to mitochondria, adhesion, and CE formation.

PMID:
26644517
PMCID:
PMC4674273
DOI:
10.1083/jcb.201404147
[Indexed for MEDLINE]
Free PMC Article
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