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1.
Cell Syst. 2018 Sep 28. pii: S2405-4712(18)30358-2. doi: 10.1016/j.cels.2018.08.011. [Epub ahead of print]

Combined Experimental and System-Level Analyses Reveal the Complex Regulatory Network of miR-124 during Human Neurogenesis.

Author information

1
Technische Universität Dresden, DFG Research Center for Regenerative Therapies, Dresden 01307, Germany.
2
Department of Computer Science, Bioinformatics Group, Interdisciplinary Center for Bioinformatics, University of Leipzig, Leipzig 04107, Germany; Faculty of Mathematics and Computer Science, Swarm Intelligence and Complex Systems Group, University of Leipzig, Leipzig 04109, Germany; Faculty for Biology, Chemistry and Pharmacy, Freie Universität Berlin, Institute for Biology, Berlin 14195, Germany.
3
Department of Computer Science, Bioinformatics Group, Interdisciplinary Center for Bioinformatics, University of Leipzig, Leipzig 04107, Germany.
4
Department of Experimental Medical Science, Laboratory of Molecular Neurogenetics, Wallenberg Neuroscience Center and Lund Stem Cell Center, Lunds Universitet, Lund 22184, Sweden.
5
Department of Computer Science, Bioinformatics Group, Interdisciplinary Center for Bioinformatics, University of Leipzig, Leipzig 04107, Germany; Max Planck Institute for Mathematics in the Sciences, Leipzig 04103, Germany; Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA.
6
Faculty for Biology, Chemistry and Pharmacy, Freie Universität Berlin, Institute for Biology, Berlin 14195, Germany.
7
Technische Universität Dresden, DFG Research Center for Regenerative Therapies, Dresden 01307, Germany. Electronic address: volker.busskamp@tu-dresden.de.

Abstract

Non-coding RNAs regulate many biological processes including neurogenesis. The brain-enriched miR-124 has been assigned as a key player of neuronal differentiation via its complex but little understood regulation of thousands of annotated targets. To systematically chart its regulatory functions, we used CRISPR/Cas9 gene editing to disrupt all six miR-124 alleles in human induced pluripotent stem cells. Upon neuronal induction, miR-124-deleted cells underwent neurogenesis and became functional neurons, albeit with altered morphology and neurotransmitter specification. Using RNA-induced-silencing-complex precipitation, we identified 98 high-confidence miR-124 targets, of which some directly led to decreased viability. By performing advanced transcription-factor-network analysis, we identified indirect miR-124 effects on apoptosis, neuronal subtype differentiation, and the regulation of previously uncharacterized zinc finger transcription factors. Our data emphasize the need for combined experimental- and system-level analyses to comprehensively disentangle and reveal miRNA functions, including their involvement in the neurogenesis of diverse neuronal cell types found in the human brain.

KEYWORDS:

AGO2-RIP-seq; ZNF787; gene regulatory network analysis; miR-124 targetome; miRNA dynamics; miRNA regulation; miRNA-transcription factor networks; neuronal differentiation from human stem cells; neuronal miRNAs; systems biology

PMID:
30292704
DOI:
10.1016/j.cels.2018.08.011
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2.
Genome Biol Evol. 2018 Aug 1;10(8):2023-2036. doi: 10.1093/gbe/evy149.

Species-Specific Changes in a Primate Transcription Factor Network Provide Insights into the Molecular Evolution of the Primate Prefrontal Cortex.

Author information

1
Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX.
2
Bioinformatics Group, Department of Computer Science, Interdisciplinary Center for Bioinformatics (IZBI), University of Leipzig, Germany.
3
Faculty for Biology, Chemistry, and Pharmacy, Freie Universität Berlin, Germany.

Abstract

The human prefrontal cortex (PFC) differs from that of other primates with respect to size, histology, and functional abilities. Here, we analyzed genome-wide expression data of humans, chimpanzees, and rhesus macaques to discover evolutionary changes in transcription factor (TF) networks that may underlie these phenotypic differences. We determined the co-expression networks of all TFs with species-specific expression including their potential target genes and interaction partners in the PFC of all three species. Integrating these networks allowed us inferring an ancestral network for all three species. This ancestral network as well as the networks for each species is enriched for genes involved in forebrain development, axonogenesis, and synaptic transmission. Our analysis allows us to directly compare the networks of each species to determine which links have been gained or lost during evolution. Interestingly, we detected that most links were gained on the human lineage, indicating increase TF cooperativity in humans. By comparing network changes between different tissues, we discovered that in brain tissues, but not in the other tissues, the human networks always had the highest connectivity. To pinpoint molecular changes underlying species-specific phenotypes, we analyzed the sub-networks of TFs derived only from genes with species-specific expression changes in the PFC. These sub-networks differed significantly in structure and function between the human and chimpanzee. For example, the human-specific sub-network is enriched for TFs implicated in cognitive disorders and for genes involved in synaptic plasticity and cognitive functions. Our results suggest evolutionary changes in TF networks that might have shaped morphological and functional differences between primate brains, in particular in the human PFC.

3.
J Theor Biol. 2018 Feb 7;438:143-150. doi: 10.1016/j.jtbi.2017.11.015. Epub 2017 Nov 23.

Temporal ordering of substitutions in RNA evolution: Uncovering the structural evolution of the Human Accelerated Region 1.

Author information

1
TFome Research Group, Bioinformatics Group, Interdisciplinary Center of Bioinformatics, Department of Computer Science, University of Leipzig, Härtelstrasse 16-18, Leipzig, D-04107, Germany; Paul-Flechsig-Institute for Brain Research, University of Leipzig, Jahnallee 59, Leipzig, D-04109, Germany; Bioinformatics Group, Department of Computer Science and Interdisciplinary Center for Bioinformatics, University of Leipzig, Leipzig, 04107, Germany. Electronic address: bia@bioinf.uni-leipzig.de.
2
Bioinformatics Group, Department of Computer Science and Interdisciplinary Center for Bioinformatics, University of Leipzig, Leipzig, 04107, Germany. Electronic address: choener@bioinf.uni-leipzig.de.
3
National Research Council Canada, Information and Communication Technologies, 100 des Aboiteaux Street, Suite 1100, NB E1A7R1, Moncton, Canada. Electronic address: dan.tulpan@nrc-cnrc.gc.ca.
4
Bioinformatics Group, Department of Computer Science and Interdisciplinary Center for Bioinformatics, University of Leipzig, Leipzig, 04107, Germany; University of Vienna, Institute for Theoretical Chemistry, Vienna A-1090, Austria; Max Planck Institute for Mathematics in the Science, Leipzig, 04103, Germany; Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, 04103, Germany; Santa Fe Institute, Santa Fe, NM, 87501, USA. Electronic address: studla@bioinf.uni-leipzig.de.
5
TFome Research Group, Bioinformatics Group, Interdisciplinary Center of Bioinformatics, Department of Computer Science, University of Leipzig, Härtelstrasse 16-18, Leipzig, D-04107, Germany; Paul-Flechsig-Institute for Brain Research, University of Leipzig, Jahnallee 59, Leipzig, D-04109, Germany; Bioinformatics Group, Department of Computer Science and Interdisciplinary Center for Bioinformatics, University of Leipzig, Leipzig, 04107, Germany; Bioinformatics, Faculty of Agricultural Sciences, Institute of Animal Science, University of Hohenheim, Garbenstrasse 13, Stuttgart, 70593, Germany; Freie Universität Berlin, Faculty for Biology, Chemistry, and Pharmacy, Institute for Biology, Königin-Luise-Strasse 1-3, Berlin, 14195, Germany. Electronic address: katja.nowick@fu-berlin.de.

Abstract

The Human Accelerated Region 1 (HAR1) is the most rapidly evolving region in the human genome. It is part of two overlapping long non-coding RNAs, has a length of only 118 nucleotides and features 18 human specific changes compared to an ancestral sequence that is extremely well conserved across non-human primates. The human HAR1 forms a stable secondary structure that is strikingly different from the one in chimpanzee as well as other closely related species, again emphasizing its human-specific evolutionary history. This suggests that positive selection has acted to stabilize human-specific features in the ensemble of HAR1 secondary structures. To investigate the evolutionary history of the human HAR1 structure, we developed a computational model that evaluates the relative likelihood of evolutionary trajectories as a probabilistic version of a Hamiltonian path problem. The model predicts that the most likely last step in turning the ancestral primate HAR1 into the human HAR1 was exactly the substitution that distinguishes the modern human HAR1 sequence from that of Denisovan, an archaic human, providing independent support for our model. The MutationOrder software is available for download and can be applied to other instances of RNA structure evolution.

KEYWORDS:

Computational modeling; Data visualisation; Dynamic programming; Human evolution; Non-coding RNA; Secondary structure

PMID:
29175608
DOI:
10.1016/j.jtbi.2017.11.015
[Indexed for MEDLINE]
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4.
PLoS Comput Biol. 2017 Sep 28;13(9):e1005739. doi: 10.1371/journal.pcbi.1005739. eCollection 2017 Sep.

A composite network of conserved and tissue specific gene interactions reveals possible genetic interactions in glioma.

Author information

1
Network Systems Biology Group, Department of Biotechnology, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.
2
Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, Leipzig, Germany.
3
Bioinformatics, Institute of Animal Science, University of Hohenheim, Stuttgart, Germany.
4
Human Biology, Institute for Biology, Free University Berlin, Berlin, Germany.
5
K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and General Practice, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.

Abstract

Differential co-expression network analyses have recently become an important step in the investigation of cellular differentiation and dysfunctional gene-regulation in cell and tissue disease-states. The resulting networks have been analyzed to identify and understand pathways associated with disorders, or to infer molecular interactions. However, existing methods for differential co-expression network analysis are unable to distinguish between various forms of differential co-expression. To close this gap, here we define the three different kinds (conserved, specific, and differentiated) of differential co-expression and present a systematic framework, CSD, for differential co-expression network analysis that incorporates these interactions on an equal footing. In addition, our method includes a subsampling strategy to estimate the variance of co-expressions. Our framework is applicable to a wide variety of cases, such as the study of differential co-expression networks between healthy and disease states, before and after treatments, or between species. Applying the CSD approach to a published gene-expression data set of cerebral cortex and basal ganglia samples from healthy individuals, we find that the resulting CSD network is enriched in genes associated with cognitive function, signaling pathways involving compounds with well-known roles in the central nervous system, as well as certain neurological diseases. From the CSD analysis, we identify a set of prominent hubs of differential co-expression, whose neighborhood contains a substantial number of genes associated with glioblastoma. The resulting gene-sets identified by our CSD analysis also contain many genes that so far have not been recognized as having a role in glioblastoma, but are good candidates for further studies. CSD may thus aid in hypothesis-generation for functional disease-associations.

PMID:
28957313
PMCID:
PMC5634634
DOI:
10.1371/journal.pcbi.1005739
[Indexed for MEDLINE]
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5.
BMC Genomics. 2017 Mar 23;18(1):256. doi: 10.1186/s12864-017-3624-7.

Erratum to: Unity in defence: honeybee workers exhibit conserved molecular responses to diverse pathogens.

Author information

1
German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany. vincent.bs.doublet@gmail.com.
2
Centre for Ecology and Conservation, University of Exeter, Penryn, UK. vincent.bs.doublet@gmail.com.
3
German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany.
4
Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.
5
Technische Hochschule Mittelhessen, Gießen, Germany.
6
INRA, UR 406 Abeilles et Environnement, Avignon, France.
7
Dipartimento di Scienze AgroAlimentari, Ambientali e Animali, Università degli Studi di Udine, Udine, Italy.
8
Institute of Life Sciences, University of Agricultural Sciences and Veterinary Medicine, Cluj-Napoca, Romania.
9
Department of Biology, East Carolina University, Greenville, NC, USA.
10
Center for Comparative Genomics and Bioinformatics, Pennsylvania State University, State College, PA, USA.
11
Present address: MRC IGMM, University of Edinburgh, Western General Hospital, Edinburgh, UK.
12
Present address: MRC Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, University of Oxford, South Parks Road, Oxford, UK.
13
School of Biological Sciences, Royal Holloway University of London, Egham, Surrey, UK.
14
Department of Biosciences, Swansea University, Swansea, UK.
15
Department of Plant Sciences and Plant Pathology, Montana State University, Bozeman, MT, USA.
16
Department of Entomology, Center for Pollinator Research, Pennsylvania State University, State College, PA, USA.
17
Department of Molecular Microbiology and Bee Diseases, Institute for Bee Research, Hohen Neuendorf, Germany.
18
Department of Microbiology and Epizootics, Freie Universität Berlin, Berlin, Germany.
19
Department of Fisheries, Wildlife, and Conservation Biology, The Monarch Joint Venture, University of Minnesota, St. Paul, MN, USA.
20
Department of Molecular Biology, Umeå University, Umeå, Sweden.
21
Institute for Biology, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.
22
Present address: International Centre of Insect Physiology and Ecology (icipe), Environmental Health Theme, Nairobi, Kenya.
23
School of Biological Sciences, Queen's University Belfast, Belfast, UK.
24
Institute of Biology, Freie Universität Berlin, Berlin, Germany.
25
Department for Materials and Environment, BAM Federal Institute for Materials Research and Testing, Berlin, Germany.
26
Department of Entomology and Nematology, University of California, Davis, CA, USA.
27
Department of Computer Science, TFome Research Group, Bioinformatics Group, Interdisciplinary Center of Bioinformatics, University of Leipzig, Leipzig, Germany.
28
Paul-Flechsig-Institute for Brain Research, University of Leipzig, Leipzig, Germany.
29
Department of Medical Microbiology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.
6.
BMC Genomics. 2017 Mar 2;18(1):207. doi: 10.1186/s12864-017-3597-6.

Unity in defence: honeybee workers exhibit conserved molecular responses to diverse pathogens.

Author information

1
German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany. vincent.bs.doublet@gmail.com.
2
Centre for Ecology and Conservation, University of Exeter, Penryn, UK. vincent.bs.doublet@gmail.com.
3
German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany.
4
Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.
5
Technische Hochschule Mittelhessen, Gießen, Germany.
6
INRA, UR 406 Abeilles et Environnement, Avignon, France.
7
Dipartimento di Scienze AgroAlimentari, Ambientali e Animali, Università degli Studi di Udine, Udine, Italy.
8
Institute of Life Sciences, University of Agricultural Sciences and Veterinary Medicine, Cluj-Napoca, Romania.
9
Department of Biology, East Carolina University, Greenville, NC, USA.
10
Center for Comparative Genomics and Bioinformatics, Pennsylvania State University, State College, PA, USA.
11
Present address: MRC IGMM, University of Edinburgh, Western General Hospital, Edinburgh, UK.
12
Present address: MRC Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, University of Oxford, South Parks Road, Oxford, UK.
13
School of Biological Sciences, Royal Holloway University of London, Egham, Surrey, UK.
14
Department of Biosciences, Swansea University, Swansea, UK.
15
Department of Plant Sciences and Plant Pathology, Montana State University, Bozeman, MT, USA.
16
Department of Entomology, Center for Pollinator Research, Pennsylvania State University, State College, PA, USA.
17
Department of Molecular Microbiology and Bee Diseases, Institute for Bee Research, Hohen Neuendorf, Germany.
18
Department of Microbiology and Epizootics, Freie Universität Berlin, Berlin, Germany.
19
Department of Fisheries, Wildlife, and Conservation Biology, The Monarch Joint Venture, University of Minnesota, St. Paul, MN, USA.
20
Department of Molecular Biology, Umeå University, Umeå, Sweden.
21
Institute for Biology, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.
22
Present address: International Centre of Insect Physiology and Ecology (icipe), Environmental Health Theme, Nairobi, Kenya.
23
School of Biological Sciences, Queen's University Belfast, Belfast, UK.
24
Institute of Biology, Freie Universität Berlin, Berlin, Germany.
25
Department for Materials and Environment, BAM Federal Institute for Materials Research and Testing, Berlin, Germany.
26
Department of Entomology and Nematology, University of California, Davis, CA, USA.
27
Department of Computer Science, TFome Research Group, Bioinformatics Group, Interdisciplinary Center of Bioinformatics, University of Leipzig, Leipzig, Germany.
28
Paul-Flechsig-Institute for Brain Research, University of Leipzig, Leipzig, Germany.
29
Department of Medical Microbiology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.

Abstract

BACKGROUND:

Organisms typically face infection by diverse pathogens, and hosts are thought to have developed specific responses to each type of pathogen they encounter. The advent of transcriptomics now makes it possible to test this hypothesis and compare host gene expression responses to multiple pathogens at a genome-wide scale. Here, we performed a meta-analysis of multiple published and new transcriptomes using a newly developed bioinformatics approach that filters genes based on their expression profile across datasets. Thereby, we identified common and unique molecular responses of a model host species, the honey bee (Apis mellifera), to its major pathogens and parasites: the Microsporidia Nosema apis and Nosema ceranae, RNA viruses, and the ectoparasitic mite Varroa destructor, which transmits viruses.

RESULTS:

We identified a common suite of genes and conserved molecular pathways that respond to all investigated pathogens, a result that suggests a commonality in response mechanisms to diverse pathogens. We found that genes differentially expressed after infection exhibit a higher evolutionary rate than non-differentially expressed genes. Using our new bioinformatics approach, we unveiled additional pathogen-specific responses of honey bees; we found that apoptosis appeared to be an important response following microsporidian infection, while genes from the immune signalling pathways, Toll and Imd, were differentially expressed after Varroa/virus infection. Finally, we applied our bioinformatics approach and generated a gene co-expression network to identify highly connected (hub) genes that may represent important mediators and regulators of anti-pathogen responses.

CONCLUSIONS:

Our meta-analysis generated a comprehensive overview of the host metabolic and other biological processes that mediate interactions between insects and their pathogens. We identified key host genes and pathways that respond to phylogenetically diverse pathogens, representing an important source for future functional studies as well as offering new routes to identify or generate pathogen resilient honey bee stocks. The statistical and bioinformatics approaches that were developed for this study are broadly applicable to synthesize information across transcriptomic datasets. These approaches will likely have utility in addressing a variety of biological questions.

KEYWORDS:

Apis mellifera; Co-expression network; DWV; IAPV; Immunity; Meta-analysis; Nosema; RNA virus; Transcriptomics; Varroa destructor

PMID:
28249569
PMCID:
PMC5333379
DOI:
10.1186/s12864-017-3597-6
[Indexed for MEDLINE]
Free PMC Article
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7.
Front Genet. 2016 Mar 8;7:31. doi: 10.3389/fgene.2016.00031. eCollection 2016.

A Consensus Network of Gene Regulatory Factors in the Human Frontal Lobe.

Author information

1
Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University LeipzigLeipzig, Germany; Paul-Flechsig Institute for Brain Research, University of LeipzigLeipzig, Germany; Department of Neuroscience, University of Texas Southwestern Medical CenterDallas, TX, USA.
2
Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University Leipzig Leipzig, Germany.
3
Department of Mathematics and Computer Sciences, University of Southern DenmarkOdense, Denmark; Institute for Theoretical Chemistry, University of ViennaVienna, Austria.
4
Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University LeipzigLeipzig, Germany; Paul-Flechsig Institute for Brain Research, University of LeipzigLeipzig, Germany.

Abstract

Cognitive abilities, such as memory, learning, language, problem solving, and planning, involve the frontal lobe and other brain areas. Not much is known yet about the molecular basis of cognitive abilities, but it seems clear that cognitive abilities are determined by the interplay of many genes. One approach for analyzing the genetic networks involved in cognitive functions is to study the coexpression networks of genes with known importance for proper cognitive functions, such as genes that have been associated with cognitive disorders like intellectual disability (ID) or autism spectrum disorders (ASD). Because many of these genes are gene regulatory factors (GRFs) we aimed to provide insights into the gene regulatory networks active in the human frontal lobe. Using genome wide human frontal lobe expression data from 10 independent data sets, we first derived 10 individual coexpression networks for all GRFs including their potential target genes. We observed a high level of variability among these 10 independently derived networks, pointing out that relying on results from a single study can only provide limited biological insights. To instead focus on the most confident information from these 10 networks we developed a method for integrating such independently derived networks into a consensus network. This consensus network revealed robust GRF interactions that are conserved across the frontal lobes of different healthy human individuals. Within this network, we detected a strong central module that is enriched for 166 GRFs known to be involved in brain development and/or cognitive disorders. Interestingly, several hubs of the consensus network encode for GRFs that have not yet been associated with brain functions. Their central role in the network suggests them as excellent new candidates for playing an essential role in the regulatory network of the human frontal lobe, which should be investigated in future studies.

KEYWORDS:

coexpression network; cognitive abilities; cognitive disorders; consensus network; prefrontal cortex (PFC); transcription factor; weighted topological overlap network

8.
Mol Biol Evol. 2016 May;33(5):1231-44. doi: 10.1093/molbev/msw007. Epub 2016 Jan 26.

Human Lineage-Specific Transcriptional Regulation through GA-Binding Protein Transcription Factor Alpha (GABPa).

Author information

1
Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University Leipzig, Leipzig, Germany Paul-Flechsig Institute for Brain Research, University of Leipzig, Leipzig, Germany.
2
Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University Leipzig, Leipzig, Germany Paul-Flechsig Institute for Brain Research, University of Leipzig, Leipzig, Germany nowick@bioinf.uni-leipzig.de Robert.Querfurth@gmx.de.
3
Institute of Genetics of Heart Diseases (IfGH), Department of Cardiovascular Medicine, University Hospital Münster, 48149 Münster, Germany Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics, Berlin, Germany.
4
European Centre for Public Heath Genomics, UNU-MERIT, Unsiversity Maastricht,PO Box 616, 6200 MD Maastricht, The Netherlands Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics, Berlin, Germany.
5
Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics, Berlin, Germany.
6
Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics, Berlin, Germany nowick@bioinf.uni-leipzig.de Robert.Querfurth@gmx.de.

Abstract

A substantial fraction of phenotypic differences between closely related species are likely caused by differences in gene regulation. While this has already been postulated over 30 years ago, only few examples of evolutionary changes in gene regulation have been verified. Here, we identified and investigated binding sites of the transcription factor GA-binding protein alpha (GABPa) aiming to discover cis-regulatory adaptations on the human lineage. By performing chromatin immunoprecipitation-sequencing experiments in a human cell line, we found 11,619 putative GABPa binding sites. Through sequence comparisons of the human GABPa binding regions with orthologous sequences from 34 mammals, we identified substitutions that have resulted in 224 putative human-specific GABPa binding sites. To experimentally assess the transcriptional impact of those substitutions, we selected four promoters for promoter-reporter gene assays using human and African green monkey cells. We compared the activities of wild-type promoters to mutated forms, where we have introduced one or more substitutions to mimic the ancestral state devoid of the GABPa consensus binding sequence. Similarly, we introduced the human-specific substitutions into chimpanzee and macaque promoter backgrounds. Our results demonstrate that the identified substitutions are functional, both in human and nonhuman promoters. In addition, we performed GABPa knock-down experiments and found 1,215 genes as strong candidates for primary targets. Further analyses of our data sets link GABPa to cognitive disorders, diabetes, KRAB zinc finger (KRAB-ZNF), and human-specific genes. Thus, we propose that differences in GABPa binding sites played important roles in the evolution of human-specific phenotypes.

KEYWORDS:

ChIP-Seq; GABP; KRAB zinc finger genes; comparative genomics.; human molecular evolution; human-specific binding sites; promoter assay

PMID:
26814189
PMCID:
PMC4839217
DOI:
10.1093/molbev/msw007
[Indexed for MEDLINE]
Free PMC Article
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9.
Mitochondrial DNA A DNA Mapp Seq Anal. 2017 Jan;28(1):116-118. doi: 10.3109/19401736.2015.1111349. Epub 2015 Dec 28.

The complete mitochondrial genome of Lacerta bilineata and comparison with its closely related congener L. Viridis.

Kolora SR1,2,3, Faria R4,5, Weigert A2,6, Schaffer S2, Grimm A7, Henle K7, Sahyoun AH3, Stadler PF3,8,9,10,11,12,13, Nowick K3,8,14, Bleidorn C1,2, Schlegel M1,2.

Author information

1
a German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig , Leipzig , Germany.
2
b Molecular Evolution and Systematics of Animals, Institute of Biology, University of Leipzig , Leipzig , Germany.
3
c Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, Universität Leipzig , Leipzig , Germany.
4
d CIBIO, Centro De Investigacßao Em Biodiversidade E Recursos Geneticos, InBio, Laboratorio Associado, Universidade Do Porto , Campus Agrário De Vairão , Vairão , Portugal.
5
e Institute of Evolutionary Biology (Universitat Pompeu Fabra-CSIC) , PRBB, Barcelona , Catalonia , Spain.
6
f Max Planck Institute for Evolutionary Anthropology , Deutscher Platz 6 , Leipzig , Germany.
7
g Department of Conservation Biology , UFZ - Helmholtz Center for Environmental Research , Leipzig , Germany.
8
h Paul-Flechsig-Institute for Brain Research, University of Leipzig , Leipzig , Germany.
9
i Max-Planck-Institute for Mathematics in the Sciences , Leipzig , Germany.
10
j Fraunhofer Institut Für Zelltherapie Und Immunologie , Leipzig , Germany.
11
k Department of Theoretical Chemistry , University of Vienna , Wien , Austria.
12
l Center for non-Coding RNA In Technology and Health, University of Copenhagen , Frederiksberg , Denmark.
13
m Santa Fe Institute , Santa Fe , NM , USA , and.
14
n TFome Research Group, Bioinformatics Group, Department of Computer Science, Interdisciplinary Center of Bioinformatics, University of Leipzig , Leipzig , Germany.

Abstract

We sequenced the mitochondrial genome of the Western green lizard (Lacerta bilineata) using Illumina technology and additional Sanger sequencing. The assembled 17 086 bp mitogenome had a GC content of 40.32% and consisted of 13 protein-coding genes, 22 tRNA genes, two rRNA genes, and one control region (CR), with a gene order identical to the chordate consensus. In addition, we re-sequenced the mitogenome of the closely related Eastern green lizard L. viridis using the same techniques as for L. bilineata. The mitogenomes of L. bilineata and L. viridis showed a sequence identity of 94.4% and 99.9%, respectively, relative to the previously published L. viridis mitogenome. The phylogenetic reconstruction based on 17 Lacertinae mitogenomes using Anolis carolinensis as the outgroup supported L. bilineata and its sister species L. viridis as distinct lineages.

KEYWORDS:

Complete mitochondrial genome; Illumina sequencing; Lacerta bilineata; Lacerta viridis; Lacertinae; phylogeny

PMID:
26709540
DOI:
10.3109/19401736.2015.1111349
[Indexed for MEDLINE]
10.
Theory Biosci. 2015 Dec;134(3-4):143-7. doi: 10.1007/s12064-015-0215-5. Epub 2015 Oct 8.

The relativity of biological function.

Author information

1
School of Life Sciences, Arizona State University, Tempe, AZ, 85287-4501, USA. manfred.laubichler@asu.edu.
2
Marine Biological Laboratory, Woods Hole, USA. manfred.laubichler@asu.edu.
3
Santa Fe Institute, 1399 Hyde Park Rd, Santa Fe, NM, 87501, USA. manfred.laubichler@asu.edu.
4
Bioinformatics Group at the Department of Computer Science, Interdisciplinary Center of Bioinformatics, LIFE - Leipzig Research Center for Civilization Diseases, and German Center for Integrative Biodiversity Research, University Leipzig, Härtelstraβe 16-18, 04107, Leipzig, Germany. studla@bioinf.uni-leipzig.de.
5
Max-Planck Institute for Mathematics in the Sciences, Inselstraße 22, 04103, Leipzig, Germany. studla@bioinf.uni-leipzig.de.
6
Fraunhofer Institut für Zelltherapie und Immunologie - IZI, Perlickstraße 1, 04103, Leipzig, Germany. studla@bioinf.uni-leipzig.de.
7
Department of Theoretical Chemistry, University of Vienna, Währingerstraße 17, 1090, Wien, Austria. studla@bioinf.uni-leipzig.de.
8
Center for non-coding RNA in Technology and Health, University of Copenhagen, Grønnegårdsvej 3, 1870, Frederiksberg C, Denmark. studla@bioinf.uni-leipzig.de.
9
Santa Fe Institute, 1399 Hyde Park Rd, Santa Fe, NM, 87501, USA. studla@bioinf.uni-leipzig.de.
10
Computational EvoDevo Group, Department of Computer Science and Interdisciplinary Center of Bioinformatics, University Leipzig, Härtelstraβe 16-18, 04107, Leipzig, Germany. sonja@bioinf.uni-leipzig.de.
11
TFome Research Group at Department of Computer Science, Paul-Flechsig-Institute for Brain Research, and Interdisciplinary Center of Bioinformatics, University Leipzig, Härtelstraβe 16-18, 04107, Leipzig, Germany. katja@bioinf.uni-leipzig.de.

Abstract

Function is a central concept in biological theories and explanations. Yet discussions about function are often based on a narrow understanding of biological systems and processes, such as idealized molecular systems or simple evolutionary, i.e., selective, dynamics. Conflicting conceptions of function continue to be used in the scientific literature to support certain claims, for instance about the fraction of "functional DNA" in the human genome. Here we argue that all biologically meaningful interpretations of function are necessarily context dependent. This implies that they derive their meaning as well as their range of applicability only within a specific theoretical and measurement context. We use this framework to shed light on the current debate about functional DNA and argue that without considering explicitly the theoretical and measurement contexts all attempts to integrate biological theories are prone to fail.

KEYWORDS:

Biological function; Biological theory; Coarse graining; ENCODE; Theory integration

PMID:
26449352
DOI:
10.1007/s12064-015-0215-5
[Indexed for MEDLINE]
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11.
BMC Genomics. 2015;16 Suppl 10:S12. doi: 10.1186/1471-2164-16-S10-S12. Epub 2015 Oct 2.

Gene-pseudogene evolution: a probabilistic approach.

Abstract

Over the last decade, methods have been developed for the reconstruction of gene trees that take into account the species tree. Many of these methods have been based on the probabilistic duplication-loss model, which describes how a gene-tree evolves over a species-tree with respect to duplication and losses, as well as extension of this model, e.g., the DLRS (Duplication, Loss, Rate and Sequence evolution) model that also includes sequence evolution under relaxed molecular clock. A disjoint, almost as recent, and very important line of research has been focused on non protein-coding, but yet, functional DNA. For instance, DNA sequences being pseudogenes in the sense that they are not translated, may still be transcribed and the thereby produced RNA may be functional.

PMID:
26449131
PMCID:
PMC4602177
DOI:
10.1186/1471-2164-16-S10-S12
[Indexed for MEDLINE]
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13.
Nat Commun. 2014 Nov 26;5:5569. doi: 10.1038/ncomms6569.

Multiple haplotype-resolved genomes reveal population patterns of gene and protein diplotypes.

Author information

1
Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics, Berlin D-14195, Germany.
2
Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA.
3
Department of Computer Science, University of Leipzig, Leipzig D-04107, Germany.
4
1] Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics, Berlin D-14195, Germany [2].

Abstract

To fully understand human biology and link genotype to phenotype, the phase of DNA variants must be known. Here we present a comprehensive analysis of haplotype-resolved genomes to assess the nature and variation of haplotypes and their pairs, diplotypes, in European population samples. We use a set of 14 haplotype-resolved genomes generated by fosmid clone-based sequencing, complemented and expanded by up to 372 statistically resolved genomes from the 1000 Genomes Project. We find immense diversity of both haploid and diploid gene forms, up to 4.1 and 3.9 million corresponding to 249 and 235 per gene on average. Less than 15% of autosomal genes have a predominant form. We describe a 'common diplotypic proteome', a set of 4,269 genes encoding two different proteins in over 30% of genomes. We show moreover an abundance of cis configurations of mutations in the 386 genomes with an average cis/trans ratio of 60:40, and distinguishable classes of cis- versus trans-abundant genes. This work identifies key features characterizing the diplotypic nature of human genomes and provides a conceptual and analytical framework, rich resources and novel hypotheses on the functional importance of diploidy.

PMID:
25424553
PMCID:
PMC4263165
DOI:
10.1038/ncomms6569
[Indexed for MEDLINE]
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14.
Curr Opin Genet Dev. 2014 Dec;29:60-7. doi: 10.1016/j.gde.2014.08.007. Epub 2014 Sep 15.

The role of gene regulatory factors in the evolutionary history of humans.

Author information

1
TFome Research Group, Bioinformatics Group, Interdisciplinary Center of Bioinformatics, Department of Computer Science, University of Leipzig, Härtelstrasse 16-18, D-04107 Leipzig, Germany; Paul-Flechsig-Institute for Brain Research, University of Leipzig, Jahnallee 59, D-04109 Leipzig, Germany.
2
TFome Research Group, Bioinformatics Group, Interdisciplinary Center of Bioinformatics, Department of Computer Science, University of Leipzig, Härtelstrasse 16-18, D-04107 Leipzig, Germany; Paul-Flechsig-Institute for Brain Research, University of Leipzig, Jahnallee 59, D-04109 Leipzig, Germany. Electronic address: nowick@bioinf.uni-leipzig.de.

Abstract

Deciphering the molecular basis of how modern human phenotypes have evolved is one of the most fascinating challenges in biology. Here, we will focus on the roles of gene regulatory factors (GRFs), in particular transcription factors (TFs) and long non-coding RNAs (lncRNAs) during human evolution. We will present examples of TFs and lncRNAs that have changed or show signs of positive selection in humans compared to chimpanzees, in modern humans compared to archaic humans, or within modern human populations. On the basis of current knowledge about the functions of these GRF genes, we speculate that they have been involved in speciation as well as in shaping phenotypes such as brain functions, skeletal morphology, and metabolic processes.

PMID:
25215414
DOI:
10.1016/j.gde.2014.08.007
[Indexed for MEDLINE]
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15.
Trends Genet. 2013 Mar;29(3):130-9. doi: 10.1016/j.tig.2012.11.007. Epub 2012 Dec 17.

A prominent role of KRAB-ZNF transcription factors in mammalian speciation?

Author information

1
Bioinformatics Group, Department of Computer Science, Interdisciplinary Center for Bioinformatics (IZBI), University of Leipzig, Härtelstrasse 16-18, D-04107 Leipzig, Germany. nowick@bioinf.uni-leipzig.de

Abstract

The mechanisms of speciation have been one of the most debated topics in evolutionary biology. Among all reproductive barriers, postzygotic reproductive isolation is perhaps the one that has attracted the most attention from geneticists. Despite remarkable advances in the identification of loci involved in Drosophila speciation, little is known about the genes, functions, and biochemical interactions of the molecules underlying hybrid sterility and inviability in mammals. Here, we discuss the main evolutionary and molecular features that make transcription factors (TFs), especially the family of zinc finger proteins with a Krüppel-associated box domain (KRAB-ZNF), strong candidates to play an important role in postzygotic reproductive isolation. Motivated by the recent identification of the gene encoding PR domain zinc finger protein 9 (Prdm9; a KRAB-ZNF gene) as the first hybrid sterility gene identified in mammals, we further propose integrative approaches to study KRAB-ZNF genes with the main goal of characterizing the molecular pathways and interactions involved in hybrid incompatibilities.

PMID:
23253430
DOI:
10.1016/j.tig.2012.11.007
[Indexed for MEDLINE]
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16.
Genome Res. 2011 Oct;21(10):1672-85. doi: 10.1101/gr.125047.111. Epub 2011 Aug 3.

A comprehensively molecular haplotype-resolved genome of a European individual.

Author information

1
Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany.

Abstract

Independent determination of both haplotype sequences of an individual genome is essential to relate genetic variation to genome function, phenotype, and disease. To address the importance of phase, we have generated the most complete haplotype-resolved genome to date, "Max Planck One" (MP1), by fosmid pool-based next generation sequencing. Virtually all SNPs (>99%) and 80,000 indels were phased into haploid sequences of up to 6.3 Mb (N50 ~1 Mb). The completeness of phasing allowed determination of the concrete molecular haplotype pairs for the vast majority of genes (81%) including potential regulatory sequences, of which >90% were found to be constituted by two different molecular forms. A subset of 159 genes with potentially severe mutations in either cis or trans configurations exemplified in particular the role of phase for gene function, disease, and clinical interpretation of personal genomes (e.g., BRCA1). Extended genomic regions harboring manifold combinations of physically and/or functionally related genes and regulatory elements were resolved into their underlying "haploid landscapes," which may define the functional genome. Moreover, the majority of genes and functional sequences were found to contain individual or rare SNPs, which cannot be phased from population data alone, emphasizing the importance of molecular phasing for characterizing a genome in its molecular individuality. Our work provides the foundation to understand that the distinction of molecular haplotypes is essential to resolve the (inherently individual) biology of genes, genomes, and disease, establishing a reference point for "phase-sensitive" personal genomics. MP1's annotated haploid genomes are available as a public resource.

PMID:
21813624
PMCID:
PMC3202284
DOI:
10.1101/gr.125047.111
[Indexed for MEDLINE]
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17.
PLoS One. 2011;6(6):e21553. doi: 10.1371/journal.pone.0021553. Epub 2011 Jun 29.

Gain, loss and divergence in primate zinc-finger genes: a rich resource for evolution of gene regulatory differences between species.

Author information

1
Institute for Genomic Biology, University of Illinois, Urbana, Illinois, United States of America.

Abstract

The molecular changes underlying major phenotypic differences between humans and other primates are not well understood, but alterations in gene regulation are likely to play a major role. Here we performed a thorough evolutionary analysis of the largest family of primate transcription factors, the Krüppel-type zinc finger (KZNF) gene family. We identified and curated gene and pseudogene models for KZNFs in three primate species, chimpanzee, orangutan and rhesus macaque, to allow for a comparison with the curated set of human KZNFs. We show that the recent evolutionary history of primate KZNFs has been complex, including many lineage-specific duplications and deletions. We found 213 species-specific KZNFs, among them 7 human-specific and 23 chimpanzee-specific genes. Two human-specific genes were validated experimentally. Ten genes have been lost in humans and 13 in chimpanzees, either through deletion or pseudogenization. We also identified 30 KZNF orthologs with human-specific and 42 with chimpanzee-specific sequence changes that are predicted to affect DNA binding properties of the proteins. Eleven of these genes show signatures of accelerated evolution, suggesting positive selection between humans and chimpanzees. During primate evolution the most extensive re-shaping of the KZNF repertoire, including most gene additions, pseudogenizations, and structural changes occurred within the subfamily homininae. Using zinc finger (ZNF) binding predictions, we suggest potential impact these changes have had on human gene regulatory networks. The large species differences in this family of TFs stands in stark contrast to the overall high conservation of primate genomes and potentially represents a potent driver of primate evolution.

PMID:
21738707
PMCID:
PMC3126818
DOI:
10.1371/journal.pone.0021553
[Indexed for MEDLINE]
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18.
Mol Biol Evol. 2010 Nov;27(11):2606-17. doi: 10.1093/molbev/msq157. Epub 2010 Jun 23.

Rapid sequence and expression divergence suggest selection for novel function in primate-specific KRAB-ZNF genes.

Author information

1
Institute for Genomic Biology, University of Illinois at Urbana-Champaign, USA.

Abstract

Recent segmental duplications (SDs), arising from duplication events that occurred within the past 35-40 My, have provided a major resource for the evolution of proteins with primate-specific functions. KRAB zinc finger (KRAB-ZNF) transcription factor genes are overrepresented among genes contained within these recent human SDs. Here, we examine the structural and functional diversity of the 70 human KRAB-ZNF genes involved in the most recent primate SD events including genes that arose in the hominid lineage. Despite their recent advent, many parent-daughter KRAB-ZNF gene pairs display significant differences in zinc finger structure and sequence, expression, and splicing patterns, each of which could significantly alter the regulatory functions of the paralogous genes. Paralogs that emerged on the lineage to humans and chimpanzees have undergone more evolutionary changes per unit of time than genes already present in the common ancestor of rhesus macaques and great apes. Taken together, these data indicate that a substantial fraction of the recently evolved primate-specific KRAB-ZNF gene duplicates have acquired novel functions that may possibly define novel regulatory pathways and suggest an active ongoing selection for regulatory diversity in primates.

PMID:
20573777
PMCID:
PMC2981486
DOI:
10.1093/molbev/msq157
[Indexed for MEDLINE]
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19.
Brief Funct Genomics. 2010 Jan;9(1):65-78. doi: 10.1093/bfgp/elp056. Epub 2010 Jan 16.

Lineage-specific transcription factors and the evolution of gene regulatory networks.

Author information

1
Department of Cell and Developmental Biology, Institute for Genomic Biology, University of Illinois, 1206 W. Gregory Drive, Urbana, IL 61802, USA.

Abstract

Nature is replete with examples of diverse cell types, tissues and body plans, forming very different creatures from genomes with similar gene complements. However, while the genes and the structures of proteins they encode can be highly conserved, the production of those proteins in specific cell types and at specific developmental time points might differ considerably between species. A full understanding of the factors that orchestrate gene expression will be essential to fully understand evolutionary variety. Transcription factor (TF) proteins, which form gene regulatory networks (GRNs) to act in cooperative or competitive partnerships to regulate gene expression, are key components of these unique regulatory programs. Although many TFs are conserved in structure and function, certain classes of TFs display extensive levels of species diversity. In this review, we highlight families of TFs that have expanded through gene duplication events to create species-unique repertoires in different evolutionary lineages. We discuss how the hierarchical structures of GRNs allow for flexible small to large-scale phenotypic changes. We survey evidence that explains how newly evolved TFs may be integrated into an existing GRN and how molecular changes in TFs might impact the GRNs. Finally, we review examples of traits that evolved due to lineage-specific TFs and species differences in GRNs.

PMID:
20081217
PMCID:
PMC3096533
DOI:
10.1093/bfgp/elp056
[Indexed for MEDLINE]
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20.
Proc Natl Acad Sci U S A. 2009 Dec 29;106(52):22358-63. doi: 10.1073/pnas.0911376106. Epub 2009 Dec 10.

Differences in human and chimpanzee gene expression patterns define an evolving network of transcription factors in brain.

Author information

1
Department of Cell and Developmental Biology, Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.

Abstract

Humans differ from other primates by marked differences in cognitive abilities and a significantly larger brain. These differences correlate with metabolic changes, as evidenced by the relative up-regulation of energy-related genes and metabolites in human brain. While the mechanisms underlying these evolutionary changes have not been elucidated, altered activities of key transcription factors (TFs) could play a pivotal role. To assess this possibility, we analyzed microarray data from five tissues from humans and chimpanzees. We identified 90 TF genes with significantly different expression levels in human and chimpanzee brain among which the rapidly evolving KRAB-zinc finger genes are markedly over-represented. The differentially expressed TFs cluster within a robust regulatory network consisting of two distinct but interlinked modules, one strongly associated with energy metabolism functions, and the other with transcription, vesicular transport, and ubiquitination. Our results suggest that concerted changes in a relatively small number of interacting TFs may coordinate major gene expression differences in human and chimpanzee brain.

PMID:
20007773
PMCID:
PMC2799715
DOI:
10.1073/pnas.0911376106
[Indexed for MEDLINE]
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