Species-specific evolving regions in the human and chimpanzee genomes

Vergleichende Genexpressionsanalysen zwischen Mensch und Schimpanse ermöglichen die Identifizierung von Kandidaten-Genen, die für die phänotypischen Unterschieden zwischen den beiden Arten verantwortlich sein können. Unterschiede in der Genexpression in frühen Stadien der Ontogenese spielen eine potentielle Schlüsselrolle bei der differentiellen Embryonalentwicklung der beiden Arten. Direkte Genexpressionsvergleiche in diesen Entwicklungsstadien sind allerdings in der Regel möglich, da embryonales Gewebe für solche Versuche weder vom Menschen noch vom Schimpansen zur Verfügung steht. In der vorliegenden Arbeit wird daher ein bioinformatischer Ansatz zur Identifizierung von Genen präsentiert, deren Expression sich in einer artspezifischen Weise verändert hat. Das Substitutionsmuster weist in transkribierten genomischen Regionen eine erhöhte Rate von A->G im Vergleich zu T->C Substitutionen auf, die auf den Effekt der transkriptions-gekoppelten Reparatur zurueckgeführt wird. Es wurden Gene im Genom von Mensch bzw. Schimpanse gesucht, deren Substitutionsmuster Anzeichen eines unterschiedlichen Ausmaßes an der transkription-gekoppelten Reparatur in den beiden Arten zeigen. Zunächst wurden spezifische Substitutionsmatrizen für 12,596 nicht-überlappende 125 Kb Alignmentfenster in der Fraktion des transkribierten Genoms für Mensch, Schimpansen und Rhesus geschätzt. Anschliessend wurde eine neue Teststatistik verwendet, mit der 717 transkribierte Genomregionen identifiziert wurden, bei denen sich die Substitutionsmatrizen von Mensch und Schimpanse signifikant unterscheiden. Die Substitutionsmatrizen unterscheiden sich hauptsächlich in ihrem relativen Ausmaß von AG im Vergleich zu TC Substitutionen. Genau ein solcher Unterschied ist zu erwarten, wenn die transkription-gekoppelte Reparatur im unterschiedlichem Maße auf die entsprechenden Gene der beiden Arten wirkt. Diese Beobachtung liefert erste Hinweise darauf, dass diese Gene während der fr\"uhen Stadien der Entwicklung von Mensch und Schimpanse differentiell exprimiert werden. Eine nachfolgende Genontologie Anreicherungsanalyse zeigt daß die entsprechenden Gene hauptsächlich eine Rolle in embryonalen Entwicklungsprozessen wie z.B. anatomische Strukturentwicklung (z.B. Skelett, Rückenmark, Gehirn, Darm), Neurogenese, Signaltransduktion, Transkriptionsregulation, Translation und Replikation spielen.Comparative analyses of the human and chimpanzee transcriptomes aim at the identification of genes whose expression pattern has changed since both species last shared a common ancestor. This approach complements the search for genes with altered function in compiling the catalogue of genetic changes responsible for the distinct phenotypes of the contemporary species. However, gene expression analyses are ultimately dependent on the availability of tissue samples. Embryonic tissues from chimpanzees are rare, if available at all. Thus, changes in the gene expression pattern in these early stages of ontogenesis, which potentially play a key role in differential development, are likely to be missed. Here I present a bioinformatics approach to identify candidates that may be expressed in a species-specific manner. Specifically, I have searched for genes in the human and chimpanzee genomes of which evolutionary sequence change shows signs of different extents of transcription-coupled-repair in the two species. Human, chimpanzee and rhesus branch-specific substitution matrices were estimated for 12,596 non-overlapping sliding windows 125 Kb in size representing the transcribed fraction in a human-chimpanzee-rhesus genome alignment. Applying a novel test statistic, 717 transcribed regions were identified in which the estimated branch-specific substitution models differ significantly between humans and chimpanzees. More specifically, it is shown that the two species differ mainly in their relative rates of AG and TC substitutions, and in the rate ratio of the two transition types. This pattern is expected when transcription-coupled-repair acts to different extents on the corresponding genes in the two species and provides initial evidence that these genes may be differentially expressed during early stages of human and chimpanzee development. A subsequent Gene Ontology enrichment analysis of the corresponding genes revealed an enrichment for embryonic developmental processes such as anatomical structure development (e.g., skeleton, spinal cord, brain, gut), neurogenesis, signal transduction and regulation processes for transcription, translation and replication

Interfacing cellular networks of <i>S. cerevisiae</i> and <i>E. coli</i>: Connecting dynamic and genetic information

BACKGROUND: In recent years, various types of cellular networks have penetrated biology and are nowadays used omnipresently for studying eukaryote and prokaryote organisms. Still, the relation and the biological overlap among phenomenological and inferential gene networks, e.g., between the protein interaction network and the gene regulatory network inferred from large-scale transcriptomic data, is largely unexplored. RESULTS: We provide in this study an in-depth analysis of the structural, functional and chromosomal relationship between a protein-protein network, a transcriptional regulatory network and an inferred gene regulatory network, for S. cerevisiae and E. coli. Further, we study global and local aspects of these networks and their biological information overlap by comparing, e.g., the functional co-occurrence of Gene Ontology terms by exploiting the available interaction structure among the genes. CONCLUSIONS: Although the individual networks represent different levels of cellular interactions with global structural and functional dissimilarities, we observe crucial functions of their network interfaces for the assembly of protein complexes, proteolysis, transcription, translation, metabolic and regulatory interactions. Overall, our results shed light on the integrability of these networks and their interfacing biological processes

B-cell lymphoma gene regulatory networks: biological consistency among inference methods

Despite the development of numerous gene regulatory network (GRN) inference methods in the last years, their application, usage and the biological significance of the resulting GRN remains unclear for our general understanding of large-scale gene expression data in routine practice. In our study, we conduct a structural and a functional analysis of B-cell lymphoma GRNs that were inferred using 3 mutual information-based GRN inference methods: C3Net, BC3Net and Aracne. From a comparative analysis on the global level, we find that the inferred B-cell lymphoma GRNs show major differences. However, on the edge-level and the functional-level - that are more important for our biological understanding - the B-cell lymphoma GRNs were highly similar among each other. Also, the ranks of the degree centrality values and major hub genes in the inferred networks are highly conserved as well. Interestingly, the major hub genes of all GRNs are associated with the G-protein-coupled receptor pathway, cell-cell signaling and cell cycle. This implies that hub genes of the GRNs can be highly consistently inferred with C3Net, BC3Net and Aracne, representing prominent targets for signaling pathways. Finally, we describe the functional and structural relationship between C3Net, BC3Net and Aracne gene regulatory networks. Our study shows that these GRNs that are inferred from large-scale gene expression data are promising for the identification of novel candidate interactions and pathways that play a key role in the underlying mechanisms driving cancer hallmarks. Overall, our comparative analysis reveals that these GRNs inferred with considerably different inference methods contain large amounts of consistent, method independent, biological information

A Consistent Phylogenetic Backbone for the Fungi

The kingdom of fungi provides model organisms for biotechnology, cell biology, genetics, and life sciences in general. Only when their phylogenetic relationships are stably resolved, can individual results from fungal research be integrated into a holistic picture of biology. However, and despite recent progress, many deep relationships within the fungi remain unclear. Here, we present the first phylogenomic study of an entire eukaryotic kingdom that uses a consistency criterion to strengthen phylogenetic conclusions. We reason that branches (splits) recovered with independent data and different tree reconstruction methods are likely to reflect true evolutionary relationships. Two complementary phylogenomic data sets based on 99 fungal genomes and 109 fungal expressed sequence tag (EST) sets analyzed with four different tree reconstruction methods shed light from different angles on the fungal tree of life. Eleven additional data sets address specifically the phylogenetic position of Blastocladiomycota, Ustilaginomycotina, and Dothideomycetes, respectively. The combined evidence from the resulting trees supports the deep-level stability of the fungal groups toward a comprehensive natural system of the fungi. In addition, our analysis reveals methodologically interesting aspects. Enrichment for EST encoded data—a common practice in phylogenomic analyses—introduces a strong bias toward slowly evolving and functionally correlated genes. Consequently, the generalization of phylogenomic data sets as collections of randomly selected genes cannot be taken for granted. A thorough characterization of the data to assess possible influences on the tree reconstruction should therefore become a standard in phylogenomic analyses

Bagging Statistical Network Inference from Large-Scale Gene Expression Data

Modern biology and medicine aim at hunting molecular and cellular causes of biological functions and diseases. Gene regulatory networks (GRN) inferred from gene expression data are considered an important aid for this research by providing a map of molecular interactions. Hence, GRNs have the potential enabling and enhancing basic as well as applied research in the life sciences. In this paper, we introduce a new method called BC3NET for inferring causal gene regulatory networks from large-scale gene expression data. BC3NET is an ensemble method that is based on bagging the C3NET algorithm, which means it corresponds to a Bayesian approach with noninformative priors. In this study we demonstrate for a variety of simulated and biological gene expression data from S. cerevisiae that BC3NET is an important enhancement over other inference methods that is capable of capturing biochemical interactions from transcription regulation and protein-protein interaction sensibly. An implementation of BC3NET is freely available as an R package from the CRAN repository

Influence of Statistical Estimators of Mutual Information and Data Heterogeneity on the Inference of Gene Regulatory Networks

The inference of gene regulatory networks from gene expression data is a difficult problem because the performance of the inference algorithms depends on a multitude of different factors. In this paper we study two of these. First, we investigate the influence of discrete mutual information (MI) estimators on the global and local network inference performance of the C3NET algorithm. More precisely, we study different MI estimators (Empirical, Miller-Madow, Shrink and Schürmann-Grassberger) in combination with discretization methods (equal frequency, equal width and global equal width discretization). We observe the best global and local inference performance of C3NET for the Miller-Madow estimator with an equal width discretization. Second, our numerical analysis can be considered as a systems approach because we simulate gene expression data from an underlying gene regulatory network, instead of making a distributional assumption to sample thereof. We demonstrate that despite the popularity of the latter approach, which is the traditional way of studying MI estimators, this is in fact not supported by simulated and biological expression data because of their heterogeneity. Hence, our study provides guidance for an efficient design of a simulation study in the context of network inference, supporting a systems approach

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Harnessing the complexity of gene expression data from cancer: from single gene to structural pathway methods

<p>Abstract</p> <p/> <p>High-dimensional gene expression data provide a rich source of information because they capture the expression level of genes in dynamic states that reflect the biological functioning of a cell. For this reason, such data are suitable to reveal systems related properties inside a cell, e.g., in order to elucidate molecular mechanisms of complex diseases like breast or prostate cancer. However, this is not only strongly dependent on the sample size and the correlation structure of a data set, but also on the statistical hypotheses tested. Many different approaches have been developed over the years to analyze gene expression data to (<monospace>I</monospace>) identify changes in single genes, (<monospace>II</monospace>) identify changes in gene sets or pathways, and (<monospace>III</monospace>) identify changes in the correlation structure in pathways. In this paper, we review statistical methods for all three types of approaches, including subtypes, in the context of cancer data and provide links to software implementations and tools and address also the general problem of multiple hypotheses testing. Further, we provide recommendations for the selection of such analysis methods.</p> <p>Reviewers</p> <p>This article was reviewed by Arcady Mushegian, Byung-Soo Kim and Joel Bader.</p

Urothelial cancer gene regulatory networks inferred from large-scale RNAseq, Bead and Oligo gene expression data

BACKGROUND: Urothelial pathogenesis is a complex process driven by an underlying network of interconnected genes. The identification of novel genomic target regions and gene targets that drive urothelial carcinogenesis is crucial in order to improve our current limited understanding of urothelial cancer (UC) on the molecular level. The inference of genome-wide gene regulatory networks (GRN) from large-scale gene expression data provides a promising approach for a detailed investigation of the underlying network structure associated to urothelial carcinogenesis. METHODS: In our study we inferred and compared three GRNs by the application of the BC3Net inference algorithm to large-scale transitional cell carcinoma gene expression data sets from Illumina RNAseq (179 samples), Illumina Bead arrays (165 samples) and Affymetrix Oligo microarrays (188 samples). We investigated the structural and functional properties of GRNs for the identification of molecular targets associated to urothelial cancer. RESULTS: We found that the urothelial cancer (UC) GRNs show a significant enrichment of subnetworks that are associated with known cancer hallmarks including cell cycle, immune response, signaling, differentiation and translation. Interestingly, the most prominent subnetworks of co-located genes were found on chromosome regions 5q31.3 (RNAseq), 8q24.3 (Oligo) and 1q23.3 (Bead), which all represent known genomic regions frequently deregulated or aberated in urothelial cancer and other cancer types. Furthermore, the identified hub genes of the individual GRNs, e.g., HID1/DMC1 (tumor development), RNF17/TDRD4 (cancer antigen) and CYP4A11 (angiogenesis/ metastasis) are known cancer associated markers. The GRNs were highly dataset specific on the interaction level between individual genes, but showed large similarities on the biological function level represented by subnetworks. Remarkably, the RNAseq UC GRN showed twice the proportion of significant functional subnetworks. Based on our analysis of inferential and experimental networks the Bead UC GRN showed the lowest performance compared to the RNAseq and Oligo UC GRNs. CONCLUSION: To our knowledge, this is the first study investigating genome-scale UC GRNs. RNAseq based gene expression data is the data platform of choice for a GRN inference. Our study offers new avenues for the identification of novel putative diagnostic targets for subsequent studies in bladder tumors. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12918-015-0165-z) contains supplementary material, which is available to authorized users