Are we overestimating the number of cell-cycling genes? The impact of background models for time series data.

Periodic processes play fundamental roles in organisms. Prominent examples are the cell cycle and the circadian clock. Microarray array technology has enabled us to screen complete sets of transcripts for possible association with such fundamental periodic processes on a system-wide level. Frequently, quite a large number of genes has been detected as periodically expressed. However, the small overlap of identified genes between different studies has shaded considerable doubts about the reliability of the detected periodic expression. In this study, we show that a major reason for the lacking agreement is the use of an inadequate background model for the determination of significance. We demonstrate that the choice of background model has considerable impact on the statistical significance of periodic expression. For illustration, we reanalyzed two microarray studies of the yeast cell cycle. Our evaluation strongly indicates that the results of previous analyses might have been overoptimistic and that the use of more suitable background model promises to give more realistic resultsinfo:eu-repo/semantics/publishedVersio

Model selection and efficiency testing for normalization of cDNA microarray data

In this study we present two novel normalization schemes for cDNA microarrays. They are based on iterative local regression and optimization of model parameters by generalized cross-validation. Permutation tests assessing the efficiency of normalization demonstrated that the proposed schemes have an improved ability to remove systematic errors and to reduce variability in microarray data. The analysis also reveals that without parameter optimization local regression is frequently insufficient to remove systematic errors in microarray data

Toward a systems-level understanding of gene regulatory, protein interaction, and metabolic networks in cyanobacteria.

Cyanobacteria are essential primary producers in marine ecosystems, playing an important role in both carbon and nitrogen cycles. In the last decade, various genome sequencing and metagenomic projects have generated large amounts of genetic data for cyanobacteria. This wealth of data provides researchers with a new basis for the study of molecular adaptation, ecology and evolution of cyanobacteria, as well as for developing biotechnological applications. It also facilitates the use of multiplex techniques, i.e., expression profiling by high-throughput technologies such as microarrays, RNA-seq, and proteomics. However, exploration and analysis of these data is challenging, and often requires advanced computational methods. Also, they need to be integrated into our existing framework of knowledge to use them to draw reliable biological conclusions. Here, systems biology provides important tools. Especially, the construction and analysis of molecular networks has emerged as a powerful systems-level framework, with which to integrate such data, and to better understand biological relevant processes in these organisms. In this review, we provide an overview of the advances and experimental approaches undertaken using multiplex data from genomic, transcriptomic, proteomic, and metabolomic studies in cyanobacteria. Furthermore, we summarize currently available web-based tools dedicated to cyanobacteria, i.e., CyanoBase, CyanoEXpress, ProPortal, Cyanorak, CyanoBIKE, and CINPER. Finally, we present a case study for the freshwater model cyanobacteria, Synechocystis sp. PCC6803, to show the power of meta-analysis, and the potential to extrapolate acquired knowledge to the ecologically important marine cyanobacteria genus, Prochlorococcus

The Unfolded Protein Response and its potential role in Huntington's disease

Huntington's disease (HD) is a progressive, neurodegenerative disease with fatal outcome. Although the disease-causing gene (huntingtin) has been known for some time, the exact cause of neuronal cell death is still unknown. One potential mechanism contributing to the massive loss of neurons in the brain of HD patients might be the unfolded protein response (UPR), which is activated by accumulation of misfolded proteins in the endoplasmatic reticulum (ER). As an adaptive response to counter-balance accumulation of un- or misfolded proteins, the UPR upregulates transcription of chaperones, temporarily attenuates new translation, and activates protein degradation via the proteasome. However, it is known that persistent ER stress and activated UPR can cause cell death by triggering of apoptosis. Nevertheless, the evidence linking UPR with HD progression remains inconclusive. Here, we present first analyses of UPR activation during HD based on available expression data. To elucidate the potential role of UPR as a disease-relevant process, we examine its connection to cell death and inflammatory processes. Due to the complexity of these molecular mechanisms, a systems biology approach was pursued

Comparison and consolidation of microarray data sets of human tissue expression

<p>Abstract</p> <p>Background</p> <p>Human tissue displays a remarkable diversity in structure and function. To understand how such diversity emerges from the same DNA, systematic measurements of gene expression across different tissues in the human body are essential. Several recent studies addressed this formidable task using microarray technologies. These large tissue expression data sets have provided us an important basis for biomedical research. However, it is well known that microarray data can be compromised by high noise level and various experimental artefacts. Critical comparison of different data sets can help to reveal such errors and to avoid pitfalls in their application.</p> <p>Results</p> <p>We present here the first comparison and integration of four freely available tissue expression data sets generated using three different microarray platforms and containing a total of 377 microarray hybridizations. When assessing the tissue expression of genes, we found that the results considerably depend on the chosen data set. Nevertheless, the comparison also revealed statistically significant similarity of gene expression profiles across different platforms. This enabled us to construct consolidated lists of platform-independent tissue-specific genes using a set of complementary measures. Follow-up analyses showed that results based on consolidated data tend to be more reliable.</p> <p>Conclusions</p> <p>Our study strongly indicates that the consolidation of the four different tissue expression data sets can increase data quality and can lead to biologically more meaningful results. The provided compendium of platform-independent gene lists should facilitate the identification of novel tissue-specific marker genes.</p

A Systems Biology Approach towards Deciphering the Unfolded Protein Response in Huntington&#x27;s Disease

Although the disease causing gene huntingtin has been known for some time, the exact cause of neuronal cell death during _Huntington&#x27;s disease_ (HD) remains unknown. One potential mechanism contributing to the massive loss of neurons in HD brains might be the _Unfolded Protein Response_ (UPR) which is activated by accumulation of misfolded proteins in the endoplasmic reticulum (ER). As an adaptive response, UPR upregulates transcription of chaperones, temporarily attenuating new translation and activates protein degradation via the proteasome. However, at high levels of ER stress, UPR signalling can contribute to neuronal apoptosis.&#xd;&#xa;&#xd;&#xa;Our primary aims include (a) construction of the UPR signalling network, (b) curation and bioinformatical identification of UPR target genes and finally (c) examination of HD gene expression data sets for UPR transcriptional signatures and differential regulation of UPR pathways.&#xd;&#xa;&#xd;&#xa;The UPR signalling pathway is reconstructed based on literature review and using the &#x22;Unified Interactome database&#x22;:http://www.unihi.org. Lists of UPR target genes detected by previous experiments or as predicted by computational analysis are compiled. This allows us to perform enrichment analysis for differential HD gene expression and to assess whether UPR expression signatures are prominent during HD pathogenesis.&#xd;&#xa;&#xd;&#xa;Results: The canonical UPR pathway is complemented with additional protein interaction data allowing us to assess its embedding into the cellular context and to identify potential modifiers as well as novel drug targets.&#xd;&#xa;&#xd;&#xa;Conclusions: The in depth systems biology analysis can give us valuable insights about the involvement of the UPR in HD.&#xd;&#xa

Integration of stemness gene signatures reveals core functional modules of stem cells and potential novel stemness genes

Stem cells encompass a variety of different cell types which converge on the dual capacity to self-renew and differentiate into one or more lineages. These characteristic features are key for the involvement of stem cells in crucial biological processes such as development and ageing. To decipher their underlying genetic substrate, it is important to identify so-called stemness genes that are common to different stem cell types and are consistently identified across different studies. In this meta-analysis, 21 individual stemness signatures for humans and another 21 for mice, obtained from a variety of stem cell types and experimental techniques, were compared. Although we observed biological and experimental variability, a highly significant overlap between gene signatures was identified. This enabled us to define integrated stemness signatures (ISSs) comprised of genes frequently occurring among individual stemness signatures. Such integrated signatures help to exclude false positives that can compromise individual studies and can provide a more robust basis for investigation. To gain further insights into the relevance of ISSs, their genes were functionally annotated and connected within a molecular interaction network. Most importantly, the present analysis points to the potential roles of several less well-studied genes in stemness and thus provides promising candidates for further experimental validation.info:eu-repo/semantics/publishedVersio

Inferring modules from human protein interactome classes

<p>Abstract</p> <p>Background</p> <p>The integration of protein-protein interaction networks derived from high-throughput screening approaches and complementary sources is a key topic in systems biology. Although integration of protein interaction data is conventionally performed, the effects of this procedure on the result of network analyses has not been examined yet. In particular, in order to optimize the fusion of heterogeneous interaction datasets, it is crucial to consider not only their degree of coverage and accuracy, but also their mutual dependencies and additional salient features.</p> <p>Results</p> <p>We examined this issue based on the analysis of modules detected by network clustering methods applied to both integrated and individual (disaggregated) data sources, which we call interactome classes. Due to class diversity, we deal with variable dependencies of data features arising from structural specificities and biases, but also from possible overlaps. Since highly connected regions of the human interactome may point to potential protein complexes, we have focused on the concept of modularity, and elucidated the detection power of module extraction algorithms by independent validations based on GO, MIPS and KEGG. From the combination of protein interactions with gene expressions, a confidence scoring scheme has been proposed before proceeding via GO with further classification in permanent and transient modules.</p> <p>Conclusions</p> <p>Disaggregated interactomes are shown to be informative for inferring modularity, thus contributing to perform an effective integrative analysis. Validation of the extracted modules by multiple annotation allows for the assessment of confidence measures assigned to the modules in a protein pathway context. Notably, the proposed multilayer confidence scheme can be used for network calibration by enabling a transition from unweighted to weighted interactomes based on biological evidence.</p

CCBE1 Is Essential for Epicardial Function during Myocardium Development

Funding: This work was supported by Fundação para a Ciência e a Tecnologia (FCT) grants (PTDC/MEC-CAR/29590/2017) to JA Belo and by the Scientific Employment Stimulus to JMI (Norma Transitória 8189/2018, FCT), and iNOVA4Health—UIDB/Multi/04462/2013, a program Int. J. Mol. Sci. 2022, 23, 12642 14 of 17 financially supported by Fundação para a Ciência e Tecnologia/Ministério da Educação e Ciência, through national funds and co-funded by FEDER under the PT2020 Partnership Agreement.The epicardium is a single cell layer of mesothelial cells that plays a critical role during heart development contributing to different cardiac cell types of the developing heart through epithelial-to-mesenchymal transition (EMT). Moreover, the epicardium is a source of secreted growth factors that promote myocardial growth. CCBE1 is a secreted extracellular matrix protein expressed by epicardial cells that is required for the formation of the primitive coronary plexus. However, the role of CCBE1 during epicardial development was still unknown. Here, using a Ccbe1 knockout (KO) mouse model, we observed that loss of CCBE1 leads to congenital heart defects including thinner and hyper-trabeculated ventricular myocardium. In addition, Ccbe1 mutant hearts displayed reduced proliferation of cardiomyocyte and epicardial cells. Epicardial outgrowth culture assay to assess epicardial-derived cells (EPDC) migration showed reduced invasion of the collagen gel by EPDCs in Ccbe1 KO epicardial explants. Ccbe1 KO hearts also displayed fewer nonmyocyte/nonendothelial cells intramyocardially with a reduced proliferation rate. Additionally, RNA-seq data and experimental validation by qRT-PCR showed a marked deregulation of EMT-related genes in developing Ccbe1 mutant hearts. Together, these findings indicate that the myocardium defects in Ccbe1 KO mice arise from disruption of epicardial development and function.publishersversionpublishe