A systems-based approach for detecting molecular interactions across tissues.

Current high-throughput gene expression experiments have a straightforward design of examining the gene expression of one group or condition relative to that of another. The data is typically analyzed as if they represent strictly intracellular events, and often treats genes as coming from a homogeneous population. Although intracellular events are crucial to nearly all biological processes, cell-cell interactions are often just as important, especially when gene expression data is generated from heterogeneous cell populations, such as from whole tissues. Cell-cell molecular interactions are generally lost in the available analytical procedures and as a result, are not examined experimentally, at least not accurately or with efficiency. Most importantly, this imposes major limitations when studying gene expression changes in multiple samples that interact with one another. In order to addresses the limitations of current techniques, we have developed a novel systems-based approach that expands the traditional analysis of gene expression in two stages. This includes a novel sequence-based meta-analytic tool, AbsIDconvert, that allows for conversion of annotated features using an interval tree for storing and querying absolute genomic coordinates for comparison of multi-scale macro-molecule identifiers across platforms and/or organisms. In addition, a systems-based heuristic algorithm is developed to find intercellular interactions between two sets of genes, potentially from different tissues by utilizing location information of each gene along with the information available in the secondary databases in the form of interactions, pathways and signaling. AbsIDconvert is shown to provide a high accuracy in identifier conversion as compared to other available methodologies (typically at an average rate of 84%) while maintaining a higher efficiency (O(n*log(n)). Our intercellular interaction approach and underlying visualization shows promise in allowing researchers to uncover novel signaling pathways in an intercellular fashion that to this point has not been possible

Detection of Molecular Paths Associated with Insulitis and Type 1 Diabetes in Non-Obese Diabetic Mouse

Recent clinical evidence suggests important role of lipid and amino acid metabolism in early pre-autoimmune stages of type 1 diabetes pathogenesis. We study the molecular paths associated with the incidence of insulitis and type 1 diabetes in the Non-Obese Diabetic (NOD) mouse model using available gene expression data from the pancreatic tissue from young pre-diabetic mice. We apply a graph-theoretic approach by using a modified color coding algorithm to detect optimal molecular paths associated with specific phenotypes in an integrated biological network encompassing heterogeneous interaction data types. In agreement with our recent clinical findings, we identified a path downregulated in early insulitis involving dihydroxyacetone phosphate acyltransferase (DHAPAT), a key regulator of ether phospholipid synthesis. The pathway involving serine/threonine-protein phosphatase (PP2A), an upstream regulator of lipid metabolism and insulin secretion, was found upregulated in early insulitis. Our findings provide further evidence for an important role of lipid metabolism in early stages of type 1 diabetes pathogenesis, as well as suggest that such dysregulation of lipids and related increased oxidative stress can be tracked to beta cells

Transcription factor target prediction using multiple short expression time series from Arabidopsis thaliana

BACKGROUND: The central role of transcription factors (TFs) in higher eukaryotes has led to much interest in deciphering transcriptional regulatory interactions. Even in the best case, experimental identification of TF target genes is error prone, and has been shown to be improved by considering additional forms of evidence such as expression data. Previous expression based methods have not explicitly tried to associate TFs with their targets and therefore largely ignored the treatment specific and time dependent nature of transcription regulation. RESULTS: In this study we introduce CERMT, Covariance based Extraction of Regulatory targets using Multiple Time series. Using simulated and real data we show that using multiple expression time series, selecting treatments in which the TF responds, allowing time shifts between TFs and their targets and using covariance to identify highly responding genes appear to be a good strategy. We applied our method to published TF - target gene relationships determined using expression profiling on TF mutants and show that in most cases we obtain significant target gene enrichment and in half of the cases this is sufficient to deliver a usable list of high-confidence target genes. CONCLUSION: CERMT could be immediately useful in refining possible target genes of candidate TFs using publicly available data, particularly for organisms lacking comprehensive TF binding data. In the future, we believe its incorporation with other forms of evidence may improve integrative genome-wide predictions of transcriptional networks

Human cognition inspired procedures for part family formation based on novel Inspection Based Clustering approach

Human cognition based procedures are promising approaches for solving different kind or problems, and this paper addresses the part family formation problem inspired by a human cognition procedure through a graph-based approach, drawing on pattern recognition. There are many algorithms which consider nature inspired models for solving a broad range of problem types. However, there is a noticeable existence of a gap in implementing models based on human cognition, which are generally characterized by “visual thinking”, rather than complex mathematical models. Hence, the natural power of reasoning - by detecting the patterns that mimic the natural human cognition - is used in this study as this paper is based on the partial implementation of graph theory in modelling and solving issues related to the grouping of the parts to be processed by one machine, regardless of their size. The obtained results have shown that most of the problems solved by using the proposed approach have provided interesting benchmark results when compared with previous results given by GRASP (Greedy Randomized Adaptive Search Procedure) heuristics.This work has been supported by national funds through FCT - Fundacao para a Ciencia e Tecnologia - under the [UID/CEC/00319/2019] project, and under the RD Units Projects Scopes: UIDP/04077/2020 and UIDB/04077/2020, UIDP/04077/2020 and UIDB/04077/2020

Understanding Metastasis Organotropism Patterns Through Within-cell and Between-cells Molecular Interaction Networks

Tese de mestrado, Bioquímica e Biomedicina, 2023, Universidade de Lisboa, Faculdade de CiênciasMetastasis is responsible for the majority of cancer-related deaths. It occurs when cells from a primary tumour disseminate and initiate new tumours at distant organ sites. Metastasizing cells have to exhibit especial characteristics that allow them to surpass all barriers and bottlenecks in their way to effective colonization. Ensuring survival throughout this process depends on how those cells communicate with the surrounding environments. Patterns of metastasis are remarkably variable between cancer types. In fact, distinct cancers seem to be predisposed to metastasize to specific organs, a feature known as metastasis organotropism. Our work is based on the hypothesis that organotropism can be partially explained by the extent of intercellular communication between metastasizing cells and cells in the secondary organ. Some proteins that establish intercellular interactions are tissue-specific and can be expressed in pre-cancerous tissue. Using RNA-seq data from non-diseased tissue, we built networks of intercellular proteinprotein interactions between cells from the primary cancer tissue and cells from a potential metastasis tissue. Controlling for other factors that affect organotropism, we found that sites where cancers metastasize more often tend to establish a larger number of intercellular interactions than sites with low incidence of metastasis. We detected 528 literature curated interactions that might play a role in metastasis formation and contribute to the observed differences in cellcell communication, some previously known to be related to cancer and/or metastasis. Finally, using a network of signalling pathways, we observed that proteins involved in metastasisassociated interactions and their closest neighbours in the network are enriched in cancer driver genes and biological processes linked to invasion and metastasis. In conclusion, we identified intercellular interactions and proteins that drive metastasis development and help explain organotropism. These insights might constitute new research and therapeutic opportunities to treat and prevent metastasis

Predicting Function and Structure using Bioinformatics Protocols:Study of the Intracellular Regions of the Jagged and Delta Protein Families

The type I membrane-spanning proteins Jagged (Jagged-i and -2) and Delta (Delta-l, - 3 and -4) are the human ligands of Notch receptors, which mediate key signaling events in cell differentiation and morphogenesis. The Jagged and Delta proteins are composed of a relatively large extracellular region and of a 100-150 residue, yet uncharacterized cytoplasmic tail, which has been recently found to be important in Notch bi-directional signaling. We applied bioinformatics methods to analyze the intracellular region of human Notch ligands, and to predict their structural and functional properties. We searched databases for orthologues, and found that while the intracellular region is evolutionaiy well conserved within the same ligand type, a wide variability is observed in different ligands. No significant similarity was found between the intracellular region of Jagged and Delta and proteins of known 3D structure. Globularity and disorder predictions indeed suggest that these regions are largely unstructured. However, secondary structure predictions show that these regions have some propensity to form local secondary structure elements. Functional predictions based on pattern recognition imply that the specificity in the Notch machinery response might be related to specific post-translational modifications and binding motifs in the ligand cytoplasmic tail, rather than to specific interactions between the receptors and the extracellular region of the ligands. We also speculate that, given the unusual amino acid composition, the cytoplasmic tail of Jagged and Delta might be involved in zinc binding

New components of the Dictyostelium PKA pathway revealed by Bayesian analysis of expression data

<p>Abstract</p> <p>Background</p> <p>Identifying candidate genes in genetic networks is important for understanding regulation and biological function. Large gene expression datasets contain relevant information about genetic networks, but mining the data is not a trivial task. Algorithms that infer Bayesian networks from expression data are powerful tools for learning complex genetic networks, since they can incorporate prior knowledge and uncover higher-order dependencies among genes. However, these algorithms are computationally demanding, so novel techniques that allow targeted exploration for discovering new members of known pathways are essential.</p> <p>Results</p> <p>Here we describe a Bayesian network approach that addresses a specific network within a large dataset to discover new components. Our algorithm draws individual genes from a large gene-expression repository, and ranks them as potential members of a known pathway. We apply this method to discover new components of the cAMP-dependent protein kinase (PKA) pathway, a central regulator of <it>Dictyostelium discoideum </it>development. The PKA network is well studied in <it>D. discoideum </it>but the transcriptional networks that regulate PKA activity and the transcriptional outcomes of PKA function are largely unknown. Most of the genes highly ranked by our method encode either known components of the PKA pathway or are good candidates. We tested 5 uncharacterized highly ranked genes by creating mutant strains and identified a candidate cAMP-response element-binding protein, yet undiscovered in <it>D. discoideum</it>, and a histidine kinase, a candidate upstream regulator of PKA activity.</p> <p>Conclusions</p> <p>The single-gene expansion method is useful in identifying new components of known pathways. The method takes advantage of the Bayesian framework to incorporate prior biological knowledge and discovers higher-order dependencies among genes while greatly reducing the computational resources required to process high-throughput datasets.</p

Patterning by cell-to-cell communication

This thesis addresses the question of how patterning may arise through cell-to-cell communication. It combines quantitative data analysis with computational techniques to understand biological patterning processes. The fi�rst section describes an investigation into the robustness of an evolved arti�ficial patterning system. Cellular automata rules were implemented sequentially according to the instructions in a simple `genome'. In this way, a set of target patterns could be evolved using a genetic algorithm. The patterning systems were tested for robustness by perturbing cell states during their development. This exposed how certain types of patterning rule had very di�fferent levels of robustness to perturbations. Rules that generated patterns with complex divergent patterns were more likely to amplify the e�ffect of a perturbation. When smaller genomes, comprising less individual rules, were evolved to match certain target patterns, these were shown to be more likely to select complex patterning rules. As a result, the developmental systems based on smaller genomes were less robust than those with larger genome sizes. Section two provides an analysis of the patterning of microchaetes in the epithelial layer of the notum of Drosophila flies. It is shown that the pattern spacing is not sufficiently described by a model of lateral inhibition through Delta-Notch signalling between adjacent cells. A computational model is used to demonstrate the viability of long range signalling through a dynamic network of �filopodia, observed in the basal layer of the epithelium. In-vivo experiments con�rm that when fi�lopodia lengths are effected by mutations the pattern spacing reduces in accordance with the model. In the fi�nal section the behaviour of simple asynchronous cellular automata are analysed. It is shown how these diff�er to the synchronous cellular automata used in the fi�rst section. A set of rules are identifi�ed whose emergent behaviour is similar to the lateral inhibition patterning process established by the Delta-Notch signalling system. Among these rules a particular subset are found to produce patterns that adjust their spacing, over the course of their development, towards a more ordered and densely packed state. A re-examination of the Delta-Notch signalling model reveals that this type of packing optimisation could take place with either dynamic �filopodial signalling, or as an alternative, transient Delta signalling at each cell. Under certain parameter regimes the patterns become more densely packed over time, whilst maintaining a minimum zone of inhibition around each Delta expressing cell. The asynchronous CA are also used to demonstrate how stripes can be formed by cell-to-cell signalling and optimised, under certain conditions, so that they align in a single direction. This is presented as a possible novel alternative to the reaction-di�ffusion mechanism that is commonly used to model the patterning of spots and stripes

Zoonotic orthopoxviruses encode a high-affinity antagonist of NKG2D

NK and T lymphocytes express both activating and inhibiting receptors for various members of the major histocompatibility complex class I superfamily (MHCISF). To evade immunologic cytotoxicity, many viruses interfere with the function of these receptors, generally by altering the displayed profile of MHCISF proteins on host cells. Using a structurally constrained hidden Markov model, we discovered an orthopoxvirus protein, itself distantly class I–like, that acts as a competitive antagonist of the NKG2D activating receptor. This orthopoxvirus MHC class I–like protein (OMCP) is conserved among cowpox and monkeypox viruses, secreted by infected cells, and bound with high affinity by NKG2D of rodents and humans (KD ∼ 30 and 0.2 nM, respectively). OMCP blocks recognition of host-encoded ligands and inhibits NKG2D-dependent killing by NK cells. This finding represents a novel mechanism for viral interference with NKG2D and sheds light on intercellular recognition events underlying innate immunity against emerging orthopoxviruses