A novel subnetwork alignment approach predicts new components of the cell cycle regulatory apparatus in Plasmodium falciparum

Background According to the World Health organization, half the world\u27s population is at risk of contracting malaria. They estimated that in 2010 there were 219 million cases of malaria, resulting in 660,000 deaths and an enormous economic burden on the countries where malaria is endemic. The adoption of various high-throughput genomics-based techniques by malaria researchers has meant that new avenues to the study of this disease are being explored and new targets for controlling the disease are being developed. Here, we apply a novel neighborhood subnetwork alignment approach to identify the interacting elements that help regulate the cell cycle of the malaria parasite Plasmodium falciparum. Results Our novel subnetwork alignment approach was used to compare networks in Escherichia coli and P. falciparum. Some 574 P. falciparum proteins were revealed as functional orthologs of known cell cycle proteins in E. coli. Over one third of these predicted functional orthologs were annotated as conserved Plasmodium proteins or putative uncharacterized proteins of unknown function. The predicted functionalities included cyclins, kinases, surface antigens, transcriptional regulators and various functions related to DNA replication, repair and cell division. Conclusions The results of our analysis demonstrate the power of our subnetwork alignment approach to assign functionality to previously unannotated proteins. Here, the focus was on proteins involved in cell cycle regulation. These proteins are involved in the control of diverse aspects of the parasite lifecycle and of important aspects of pathogenesis

Predicting and exploring network components involved in pathogenesis in the malaria parasite via novel subnetwork alignments

Background Malaria is a major health threat, affecting over 40% of the world\u27s population. The latest report released by the World Health Organization estimated about 207 million cases of malaria infection, and about 627,000 deaths in 2012 alone. During the past decade, new therapeutic targets have been identified and are at various stages of characterization, thanks to the emerging omics-based technologies. However, the mechanism of malaria pathogenesis remains largely unknown. In this paper, we employ a novel neighborhood subnetwork alignment approach to identify network components that are potentially involved in pathogenesis. Results Our module-based subnetwork alignment approach identified 24 functional homologs of pathogenesis-related proteins in the malaria parasite P. falciparum, using the protein-protein interaction networks in Escherichia coli as references. Eighteen out of these 24 proteins are associated with 418 other proteins that are related to DNA replication, transcriptional regulation, translation, signaling, metabolism, cell cycle regulation, as well as cytoadherence and entry to the host. Conclusions The subnetwork alignments and subsequent protein-protein association network mining predicted a group of malarial proteins that may be involved in parasite development and parasite-host interaction, opening a new systems-level view of parasite pathogenesis and virulence

Computational techniques for cell signaling

Cells can be viewed as sophisticated machines that organize their constituent components and molecules to receive, process, and respond to signals. The goal of the scientist is to uncover both the individual operations underlying these processes and the mechanism of the emergent properties of interest that give rise to the various phenomena such as disease, development, recovery or aging. Cell signaling plays a crucial role in all of these areas. The complexity of biological processes coupled with the physical limitations of experiments to observe individual molecular components across small to large scales limits the knowlege that can be gleaned from direct observations. Mathematical modeling can be used to estimate parameters that are hidden or too difficult to observe in experiments, and it can make qualitative predictions that can distinguish between hypotheses of interest. Statistical analysis can be employed to explore the large amounts of data generated by modern experimental techniques such as sequencing and high-throughput screening, and it can integrate the observations from many individual experiments or even separate studies to generate new hypotheses. This dissertation employs mathematical and statistical analyses for three prominent aspects of cell signaling: the physical transfer of signaling molecules between cells, the intracellular protein machinery that organizes into pathways to process these signals, and changes in gene expression in response to cell signaling. Computational biology can be described as an applied discipline in that it aims to further the knowledge of a discipline that is distinct from itself. However, the richness of the problems encountered in biology requires continuous development of better methods equipped to handle the complexity, size, or uncertainty of the data, and to build in constraints motivated by the reality of the underlying biological system. In addition, better computational and mathematical methods are also needed to model the emergent behavior that arises from many components. The work presented in this dissertation fulfills both of these roles. We apply known and existing techniques to analyse experimental data and provide biological meaning, and we also develop new statistical and mathematical models that add to the knowledge and practice of computational biology. Much of cell signaling is initiated by signal transduction from the exterior, either by sensing the environmental conditions or the recpetion of specific signals from other cells. The phenomena of most immediate concern to our species, that of human health and disease, are usually also generated from, and manifest in, our tissues and organs due to the interaction and signaling between cells. A modality of inter-cellular communication that was regarded earlier as an obscure phenomenon but has more recently come to the attention of the scientific community is that of tunneling nanotubes (TNs). TNs have been observed as thin (of the order of 100 nanometers) extensions from a cell to another closely located one. The formation of such structures along with the intercellular exchange of molecules through them, and their interaction with the cytoskeleton, could be involved in many important processes, such as tissue formation and cancer growth. We describe a simple model of passive transport of molecules between cells due to TNs. Building on a few basic assumptions, we derive parametrized, closed-form expressions to describe the concentration of transported molecules as a function of distance from a population of TN-forming cells. Our model predicts how the perfusion of molecules through the TNs is affected by the size of the transferred molecules, the length and stability of nanotube formation, and the differences between membrane-bound and cytosolic proteins. To our knowledge, this is the first published mathematical model of intercellular transfer through tunneling nanotubes. We envision that experimental observations will be able to confirm or improve the assumptions made in our model. Furthermore, quantifying the form of inter-cellular communication in the basic scenario envisioned in our model can help suggest ways to measure and investigate cases of possible regulation of either formation of tunneling nanotubes or transport through them. The next problem we focus on is uncovering how the interactions between the genes and proteins in a cell organize into pathways to process call signals or perform other tasks. The ability to accurately model and deeply understand gene and protein interaction networks of various kinds can be very powerful for prioritizing candidate genes and predicting their role in various signaling pathways and processes. A popular technique for gene prioritization and function prediction is the graph diffusion kernel. We show how the graph diffusion kernel is mathematically similar to the Ising spin graph, a model popular in statistical physics but not usually employed on biological interaction networks. We develop a new method for calculating gene association based on the Ising spin model which is different from the methods common in either bioinformatics or statistical physics. We show that our method performs better than both the graph diffusion kernel and its commonly used equivalent in the Ising model. We present a theoretical argument for understanding its performance based on ideas of phase transitions on networks. We measure its performance by applying our method to link prediction on protein interaction networks. Unlike candidate gene prioritization or function prediction, link prediction does not depend on the existing annotation or characterization of genes for ground truth. It helps us to avoid the confounding noise and uncertainty in the network and annotation data. As a purely network analysis problem, it is well suited for comparing network analysis methods. Once we know that we are accurately modeling the interaction network, we can employ our model to solve other problems like gene prioritization using interaction data. We also apply statistical analysis for a specific instance of a cell signaling process: the drought response in Brassica napus, a plant of scientific and economic importance. Important changes in the cell physiology of guard cells are initiated by abscisic acid, an important phytohormone that signals water deficit stress. We analyse RNA-seq reads resulting from the sequencing of mRNA extracted from protoplasts treated with abscisic acid. We employ sequence analysis, statisitical modeling, and the integration of cross-species network data to uncover genes, pathways, and interactions important in this process. We confirm what is known from other species and generate new gene and interaction candidates. By associating functional and sequence modification, we are also able to uncover evidence of evolution of gene specialization, a process that is likely widespread in polyploid genomes. This work has developed new computational methods and applied existing tools for understanding cellular signaling and pathways. We have applied statistical analysis to integrate expression, interactome, pathway, regulatory elements, and homology data to infer \textit{Brassica napus} genes and their roles involved in drought response. Previous literature suggesting support for our findings from other species based on independent experiments is found for many of of these findings. By relating the changes in regulatory elements, our RNA-seq results and common gene ancestry, we present evidence of its evolution in the context of polyploidy. Our work can provide a scientific basis for the pursuit of certain genes as targets of breeding and genetic engineering efforts for the development of drought tolerant oil crops. Building on ideas from statistical physics, we developed a new model of gene associations in networks. Using link prediction as a metric for the accuracy of modeling the underlying structure of a real network, we show that our model shows improved performance on real protein interaction networks. Our model of gene associations can be use to prioritize candidate genes for a disease or phenotype of interest. We also develop a mathematical model for a novel inter-cellular mode of biomolecule transfer. We relate hypotheses about the dynamics of TN formation, stability, and nature of molecular transport to quantitative predictions that may be tested by suitable experiments. In summary, this work demostrates the application and development of computational analysis of cell signaling at the level of the transcriptome, the interactome, and physical transport

Comparative metabolomics of erythroid lineage and Plasmodium life stages reveal novel host and parasite metabolism

Malaria, caused by the Apicomplexan parasite Plasmodium is a deadly disease which poses a huge health and economic burden over many populations in the world, mostly in sub-Saharan Africa and Asia. To design new intervention strategies and to improve upon existing drugs against malaria, it is important to understand the biochemistry of the Plasmodium parasite and its interaction with the host. We used metabolomics to dissect the biology of the reticulocyte preferring rodent malaria parasite Plasmodium berghei and showed that metabolic reserves in the reticulocytes can aid in survival of malaria parasites when their metabolism is genetically or chemically disrupted, pointing towards a direct role of host cell metabolism in parasite survival. These results have implications for currently used ways of intermediation in malaria infections which target only parasite metabolism against the human malaria parasites, Plasmodium vivax which prefers to infect reticulocytes and Plasmodium falciparum which is capable of infecting all erythrocytes. We also used metabolomics to show the biochemical differences between the asexual and sexual stages of P. berghei parasites and our data gave additional insights into the preparatory phase of the gametocyte stage at the metabolic level with the discovery of a phosphagen system which plays a role in gametogenesis. Targeted metabolomics of P. berghei life stages using isotopic labelling showed that TCA cycle metabolism is predominant in the mosquito stages. Discovery of a reductive arm of TCA metabolism in reticulocytes pointed towards the existence of rudimentary mitochondria in young erythrocytes. Another surprising discovery was the presence of up regulated γ-Aminobutyric acid (GABA) metabolism in the ookinete stage in P. berghei which may act as an energy source during the ookinete to oocyst transition in the mosquito. This pathway presented novel candidates for transmission blocking

DECIPHERING THE ROLE OF HSP110 CHAPERONES IN DISEASES OF PROTEIN MISFOLDING

Molecular chaperones maintain protein homeostasis (proteostasis) by ensuring the proper folding of polypeptides. Loss of proteostasis has been linked to the onset of numerous neurodegenerative disorders including Alzheimer’s, Parkinson’s, and Huntington’s disease. Hsp110 is a member of the Hsp70 class of molecular chaperones and acts as a nucleotide exchange factor (NEF) for Hsp70, the preeminent Hsp70-family protein folding chaperone. Hsp110 promotes rapid cycling of ADP for ATP, allowing Hsp70 to properly fold nascent or unfolded polypeptides in iterative cycles. In addition to its NEF activity, Hsp110 possesses an Hsp70-like substrate binding domain (SBD) whose biological roles are undefined. Previous work in Drosophila melanogaster has shown that loss of the sole Hsp110 gene (Hsc70Cb) accelerates the aggregation of polyglutamine-expanded human huntingtin (HTT), the causative agent of Huntington’s disease; while its overexpression protects against polyQ-mediated neuronal cell death. I hypothesize that in addition to its role as an Hsp70 NEF, Drosophila Hsp110 (Hsc70Cb) may function as a protective protein “holdase”, preventing the aggregation of unfolded polypeptides via the SBD-β subdomain. In the process of generating deletion mutants in Hsc70Cb to dissect the role of the SBD-β subdomain in holdase activity, I uncovered a redundant and heretofore unknown potent holdase capacity in a 138-amino acid region carboxyl-terminal to both SBD-β and SBD-α (henceforth called the C-terminal extension). This sequence is highly conserved in animal Hsp110 genes and completely absent from fungal representatives, including Saccharomyces cerevisiaeSSE1. Furthermore, the human Hsp110s, Apg-1 and Hsp105α, also contain a C-terminal extension substrate binding region, indicating this site may be conserved among some metazoans. Upon further analysis, I determined C-terminal extension chaperoning is mediated by a predicted short intrinsically disordered region (IDR) in both fly and human Hsp110s. I demonstrate for the first time the carboxy-terminal IDRs in fly and human Hsp110s effectively prevent aggregation of numerous substrates, including amyloidogenic peptides Aβ1-42 and α-synuclein, in vitro. Additionally, I use the Hsc70CbΔSBD-β construct to show the C-terminal extension is essential for fly embryonic development, and can prevent HTT aggregation in an in vivo disease model. These data indicate Hsc70Cb modulates neurodegeneration by blocking aggregation via a combination of its holdase and nucleotide exchange activities. Through my work I have attributed a biological role to Hsp110 substrate binding, while bestowing molecular and biological significance to a previously undiscovered chaperoning domain contained within the C-terminal extension

IN SILICO METHODS FOR DRUG DESIGN AND DISCOVERY

Computer-aided drug design (CADD) methodologies are playing an ever-increasing role in drug discovery that are critical in the cost-effective identification of promising drug candidates. These computational methods are relevant in limiting the use of animal models in pharmacological research, for aiding the rational design of novel and safe drug candidates, and for repositioning marketed drugs, supporting medicinal chemists and pharmacologists during the drug discovery trajectory.Within this field of research, we launched a Research Topic in Frontiers in Chemistry in March 2019 entitled “In silico Methods for Drug Design and Discovery,” which involved two sections of the journal: Medicinal and Pharmaceutical Chemistry and Theoretical and Computational Chemistry. For the reasons mentioned, this Research Topic attracted the attention of scientists and received a large number of submitted manuscripts. Among them 27 Original Research articles, five Review articles, and two Perspective articles have been published within the Research Topic. The Original Research articles cover most of the topics in CADD, reporting advanced in silico methods in drug discovery, while the Review articles offer a point of view of some computer-driven techniques applied to drug research. Finally, the Perspective articles provide a vision of specific computational approaches with an outlook in the modern era of CADD

Knowledge derivation and data mining strategies for probabilistic functional integrated networks

PhDOne of the fundamental goals of systems biology is the experimental verification of the interactome: the entire complement of molecular interactions occurring in the cell. Vast amounts of high-throughput data have been produced to aid this effort. However these data are incomplete and contain high levels of both false positives and false negatives. In order to combat these limitations in data quality, computational techniques have been developed to evaluate the datasets and integrate them in a systematic fashion using graph theory. The result is an integrated network which can be analysed using a variety of network analysis techniques to draw new inferences about biological questions and to guide laboratory experiments. Individual research groups are interested in specific biological problems and, consequently, network analyses are normally performed with regard to a specific question. However, the majority of existing data integration techniques are global and do not focus on specific areas of biology. Currently this issue is addressed by using known annotation data (such as that from the Gene Ontology) to produce process-specific subnetworks. However, this approach discards useful information and is of limited use in poorly annotated areas of the interactome. Therefore, there is a need for network integration techniques that produce process-specific networks without loss of data. The work described here addresses this requirement by extending one of the most powerful integration techniques, probabilistic functional integrated networks (PFINs), to incorporate a concept of biological relevance. Initially, the available functional data for the baker’s yeast Saccharomyces cerevisiae was evaluated to identify areas of bias and specificity which could be exploited during network integration. This information was used to develop an integration technique which emphasises interactions relevant to specific biological questions, using yeast ageing as an exemplar. The integration method improves performance during network-based protein functional prediction in relation to this process. Further, the process-relevant networks complement classical network integration techniques and significantly improve network analysis in a wide range of biological processes. The method developed has been used to produce novel predictions for 505 Gene Ontology biological processes. Of these predictions 41,610 are consistent with existing computational annotations, and 906 are consistent with known expert-curated annotations. The approach significantly reduces the hypothesis space for experimental validation of genes hypothesised to be involved in the oxidative stress response. Therefore, incorporation of biological relevance into network integration can significantly improve network analysis with regard to individual biological questions

Genetic Engineering of Plant Seeds to Increase Thiamin (Vitamin B1) Content

Thiamine (Vitamin B1) in the form of thiamine pyrophosphate (TPP) is an essential cofactor for the function of numerous enzymes which are involved in central metabolism such as citric acid cycle, pentose phosphate pathway, Calvin cycle, isoprenoid biosynthesis, and branched-chain amino acid biosynthesis. All living organisms need thiamine. However, human and animals can synthesize TPP from thiamine, but they are not able to synthesize thiamine de novo. Therefore, human and animals must obtain thiamine from their diet to maintain a normal metabolism. Severe thiamine deficiency causes the lethal disease beriberi and Wernicke-Korsakoff syndrome in humans. The enzymes involved in thiamine de novo biosynthesis pathway are well known in microorganisms and plants, but little is known regarding the salvage pathways in plants. In order to have better insight about the thiamine salvage pathways in plants, the homologs of bacterial ThiM (thiazole kinase) were analyzed. It has been revealed that this protein in plants has thazole kinase activity which is important for thiamine salvage. In addition, analyzing the TenA_E proteins in plants shows that these proteins have amidohydrolase and aminohydrolase activity to form 4-amino-5-hydroxymethyl-2-methylpyrimidine (HMP) from the salvage of thiamine breakdown products. Thiamine plays a vital role in resistance against biotic and abiotic stresses in plants in addition to its role as a cofactor. It has been shown that elevated levels of thiamine content achieved by the seed overexpression of Thi4, ThiC, and ThiE genes can enhance the seed germination and seedlings viability under abiotic stress conditions. Additionally, thiamine and TPP over-producing lines shows altered seed carbon partitioning

Comparative genomics and systems biology of environmental stress responses relevant to fungal virulence

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