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

Parrilla urethra: A sequalae of electric shock torture to genitals in men. A 40 case series in Kashmir (India)

Introduction: Since the 20th century, electric shock torture has become one of the most prevalent methods of torture partly because it produces sequelae that are more challenging to visibly detect, particularly when administered using high voltage and low current. In sexual torture, a wire is wrapped around the head of the penis and a wire electrode is inserted into the urethra.This produces unbearable pain and can lead to urethral strictures with devastating physical and psychological consequences. Objective: To document electric shock torture to genitals as an etiologic agent in urethral stricture and erectile dysfunction amongst survivors of electric torture introducing the term “parrilla urethra” for the electric shock torture urethral stricture. Materials and methods: The study included 40 patients who attended the Department of Urology, Directorate of Health services, Srinagar, Kashmir, India with obstructive lower urinary tract symptoms (LUTS) / obstructive uroflowmetry between March 2010 and November 2014. All cases had an antecedent of electric shock torture to genitals six months to one year prior to examination. Pre-post psychological impact and well-being was used through Global Assessment of Functioning (GAF) scores. Results: The mean age of patients was 35.6 years. Most of the urethral strictures were located in the anterior urethra. Some degree of erectile dysfunction was present in all (100%) of patients. Psychological sequelae including depression, anxiety, acute stress disorder and symptoms of post-traumatic stress disorder were observed. Patients were treated with standard urethroplasty procedures after addressing the urethral stricture.This improved both physical and psychological sequelae of torture. &nbsp

Development of a real-time quantitative PCR assay for detection of a stable genomic region of BK virus

<p>Abstract</p> <p>Background</p> <p>BK virus infections can have clinically significant consequences in immunocompromised individuals. Detection and monitoring of active BK virus infections in certain situations is recommended and therefore PCR assays for detection of BK virus have been developed. The performance of current BK PCR detection assays is limited by the existence of viral polymorphisms, unknown at the time of assay development, resulting in inconsistent detection of BK virus. The objective of this study was to identify a stable region of the BK viral genome for detection by PCR that would be minimally affected by polymorphisms as more sequence data for BK virus becomes available.</p> <p>Results</p> <p>Employing a combination of techniques, including amino acid and DNA sequence alignment and interspecies analysis, a conserved, stable PCR target region of the BK viral genomic region was identified within the VP2 gene. A real-time quantitative PCR assay was then developed that is specific for BK virus, has an analytical sensitivity of 15 copies/reaction (450 copies/ml) and is highly reproducible (CV ≤ 5.0%).</p> <p>Conclusion</p> <p>Identifying stable PCR target regions when limited DNA sequence data is available may be possible by combining multiple analysis techniques to elucidate potential functional constraints on genomic regions. Applying this approach to the development of a real-time quantitative PCR assay for BK virus resulted in an accurate method with potential clinical applications and advantages over existing BK assays.</p

Evolved Resistance to Placental Invasion Secondarily Confers Increased Survival in Melanoma Patients

Mammals exhibit large differences in rates of cancer malignancy, even though the tumor formation rates may be similar. In placental mammals, rates of malignancy correlate with the extent of placental invasion. Our Evolved Levels of Invasibility (ELI) framework links these two phenomena identifying genes that potentially confer resistance in stromal fibroblasts to limit invasion, from trophoblasts in the endometrium, and from disseminating melanoma in the skin. Herein, using patient data from The Cancer Genome Atlas (TCGA), we report that these anti-invasive genes may be crucial in melanoma progression in human patients, and that their loss is correlated with increased cancer spread and lowered survival. Our results suggest that, surprisingly, these anti-invasive genes, which have lower expression in humans compared to species with non-invasive placentation, may potentially prevent stromal invasion, while a further reduction in their levels increases the malignancy and lethality of melanoma. Our work links evolution, comparative biology, and cancer progression across tissues, indicating new avenues for using evolutionary medicine to prognosticate and treat human cancers

Evolved Resistance to Placental Invasion Secondarily Confers Increased Survival in Melanoma Patients

Mammals exhibit large differences in rates of cancer malignancy, even though the tumor formation rates may be similar. In placental mammals, rates of malignancy correlate with the extent of placental invasion. Our Evolved Levels of Invasibility (ELI) framework links these two phenomena identifying genes that potentially confer resistance in stromal fibroblasts to limit invasion, from trophoblasts in the endometrium, and from disseminating melanoma in the skin. Herein, using patient data from The Cancer Genome Atlas (TCGA), we report that these anti-invasive genes may be crucial in melanoma progression in human patients, and that their loss is correlated with increased cancer spread and lowered survival. Our results suggest that, surprisingly, these anti-invasive genes, which have lower expression in humans compared to species with non-invasive placentation, may potentially prevent stromal invasion, while a further reduction in their levels increases the malignancy and lethality of melanoma. Our work links evolution, comparative biology, and cancer progression across tissues, indicating new avenues for using evolutionary medicine to prognosticate and treat human cancers

Metastatic Transition of Pancreatic Ductal Cell Adenocarcinoma Is Accompanied by the Emergence of Pro-Invasive Cancer-Associated Fibroblasts

Cancer-associated fibroblasts (CAFs) are now appreciated as key regulators of cancer metastasis, particularly in cancers with high stromal content, e.g., pancreatic ductal cell carcinoma (PDAC). However, it is not yet well understood if fibroblasts are always primed to be cooperative in PDAC transition to metastasis, if they undergo transformation which ensures their cooperativity, and if such transformations are cancer-driven or intrinsic to fibroblasts. We performed a fibroblast-centric analysis of PDAC cancer, as it transitioned from the primary site to trespass stromal compartment reaching the lymph node using published single-cell RNA sequencing data by Peng et al. We have characterized the change in fibroblast response to cancer from a normal wound healing response in the initial stages to the emergence of subclasses with myofibroblast and inflammatory fibroblasts such as signatures. We have previously posited &ldquo;Evolved Levels of Invasibility (ELI)&rdquo;, a framework describing the evolution of stromal invasability as a selected phenotype, which explains the large and correlated reduction in stromal invasion by placental trophoblasts and cancer cells in certain mammals. Within PDAC samples, we found large changes in fibroblast subclasses at succeeding stages of PDAC progression, with the emergence of specific subclasses when cancer trespasses stroma to metastasize to proximal lymph nodes (stage IIA to IIB). Surprisingly, we found that the initial metastatic transition is accompanied by downregulation of ELI-predicted pro-resistive genes, and the emergence of a subclass of fibroblasts with ELI-predicted increased invasibility. Interestingly, this trend was also observed in stellate cells. Using a larger cohort of bulk RNAseq data from The Cancer Genome Atlas for PDAC cancers, we confirmed that genes describing this emergent fibroblast subclass are also correlated with lymph node metastasis of cancer cells. Experimental testing of selected genes characterizing pro-resistive and pro-invasive fibroblast clusters confirmed their contribution in regulating stromal invasability as a phenotype. Our data confirm that the complexity of stromal response to cancer is really a function of stage-wise emergence of distinct fibroblast clusters, characterized by distinct gene sets which confer initially a predominantly pro-resistive and then a pro-invasive property to the stroma. Stromal response therefore transitions from being tumor-limiting to a pro-metastatic state, facilitating stromal trespass and the onset of metastasis

Abstract

Identifying neuronal assemblies with local and global connectivity with scale space spectral clusterin