No description available.

RNA (Ribonuceic Acid) sequencing technology is a powerful technology used to give re- searchers essential information about the functionality of genes. The transcriptomic study and downstream analysis highlight the functioning of the genes associated with a specific biological process/treatment. In practice, differentially expressed genes associated with a particular treatment or genotype are subjected to downstream analysis to find some critical set of genes. This critical set of genes/ genes pathways infers the effect of the treatment in a cell or tissue. This disserta- tion describes the multiple stages framework of finding these critical sets of genes using different analysis methodologies and inference algorithms. RNA sequencing technology helps to find the differentially expressed genes associated with the treatments and genotypes. The preliminary step of RNA-seq analysis consists of extracting the mRNA(messenger RNA) followed by mRNA libraries’ preparation and sequencing using the Illumina HiSeq 2000 platform. The later stage analysis starts with mapping the RNA sequencing data (obtained from the previous step) to the genome annotations and counting each annotated gene’s reads to produce the gene expression data. The second step involves using the statistical method such as linear model fit, clustering, and probabilistic graphical modeling to analyze genes and gene networks’ role in treatment responses. In this dissertation, an R software package is developed that compiles all the RNA sequencing steps and the downstream analysis using the R software and Linux environment. Inference methodology based on loopy belief propagation is conducted on the gene networks to infer the differential expression of the gene in the further step. The loopy belief propagation algorithm uses a computational modeling framework that takes the gene expression data and the transcriptional Factor interacting with the genes. The inference method starts with constructing a gene-Transcriptional Factor network. The construction of the network uses an undirected proba- bilistic graphical modeling approach. Later the belief message is propagated across all the nodes of the graphs. The analysis and inference methods explained in the dissertation were applied to the Arabidopsis plant with two different genotypes subjected to two different stress treatments. The results for the analysis and inference methods are reported in the dissertation.

京都大学 / 新制・課程博士 / 博士(医学) / 甲第23572号 / 医博第4786号 / 新制||医||1054(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 妹尾 浩, 教授 武藤 学, 教授 小川 誠司 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

Single-cell RNA-sequencing (scRNA-seq) technology enables researchers to investigate a genome at the cellular level with unprecedented resolution. An organism consists of a heterogeneous collection of cell types, each of which plays a distinct role in various biological processes. Hence, the first step of scRNA-seq data analysis often is to distinguish cell types so that they can be investigated separately. Researchers have recently developed several automated cell type annotation tools based on supervised machine learning algorithms, requiring neither biological knowledge nor subjective human decisions. Dropout is a crucial characteristic of scRNA-seq data which is widely utilized in differential expression analysis but not by existing cell annotation methods. We present scAnnotate, a cell annotation tool that fully utilizes dropout information. We model every gene’s marginal distribution using a mixture model, which describes both the dropout proportion and the distribution of the non-dropout expression levels. Then, using an ensemble machine learning approach, we combine the mixture models of all genes into a single model for cell-type annotation. This combining approach can avoid estimating numerous parameters in the high-dimensional joint distribution of all genes. Using fourteen real scRNA-seq datasets, we demonstrate that scAnnotate is competitive against nine existing annotation methods, and that it accurately annotates cells when training and test data are (1) similar, (2) cross-platform, and (3) cross-species. Of the cells that are incorrectly annotated by scAnnotate, we find that a majority are different from those of other methods. / Graduate / 2023-07-27

No description available.

Clostridium perfringens is a Gram-positive (Gr+) anaerobic pathogen that was found to contain Type IV pilus (T4P) system genes within the genomes of all its sequenced strains. T4P are widely used in Gram-negative organisms for aggregation, biofilm formation, adherence, and DNA uptake. Because few examples of T4P-utilizing Gram-positive bacteria are studied to date, we wanted to characterize the T4P system in this Gr+ bacterium. To understand the regulation of T4P genes and therefore better understand their expression, we employed the highly powerful next-generation sequencing tool RNA-seq in a variety of conditions. RNA-seq uncovered previously unknown regulatory mechanisms surrounding T4P genes as well as provided transcriptional information for most of the genes in the C. perfringens strain 13 genome. We also utilized reporter gene assays to look at post-transcriptional regulation of T4P promoters. The wealth of RNA-seq data acted as a jumping-off point for many smaller projects involving transcriptional regulators that may influence T4P expression. We investigated a novel small RNA in close proximity to the major T4P operon, as well as two little-characterized transcriptional regulators that function in the same conditions as T4P genes. RNA-seq also provided data to develop a method for protein purification from C. perfringens without induction. / Master of Science / Clostridium perfringens is a ubiquitous bacterium that causes many diseases that negatively impact the public, including gas gangrene and food poisoning. This bacterium is able to infect through its ability to adhere to muscle or intestinal cells, and its infection results in breakdown of muscle tissue or severe diarrhea. In order to investigate how this bacterium senses its environment and consequently infects human beings, we looked at which genes the bacteria used in different environments, particularly on solid surfaces and in liquids. We also looked at a profile of different nutrients in order to determine which conditions cause the bacterium to use genes that start the infection process. This study impacts the literature on Clostridium perfringens by highlighting what physical cues signal this bacterium to start infecting, in hopes of disrupting this process and provide relief from C. perfringens infections in the medical community in the future.

Aedes aegypti is the primary vector for dengue, zika, chikungunya, and yellow fever viruses. Disease transmission through this mosquito places over 40% of the world's population at risk of contracting one or more of these pathogens. Current control strategies such as insecticide application have failed or carry additional burdens, such as off-target toxicity to mammals and birds. Our lab proposes utilizing a conserved arthropod hormone pathway, juvenile hormone (JH), related to growth and reproduction to curb these vector populations and reduce disease transmission. Additionally, JH is nontoxic to birds and mammals; it requires incredibly high doses to have lethal effects. We hypothesize that JH-responsive genes expressed early in the adult are responsible for her reproductive capacity and by manipulating the signaling downstream of the receptor, we will be able to decrease the female's fecundity and limit vector populations. Via bioinformatics screening of RNA-sequencing data using the New Tuxedo pipeline, we identified 47 potential transcription factor candidates. With the use of in vitro culturing of the mosquito's reproductive tissues in the presence of a translation inhibitor, we identified two early JH responsive gene candidates, FoxA and zinc finger 519, p-value <0.05. The functional characterization of these two remains to be seen, however, in Drosophila melanogaster, they both have roles in chromatin remodeling and require protein partners to carry out long range interactions. / Master of Science in Life Sciences / The mosquito, Aedes aegypti, is responsible for the spread of a myriad of viruses such as dengue, zika, and chikungunya. Currently, these infections have no vaccine or treatment available and transmission rates continue to steeply rise in response to the spread of breeding grounds. Popular insecticides carry detriments such as off-species toxicity and continuous application to treatment areas. Our lab proposes an alternative to these chemical insecticides by manipulating a developmental pathway in the mosquito. The Juvenile Hormone pathway is conserved in arthropods, responsible for growth and reproduction, and the hormone is nontoxic to mammals. Through the combination of bioinformatics and genomics studies, we have identified two JH-responsive gene candidates that are potential regulators of this pathway.

Yes / Maintenance of skeletal muscle mass and function is critical to health and wellbeing throughout the lifespan. However, disuse through reduced physical activity (e.g., sedentarism), immobilisation, bed rest or microgravity has significant adverse effects on skeletal muscle health. Conversely, resistance exercise training (RET) induces positive muscle mass and strength adaptations. Several studies have employed microarray technology to understand the transcriptional basis of muscle atrophy and hypertrophy after disuse and RET, respectively, to devise fully effective therapeutic interventions. More recently, rapidly falling costs have seen RNA-sequencing (RNA-seq) increasingly applied in exploring muscle adaptations to RET and disuse. The aim of this review is to summarise the transcriptional responses to RET or disuse measured via RNA-seq in young and older adults. We also highlight analytical considerations to maximise the utility of RNA-seq in the context of skeletal muscle research. The limited number of muscle transcriptional signatures obtained thus far with RNA-seq are generally consistent with those obtained with microarrays. However, RNA-seq may provide additional molecular insight, particularly when combined with data-driven approaches such as correlation network analyses. In this context, it is essential to consider the most appropriate study design parameters as well as bioinformatic and statistical approaches. This will facilitate the use of RNA-seq to better understand the transcriptional regulators of skeletal muscle plasticity in response to increased or decreased use.

This thesis explores the application of hyperbolic geometry to deep variational autoencoders (VAEs) for learning low-dimensional latent representations of data. Hyperbolic geometry has gained increasing attention in machine learning due to its potential to embed hierarchical data structures in continuous, differentiable manifolds. We extend previous work investi- gating the Poincaré ball model of hyperbolic geometry and its integration into VAEs. By evaluating hyperbolic VAEs on the MNIST handwritten digit dataset and a single-cell RNA sequencing dataset of metastatic melanoma, we assess whether the inductive bias and math- ematical properties of hyperbolic spaces result in improved data representations compared to standard Euclidean VAEs, especially for single-cell RNA sequencing data. Our findings demonstrate the potential advantages of leveraging hyperbolic geometry for representation learning, while also highlighting some challenges. This work contributes to the growing field of geometric deep learning and provides insights for future research on non-Euclidean approaches to representation learning.

Yes / Functional genomics research can give us valuable insights into bacterial gene function. RNA Sequencing (RNA-seq) can generate information on transcript abundance in bacteria following abiotic stress treatments. In this study, we used the RNA-seq technique to study the transcriptomes of the opportunistic nosocomial pathogen Pseudomonas aeruginosa PAO1 following heat shock. Samples were grown at both the human body temperature (37 C) and an arbitrarily-selected temperature of 46 C. In this work using RNA-seq, we identified 133 genes that are differentially expressed at 46 C compared to the human body temperature. Our work identifies some key P. aeruginosa PAO1 genes whose products have importance in both environmental adaptation as well as in vivo infection in febrile hosts. More importantly, our transcriptomic results show that many genes are only expressed when subjected to heat shock. Because the RNA-seq can generate high throughput gene expression profiles, our work reveals many unanticipated genes with further work to be done exploring such genes products. / University of Malaya High Impact Research (HIR) UM-MOHE HIR Grants (UM.C/625/1/HIR/MOHE/CHAN/14/1, No. H-50001-A000027; UM.C/625/1/HIR/MOHE/CHAN/01, No. A000001-50001); PPP Grant (PG081-2015B)