Thesis (Ph. D.)--University of Texas at Austin, 2007. / Vita. Includes bibliographical references.

Thesis (Ph. D.)--University of Washington, 2006. / Vita. Includes bibliographical references (p. 118-126).

This qualification work is trying to shed a little bit more light on some proteins present in higher plants, which structure and function in photosynthetic reaction remain unclear. In particular it treats proteins of photosystem II, called PsbR, PsbW and PsbX that are responsible for photosynthetic reaction optimization. This thesis contains data about proteins acquisition and their sequences elucidation.

Copy number variation, causing by the genome rearrangement, generally refers to the copy numbers increased or decreased of large genome segments whose lengths are more than 1kb. Such copy number variations mainly appeared as the sub-microscopic level of deletion and duplication. Copy number variation is an important component of genome structural variation, and is one of pathogenic factors of human diseases. Next generation sequencing technology is a popular CNV detection method and it has been widely used in various fields of life science research. It possesses the advantages of high throughput and low cost. By tailoring NGS technology, it is plausible to sequence individual cells. Such single cell sequencing can reveal the gene expression status and genomic variation profile of a single-cell. Single cell sequencing is promising in the study of tumor, developmental biology, neuroscience and other fields. However, there are two challenging problems encountered in CNV detection for NGS data. The first one is that since single-cell sequencing requires a special genome amplification step to accumulate enough samples, a large number of bias is introduced, making the calling of copy number variants rather challenging. The performances of many popular copy number calling methods, designed for bulk sequencings, are not consistent and cannot be applied on single-cell sequenced data directly. The second one is to simultaneously analyze genome data for multiple samples, thus achieving assembling and subgrouping similar cells accurately and efficiently. The high level of noises in single-cell-sequencing data negatively affects the reliability of sequence reads and leads to inaccurate patterns of variations. To handle the problem of reliably finding CNVs in NGS data, in this thesis, we firstly establish a workflow for analyzing NGS and single-cell sequencing data. The CNVs identification is formulated as a quadratic optimization problem with both constraints of sparsity and smoothness. Tailored from alternating direction minimization (ADM) framework, an efficient numerical solution is designed accordingly. The proposed model was tested extensively to demonstrate its superior performances. It is shown that the proposed approach can successfully reconstruct CNVs especially somatic copy number alteration patterns from raw data. By comparing with existing counterparts, it achieved superior or comparable performances in detection of the CNVs. To tackle this issue of recovering the hidden blocks within multiple single-cell DNA-sequencing samples, we present an permutation based model to rearrange the samples such that similar ones are positioned adjacently. The permutation is guided by the total variational (TV) norm of the recovered copy number profiles, and is continued until the TV-norm is minimized when similar samples are stacked together to reveal block patterns. Accordingly, an efficient numerical scheme for finding this permutation is designed, tailored from the alternating direction method of multipliers. Application of this method to both simulated and real data demonstrates its ability to recover the hidden structures of single-cell DNA sequences.

In order to identify Leishmania proteins which may be immunologically relevant or may play a role in interactions between Leishmania and its mammalian host, a Leishmania major genomic DNA library was constructed in the vector λgt11 and screened with antibodies raised to Leishmania major promastigote membranes. Two recombinant DNA clones were identified which encoded repetitive sequences (Clone 20 and Clone 39). Clone 20 encoded a repetitive peptide of 14 amino acids and clone 39 encoded an unrelated repetitive peptide of 10 amino acids. Analysis of one of these clones, Clone 20, indicated that there were two RNA transcripts of 9500 and 5200 nucleotides expressed which corresponded to this clone in Leishmania major and Leishmania donovani and this expression was not stage-specific. The results of genomic DNA analysis and isolation of additional clones encoding Clone 20 sequences indicated that there were two genes which corresponded to Clone 20 in both Leishmania major and Leishmania donovani and that these genes differed from one another with respect to the number of repeats which they contained. Antibodies against the fusion protein produced by Clone 20 recognized a series of Leishmania major proteins of apparent mol wt 250,000. Analysis of Clone 39 indicated that there was a single transcript of 7500 nucleotides expressed which corresponded to this clone in both Leishmania major and Leishmania donovani and that there was a single gene (or two identical genes) which encoded this transcript. The genomes of many protozoan parasites exhibit a high degree of plasticity with respect to chromosome size and number. The presence of highly repetitive regions within their DNA may be involved in maintaining this plasticity, allowing the parasite to evolve rapidly under selective pressure. Repetitive regions have been identified within many Plasmodia antigens and have been implicated in the ability of this parasite to evade the host immune system. The presence of Leishmania genes encoding proteins containing tandemly repeating peptides may indicate that these proteins play a similar role in evading the host immune system during the course of Leishmania infections. The possible evolution and functions of repetitive proteins in protozoan parasites is discussed. / Medicine, Faculty of / Medical Genetics, Department of / Graduate

Advances in DNA sequencing of microbial communities have revealed a complex relationship between the human microbiome and our health. Community dynamics, host-microbe interactions, and changing environmental pressures create a dynamic ecosystem that is just beginning to be understood. In this work, we develop methods for investigating the dynamics of the microbiome. First, we develop a model for describing community dynamics. We show that the proposed approaches accurately describes community trajectories over time. Next, we develop a method for modeling and eliminating technical noise from longitudinal data. We demonstrate that the method can accurately reconstruct microbial trajectories from noisy data. Finally, we develop a method for estimating bacterial growth rates from metagenomic sequencing. Using a case-control cohort of individuals with irritable bowel disease, we show how growth rates can be associated with disease status, community states, and metabolites. Altogether, these models can be used to help uncover the relationship between microbial dynamics, human health, and disease.

No description available.

Indiana University-Purdue University Indianapolis (IUPUI) / There are many mechanisms for the creation of new genes. In this study, the newborn genes i.e. de novo genes are the genes that are created from scratch. These are created by two mechanisms, polymerization (de novo genes produced from non-coding regions) and overprinting (de novo genes produced from overlapping frames). Rancurel et al has found that de novo genes in overlapping coding regions tend to be more disordered than their ancestral counterparts. It was suggested that it is natural for the newborn genes to be disordered, as it must be very difficult for newborn genes to obtain order at such an early stage, so that the structure is only developed after the evolutionary development. The two hypotheses tested in this study state (1) that genes generated de novo will have a tendency to be disordered, and (2) this tendency is due to a natural inclination of these genes to be disordered at birth. The origin and evolution of some de novo coding regions have been studied in detail. We analyzed genes reported in literature that have been produced de novo; either by overprinting or by polymerization, and their tendency for disorder was evaluated using the VSL2 disorder predictor. The de novo coding regions produced by both ways indeed shows a tendency towards disorder, which supports hypothesis 1. For hypothesis 2 to be tested on a larger dataset the exonic and intronic materials of two human chromosomes were studied and the tendency for disorder was assessed for any new peptide sequence arising from the translation of non-coding sequences arising from introns and exons (overlapping frames). It was shown that the tendency of disorder for protein products of newborn genes arising from introns were not inclined towards being ordered or disordered, but they can become disordered by evolution. The new exonic material created from the existing exons tends to be more disordered when translated, and this tendency does not seem to be dependent upon the disorder content of the original exons. This difference could be a consequence of the fact that the overlapping frames of coding sequences have indirectly been subjected to evolutionary pressure along with the original exon, whereas intronic sequences do not seem to have this constraint, but the exact nature of this discrepancy needs further study to be explained. The tendency of disorder in the existing new exons seems to be higher than the artificial exons (generated in this study). We conclude that the intrinsic disorder in the protein products of de novo genes is selected by the evolution rather than an initial condition. Thus, the newborn genes were not born disordered. / indefinitely

While single-cell RNA sequencing provides a remarkable window on pathophysiologic tissue biology and heterogeneity, its high gene-dropout rate and low signal-to-noise ratio challenge quantitative analyses and mechanistic understanding. This thesis addresses this issue by developing PISCES, a pipeline for regulatory network-based single-cell analysis of mammalian tissues. PISCES accurately estimates the mechanistic contribution of regulatory and signaling proteins to cell state implementation and maintenance based on the expression of their lineage-specific transcriptional targets, inferring protein activity for a putative set of transcriptional regulators and cell-state markers. Experimental validation assays – including technical analysis via downsampling of high depth data and biological analysis by assessing concordance with CITE-Seq-based measurements – show a significant improvement in the ability to identify rare subpopulations and to elucidate key lineage markers compared to gene expression analysis. The improved ability to identify biologically meaningful cellular subpopulations makes PISCES an ideal tool to deconvolute heterogeneity in a wide variety of biological contexts. A systematic analysis of single-cell gene expression profiles in the Human Protein Atlas (HPA) by PISCES generated tissue-specific clustering and master regulator analyses across 26 human tissues, as well as a publicly available repository of ready-to-use regulatory networks specific to cell-lineages in each tissue. This resource will allow researchers to access the algorithmic advantages of PISCES without requiring prohibitively expensive or technically challenging computational resources. Additionally, PISCES is able to unravel the heterogeneous stromal environment of Pancreatic Ductal Adenocarcinoma, a malignancy defined by a large and complicated stromal compartment. This analysis reveals several novel candidate subpopulations, including a fibroblast subtype that has never been observed in humans, a potential pro-metastatic population of endothelial cells, and a population of immune-suppressing stellate cells. PISCES is also able to deconvolute more continuous forms of heterogeneity, as demonstrated by an analysis of epithelial cells in the developing murine lung. Here, PISCES is able to computationally reconstruct a developmental trajectory between Sox9+ distal cells and Sox2+ proximal cells, which is then leveraged to identify several novel markers of the critical intermediate population. Subsequent analysis suggests that these transition zone cells may share programs similar to those seen in injury repair and identifies a candidate therapeutic target that can drive cells into or out of this transition state. Finally, protein activity measured by PISCES is used to refine faulty experimental labels through differential density analysis. This analysis lead to the development of a machine learning classifier that accurately predicted increased degrees of stemness in experimentally transduced populations. Additionally, the density analysis paradigm has been extended to unsupervised settings, allowing for the detection of stable cellular populations and transitory trajectories.

No description available.