UC Riverside

ABSTRACTHistoplasmosis is an endemic mycosis in North America frequently reported along the Ohio and Mississippi River Valleys, although autochthonous cases occur in non-endemic areas. In the United States, the disease is provoked by two genetically distinct clades of Histoplasma capsulatum sensu lato, Histoplasma mississippiense (Nam1) and H. ohiense (Nam2). To bridge the molecular epidemiological gap, we genotyped 93 Histoplasma isolates (62 novel genomes) including clinical, environmental, and veterinarian samples from a broader geographical range by whole-genome sequencing, followed by evolutionary and species niche modelling analyses. We show that histoplasmosis is caused by two major lineages, H. ohiense and H. mississippiense; with sporadic cases caused by H. suramericanum in California and Texas. While H. ohiense is prevalent in eastern states, H. mississipiense was found to be prevalent in the central and western portions of the United States, but also geographically overlapping in some areas suggesting that these species might co-occur. Species Niche Modelling revealed that H. ohiense thrives in places with warmer and drier conditions, while H. mississippiense is endemic to areas with cooler temperatures and more precipitation. In addition, we predicted multiple areas of secondary contact zones where the two species co-occur, potentially facilitating gene exchange and hybridization. This study provides the most comprehensive understanding of the genomic epidemiology of histoplasmosis in the USA and lays a blueprint for the study of invasive fungal diseases.

Evolutionary constraints greatly favor compact genomes that efficiently encode proteins. However, several eukaryotic organisms, including apicomplexan parasites such as Toxoplasma gondii, Plasmodium falciparum and Babesia duncani, the causative agents of toxoplasmosis, malaria and babesiosis, respectively, encode very large proteins, exceeding 20 times their average protein size. Although these large proteins represent <1% of the total protein pool and are generally expressed at low levels, their persistence throughout evolution raises important questions about their functions and possible evolutionary pressures to maintain them. In this study, we examined the trends in gene and protein size, function and expression patterns within seven apicomplexan pathogens. Our analysis revealed that certain large proteins in apicomplexan parasites harbor domains potentially important for functions such as antigenic variation, erythrocyte invasion and immune evasion. However, these domains are not limited to or strictly conserved within large proteins. While some of these proteins are predicted to engage in conventional metabolic pathways within these parasites, others fulfill specialized functions for pathogen-host interactions, nutrient acquisition and overall survival.

The total oxidizable precursor (TOP) assay has been extensively used for detecting PFAS pollutants that do not have analytical standards. It uses hydroxyl radicals (HO•) from the heat activation of persulfate under alkaline pH to convert H-containing precursors to perfluoroalkyl carboxylates (PFCAs) for target analysis. However, the current TOP assay oxidation method does not apply to emerging PFAS because (i) many structures do not contain C-H bonds for HO• attack and (ii) the transformation products are not necessarily PFCAs. In this study, we explored the use of classic acidic persulfate digestion, which generates sulfate radicals (SO4-•), to extend the capability of the TOP assay. We examined the oxidation of Nafion-related ether sulfonates that contain C-H or -COO-, characterized the oxidation products, and quantified the F atom balance. The SO4-• oxidation greatly expanded the scope of oxidizable precursors. The transformation was initiated by decarboxylation, followed by various spontaneous steps, such as HF elimination and ester hydrolysis. We further compared the oxidation of legacy fluorotelomers using SO4-• versus HO•. The results suggest novel product distribution patterns, depending on the functional group and oxidant dose. The general trends and strategies were also validated by analyzing a mixture of 100000- or 10000-fold diluted aqueous film-forming foam (containing various fluorotelomer surfactants and organics) and a spiked Nafion precursor. Therefore, (1) the combined use of SO4-• and HO• oxidation, (2) the expanded list of standard chemicals, and (3) further elucidation of SO4-• oxidation mechanisms will provide more critical information to probe emerging PFAS pollutants.

Isomerized amino acid residues have been identified in many peptides extracted from tissues or excretions of humans and animals. These isomerized residues can play key roles by affecting biological activity or by exerting an influence on the process of aging. Isomerization occurs spontaneously and does not result in a mass shift. Thus, identifying and localizing isomerized residues in biological samples is challenging. Herein, we introduce a fast and efficient method using tandem mass spectrometry (MS) to locate isomerized residues in peptides. Although MS2 spectra are useful for identifying peptides that contain an isomerized residue, they cannot reliably localize isomerization sites. We show that this limitation can be overcome by utilizing MS3 experiments to further evaluate each fragment ion from the MS2 stage. Comparison at the MS3 level, utilizing statistical analyses, reveals which MS2 fragments differ between samples and, therefore, must contain the isomerized sites. The approach is similar to previous work relying on ion mobility to discriminate MS2 product ions by collision cross-section. The MS3 approach can be implemented using either ion-trap or beam-type collisional activation and is compatible with the quantification of isomer mixtures when coupled to a calibration curve. The method can also be implemented in combination with liquid chromatography in a targeted approach. Enabling the identification and localization of isomerized residues in peptides with an MS-only methodology will expand accessibility to this important information.

Connected and Autonomous Vehicles (CAV) require positioning that is consistently reliable and accurate. This is achieved through the choice of sensors and the real-time selection of high-quality measurements. Global Navigation Satellite Systems (GNSS) are the foundation to achieve accurate absolute positioning. GNSS Common-mode Errors (CME)mitigation can be realized with Differential GNSS (DGNSS) approach and Precise Point Positioning (PPP) techniques. With the evolution of the International GNSS Service (IGS) Multi-GNSS Experiment (MGEX), Real-time PPP (RT-PPP) corrections for multi-GNSS have only recently become accessible.

GNSS measurements are prone to outliers. This results in an inherent performance versus risk trade-off in CAV state estimation applications. Recently proposed Risk-Averse Performance Specified (RAPS) methods address this trade-off by optimally selecting a subset of measurements to minimize risk while achieving a target performance. The existing RAPS literature presents cases where the performance specification is stated for the full information matrix. However, those methods are not computationally efficient as required for real-time and do not address situations where that specification is infeasible.

This dissertation focuses on the Diagonal Performance-Specified RAPS (DiagRAPS) formulation. This dissertation begins with a review of GNSS measurement models and real-time CME mitigation techniques, such as DGNSS, PPP, and Virtual Network DGNSS (VN-DGNSS). It then develops the theory of DiagRAPS for both binary and non-binary measurement selection variables. Algorithms suitable for real-time applications are proposed within Linear Programming (LP) and Mixed-Integer Linear Programming (ILP) optimization frameworks, achieving polynomial time complexity. The convergence and computation costs of these algorithms are discussed. For binary DiagRAPS, a novel convex reformulation is derived, leading to a globally optimal solution that can be solved using existing tools. Additionally, a soft constraint optimization approach is proposed for situations when the specified performance is unfeasible. Finally, this dissertation evaluates DiagRAPS state estimation approaches using real-world multi-GNSS data from challenging environments for both DGNSS and RT-PPP applications. The results reveal that the locally optimal approach achieves state estimation performance comparable to the global solution. Both binary and non-binary DiagRAPS outperform traditional methods. Notably, the non-binary approach yielded the lowest computation cost and the best overall performance.

Chronic myeloid leukemia (CML) is initiated and maintained by BCR::ABL which is clinically targeted using tyrosine kinase inhibitors (TKIs). TKIs can induce long-term remission but are also not curative. Thus, CML is an ideal system to test our hypothesis that transcriptome-based state-transition models accurately predict cancer evolution and treatment response. We collected time-sequential blood samples from tetracycline-off (Tet-Off) BCR::ABL-inducible transgenic mice and wild-type controls. From the transcriptome, we constructed a CML state-space and a three-well leukemogenic potential landscape. The potential's stable critical points defined observable disease states. Early states were characterized by anti-CML genes opposing leukemia; late states were characterized by pro-CML genes. Genes with expression patterns shaped similarly to the potential landscape were identified as drivers of disease transition. Re-introduction of tetracycline to silence the BCR::ABL gene returned diseased mice transcriptomes to a near healthy state, without reaching it, suggesting parts of the transition are irreversible. TKI only reverted the transcriptome to an intermediate disease state, without approaching a state of health; disease relapse occurred soon after treatment. Using only the earliest time-point as initial conditions, our state-transition models accurately predicted both disease progression and treatment response, supporting this as a potentially valuable approach to time clinical intervention, before phenotypic changes become detectable.

Impaired healing wounds do not proceed through the normal healing processes in a timely and orderly manner, and while they do eventually heal, their healing is not optimal. Chronic wounds, on the other hand, remain unhealed for weeks or months. In the US alone, chronic wounds impact ~8.5 million people and cost ~USD 28-90 billion per year, not accounting for the psychological and physical pain and emotional suffering that patients endure. These numbers are only expected to rise in the future as the elderly populations and the incidence of comorbidities such as diabetes, hypertension, and obesity increase. Over the last few decades, scientists have used a variety of approaches to treat chronic wounds, but unfortunately, to date, there is no effective treatment. Indeed, while there are thousands of drugs to combat cancer, there is only one single drug approved for the treatment of chronic wounds. This is in part because wound healing is a very complex process involving many phases that must occur sequentially and in a timely manner. Furthermore, models that fully mimic human chronic wounds have not been developed. In this review, we assess various models currently being used to study the biology of impaired healing and chronic non-healing wounds. Among them, this paper also highlights one model which shows significant promise; this model uses aged and obese db/db-/- mice and the chronic wounds that develop show characteristics of human chronic wounds that include increased oxidative stress, chronic inflammation, damaged microvasculature, abnormal collagen matrix deposition, a lack of re-epithelialization, and the spontaneous development of multi-bacterial biofilm. We also discuss how important it is that we continue to develop chronic wound models that more closely mimic those of humans and that can be used to test potential treatments to heal chronic wounds.

Reptiles and amphibians (herptiles) are some of the most endangered and threatened species on the planet and numerous conservation strategies are being implemented with the goal of ensuring species recovery. Little is known, however, about the gut microbiome of wild herptiles and how it relates to the health of these populations. Here, we report results from the gut microbiome characterization of both a broad survey of herptiles, and the correlation between the fungus Basidiobolus, and the bacterial community supported by a deeper, more intensive sampling of Plethodon glutinosus, known as slimy salamanders. We demonstrate that bacterial communities sampled from frogs, lizards, and salamanders are structured by the host taxonomy and that Basidiobolus is a common and natural component of these wild gut microbiomes. Intensive sampling of multiple hosts across the ecoregions of Tennessee revealed that geography and host:geography interactions are strong predictors of distinct Basidiobolus operational taxonomic units present within a given host. Co-occurrence analyses of Basidiobolus and bacterial community diversity support a correlation and interaction between Basidiobolus and bacteria, suggesting that Basidiobolus may play a role in structuring the bacterial community. We further the hypothesis that this interaction is advanced by unique specialized metabolism originating from horizontal gene transfer from bacteria to Basidiobolus and demonstrate that Basidiobolus is capable of producing a diversity of specialized metabolites including small cyclic peptides.IMPORTANCEThis work significantly advances our understanding of biodiversity and microbial interactions in herptile microbiomes, the role that fungi play as a structural and functional members of herptile gut microbiomes, and the chemical functions that structure microbiome phenotypes. We also provide an important observational system of how the gut microbiome represents a unique environment that selects for novel metabolic functions through horizontal gene transfer between fungi and bacteria. Such studies are needed to better understand the complexity of gut microbiomes in nature and will inform conservation strategies for threatened species of herpetofauna.