Hypoxia is a common patho-physiological phenomenon in many types of diseases, including tumors, myocardial infarction and cerebral ischemia. It is believed that hypoxia not only affects the cellular regulation pathways, but also interferes genome, transcriptome and proteome inside tumor, eventually enhances tumor development by increasing malignancy and metastatic potential, induction of resistance towards radiotherapy and chemotherapy, activation of angiogenic mechanism, etc. One of the major biological events for hypoxia is induction of apoptosis, which is believed to provide a selective pressure for tumor progression. However, the mechanism of hypoxia induced apoptosis is not well established. In the present study, the molecular mechanism of hypoxia induced apoptosis was investigated and was found to be different in human squamous carcinoma A431 cells and human hepatocellular carcinoma HepG2 cells. In HepG2 cells, the conventional intrinsic apoptotic pathway that involved the activation of caspase-9 and -3 was found to be triggered by hypoxia through a newly identified p53 - Bnip-3 shunt. On the other hand, caspase-4 and -10 were found to be activated under hypoxia and may be related to hypoxia induced DNA fragmentation in A431 cells. Reoxygenation prior to hypoxia is the event after blood reperfusion in tumor vasculature. It is demonstrated in this study that reoxygenation is a distinctive stress from hypoxia, and it is very likely to be induced by reactive oxygen species. Apart from apoptosis, the mechanism for the development of drug resistance after hypoxia is also not yet clearly identified. In this study, resistance towards several common chemotherapeutic drugs after cells were subjected to hypoxia/reoxygenation cycles were demonstrated. Among them, the possible role of the genes related to methotrexate and cisplatin resistance were also investigated. / Ho Yiu Fung. / "August 2006." / Adviser: Tim-Tak Kwok. / Source: Dissertation Abstracts International, Volume: 68-03, Section: B, page: 1393. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (p. 159-176). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

Zhu, Ranxu. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 123-142). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

O câncer é caracterizado pelo crescimento anormal de células em consequência ao acúmulo de alterações no DNA. Diferentes tecidos apresentam variadas incidências de tumores. Em 2015, Tomasetti & Vogelstein demonstraram uma forte correlação positiva (r = 0.804) entre o número de divisões de células tronco e o risco de câncer em diferentes tecidos, em que tecidos com maior número de divisões de células tronco estão mais susceptíveis aos efeitos estocásticos da replicação do DNA, e, assim, mais propícios a desenvolverem tumores. Assim, esta correlação é justificada pelo número de mutações. Neste trabalho, propomos e testamos in silico um novo conceito para justificar parte desta correlação positiva entre o número de divisões de células tronco e o risco de câncer entre diferentes tecidos, o qual denominamos concentração de ancestrais (AC). Em nossa hipótese, tecidos com alta taxa de proliferação concentram mais seus ancestrais, amplificando as chances de perpetuar células ancestrais mutadas, e, por isso, estão relacionados a maiores riscos de câncer. Assim, tecidos com altos valores de divisão de células tronco apresentam um perfil de alto AC e tecidos com baixo número de divisão de células tronco apresentam um perfil de baixo AC Para comprovar nossa hipótese, simulamos a evolução tumoral através do software esi- Cancer e aplicamos diferentes valores de proliferação e morte (CTOR). Os resultados demonstraram uma correlação positiva de 0.995 entre o valor de CTOR e o perfil de AC (P = 0:002). Como esperado, demonstramos que maiores CTORs estão relacionados a maiores médias de gerações com esiTumors para valores totais de mutações por divisão iguais. Entretanto, esta relação se mantém quando aplicadas valores corrigidos conforme o número de divisões para os diferentes CTORs, a fim de o número de mutações totais ser igual. Logo, apenas variações não são suficientes para explicar a incidência observada em diferentes tecidos. Nossos resultados demonstram que tecidos com maior número de divisões de células tronco apresentam um perfil de alto AC, o qual amplifica as chances de concentrar ancestrais mutados, aumentando as chances de desenvolver tumores. Assim, justificando parte da correlação encontrada por Tomasetti & Vogelstein (2015). / Cancer is characterized by an abnormal replication of somatic cells as a result of DNA alterations. Different types of tissues present differences in cancer incidence. Tomasetti & Vogelstein (2015) have shown that lifetime cancer risk of different tissues presents a strong correlation of 0.804 with the number of stem cells divisions, in which tissues with higher number of stem cells divisions are more susceptible to stochastic effects of DNA replication and thus more likely to develop cancer. Thus, the number of mutations was used to explain this correlation. In our work, we propose and test in silico a new concept to explain this positive correlation, which we denominated ancestral concentration (AC). In our hypothesis, a tissue with high proliferation rates concentrates more their ancestral cells and increases the chance of a mutated ancestral to persist; which result in a higher risk of cancer. Tissues with a high number of stem cells divisions presents a high AC profile whereas tissues with a low number of stem cells divisions presents a low AC profile To prove our hypothesis, we simulated tumor evolution using esiCancer software and applied different initial rates of proliferation and death (CTOR). We observed a positive correlation of 0.995 between CTOR values and the AC profile (P = 0:002). Besides, higher CTOR values are associated to higher mean generations with esiTumors when equal mutation rates are applied. Nevertheless, this association still exist in simulations with mutation rates corrected by total number of divisions, whereas the total mutation rate is similar for different CTORs. This way, modifications of mutations solely are not sufficient to explain the observed cancer risks in different tissues. Our results showed that tissues with higher number of stem cells divisions present a high AC profile, which rises the probabilities of concentrate mutated ancestral cells, increasing the tumor risk. In this way, justifying partly the correlation that was founded by Tomasetti & Vogelstein (2015).

O câncer é caracterizado pelo crescimento anormal de células em consequência ao acúmulo de alterações no DNA. Diferentes tecidos apresentam variadas incidências de tumores. Em 2015, Tomasetti & Vogelstein demonstraram uma forte correlação positiva (r = 0.804) entre o número de divisões de células tronco e o risco de câncer em diferentes tecidos, em que tecidos com maior número de divisões de células tronco estão mais susceptíveis aos efeitos estocásticos da replicação do DNA, e, assim, mais propícios a desenvolverem tumores. Assim, esta correlação é justificada pelo número de mutações. Neste trabalho, propomos e testamos in silico um novo conceito para justificar parte desta correlação positiva entre o número de divisões de células tronco e o risco de câncer entre diferentes tecidos, o qual denominamos concentração de ancestrais (AC). Em nossa hipótese, tecidos com alta taxa de proliferação concentram mais seus ancestrais, amplificando as chances de perpetuar células ancestrais mutadas, e, por isso, estão relacionados a maiores riscos de câncer. Assim, tecidos com altos valores de divisão de células tronco apresentam um perfil de alto AC e tecidos com baixo número de divisão de células tronco apresentam um perfil de baixo AC Para comprovar nossa hipótese, simulamos a evolução tumoral através do software esi- Cancer e aplicamos diferentes valores de proliferação e morte (CTOR). Os resultados demonstraram uma correlação positiva de 0.995 entre o valor de CTOR e o perfil de AC (P = 0:002). Como esperado, demonstramos que maiores CTORs estão relacionados a maiores médias de gerações com esiTumors para valores totais de mutações por divisão iguais. Entretanto, esta relação se mantém quando aplicadas valores corrigidos conforme o número de divisões para os diferentes CTORs, a fim de o número de mutações totais ser igual. Logo, apenas variações não são suficientes para explicar a incidência observada em diferentes tecidos. Nossos resultados demonstram que tecidos com maior número de divisões de células tronco apresentam um perfil de alto AC, o qual amplifica as chances de concentrar ancestrais mutados, aumentando as chances de desenvolver tumores. Assim, justificando parte da correlação encontrada por Tomasetti & Vogelstein (2015). / Cancer is characterized by an abnormal replication of somatic cells as a result of DNA alterations. Different types of tissues present differences in cancer incidence. Tomasetti & Vogelstein (2015) have shown that lifetime cancer risk of different tissues presents a strong correlation of 0.804 with the number of stem cells divisions, in which tissues with higher number of stem cells divisions are more susceptible to stochastic effects of DNA replication and thus more likely to develop cancer. Thus, the number of mutations was used to explain this correlation. In our work, we propose and test in silico a new concept to explain this positive correlation, which we denominated ancestral concentration (AC). In our hypothesis, a tissue with high proliferation rates concentrates more their ancestral cells and increases the chance of a mutated ancestral to persist; which result in a higher risk of cancer. Tissues with a high number of stem cells divisions presents a high AC profile whereas tissues with a low number of stem cells divisions presents a low AC profile To prove our hypothesis, we simulated tumor evolution using esiCancer software and applied different initial rates of proliferation and death (CTOR). We observed a positive correlation of 0.995 between CTOR values and the AC profile (P = 0:002). Besides, higher CTOR values are associated to higher mean generations with esiTumors when equal mutation rates are applied. Nevertheless, this association still exist in simulations with mutation rates corrected by total number of divisions, whereas the total mutation rate is similar for different CTORs. This way, modifications of mutations solely are not sufficient to explain the observed cancer risks in different tissues. Our results showed that tissues with higher number of stem cells divisions present a high AC profile, which rises the probabilities of concentrate mutated ancestral cells, increasing the tumor risk. In this way, justifying partly the correlation that was founded by Tomasetti & Vogelstein (2015).

O câncer é caracterizado pelo crescimento anormal de células em consequência ao acúmulo de alterações no DNA. Diferentes tecidos apresentam variadas incidências de tumores. Em 2015, Tomasetti & Vogelstein demonstraram uma forte correlação positiva (r = 0.804) entre o número de divisões de células tronco e o risco de câncer em diferentes tecidos, em que tecidos com maior número de divisões de células tronco estão mais susceptíveis aos efeitos estocásticos da replicação do DNA, e, assim, mais propícios a desenvolverem tumores. Assim, esta correlação é justificada pelo número de mutações. Neste trabalho, propomos e testamos in silico um novo conceito para justificar parte desta correlação positiva entre o número de divisões de células tronco e o risco de câncer entre diferentes tecidos, o qual denominamos concentração de ancestrais (AC). Em nossa hipótese, tecidos com alta taxa de proliferação concentram mais seus ancestrais, amplificando as chances de perpetuar células ancestrais mutadas, e, por isso, estão relacionados a maiores riscos de câncer. Assim, tecidos com altos valores de divisão de células tronco apresentam um perfil de alto AC e tecidos com baixo número de divisão de células tronco apresentam um perfil de baixo AC Para comprovar nossa hipótese, simulamos a evolução tumoral através do software esi- Cancer e aplicamos diferentes valores de proliferação e morte (CTOR). Os resultados demonstraram uma correlação positiva de 0.995 entre o valor de CTOR e o perfil de AC (P = 0:002). Como esperado, demonstramos que maiores CTORs estão relacionados a maiores médias de gerações com esiTumors para valores totais de mutações por divisão iguais. Entretanto, esta relação se mantém quando aplicadas valores corrigidos conforme o número de divisões para os diferentes CTORs, a fim de o número de mutações totais ser igual. Logo, apenas variações não são suficientes para explicar a incidência observada em diferentes tecidos. Nossos resultados demonstram que tecidos com maior número de divisões de células tronco apresentam um perfil de alto AC, o qual amplifica as chances de concentrar ancestrais mutados, aumentando as chances de desenvolver tumores. Assim, justificando parte da correlação encontrada por Tomasetti & Vogelstein (2015). / Cancer is characterized by an abnormal replication of somatic cells as a result of DNA alterations. Different types of tissues present differences in cancer incidence. Tomasetti & Vogelstein (2015) have shown that lifetime cancer risk of different tissues presents a strong correlation of 0.804 with the number of stem cells divisions, in which tissues with higher number of stem cells divisions are more susceptible to stochastic effects of DNA replication and thus more likely to develop cancer. Thus, the number of mutations was used to explain this correlation. In our work, we propose and test in silico a new concept to explain this positive correlation, which we denominated ancestral concentration (AC). In our hypothesis, a tissue with high proliferation rates concentrates more their ancestral cells and increases the chance of a mutated ancestral to persist; which result in a higher risk of cancer. Tissues with a high number of stem cells divisions presents a high AC profile whereas tissues with a low number of stem cells divisions presents a low AC profile To prove our hypothesis, we simulated tumor evolution using esiCancer software and applied different initial rates of proliferation and death (CTOR). We observed a positive correlation of 0.995 between CTOR values and the AC profile (P = 0:002). Besides, higher CTOR values are associated to higher mean generations with esiTumors when equal mutation rates are applied. Nevertheless, this association still exist in simulations with mutation rates corrected by total number of divisions, whereas the total mutation rate is similar for different CTORs. This way, modifications of mutations solely are not sufficient to explain the observed cancer risks in different tissues. Our results showed that tissues with higher number of stem cells divisions present a high AC profile, which rises the probabilities of concentrate mutated ancestral cells, increasing the tumor risk. In this way, justifying partly the correlation that was founded by Tomasetti & Vogelstein (2015).

DNA double strand breaks (DSB) are cytotoxic lesions that can lead to genome rearrangements and genomic instability, which are hallmarks of cancer. The two main DSB repair pathways are non-homologous end joining and homologous recombination (HR). While HR is generally highly accurate, it has the potential for gross chromosomal rearrangements (GCRs) that occur directly or through intermediates generated during the repair process. Whole genome sequencing of cancers has revealed numerous types of structural rearrangement signatures that are often indicative of repair mediated by sequence homology. However, it can be challenging to delineate repair mechanisms from sequence analysis of rearrangement end products from cancer genomes, or even model systems, because the same rearrangements can be generated by different pathways. Numerous studies have provided insights into the types of spontaneous GCRs that can occur in various Saccharomyces cerevisiae mutants. However, understanding the mechanism and frequency of formation of these GCR without knowledge of the initiating lesions is limited. Here, we focus on DSB-induced repair pathways that lead to GCRs. Inverted duplications occur at a surprisingly high frequency when a DSB is formed near short inverted repeats in cells deficient for the nuclease activity of Mre11. Similar to previously proposed models, the inverted duplications occur through intra-strand foldback annealing at resected inverted repeats to form a hairpin-capped chromosome that is a precursor to dicentric chromosomes. Surprisingly, we found that DNA polymerase δ proof-reading activity but not the Rad1-Rad10 nuclease is required for inverted duplication formation, suggesting a role for Pol δ in the removal of the heterologous tails formed during foldback annealing. Contrary to previous work on spontaneous inverted duplications, we find that DSB-induced inverted duplications require the Pol δ processivity subunit Pol32 and that RPA plays little role in their inhibition, suggesting that spontaneous inverted duplications arise differently than those induced by DSBs. We show that stabilization of dicentric chromosomes after breakage involves telomere capture through a strand-invasion step mediated by repeat sequences and requires Rad51. Previous work on spontaneous inverted duplications suggested that Tel1, but not Mre11-Sae2, inhibits inverted duplications that initiate from inverted repeats separated by long spacers (> 12 bp). However, we do not find evidence for this requirement. Cells with Tel1 deletion can still resolve hairpins containing loops up to 30 nt long. Furthermore, deletion of Sae2, but not Tel1, increases the frequency of inverted duplications when a DSB is induced near an inverted repeat separated by a 20 bp-long spacer. This highlights another difference between spontaneous and DSB-induced GCRs. Finally, we find that the sequence context of a DSB affects the type of GCR outcome. Inverted repeats are required for the formation of inverted duplications, as the deletion of a DSB-proximal inverted repeat significantly reduces the incidence of this type of rearrangement. Furthermore, introduction of a DSB near short telomere-like sequence is required for chromosome truncations stabilized by de novo telomere addition. The effect of the sequence context can partly explain how repair pathways can be channeled to different mutagenic outcomes. Our results highlight the importance of considering how the initiating lesion can affect the type of resulting GCRs and the mechanisms by which they occur.

Trillions of microbes line the gastrointestinal tract to form the gut microbiome, a symbiotic organ whose supportive functions include energy production, immune homeostasis, and defense against pathogens. Disturbances to gut microbial composition, in turn, drive the pathogenesis of various metabolic, inflammatory, and carcinogenic diseases. Much effort has been dedicated to elucidating environmental triggers of gut dysbiosis, not the least of which is the consumption of medications. Antibiotics eradicate keystone commensals and enhance pathogenic behaviors of persisting pathobionts, whose resistance mechanisms can have off-target effects on human physiology and treatment response. Recent evidence indicates that the spectrum of antimicrobial compounds that disturb the gut microbiome extends far beyond traditional antibiotics, and includes commonly prescribed cardiovascular, neuropsychiatric, metabolic, and cancer medications. Although the capacity of non-antibiotic pharmaceuticals to induce gut dysbiosis is well appreciated, their impact on gut microbial function has not been studied systematically. Bacterial multi-omic profiling offers a cost-effective, high-throughput approach to understanding bacterial genetic responses to chemical perturbations, and how these functional changes might reciprocally impact relevant human phenotypes. Our laboratory, which houses a personal strain biobank of over 30,000 gut bacterial isolates spanning over 400 taxa, has established scalable pipelines for bacterial genomic and transcriptomic profiling that are readily applicable to diverse non-model gut microbes. We applied these methodologies to healthy fecal samples and bacterial isolates to elucidate strain-level responses to common pharmaceuticals with known gut microbiome associations. We first performed a gut microbiota transcriptomic screen of 19 representative fecal isolates against 20 top-prescribed orally delivered medications. Computational analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed induction of pathways associated with metabolism and multidrug resistance, including upregulation of efflux machinery by lipid-lowering drugs, antidepressants and cardiovascular medications. We discovered many bacterial responses with clinical significance, which we computationally validated using clinical metagenomic datasets. Most importantly, we showed that statin-mediated overexpression of the AcrAB-TolC efflux pump generates collateral toxicity in dietary retinol and secondary bile acids, resulting in depletion of pump-containing Bacteroidales species from patient microbiomes. We next performed the first comprehensive screen for antimicrobial activity in cancer drugs by exposing three healthy fecal samples to a panel of 41 first-line cancer therapeutics. Using 16S-genomic profiling, we identified several members of the targeted kinase inhibitor (TKI) class that induced gut dysbiosis, including first-line hepatocellular carcinoma (HCC) treatment sorafenib. We profiled natural bacterial isolates exposed to different TKI HCC treatments, and again observed transcriptional induction of conserved multidrug efflux pumps. Adaptive evolution assays identified Resistance-Nodulation-Division (RND) efflux pumps as effectors of TKI resistance. Remarkably, we demonstrated that acquired TKI resistance in evolved Bacteroidales lineages generated strain-specific cross-resistances and collateral sensitivities to several unrelated antibiotics. Collectively, our work demonstrates the importance of profiling xenobiotic impacts on the gut microbial resistome, as bacterial adaptations to pharmaceutical toxicities can feed back onto microbiome communities and the human host to affect health outcomes.

Recent studies have revealed that crosstalk between glioma cells and the brain microenvironment is a crucial regulator of cancer initiation and progression. A vast majority of glioma patients suffer from seizures, and this pathological neuronal activity has been proposed to contribute to increased glioma cell proliferation. Glioma patients also suffer from additional neurological symptoms, including deficits in attention, concentration, memory, and language. These neurological effects of gliomas along with the limited therapeutic options underscore the need for novel therapies. This thesis investigates the neuronal alterations at the margins of glioma which contribute to the neurological symptoms (Chapter 2), and on the effect of sensory stimulation on the glioma cells and microglia in the glioma microenvironment (Chapter 3). The work describes the development of new mouse models in which glioma cells are infiltrating the somatosensory cortex in mice that express GCaMP in neurons or microglia. Methodologies include a combination of in vivo two-photon calcium imaging and tissue-based analysis to determine the glioma-induced alteration on whisker stimulation-evoked responses of these different cell types. This work also tests the effects of pharmacologically inhibiting mTOR signaling on neuronal responses (Chapter 2) or purinergic signaling on microglial responses (Chapter 3). Together these studies demonstrate that glioma infiltration induces local effects in functionally-responsive cortex, and that many of these glioma-induced effects on neurons and microglia are ameliorated by pharmacological inhibition of mTOR or purinergic signaling. This reveals a highly dynamic and plastic nature of the glioma-induced alterations, and points towards new strategies to treat glioma-associated neurological symptoms while potentially slowing tumor progression.

The glyceryl enol ether fecapentaene-12 (FP-12) is a direct-acting mutagen that is formed by bacteria in the lower part of the gastrointestinal tract from a precursor of unknown structure. Two major unsolved questions concerning FP-12 are the structure of its precursor and the nature of its interaction (if any) with DNA. The structure of the biosynthetic precursor of FP-12 is thought to be that of a plasmalogen with an intact pentaenyl ether moiety. A synthesis of the perhydro analog of the proposed precursor structure is described, and approaches to the synthesis of the precursor itself are also described. Comparison of chromatographic data for the saturated model precursor and natural precursor provided evidence for the structure of the latter. The nature of the interactions of FP-12 with DNA was probed by model studies of the reaction of nucleoside bases with FP-12 and two proposed FP-12 metabolites. No adducts were formed between FP-12 or between the various putative polyenal metabolites and guanosine, cytosine, or thymidine. A model epoxy ether did react with a guanosine derivative, however, indicating that an epoxy ether derivative of FP-12, if formed, would be capable of reacting with DNA. / Ph. D.

Functional data analysis (FDA) offers a robust statistical framework for handling complex data arising from a variety of fields. The presented dissertation focuses on the development and application of innovative FDA methods to analyze scientific data. We introduce three novel approaches tailored to distinct health-related topics: the mechanisms of neural activation during skilled movement and the role of the menstrual cycle in clinical studies performed on women. In the realm of neuroscience, we propose a functional clustering method designed to analyze high-dimensional, temporal data collected across multiple trials of varying conditions. Leveraging two datasets involving motor neuron behavior in mice, our method identifies latent neuron subgroups and conducts group-specific dimensionality reduction. Through simulations and real-data analyses, we demonstrate the method's efficacy in capturing subtle differences between groups, offering insights into the underlying mechanisms of voluntary movement. Turning to women's health, we address the often-overlooked effects of the menstrual cycle in clinical research. We develop a method to estimate menstrual cycle day using hormone values derived from a single spot urine sample. We leverage patterns of hormonal variation obtained from two sources of data, which follow a collection of women across a full cycle. Through simulations and real data applications, we demonstrate our ability to obtain accurate estimations of cycle day within three days of the truth in optimal settings. This work paves the way for improved model accuracy and statistical power in clinical studies performed on women. Furthermore, we propose an innovative analysis strategy to model menstrual cycle day as an effect modifier on the relationship between hormone levels and breast cancer risk, providing insights into the cyclic variations of hormone levels and their implications on breast cancer etiology. This dissertation aims to advance our understanding of complex health-related processes and empower clinicians and researchers to develop more personalized interventions. The dissertation structure comprises detailed chapters discussing the development, application, and results of each method, highlighting the pivotal role of functional data analysis in advancing scientific research and discovery.