Antibiotic resistance (AR) is a critical and looming threat to human health that requires action across the One Health continuum (humans, animals, environment). Coordinated surveillance within the environmental sector is largely underdeveloped in current National Action Plans to combat the spread of AR, and a lack of effective study approaches and standard analytical methods have led to a dearth of impactful environmental monitoring data on the prevalence and risk of antibiotic resistant bacteria (ARB) and antibiotic resistance genes (ARGs) in aquatic environments. In this dissertation, integrated surveillance approaches of surface water and wastewater systems are demonstrated, and efforts are made towards standardizing both metagenomic- and culture-based techniques for globally comparable environmental monitoring. A field study of differentially-impacted watersheds on the island of Puerto Rico post-Hurricane Maria demonstrated the effectiveness of metagenomics in defining direct impact of anthropogenic stress and human fecal contamination on the proliferation of ARGs in riverine systems. The contribution of treated wastewater effluents to the dissemination of highly mobile and clinically-relevant ARGs and their connection to local clinical settings was also revealed. At the international scale, a transect of conventional activated sludge wastewater treatment plants (WWTPs), representing both US/European and Asian regions, were found to significantly attenuate ARG abundance through the removal of total bacterial load and human fecal indicators, regardless of influent ARG compositions. Strong structural symmetry between microbiome and ARG compositions through successional treatment stages suggested that horizontal gene transfer plays a relatively minor role in actively shaping resistomes during treatment. Risk assessment models, however, indicated high-priority plasmid-borne ARGs in final treated effluents discharged around the world, indicating potentially increased transmission risks in downstream environments. Advancements were also made toward standardizing methods for the generation of globally representative and comparable metagenomic- and culture-based AR monitoring data via two comprehensive and critical literature reviews. The first review provides guidance in next-generation sequencing (NGS) studies of environmental AR, proposing a framework for experimental controls, adequate sequencing depths, appropriate use of public databases, and the derivation of datatypes that are conducive for risk assessment. The second review focuses on antibiotic-resistant Enterococcus spp. as robust monitoring targets and an attractive alternative to more widely adopted Gram-negative organisms, while proposing workflows that generate universally equivalent datatypes. Finally, quantitative metagenomic (qMeta) techniques were benchmarked using internal reference standards for high-throughput quantification of ARGs with statistical reproducibility. / Doctor of Philosophy / Antimicrobials have contributed to the reduction of infectious diseases in humans and animals since the early 20th century, increasing productivity and saving countless lives. However, their industrial-scale application across human, animal, and agricultural sectors over the last several decades, especially the use of antibiotics, have engendered the proliferation of antibiotic resistance (AR). AR occurs when changes in bacteria cause the drugs used to treat infections to become less effective and has become one of the leading public health threats of the 21st century. The global spread of AR through the transmission and evolution of antibiotic resistant bacteria (ARB; known colloquially as "superbugs") and antibiotic resistance genes (ARGs) across the One Health continuum (i.e., humans, animals, and the environment) is resulting in increased hospitalization, length of hospital stays, suffering, death, and overall health-care associated costs globally. This dissertation demonstrates the use of metagenomics, the sequencing of all genetic material (e.g., DNA) recovered from a microbial community, for the comprehensive monitoring of ARB and ARGs in aquatic environments, a key pathway for the dissemination of AR into and out of human populations. In order to impede the proliferation of AR, surveillance systems are currently in place to track the spread and evolution of ARB and ARGs in humans and livestock, as well as agri-food sectors. However, the surveillance in natural and built environments (i.e., rivers and domestic sewage) has significantly lagged due to the lack of standard monitoring targets and methodologies. It is also a goal of this dissertation to suggest guidance for the collection of metagenomic- and culture-based AR monitoring data to generate universally comparable results that can be included in centralized databases. Riverine systems are ideal models for tracking input of antibiotic resistance to the natural environment by human activity. After Hurricane-Maria, many of Puerto Rico's wastewater treatment plants (WWTPs) went offline, discharging raw sewage to local surface waters. In a cross-sectional study of watersheds impacted by WWTPs, the abundance of ARGs was directly correlated to increases in local population density. Also, highly mobile and clinically-relevant ARGs were found directly downstream of WWTPs across the island. We found that many of these ARGs corresponded well to forms AR endemic to the region. WWTPs are the primary engineering controls put in place to curb the spread of human and animal waste streams and can help to reduce AR. An international transect of conventional activated sludge WWTPs representing US/Europe and Asia were sampled to garner a mechanistic understanding of the fate or ARGs through treatment. Although WWTPs remove total bacteria, human fecal indicators, and much of the abundance of ARGs, mobile and clinically-relevant ARGs are discharged around the world in large quantities. Consideration is needed in certain regions of iv the world where the managing of human waste streams is the first line of defense against the dissemination of resistance to local communities. Two comprehensive critical literature reviews were conducted to evaluate the various methodologies for generating and analyzing metagenomic- and culture-based AR monitoring data. These reviews address the need for experimental rigor and disclosure of extensive metadata for inclusion in future, centralized databases. The articles further provide guidance with respect to universally comparable datatypes and efficient workflows that will aid in the scale-up of the collection of environmental monitoring data within a global surveillance framework. Finally, a study was conducted to benchmark the use of internal DNA reference standards for the absolute quantification of ARGs (i.e., on a ARG copy per volume of sample basis). The statistical framework for ARG detection and its implications for wastewater-based surveillance systems of AR are also discussed.

Antimicrobial resistance (AMR) threatens our modern standard of living with the potential return to a pre-antibiotic condition where deadly infections are no longer treatable. Wastewater treatment plants (WWTPs) are vital components in water sanitation infrastructure and are now also being recognized as valuable monitoring points for antibiotics, antibiotic resistant bacteria (ARB), and antibiotic resistance genes (ARGs) disposed of or excreted by human populations. Hospital waste water is of special interest as a potential focused monitoring point and in general research is needed to establish the benefits of both on-site and community-scale wastewater treatment as important barriers to the disseminators of ARGs into the environment. The research aims described herein examine these components of wastewater treatment and how they relate to AMR indicators identified through metagenomic sequencing. Through monitoring of local WWTPs, it was found that AMR indicators shifted over time and in relation to human behavior that changed due to the COVID-19 pandemic. Hospital wastewater did not measurably impact the microbiome during simulated activated sludge wastewater treatment according to broad-scale metagenomic ARG profiling; however, some clinically-relevant ARGs escaped treatment. Lastly, a study of a transect of WWTPs indicated impacts on the abundance of certain ARGs in downstream riverine receiving environments. Nonetheless, there appeared to be a number of other factors at play, and upstream and downstream resistomes tended to remain similar, calling for further research to delineate impacts of various wastewaters and treatments on ARGs in affected aquatic environments. / Doctor of Philosophy / Antimicrobial resistance (AMR) occurs when bacteria, viruses, and fungi are able to survive in the presence of antibiotics because they carry antibiotic resistance genes (ARGs) encoded in their DNA. AMR is a major public health concern as it makes it so that antibiotics are no longer effective against potentially deadly infections. Wastewater treatment plants (WWTPs) are being discovered as a hub of opportunity for monitoring potential AMR problems in a community. WWTPs receive sewage from homes and various industries. This sewage contains rich information for researchers to examine in terms of which antibiotics, bacteria, and ARGs are circulating in the community. This makes it possible to find out which antibiotics are being consumed in the community and which ARGs might be prevalent. The purpose of this research was to better understand both how WWTPs can be used as monitoring points for AMR and how they can be improved to help reduce ARGs emitted to rivers and streams where treated water is discharged. It was found that the types of ARGs prevalent in wastewater changed over time, especially during the COVID-19 pandemic as people worked from home and changed habits regarding doctors' visits, which impacted antibiotic use. Hospital sewage was studied as a useful indicator of pathogens and ARGs that are challenging a community and also the antibiotics being used. This research explored what happened to ARGs during the treatment of domestic (i.e., from people's homes) wastewater along with hospital wastewater and found that hospital wastewater introduced some ARGs that are typically found in clinical settings, but did not negatively impact the overall wastewater treatment process. Finally, the impact that WWTPs have on rivers to which treated water is discharged was explored. The results indicated that certain ARGs were elevated downstream of the WWTPs. However, when examining all ARGs together, no major shifts due to the treated wastewater were apparent.

The main focus of this research was the development of microfluidic technology for ultrasensitive and fast molecular analysis of cells. Chromatin immunoprecipitation (ChIP) assay followed by next generation sequencing serves as the primary technique to characterize the genomic locations associated with histone modifications. However, conventional ChIP-seq assay requires large numbers of cells. We demonstrate a novel microfluidics-based ChIP-seq assay which dramatically reduced the required cell number. Coupled with next generation sequencing, the assay permitted the analysis of histone modifications at the whole genome from as few as ~100 cells. Using the same device, we demonstrated that MeDIP-seq with tiny amount of DNA (<5ng) generated high quality genome-wide profiles of DNA methylation. Off-chip sonication often leads to sample loss due to multiple tube transferring. In addition, conventional sonicators are not able to manipulate samples with small volume. We developed a novel microfluidic sonicator, which is able to achieve on-chip DNA/chromatin shearing into ideal fragment size (100~600bp) for both chromatin immunoprecipitation (ChIP) and methylated DNA immunoprecipitation (MeDIP). The integrated on-chip sonication followed by immunoprecipitation (IP) reaction can significantly reduce sample loss and contamination. Simple and accessible detection methods that can rapidly screen a large cell population with single cell resolution have been seriously lacking. We demonstrate a simple protocol for detecting translocation of native proteins using a common flow cytometer which detects fluorescence intensity without imaging. Using our approach, we successfully detected the translocation of native NF-kappa B (an important transcription factor) at its native expression level and examine the temporal dynamics in the process. Droplets with encapsulated beads and cells have been increasingly used for studying molecular and cellular biology. However, a mixed population of droplets with an uneven number or type of encapsulated particles is resulted and used for screening. We developed a fluorescence-activated microfluidic droplet sorter that integrated a simple deflection mechanism. By passing droplets through a narrow interrogation channel, the encapsulated particles were detected individually. The microcontroller conducted the computation to determine the number and type of encapsulated particles in each droplet and made the sorting decision. Our results showed high efficiency and accuracy for sorting and enrichment. / Ph. D.

The shift of medical technology from a doctor's application of individualized medicine toward precision medicine has been accelerated by the advent of Next Generation Sequencing. Individualized medicine is where a doctor tries to understand the intricacies of a patient's medical state, where precision medicine uses a wealth of data to understand the individuality of a patient on a biological level to determine treatment course. Next Generation Sequencing allows for the collection of genome wide analyses such as genomic, transcriptomic, and epigenomic sequencing, which provides the backbone of the data driven precision medicine. In order to obtain and use this data, it needs to be produced from minimal amounts of patient tissue, such as the amount from a needle biopsy. In order to perform so many different assays it is paramount that we develop high sensitivity methodologies, such that we can gain an understanding of the patient's physiology without causing much discomfort in gathering large amounts of sample. In pursuit of making more tests, data, and assays available for use in precision medicine, we have developed 3 different microfluidic technologies, which automate and simplify the assays needed for the data collection at a high sensitivity, as well as a versatile platform for therapeutic production. First, we developed a epigenomic assay for chromatin immunoprecipitation, which gives us information on histone modifications across the genome. These histone modifications heavily impact gene expression and how the chromatin is organized, as well promoting or inhibiting transcription of genes. Our technology allowed us to perform multiple parallel assays from as few as 50 cells quickly and reliably using our fluidized bed technology. Next, we developed a library preparation system, which reduces the cost of library preparation by 20x and reduces operator pipetting by 100x. Our system uses a droplet based reactor to quickly and reliably prepare sequencing libraries using the lowest amount of DNA to date, 10 pg. Finally, we designed a therapeutics-on-a-chip platform which is capable of producing clinically relevant proteins on demand from temperature stable components. Using our system, we are capable of producing a number of different therapeutics on demand quickly without rearrangement of the system. / Doctor of Philosophy / Technical advances in the healthcare industry have made a range of new data available to physicians and patients. Home use DNA testing kits have made it possible to examine one’s predisposition to certain genetic diseases. Using these advanced methods, we are able to gain insights into a patient’s disease state where we were previously unable. Unfortunately, some of these new analyses currently require large amounts of patient sample, which make the examinations largely impractical to perform. In order to overcome the sample requirements, which make these analyses impractical, we develop microscale reactor systems capable of reducing the amount of material required for these new analyses. Here I demonstrate our developed technologies to automate 3 different processes aimed at enabling the study of protein-DNA interactions and produce therapeutics at the point of care. First, we developed an analytical system to study protein-DNA interactions (which are important to understanding patient responses to treatment), that allow for parallel analyses which can be done with sample from less than one needle biopsy, where existing methods would require dozens or more (50 vs 10,000,000 cells.) Next, we developed automated system for preparing DNA sequencing libraries using as little as 10 pg DNA (~2 cells of DNA). The device run multiple reactions simultaneously while reducing batch to batch variation and operator hands-on time. Finally, we developed a v Therapeutics-On-a-Chip platform that produces clinically relevant therapeutic proteins in clinically relevant dosages using a cell-free approach, while saving the trouble and cost associated with protein storage and transportation.

The innate immune response to lipopolysaccharide (LPS) mediated by toll-like receptor 4 (TLR4) contributes substantially to the morbidity of equine gastrointestinal disease, neonatal sepsis and other diseases. MicroRNAs (miRNAs), small non-coding RNA molecules acting as post-transcriptional regulators of gene expression, have key roles in TLR4 signaling regulation in other species. The central hypothesis of this study was that LPS induces differential expression of miRNAs in equine peripheral blood mononuclear cells (PBMCs). PBMCs were isolated from healthy adult horses and cultured with LPS or medium only for 2, 4 and 8 hours. Concentrations of inflammatory cytokines were measured in supernatants by immunoassay. Illumina Next-Generation Sequencing of the miRNA transcriptome was performed in PBMCs at 0, 2 and 4 hours. Selected expression changes were verified by qRT-PCR. 327 mature miRNAs were detected in equine PBMCs. Only miR-155 was significantly upregulated by LPS. 9 miRNAs showed statistically significant expression changes with time. Tumor necrosis factor-α concentration was significantly higher in supernatants from LPS-treated cells than controls from 2 hours, while interleukin-10 and interferon-γ were increased at 8 hours. miR-155 expression was correlated to all three cytokines. These data provide a foundation for future research into miRNA involvement in equine inflammatory responses. miR-155 is the principal LPS-induced miRNA in horses. Bioinformatic target predictions support roles in regulation of innate and adaptive immune responses including TLR4 signaling, as in humans. It is thus likely to influence the acute inflammatory response to LPS. Further research will be necessary to establish its role in naturally occurring disease. / Master of Science

Background: Prostate cancer (PCa) is one of the most prevalent cancers worldwide. The clinical manifestations and molecular characteristics of PCa are highly variable. Aggressive types require radical treatment, whereas indolent ones may be suitable for active surveillance or organ-preserving focal therapies. Patient stratification by clinical or pathological risk categories still lacks sufficient precision. Incorporating molecular biomarkers, such as transcriptome-wide expression signatures, improves patient stratification but so far excludes chromosomal rearrangements. In this study, we investigated gene fusions in PCa, characterized potential novel candidates, and explored their role as prognostic markers for PCa progression. Methods: We analyzed 630 patients in four cohorts with varying traits regarding sequencing protocols, sample conservation, and PCa risk group. The datasets included transcriptome-wide expression and matched clinical follow-up data to detect and characterize gene fusions in PCa. With the fusion calling software Arriba, we computationally predicted gene fusions. Following detection, we annotated the gene fusions using published databases for gene fusions in cancer. To relate the occurrence of gene fusions to Gleason Grading Groups and disease prognosis, we performed survival analyses using the Kaplan–Meier estimator, log-rank test, and Cox regression. Results: Our analyses identified two potential novel gene fusions, MBTTPS2,L0XNC01::SMS and AMACR::AMACR . These fusions were detected in all four studied cohorts, providing compelling evidence for the validity of these fusions and their relevance in PCa. We also found that the number of gene fusions detected in a patient sample was significantly associated with the time to biochemical recurrence in two of the four cohorts (log-rank test, p-value < 0.05 for both cohorts). This was also confirmed after adjusting the prognostic model for Gleason Grading Groups (Cox regression, p-values < 0.05). Conclusions: Our gene fusion characterization workflow revealed two potential novel fusions specific for PCa. We found evidence that the number of gene fusions was associated with the prognosis of PCa. However, as the quantitative correlations were only moderately strong, further validation and assessment of clinical value is required before potential application.

Microfluidics has revolutionized how molecular biology studies are conducted. It permits profiling of genomic and epigenomic features for a wide range of applications. Microfluidics has been proven to be highly complementary to NGS technology with its unique capabilities for handling small volumes of samples and providing platforms for automation, integration, and multiplexing. In this thesis, we focus on three projects (diffusion-based PCR, MID-RRBS, and SurfaceChIP-seq), which improved the sensitivities of conventional assays by coupling with microfluidic technology. MID-RRBS and SurfaceChIP-seq projects were designed to profiling genome-wide DNA methylation and histone modifications, respectively. These assays dramatically improved the sensitivities of conventional approaches over 1000 times without compromising genomic coverages. We applied these assays to examine the neuronal/glial nuclei isolated from mouse brain tissues. We successfully identified the distinctive epigenomic signatures from neurons and glia. Another focus of this thesis is applying electrical field to investigate the intracellular contents. We report two projects, drug delivery to encapsulated bacteria and mRNA extraction under ultra-high electrical field intensity. We envision rapid growth in these directions, driven by the needs for testing scarce primary cells samples from patients in the context of precision medicine. / Ph. D. / Microfluidics is a technology that manipulates solution with extremely small volume. It is an emerging platform that has revolutionized how molecular biology studies are conducted. It permits profiling of genome wide DNA changes or DNA-related changes (e.g. epigenomics) for a wide range of applications. One of the major contribution of microfluidics is to improve the next generation sequencing (NGS) technologies with its unique capabilities for handling small volumes of samples and providing platforms for automation, integration, and multiplexing. In this thesis, we focus on three projects (diffusion-based PCR, MID-RRBS, and SurfaceChIP-seq), which improved the sensitivities of conventional assays by coupling with microfluidic technology. MID-RRBS and SurfaceChIP-seq projects were designed to profiling genome-wide DNA methylation and histone modifications, respectively. DNA methylation and histone modification have been proved to affect a lot of biological processes, such as disease development. These developed technologies would benefit the development of precision medicine (a medical model that proposes the customization of healthcare) and treatment to various diseases. We applied these technologies to study the epigenomic differences between several cell types in the mouse brain.

The Internet has made tremendous progress since its inception. The kingpin has been the transmission control protocol (TCP), which supports a large fraction of communication. With the Internet's wide-spread access, users now have increased expectations. The demands have evolved to an extent which TCP was never designed to support. Since network stacks do not provide the necessary functionality for modern applications, developers are forced to implement them over and over again --- as part of the application or supporting libraries. Consequently, application developers not only bear the burden of developing application features but are also responsible for building networking libraries to support sophisticated scenarios. This leads to considerable duplication of effort. The challenge for TCP in supporting modern use cases is mostly due to limiting assumptions, simplistic communication abstractions, and (once expedient) implementation shortcuts. To further add to the complexity, the limited TCP options space is insufficient to support extensibility and thus, contemporary communication patterns. Some argue that radical changes are required to extend the networks functionality; some researchers believe that a clean slate approach is the only path forward. Others suggest that evolution of the network stack is necessary to ensure wider adoption --- by avoiding a flag day. In either case, we see that the proposed solutions have not been adopted by the community at large. This is perhaps because the cost of transition from the incumbent to the new technology outweighs the value offered. In some cases, the limited scope of the proposed solutions limit their value. In other cases, the lack of backward compatibility or significant porting effort precludes incremental adoption altogether. In this dissertation, we focus on the development of a communication model that explicitly acknowledges the context of the conversation and describes (much of) modern communications. We highlight how the communication stack should be able to discover, interact with and use available resources to compose richer communication constructs. The model is able to do so by using session, flow and endpoint abstractions to describe communications between two or more endpoints. These abstractions provide means to the application developers for setting up and manipulating constructs, while the ability to recognize change in the operating context and reconfigure the constructs allows applications to adapt to the changing requirements. The model considers two or more participants to be involved in the conversation and thus enables most modern communication patterns, which is in contrast with the well-established two-participant model. Our contributions also include an implementation of a framework that realizes such communication methods and enables future innovation. We substantiate our claims by demonstrating case studies where we use the proposed abstractions to highlight the gains. We also show how the proposed model may be implemented in a backwards compatible manner, such that it does not break legacy applications, network stacks, or middleboxes in the network infrastructure. We also present use cases to substantiate our claims about backwards compatibility. This establishes that incremental evolution is possible. We highlight the benefits of context awareness in setting up complex communication constructs by presenting use cases and their evaluation. Finally, we show how the communication model may open the door for new and richer communication patterns. / PHD / In this dissertation, we focus on the development of a communication model that explicitly acknowledges the context of the conversation and describes (much of) modern communications. We highlight how the networking software should be able to discover, interact with and use available resources. The model is able to do so by using abstractions that describe communications between participants as if human beings were having a conversation i.e., the semantics of interactions between participants are defined in terms of a conversation session. These abstractions provide means to the application developers for describing communications in a holistic manner, recognizing change in the context and reconfigure communications to allow adaptation to changing requirements. The model considers two or more participants to be involved in the conversation and thus enables most modern communication patterns, which is in contrast with the well-established two-participant legacy model. Our contributions also include an implementation of a framework that realizes such communication methods and enables future innovation. We substantiate our claims by demonstrating case studies where we use the proposed abstractions to highlight the gains. We also show how the proposed model may be implemented in a backwards compatible manner, such that it does not break legacy applications, networking software, or network infrastructure. We also present use cases to substantiate our claims about backwards compatibility. This establishes that incremental evolution is possible. We highlight the benefits of context awareness in setting up complex communication constructs by presenting use cases and their evaluation. Finally, we show how the communication model may open the door for new and richer communication patterns.

Large-scale bisulfite treatment and short reads sequencing technology allows comprehensive estimation of methylation states of Cs in the genomes of different tissues, cell types, and developmental stages. Accurate characterization of DNA methylation is essential for understanding genotype phenotype association, gene and environment interaction, diseases, and cancer. The thesis work first evaluates the performance of several commonly used bisulfite short read mappers and investigates how pre-processing data might affect the performance. Aligning bisulfite short reads to a reference genome remains a challenging task. In practice, only a limited proportion of bisulfite treated DNA reads can be mapped uniquely (around 50-70%) while a significant proportion of reads (called multireads) are aligned to multiple genomic locations. The thesis outlines a strategy to improve the mapping efficiencies of the existing bisulfite short reads software by finding unique locations for multireads. Analyses of both simulated data and real hairpin bisulfite sequencing data show that our strategy can effectively assign approximately 70% of the multireads to their best locations with up to 90% accuracy, leading to a significant increase in the overall mapping efficiency. The most common and essential downstream task in DNA methylation analysis is to detect differential methylated cytosines (DMCs). Although many statistical methods have been applied to detect DMCs, inconsistency in detecting differential methylated sites among statistical tools remains. We adapt the wavelet-based functional mixed models (WFMM) to detect DMCs. Analyses of simulated Arabidopsis data show that WFMM has higher sensitivities and specificities in detecting DMCs compared to existing methods especially when methylation differences are small. Analyses of monozygotic twin data who have different pain sensitivity also show that WFMM can find more relevant DMCs related to pain sensitivity compared to methylKit. In addition, we provide a strategy to modify the default settings in both WFMM and methylKit to be more tailored to a given methylation profile, thus improving the accuracy of detecting DMCs. Population growth and climate change leave billions of people around the world living in water scarcity conditions. Therefore, utility of reclaimed water (treated wastewater) is pivotal for water sustainability. Recently, researchers discovered microbial regrowth problems in reclaimed water distribution systems (RWDs). The third part of the thesis involves: 1) identifying fundamental conditions that affect proliferation of antibiotic resistance genes (ARGs), 2) identifying the effect of water chemistry and water age on microbial regrowth, and 3) characterizing co-occurrence of ARGs and/or mobile genetics elements (MGEs), i.e., plasmids in simulated RWDs. Analyses of preliminary results from simulated RWDs show that biofilms, bulk water environment, temperature, and disinfectant types have significant influence on shaping antibiotic resistant bacteria (ARB) communities. In particular, biofilms create a favorable environment for ARGs to diversify but with lower total ARG populations. ARGs are the least diverse at 300C and the most diverse at 220C. Disinfectants reduce ARG populations as well as ARG diversity. Chloramines keep ARG populations and diversity at the lowest rate. Disinfectants work better in bulk water environment than in biofilms in terms of shaping resistome. Network analysis on assembly data is done to determine which ARG pairs are the most co-occurred. Bayesian network is more consistent with the co-occurrence network constructed from assembly data than the network based on Spearman's correlation network of ARG abundance profiles. / Ph. D. / Human genome project has been lately attracting a lot of public attention. With the flood of big genomic data, understanding and extracting valuable information from the data remain challenge. The thesis work first evaluates the performance of different genome analysis tools. After that, the thesis outlies a strategy to improve the overall performance of whole-genome analysis tools, thus contributing to more accurate identification of mutations that are responsible for cancer and diseases. Population growth and climate change leave billions of people around the world living in water scarcity conditions. Therefore, utility of reclaimed water (treated wastewater) is pivotal for water sustainability. Recently, researchers discovered microbial regrowth problems in reclaimed water distribution systems which can worsen the existing problem of antibiotics resistance spread. The thesis identifies fundamental factors that help shape the microbial communities in reclaimed water systems in order to limit the spread of antibiotics resistance.

Lymphoma is a common hematopoietic malignancy in canines. Current diagnostic techniques to diagnose lymphoma are often invasive and expensive. Additionally, tumor heterogeneity complicates the accurate classification and diagnosis of specific subtypes, hindering the development of targeted therapy and prognostic assessments. We propose a minimally invasive liquid biopsy technique involving blood collection to detect cell-free DNA from tumors using Next-generation sequencing. We hypothesize that identical tumor aberrations can be found in matching plasma and tumor DNA. Five dogs diagnosed with B-cell lymphoma through flow cytometry or PAAR were enrolled in the study. Samples collected included: (1) blood for plasma (cfDNA), (2) tumor tissue fine-needle aspirates (tumor DNA), and (3) buccal swabs (genomic DNA, germline control). Whole Genome Sequencing was performed using Illumina NovaSeq 6000, and the sequenced output was analyzed with bioinformatics tools to detect somatic variants in plasma and tumor samples. Our results revealed many shared somatic variants between matched cfDNA and tumor DNA samples, with 1.7-49% of tumor variants also found in corresponding plasma samples. Shared variants constituted only 0.5-9% of all plasma somatic variants. Specific B-cell lymphoma mutations were identified in cfDNA, including MYC, POT1, and TRAF3, alongside other cancer-related genes. Tumor samples showed mutations in genes associated with canine and human B-cell lymphoma. This study suggests that tumor-specific genomic mutations can be detected in plasma, supporting the potential of liquid biopsy as a less invasive diagnostic tool. However, cfDNA may not capture the full genetic heterogeneity of tumors due to low tumor-derived DNA content in limited plasma volumes. / Master of Science / Lymphoma is a type of blood cancer affecting white blood cells. Canine lymphoma is a common neoplasia, with an incidence rate of 20 to 100 cases per 100,000 dogs, making it a significant research focus. Current diagnostic methods are invasive and costly. Additionally, the wide variety of tumor types in lymphoma makes it challenging to determine the exact subtypes, which is crucial for selecting the best treatment approach. To overcome these challenges, we proposed a less invasive method known as "liquid biopsy". This technique involves taking a blood sample of a dog to find cell-free DNA from tumor cells using Next-Generation Sequencing technologies. We aimed to see if blood DNA could provide the same information as tumor DNA. In our study, we worked with five dogs diagnosed with B-cell lymphoma through traditional methods. We collected blood, tissue from needle biopsies, and buccal swabs from each dog. We then performed DNA extraction and sequencing on these samples. Our findings showed that 1.7-50% of the mutations in tumor DNA were also detected in matched blood DNA, though these represented only a small fraction of all changes found in blood samples. Additionally, the blood samples also revealed mutations related to canine B-cell lymphoma in genes like MYC, POT1, and TRAF3. In conclusion, our study supports the use of liquid biopsy as a feasible and less invasive method to diagnose lymphoma in dogs. However, they might not show all genetic variations of the tumor due to limited tumor DNA content.