Overall, the work presented in this thesis explored the impact of cigarette smoke on the immunopathogenesis of respiratory disease. This thesis highlighted the determinantal impact of cigarette smoke on (auto)antibody levels and pulmonary macrophage composition. Work completed by Steven P Cass 2016-2021. / Cigarette smoke is an insidious insult that is associated with a spectrum of respiratory diseases that range from cancer to obstructive diseases such as chronic obstructive pulmonary disease (COPD), to restrictive diseases such as idiopathic pulmonary fibrosis (IPF). In this thesis, we explore how cigarette smoke impacts immune components that contribute to respiratory disease. To begin, we assessed the impact of cigarette smoke on airway antibody and autoantibody levels. We assessed sputum, a non-invasive method to sample the lower airways, to directly assess the presence of antibodies and autoantibodies in COPD. Total immunoglobulin M (IgM), IgG and IgA were detectable in the sputum of subjects. Notably, in patients with mild to moderate COPD, current smoking status was associated with decreased IgM and IgG. Next, using a comprehensive autoantigen array, we measured matched sputum and serum autoantibodies in 224 individuals. Serum autoantibodies were more abundant than sputum autoantibodies and correlated strongly between two independent COPD cohorts. Overall, the autoantibody profile of a patient with COPD was the same as a control subject. A proportion of autoantibody specificities were differentially expressed in patients with COPD with anti-tissue autoantibodies weakly associated with measures of emphysema. Taken together, these data suggested chronic cigarette smoke exposure was associated with limited differential expression of autoantibodies, but these changes were not a reliable method to identify COPD status. In our third study, we assessed the impact of cigarette smoke exposure on the composition and function of pulmonary macrophage subpopulations. Macrophages perform a central role in respiratory host defence and are implicated in the pathobiology of several respiratory diseases. Using a mouse model of cigarette smoke exposure, we reported cigarette smoke-induced expansion of CD11b+ macrophage subpopulations including monocyte-derived alveolar macrophages and interstitial macrophages. The altered pulmonary macrophage composition following cigarette smoke exposure contributed to attenuated fibrogenesis in a model of bleomycin-induced lung injury. This study offered insight to pulmonary macrophage composition and function following cigarette smoke exposure. This thesis summarises the original contributions and work completed during the course of this Ph.D., aimed at understanding the impact of cigarette smoke exposure on immune components central to respiratory disease. In conclusion, these findings shed light on the presence of (auto)antibodies in patients with COPD and the composition of macrophage subpopulations following cigarette smoke exposure. / Thesis / Doctor of Philosophy (PhD) / Currently there are 1.3 billion people who use tobacco across the world. The most common method to consume tobacco is by smoking cigarettes. Cigarette smoking is well-known to cause disease; however, smoking rates are still increasing with more daily cigarette smokers in 2012 than there were in 1980. In this thesis, we explore the impact of cigarette smoke upon the immune system. We first assessed whether cigarette smoking impacts the levels of antibodies, proteins that are produced by the immune system to protect against foreign bodies, in healthy individuals, cigarette smokers without disease and patients with chronic obstructive pulmonary disease (COPD). We found that current smokers had decreased antibodies in the airways, thus predisposing cigarette smokers to increased damage. In our second study, we measured the presence of airway and blood autoantibodies. These are antibodies that target self and have the potential to inflict damage. We discovered that patients with COPD had minor changes in autoantibodies and these changes were weakly associated with emphysema. In our third study, we evaluated the impact of cigarette smoke on lung macrophages, cells that eat and destroy foreign bodies, in a mouse model of cigarette smoke exposure. Cigarette smoke increased the number of bone marrow-derived macrophages and this change in macrophage populations was associated with a reduced wound healing ability. Overall, these studies offer insight into how cigarette smoke impairs the function of the immune system and contributes to lung disease.

<p> Pasteurella multocida is a respiratory pathogen that is frequently isolated from cattle suffering  from bovine respiratory disease (BRD), the leading cause of mortality and morbidity on modern day cattle farms. Treatment involves the use of antimicrobials which have been shown to fail for  about 30% of BRD cases, leading to the suspicion that etiologic agents, such as P. multocida, may  be resistant. Phenotypic resistance can be confirmed via laboratory antibiotic susceptibility testing  (AST) but this requires several days to complete. Genotypic resistance could be quickly assessed  via nucleic acid assays based on the presence of known antibiotic resistance genes (ARGs). In  human medicine, resistant genes associated with common antibiotics (i.e., ampicillin and penicillin)  in common pathogens (i.e., Salmonella) are very accurate in predicting phenotypic resistance;  however, ARGs associated with antibiotics used to treat BRD, such as enrofloxacin and  tulathromycin, have shown low genotype-phenotype concordance. Hence, this study aims to  improve P. multocida genotype-phenotype concordance by applying a machine learning (ML)  algorithm to identify novel genomic sequences (biomarkers) that have greater accuracy in  predicting resistance to antibiotics commonly used to treat BRD compared to known ARGs.  Cultures of P. multocida were isolated from cattle with clinical signs of BRD. Antibiotic  susceptibility testing was performed and recorded for each isolate. Genomes were sequenced and  assembled, followed by annotating and identifying ARGs using the comprehensive antibiotic  resistance database (CARD). Assembled genomes were then split into 31-base long segments (31- mers), and these segments along with phenotypic antibiotic susceptibility were used as input data  for the ML algorithm. Important genomic biomarkers for four out of the six tested antibiotics were  found to have greater accuracy when predicting resistance phenotype compared to known ARGs.  The biomarker for enrofloxacin had the highest accuracy of 100% whereas the accuracy for the  12 tulathromycin biomarker was 81% but was still greater than the accuracy given by ARGs of 63%.  On the other hand, resistance genes for florfenicol and tetracycline showed greater genotype?phenotype concordance, with accuracies of 95% and 91%, respectively. Annotations to important  rulesets determined by ML were associated with clustered regularly interspaced short palindromic  repeats (CRISPR) sequences, ligases that function to recycle murein into the peptidoglycan (PDG) layer, and transferases that control the synthesis and modulation of the lipopolysaccharide (LPS).  External validation revealed that phenotypic resistance could be accurately predicted for  danofloxacin and enrofloxacin using genomic biomarkers determined by ML, and for florfenicol  using the floR gene. This study demonstrated that genomic biomarkers determined by ML can provide an accurate prediction of antibiotic resistance within Pasteurella multocida isolates.  Assays could be developed to target ML-generated biomarkers and known ARGs to predict resistance in sick animals and to limit treatment failures associated with antibiotic resistance in  cattle suffering from BRD. </p>

Bovine respiratory disease (BRD) is a highly dynamic disease complex that results from host, microbial agent, and environmental interactions. Despite nearly a century of targeted research, BRD remains the most economically damaging disease in beef cattle production and appears to be increasing in global incidence. While modern modalities for BRD detection exist, clinical diagnosis and management decisions largely depend upon clinical observations and their associated risk of disease. Though these approaches lack precision, they remain in use for many reasons, including fiscal and time constraints within beef production systems. Advancements in high-throughput sequencing have demonstrated the ability to provide insight into complex biological disorders, leading to the development of predictive biomarkers and individualized therapy. Through the use of observational research methods and previously published data, transcriptome analyses were used to capture biological information related to the host-disease or host-pathogen relationship. These studies independently elaborated findings related to host management of inflammation, ultimately being associated with both acquisition and severity of BRD. Through advances in sequencing technology and data analysis methodology, novel components related to host inflammatory mitigation and antimicrobial defense are described for clinical BRD. Factors related to increased alternative complement activation, decreased specialized proresolving lipid mediator biosynthesis, decreased antimicrobial peptide production, and increased type I interferon stimulation were associated with severe clinical BRD. These findings define molecular networks, mechanisms, and pathways that are associated with BRD outcome, and may serve as a foundation for precision medicine in beef cattle.

Bovine respiratory disease (BRD) is a significant health problem for cattle producers in terms of economic cost and animal welfare. In the United States (US), it is one of the leading causes of sickness and death in beef calves prior to weaning. Although much research has been conducted to develop vaccines for prevention and antibiotics for treatment, the morbidity and mortality of BRD in beef calves prior to weaning has not improved over the years. The identification of risk factors associated with BRD is an area of focus which might ultimately allow producers to minimize morbidity and mortality from BRD. Little research has been performed to understand factors contributing to the risk of BRD in beef calves prior to weaning. BRD affects the beef cattle industry through losses due to mortality, prevention cost, treatment cost, or morbidity effect on productivity. Currently, the economic losses due to BRD for beef calves prior to weaning is not available. Price paid for feeder cattle is a major factor influencing the income of producers. The effect of BRD is a complicated problem since the parameters associated with the cost of BRD in beef cow-calf production are variable and interrelated. To better understand the economic effect of BRD in beef calves prior to weaning, concepts of uncertainty, variability, stochasticity, nonlinearity, and feedback might be involved during the process of assessing risk. The objectives of this dissertation are the following: 1) to test if calf sex, birth weight, and age of dam are associated with BRD of beef calves prior to weaning in different age periods; 2) to identify factors affecting the national market price of beef feeder cattle in the US and how the prices change over time; 3) to investigate the prevention and treatment cost of BRD in beef calves prior to weaning; 4) to estimate the economic cost of BRD in US beef calves prior to weaning; and 5) to understand the effect of BRD occurrence or absence on the national net income of the US beef cow-calf industry.

No description available.

<p>Asthma is a chronic immune-inflammatory disease of the airways, characterized by reversible airflow obstruction and airway hyperresponsiveness (AHR), and is associated with the development of airway remodeling. While our understanding of the pathophysiology of allergic asthma has increased remarkably in the last few decades, the origins of the disease remain elusive. Indeed, studies indicate that the prevalence of allergic asthma, has increased dramatically over the last 30 years. Within this context, a number of environmental factors including respiratory viral infections have been associated with the onset of this disease but causal evidence is lacking. The work presented in this thesis examines the interactions between a respiratory viral infection, specifically influenza A, and the common aeroallergen house dust mite (HDM) in an experimental murine model. To this end, we investigated the impact of an acute influenza A infection on the exposure to a subclinical dose of HDM (Chapter 2) and addressed potential underlying immune mechanisms using a global, genomic approach (Chapter 3). Our data demonstrate an enhancement of immune inflammatory responses to HDM and reveals multiple immune pathways by which influenza A may enhance the response to subsequent allergen exposure. Collectively these immune pathways are capable of lowering the threshold of HDM responsiveness. Lastly, as allergic asthma develops in most instances during infancy, we investigated the impact of an influenza A infection on allergen responses in infant mice (Chapter 4). In this setting, acute influenza A infection subverts constitutive allergen hyporesponsiveness thus resulting in sensitization, airway inflammation and, ultimately, structural and functional alterations persisting into adulthood.</p> / Doctor of Philosophy (PhD)

Drywall finishing operations have been associated with worker exposure to dust that contains known particulate respiratory health hazards, such as silica, talc, and mica. Despite the existence of engineering, work-practice, and personal-protective-equipment (PPE) control technologies for the mitigation of this hazard, worker exposures persist in the drywall finishing industry. This research employed a macroergonomic framework to evaluate this problem and identify barriers to dust control technology adoption in the key subsystems: personnel, technological, and organizational. In the first study, the organizational subsystem was evaluated through a telephone interview of 264 drywall finishing firm owners. This study found the most commonly used dust control technology was respiratory protection. Cost, usability, environmental factors, and productivity were barriers identified in preventing adoption of other technologies. In the second study, of the technological subsystem, 16 participants performed simulated drywall finishing tasks with each of four methods, in a laboratory setting. Dust particles were monitored and compared among the technologies used. Participants performed usability evaluations of the four tools. The ventilated sander produced less respirable-size class dust than did the other three tools. The block sander produced more dust than the other three tools. Usability evaluations revealed that the block sander was easiest to learn, easiest to use, and perceived to be the best overall, while the wet method and pole sander were considered to have poor usability in terms of ease of use and productivity. Usability problems associated with perceived comfort and ease of use were identified for the ventilated sander, but it was tied for "overall best" with the block sander. The third study, of drywall finishing worker perceptions, employed the Health Belief Model to assess barriers to technology adoption, risk, susceptibility, and benefits. Results showed that workers have a high perception of the risk associated with drywall dust, but a lower perception of individual susceptibility to disease as a result of occupational exposure. Barriers to the use of dust control technologies were identified as being associated with organizational and usability factors. Most participants indicated having access only to respiratory protection, among the available dust control methods. / Ph. D.

No description available.

There is an urgent need for detection of viral respiratory pathogens to identify the causal agent(s) involved and to prevent the spread of related diseases. The first part of this dissertation focuses on development, optimization and validation of Real-time reverse transcription polymerase chain reaction (rRT-PCR) assays for the detection of several common equine viral pathogens: equine arteritis virus (EAV), equine influenza virus and equine rhinitis viruses A and B. Emphasis of the second part of this dissertation is on studying the role of EAV envelope proteins in virus attachment and entry. Using an infectious cDNA clone of EAV and reverse genetics, a panel of chimeric viruses was generated by swapping the N-terminal ectodomains and full-lengths of the two major envelope proteins (GP5 and M) from porcine reproductive and respiratory syndrome virus (PRRSV). The recombinant viruses expressing the N-terminal ectodomain of PRRSV GP5 or M or both (GP5ecto, Mecto, and GP5&Mecto, respectively) in an EAV backbone were viable and genetically stable. Compared to the parental virus, these three chimeric viruses produced lower titers and smaller plaque sizes indicating that they have a crippled phenotype. Interestingly, the three chimeric viruses could only infect EAV susceptible cell lines but not the PRRSV susceptible cell line. Therefore, the exchange of GP5 and/or M protein N-terminal ectodomains from PRRSV did not alter the cellular tropism of the chimeric viruses. We also investigated the role of one of the minor envelope proteins (E) of EAV in virus attachment and entry. The results showed that EAV infection of equine endothelial cells is heparin-dependent and the Cterminus of the E protein contains a putative heparin-binding domain. We generated a panel of arginine to glycine mutations in the conserved region of both the full-length EAV infectious cDNA clone and individual E protein expression vectors. The triple mutation R52,60,65G construct grew significantly slower and produced much smaller plaques. The double mutant R52,60G completely blocked the interaction between E protein and heparin. Taken together, these data indicated that E protein interacts with heparin to facilitate virus attachment and plays a major role in EAV infection.

[pt] Esta dissertação apresenta um estudo de validação das estimações dos efeitos da poluição atmosférica na saúde da população quando se utilizam dados com periodicidade de seis dias. O estudo foi realizado utilizando duas abordagens complementares. A primeira consiste em comparar os efeitos estimados a partir da análise de duas séries de morbidade diárias na cidade do Rio de Janeiro com aqueles obtidos particionando-se estas mesmas séries em seis séries distintas, cada qual com periodicidade de seis dias. As estimativas dos efeitos nas series particionadas de seis dias variaram substancialmente em relação à série diária para contagem de internações por doenças respiratórias em crianças. Para a mesma análise feita para a série de idosos, não foram detectadas diferenças tão significativas. Para complementar esta análise, realizou-se um estudo de Monte Carlo considerando diferentes cenários quanto aos padrões de poluição do ar. Os resultados mostraram que quanto maior a quantidade de dia atípicos por mês, maior pode ser a variação entre as estimações das séries diárias e as séries com periodicidade de 6 dias. Ao fim deste trabalho são apresentados resultados utilizando dados reais com periodicidade de 6 dias. Os efeitos estimados de PM10 para doenças respiratórias em crianças foram de 8.1% (IC: 5.4% ; 10.8%) para o dia corrente e 7.3% (IC: 4.5% ; 10.2%) para 1 dia após a exposição à poluição do ar. Para idosos, houve um aumento estatisticamente significativo apenas para o dia corrente de 3.36% (IC: 1.19% ;5.58%). / [en] This dissertation presents a validation study on the analysis of the effect of atmospheric pollution on morbidity using data sampled every sixth day. This has been investigated using two complementary frameworks. We first compared such pollution effects using two morbidity daily time series in Rio de Janeiro city, which have been sampled every sixth day, thus generating, for each series, a set of six sampled series. For the daily counts of hospital events for children due to respiratory diseases the estimated pollution effect for the six sampled series was markedly different from the same effect estimated on the original daily time series, while for elderly people such difference has not been observed. The second part of our analysis was carried over using a Monte Carlo study. Finally we conclude our work presenting risk estimates using real data sampled every six days. The estimated relative risks of particulate material (PM10) on respiratory diseases for inhabitants of Rio de Janeiro city were as follows. For children the risk was estimated in 8.1% (5.4%; 10.8%) for current day exposure and 7.3% (4.5%; 10.2%) for exposition lagging one day. For elderly people it was observed a significant increase on hospital attendances due to pollution on the same day of exposition. and the estimated risk was 3.36% (1.19%; 5.58%).