Death of a bacterium: exploring the inhibition of Staphylococcus aureus by Burkholderia cenocepacia.

Antimicrobial resistance is a phenomenon of increasing concern as antimicrobial overuse and misuse eliminate current therapeutic options, ushering society into a post-antimicrobial era. Antibiotic discovery and synthesis efforts are urgently needed to counter the increasing burden of antimicrobial resistance. Staphylococcus aureus is a causative agent of a variety of clinical manifestations including bacteremia, endocarditis, soft tissue infection, osteomyelitis, and device-related infections. S. aureus infection presents additional treatment challenges due to its capacity for biofilm formation, which is a mode of growth that confers protection from antibiotics and physical elimination, and the emergence of antibiotic resistant strains, including methicillin-resistant S. aureus and vancomycin-resistant S. aureus. Infection with antibiotic-resistant strains occurs within both nosocomial and community settings, broadening the potential impact of this organism. Bacteria within the genus Burkholderia hold vast potential as sources of antimicrobial agents. Our analysis of patient culture data, provided by the Cystic Fibrosis Foundation, suggests a negative relationship between members of the Burkholderia cepacia complex and Staphylococcus aureus. An in vitro screen for activity against S. aureus indicated several clinical strains of Burkholderia cenocepacia confer potent anti-Staphylococcus activity. This dissertation characterizes the deleterious effect of the presence of B. cenocepacia J2315 and H111, two clinical isolates from cystic fibrosis patients, against S. aureus. Co-culture biofilm-associated survival of both methicillin-sensitive and methicillin-resistant strains was, overall, decreased with both B. cenocepacia J2315 and H111. I further established the breadth of antibiotic activity of these two strains in co-culture with multiple Staphylococcus and other Gram-positive species, including Enterococcus, Bacillus and Listeria. While both B. cenocepacia strains demonstrated detrimental effects against survival of co-inoculated Staphylococcus species, the extent of inhibition of other Gram-positive species differed. Antagonistic activity against the Enterococcus and Bacillus strains assessed in co-culture with B. cenocepacia H111 was profound, with reduction of many co-cultured strains to below the limit of detection. Co-culture survival of the same Gram-positive species with B. cenocepacia J2315 indicated no significant reduction versus cognate mono-culture. Inhibition of S. aureus by both B. cenocepacia strains occurs via a secreted compound, as evidenced by reduction in survival of S. aureus when exposed to B. cenocepacia sterile biofilm supernatants. The inhibitory substance, at least for B. cenocepacia J2315 is secreted in larger quantities in response to the presence of S. aureus. Enzymatic treatment of the supernatants suggests that a protein and an RNA, or a nucleoprotein, are involved in the B. cenocepacia J2315-mediated antagonism of S. aureus, but that inhibition by B. cenocepacia H111 involves a different mechanism. The inhibitory effect is largely dependent upon culture medium, and B. cenocepacia J2315 is more sensitive to differences in nutrient composition of the growth medium than B. cenocepacia H111. Further, this decrease in viable S. aureus is not simply due to B. cenocepacia causing a release of S. aureus from biofilms but is due to killing of S. aureus. Collectively, these data confirm the biotechnological potential of two B. cenocepacia strains and serve to optimize conditions for observation and analysis of this phenomenon

Biofilm Associated Staphylococcus Aureus Viability is Altered By Burkholderia Cenocepacia

Respiratory failure caused by chronic and recurrent microbial infections is the most common cause of death for people with cystic fibrosis (CF)1, a disease causing the formation of thick mucus in the lungs2. Most bacteria can form biofilms, collections of sessile cells adhered to a surface by a secreted substance. Biofilm-associated cells develop antibiotic resistance at higher rates3. The thicker mucus in CF lungs is extremely difficult to clear via action of the mucociliary escalator and its presence fosters the formation of bacterial biofilms. Staphylococcus aureus and Burkholderia cenocepacia are two pathogens commonly found in the CF lung. Previous work in the Yoder-Himes laboratory established an antagonistic relationship between members of the B. cepacia complex and S. aureus biofilms4. To understand this antagonism, it is crucial to identify the biofilm changes occurring when S. aureus and B. cenocepacia interact. This work provides insight into the changes that may be responsible for the reduced viability of S. aureus in biofilms. Using crystal violet to measure biofilm biomass, confocal laser scanning microscopy, and assessing differences in antibiotic susceptibility, S. aureus and B. cenocepacia were examined in both monoculture and co-culture conditions. The results of this experiment indicate S. aureus and B. cenocepacia biofilm formation increases over time and is greater in nutrient-rich media. Additionally, B. cenocepacia inhibits biofilm formation of S. aureus. These findings provide information that can be used for understanding the interactions between pathogenic bacteria in the lungs of CF patients, leading to the development of more effective therapeutics.https://ir.library.louisville.edu/uars/1038/thumbnail.jp

SNAPSHOT USA 2019 : a coordinated national camera trap survey of the United States

This article is protected by copyright. All rights reserved.With the accelerating pace of global change, it is imperative that we obtain rapid inventories of the status and distribution of wildlife for ecological inferences and conservation planning. To address this challenge, we launched the SNAPSHOT USA project, a collaborative survey of terrestrial wildlife populations using camera traps across the United States. For our first annual survey, we compiled data across all 50 states during a 14-week period (17 August - 24 November of 2019). We sampled wildlife at 1509 camera trap sites from 110 camera trap arrays covering 12 different ecoregions across four development zones. This effort resulted in 166,036 unique detections of 83 species of mammals and 17 species of birds. All images were processed through the Smithsonian's eMammal camera trap data repository and included an expert review phase to ensure taxonomic accuracy of data, resulting in each picture being reviewed at least twice. The results represent a timely and standardized camera trap survey of the USA. All of the 2019 survey data are made available herein. We are currently repeating surveys in fall 2020, opening up the opportunity to other institutions and cooperators to expand coverage of all the urban-wild gradients and ecophysiographic regions of the country. Future data will be available as the database is updated at eMammal.si.edu/snapshot-usa, as well as future data paper submissions. These data will be useful for local and macroecological research including the examination of community assembly, effects of environmental and anthropogenic landscape variables, effects of fragmentation and extinction debt dynamics, as well as species-specific population dynamics and conservation action plans. There are no copyright restrictions; please cite this paper when using the data for publication.Publisher PDFPeer reviewe

Mammal responses to global changes in human activity vary by trophic group and landscape

Wildlife must adapt to human presence to survive in the Anthropocene, so it is critical to understand species responses to humans in different contexts. We used camera trapping as a lens to view mammal responses to changes in human activity during the COVID-19 pandemic. Across 163 species sampled in 102 projects around the world, changes in the amount and timing of animal activity varied widely. Under higher human activity, mammals were less active in undeveloped areas but unexpectedly more active in developed areas while exhibiting greater nocturnality. Carnivores were most sensitive, showing the strongest decreases in activity and greatest increases in nocturnality. Wildlife managers must consider how habituation and uneven sensitivity across species may cause fundamental differences in human–wildlife interactions along gradients of human influence.Peer reviewe

Genomic Relationships, Novel Loci, and Pleiotropic Mechanisms across Eight Psychiatric Disorders

Genetic influences on psychiatric disorders transcend diagnostic boundaries, suggesting substantial pleiotropy of contributing loci. However, the nature and mechanisms of these pleiotropic effects remain unclear. We performed analyses of 232,964 cases and 494,162 controls from genome-wide studies of anorexia nervosa, attention-deficit/hyper-activity disorder, autism spectrum disorder, bipolar disorder, major depression, obsessive-compulsive disorder, schizophrenia, and Tourette syndrome. Genetic correlation analyses revealed a meaningful structure within the eight disorders, identifying three groups of inter-related disorders. Meta-analysis across these eight disorders detected 109 loci associated with at least two psychiatric disorders, including 23 loci with pleiotropic effects on four or more disorders and 11 loci with antagonistic effects on multiple disorders. The pleiotropic loci are located within genes that show heightened expression in the brain throughout the lifespan, beginning prenatally in the second trimester, and play prominent roles in neurodevelopmental processes. These findings have important implications for psychiatric nosology, drug development, and risk prediction.Peer reviewe

Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors

Background Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. Methods We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors. Results Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged. Conclusions Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders.Peer reviewe

Image_1_Racial, skin tone, and sex disparities in automated proctoring software.tif

Students of color, particularly women of color, face substantial barriers in STEM disciplines in higher education due to social isolation and interpersonal, technological, and institutional biases. For example, online exam proctoring software often uses facial detection technology to identify potential cheating behaviors. Undetected faces often result in flagging and notifying instructors of these as “suspicious” instances needing manual review. However, facial detection algorithms employed by exam proctoring software may be biased against students with certain skin tones or genders depending on the images employed by each company as training sets. This phenomenon has not yet been quantified nor is it readily accessible from the companies that make this type of software. To determine if the automated proctoring software adopted at our institution and which is used by at least 1,500 universities nationally, suffered from a racial, skin tone, or gender bias, the instructor outputs from ∼357 students from four courses were examined. Student data from one exam in each course was collected, a high-resolution photograph was used to manually categorize skin tone, and the self-reported race and sex for each student was obtained. The likelihood that any groups of students were flagged more frequently for potential cheating was examined. The results of this study showed a significant increase in likelihood that students with darker skin tones and Black students would be marked as more in need of instructor review due to potential cheating. Interestingly, there were no significant differences between male and female students when considered in aggregate but, when examined for intersectional differences, women with the darkest skin tones were far more likely than darker skin males or lighter skin males and females to be flagged for review. Together, these results suggest that a major automated proctoring software may employ biased AI algorithms that unfairly disadvantage students. This study is novel as it is the first to quantitatively examine biases in facial detection software at the intersection of race and sex and it has potential impacts in many areas of education, social justice, education equity and diversity, and psychology.</p

Image_2_Racial, skin tone, and sex disparities in automated proctoring software.TIF

Students of color, particularly women of color, face substantial barriers in STEM disciplines in higher education due to social isolation and interpersonal, technological, and institutional biases. For example, online exam proctoring software often uses facial detection technology to identify potential cheating behaviors. Undetected faces often result in flagging and notifying instructors of these as “suspicious” instances needing manual review. However, facial detection algorithms employed by exam proctoring software may be biased against students with certain skin tones or genders depending on the images employed by each company as training sets. This phenomenon has not yet been quantified nor is it readily accessible from the companies that make this type of software. To determine if the automated proctoring software adopted at our institution and which is used by at least 1,500 universities nationally, suffered from a racial, skin tone, or gender bias, the instructor outputs from ∼357 students from four courses were examined. Student data from one exam in each course was collected, a high-resolution photograph was used to manually categorize skin tone, and the self-reported race and sex for each student was obtained. The likelihood that any groups of students were flagged more frequently for potential cheating was examined. The results of this study showed a significant increase in likelihood that students with darker skin tones and Black students would be marked as more in need of instructor review due to potential cheating. Interestingly, there were no significant differences between male and female students when considered in aggregate but, when examined for intersectional differences, women with the darkest skin tones were far more likely than darker skin males or lighter skin males and females to be flagged for review. Together, these results suggest that a major automated proctoring software may employ biased AI algorithms that unfairly disadvantage students. This study is novel as it is the first to quantitatively examine biases in facial detection software at the intersection of race and sex and it has potential impacts in many areas of education, social justice, education equity and diversity, and psychology.</p

Table_1_Racial, skin tone, and sex disparities in automated proctoring software.XLSX

Students of color, particularly women of color, face substantial barriers in STEM disciplines in higher education due to social isolation and interpersonal, technological, and institutional biases. For example, online exam proctoring software often uses facial detection technology to identify potential cheating behaviors. Undetected faces often result in flagging and notifying instructors of these as “suspicious” instances needing manual review. However, facial detection algorithms employed by exam proctoring software may be biased against students with certain skin tones or genders depending on the images employed by each company as training sets. This phenomenon has not yet been quantified nor is it readily accessible from the companies that make this type of software. To determine if the automated proctoring software adopted at our institution and which is used by at least 1,500 universities nationally, suffered from a racial, skin tone, or gender bias, the instructor outputs from ∼357 students from four courses were examined. Student data from one exam in each course was collected, a high-resolution photograph was used to manually categorize skin tone, and the self-reported race and sex for each student was obtained. The likelihood that any groups of students were flagged more frequently for potential cheating was examined. The results of this study showed a significant increase in likelihood that students with darker skin tones and Black students would be marked as more in need of instructor review due to potential cheating. Interestingly, there were no significant differences between male and female students when considered in aggregate but, when examined for intersectional differences, women with the darkest skin tones were far more likely than darker skin males or lighter skin males and females to be flagged for review. Together, these results suggest that a major automated proctoring software may employ biased AI algorithms that unfairly disadvantage students. This study is novel as it is the first to quantitatively examine biases in facial detection software at the intersection of race and sex and it has potential impacts in many areas of education, social justice, education equity and diversity, and psychology.</p