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Burkholderia pseudomallei, the causative agent of melioidosis, is a gram-negative soil bacterium well recognized in Southeast Asia and northern Australia. However, wider and expanding global distribution of B. pseudomallei has been elucidated. Early diagnosis is critical for commencing the specific therapy required to optimize outcome. Serological testing using the indirect hemagglutination (IHA) antibody assay has long been used to augment diagnosis of melioidosis and to monitor progress. However, cross reactivity and prior exposure may complicate the diagnosis of current clinical disease (melioidosis). The goal of our study was to develop and initially evaluate a serology assay (BurkPx) that capitalized upon host response to multiple antigens. Antigens were selected from previous studies for expression/purification and conjugation to microspheres for multiantigen analysis. Selected serum samples from non-melioidosis controls and serial samples from culture-confirmed melioidosis patients were used to characterize the diagnostic power of individual and combined antigens at two times post admission. Multiple variable models were developed to evaluate multivariate antigen reactivity, identify important antigens, and determine sensitivity and specificity for the diagnosis of melioidosis. The final multiplex assay had a diagnostic sensitivity of 90% and specificity of 93%, which was superior to any single antigen in side-by-side comparisons. The sensitivity of the assay started at >85% for the initial serum sample after admission and increased to 94% 21 days later. Weighting antigen contribution to each model indicated that certain antigen contributed to diagnosis more than others, which suggests that the number of antigens in the assay can be decreased. In summation, the BurkPx assay can facilitate the diagnosis of melioidosis and potentially improve on currently available serology assays. Further evaluation is now required in both melioidosis-endemic and non-endemic settings.
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Burkholderia pseudomallei , Melioidose , Humanos , Anticorpos Antibacterianos , Melioidose/microbiologia , Antígenos de Bactérias , Sensibilidade e EspecificidadeRESUMO
BACKGROUND: Melioidosis is a disease caused by the bacterium Burkholderia pseudomallei, infecting humans and non-human primates (NHP) through contaminated soil or water. World-wide there are an estimated 165,000 human melioidosis cases each year, but recordings of NHP cases are sporadic. Clinical detection of melioidosis in humans is primarily by culturing B. pseudomallei, and there are no standardized detection protocols for NHP. NHP are an important animal model for melioidosis research including clinical trials and development of biodefense countermeasures. METHODOLOGY/PRINCIPLE FINDINGS: We evaluated the diagnostic potential of the multiple antigen serological assay, BurkPx, in NHP using two sera sets: (i) 115 B. pseudomallei-challenged serum samples from 80 NHP collected each week post-exposure (n = 52) and at euthanasia (n = 47), and (ii) 126 B. pseudomallei-naïve/negative serum samples. We observed early IgM antibody responses to carbohydrate antigens followed by IgG antibody recognition to multiple B. pseudomallei protein antigens during the second week of infection. B. pseudomallei negative serum samples had low to intermediate antibody cross reactivity to the antigens in this assay. Infection time was predicted as the determining factor in the variation of antibody responses, with 77.67% of variation explained by the first component of the principal component analysis. A multiple antigen model generated a binary prediction metric ([Formula: see text]), which when applied to all data resulted in 100% specificity and 63.48% sensitivity. Removal of week 1 B. pseudomallei challenged serum samples increased the sensitivity of the model to 95%. CONCLUSION/SIGNIFICANCE: We employed a previously standardized assay for humans, the BurkPx assay, and assessed its diagnostic potential for detection of B. pseudomallei exposure in NHP. The assay is expected to be useful for surveillance in NHP colonies, in investigations of suspected accidental releases or exposures, and for identifying vaccine correlates of protection.
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Burkholderia pseudomallei , Melioidose , Animais , Humanos , Melioidose/diagnóstico , Melioidose/veterinária , Melioidose/epidemiologia , Anticorpos Antibacterianos , Antígenos de Bactérias , PrimatasRESUMO
BACKGROUND: Research has shown children disproportionately gain excess weight over the summer months (vs. school months), with stronger effects for children with obesity. However, the question has not been investigated among children receiving care in paediatric weight management (PWM) programs. OBJECTIVE: To test for seasonal variability in weight change among youth with obesity in PWM care enrolled in the Pediatric Obesity Weight Evaluation Registry (POWER). METHOD: Longitudinal evaluation of a prospective cohort from 2014 to 2019 among youth in 31 PWM programs. Change in percentage of the 95th percentile for BMI (%BMIp95) was compared by quarter. RESULTS: Participants (N = 6816) were primarily ages 6-11 (48%), female (54%), 40% non-Hispanic White, 26% Hispanic and 17% Black, and 73% had severe obesity. Children were enrolled on average 424.9 ± 401.5 days. Participants reduced their %BMIp95 every season, but compared with Quarter 3 (July-September), reductions were significantly greater in Q1 (Jan-March, b = -0.27, 95%CI -0.46, -0.09), Q2 (April-June, b = -0.21, CI -0.40, -0.03), and Q4 (October-December, b = -0.44, CI -0.63, -0.26). CONCLUSION AND RELEVANCE: Across 31 clinics nationwide, children reduced their %BMIp95 every season, but reductions were significantly smaller during the summer quarter. While PWM successfully mitigated excess weight gain during every period, summer remains a high-priority time.
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Obesidade Infantil , Adolescente , Criança , Humanos , Feminino , Índice de Massa Corporal , Obesidade Infantil/epidemiologia , Obesidade Infantil/prevenção & controle , Estações do Ano , Estudos Prospectivos , Aumento de Peso , Sistema de RegistrosRESUMO
Nearly half a century ago, two papers postulated the likelihood of lunar lava tube caves using mathematical models. Today, armed with an array of orbiting and fly-by satellites and survey instrumentation, we have now acquired cave data across our solar system-including the identification of potential cave entrances on the Moon, Mars, and at least nine other planetary bodies. These discoveries gave rise to the study of planetary caves. To help advance this field, we leveraged the expertise of an interdisciplinary group to identify a strategy to explore caves beyond Earth. Focusing primarily on astrobiology, the cave environment, geology, robotics, instrumentation, and human exploration, our goal was to produce a framework to guide this subdiscipline through at least the next decade. To do this, we first assembled a list of 198 science and engineering questions. Then, through a series of social surveys, 114 scientists and engineers winnowed down the list to the top 53 highest priority questions. This exercise resulted in identifying emerging and crucial research areas that require robust development to ultimately support a robotic mission to a planetary cave-principally the Moon and/or Mars. With the necessary financial investment and institutional support, the research and technological development required to achieve these necessary advancements over the next decade are attainable. Subsequently, we will be positioned to robotically examine lunar caves and search for evidence of life within Martian caves; in turn, this will set the stage for human exploration and potential habitation of both the lunar and Martian subsurface.
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INTRODUCTION: Several studies have reported that children gain more weight during the summer season. Despite high obesity rates, little research has included American Indian/Alaskan Native children, and few studies have been longitudinal. This observational study examines seasonal weight variability over 3.5 years among ethnically diverse children, including 2,184 American Indian/Alaskan Native children. METHODS: Children's height and weight were measured before and after the summer from 2012-2015 and analyzed in 2019-2020, including children with ≥2 consecutive measurements (N=7,890, mean age=8.4 [SD=2.8] years). Mixed-effects models tested whether the percentage of the 95th BMI percentile and BMI differed by season (summer versus the rest of the year) and ethnicity. RESULTS: American Indian/Alaskan Native (23.7%), Hispanic (19.8%), and Black (17.8%) children had significantly higher baseline obesity rates than White children (7.1%). The percentage of the 95th BMI percentile significantly increased during the summer compared with the percentage during the rest of the year, with the strongest effects for children who were obese (b=2.69, 95% CI=1.35, 4.03, p<0.001) or overweight (b=1.47, 95% CI=0.56, 2.35, p<0.01). In BMI units, summer BMI increase was 0.50 kg/m2 higher (obese model) and 0.27 kg/m2 higher (overweight) than that of the rest of the year. Seasonal effects were significantly less pronounced for American Indian/Alaskan Native children than for White children. CONCLUSIONS: Children gained significantly more weight during the summer season, with the strongest effects for children who were obese. American Indian/Alaskan Native children had less seasonal variability than White children, but higher overall obesity rates. These data underscore summer as a critical time for obesity prevention among children who are overweight/obese but suggest that seasonal patterns may vary for American Indian/Alaskan Native children.
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Negro ou Afro-Americano , Indígenas Norte-Americanos , Índice de Massa Corporal , Criança , Hispânico ou Latino , Humanos , Estações do Ano , Aumento de Peso , Indígena Americano ou Nativo do AlascaRESUMO
In this study we examined changes to the human gut microbiome resulting from an eight-week intervention of either cardiorespiratory exercise (CRE) or resistance training exercise (RTE). Twenty-eight subjects (21 F; aged 18-26) were recruited for our CRE study and 28 subjects (17 F; aged 18-33) were recruited for our RTE study. Fecal samples for gut microbiome profiling were collected twice weekly during the pre-intervention phase (three weeks), intervention phase (eight weeks), and post-intervention phase (three weeks). Pre/post VO2max, three repetition maximum (3RM), and body composition measurements were conducted. Heart rate ranges for CRE were determined by subjects' initial VO2max test. RTE weight ranges were established by subjects' initial 3RM testing for squat, bench press, and bent-over row. Gut microbiota were profiled using 16S rRNA gene sequencing. Microbiome sequence data were analyzed with QIIME 2. CRE resulted in initial changes to the gut microbiome which were not sustained through or after the intervention period, while RTE resulted in no detectable changes to the gut microbiota. For both CRE and RTE, we observe some evidence that the baseline microbiome composition may be predictive of exercise gains. This work suggests that the human gut microbiome can change in response to a new exercise program, but the type of exercise likely impacts whether a change occurs. The changes observed in our CRE intervention resemble a disturbance to the microbiome, where an initial shift is observed followed by a return to the baseline state. More work is needed to understand how sustained changes to the microbiome occur, resulting in differences that have been reported in cross sectional studies of athletes and non-athletes.
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The original publication of this Article included analysis of virus and microbial cell abundances and virus-to-microbial cell ratios. Data in the Article came from 25 studies intended to be exclusively from marine sites. However, 3 of the studies included in the original unified dataset were erroneously classified as marine sites during compilation. The records with mis-recorded longitude and latitude values were, in fact, taken from inland, freshwater sources. The three inland, freshwater datasets are ELA, TROUT and SWAT. The data from these three studies represent 163 of the 5,671 records in the original publication. In the updated version of the Article, all analyses have been recalculated using the same statistical analysis pipeline released via GitHub as part of the original publication. Removal of the three studies reduces the unified dataset to 5,508 records. Analyses involving all grouped datasets have been updated with changes noted in each figure. All key results remain qualitatively unchanged. All data and scripts used in this correction have been made available as a new, updated GitHub release to reflect the updated dataset and figures.
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In the United States, humans spend the majority of their time indoors, where they are exposed to the microbiome of the built environment (BE) they inhabit. Despite the ubiquity of microbes in BEs and their potential impacts on health and building materials, basic questions about the microbiology of these environments remain unanswered. We present a study on the impacts of geography, material type, human interaction, location in a room, seasonal variation, and indoor and microenvironmental parameters on bacterial communities in offices. Our data elucidate several important features of microbial communities in BEs. First, under normal office environmental conditions, bacterial communities do not differ on the basis of surface material (e.g., ceiling tile or carpet) but do differ on the basis of the location in a room (e.g., ceiling or floor), two features that are often conflated but that we are able to separate here. We suspect that previous work showing differences in bacterial composition with surface material was likely detecting differences based on different usage patterns. Next, we find that offices have city-specific bacterial communities, such that we can accurately predict which city an office microbiome sample is derived from, but office-specific bacterial communities are less apparent. This differs from previous work, which has suggested office-specific compositions of bacterial communities. We again suspect that the difference from prior work arises from different usage patterns. As has been previously shown, we observe that human skin contributes heavily to the composition of BE surfaces. IMPORTANCE Our study highlights several points that should impact the design of future studies of the microbiology of BEs. First, projects tracking changes in BE bacterial communities should focus sampling efforts on surveying different locations in offices and in different cities but not necessarily different materials or different offices in the same city. Next, disturbance due to repeated sampling, though detectable, is small compared to that due to other variables, opening up a range of longitudinal study designs in the BE. Next, studies requiring more samples than can be sequenced on a single sequencing run (which is increasingly common) must control for run effects by including some of the same samples in all of the sequencing runs as technical replicates. Finally, detailed tracking of indoor and material environment covariates is likely not essential for BE microbiome studies, as the normal range of indoor environmental conditions is likely not large enough to impact bacterial communities.
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Marine viruses are critical drivers of ocean biogeochemistry, and their abundances vary spatiotemporally in the global oceans, with upper estimates exceeding 10(8) per ml. Over many years, a consensus has emerged that virus abundances are typically tenfold higher than microbial cell abundances. However, the true explanatory power of a linear relationship and its robustness across diverse ocean environments is unclear. Here, we compile 5,671 microbial cell and virus abundance estimates from 25 distinct marine surveys and find substantial variation in the virus-to-microbial cell ratio, in which a 10:1 model has either limited or no explanatory power. Instead, virus abundances are better described as nonlinear, power-law functions of microbial cell abundances. The fitted scaling exponents are typically less than 1, implying that the virus-to-microbial cell ratio decreases with microbial cell density, rather than remaining fixed. The observed scaling also implies that viral effect sizes derived from 'representative' abundances require substantial refinement to be extrapolated to regional or global scales.
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Organismos Aquáticos/crescimento & desenvolvimento , Densidade Demográfica , Água do Mar/microbiologia , Água do Mar/virologia , Vírus/crescimento & desenvolvimento , Oceanos e Mares , Análise Espaço-TemporalRESUMO
Q-fever is an underreported disease caused by the bacterium Coxiella burnetii, which is highly infectious and has the ability to disperse great distances. It is a completely clonal pathogen with low genetic diversity and requires whole-genome analysis to identify discriminating features among closely related isolates. C. burnetii, and in particular one genotype (ST20), is commonly found in cow's milk across the entire dairy industry of the USA. This single genotype dominance is suggestive of host-specific adaptation, rapid dispersal and persistence within cattle. We used a comparative genomic approach to identify SNPs for high-resolution and high-throughput genotyping assays to better describe the dispersal of ST20 across the USA. We genotyped 507 ST20 cow milk samples and discovered three subgenotypes, all of which were present across the entire country and over the complete time period studied. Only one of these sub-genotypes was observed in a single dairy herd. The temporal and geographic distribution of these sub-genotypes is consistent with a model of large-scale, rapid, frequent and continuous dissemination on a continental scale. The distribution of subgenotypes is not consistent with wind-based dispersal alone, and it is likely that animal husbandry and transportation practices, including pooling of milk from multiple herds, have also shaped the patterns. On the scale of an entire country, there appear to be few barriers to rapid, frequent and large-scale dissemination of the ST20 subgenotypes.
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Doenças dos Bovinos/microbiologia , Coxiella burnetii/fisiologia , Febre Q/veterinária , Animais , Bovinos , Doenças dos Bovinos/epidemiologia , Doenças dos Bovinos/transmissão , Coxiella burnetii/genética , Indústria de Laticínios , Feminino , Genótipo , Leite/microbiologia , Polimorfismo de Nucleotídeo Único/genética , Febre Q/epidemiologia , Febre Q/microbiologia , Febre Q/transmissão , Meios de Transporte , Estados Unidos/epidemiologiaRESUMO
Viral lysis of microbial hosts releases organic matter that can then be assimilated by nontargeted microorganisms. Quantitative estimates of virus-mediated recycling of carbon in marine waters, first established in the late 1990s, were originally extrapolated from marine host and virus densities, host carbon content and inferred viral lysis rates. Yet, these estimates did not explicitly incorporate the cascade of complex feedbacks associated with virus-mediated lysis. To evaluate the role of viruses in shaping community structure and ecosystem functioning, we extend dynamic multitrophic ecosystem models to include a virus component, specifically parameterized for processes taking place in the ocean euphotic zone. Crucially, we are able to solve this model analytically, facilitating evaluation of model behavior under many alternative parameterizations. Analyses reveal that the addition of a virus component promotes the emergence of complex communities. In addition, biomass partitioning of the emergent multitrophic community is consistent with well-established empirical norms in the surface oceans. At steady state, ecosystem fluxes can be probed to characterize the effects that viruses have when compared with putative marine surface ecosystems without viruses. The model suggests that ecosystems with viruses will have (1) increased organic matter recycling, (2) reduced transfer to higher trophic levels and (3) increased net primary productivity. These model findings support hypotheses that viruses can have significant stimulatory effects across whole-ecosystem scales. We suggest that existing efforts to predict carbon and nutrient cycling without considering virus effects are likely to miss essential features of marine food webs that regulate global biogeochemical cycles.
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Biomassa , Ecossistema , Cadeia Alimentar , Vírus , Animais , Bactérias/virologia , Carbono , Cianobactérias/metabolismo , Interações Microbianas , Oceanos e Mares , Microbiologia da Água , Zooplâncton/metabolismoRESUMO
Deserts are considered 'below-ground dominated', yet little is known about the impact of rising CO(2) in combination with natural weather cycles on long-term dynamics of root biomass. This study quantifies the temporal dynamics of fine-root production, loss and standing crop in an intact desert ecosystem exposed to 10 yr of elevated CO(2). We used monthly minirhizotron observations from 4 yr (2003-2007) for two dominant shrub species and along community transects at the Nevada Desert free-air CO(2) enrichment Facility. Data were synthesized within a Bayesian framework that included effects of CO(2) concentration, cover type, phenological period, antecedent soil water and biological inertia (i.e. the influence of prior root production and loss). Elevated CO(2) treatment interacted with antecedent soil moisture and had significantly greater effects on fine-root dynamics during certain phenological periods. With respect to biological inertia, plants under elevated CO(2) tended to initiate fine-root growth sooner and sustain growth longer, with the net effect of increasing the magnitude of production and mortality cycles. Elevated CO(2) interacts with past environmental (e.g. antecedent soil water) and biological (e.g. biological inertia) factors to affect fine-root dynamics, and such interactions are expected to be important for predicting future soil carbon pools.
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Dióxido de Carbono/farmacologia , Clima Desértico , Raízes de Plantas/efeitos dos fármacos , Raízes de Plantas/fisiologia , Produtos Agrícolas/fisiologia , Umidade , Modelos Biológicos , Nevada , Chuva , Solo/química , Fatores de Tempo , ÁguaRESUMO
The effect that measurement error of predictor variables has on regression inference is well known in the statistical literature. However, the influence of measurement error on the ability to quantify relationships between chemical stressors and biological responses has received little attention in ecotoxicology. We present a common data-collection scenario and demonstrate that the relationship between explanatory and response variables is consistently underestimated when measurement error is ignored. A straightforward extension of the regression calibration method is to use a nonparametric method to smooth the predictor variable with respect to another covariate (e.g., time) and using the smoothed predictor to estimate the response variable. We conducted a simulation study to compare the effectiveness of the proposed method to the naive analysis that ignores measurement error. We conclude that the method satisfactorily addresses the problem when measurement error is moderate to large, and does not result in a noticeable loss of power in the case where measurement error is absent.