Browsing by Subject "Cluster Analysis"
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Item Effects of temperature on bacterial microbiome composition in Ixodes scapularis ticks(John Wiley & Sons, Inc., 2018-09-21) Thapa, Santosh; Zhang, Yan; Allen, Michael S.Ixodes scapularis, the blacklegged deer tick, is the principal vector of Lyme disease in North America. Environmental factors are known to influence regional and seasonal incidence of Lyme disease and possibly the endemicity of the disease to the northeastern and upper mid-western regions of the United States. With a goal to understand the impact of environmental temperature on microbial communities within the tick, we investigated the bacterial microbiome of colony-reared I. scapularis ticks statically incubated at different temperatures (4, 20, 30, and 37°C) at a constant humidity in a controlled laboratory setting by comparison of sequenced amplicons of the bacterial 16S V4 rRNA gene to that of the untreated baseline controls. The microbiomes of colony-reared I. scapularis males were distinct than that of females, which were entirely dominated by Rickettsia. In silico removal of Rickettsia sequences from female data revealed the underlying bacterial community, which is consistent in complexity with those seen among male ticks. The bacterial community composition of these ticks changes upon incubation at 30°C for a week and 37°C for more than 5 days. Moreover, the male ticks incubated at 30 and 37°C exhibited significantly different bacterial diversity compared to the initial baseline microbiome, and the change in bacterial diversity was dependent upon duration of exposure. Rickettsia-free data revealed a significantly different bacterial diversity in female ticks incubated at 37°C compared to that of 4 and 20°C treatments. These results provide experimental evidence that environmental temperature can impact the tick bacterial microbiome in a laboratory setting.Item Genetically engineered probiotic for the treatment of phenylketonuria (PKU); assessment of a novel treatment in vitro and in the PAHenu2 mouse model of PKU(PLOS, 2017-05-17) Durrer, Katherine E.; Allen, Michael S.; Hunt von Herbing, IonePhenylketonuria (PKU) is a genetic disease characterized by the inability to convert dietary phenylalanine to tyrosine by phenylalanine hydroxylase. Given the importance of gut microbes in digestion, a genetically engineered microbe could potentially degrade some ingested phenylalanine from the diet prior to absorption. To test this, a phenylalanine lyase gene from Anabaena variabilis (AvPAL) was codon-optimized and cloned into a shuttle vector for expression in Lactobacillus reuteri 100-23C (pHENOMMenal). Functional expression of AvPAL was determined in vitro, and subsequently tested in vivo in homozygous PAHenu2 (PKU model) mice. Initial trials of two PAHenu2 homozygous (PKU) mice defined conditions for freeze-drying and delivery of bacteria. Animals showed reduced blood phe within three to four days of treatment with pHENOMMenal probiotic, and blood phe concentrations remained significantly reduced (P < 0.0005) compared to untreated controls during the course of experiments. Although pHENOMMenal probiotic could be cultured from fecal samples at four months post treatment, it could no longer be cultivated from feces at eight months post treatment, indicating eventual loss of the microbe from the gut. Preliminary screens during experimentation found no immune response to AvPAL. Collectively these studies provide data for the use of a genetically engineered probiotic as a potential treatment for PKU.Item Precision DNA Mixture Interpretation with Single-Cell Profiling(MDPI, 2021-10-20) Ge, Jianye; King, Jonathan L.; Smuts, Amy; Budowle, BruceWet-lab based studies have exploited emerging single-cell technologies to address the challenges of interpreting forensic mixture evidence. However, little effort has been dedicated to developing a systematic approach to interpreting the single-cell profiles derived from the mixtures. This study is the first attempt to develop a comprehensive interpretation workflow in which single-cell profiles from mixtures are interpreted individually and holistically. In this approach, the genotypes from each cell are assessed, the number of contributors (NOC) of the single-cell profiles is estimated, followed by developing a consensus profile of each contributor, and finally the consensus profile(s) can be used for a DNA database search or comparing with known profiles to determine their potential sources. The potential of this single-cell interpretation workflow was assessed by simulation with various mixture scenarios and empirical allele drop-out and drop-in rates, the accuracies of estimating the NOC, the accuracies of recovering the true alleles by consensus, and the capabilities of deconvolving mixtures with related contributors. The results support that the single-cell based mixture interpretation can provide a precision that cannot beachieved with current standard CE-STR analyses. A new paradigm for mixture interpretation is available to enhance the interpretation of forensic genetic casework.Item Spatial and temporal patterns of dengue incidence in northeastern Thailand 2006-2016(BioMed Central Ltd., 2019-08-23) Phanitchat, Thipruethai; Zhao, Bingxin; Haque, Ubydul; Pientong, Chamsai; Ekalaksananan, Tipaya; Aromseree, Sirinart; Thaewnongiew, Kesorn; Fustec, Benedicte; Bangs, Michael J.; Alexander, Neal; Overgaard, Hans J.BACKGROUND: Dengue, a viral disease transmitted by Aedes mosquitoes, is an important public health concern throughout Thailand. Climate variables are potential predictors of dengue transmission. Associations between climate variables and dengue have usually been performed on large-scale first-level national administrative divisions, i.e. provinces. Here we analyze data on a finer spatial resolution in one province, which is often more relevant for effective disease control design. The objective of this study was to investigate the effect of seasonal variations, monthly climate variability, and to identify local clusters of symptomatic disease at the sub-district level based on reported dengue cases. METHODS: Data on dengue cases were retrieved from the national communicable disease surveillance system in Thailand. Between 2006 and 2016, 15,167 cases were recorded in 199 sub-districts of Khon Kaen Province, northeastern Thailand. Descriptive analyses included demographic characteristics and temporal patterns of disease and climate variables. The association between monthly disease incidence and climate variations was analyzed at the sub-district level using Bayesian Poisson spatial regression. A hotspot analysis was used to assess the spatial patterns (clustered/dispersed/random) of dengue incidence. RESULTS: Dengue was predominant in the 5-14 year-old age group (51.1%). However, over time, dengue incidence in the older age groups (> 15 years) gradually increased and was the most affected group in 2013. Dengue outbreaks coincide with the rainy season. In the spatial regression model, maximum temperature was associated with higher incidence. The hotspot analysis showed clustering of cases around the urbanized area of Khon Kaen city and in rural areas in the southwestern portion of the province. CONCLUSIONS: There was an increase in the number of reported dengue cases in older age groups over the study period. Dengue incidence was highly seasonal and positively associated with maximum ambient temperature. However, climatic variables did not explain all the spatial variation of dengue in the province. Further analyses are needed to clarify the detailed effects of urbanization and other potential environmental risk factors. These results provide useful information for ongoing prediction modeling and developing of dengue early warning systems to guide vector control operations.Item Water T2 as an early, global and practical biomarker for metabolic syndrome: an observational cross-sectional study(BioMed Central Ltd., 2017-12-19) Robinson, Michelle D.; Mishra, Ina; Deodhar, Sneha; Patel, Vipulkumar; Gordon, Katrina V.; Vintimilla, Raul; Brown, Kim; Johnson, Leigh A.; O'Bryant, Sid E.; Cistola, David P.BACKGROUND: Metabolic syndrome (MetS) is a highly prevalent condition that identifies individuals at risk for type 2 diabetes mellitus and atherosclerotic cardiovascular disease. Prevention of these diseases relies on early detection and intervention in order to preserve pancreatic beta-cells and arterial wall integrity. Yet, the clinical criteria for MetS are insensitive to the early-stage insulin resistance, inflammation, cholesterol and clotting factor abnormalities that characterize the progression toward type 2 diabetes and atherosclerosis. Here we report the discovery and initial characterization of an atypical new biomarker that detects these early conditions with just one measurement. METHODS: Water T2, measured in a few minutes using benchtop nuclear magnetic resonance relaxometry, is exquisitely sensitive to metabolic shifts in the blood proteome. In an observational cross-sectional study of 72 non-diabetic human subjects, the association of plasma and serum water T2 values with over 130 blood biomarkers was analyzed using bivariate, multivariate and logistic regression. RESULTS: Plasma and serum water T2 exhibited strong bivariate correlations with markers of insulin, lipids, inflammation, coagulation and electrolyte balance. After correcting for confounders, low water T2 values were independently and additively associated with fasting hyperinsulinemia, dyslipidemia and subclinical inflammation. Plasma water T2 exhibited 100% sensitivity and 87% specificity for detecting early insulin resistance in normoglycemic subjects, as defined by the McAuley Index. Sixteen normoglycemic subjects with early metabolic abnormalities (22% of the study population) were identified by low water T2 values. Thirteen of the 16 did not meet the harmonized clinical criteria for metabolic syndrome and would have been missed by conventional screening for diabetes risk. Low water T2 values were associated with increases in the mean concentrations of 6 of the 16 most abundant acute phase proteins and lipoproteins in plasma. CONCLUSIONS: Water T2 detects a constellation of early abnormalities associated with metabolic syndrome, providing a global view of an individual's metabolic health. It circumvents the pitfalls associated with fasting glucose and hemoglobin A1c and the limitations of the current clinical criteria for metabolic syndrome. Water T2 shows promise as an early, global and practical screening tool for the identification of individuals at risk for diabetes and atherosclerosis.