Semi-automated monitoring of longitudinal microbial metabolic dynamics
Ontology highlight
ABSTRACT:
INSTRUMENT(S): GCMS-QP2010SE
ORGANISM(S): Trichoderma Reesei (ncbitaxon:51453) Phanerochaete Chrysosporium (ncbitaxon:5306)
SUBMITTER: 
Ricardo Roberto da Silva
PROVIDER: MSV000096132 | MassIVE | Fri Oct 18 18:05:00 BST 2024
REPOSITORIES: MassIVE
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Project description:Raw data from GC-MS analysis of samples collected from P. chrysosporium and T. reesei in mono and co-culture for evaluation of lignin degradation.
2024-10-18 | MSV000096132 | GNPS
Project description:"Omics" technologies have been developed to understand the whole complex microbial systems; however, most omics studies reported so far were utilized to analyze the living matters of “single-species”. To understand the cell-cell interaction in the gut microbial complex, we selected to examine the interaction of Escherichia coli O157:H7 (O157) and Bifidobacterium longum (BL), known as a pathogenic and a commensal bacteria, as a first step for understanding the whole gut microbial complex. We have developed a novel time-lapse 2D-NMR metabolic profiling system in order to measure the extracellular metabolites, which are considered a key factor to understand the bacterial crosstalk. Furthermore, in combination with transcriptome and proteome analysis, we found that the relationship between BL and O157 could be partially regarded as the producer and the consumer of nutrients, especially in the case of serine and aspartate metabolism. These findings suggest that our novel profiling systems could be a powerful tool toward understanding crosstalk of the whole microbial complex such as the gut, industrial bioreactors or environmental microbial communities. In vitro mono and coculture were performed. All experiments were performed in duplicate.
2011-09-30 | E-GEOD-18474 | biostudies-arrayexpress
Project description:Climate change is affecting Antarctica and minimally destructive long-term monitoring of its unique ecosystems is vital to detect biodiversity trends, and to understand how change is affecting these communities. The use of automated or semi-automated methods is especially valuable in harsh polar environments, as access is limited and conditions extreme. We assessed moss health and cover at six time points between 2003 and 2014 at two East Antarctic sites. Semi-automatic object-based image analysis (OBIA) was used to classify digital photographs using a set of rules based on digital red, green, blue (RGB) and hue-saturation-intensity (HSI) value thresholds, assigning vegetation to categories of healthy, stressed or moribund moss and lichens. Comparison with traditional visual estimates showed that estimates of percent cover using semi-automated OBIA classification fell within the range of variation determined by visual methods. Overall moss health, as assessed using the mean percentages of healthy, stressed and moribund mosses within quadrats, changed over the 11 years at both sites. A marked increase in stress and decline in health was observed across both sites in 2008, followed by recovery to baseline levels of health by 2014 at one site, but with significantly more stressed or moribund moss remaining within the two communities at the other site. Our results confirm that vegetation cover can be reliably estimated using semi-automated OBIA, providing similar accuracy to visual estimation by experts. The resulting vegetation cover estimates provide a sensitive measure to assess change in vegetation health over time and have informed a conceptual framework for the changing condition of Antarctic mosses. In demonstrating that this method can be used to monitor ground cover vegetation at small scales, we suggest it may also be suitable for other extreme environments where repeat monitoring via images is required.
| S-EPMC7296125 | biostudies-literature
Project description:Fluorescent staining coupled with flow cytometry (FCM) is often used for the monitoring, quantification and characterization of bacteria in engineered and environmental aquatic ecosystems including seawater, freshwater, drinking water, wastewater, and industrial bioreactors. However, infrequent grab sampling hampers accurate characterization and subsequent understanding of microbial dynamics in all of these ecosystems. A logic technological progression is high throughput and full automation of the sampling, staining, measurement, and data analysis steps. Here we assess the feasibility and applicability of automated FCM by means of actual data sets produced with prototype instrumentation. As proof-of-concept we demonstrate examples of microbial dynamics in (i) flowing tap water from a municipal drinking water supply network and (ii) river water from a small creek subject to two rainfall events. In both cases, automated measurements were done at 15-min intervals during 12-14 consecutive days, yielding more than 1000 individual data points for each ecosystem. The extensive data sets derived from the automated measurements allowed for the establishment of baseline data for each ecosystem, as well as for the recognition of daily variations and specific events that would most likely be missed (or miss-characterized) by infrequent sampling. In addition, the online FCM data from the river water was combined and correlated with online measurements of abiotic parameters, showing considerable potential for a better understanding of cause-and-effect relationships in aquatic ecosystems. Although several challenges remain, the successful operation of an automated online FCM system and the basic interpretation of the resulting data sets represent a breakthrough toward the eventual establishment of fully automated online microbiological monitoring technologies.
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Project description:BackgroundThe haemostatic balance is an equilibrium of pro- and anticoagulant factors that work synergistically to prevent bleeding and thrombosis. As thrombin is the central enzyme in the coagulation pathway, it is desirable to measure thrombin generation (TG) in order to detect possible bleeding or thrombotic phenotypes, as well as to investigate the capacity of drugs affecting the formation of thrombin. By investigating the underlying processes of TG (i.e., prothrombin conversion and inactivation), additional information is collected about the dynamics of thrombin formation.ObjectivesTo obtain reference values for thrombin dynamics (TD) analysis in 112 healthy donors using an automated system for TG.MethodsTG was measured on the ST Genesia, fibrinogen on the Start, anti-thrombin (AT) on the STA R Max and α2Macroglobulin (α2M) with an in-house chromogenic assay.ResultsTG was measured using STG-BleedScreen, STG-ThromboScreen and STG-DrugScreen. The TG data was used as an input for TD analysis, in combination with plasma levels of AT, α2M and fibrinogen that were 113% (108-118%), 2.6 μM (2.2 μM-3.1 μM) and 2.9 g/L (2.6-3.2 g/L), respectively. The maximum rate of the prothrombinase complex (PCmax) and the total amount of prothrombin converted (PCtot) increased with increasing tissue factor (TF) concentration. PCtot increased from 902 to 988 nM, whereas PCmax increased from 172 to 508 nM/min. Thrombin (T)-AT and T-α2M complexes also increased with increasing TF concentration (i.e., from 860 to 955 nM and from 28 to 33 nm, respectively). PCtot, T-AT and T-α2M complex formation were strongly inhibited by addition of thrombomodulin (-44%, -43%, and -48%, respectively), whereas PCmax was affected less (-24%). PCtot, PCmax, T-AT, and T-α2M were higher in women using oral contraceptives (OC) compared to men/women without OC, and inhibition by thrombomodulin was also significantly less in women on OC (p < 0.05).ConclusionsTG measured on the ST Genesia can be used as an input for TD analysis. The data obtained can be used as reference values for future clinical studies as the balance between prothrombin conversion and thrombin inactivation has shown to be useful in several clinical settings.
| S-EPMC9360406 | biostudies-literature
Project description:Analysis of therapeutic IgG aggregates in serum is a potential area of investigation as it can give deeper insights about the function, immunogenic issues and protein interaction associated with the aggregates. To overcome various complexities associated with the existing analytical techniques for analyzing aggregates in serum, a novel florescence microscopy-based image processing approach was developed. The monoclonal antibody (mAb) was tagged with a fluorescent dye, fluorescein isothiocyanate (FITC). Aggregates, generated by stirring, were spiked into serum and images were captured at various time points. After denoising, thresholding by weighted median, 1D Otsu, and 2D Otsu was attempted and a modified 2D Otsu, a new mode of thresholding, was developed. This thresholding method was found to be highly effective in removing noises and retaining analyte sizes. Out of 0-255, the optimized threshold value obtained for the images discussed in modified 2D Otsu was 9 while 2D Otsu's overestimated values were 38 and 48. Other morphological operations were applied after thresholding and the area, perimeter, circularity, and radii of the aggregates in these images were calculated. The proposed algorithm offers an approach for analysis of aggregates in serum that is simpler to implement and is complementary to existing approaches.
| S-EPMC8166854 | biostudies-literature
Project description:We developed a semi-automated active monitoring system that uses sequential matched-cohort analyses to assess drug safety across a distributed network of longitudinal electronic health-care data. In a retrospective analysis, we show that the system would have identified cerivastatin-induced rhabdomyolysis. In this study, we evaluated whether the system would generate alerts for three drug-outcome pairs: rosuvastatin and rhabdomyolysis (known null association), rosuvastatin and diabetes mellitus, and telithromycin and hepatotoxicity (two examples for which alerting would be questionable). Over >5 years of monitoring, rate differences (RDs) in comparisons of rosuvastatin with atorvastatin were -0.1 cases of rhabdomyolysis per 1,000 person-years (95% confidence interval (CI): -0.4, 0.1) and -2.2 diabetes cases per 1,000 person-years (95% CI: -6.0, 1.6). The RD for hepatotoxicity comparing telithromycin with azithromycin was 0.3 cases per 1,000 person-years (95% CI: -0.5, 1.0). In a setting in which false positivity is a major concern, the system did not generate alerts for the three drug-outcome pairs.
| S-EPMC3947906 | biostudies-literature
Project description:Cell proliferation is crucial to tissue growth and form during embryogenesis, yet dynamic tracking of cell cycle progression and cell position presents a challenging roadblock. We have developed a fluorescent cell cycle indicator and single cell analysis method, called CycleTrak, which allows for better spatiotemporal resolution and quantification of cell cycle phase and cell position than current methods. Our method was developed on the basis of the existing Fucci method. CycleTrak uses a single lentiviral vector that integrates mKO2-hCdt1 (30/120), and a nuclear-localized eGFP reporter. The single vector and nuclear localized fluorescence signals simplify delivery into cells and allow for rapid, automated cell tracking and cell cycle phase readout in single and subpopulations of cells. We validated CycleTrak performance in metastatic melanoma cells and identified novel cell cycle dynamics in vitro and in vivo after transplantation and 3D confocal time-lapse imaging in a living chick embryo.
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Project description:Objective:Managing registries with continual data collection poses challenges, such as following reproducible research protocols and guaranteeing data accessibility. The University of Kansas (KU) Alzheimer's Disease Center (ADC) maintains one such registry: Curated Clinical Cohort Phenotypes and Observations (C3PO). We created an automated and reproducible process by which investigators have access to C3PO data. Materials and Methods:Data was input into Research Electronic Data Capture. Monthly, data part of the Uniform Data Set (UDS), that is data also collected at other ADCs, was uploaded to the National Alzheimer's Coordinating Center (NACC). Quarterly, NACC cleaned, curated, and returned the UDS to the KU Data Management and Statistics (DMS) Core, where it was stored in C3PO with other quarterly curated site-specific data. Investigators seeking to utilize C3PO submitted a research proposal and requested variables via the publicly accessible and searchable data dictionary. The DMS Core used this variable list and an automated SAS program to create a subset of C3PO. Results:C3PO contained 1913 variables stored in 15 datasets. From 2017 to 2018, 38 data requests were completed for several KU departments and other research institutions. Completing data requests became more efficient; C3PO subsets were produced in under 10 seconds. Discussion:The data management strategy outlined above facilitated reproducible research practices, which is fundamental to the future of research as it allows replication and verification to occur. Conclusion:We created a transparent, automated, and efficient process of extracting subsets of data from a registry where data was changing daily.
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Project description:BackgroundClinical data collection requires correct and complete data sets in order to perform correct statistical analysis and draw valid conclusions. While in randomized clinical trials much effort concentrates on data monitoring, this is rarely the case in observational studies- due to high numbers of cases and often-restricted resources. We have developed a valid and cost-effective monitoring tool, which can substantially contribute to an increased data quality in observational research.MethodsAn automated digital monitoring system for cohort studies developed by the German Rheumatism Research Centre (DRFZ) was tested within the disease register RABBIT-SpA, a longitudinal observational study including patients with axial spondyloarthritis and psoriatic arthritis. Physicians and patients complete electronic case report forms (eCRF) twice a year for up to 10 years. Automatic plausibility checks were implemented to verify all data after entry into the eCRF. To identify conflicts that cannot be found by this approach, all possible conflicts were compiled into a catalog. This "conflict catalog" was used to create queries, which are displayed as part of the eCRF. The proportion of queried eCRFs and responses were analyzed by descriptive methods. For the analysis of responses, the type of conflict was assigned to either a single conflict only (affecting individual items) or a conflict that required the entire eCRF to be queried.ResultsData from 1883 patients was analyzed. A total of n = 3145 eCRFs submitted between baseline (T0) and T3 (12 months) had conflicts (40-64%). Fifty-six to 100% of the queries regarding eCRFs that were completely missing were answered. A mean of 1.4 to 2.4 single conflicts occurred per eCRF, of which 59-69% were answered. The most common missing values were CRP, ESR, Schober's test, data on systemic glucocorticoid therapy, and presence of enthesitis.ConclusionProviding high data quality in large observational cohort studies is a major challenge, which requires careful monitoring. An automated monitoring process was successfully implemented and well accepted by the study centers. Two thirds of the queries were answered with new data. While conventional manual monitoring is resource-intensive and may itself create new sources of errors, automated processes are a convenient way to augment data quality.
| S-EPMC8262053 | biostudies-literature