scholarly journals COMMIT: Consideration of metabolite leakage and community composition improves microbial community models

2021 ◽  
Author(s):  
Philipp Wendering ◽  
Zoran Nikoloski
Keyword(s):  
Microbial Communities ◽  
Community Composition ◽  
Large Scale ◽  
Culture Collection ◽  
Enzymatic Reactions ◽  
Gap Filling ◽  
Soil Communities ◽  
Scale Modelling ◽  
Metabolite Leakage ◽  
Community Roles

Composition and functions of microbial communities affect important traits in diverse hosts, from crops to humans. Yet, mechanistic understanding of how metabolism of individual microbes is affected by the community composition and metabolite leakage is lacking. Here, we first show that the consensus of automatically generated metabolic models improves the quality of the draft models, measured by the genomic evidence for considered enzymatic reactions. We then devise an approach for gap filling, termed COMMIT, that considers exchangeable metabolites based on their permeability and the composition of the community. By applying COMMIT with two soil communities from the Arabidopsis thaliana culture collection, we could significantly reduce the gap-filling solution in comparison to filling gaps in individual models. Inspection of the metabolic interactions in the soil communities allows us to identify microbes with community roles of helpers and beneficiaries. Therefore, COMMIT offers a versatile automated solution for large-scale modelling of microbial communities for diverse biotechnological applications.

scholarly journals Slope Aspect Affects The Soil Microbial Communities In Karst Tiankeng Negative Landforms

2021 ◽  
Author(s):  
Cong Jiang ◽  
Wei Shui ◽  
Su-Feng Zhu ◽  
Jie Feng
Keyword(s):  
Microbial Community ◽  
Microbial Communities ◽  
Community Composition ◽  
Large Scale ◽  
Microbial Community Composition ◽  
Soil Microbial Communities ◽  
Slope Aspect ◽  
Soil Conditions ◽  
Metagenomic Sequencing ◽  
Soil Microbial

Abstract Background: Karst tiankeng is a large-scale negative surface terrain, and slope aspect affect the soil conditions, vegetation and microbial flora in the tiankeng. However, the influence of the slope aspect on the soil microbial community in tiankeng has not been elucidated. Methods: In this study, metagenomic sequencing technology was used to analyzed the soil microbial communities and metabolic function on the shady and sunny slopes of karst tiankeng. Results: The Shannon-Wiener diversity of microbial communities on shady slopes was significantly higher than that on shady slopes. Shady and sunny slopes have similar microbial community composition, but there are differences in abundance. The linear discriminate analysis (LDA) results showed that biomarkers mainly belongs to Actinobacteria, Chloroflexi and Proteobacteria. Functional pathways and CAZy (Carbohydrate-Active Enzymes) genes also had a remarkable response to slope aspect change. LEfSe results indicated several biomarker pathways in sunny slope involved in human disease. Moreover, the abundance of CAZy genes was higher in shady slope and had stronger ability in decomposing litter. The microbial communities were mainly correlation with the vegetation characteristics (species richness and coverage) and soil properties (SOM and pH). Conclusions: These results indicate slope aspect has a pronounced influence on microbial community composition, structure and function at karst tiankeng. In the future, the conservation of karst tiankeng biodiversity should pay more attention to topographical factors.

scholarly journals General Unified Microbiome Profiling Pipeline (GUMPP) for Large Scale, Streamlined and Reproducible Analysis of Bacterial 16S rRNA Data to Predicted Microbial Metagenomes, Enzymatic Reactions and Metabolic Pathways

Metabolites ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 336
Author(s):  
Boštjan Murovec ◽  
Leon Deutsch ◽  
Blaž Stres
Keyword(s):  
16S Rrna ◽  
Metabolic Pathways ◽  
Large Scale ◽  
Enzymatic Reactions ◽  
Operational Taxonomic Units ◽  
Biochemical Pathways ◽  
Meaningful Information ◽  
Novel Biomarkers ◽  
Reproducible Analysis ◽  
Microbiome Profiling

General Unified Microbiome Profiling Pipeline (GUMPP) was developed for large scale, streamlined and reproducible analysis of bacterial 16S rRNA data and prediction of microbial metagenomes, enzymatic reactions and metabolic pathways from amplicon data. GUMPP workflow introduces reproducible data analyses at each of the three levels of resolution (genus; operational taxonomic units (OTUs); amplicon sequence variants (ASVs)). The ability to support reproducible analyses enables production of datasets that ultimately identify the biochemical pathways characteristic of disease pathology. These datasets coupled to biostatistics and mathematical approaches of machine learning can play a significant role in extraction of truly significant and meaningful information from a wide set of 16S rRNA datasets. The adoption of GUMPP in the gut-microbiota related research enables focusing on the generation of novel biomarkers that can lead to the development of mechanistic hypotheses applicable to the development of novel therapies in personalized medicine.

Climate change refugia: landscape, stand and tree-scale microclimates in epiphyte community composition

2021 ◽  
Vol 53 (1) ◽  
pp. 135-148
Author(s):  
Christopher J. Ellis ◽  
Sally Eaton
Keyword(s):  
Climate Change ◽  
Community Composition ◽  
Large Scale ◽  
Biological Response ◽  
Tree Age ◽  
Local Effects ◽  
Vulnerability To Climate Change ◽  
Or Gene ◽  
Water Holding ◽  
Epiphyte Community

AbstractThere is growing evidence that species and communities are responding to, and will continue to be affected by, climate change. For species at risk, vulnerability can be reduced by ensuring that their habitat is extensive, connected and provides opportunities for dispersal and/or gene flow, facilitating a biological response through migration or adaptation. For woodland epiphytes, vulnerability might also be reduced by ensuring sufficient habitat heterogeneity, so that microhabitats provide suitable local microclimates, even as the larger scale climate continues to change (i.e. microrefugia). This study used fuzzy set ordination to compare bryophyte and lichen epiphyte community composition to a large-scale gradient from an oceanic to a relatively more continental macroclimate. The residuals from this relationship identified microhabitats in which species composition reflected a climate that was more oceanic or more continental than would be expected given the prevailing macroclimate. Comparing these residuals to features that operate at different scales to create the microclimate (landscape, stand and tree-scale), it was possible to identify how one might engineer microrefugia into existing or new woodland, in order to reduce epiphyte vulnerability to climate change. Multimodel inference was used to identify the most important features for consideration, which included local effects such as height on the bole, angle of bole lean and bark water holding capacity, as well as tree species and tree age, and within the landscape, topographic wetness and physical exposure.

scholarly journals Multi-scale modelling of a large scale shell-and-tube latent heat storage system for direct steam generation power plants

2020 ◽  
Author(s):  
Clément Beust ◽  
Erwin Franquet ◽  
Jean-Pierre Bédécarrats ◽  
Pierre Garcia ◽  
Jérôme Pouvreau ◽  
...  
Keyword(s):  
Power Plants ◽  
Large Scale ◽  
Storage System ◽  
Steam Generation ◽  
Latent Heat Storage ◽  
Multi Scale ◽  
Direct Steam ◽  
Scale Modelling ◽  
Shell And Tube ◽  
Generation Power

scholarly journals Evaluating Machine Learning and Geostatistical Methods for Spatial Gap-filling of Monthly ESA CCI Soil Moisture in China

2021 ◽  
Vol 13 (14) ◽  
pp. 2848
Author(s):  
Hao Sun ◽  
Qian Xu
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Water Resource Management ◽  
Large Scale ◽  
Gap Filling ◽  
Spatial Continuity ◽  
Study Results ◽  
Data Gaps

Obtaining large-scale, long-term, and spatial continuous soil moisture (SM) data is crucial for climate change, hydrology, and water resource management, etc. ESA CCI SM is such a large-scale and long-term SM (longer than 40 years until now). However, there exist data gaps, especially for the area of China, due to the limitations in remote sensing of SM such as complex topography, human-induced radio frequency interference (RFI), and vegetation disturbances, etc. The data gaps make the CCI SM data cannot achieve spatial continuity, which entails the study of gap-filling methods. In order to develop suitable methods to fill the gaps of CCI SM in the whole area of China, we compared typical Machine Learning (ML) methods, including Random Forest method (RF), Feedforward Neural Network method (FNN), and Generalized Linear Model (GLM) with a geostatistical method, i.e., Ordinary Kriging (OK) in this study. More than 30 years of passive–active combined CCI SM from 1982 to 2018 and other biophysical variables such as Normalized Difference Vegetation Index (NDVI), precipitation, air temperature, Digital Elevation Model (DEM), soil type, and in situ SM from International Soil Moisture Network (ISMN) were utilized in this study. Results indicated that: 1) the data gap of CCI SM is frequent in China, which is found not only in cold seasons and areas but also in warm seasons and areas. The ratio of gap pixel numbers to the whole pixel numbers can be greater than 80%, and its average is around 40%. 2) ML methods can fill the gaps of CCI SM all up. Among the ML methods, RF had the best performance in fitting the relationship between CCI SM and biophysical variables. 3) Over simulated gap areas, RF had a comparable performance with OK, and they outperformed the FNN and GLM methods greatly. 4) Over in situ SM networks, RF achieved better performance than the OK method. 5) We also explored various strategies for gap-filling CCI SM. Results demonstrated that the strategy of constructing a monthly model with one RF for simulating monthly average SM and another RF for simulating monthly SM disturbance achieved the best performance. Such strategy combining with the ML method such as the RF is suggested in this study for filling the gaps of CCI SM in China.

scholarly journals Habitat Elevation Shapes Microbial Community Composition and Alter the Metabolic Functions in Wild Sable (Martes zibellina) Guts

Animals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 865
Author(s):  
Lantian Su ◽  
Xinxin Liu ◽  
Guangyao Jin ◽  
Yue Ma ◽  
Haoxin Tan ◽  
...  
Keyword(s):  
Microbial Community ◽  
Environmental Factors ◽  
Microbial Communities ◽  
Community Composition ◽  
16S Rrna Genes ◽  
Rrna Genes ◽  
Functional Genes ◽  
Martes Zibellina ◽  
Clear Cutting ◽  
Metabolic Functions

In recent decades, wild sable (Carnivora Mustelidae Martes zibellina) habitats, which are often natural forests, have been squeezed by anthropogenic disturbances such as clear-cutting, tilling and grazing. Sables tend to live in sloped areas with relatively harsh conditions. Here, we determine effects of environmental factors on wild sable gut microbial communities between high and low altitude habitats using Illumina Miseq sequencing of bacterial 16S rRNA genes. Our results showed that despite wild sable gut microbial community diversity being resilient to many environmental factors, community composition was sensitive to altitude. Wild sable gut microbial communities were dominated by Firmicutes (relative abundance 38.23%), followed by Actinobacteria (30.29%), and Proteobacteria (28.15%). Altitude was negatively correlated with the abundance of Firmicutes, suggesting sable likely consume more vegetarian food in lower habitats where plant diversity, temperature and vegetation coverage were greater. In addition, our functional genes prediction and qPCR results demonstrated that energy/fat processing microorganisms and functional genes are enriched with increasing altitude, which likely enhanced metabolic functions and supported wild sables to survive in elevated habitats. Overall, our results improve the knowledge of the ecological impact of habitat change, providing insights into wild animal protection at the mountain area with hash climate conditions.

scholarly journals ON GENERATING DIGITAL ELEVATION MODELS FROM LIDAR DATA – RESOLUTION VERSUS ACCURACY AND TOPOGRAPHIC WETNESS INDEX INDICES IN NORTHERN PEATLANDS

2012 ◽  
Vol 38 (2) ◽  
pp. 57-69 ◽  
Author(s):  
Abdulghani Hasan ◽  
Petter Pilesjö ◽  
Andreas Persson
Keyword(s):  
Large Scale ◽  
Drainage Area ◽  
Data Sets ◽  
Topographic Wetness Index ◽  
Absolute Deviation ◽  
Digital Elevation ◽  
Elevation Data ◽  
Scale Modelling ◽  
Data Points ◽  
Emission Modelling

Global change and GHG emission modelling are dependent on accurate wetness estimations for predictions of e.g. methane emissions. This study aims to quantify how the slope, drainage area and the TWI vary with the resolution of DEMs for a flat peatland area. Six DEMs with spatial resolutions from 0.5 to 90 m were interpolated with four different search radiuses. The relationship between accuracy of the DEM and the slope was tested. The LiDAR elevation data was divided into two data sets. The number of data points facilitated an evaluation dataset with data points not more than 10 mm away from the cell centre points in the interpolation dataset. The DEM was evaluated using a quantile-quantile test and the normalized median absolute deviation. It showed independence of the resolution when using the same search radius. The accuracy of the estimated elevation for different slopes was tested using the 0.5 meter DEM and it showed a higher deviation from evaluation data for steep areas. The slope estimations between resolutions showed differences with values that exceeded 50%. Drainage areas were tested for three resolutions, with coinciding evaluation points. The model ability to generate drainage area at each resolution was tested by pair wise comparison of three data subsets and showed differences of more than 50% in 25% of the evaluated points. The results show that consideration of DEM resolution is a necessity for the use of slope, drainage area and TWI data in large scale modelling.

scholarly journals Biotic stress accelerates formation of climate-relevant aerosols in boreal forests

2015 ◽  
Vol 15 (21) ◽  
pp. 12139-12157 ◽  
Author(s):  
J. Joutsensaari ◽  
P. Yli-Pirilä ◽  
H. Korhonen ◽  
A. Arola ◽  
J. D. Blande ◽  
...  
Keyword(s):  
Boreal Forests ◽  
Large Scale ◽  
Transport Model ◽  
Cloud Condensation Nuclei ◽  
Fold Increase ◽  
Global Scale ◽  
Satellite Observations ◽  
Insect Outbreaks ◽  
Voc Emissions ◽  
Scale Modelling

Abstract. Boreal forests are a major source of climate-relevant biogenic secondary organic aerosols (SOAs) and will be greatly influenced by increasing temperature. Global warming is predicted to not only increase emissions of reactive biogenic volatile organic compounds (BVOCs) from vegetation directly but also induce large-scale insect outbreaks, which significantly increase emissions of reactive BVOCs. Thus, climate change factors could substantially accelerate the formation of biogenic SOAs in the troposphere. In this study, we have combined results from field and laboratory experiments, satellite observations and global-scale modelling in order to evaluate the effects of insect herbivory and large-scale outbreaks on SOA formation and the Earth's climate. Field measurements demonstrated 11-fold and 20-fold increases in monoterpene and sesquiterpene emissions respectively from damaged trees during a pine sawfly (Neodiprion sertifer) outbreak in eastern Finland. Laboratory chamber experiments showed that feeding by pine weevils (Hylobius abietis) increased VOC emissions from Scots pine and Norway spruce seedlings by 10–50 fold, resulting in 200–1000-fold increases in SOA masses formed via ozonolysis. The influence of insect damage on aerosol concentrations in boreal forests was studied with a global chemical transport model GLOMAP and MODIS satellite observations. Global-scale modelling was performed using a 10-fold increase in monoterpene emission rates and assuming 10 % of the boreal forest area was experiencing outbreak. Results showed a clear increase in total particulate mass (local max. 480 %) and cloud condensation nuclei concentrations (45 %). Satellite observations indicated a 2-fold increase in aerosol optical depth over western Canada's pine forests in August during a bark beetle outbreak. These results suggest that more frequent insect outbreaks in a warming climate could result in substantial increase in biogenic SOA formation in the boreal zone and, thus, affect both aerosol direct and indirect forcing of climate at regional scales. The effect of insect outbreaks on VOC emissions and SOA formation should be considered in future climate predictions.

scholarly journals Correlation-Centric Network (CCN) representation for microbial co-occurrence patterns: new insights for microbial ecology

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Pengshuo Yang ◽  
Chongyang Tan ◽  
Maozhen Han ◽  
Lin Cheng ◽  
Xuefeng Cui ◽  
...  
Keyword(s):  
Microbial Communities ◽  
Large Scale ◽  
Network Connectivity ◽  
Small Range ◽  
Environmental Disturbance ◽  
Target Species ◽  
Important Species ◽  
Metagenome Analysis ◽  
Occurrence Patterns ◽  
Edge Networks

Abstract Mainstream studies of microbial community focused on critical organisms and their physiology. Recent advances in large-scale metagenome analysis projects initiated new researches in the complex correlations between large microbial communities. Specifically, previous studies focused on the nodes (i.e. species) of the Species-Centric Networks (SCNs). However, little was understood about the change of correlation between network members (i.e. edges of the SCNs) when the network was disturbed. Here, we introduced a Correlation-Centric Network (CCN) to the microbial research based on the concept of edge networks. In CCN, each node represented a species–species correlation, and edge represented the species shared by two correlations. In this research, we investigated the CCNs and their corresponding SCNs on two large cohorts of microbiome. The results showed that CCNs not only retained the characteristics of SCNs, but also contained information that cannot be detected by SCNs. In addition, when the members of microbial communities were decreased (i.e. environmental disturbance), the CCNs fluctuated within a small range in terms of network connectivity. Therefore, by highlighting the important species correlations, CCNs could unveil new insights when studying not only the functions of target species, but also the stabilities of their residing microbial communities.

scholarly journals Antarctic Water Tracks: Microbial Community Responses to Variation in Soil Moisture, pH, and Salinity

2021 ◽  
Vol 12 ◽  
Author(s):  
Scott F. George ◽  
Noah Fierer ◽  
Joseph S. Levy ◽  
Byron Adams
Keyword(s):  
Microbial Community ◽  
Microbial Communities ◽  
Community Composition ◽  
Environmental Conditions ◽  
Microbial Community Composition ◽  
Soil Microbial Communities ◽  
Vapor Flow ◽  
Arid Soils ◽  
Opposite Pattern ◽  
Dry Valley

Ice-free soils in the McMurdo Dry Valleys select for taxa able to cope with challenging environmental conditions, including extreme chemical water activity gradients, freeze-thaw cycling, desiccation, and solar radiation regimes. The low biotic complexity of Dry Valley soils makes them well suited to investigate environmental and spatial influences on bacterial community structure. Water tracks are annually wetted habitats in the cold-arid soils of Antarctica that form briefly each summer with moisture sourced from snow melt, ground ice thaw, and atmospheric deposition via deliquescence and vapor flow into brines. Compared to neighboring arid soils, water tracks are highly saline and relatively moist habitats. They represent a considerable area (∼5–10 km2) of the Dry Valley terrestrial ecosystem, an area that is expected to increase with ongoing climate change. The goal of this study was to determine how variation in the environmental conditions of water tracks influences the composition and diversity of microbial communities. We found significant differences in microbial community composition between on- and off-water track samples, and across two distinct locations. Of the tested environmental variables, soil salinity was the best predictor of community composition, with members of the Bacteroidetes phylum being relatively more abundant at higher salinities and the Actinobacteria phylum showing the opposite pattern. There was also a significant, inverse relationship between salinity and bacterial diversity. Our results suggest water track formation significantly alters dry soil microbial communities, likely influencing subsequent ecosystem functioning. We highlight how Dry Valley water tracks could be a useful model system for understanding the potential habitability of transiently wetted environments found on the surface of Mars.

Sign in / Sign up

Export Citation Format

Share Document