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The effects of expression of a hyperthermophilic endoglucanase and nutrient conditions on poplar growth and physiology
Abstract Efficient production and processing of poplar biomass feedstock requires costly pretreatments and enzyme additives. Transgenic alterations of poplar can reduce the need for these inputs by increasing biomass, improving lignocellulose quality and enhancing nutrient uptake. Previously, a transgenic line of poplar expressing a bacterial hyperthermophilic endoglucanase (TnCelB) in Populus alba × grandidentata (P39) was developed and characterized. This study reports the effects on the TnCelB transgenic poplar line under a reduced nutrient treatment. Overall, the nutrient treatment was the source of more observed significant differences than the genotype. Wild type and TnCelB poplar had similar responses in biomass allocation and net photosynthesis. TnCelB trees had a wrinkled leaf phenotype and relative to wild type, had reduced total biomass, reduced water-use efficiency, and a decreased proportion of cellulose to hemicellulose and lignin. In low nutrient conditions, TnCelB trees had increased structural carbohydrates with stable lignin values. The TnCelB line presents a viable option for poplar biomass feedstock, offering biomass comparable to wild type poplar and more efficient processing, with only mild negative phenotypes.
The physiological basis for estimating photosynthesis from Chl>em>a fluorescence
We report solar-induced Chl fluorescence (SIF) offers the potential to curb large uncertainties in the estimation of photosynthesis across biomes and climates, and at different spatiotemporal scales. However, it remains unclear how SIF should be used to mechanistically estimate photosynthesis. In this study, we built a quantitative framework for the estimation of photosynthesis, based on a mechanistic light reaction model with the Chla fluorescence of Photosystem II (SIFPSII) as an input (MLR-SIF). Utilizing 29 C 3 and C 4 plant species that are representative of major plant biomes across the globe, we confirmed the validity of this framework at the leaf level. The MLR-SIF model is capable of accurately reproducing photosynthesis for all C 3 and C 4 species under diverse light, temperature, and CO 2 conditions. We further tested the robustness of the MLR-SIF model using Monte Carlo simulations, and found that photosynthesis estimates were much less sensitive to parameter uncertainties relative to the conventional Farquhar, von Caemmerer, Berry (FvCB) model because of the additional independent information contained in SIF PSII . Once inferred from direct observables of SIF, SIF PSII provides ‘parameter savings’ to the MLR-SIF model, compared to the mechanistically equivalent FvCB model, and thus avoids the uncertainties arising as a result of imperfect model parameterization. Our findings set the stage for future efforts to employ SIF mechanistically to improve photosynthesis estimates across a variety of scales, functional groups, and environmental conditions.
Integration of physiologically relevant photosynthetic energy flows into whole genome models of light‐driven metabolism
SUMMARY Characterizing photosynthetic productivity is necessary to understand the ecological contributions and biotechnology potential of plants, algae, and cyanobacteria. Light capture efficiency and photophysiology have long been characterized by measurements of chlorophyll fluorescence dynamics. However, these investigations typically do not consider the metabolic network downstream of light harvesting. By contrast, genome‐scale metabolic models capture species‐specific metabolic capabilities but have yet to incorporate the rapid regulation of the light harvesting apparatus. Here, we combine chlorophyll fluorescence parameters defining photosynthetic and non‐photosynthetic yield of absorbed light energy with a metabolic model of the pennate diatom Phaeodactylum tricornutum. This integration increases the model predictive accuracy regarding growth rate, intracellular oxygen production and consumption, and metabolic pathway usage. Through the quantification of excess electron transport, we uncover the sequential activation of non‐radiative energy dissipation processes, cross‐compartment electron shuttling, and non‐photochemical quenching as the rapid photoacclimation strategy in P. tricornutum. Interestingly, the photon absorption thresholds that trigger the transition between these mechanisms were consistent at low and high incident photon fluxes. We use this understanding to explore engineering strategies for rerouting cellular resources and excess light energy towards bioproducts in silico . Overall, we present a methodology for incorporating a common, informative data type into computational models of light‐driven metabolism and show its utilization within the design–build–test–learn cycle for engineering of photosynthetic organisms.
Carbon isotope evidence for the global physiology of Proterozoic cyanobacteria
Ancestral cyanobacteria are assumed to be prominent primary producers after the Great Oxidation Event [≈2.4 to 2.0 billion years (Ga) ago], but carbon isotope fractionation by extant marine cyanobacteria (α-cyanobacteria) is inconsistent with isotopic records of carbon fixation by primary producers in the mid-Proterozoic eon (1.8 to 1.0 Ga ago). To resolve this disagreement, we quantified carbon isotope fractionation by a wild-type planktic β-cyanobacterium (Synechococcus sp. PCC 7002), an engineered Proterozoic analog lacking a CO 2 -concentrating mechanism, and cyanobacterial mats. At mid-Proterozoic pH and pCO 2 values, carbon isotope fractionation by the wild-type β-cyanobacterium is fully consistent with the Proterozoic carbon isotope record, suggesting that cyanobacteria with CO 2 -concentrating mechanisms were apparently the major primary producers in the pelagic Proterozoic ocean, despite atmospheric CO 2 levels up to 100 times modern. The selectively permeable microcompartments central to cyanobacterial CO 2 -concentrating mechanisms (“carboxysomes”) likely emerged to shield rubisco from O 2 during the Great Oxidation Event.
Physiological and genomic evidence of cysteine degradation and aerobic hydrogen sulfide production in freshwater bacteria
The sulfur-containing amino acid cysteine is abundant in the environment, including in freshwater lakes. Biological cysteine degradation can result in hydrogen sulfide (H 2 S), a toxic and ecologically relevant compound that is a central player in biogeochemical cycling in aquatic environments. Here, we investigated the ecological significance of cysteine in oxic freshwater, using isolated cultures, controlled experiments, and multiomics. We screened bacterial isolates enriched from natural lake water for their ability to produce H 2 S when provided cysteine. We identified 29 isolates (Bacteroidota, Proteobacteria, and Actinobacteria) that produced H 2 S. To understand the genomic and genetic basis for cysteine degradation and H 2 S production, we further characterized three isolates using whole-genome sequencing (using a combination of short-read and long-read sequencing) and tracked cysteine and H 2 S levels over their growth ranges: Stenotrophomonas maltophilia (Gammaproteobacteria), S. bentonitica (Gammaproteobacteria), and Chryseobacterium piscium (Bacteroidota). Cysteine decreased and H 2 S increased, and all three genomes had genes involved in cysteine degradation. Finally, to assess the presence of these organisms and genes in the environment, we surveyed a 5-year time series of metagenomic data from the same isolation source (Lake Mendota, Madison, WI, USA) and identified their presence throughout the time series. Overall, our study shows that diverse isolated bacterial strains can use cysteine and produce H 2 S under oxic conditions, and we show evidence using metagenomic data that this process may occur more broadly in natural freshwater lakes. Future considerations of sulfur cycling and biogeochemistry in oxic environments should account for H 2 S production from the degradation of organosulfur compounds.
Plant physiology, shrub size, thaw depth and soil water content, Seward Peninsula, Alaska, 2023
Stomatal response to changing irradiance measurements (m curves), CO2 response (ACi) and light response (AQ) curves on common tussock tundra shrub, graminoid and forb species. Data were collected in July 2023 from three sites on the Seward Peninsula, Kougarok Mile 64, Teller Mile 27 and Council. Measurements were made with LI-COR LI-6800 gas exchange systems on 18 species. For samples from Teller and Kougarok, thaw depth, soil moisture content and dGPS locations are also provided for most samples. Canopy height and canopy diameter are provided for shrubs measured at the Teller site. The data package files include data files, metadata files and the complete instrument output for all gas exchange measurements in .csv format, and a pdf describing the experimental protocols. See the related data package NGA508 for foliar trait data (leaf mass per area, leaf nitrogen concentration).The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
Vismia guianensis leaf age physiology in Manaus, Brazil, from July 2014 - July 2015
This data package contains net photosynthesis rates and methanol and isoprene leaf emissions as a function of light, temperature, and leaf age. Data was collected from the species V. guianensis at the NGEE Tropics site in Manaus, Brazil (BR-Ma2). This data is used in the publication "Methanol and Isoprene Emissions from the Fast Growing Tropical Pioneer Species Vismia guianensis (Aubl.) Pers. (Hypericaceae) in the Amazon Basin." Isoprene (Is) emissions by plants represent a loss of carbon and energy resources leading to the initial hypothesis that fast growing pioneer species in secondary tropical forests allocate carbon primarily to growth at the expense of isoprenoid defenses. In this study, we quantified leaf isoprene and methanol emissions from the abundant pantropical pioneer tree species Vismia guianensis and ambient isoprene concentrations above a diverse secondary forest in the central Amazon. See the below field, Dataset References, for the full citation. Included in the data package is a zip file with six datasets, three in Excel format and additional versions in CSV format. Also included are the figures from the related publication.
DETECTING PHYSIOLOGICAL STATUS OF MICROBIAL CULTURES, IN-SITU
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Physiological Characterization of Language Comprehension
In this project, our goal was to develop methods that would allow us to make accurate predictions about individual differences in human cognition. Understanding such differences is important for maximizing human and human-system performance. There is a large body of research on individual differences in the academic literature. Unfortunately, it is often difficult to connect this literature to applied problems, where we must predict how specific people will perform or process information. In an effort to bridge this gap, we set out to answer the question: can we train a model to make predictions about which people understand which languages? We chose language processing as our domain of interest because of the well- characterized differences in neural processing that occur when people are presented with linguistic stimuli that they do or do not understand. Although our original plan to conduct several electroencephalography (EEG) studies was disrupted by the COVID-19 pandemic, we were able to collect data from one EEG study and a series of behavioral experiments in which data were collected online. The results of this project indicate that machine learning tools can make reasonably accurate predictions about an individual?s proficiency in different languages, using EEG data or behavioral data alone.
Utilizing Physics-informed and Machine Learning Methods to Enhance Remote Monitoring of Physiological Signatures
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Remote Monitoring of Physiological Signatures: The Impact of Hyperspectral Imaging
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Detection of Chemical and Biological Exposure via Non-contact Measurement of Human Physiological Signatures
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From tides to seasons: How cyclic tidal drivers and plant physiology interact to affect carbon cycling at the terrestrial-estuarine boundary (Final technical report)
Coastal ecosystems are among the most biologically and biogeochemically active and diverse systems on Earth. Because they act as important linkages between terrestrial ecosystems and the open ocean, their incorporation in Earth system models (ESMs) is critical to predict coastal and global responses to environmental changes. However, they vary greatly in the magnitude of tides and the volume and timing of freshwater input from land, making it challenging to model the major biogeochemical reactions that control productivity and greenhouse gas emissions across coastal terrestrial aquatic interfaces (TAIs). Our overall objective was to improve mechanistic process understanding and modeling of tidal wetland hydro-biogeochemistry in coastal TAIs. We established a new flux tower site (Ameriflux US-PLo) in the oligohaline part of the Parker River to continuously monitor ecosystem-scale carbon fluxes under temporally varying salinity conditions. The site is co-located with long-term monitoring plots of the Plum Island Ecosystems LTER project. We installed wells and redox sensors in the marsh interior and creek bank, established biomass monitoring plots and deployed novel optode sensors in both locations. We used this data to parameterize plant-mediated transport in PFLOTRAN and tested the impact of soil heterogeneity on porewater constituents and gas fluxes. We collected observations of root oxygen release with a novel planar optode system in the field. Flux data collected during the measurement period encompasses a large variation in salinity ranging from drought to record precipitation years. We developed a method to extract functional relationships from the flux data using artificial neural networks, identifying salinity thresholds for CH 4 fluxes. Finally, we are using the coupled ELM-PFLOTRAN model to test the impact of antecedent hydrological conditions on the salinity-CH 4 flux relationship. This grant contributed to the professional development of one postdoc, three research assistants and one graduate student. The sensor data has been shared with external collaborators.
Plant Physiology as the Bridge Between Fire Behavior and Ecosystem Effects
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Detection of Chemical and Biological Exposure via Non-contact Measurement of Human Physiological Signatures
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Five-year yield and physiology data for Populus deltoides grown in South Carolina
A 6-year study in Sumter Co., SC, to determine crown and leaf attributes, supplemental water and fertilization effects on dry weight productivity, and clonal variation accounts in biomass traits. Eight commercially available clones -- ‘ST148’, ‘S2C20’, ‘3167’, ‘ST72’ ‘20WV’, ‘S7C1’, ‘ST70’, and ‘S7C15’, were studied. Drip irrigation rates were used to maintain soil tension of 33 kPa at 15% volumetric soil moisture or 1500 kPa at 10%. Application of macro- and micronutrients occurred via drip irrigation at a high and low rate. The high rate included an initial application of 85 kg elemental nitrogen ha-1, which increased to 182 kg ha-1 at age 3, which then was reduced to 135 kg ha-1 for remainder of the study. The low treatment received 50% (w/v) of the high treatment. Individual tree measurements across time are provided in this dataset.