Evaluation of 3D seed structure and cellular traits in-situ using X-ray microscopy
Not Available
Griffiths, Marcus↗
SEARCH · Search NASA
Results for “Trait”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Identification and functional analysis of strigolactone pathway genes regulating tillering traits in sugarcane
Abstract Saccharum officinarum and Saccharum spontaneum are two fundamental species of modern sugarcane cultivars, exhibiting divergent tillering patterns crucial for sugarcane architecture and yield. Strigolactones (SLs), a class of plant hormones, are considered to play a central role in shaping plant form and regulating tillering. Our study highlights the distinct tillering patterns observed between S. officinarum and S. spontaneum and implicates significant differences in SL levels in root exudates between the two species. Treatment with rac-GR24 (an artificial SL analog) suppressed tillering in S. spontaneum. Based on transcriptome analysis, we focused on two genes, TRANSCRIPTION ELONGATION FACTOR 1 (TEF1) and CIRCADIAN CLOCK ASSOCIATED1 (CCA1), which show higher expression in S. spontaneum or S. officinarum, respectively. While the overexpression of SoCCA1 did not lead to significant phenotypic differences, overexpression of SsTEF1 in rice stimulated tillering and inhibited plant height, demonstrating its role in tillering regulation. However, the overexpression of suggests that SoCCA1 may not be the key regulator of sugarcane tillering. Yeast one-hybrid assays identified four transcription factors (TFs) regulating SsTEF1 and four and five TFs regulating SsCCA1 and SoCCA1. This study provides a theoretical foundation for deciphering the molecular mechanisms underlying the different tillering behaviors between S. officinarum and S. spontaneum, providing valuable insights for the molecular-based design of sugarcane breeding strategies.
Qi, Yiying↗
Changing Interactions Between Trace Gas Fluxes, Belowground Chemistry, and Plant Traits Across an Arctic Thermokarst Landscape
Arctic permafrost soils are increasingly subject to thermokarst that is, abrupt ground subsidence caused by thaw. Wetlands can form within these depressions, leading to changes in organic matter decomposition and gas fluxes (CO 2 , CH 4 , N 2 O, NH 3 ). Thermokarst wetlands tend to be dominated by graminoids, while surrounding upland tussock tundra tends to be dominated by mixed communities of shrubs and graminoids. Here, to investigate how thermokarst alters the land-atmosphere exchange of C and N gases in Arctic tundra, we analyzed soil, porewater, above- and belowground biomass, and measured gas fluxes across dominant plant functional types (PFTs) within a lowland thermokarst wetland and adjacent upland tussock tundra. Both locations were overall sinks of CO 2 , sources of CH 4 , and sources of both N 2 O and NH 3 . We found that thermokarst wetlands emitted enough CH 4 to generate a positive radiative forcing in CO 2 equivalents (+1.2 μmol m −2 s −1 CO 2 -eq), counteracting the high CO 2 uptake. In contrast, the upland tussock tundra had a net negative radiative forcing (−1.2 μmol m −2 s −1 CO 2 -eq). Differences in gas flux and soil chemistry between upland and lowland are primarily driven by flooded conditions present in thermokarst wetland. Additionally, root biomass from graminoids across both lowlands and uplands significantly correlated with CH 4 fluxes, supporting previous observations of plant-mediated transport of CH 4 . Graminoid cover was correlated with increases in low molecular weight dissolved organic carbon, possibly associated with root exudates that fuel methanogenesis. Forb cover in the upland tussock tundra was significantly correlated with nine soil chemical variables, indicating that forbs may influence local soil chemistry or conversely, that soil chemistry controls where forbs grow. Overall, our findings indicate the variability in gas fluxes in the upland tussock tundra is partially controlled by PFT cover, while thermokarst wetlands emit enough CH 4 to counteract CO 2 uptake, with implications for carbon budget changes in Arctic systems.
Land-atmosphere feedback↗
Habitat Affinity of Riverine Dissolved Organic Matter Linked to Molecular Traits
Fluxes of organic matter across Earth habitats shape ecosystem function and carbon storage. Yet, it is challenging to predict howthe molecules are preferentially accumulated in specific habitats, that is, the habitat affinities, due to their diverse character-istics. Here, we develop an indicator of compositional-level habitat affinity for dissolved organic matter (DOM) by quantifyingthe aggregated affinities of individual molecules that are over- or under-represented relative to a reference habitat. Applyingthis indicator to 93 paired riverine water-sediment sites, we find that 65.1% of molecules show non-significant habitat affinitiesfor water or sediment and are thermodynamically favorable and susceptible to microbial degradation. Consistently, the habitataffinities of DOM assemblages decrease with lower molecular weights and recalcitrance for waters, and with higher thermody-namic favorability for sediments. These affinities are both reduced by elevated temperatures and anthropogenic pressures, suchas nitrogen loading and land-use change. Our projection of habitat affinities of global riverine DOM shows that regions withintense anthropogenic disturbances or warm climates, such as East Asia, Western Europe, and the Amazon, tend to exhibit loweraffinities. These findings suggest enhanced molecular homogenization between water and sediment habitats is associated withenvironmental changes and underscore the importance of habitat affinities for predicting the potential trajectories of organiccarbon across ecosystems
Cui, Yifan↗
Future tree mortality is impossible to observe, but a new model reveals why tropical tree traits matter more than climate change variability for predicting hydraulic failure
This article is a Commentary on Robbins et al . (2024), 244 : 2239–2250 .
Mackay, D. Scott↗
Nutrient limitation shapes functional traits of mycorrhizal fungi and phosphorus-cycling bacteria across an elevation gradient
In nutrient-limited high-elevation ecosystems, plants rely on arbuscular mycorrhizal (AM) fungi to provide mineral phosphorus (P) in the form of phosphate (PO43-). AM fungi gather these nutrients from phosphorus-cycling bacteria (PCBs) that can mineralize PO43- from organic matter and solubilize mineral-bound P. How climate, soil factors, and nutrient limitation influence AM fungi and PCB assembly remains unclear. We collected soil from montane meadows across a 1,000-m elevation gradient on three replicate mountainsides and analyzed AM fungal marker genes, P-cycling genes from shotgun metagenomes, and edaphic measurements. High-elevation soils had nearly 50-fold less soil PO₄³⁻ and 60% more AM fungal hyphae than low-elevation soils. AM fungal turnover was linked to changes in pH, organic carbon, and PO₄³-. The composition of 198 P-cycling genes was influenced by the AM fungal community structure. Drivers of individual PCB functional genes, including pH and organic carbon, varied with gene phylogeny. We found a trade-off in P-cycling strategies across elevation: P-rich, low-elevation soils supported root-colonizing AM fungi and organic P-mineralizing bacteria. P-poor, high-elevation soils were dominated by stress-tolerant AM fungi and mineral P-solubilizing bacteria. Our results suggest that AM fungi and PCB community turnover across elevation are both shaped by pH, organic carbon, and P availability. With continued climate warming, the structure and function of mountaintop ecosystems might shift to resemble lower elevations, disrupting long-established and specialized microbial assemblages, with consequences for P-cycling dynamics and the total P available to plant communities.IMPORTANCEPhosphorus (P) limits plant productivity in high-elevation ecosystems, yet the microbial networks that mobilize P, including arbuscular mycorrhizal (AM) fungi and phosphorus-cycling bacteria (PCBs), remain under-characterized in these nutrient-poor soils. We show that across a 10,00-m elevation gradient, AM fungi and P-cycling gene assemblages shift predictably with pH, organic carbon, and phosphate availability. Higher elevations, with less available P, select for stress-tolerant AM fungal taxa and PCB strategies geared toward mineral solubilization, while low-elevation sites favor root colonization by AM fungi and organic P mineralization. These results suggest that nutrient limitation can constrain microbial community assembly in consistent ways across landscapes. High mountain soils are low in P and rely on a network of underground AM fungi and PCB to deliver nutrients to plants. This study shows how those underground relationships reorganize with elevation and how climate change could collapse long-standing microbial strategies by pushing high-elevation ecosystems toward lowland conditions. As soils warm and dry, the microbial scaffolding that supports alpine plant life may become increasingly unstable.
arbuscular mycorrhizal fungi↗
Pangenome analysis of Liriodendron reveals presence/absence variations associated with growth traits
Not provided.
Plant Sciences↗
The SEMA3F-NRP1/NRP2 axis is a key factor in the acquisition of invasive traits in in situ breast ductal carcinoma
A better understanding of ductal carcinoma in situ (DCIS) is urgently needed to identify these preinvasive lesions as distinct clinical entities. Semaphorin 3F (SEMA3F) is a soluble axonal guidance molecule, and its coreceptors Neuropilin 1 (NRP1) and NRP2 are strongly expressed in invasive epithelial BC cells. We utilized two cell line models to represent the progression from a healthy state to the mild-aggressive or ductal carcinoma in situ (DCIS) stage and, ultimately, to invasive cell lines. Additionally, we employed in vivo models and conducted analyses on patient databases to ensure the translational relevance of our results. We revealed SEMA3F as a promoter of invasion during the DCIS-to-invasive ductal carcinoma transition in breast cancer (BC) through the action of NRP1 and NRP2. In epithelial cells, SEMA3F activates epithelialmesenchymal transition, whereas it promotes extracellular matrix degradation and basal membrane and myoepithelial cell layer breakdown. Together with our patient database data, these proof-of-concept results reveal new SEMA3F-mediated mechanisms occurring in the most common preinvasive BC lesion, DCIS, and represent potent and direct activation of its transition to invasion. Moreover, and of clinical and therapeutic relevance, the effects of SEMA3F can be blocked directly through its coreceptors, thus preventing invasion and keeping DCIS lesions in the preinvasive state.
60 APPLIED LIFE SCIENCES↗
SPRUCE: Shrub Leaf and Stem Functional Traits from 10-year Shrub Layer Biomass Harvests (August 2025)
This data set contains Leaf Mass Area (LMA), Specific Leaf Area (SLA), Leaf Area Index (LAI), and Huber value (Hv) measurements from the 10-year Shrub-layer Biomass harvests in the Spruce and Peatland Responses Under Environmental Change (SPRUCE) experiment in August 2025. The total ground-level cross-sectional sapwood area of Chamaedaphne calyculata (CHCA), Rhododendron groenlandicum (RHGR), and Vaccinium angustifolium (VAAN) were calculated for each of the three shrub-layer community survey plots located in each experimental enclosure. Representative current-year leaves were removed, scanned, dried, and weighed to derive SLA. Grab-samples for VAAN were used to calculate SLA when no biomass was harvested in the shrub-layer community plots. These SLA values were used to infer total one-sided leave surface area from the total species lead dry mass for calculation of LAI and Hv.
Birkebak, Joshua [ORNL] (ORCID:0009000955611494)↗
VLM4Bio: A Benchmark Dataset to Evaluate Pretrained Vision-Language Models for Trait Discovery from Biological Images
Images are increasingly becoming the currency for documenting biodiversity on the planet, providing novel opportunities for accelerating scientific discoveries in the field of organismal biology, especially with the advent of large vision-language models (VLMs). We ask if pre-trained VLMs can aid scientists in answering a range of biologically relevant questions without any additional fine-tuning. In this paper, we evaluate the effectiveness of 12 state-of-the-art (SOTA) VLMs in the field of organismal biology using a novel dataset, VLM4Bio, consisting of 469K question8 answer pairs involving 30K images from three groups of organisms: fishes, birds, and butterflies, covering five biologically relevant tasks. We also explore the effects of applying prompting techniques and tests for reasoning hallucination on the performance of VLMs, shedding new light on the capabilities of current SOTA VLMs in answering biologically relevant questions using images
Maruf, M [Virginia Tech, Blacksburg]↗
Multiplex characterization of microbial traits using dual barcoded nucleic acid fragment expression library
Disclosed herein are barcoded expression libraries comprising a plurality of expression vectors, wherein each expression vector comprises a nucleic acid fragment flanked by a first barcode and a second barcode. Further disclosed herein are methods of making the barcoded expression libraries and methods of conducting functional analysis using the barcoded expression libraries.
Mutalik, Vivek K.↗
Data for Spatial Analysis of Cell Patterning to Aid Genetic and Phenotypic Understanding of Grass Stomatal Density: A Case Study in Maize
Biological processes involve complex hierarchies where composite traits result from multiple component traits. However, holistically understanding of how sets of component traits interact to underpin genotype-to-phenotype relationships is generally lacking. Stomatal density (SD) is a tractable model system for exploring how high-throughput phenotyping (HTP) data could be exploited by a new spatial analysis approach to better understand a developmentally and functionally important trait. SD is a composite trait, resulting from various components related to cell identity and size, which are themselves governed by a series of spatio-developmental processes. Data from 192 recombinant inbred lines of maize [Zea mays (L.)] were analyzed by a new stomatal patterning phenotype (SPP) to (1) describe the average spatial probability distribution of the nearest neighboring stomata; (2) derive a core set of component traits related to cell size, cell packing, and positional probabilities; (3) build a structural equation model of component traits underlying SD; and (4) identify stomatal patterning quantitative trait loci (QTL). The core set of SPP-derived traits explained 74% of the variation in SD. Analyzing SPP component traits allowed some loci previously identified as generic SD QTL to be recognized as specific to lateral versus longitudinal elements of stomatal patterning. Therefore, this study highlights how novel insights can be gained by decomposing a composite trait (e.g., SD) into a set of component traits that were present in HTP data but not previously exploited.
AI/ML↗
Spatial analysis of cell patterning to aid genetic and phenotypic understanding of grass stomatal density: A case study in maize
Biological processes involve complex hierarchies where composite traits result from multiple component traits. However, holistically understanding of how sets of component traits interact to underpin genotype-to-phenotype relationships is generally lacking. Stomatal density (SD) is a tractable model system for exploring how high-throughput phenotyping (HTP) data could be exploited by a new spatial analysis approach to better understand a developmentally and functionally important trait. SD is a composite trait, resulting from various components related to cell identity and size, which are themselves governed by a series of spatio-developmental processes. Data from 192 recombinant inbred lines of maize [Zea mays (L.)] were analyzed by a new stomatal patterning phenotype (SPP) to (1) describe the average spatial probability distribution of the nearest neighboring stomata; (2) derive a core set of component traits related to cell size, cell packing, and positional probabilities; (3) build a structural equation model of component traits underlying SD; and (4) identify stomatal patterning quantitative trait loci (QTL). The core set of SPP-derived traits explained 74% of the variation in SD. Analyzing SPP component traits allowed some loci previously identified as generic SD QTL to be recognized as specific to lateral versus longitudinal elements of stomatal patterning. Therefore, this study highlights how novel insights can be gained by decomposing a composite trait (e.g., SD) into a set of component traits that were present in HTP data but not previously exploited.
59 BASIC BIOLOGICAL SCIENCES↗
Convergent expansions of keystone gene families drive metabolic innovation in Saccharomycotina yeasts
Many remarkable phenotypes have repeatedly occurred across vast evolutionary distances. When convergent traits emerge on the tree of life, they are sometimes driven by the same underlying gene families, while other times, many different gene families are involved. Conversely, a gene family may be repeatedly recruited for a single trait or many different traits. To understand the general rules governing convergence at both genomic and phenotypic levels, we systematically tested associations between 56 binary metabolic traits and gene count in 14,785 gene families from 993 Saccharomycotina yeasts. Using a recently developed phylogenetic approach that reduces spurious correlations, we found that gene family expansion and contraction were significantly linked to trait gain and loss in 45/56 (80%) traits. While 595/739 (81%) significant gene families were associated with only one trait, we also identified several “keystone” gene families that were significantly associated with up to 13/56 (23%) of all traits. Strikingly, most of these families are known to encode metabolic enzymes and transporters, including all members of the industrially relevant MAL tose fermentation loci in the baker’s yeast Saccharomyces cerevisiae. These results indicate that convergent evolution on the gene family level may be more widespread across deeper timescales than previously believed.
59 BASIC BIOLOGICAL SCIENCES↗
Hyperspectral leaf reflectance of grasses varies with evolutionary lineage more than with site
Abstract To predict ecological responses at broad environmental scales, grass species are commonly grouped into two broad functional types based on photosynthetic pathway. However, closely related species may have distinctive anatomical and physiological attributes that influence ecological responses, beyond those related to photosynthetic pathway alone. Hyperspectral leaf reflectance can provide an integrated measure of covarying leaf traits that may result from phylogenetic trait conservatism and/or environmental conditions. Understanding whether spectra‐trait relationships are lineage specific or reflect environmental variation across sites is necessary for using hyperspectral reflectance to predict plant responses to environmental changes across spatial scales. We measured hyperspectral leaf reflectance (400–2400 nm) and 12 structural, biochemical, and physiological leaf traits from five grass‐dominated sites spanning the Great Plains of North America. We assessed if variation in leaf reflectance spectra among grass species is explained more by evolutionary lineage (as captured by tribes or subfamilies), photosynthetic pathway (C 3 or C 4 ), or site differences. We then determined whether leaf spectra can be used to predict leaf traits within and across lineages. Our results using redundancy analysis ordination (RDA) show that grass tribe identity explained more variation in leaf spectra (adjusted R 2 = 0.12) than photosynthetic pathway, which explained little variation in leaf spectra (adjusted R 2 = 0.00). Furthermore, leaf reflectance from the same tribe across multiple sites was more similar than leaf reflectance from the same site across tribes (adjusted R 2 = 0.12 and 0.08, respectively). Across all sites and species, trait predictions based on spectra ranged considerably in predictive accuracies ( R 2 = 0.65 to <0.01), but R 2 was >0.80 for certain lineages and sites. The relationship between Vc max , a measure of photosynthetic capacity, and spectra was particularly promising. Chloridoideae, a lineage more common at drier sites, appears to have distinct spectra‐trait relationships compared with other lineages. Overall, our results show that evolutionary relatedness explains more variation in grass leaf spectra than photosynthetic pathway or site, but consideration of lineage‐ and site‐specific trait relationships is needed to interpret spectral variation across large environmental gradients.
Pau, Stephanie [Department of Geography University↗
The Emergence and Promise of Functional Chemogeography of Organic Matter
Organisms in ecosystems continuously release a myriad of organic matter molecules that undergo microbial and abiotic transformation, processes that critically influence carbon storage and climate feedbacks. Yet, a systematic understanding of what determines the transformation and persistence of organic matter across spatiotemporal scales remains elusive. We propose an emerging framework, termed “functional chemogeography,” to understand transformation and persistence of organic matter based on the chemical traits of molecules. This framework extends beyond a sole focus on intrinsic traits, which remain relatively constant across spatiotemporal scales, to emphasize extrinsic traits such as biochemical transformations and environmental responses, which vary spatiotemporally and are shaped by both intrinsic traits and the environment. When upscaled to the assemblage level using functional diversity indices, these extrinsic traits reveal a significant, and in some cases superior, capacity than intrinsic traits to explain biogeochemical processes, as demonstrated through a case study of dissolved organic matter in China's lakes. In conclusion, by integrating trait-based perspectives into predictive models, this framework helps bridge chemical complexity with ecosystem biogeochemistry, thereby advancing our ability to predict the fate of global organic carbon under environmental change.
chemical traits↗
Longitudinal genome-wide association study reveals early QTL that predict biomass accumulation under cold stress in sorghum
Sorghum bicolor is a promising cellulosic feedstock crop for bioenergy due to its high biomass yields. However, early growth phases of sorghum are sensitive to cold stress, limiting its planting in temperate environments. Cold adaptability is crucial for cultivating bioenergy and grain sorghum at higher latitudes and elevations, or for extending the growing season. Identifying genes and alleles that enhance biomass accumulation under early cold stress can lead to improved sorghum varieties through breeding or genetic engineering. We conducted image-based phenotyping on 369 accessions from the sorghum Bioenergy Association Panel (BAP) in a controlled environment with early cold treatment. The BAP includes diverse accessions with dense genotyping and varied racial, geographical, and phenotypic backgrounds. Daily, non-destructive imaging allowed temporal analysis of growth-related traits and water use efficiency (WUE). A genome-wide association study (GWAS) was performed to identify genomic intervals and genes associated with cold stress response. The GWAS identified transient quantitative trait loci (QTL) strongly associated with growth-related traits, enabling an exploration of the genetic basis of cold stress response at different developmental stages. This analysis of daily growth traits, rather than endpoint traits, revealed early transient QTL predictive of final phenotypes. The study identified both known and novel candidate genes associated with growth-related traits and temporal responses to cold stress. The identified QTL and candidate genes contribute to understanding the genetic mechanisms underlying sorghum's response to cold stress. These findings can inform breeding and genetic engineering strategies to develop sorghum varieties with improved biomass yields and resilience to cold, facilitating earlier planting, extended growing seasons, and cultivation at higher latitudes and elevations.
59 BASIC BIOLOGICAL SCIENCES↗
Data driven discovery and quantification of hyperspectral leaf reflectance phenotypes across a maize diversity panel
Abstract Estimates of plant traits derived from hyperspectral reflectance data have the potential to efficiently substitute for traits, which are time or labor intensive to manually score. Typical workflows for estimating plant traits from hyperspectral reflectance data employ supervised classification models that can require substantial ground truth datasets for training. We explore the potential of an unsupervised approach, autoencoders, to extract meaningful traits from plant hyperspectral reflectance data using measurements of the reflectance of 2151 individual wavelengths of light from the leaves of maize ( Zea mays ) plants harvested from 1658 field plots in a replicated field trial. A subset of autoencoder‐derived variables exhibited significant repeatability, indicating that a substantial proportion of the total variance in these variables was explained by difference between maize genotypes, while other autoencoder variables appear to capture variation resulting from changes in leaf reflectance between different batches of data collection. Several of the repeatable latent variables were significantly correlated with other traits scored from the same maize field experiment, including one autoencoder‐derived latent variable (LV8) that predicted plant chlorophyll content modestly better than a supervised model trained on the same data. In at least one case, genome‐wide association study hits for variation in autoencoder‐derived variables were proximal to genes with known or plausible links to leaf phenotypes expected to alter hyperspectral reflectance. In aggregate, these results suggest that an unsupervised, autoencoder‐based approach can identify meaningful and genetically controlled variation in high‐dimensional, high‐throughput phenotyping data and link identified variables back to known plant traits of interest.
Tross, Michael C.↗