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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.

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Environmental matrix and moisture influence soil microbial phenotypes in a simplified porous media incubation

Soil moisture and porosity regulate microbial metabolism by influencing factors, such as system chemistry, substrate availability, and soil connectivity. However, accurately representing the soil environment and establishing a tractable microbial community that limits confounding variables is difficult. Here, we use a reduced-complexity microbial consortium grown in a glass bead porous media amended with chitin to test the effects of moisture and a structural matrix on microbial phenotypes. Leveraging metagenomes, metatranscriptomes, metaproteomes, and metabolomes, we saw that our porous media system significantly altered microbial phenotypes compared with the liquid incubations, denoting the importance of incorporating pores and surfaces for understanding microbial phenotypes in soils. These phenotypic shifts were mainly driven by differences in expression of Streptomyces and Ensifer, which included a significant decrease in overall chitin degradation between porous media and liquid. Our findings suggest that the success of Ensifer in porous media is likely related to its ability to repurpose carbon via the glyoxylate shunt amidst a lack of chitin degradation byproducts while potentially using polyhydroxyalkanoate granules as a C source. We also identified traits expressed by Ensifer and others, including motility, stress resistance, and carbon conservation, that likely influence the metabolic profiles observed across treatments. Together, these results demonstrate that porous media incubations promote structure-induced microbial phenotypes and are likely a better proxy for soil conditions than liquid culture systems. Furthermore, they emphasize that microbial phenotypes encompass not only the multi-enzyme pathways involved in metabolism but also include the complex interactions with the environment and other community members.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

TREM2 modulates microglia phenotypes in the neuroinflammation of Parkinson's disease

Highlights: • TREM2, Arginase-1 and proinflammatory cytokines increased in the midbrain of PD mice. • Knockdown of TREM2 in BV2 microglia exaggerated M1 microglial inflammatory responses. • TREM2 overexpression promoted M2 polarization and alleviated microglial inflammation. • TREM2 plays a crucial role in altering M1 microglia to M2 phenotype. Neuroinflammation and overactivated microglia underlies the pathogenesis of Parkinson's disease (PD). Furthermore, microglia could polarize into classic inflammatory M1 and immunosuppressive M2 phenotype. Thus, inhibiting the overactivated inflammatory M1 microglia by promoting the transformation of microglia to the protective M2 phenotype provides potential therapy for PD, but the mechanism that modulates microglia polarization remains unknown. Triggering receptor expressed on myeloid cells-2 (TREM2) is a recently identified immune receptor expressed by the microglia in the brain. Emerging evidence indicates that TREM2 enhances the phagocytosis function of microglia and suppress inflammation. Based on these evidence, we hypothesized that TREM2 might play a protective role through regulating microglia polarization. Here, we employ a lentiviral strategy to overexpress or suppress TREM2 on microglia and found that TREM2 was essential for M2 microglia polarization. Knockdown of TREM2 in BV2 microglia inhibited M2 polarization and lead to exaggeration of M1 microglial inflammatory responses, whereas overexpression of TREM2 promoted M2 polarization and alleviated microglial inflammation. We also observed that the TREM2 level was higher in the midbrain of PD mice, which was accompanied by an elevated level of Arginase-1 and increased proinflammatory cytokines, suggesting that TREM2 is an important factor in switching the microglia phenotypes. Taken together, these findings indicate that TREM2 plays a crucial role in altering the proinflammatory M1 microglia to M2 phenotype and has beneficial effects in the immune pathogenesis of PD.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Single-cell, whole-embryo phenotyping of mammalian developmental disorders

Mouse models are a critical tool for studying human diseases, particularly developmental disorders. However, conventional approaches for phenotyping may fail to detect subtle defects throughout the developing mouse. Here we set out to establish single-cell RNA sequencing of the whole embryo as a scalable platform for the systematic phenotyping of mouse genetic models. We applied combinatorial indexing-based single-cell RNA sequencing to profile 101 embryos of 22 mutant and 4 wild-type genotypes at embryonic day 13.5, altogether profiling more than 1.6 million nuclei. The 22 mutants represent a range of anticipated phenotypic severities, from established multisystem disorders to deletions of individual regulatory regions. We developed and applied several analytical frameworks for detecting differences in composition and/or gene expression across 52 cell types or trajectories. Some mutants exhibit changes in dozens of trajectories whereas others exhibit changes in only a few cell types. We also identify differences between widely used wild-type strains, compare phenotyping of gain- versus loss-of-function mutants and characterize deletions of topological associating domain boundaries. Notably, some changes are shared among mutants, suggesting that developmental pleiotropy might be ‘decomposable’ through further scaling of this approach. Overall, our findings show how single-cell profiling of whole embryos can enable the systematic molecular and cellular phenotypic characterization of mouse mutants with unprecedented breadth and resolution.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Phenotypic plasticity of fungal traits in response to moisture and temperature

Phenotypic plasticity of traits is commonly measured in plants to improve understanding of organismal and ecosystem responses to climate change but is far less studied for microbes. Specifically, decomposer fungi are thought to display high levels of phenotypic plasticity and their functions have important implications for ecosystem dynamics. Assessing the phenotypic plasticity of fungal traits may therefore be important for predicting fungal community response to climate change. Here, we assess the phenotypic plasticity of 15 fungal isolates (12 species) from a Southern California grassland. Fungi were incubated on litter at five moisture levels (ranging from 4–50% water holding capacity) and at five temperatures (ranging from 4–36 °C). After incubation, fungal biomass and activities of four extracellular enzymes (cellobiohydrolase (CBH), β -glucosidase (BG), β -xylosidase (BX), and N-acetyl- β - D -glucosaminidase (NAG)) were measured. We used response surface methodology to determine how fungal phenotypic plasticity differs across the moisture-temperature gradient. We hypothesized that fungal biomass and extracellular enzyme activities would vary with moisture and temperature and that the shape of the response surface would vary between fungal isolates. We further hypothesized that more closely related fungi would show more similar response surfaces across the moisture-temperature gradient. In support of our hypotheses, we found that plasticity differed between fungi along the temperature gradient for fungal biomass and for all the extracellular enzyme activities. Plasticity also differed between fungi along the moisture gradient for BG activity. These differences appear to be caused by variation mainly at the moisture and temperature extremes. We also found that more closely related fungi had more similar extracellular enzymes activities at the highest temperature. Altogether, this evidence suggests that with global warming, fungal biodiversity may become increasingly important as functional traits tend to diverge along phylogenetic lines at higher temperatures.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

The Monarch Initiative in 2019: an integrative data and analytic platform connecting phenotypes to genotypes across species

In biology and biomedicine, relating phenotypic outcomes with genetic variation and environmental factors remains a challenge: patient phenotypes may not match known diseases, candidate variants may be in genes that haven’t been characterized, research organisms may not recapitulate human or veterinary diseases, environmental factors affecting disease outcomes are unknown or undocumented, and many resources must be queried to find potentially significant phenotypic associations. The Monarch Initiative (https://monarchinitiative.org) integrates information on genes, variants, genotypes, phenotypes and diseases in a variety of species, and allows powerful ontology-based search. We develop many widely adopted ontologies that together enable sophisticated computational analysis, mechanistic discovery and diagnostics of Mendelian diseases. Our algorithms and tools are widely used to identify animal models of human disease through phenotypic similarity, for differential diagnostics and to facilitate translational research. Launched in 2015, Monarch has grown with regards to data (new organisms, more sources, better modeling); new API and standards; ontologies (new Mondo unified disease ontology, improvements to ontologies such as HPO and uPheno); user interface (a redesigned website); and community development. Monarch data, algorithms and tools are being used and extended by resources such as GA4GH and NCATS Translator, among others, to aid mechanistic discovery and diagnostics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Temperature and nutrients drive eco-phenotypic dynamics in a microbial food web

Anthropogenic increases in temperature and nutrient loads will likely impact food web structure and stability. Although their independent effects have been reasonably well studied, their joint effects—particularly on coupled ecological and phenotypic dynamics—remain poorly understood. Here we experimentally manipulated temperature and nutrient levels in microbial food webs and used time-series analysis to quantify the strength of reciprocal effects between ecological and phenotypic dynamics across trophic levels. We found that (1) joint—often interactive—effects of temperature and nutrients on ecological dynamics are more common at higher trophic levels, (2) temperature and nutrients interact to shift the relative strength of top-down versus bottom-up control, and (3) rapid phenotypic change mediates observed ecological responses to changes in temperature and nutrients. Our results uncover how feedback between ecological and phenotypic dynamics mediate food web responses to environmental change. As a result, this suggests important but previously unknown ways that temperature and nutrients might jointly control the rapid eco-phenotypic feedback that determine food web dynamics in a changing world.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Identifying intragenic functional modules of genomic variations associated with cancer phenotypes by learning representation of association networks

Background Genome-wide Association Studies (GWAS) aims to uncover the link between genomic variation and phenotype. They have been actively applied in cancer biology to investigate associations between variations and cancer phenotypes, such as susceptibility to certain types of cancer and predisposed responsiveness to specific treatments. Since GWAS primarily focuses on finding associations between individual genomic variations and cancer phenotypes, there are limitations in understanding the mechanisms by which cancer phenotypes are cooperatively affected by more than one genomic variation. Results This paper proposes a network representation learning approach to learn associations among genomic variations using a prostate cancer cohort. The learned associations are encoded into representations that can be used to identify functional modules of genomic variations within genes associated with early- and late-onset prostate cancer. The proposed method was applied to a prostate cancer cohort provided by the Veterans Administration’s Million Veteran Program to identify candidates for functional modules associated with early-onset prostate cancer. The cohort included 33,159 prostate cancer patients, 3181 early-onset patients, and 29,978 late-onset patients. The reproducibility of the proposed approach clearly showed that the proposed approach can improve the model performance in terms of robustness. Conclusions To our knowledge, this is the first attempt to use a network representation learning approach to learn associations among genomic variations within genes. Associations learned in this way can lead to an understanding of the underlying mechanisms of how genomic variations cooperatively affect each cancer phenotype. This method can reveal unknown knowledge in the field of cancer biology and can be utilized to design more advanced cancer-targeted therapies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Design of synthetic bacterial communities for predictable plant phenotypes

Specific members of complex microbiota can influence host phenotypes, depending on both the abiotic environment and the presence of other microorganisms. Therefore, it is challenging to define bacterial combinations that have predictable host phenotypic outputs. We demonstrate that plant–bacterium binary-association assays inform the design of small synthetic communities with predictable phenotypes in the host. Specifically, we constructed synthetic communities that modified phosphate accumulation in the shoot and induced phosphate starvation–responsive genes in a predictable fashion. We found that bacterial colonization of the plant is not a predictor of the plant phenotypes we analyzed. In conclusion, we demonstrated that characterizing a subset of all possible bacterial synthetic communities is sufficient to predict the outcome of untested bacterial consortia. Our results demonstrate that it is possible to infer causal relationships between microbiota membership and host phenotypes and to use these inferences to rationally design novel communities.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

OPEN-Augmented Reality GUI for Bioenergy Crop Phenotyping and Precision Agriculture (Donald Danforth Plant Science Center Final Scientific Technical Report)

The project led by the Donald Danforth Plant Science Center, in collaboration with Arizona State University, George Washington University, and Saint Louis University, has made significant strides in advancing the phenotypic analysis of bioenergy crops through the development of an innovative AI processing pipeline. This initiative was primarily funded by ARPA-E, with additional cost-sharing provided by the participating institutions. The project successfully utilized a variety of sensors—3D scanners, thermal, RGB, and hyperspectral—to refine algorithms for data-driven trait signature identification and improve the classification and visualization of plant traits. The developed AI processing pipeline is capable of handling the complex, multidimensional data characteristic of dynamic agricultural environments. 1) Contributions to understanding: The research has advanced the field of plant phenomics by showcasing the synergistic use of various sensor data to enhance the precision of trait analysis in bioenergy crops. Through the integration of 3D scanners, thermal, RGB, and hyperspectral sensors, the project has developed robust data-driven trait signature algorithms and visualization techniques. These innovations have facilitated detailed monitoring and management of plant traits, providing vital insights into plant growth dynamics and stress responses. Further, the project has broadened our understanding of how machine learning can be effectively applied in multi-sensor environments to refine trait analysis. By leveraging diverse datasets, the research has not only improved the accuracy of phenotypic assessments but also established a versatile methodological framework that can be extended beyond agriculture to other fields requiring detailed phenotypic analysis. 2) Technical effectiveness and economic feasibility: The AI processing pipeline developed in this project demonstrated significant technical effectiveness, achieving high throughput analysis of extensive phenotypic data and meeting targeted accuracies. This system exemplified the capability of advanced machine learning technologies to efficiently manage and analyze large, complex datasets. Economically, the implementation of the project-developed pipelines may offer substantial cost savings across multiple sectors. It enhances data analysis processes and significantly reduces the need for manual data interpretation, thereby decreasing both the time and resources required. 3) Public benefit: The project has significantly broadened the scope of agricultural methodologies to enhance phenotypic analysis, with potential applications in various sectors beyond agriculture. Additionally, the initiative fostered an enriching educational and collaborative environment, significantly enhancing the technical skills of participants. It also made substantial contributions to the scientific community by providing open-access data sets and tools, encouraging ongoing research and development across various disciplines. Overall, the project not only met its scientific goals but also showcased the extensive utility of integrating advanced machine learning and sensor data analysis technologies. These advancements have proven instrumental in driving forward both theoretical research and practical applications, setting a strong foundation for future explorations and innovations in data-driven science.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Robust High-Throughput Phenotyping with Deep Segmentation Enabled by a Web-Based Annotator

The abilities of plant biologists and breeders to characterize the genetic basis of physiological traits are limited by their abilities to obtain quantitative data representing precise details of trait variation, and particularly to collect this data at a high-throughput scale with low cost. Although deep learning methods have demonstrated unprecedented potential to automate plant phenotyping, these methods commonly rely on large training sets that can be time-consuming to generate. Intelligent algorithms have therefore been proposed to enhance the productivity of these annotations and reduce human efforts. We propose a high-throughput phenotyping system which features a Graphical User Interface (GUI) and a novel interactive segmentation algorithm: Semantic-Guided Interactive Object Segmentation (SGIOS). By providing a user-friendly interface and intelligent assistance with annotation, this system offers potential to streamline and accelerate the generation of training sets, reducing the effort required by the user. Our evaluation shows that our proposed SGIOS model requires fewer user inputs compared to the state-of-art models for interactive segmentation. As a case study of the use of the GUI applied for genetic discovery in plants, we present an example of results from a preliminary genome-wide association study (GWAS) of in planta regeneration in Populus trichocarpa (poplar). We further demonstrate that the inclusion of a semantic prior map with SGIOS can accelerate the training process for future GWAS, using a sample of a dataset extracted from a poplar GWAS of in vitro regeneration. The capabilities of our phenotyping system surpass those of unassisted humans to rapidly and precisely phenotype our traits of interest. The scalability of this system enables large-scale phenomic screens that would otherwise be time-prohibitive, thereby providing increased power for GWAS, mutant screens, and other studies relying on large sample sizes to characterize the genetic basis of trait variation. Our user-friendly system can be used by researchers lacking a computational background, thus helping to democratize the use of deep segmentation as a tool for plant phenotyping.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Quantitative Phenotypic Analysis of Arabidopsis Thaliana Grown in Microgravity Using Soap, an Applied Artificial Intelligence

Phenotypic analysis is an essential step in studying the gravitropic responses and gravitational stress experienced by plants grown in microgravity. Many of the phenotypic traits analyzed in gravitropism studies, such as root length, leaf area, secondary root count, and number of root hairs, currently rely upon manual measurement methods for quantification. However, new advances in data analysis technology using artificial intelligence offer an opportunity for more efficient phenotypic analysis and a reduction of time spent in the data collection phase. In this project, the ability of a new artificially intelligent data collection software, SOAP (Simple Object Access Protocol), to collect and quantify phenotypic traits of Arabidopsis thaliana will be assessed. This project will test the measurements taken by an initial draft of the software. SOAP will take measurements of shoot length, a key phenotype used to assess A. thaliana stress response when grown in microgravity conditions. Shoot length measurements made by SOAP will be compared against a series of manual shoot length measurements. The comparison between the two methods of data measurement will provide valuable insight into the relative accuracy of SOAP and the margin of human error when conducting lab measurements.

Arabidopsis↗
DOE OSTI2024

Non-destructive, whole-plant phenotyping reveals dynamic changes in water use efficiency, photosynthesis, and rhizosphere acidification of sorghum accessions under osmotic stress

Noninvasive phenotyping can quantify dynamic plant growth processes at higher temporal resolution than destructive phenotyping and can reveal phenomena that would be missed by end-point analysis alone. Additionally, whole-plant phenotyping can identify growth conditions that are optimal for both above- and below-ground tissues. However, noninvasive, whole-plant phenotyping approaches available today are generally expensive, complex, and non-modular. We developed a low-cost and versatile approach to noninvasively measure whole-plant physiology over time by growing plants in isolated hydroponic chambers. We demonstrate the versatility of our approach by measuring whole-plant biomass accumulation, water use, and water use efficiency every two days on unstressed and osmotically stressed sorghum accessions. We identified relationships between root zone acidification and photosynthesis on whole-plant water use efficiency over time. Our system can be implemented using cheap, basic components, requires no specific technical expertise, and should be suitable for any non-aquatic vascular plant species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Characterizing Long COVID: Deep Phenotype of a Complex Condition

Background: Numerous publications describe the clinical manifestations of post-acute sequelae of SARS-CoV-2 (PASC or "long COVID"), but they are difficult to integrate because of heterogeneous methods and the lack of a standard for denoting the many phenotypic manifestations. Patient-led studies are of particular importance for understanding the natural history of COVID-19, but integration is hampered because they often use different terms to describe the same symptom or condition. This significant disparity in patient versus clinical characterization motivated the proposed ontological approach to specifying manifestations, which will improve capture and integration of future long COVID studies. Methods: The Human Phenotype Ontology (HPO) is a widely used standard for exchange and analysis of phenotypic abnormalities in human disease but has not yet been applied to the analysis of COVID-19. Funding: We identified 303 articles published before April 29, 2021, curated 59 relevant manuscripts that described clinical manifestations in 81 cohorts three weeks or more following acute COVID-19, and mapped 287 unique clinical findings to HPO terms. We present layperson synonyms and definitions that can be used to link patient self-report questionnaires to standard medical terminology. Long COVID clinical manifestations are not assessed consistently across studies, and most manifestations have been reported with a wide range of synonyms by different authors. Across at least 10 cohorts, authors reported 31 unique clinical features corresponding to HPO terms; the most commonly reported feature was Fatigue (median 45.1%) and the least commonly reported was Nausea (median 3.9%), but the reported percentages varied widely between studies. Interpretation: Translating long COVID manifestations into computable HPO terms will improve analysis, data capture, and classification of long COVID patients. If researchers, clinicians, and patients share a common language, then studies can be compared/pooled more effectively. Furthermore, mapping lay terminology to HPO will help patients assist clinicians and researchers in creating phenotypic characterizations that are computationally accessible, thereby improving the stratification, diagnosis, and treatment of long COVID.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Tradeoffs between phenotypic plasticity and local adaptation influence the ecophysiology of the moss, Sphagnum magellanicum

Bryophytes are a diverse plant group and are functionally different from vascular plants. Yet, their peculiarities are rarely considered in the theoretical frameworks for plants. Currently, we lack information about the magnitude and the importance of intraspecific variability in the ecophysiology of bryophytes and how these might translate to local adaptation—a prerequisite for adaptive evolution. Capitalizing on two ecologically distinct (hummock and hollow) phenotypes of Sphagnum magellanicum, we explored the magnitude and pattern of intraspecific variability in this species and asked whether the environmental-mediated changes in shoot and physiological traits are due to phenotypic plasticity or local adaptation. Size, pigmentation, and habitat type that distinguished the phenotypes in the field did not influence the trait responses under a transplant and factorial experiment. In addition, the magnitude and pattern of trait variability (e.g., branch, stem and capitulum mass) changed with the treatments, which suggest that trait responses were due largely to phenotypic plasticity. The trait responses also suggest that the ecophysiological needs for mosses to grow in clumps, where they maintain a uniform growth may have an overriding effect over the potential for a fixed adaptive response to environmental heterogeneity, which would constrain local adaptation. In this paper, we conclude that extending the trait-based framework to mosses or making comparisons between mosses and vascular plants under any theoretical framework would only be meaningful to the extent that growth form and dispersal strategies are considered.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Monocyte-lymphocyte fusion induced by the HIV-1 envelope generates functional heterokaryons with an activated monocyte-like phenotype

Enveloped viruses induce cell-cell fusion when infected cells expressing viral envelope proteins interact with target cells, or through the contact of cell-free viral particles with adjoining target cells. CD4{sup +} T lymphocytes and cells from the monocyte-macrophage lineage express receptors for HIV envelope protein. We have previously reported that lymphoid Jurkat T cells expressing the HIV-1 envelope protein (Env) can fuse with THP-1 monocytic cells, forming heterokaryons with a predominantly myeloid phenotype. This study shows that the expression of monocytic markers in heterokaryons is stable, whereas the expression of lymphoid markers is mostly lost. Like THP-1 cells, heterokaryons exhibited FcγR-dependent phagocytic activity and showed an enhanced expression of the activation marker ICAM-1 upon stimulation with PMA. In addition, heterokaryons showed morphological changes compatible with maturation, and high expression of the differentiation marker CD11b in the absence of differentiation-inducing agents. No morphological change nor increase in CD11b expression were observed when an HIV-fusion inhibitor blocked fusion, or when THP-1 cells were cocultured with Jurkat cells expressing a non-fusogenic Env protein, showing that differentiation was not induced merely by cell-cell interaction but required cell-cell fusion. Inhibition of TLR2/TLR4 signaling by a TIRAP inhibitor greatly reduced the expression of CD11b in heterokaryons. Thus, lymphocyte-monocyte heterokaryons induced by HIV-1 Env are stable and functional, and fusion prompts a phenotype characteristic of activated monocytes via intracellular TLR2/TLR4 signaling. - Highlights: • Jurkat T cells expressing the HIV-1 envelope fuse with THP-1 monocytes. • Heterokaryons display a dominant myeloid phenotype and monocyte function. • Heterokaryons exhibit activation features in the absence of activation agents. • Activation is not due to cell-cell interaction but requires cell-cell fusion. • The activated monocyte-like phenotype is mediated by TLR2/TLR4 signaling.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Interactive Effects of Microbes and Nitrogen on Panicum virgatum Root Functional Traits and Patterns of Phenotypic Selection

Premise of research. Plants in natural and agricultural systems are influenced in myriad ways by their microbial communities, particularly by providing goods and services that change plant functional traits. Microbes are considered an influential part of the environmental context that change plant trait expression, but often, microbe-mediated effects are contingent on local resources, such as nitrogen. Here, we ask how microbes and nitrogen affect belowground functional traits and patterns of phenotypic selection. Methodology. We performed a fully factorial greenhouse experiment with switchgrass (Panicum virgatum), manipulating microbial community composition and nitrogen availability. We measured plant performance and belowground functional traits and performed 16S amplicon sequencing of the root-associated microbial communities. We looked for correlations between microbial taxa and root functional traits, and we performed phenotypic selection analysis on five belowground functional traits to determine how traits affect plant relative performance across biotic and abiotic contexts. Pivotal results. All belowground plant functional traits except root tissue density were affected by adding nitrogen. We found that a microbial taxon (amplicon sequence variant [ASV]) in the genus Micromonospora correlated with shorter root lengths. We also found strong positive selection for longer roots regardless of the abiotic or biotic environment. In contrast, selection favored lower root-to-shoot ratios in high-nitrogen conditions, and selection on root tissue density was highest in treatments that had high nitrogen levels and perturbed microbial communities. Conclusions. We did not detect microbial effects on the expression of plant traits (ecological effects); however, patterns of phenotypic selection (evolutionary effects) on root tissue density differed depending on the biotic and abiotic environment. Additionally, we detected strong selection for increased root length across treatments; we also found that one ASV correlated with decreased root length, indicating potential conflict between root microbiome components and plant fitness. Future work would be to include microbial taxa in phenotypic selection analysis and to conduct manipulations of the microbes correlated with functional traits to determine causality.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

VipariNama: RNA viral vectors to rapidly elucidate the relationship between gene expression and phenotype

Synthetic transcription factors have great promise as tools to help elucidate relationships between gene expression and phenotype by allowing tunable alterations of gene expression without genomic alterations of the loci being studied. However, the years-long timescales, high cost, and technical skill associated with plant transformation have limited their use. In this work, we developed a technology called VipariNama (ViN) in which vectors based on the tobacco rattle virus are used to rapidly deploy Cas9-based synthetic transcription factors and reprogram gene expression in planta. We demonstrate that ViN vectors can implement activation or repression of multiple genes systemically and persistently over several weeks in Nicotiana benthamiana, Arabidopsis (Arabidopsis thaliana), and tomato (Solanum lycopersicum). By exploring strategies including RNA scaffolding, viral vector ensembles, and viral engineering, we describe how the flexibility and efficacy of regulation can be improved. We also show how this transcriptional reprogramming can create predictable changes to metabolic phenotypes, such as gibberellin biosynthesis in N. benthamiana and anthocyanin accumulation in Arabidopsis, as well as developmental phenotypes, such as plant size in N. benthamiana, Arabidopsis, and tomato. These results demonstrate how ViN vector-based reprogramming of different aspects of gibberellin signaling can be used to engineer plant size in a range of plant species in a matter of weeks. In summary, ViN accelerates the timeline for generating phenotypes from over a year to just a few weeks, providing an attractive alternative to transgenesis for synthetic transcription factor-enabled hypothesis testing and crop engineering.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

The genotype-phenotype landscape of an allosteric protein

Allostery is a fundamental biophysical mechanism that underlies cellular sensing, signaling, and metabolism. Yet a quantitative understanding of allosteric genotype-phenotype relationships remains elusive. Here, we report the large-scale measurement of the genotype-phenotype landscape for an allosteric protein: the lac repressor from Escherichia coli , LacI. Using a method that combines long-read and short-read DNA sequencing, we quantitatively measure the dose-response curves for nearly 10 5 variants of the LacI genetic sensor. The resulting data provide a quantitative map of the effect of amino acid substitutions on LacI allostery and reveal systematic sequence-structure-function relationships. We find that in many cases, allosteric phenotypes can be quantitatively predicted with additive or neural-network models, but unpredictable changes also occur. For example, we were surprised to discover a new band-stop phenotype that challenges conventional models of allostery and that emerges from combinations of nearly silent amino acid substitutions.

59 BASIC BIOLOGICAL SCIENCES↗