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NASA's GeneLab: An Integrated Omics Data Commons and Workbench

GeneLab (http://genelab.nasa.gov) is a NASA initiative designed to accelerate “open science” biomedical research in support of the human exploration of space and the improvement of life on earth. The GeneLab Data Systems (GLDS) were developed to help investigators corroborate findings from “omics” (genomics, transcriptomics, proteomics, and metabolomics) assays and translate them into systems biology knowledge and, eventually, therapeutics, including countermeasures to support life in space. Phase I of the project (completed) emphasized developing key capabilities for submission, curation, storage, search, and retrieval of omics data from biomedical research in and of space environments. The development focus for Phase II (completed) was federated data search and retrieval of these kinds of data from other open-access repositories. The last phase of the project (in work) entails developing an omics analysis tool set, and a portal to visualize processed omics data, emphasizing integration with the data repository and search functions developed during the prior phases. The final product will be an open-access system where users can individually or collaboratively publish, search, integrate, analyze, and visualize omics data.

genome↗

NASA's GeneLab: An Integrated Omics Data Commons and Workbench

GeneLab (http://genelab.nasa.gov) is a NASA initiative designed to accelerate "open science" biomedical research in support of the human exploration of space and the improvement of life on earth. The GeneLab Data Systems (GLDS) were developed to help investigators corroborate findings from "omics" (genomics, transcriptomics, proteomics, and metabolomics) assays and translate them into systems biology knowledge and, eventually, therapeutics, including countermeasures to support life in space. Phase I of the project (completed) emphasized developing key capabilities for submission, curation, storage, search, and retrieval of omics data from biomedical research in and of space environments. The development focus for Phase II (completed) was federated data search and retrieval of these kinds of data from other open-access repositories. The last phase of the project (in work) entails developing an omics analysis tool set, and a portal to visualize processed omics data, emphasizing integration with the data repository and search functions developed during the prior phases. The final product will be an open-access system where users can individually or collaboratively publish, search, integrate, analyze, and visualize omics data.

genome↗

Modification and analysis of context-specific genome-scale metabolic models: methane-utilizing microbial chassis as a case study

ABSTRACT Context-specific genome-scale model (CS-GSM) reconstruction is becoming an efficient strategy for integrating and cross-comparing experimental multi-scale data to explore the relationship between cellular genotypes, facilitating fundamental or applied research discoveries. However, the application of CS modeling for non-conventional microbes is still challenging. Here, we present a graphical user interface that integrates COBRApy, EscherPy, and RIPTiDe, Python-based tools within the BioUML platform, and streamlines the reconstruction and interrogation of the CS genome-scale metabolic frameworks via Jupyter Notebook. The approach was tested using -omics data collected for Methylotuvimicrobium alcaliphilum 20Z R , a prominent microbial chassis for methane capturing and valorization. We optimized the previously reconstructed whole genome-scale metabolic network by adjusting the flux distribution using gene expression data. The outputs of the automatically reconstructed CS metabolic network were comparable to manually optimized i IA409 models for Ca-growth conditions. However, the CS model questions the reversibility of the phosphoketolase pathway and suggests higher flux via primary oxidation pathways. The model also highlighted unresolved carbon partitioning between assimilatory and catabolic pathways at the formaldehyde-formate node. Only a very few genes and only one enzyme with a predicted function in C1 metabolism, a homolog of the formaldehyde oxidation enzyme ( fae1-2 ), showed a significant change in expression in La-growth conditions. The CS-GSM predictions agreed with the experimental measurements under the assumption that the Fae1-2 is a part of the tetrahydrofolate-linked pathway. The cellular roles of the tungsten (W)-dependent formate dehydrogenase ( fdhAB ) and fae homologs ( fae1-2 and fae3 ) were investigated via mutagenesis. The phenotype of the f dhAB mutant followed the model prediction. Furthermore, a more significant reduction of the biomass yield was observed during growth in La-supplemented media, confirming a higher flux through formate. M. alcaliphilum 20Z R mutants lacking fae1-2 did not display any significant defects in methane or methanol-dependent growth. However, contrary to fae1, the fae1-2 homolog failed to restore the formaldehyde-activating enzyme function in complementation tests. Overall, the presented data suggest that the developed computational workflow supports the reconstruction and validation of CS-GSM networks of non-model microbes. IMPORTANCE The interrogation of various types of data is a routine strategy to explore the relationship between genotype and phenotype. An efficient approach for integrating and cross-comparing experimental multi-scale data in the context of whole-genome-based metabolic network reconstruction becomes a powerful tool that facilitates fundamental and applied research discoveries. The present study describes the reconstruction of a context-specific (CS) model for the methane-utilizing bacterium, Methylotuvimicrobium alcaliphilum 20Z R . M. alcaliphilum 20Z R is becoming an attractive microbial platform for the production of biofuels, chemicals, pharmaceuticals, and bio-sorbents for capturing atmospheric methane. We demonstrate that this pipeline can help reconstruct metabolic models that are similar to manually curated networks. Furthermore, the model is able to highlight previously overlooked pathways, thus advancing fundamental knowledge of non-model microbial systems or promoting their development toward biotechnological or environmental implementations.

Kulyashov, M. A.↗

Single-nuclei transcriptome analysis of channel catfish spleen provides insight into the immunome of an aquaculture-relevant species

The catfish industry is the largest sector of U.S. aquaculture production. Given its role in food production, the catfish immune response to industry-relevant pathogens has been extensively studied and has provided crucial information on innate and adaptive immune function during disease progression. To further examine the channel catfish immune system, we performed single-cell RNA sequencing on nuclei isolated from whole spleens, a major lymphoid organ in teleost fish. Libraries were prepared using the 10X Genomics Chromium X with the Next GEM Single Cell 3’ reagents and sequenced on an Illumina sequencer. Each demultiplexed sample was aligned to the Coco_2.0 channel catfish reference assembly, filtered, and counted to generate feature-barcode matrices. From whole spleen samples, outputs were analyzed both individually and as an integrated dataset. The three splenic transcriptome libraries generated an average of 278,717,872 reads from a mean 8,157 cells. The integrated data included 19,613 cells, counts for 20,121 genes, with a median 665 genes/cell. Cluster analysis of all cells identified 17 clusters which were classified as erythroid, hematopoietic stem cells, B cells, T cells, myeloid cells, and endothelial cells. Subcluster analysis was carried out on the immune cell populations. Here, distinct subclusters such as immature B cells, mature B cells, plasma cells, γδ T cells, dendritic cells, and macrophages were further identified. Differential gene expression analyses allowed for the identification of the most highly expressed genes for each cluster and subcluster. This dataset is a rich cellular gene expression resource for investigation of the channel catfish and teleost splenic immunome.

Science & Technology - Other Topics↗

Discovery of genes that positively affect biomass and stress associated traits in poplar

Woody biomass serves as a renewable resource for various industries, including pulp and paper production, construction, biofuels, and electricity generation. However, the molecular mechanisms behind biomass traits are poorly understood, which significantly curtails the speed and efficiency of their improvement. We used activation tagging to discover genes that can positively affect tree biomass-associated traits. We generated and screened under greenhouse conditions a population of 2,700 independent activation tagging lines. A total of 761 lines, which had significantly and positively affected at least one biomass-associated trait, were discovered. The tag was positioned in the genome for forty lines which were affected in multiple traits and activation of proximal genes validated for a subset. For two lines we fully recapitulated the phenotype of the original lines through overexpression. Moreover, the overexpression led to more pronounced and additional improvements, not observed in the original lines. Importantly, the overexpression of a Fasciclin-like gene (PtaFLA10) and a Patatin-like gene (PtaPAT) was found to substantially improve biomass, with a 40% increase in dry-stem weight, and enhance drought tolerance, respectively. Additionally, PtaPAT overexpression increased cellulose content, which is crucial for biofuel production. Our work shows that the activation tagging approach applied even on a non-genome saturation scale in a poplar tree can be successfully used for the discovery of genes positively modify biomass productivity. Such dominant forward genetics approaches can aid in biotechnological manipulation of woody biomass traits and help unravel the functions and mechanisms of individual genes, gene families, and regulatory modules.

Plant Sciences↗

Latent Viruses: A Space Travel Hazard??

A major issue associated with long-duration space flight is the possibility of infectious disease causing an unacceptable medical risk to crew members. Our proposal is designed to gain information that addresses several issues outlined in the Immunology/Infectious disease critical path. The major hypothesis addressed is that space flight causes alterations in the immune system that may allow latent viruses which are endogenous in the human population to reactivate and shed to higher levels than normal which can affect the health of crew members during a long term space-flight mission. We will initially focus our studies on the human herpesviruses and human polyomaviruses which are important pathogens known to establish latent infections in the human population. Both primary infection and reactivation from latent infection with this group of viruses can cause a variety of illnesses that result in morbidity and occasionally mortality of infected individuals. Effective vaccines exist for only one of the eight known human herpesviruses and the vaccine itself can still reactivate from latent infection. Available antivirals are of limited use and are effective against only a few of the human herpesviruses. Although most individuals display little if any clinical consequences from latent infection, events which alter immune function such as immunosuppressive therapy following solid organ transplantation are known to increase the risk of developing complications as a result of latent virus reactivation. This proposal will measure both the frequency and magnitude of viral shedding and genome loads in the blood from humans participating in activities that serve as ground based models of space flight conditions. Our initial goal is to develop sensitive quantitative competitive PCR- based assays (QC-PCR) to detect the herpesvirus Epstein-Barr virus (EBV), and the polyomaviruses SV40, BKV, and JCV. Using these assays we will establish baseline patterns of viral genome load in the blood and viral shedding from normal volunteers in a longitudinal study over I year in length. As a comparison, we will measure patterns of viral genome loads and shedding from individuals who are severely immunosuppressed, in whom herpesvirus reactivation or primary infection with a herpesvirus is known to cause complications. In addition, we will proceed to testing ground based analogs in collaboration with Dr. Duane Pierson (Lyndon B. Johnson Space Center). This will include measuring samples obtained from individuals living and working in the extreme environment of Antarctica. We expect to detect viral shedding or reactivation from most of the test groups, although the magnitude of shedding or reactivation cannot be predicted. The data accumulated from studies in this proposal should allow us to evaluate whether events that simulate certain aspects of space flight reactivate viral infections severe enough in nature that they may compromise the success of long-term space flight missions. These studies will also provide a foundation to monitor viral reactivation and shedding from crew members participating in actual space flight missions. We will present data showing the establishment of our QC-PCR assay for detection of EBV.

Ling, P. D.↗

Simulations of DSB Yields and Radiation-induced Chromosomal Aberrations in Human Cells Based on the Stochastic Track Structure Induced by HZE Particles

The formation of double‐strand breaks (DSBs) and chromosomal aberrations (CAs) is of great importance in radiation research and, specifically, in space applications. We are presenting a new particle track and DNA damage model, in which the particle stochastic track structure is combined with the random walk (RW) structure of chromosomes in a cell nucleus. The motivation for this effort stems from the fact that the model with the RW chromosomes, NASARTI (NASA radiation track image) previously relied on amorphous track structure, while the stochastic track structure model RITRACKS (Relativistic Ion Tracks) was focused on more microscopic targets than the entire genome. We have combined chromosomes simulated by RWs with stochastic track structure, which uses nanoscopic dose calculations performed with the Monte‐Carlo simulation by RITRACKS in a voxelized space. The new simulations produce the number of DSBs as function of dose and particle fluence for high‐energy particles, including iron, carbon and protons, using voxels of 20 nm dimension. The combined model also calculates yields of radiation‐induced CAs and unrejoined chromosome breaks in normal and repair deficient cells. The joined computational model is calibrated using the relative frequencies and distributions of chromosomal aberrations reported in the literature. The model considers fractionated deposition of energy to approximate dose rates of the space flight environment. The joined model also predicts of the yields and sizes of translocations, dicentrics, rings, and more complex‐type aberrations formed in the G0/G1 cell cycle phase during the first cell division after irradiation. We found that the main advantage of the joined model is our ability to simulate small doses: 0.05‐0.5 Gy. At such low doses, the stochastic track structure proved to be indispensable, as the action of individual delta‐rays becomes more important.

Ponomarev, Artem↗

Simulations of DSB Yields and Radiation-induced Chromosomal Aberrations in Human Cells Based on the Stochastic Track Structure iIduced by HZE Particles

The formation of double‐strand breaks (DSBs) and chromosomal aberrations (CAs) is of great importance in radiation research and, specifically, in space applications. We are presenting a new particle track and DNA damage model, in which the particle stochastic track structure is combined with the random walk (RW) structure of chromosomes in a cell nucleus. The motivation for this effort stems from the fact that the model with the RW chromosomes, NASARTI (NASA radiation track image) previously relied on amorphous track structure, while the stochastic track structure model RITRACKS (Relativistic Ion Tracks) was focused on more microscopic targets than the entire genome. We have combined chromosomes simulated by RWs with stochastic track structure, which uses nanoscopic dose calculations performed with the Monte‐Carlo simulation by RITRACKS in a voxelized space. The new simulations produce the number of DSBs as function of dose and particle fluence for high‐energy particles, including iron, carbon and protons, using voxels of 20 nm dimension. The combined model also calculates yields of radiation‐induced CAs and unrejoined chromosome breaks in normal and repair deficient cells. The joined computational model is calibrated using the relative frequencies and distributions of chromosomal aberrations reported in the literature. The model considers fractionated deposition of energy to approximate dose rates of the space flight environment. The joined model also predicts of the yields and sizes of translocations, dicentrics, rings, and more complex‐type aberrations formed in the G0/G1 cell cycle phase during the first cell division after irradiation. We found that the main advantage of the joined model is our ability to simulate small doses: 0.05‐0.5 Gy. At such low doses, the stochastic track structure proved to be indispensable, as the action of individual delta‐rays becomes more important.

Ponomarev, Artem↗

Multi‐season analysis reveals hundreds of drought‐responsive genes in sorghum

Persistent drought affects global crop production and is becoming more severe in many parts of the world in recent decades. Deciphering how plants respond to drought will facilitate the development of flexible mitigation strategies. Sorghum bicolor L. Moench (sorghum), a major cereal crop and an emerging bioenergy crop, exhibits remarkable resilience to drought. To better understand the molecular traits that underlie sorghum's remarkable drought tolerance, we undertook a large-scale sorghum gene expression profiling effort, totaling nearly 1500 transcriptome profiles, across a 3-year field study with replicated plots in California's Central Valley. This study included time-resolved gene expression data from roots and leaves of two sorghum genotypes, BTx642 and RTx430, with different pre-flowering and post-flowering drought-tolerance adaptations under control and drought conditions. Quantification of genotype-specific drought tolerance effects was enabled by de novo sequencing, assembly, and annotation of both BTx642 and RTx430 genomes. These reference-quality genomes were used to construct a pangene set for characterizing conserved and genotype-specific expression. By integrating time-resolved transcriptomic responses to drought in the field across three consecutive years, we identified a set of 726 drought-responsive genes that responded similarly in all 3 years of our field study. Functional enrichment analysis identified abiotic stress, secondary cell wall-related processes and metabolism as particularly affected under both types of drought stress. We also found that some glyoxylate cycle pathway genes, including malate synthase and isocitrate lyase, are differentially regulated particularly during post-flowering drought stress, implicating this pathway as potentially important for drought responsiveness. This expansive dataset represents a unique resource for sorghum and drought research communities and provides a methodological framework for the integration of multi-faceted time-resolved transcriptomic datasets.

Cole, Benjamin [USDOE Joint Genome Institute (JGI)↗

The relationship between gene traits and transcription in soil microbial communities varies by environmental stimulus

Codon and nucleotide frequencies are known to relate to the rate of gene transcription, yet how these traits shape transcriptional profiles of soil microbial communities remains unclear. Here we test the prediction that functional genes with high codon optimization and energetically lower cost nucleotides (i.e., nucleotides requiring less adenosine triphosphate (ATP) for synthesis) have higher transcriptional expression in a soil microbial community. In laboratory incubations, we subjected an agricultural soil to two separate short-term environmental changes: labile carbon (glucose) addition or a sudden 30-min increase in temperature from 20 °C to 60 °C. Using the total genomic codon frequencies to predict preferred codon usage for each taxon, we then estimated codon optimization for each transcript. On the community level, we found a higher average level of codon optimization after the addition of glucose. Synonymous nucleotide composition in the transcript pool also shifted towards energetically cheaper nucleotides, favoring uracil (U) over adenine (A) and cytosine (C) over guanine (G). Similarly, we found that encoded amino acid usage shifted towards energetically cheaper amino acids in response to labile carbon. In contrast, in communities responding to heat shock, there were no significant differences in the averaged gene traits of expressed transcripts. We used metagenome-assembled-genomes to further examine the ability of gene traits to predict transcriptional responses within and between taxa. We found that traits of individual genes could not reliably predict the level of transcription of a gene within or between taxa—highlighting the limits of this approach. However, we did find that when traits were averaged across several related genes, codon optimization was able to predict levels of transcription in metabolic pathways associated with growth and nutrient uptake in response to glucose. Similar relationships were not observed in response to heat, or for functions associated with stress—such as genes associated with sporulation or heat shock. These results demonstrate that gene traits, such as codon usage, nucleotide selection, and amino acid selection, relate to the transcriptional expression of genes in soil microbial communities and suggests that these relationships may be dependent on both gene function and the specific type of environmental stimuli.

Biological and medical sciences↗

Physiological Observations and Omics to Develop Personalized Sensormotor Adaptability Countermeasures Using Bed Rest and Space Flight Data

Astronauts experience sensorimotor disturbances during the initial exposure to microgravity and during the re-adapation phase following a return to an earth-gravitational environment. These alterations may disrupt the ability to perform mission critical functional tasks requiring ambulation, manual control and gaze stability. Interestingly, astronauts who return from space flight show substantial differences in their abilities to readapt to a gravitational environment. The ability to predict the manner and degree to which individual astronauts would be affected would improve the effectiveness of countermeasure training programs designed to enhance sensorimotor adaptability. For such an approach to succeed, we must develop predictive measures of sensorimotor adaptability that will allow us to foresee, before actual space flight, which crewmembers are likely to experience the greatest challenges to their adaptive capacities. The goals of this project are to identify and characterize this set of predictive measures that include: 1) behavioral tests to assess sensory bias and adaptability quantified using both strategic and plastic-adaptive responses; 2) imaging to determine individual brain morphological and functional features using structural magnetic resonance imaging (MRI), diffusion tensor imaging, resting state functional connectivity MRI, and sensorimotor adaptation task-related functional brain activation; 3) genotype markers for genetic polymorphisms in Catechol-O-Methyl Transferase, Dopamine Receptor D2, Brain-derived neurotrophic factor and genetic polymorphism of alpha2-adrenergic receptor that play a role in the neural pathways underlying sensorimotor adaptation. We anticipate these predictive measures will be significantly correlated with individual differences in sensorimotor adaptability after long-duration space flight and an analog bed rest environment. We will be conducting a retrospective study leveraging data already collected from relevant ongoing/completed bed rest and space flight studies. These data will be combined with predictor metrics that will be collected prospectively - behavioral, brain imaging and genomic measures; from these returning subjects to build models for predicting post-mission (bed rest - non-astronauts or space flight - astronauts) adaptive capability as manifested in their outcome measures. Comparisons of model performance will allow us to better design and implement sensorimotor adaptability training countermeasures that are customized for each crewmember's sensory biases, adaptive capacity, brain structure and functional capacities, and genetic predispositions against decrements in post-mission adaptive capability. This ability will allow more efficient use of crew time during training and will optimize training prescriptions for astronauts to ensure expected outcomes.

Mulavara, A. P.↗

Signal sequences target enzymes and structural proteins to bacterial microcompartments and are critical for microcompartment formation

ABSTRACT Spatial organization of pathway enzymes has emerged as a promising tool to address several challenges in metabolic engineering, such as flux imbalances and off-target product formation. Bacterial microcompartments (MCPs) are a spatial organization strategy used natively by many bacteria to encapsulate metabolic pathways that produce toxic, volatile intermediates. Several recent studies have focused on engineering MCPs to encapsulate heterologous pathways of interest, but how this engineering affects MCP assembly and function is poorly understood. In this study, we investigated the role of signal sequences, short domains that target proteins to the MCP core, in the assembly of 1,2-propanediol utilization (Pdu) MCPs. We characterized two novel Pdu signal sequences on the structural proteins PduM and PduB, which constitute the first report of metabolosome signal sequences on structural proteins rather than enzymes. We then explored the role of enzymatic and structural Pdu signal sequences on MCP assembly by deleting their encoding sequences from the genome alone and in combination. Deleting enzymatic signal sequences decreased the MCP formation, but this defect could be recovered in some cases by overexpressing genes encoding the knocked-out signal sequence fused to a heterologous protein. By contrast, deleting structural signal sequences caused similar defects to knocking out the genes encoding the full-length PduM and PduB proteins. Our results contribute to a growing understanding of how MCPs form and function in bacteria and provide strategies to mitigate assembly disruption when encapsulating heterologous pathways in MCPs. IMPORTANCE Spatially organizing biosynthetic pathway enzymes is a promising strategy to increase pathway throughput and yield. Bacterial microcompartments (MCPs) are proteinaceous organelles that many bacteria natively use as a spatial organization strategy to encapsulate niche metabolic pathways, providing significant metabolic benefits. Encapsulating heterologous pathways of interest in MCPs could confer these benefits to industrially relevant pathways. Here, we investigate the role of signal sequences, short domains that target proteins for encapsulation in MCPs, in the assembly of 1,2-propanediol utilization (Pdu) MCPs. We characterize two novel signal sequences on structural proteins, constituting the first Pdu signal sequences found on structural proteins rather than enzymes, and perform knockout studies to compare the impacts of enzymatic and structural signal sequences on MCP assembly. Our results demonstrate that enzymatic and structural signal sequences play critical but distinct roles in Pdu MCP assembly and provide design rules for engineering MCPs while minimizing disruption to MCP assembly.

Johnson, Elizabeth R. (ORCID:0000000179236881)↗

Data for Phylogenetic diversity of light-dependent phosphorylation of Thr78 in Rubisco activase

Rubisco activase is an ATP-dependent chaperone that facilitates dissociation of inhibitory sugar phosphates from the catalytic sites of Rubisco during photosynthesis. In Arabidopsis, Rubisco activase is negatively regulated by dark-dependent phosphorylation of Thr78. The prevalence of Thr78 in Rubisco activase was investigated across sequences from 91 plant species, finding that 29 (∼32%) species shared a threonine in the same position. Analysis of seven C3 species with an antibody raised against a Thr78 phospho-peptide demonstrated that this position is phosphorylated in multiple genera. However, light-dependent dephosphorylation of Thr78 was observed only in Arabidopsis. Further, phosphorylation of Thr78 could not be detected in any of the four C4 grass species examined. The results suggest that despite conservation of Thr78 in Rubisco activase from a wide range of species, a regulatory role for phosphorylation at this site is more limited. This provides a case study for how variation in post-translational regulation can amplify functional divergence across the phylogeny of plants beyond what is explained by sequence variation in a metabolically important protein.

photosynthesis↗

Climate adaptation and sustainability in switchgrass: exploring plant-microbe-soil interactions across continental scale environmental gradients

Less carbon-intensive energy sources are needed to reduce greenhouse gas emissions and their predicted role in climate change. There is growing interest in the potential of biofuels for meeting this need. A critical question is whether large-scale biofuel production can be sustainable over the time scales needed to mitigate our carbon debt from fossil fuel consumption. The carbon balance and ultimately the sustainability of biofuel feedstock production is the result of complex climate-coupled interactions between carbon fixation, sequestration, and release through combustion. Similarly, the long-term productivity of biofuels depends on the environmental factors limiting plant growth. These factors are often related to soil resources which involve complex interactions at the plant-microbe-soil interface impacting their availability and cycling. Our collaborative project addressed sustainable switchgrass (Panicum virgatum) production by exploring Plant Systems, Plant-Microbiome Interactions, and Ecosystem Processes through the integrating lens of Multi-Scale Modeling. Our research was based on detailed characterization of genetically diverse switchgrass genotypes planted in common gardens across a continental latitudinal gradient. The underlying theme of our Plant Systems research was the use of locally adapted plant material to explore plant function, to understand the mechanistic basis of environmental interactions, and to discover the plant genes important for adaptation and sustainability in the face of climate change. Our Plant-Microbiome Interaction project characterized the microbial communities associated with switchgrass using genomic tools. Our Ecosystem Processes research focused on carbon cycle responses at the ecosystem level using stand level plantings. Finally, our Multi-Scale Modeling helped to define conditions of a sustainable biofuel system and identify key tradeoffs between genetic diversity, productivity, and ecosystem services. Genome-wide association analyses were used to identify alleles that contribute to successful establishment and biomass production across North America. Together, our work provided a baseline analyses of the potential of switchgrass as a biofuel feedstock. Our project resulted in a number of successful outcomes. First, we were successful in collecting switchgrass germplasm across the species range, propagating the material, and establishing common garden experiments across the species range. In collaboration with DOE JGI, we successfully assembled the first tetraploid switchgrass genome and published this resource with an analyses of the genetic basis local adaptation from our gardens (Lowry et al. 2019, Lovell et al. 2021). The gardens were used to characterize the genetic architecture for a number of important plant phenotypes. Our project also conducted extensive sampling and sequencing to characterize the bacterial and fungal associates of switchgrass roots and leaves. We showed that host genotype, location, and harvesting practices can play a role in microbiome assembly (Singer et al. 2019 & 2022, Van Wallendael et al. 2020 & 2022, Edwards et al. 2023). Our ecosystem processes work created baseline dataset of carbon and nutrient cycling in realistic stand plantings of switchgrass. Data from this experiment provided new insight into the role of plant traits, phenology, and local environments in ecosystem processes like soil respiration, net-ecosystem exchange, and dynamics of soil and plant nutrients (Ricketts et al. 2023). Finally, our crop modelling experiments help to characterize the sensitivity of common modeling frameworks to parameters, identify key limiters of productivity across large geographic scales, and leverage patterns of local adaptation in prediction. Ultimately, these studies help to identify critical plant-microbe-soil traits that may be manipulated, through breeding or agronomic management, to improve the sustainability of biofuel feedstocks.

09 BIOMASS FUELS↗

Sac1 links phosphoinositide turnover to cryptococcal virulence

Cryptococcus neoformans is an environmentally acquired fungal pathogen that causes over 140,000 deaths per year. Cryptococcal infection occurs when infectious particles are deposited into the lung, where they encounter host phagocytic cells. C. neoformans may be engulfed by these phagocytes, an important step of infection that leads to outcomes ranging from termination of infection to cryptococcal dissemination. To study this critical process, we screened approximately 4,700 cryptococcal gene deletion mutants for altered uptake, using primary mouse and human phagocytic cells. Among the hits of these two screens, we identified 93 mutants with perturbed uptake in both systems, as well as others with differences in uptake by only one cell type. We further screened the hits for changes in thickness of the capsule, a protective polysaccharide layer around the cell which is an important cryptococcal virulence factor. The combination of our three screens yielded 45 mutants, including one lacking the phosphatidylinositol-4-phosphate phosphatase Sac1. In this work, we implicate Sac1 in both host cell uptake and capsule production. We found that sac1 mutants exhibit lipid trafficking defects, reductions in secretory system function, and changes in capsule size and composition. Many of these changes occur specifically in tissue culture media, highlighting the role of Sac1 phosphatase activity in responding to the stress of host-like conditions. Overall, these findings show how genome-scale screening can identify cellular factors that contribute to our understanding of cryptococcal biology and demonstrate the role of Sac1 in determining fungal virulence.

59 BASIC BIOLOGICAL SCIENCES↗

The Omics of Stem Cell Mediated Regeneration: A Pilot Single Cell RNA-Seq Study of Mechanotransduction

Mechanical forces are potent modulators of stem cell based tissue regenerative mechanisms, inducing cell fate decisions and tissue specific commitment. A unique platform for investigating mechanotransduction is spaceflight, where microgravity and altered fluid mechanics provide a loading-null experimental condition. Seminal investigations of regenerative capacity in a wholly regenerative species, the newt model, and in a variety of totipotent and adult stem cell populations have demonstrated the detrimental effects of unloading on maintenance of stem cell based regeneration. Of particular interest is the observation that unloading interferes with the transition of stem cell pools from proliferative state to differentiation commitment. In this work we sought to test the hypothesis that gravity mechanotransduction regulates stem cell tissue regenerative processes by modulating stem cell proliferation and differentiation fates at specific cell cycle stages. To do this, clonally-derived ESCs were plated on a collagen matrix and expanded for 36 hours before re-plating on a non-adherent culture dish in the absence of leukemia inhibitory factor (LIF) to form spheroid aggregate EBs. After formation, the EBs were transferred to a collagen matrix coated culture dishes and given 4 days to allow implantation and outgrowth. In parallel, totipotent ESCs were plated 24 hours before mechanical stimulation on collagen matrix culture dishes in the presence of LIF to maintain totipotency and serve as un-differentiation committed controls. The EBs and ESCs were then subjected to either a 60 minute pulse of gravity (static loading) or 60 minutes of cyclic stretch (dynamic loading) mechanotransduction. Six hours post-stimulation, we used a 10X Genomics Single Cell controller to generate bar-coded single cell Illumina libraries and sequenced expressomes for 5,000 static loaded cells, representative of a change in gravity mechanotransduction, 5,000 dynamic loaded cells, representative of tissue loading associate with physiologic function, and 5,000 unstimulated 1g control cells. The comparison of these 3 libraries by cluster assignment based on like gene expression patterns show substantial alteration in cluster geometry due to mechanical loading. Specifically the mechanically loaded EB outgrowth cells to retain potency markers (PAX6, SOX2, CD34) and suppress early commitment markers (Dhh, VCAN, Igf1). Whereas the EBs cultured under the non-stimulated conditions display clear departure from the ESC expressome with lineage commitment markers upregulated and several tissue specific markers being expressed (BMP "early musculoskeletal development, Mesp1" early cardiovascular cell lineage). These markers are not seen in the mechano-stimulated cultures or the totipotent ESC cultures. Comparison of like clusters between our experimental conditions revealed an array of regenerative and stem cell genes are significantly mechano-regulated. Of particular importance CDKN1a/p21, a gene shown by previous investigation of our research team to be significantly upregulated in unloading, was suppressed in the static and dynamic loaded EBS. In addition to CDKN1a/p21 many genes related to cell cycle and transitory differentiation markers had elevated expression in the mechano-stimulated EBs, but surprisingly these trends were not observed in the ESC cultures. This study is the first of its kind investigating for mechano-signaling and mechano-regulated pathways, and has alre

Single cell RNA-sequecing↗

Biomolecule Sequencer: Next-Generation DNA Sequencing Technology for In-Flight Environmental Monitoring, Research, and Beyond

On the International Space Station (ISS), technologies capable of rapid microbial identification and disease diagnostics are not currently available. NASA still relies upon sample return for comprehensive, molecular-based sample characterization. Next-generation DNA sequencing is a powerful approach for identifying microorganisms in air, water, and surfaces onboard spacecraft. The Biomolecule Sequencer payload, manifested to SpaceX-9 and scheduled on the Increment 4748 research plan (June 2016), will assess the functionality of a commercially-available next-generation DNA sequencer in the microgravity environment of ISS. The MinION device from Oxford Nanopore Technologies (Oxford, UK) measures picoamp changes in electrical current dependent on nucleotide sequences of the DNA strand migrating through nanopores in the system. The hardware is exceptionally small (9.5 x 3.2 x 1.6 cm), lightweight (120 grams), and powered only by a USB connection. For the ISS technology demonstration, the Biomolecule Sequencer will be powered by a Microsoft Surface Pro3. Ground-prepared samples containing lambda bacteriophage, Escherichia coli, and mouse genomic DNA, will be launched and stored frozen on the ISS until experiment initiation. Immediately prior to sequencing, a crew member will collect and thaw frozen DNA samples, connect the sequencer to the Surface Pro3, inject thawed samples into a MinION flow cell, and initiate sequencing. At the completion of the sequencing run, data will be downlinked for ground analysis. Identical, synchronous ground controls will be used for data comparisons to determine sequencer functionality, run-time sequence, current dynamics, and overall accuracy. We will present our latest results from the ISS flight experiment the first time DNA has ever been sequenced in space and discuss the many potential applications of the Biomolecule Sequencer for environmental monitoring, medical diagnostics, higher fidelity and more adaptable Space Biology Human Research Program investigations, and even life detection experiments for astrobiology missions.

Sequencer↗

Building a FAIR data ecosystem for incorporating single-cell transcriptomics data into agricultural genome to phenome research

Introduction The agriculture genomics community has numerous data submission standards available, but the standards for describing and storing single-cell (SC, e.g., scRNA- seq) data are comparatively underdeveloped. Methods To bridge this gap, we leveraged recent advancements in human genomics infrastructure, such as the integration of the Human Cell Atlas Data Portal with Terra, a secure, scalable, open-source platform for biomedical researchers to access data, run analysis tools, and collaborate. In parallel, the Single Cell Expression Atlas at EMBL-EBI offers a comprehensive data ingestion portal for high-throughput sequencing datasets, including plants, protists, and animals (including humans). Developing data tools connecting these resources would offer significant advantages to the agricultural genomics community. The FAANG data portal at EMBL-EBI emphasizes delivering rich metadata and highly accurate and reliable annotation of farmed animals but is not computationally linked to either of these resources. Results Herein, we describe a pilot-scale project that determines whether the current FAANG metadata standards for livestock can be used to ingest scRNA-seq datasets into Terra in a manner consistent with HCA Data Portal standards. Importantly, rich scRNA-seq metadata can now be brokered through the FAANG data portal using a semi-automated process, thereby avoiding the need for substantial expert curation. We have further extended the functionality of this tool so that validated and ingested SC files within the HCA Data Portal are transferred to Terra for further analysis. In addition, we verified data ingestion into Terra, hosted on Azure, and demonstrated the use of a workflow to analyze the first ingested porcine scRNA-seq dataset. Additionally, we have also developed prototype tools to visualize the output of scRNA-seq analyses on genome browsers to compare gene expression patterns across tissues and cell populations. This JBrowse tool now features distinct tracks, showcasing PBMC scRNA-seq alongside two bulk RNA-seq experiments. Discussion We intend to further build upon these existing tools to construct a scientist-friendly data resource and analytical ecosystem based on Findable, Accessible, Interoperable, and Reusable (FAIR) SC principles to facilitate SC-level genomic analysis through data ingestion, storage, retrieval, re-use, visualization, and comparative annotation across agricultural species.

Genetics & Heredity↗