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All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Variability in Galactic Cosmic Radiation- Induced DNA Damage Response in Inbred Mice Is Modulated by Genetics

In radiation biology, the ability to predict cancer risk associated with exposure to low doses of high-LET (Linear Energy Transfer) ionizing radiation remains a challenge. Epidemiological methods lack the sensitivity and power to provide detailed risk estimates for cancer and ignore individual sensitivity. We have hypothesized that DNA repair capacity is the primary factor differentiating peoples radiation sensitivity. We previously showed in immortalized human cell lines that characterizing the dose and time dependence of p53-binding protein 1 (53BP1) foci formation in the nucleus following X-rays exposure is sufficient to predict DNA repair response to any other LET in the same cell line. We now tested this hypothesis across a population of mice with different genetic background. Fibroblast cells were extracted and cultivated from 76 individual mice from 15 different strains and exposed to HZE (high (H) atomic number (Z) and energy (E) galactic cosmic ray particles) particles and X-rays. Individual radiation sensitivities were investigated by high throughput measurement of DNA repair kinetics that evaluated 53bp1 foci numbers as a surrogate for DNA double-strand breaks at various times post-irradiation. Instead of just counting foci which can be hard to distinguish for high-LET or high doses, we also took into account the track structure of high-LET particles to compute the remaining number of unrepaired tracks as a function of time post-irradiation. As expected, the percentage of unrepaired track over a 48 hours follow-up period increased with LET. In addition, repair rate was modulated by genetics, with animals from the same strain showing small variance while large rate differences were observed between strains. Radiation strain sensitivity ranking was estimated based on repair rates from exposure to each LET evaluated in this work, and ranking for high-LET correlated better with ranking from high dose of X-ray, not low dose. At the in-vivo level, drops in T-cells and B-cells number measured 24 hours after 0.1 Gy (Gray) X-ray exposure, correlated with slower DNA repair kinetic in fibroblast cells of the same strains of mice. At the genomic level, mouse genome wide association (GWA) analysis identified seven significant genetic loci on chromosomes 2, 3, 7, 10, 11, 13 and 19 with different significance depending on the LET. Interestingly, for the two highest LET, a common locus on Chromosome 10 was identified with high enrichment for DNA repair associated genes.Overall, this work suggests that repair kinetics of primary skin fibroblasts is a good surrogate marker for in-vivo radiation sensitivities in other tissues and that this response is modulated by genetics. Our study also confirms that DNA repair kinetics following high doses of X-ray can be used to predict radiation sensitivity to high-LET.

High-LET Radiation↗
DOE OSTI2021

Metagenomic strategies identify diverse integron–integrase and antibiotic resistance genes in the Antarctic environment

The objective of this study is to identify and analyze integrons and antibiotic resistance genes (ARGs) in samples collected from diverse sites in terrestrial Antarctica. Integrons were studied using two independent methods. One involved the construction and analysis of intI gene amplicon libraries. In addition, we sequenced 17 metagenomes of microbial mats and soil by high-throughput sequencing and analyzed these data using the IntegronFinder program. As expected, the metagenomic analysis allowed for the identification of novel predicted intI integrases and gene cassettes (GCs), which mostly encode unknown functions. However, some intI genes are similar to sequences previously identified by amplicon library analysis in soil samples collected from non-Antarctic sites. ARGs were analyzed in the metagenomes using ABRIcate with CARD database and verified if these genes could be classified as GCs by IntegronFinder. We identified 53 ARGs in 15 metagenomes, but only four were classified as GCs, one in MTG12 metagenome (Continental Antarctica), encoding an aminoglycoside-modifying enzyme (AAC(6´)acetyltransferase) and the other three in CS1 metagenome (Maritime Antarctica). One of these genes encodes a class D β-lactamase (blaOXA-205) and the other two are located in the same contig. One is part of a gene encoding the first 76 amino acids of aminoglycoside adenyltransferase (aadA6), and the other is a qacG2 gene.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Artificial Intelligence and Machine Learning for Bioenergy Research: Opportunities and Challenges

The integration of artificial intelligence and machine learning (AI/ML) with automated experimentation, genomics, biosystems design, and bioprocessing technologies is poised to revolutionize scientific investigation and, particularly, bioenergy research. To identify the opportunities and challenges in this emerging research area, the U.S. Department of Energy’s (DOE) Biological and Environmental Research program (BER) and Bioenergy Technologies Office (BETO) held a joint virtual workshop on AI/ML for Bioenergy Research (AMBER) on August 23–25, 2022. These interests have since been amplified in a September 2022 Executive Order, “Advancing Biotechnology and Biomanufacturing Innovation for a Sustainable, Safe, and Secure U.S. Bioeconomy,” to promote a whole-of government approach to biotechnology development (White House 2022). Approximately 50 scientists with various backgrounds and expertise from academia, industry, and DOE national laboratories met to discuss the opportunities and challenges of AI/ML for bioenergy research. Workshop participants were tasked with assessing the potential for AI/ML and laboratory automation to advance biological understanding and engineering in general. They particularly examined how integrating AI/ML tools with laboratory automation could accelerate biosystems design and optimize biomanufacturing. Discussions included the data and computational infrastructure needed to augment biosystems design applications and the expertise and workforce development efforts urgently required to shift integrated systems toward bioenergy research more broadly. Participants discussed many existing and future applications of AI/ML for biosystems design ranging from enzymes to plants and microbes, microbiomes, and bioprocess development. They also identified three key categories of scientific and technical opportunities and challenges: high-quality data, AI/ML algorithms, and laboratory automation. Several main takeaways emerged from the workshop: 1. Numerous AI/ML and automated experimentation applications exist for a variety of DOE mission needs in energy and the environment; 2. Exemplary research grand challenges for which AI/ML could provide solutions include: building microbes and microbial communities to specifications, developing closed-loop autonomous design and control for biosystems design, and advancing scale-up and automation; 3. Lack of sufficient high-quality, annotated data hinders the development of AI/ML applications; 4. New and improved AI/ML tools are needed, particularly those meeting the specific needs of the BER and BETO research communities; 5. Trade-offs in performance, cost, and reliability exist between deploying commercially available versus building custom-developed instrumentation and software for automated or autonomous experimentation; translation of manual to automated or autonomous methods is often a nontrivial endeavor; 6. Training a new generation of young scientists who can develop and apply AI/ML tools is needed to solve long-standing scientific challenges in bioenergy research. The integration of AI/ML tools and automated experimentation represents a new data-driven research paradigm complementary to the traditional hypothesis-driven research paradigm. This paradigm accelerates design and optimization of biological systems and processes for a variety of DOE mission needs in energy and the environment. The AMBER workshop broadly explored the potential of this new paradigm for bioenergy research, of particular interest to BER and BETO, and identified key challenges and opportunities that DOE can address in the coming years by leveraging its unique capabilities and resources.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2001

Extrapolation of the dna fragment-size distribution after high-dose irradiation to predict effects at low doses

The patterns of DSBs induced in the genome are different for sparsely and densely ionizing radiations: In the former case, the patterns are well described by a random-breakage model; in the latter, a more sophisticated tool is needed. We used a Monte Carlo algorithm with a random-walk geometry of chromatin, and a track structure defined by the radial distribution of energy deposition from an incident ion, to fit the PFGE data for fragment-size distribution after high-dose irradiation. These fits determined the unknown parameters of the model, enabling the extrapolation of data for high-dose irradiation to the low doses that are relevant for NASA space radiation research. The randomly-located-clusters formalism was used to speed the simulations. It was shown that only one adjustable parameter, Q, the track efficiency parameter, was necessary to predict DNA fragment sizes for wide ranges of doses. This parameter was determined for a variety of radiations and LETs and was used to predict the DSB patterns at the HPRT locus of the human X chromosome after low-dose irradiation. It was found that high-LET radiation would be more likely than low-LET radiation to induce additional DSBs within the HPRT gene if this gene already contained one DSB.

NASA Discipline Radiation Health↗
NASA NTRS2000

A polymer, random walk model for the size-distribution of large DNA fragments after high linear energy transfer radiation

DNA double-strand breaks (DSBs) produced by densely ionizing radiation are not located randomly in the genome: recent data indicate DSB clustering along chromosomes. Stochastic DSB clustering at large scales, from > 100 Mbp down to < 0.01 Mbp, is modeled using computer simulations and analytic equations. A random-walk, coarse-grained polymer model for chromatin is combined with a simple track structure model in Monte Carlo software called DNAbreak and is applied to data on alpha-particle irradiation of V-79 cells. The chromatin model neglects molecular details but systematically incorporates an increase in average spatial separation between two DNA loci as the number of base-pairs between the loci increases. Fragment-size distributions obtained using DNAbreak match data on large fragments about as well as distributions previously obtained with a less mechanistic approach. Dose-response relations, linear at small doses of high linear energy transfer (LET) radiation, are obtained. They are found to be non-linear when the dose becomes so large that there is a significant probability of overlapping or close juxtaposition, along one chromosome, for different DSB clusters from different tracks. The non-linearity is more evident for large fragments than for small. The DNAbreak results furnish an example of the RLC (randomly located clusters) analytic formalism, which generalizes the broken-stick fragment-size distribution of the random-breakage model that is often applied to low-LET data.

DNA/radiation effects↗
NASA NTRSDate not supplied

BLOOD-BASED MULTI-SCALE MODEL FOR CANCER RISK FROM GCR IN GENETICALLY DIVERSE POPULATIONS

OBJECTIVES AND METHODS This project addresses the challenge of understanding and predicting individual radiation sensitivity by integrating genetics, demographics and biomarker characteristics across species (mice and humans). We hypothesize that ex vivo DNA repair response to GCR components is a central determinant of cancer risk from space radiation and can serve as a biomarker of radiation risk in combination with genetics. Automated image quantification of 53BP1+ radiation-induced foci (RIF) during the first 4-48 h post-irradiation was performed as a function of dose and LET in non-immortalized primary skin fibroblasts derived from 76 mice across 15 strains (5 inbred reference strains and 10 collaborative-cross strains) exposed to X rays (0.1, 1 and 4 Gy), 350 MeV/n 40Ar and 600 MeV/n 56Fe (1.1 and 3 particles/100μm2), as well as in peripheral blood mononuclear cells (PBMCs) from 768 healthy donors (matched ethnicity, 50/50 male/female, 18-70 years old) exposed to gamma rays (0.1 and 1 Gy), 350 MeV/n 28Si, 350 MeV/n 40Ar and 600 MeV/n 56Fe (1.1 and 3 particles/100μm2). QUANTIFICATION OF 53BP1+ FOCI IN VITRO AND ASSOCIATIONS TO IN VIVO RADIATION SUSCEPTIBILITY IN 15 MOUSE STRAINS We reported in vitro repair kinetic and repairable fractions of RIF for the 15 mouse strains and introduced a mathematical model for RIF as a function of time, dose and LET. We noted that the metabolic activity of cells modulates the RIF response, and we introduced the open access tool terRIFic (Tool for Enhanced Results of RIF In Cells, https://radbiolab.shinyapps.io/terrific/) to correct for such bias using confluence level. Notably, at 4h post-irradiation, RIF/Gy decreased with dose or LET: as the dose or LET increases, so does the proximity of DNA double-strand-breaks (DSB) and our data suggest that proximal DSBs are brought together inside isolated RIF for repair. The RIF/Gy trend was inverted at 24h, suggesting RIF with high DSB content are more difficult to repair. We showed that in vitro metrics correlate with in vivo measurements in the same 15 mouse strains, such as survival levels of immune cells or spontaneous cancer incidence, suggesting a relationship between the efficiency of DSB repair and cancer risk or radiation toxicity. In addition to the efficiency of repair and persistent RIF, the amount of spontaneous foci before irradiation was also found to be strain dependent. Finally, we performed genome-wide association study in the same 15 mouse strains using all RIF phenotypes measured in vitro, identifying genes of interest and validating RIF as an ideal biomarker for individual radiation sensitivity. BASELINE 53BP1+ FOCI PREDICTS INDIVIDUAL HUMAN RESPONSE TO GCR COMPONENTS Based on the analysis of radiation responses of 576 donor PBMCs (using quantification of 53BP1+ foci, oxidative stress and cell death), we observed a wide variability of subject- and LET-dependent radiation responses, with radiation-induced DNA repair foci increasing with LET, though oxidative stress being notably reduced by high-LET irradiation, potentially due to a switch between hydrogen peroxide and oxygen radical-based mechanisms. We identified a relationship between few spontaneous DNA foci at baseline and increased DNA repair after irradiation, accompanied by an alteration in immunoregulatory cytokine secretion, which might be adapted as biomarkers to predict ionizing radiation sensitivity. Among demographic variables, only latent cytomegalovirus infection and age were predictive of high baseline foci formation. Finally, we have performed low-throughput whole genome sequencing of all samples and are currently in the process of identifying the genes and pathways associated with low and high-LET ionizing radiation sensitivity in humans.

53BP1↗
DOE OSTI2024

High-throughput single-cell transcriptomics of bacteria using combinatorial barcoding

Microbial split-pool ligation transcriptomics (microSPLiT) is a high-throughput single-cell RNA sequencing method for bacteria. With four combinatorial barcoding rounds, microSPLiT can profile transcriptional states in hundreds of thousands of Gram-negative and Gram-positive bacteria in a single experiment without specialized equipment. As bacterial samples are fixed and permeabilized before barcoding, they can be collected and stored ahead of time. During the first barcoding round, the fixed and permeabilized bacteria are distributed into a 96-well plate, where their transcripts are reverse transcribed into cDNA and labeled with the first well-specific barcode inside the cells. The cells are mixed and redistributed two more times into new 96-well plates, where the second and third barcodes are appended to the cDNA via in-cell ligation reactions. Finally, the cells are mixed and divided into aliquot sub-libraries, which can be stored until future use or prepared for sequencing with the addition of a fourth barcode. It takes 4 days to generate sequencing-ready libraries, including 1 day for collection and overnight fixation of samples. Here, the standard plate setup enables single-cell transcriptional profiling of up to 1 million bacterial cells and up to 96 samples in a single barcoding experiment, with the possibility of expansion by adding barcoding rounds. The protocol requires experience in basic molecular biology techniques, handling of bacterial samples and preparation of DNA libraries for next-generation sequencing. It can be performed by experienced undergraduate or graduate students. Data analysis requires access to computing resources, familiarity with Unix command line and basic experience with Python or R.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

BONCAT-Live for isolation and cultivation of active environmental bacteria

In diverse environments, microbes drive a myriad of processes, from geochemical and nutrient cycling to interspecies interactions, including associations with plants and animals. Their physiological state is dynamic and impacted by abiotic and biotic conditions, responding to environmental fluctuations by changes in cellular metabolism, according to their genetic potential. Molecular, cellular, and genomic approaches can identify and measure microbial responses and adaptation to environmental changes in native communities. However, isolating individual microbial cells that respond to specific changes for cultivation has been difficult. To address this, we implemented a novel bacterial isolation approach (BONCAT-Live) by integrating bio-orthogonal non-canonical amino acid tagging (BONCAT) in diverse native communities, with isolation and cultivation of cells responding to specific stimuli, at different time scales. In frozen Arctic permafrost samples, we identified and isolated dormant bacteria that become active after thawing under native or nutrient-enriched conditions. From the Populus tree rhizosphere, we isolated strains that thrive under high concentrations of root exudates that act as defense compounds and nutrients. In the human microbiome, we identified and isolated bacteria that rapidly proliferated when exposed to metabolites provided by the host or other co-occurring microbes. Further characterization of isolated bacterial strains will provide opportunities for in-depth determination of how these microbes adapt to changes in their environments, individually and as part of model communities.

Analytical Methods↗
NASA NTRS2019

Predicting Cancer Risk from Ionizing Radiation

The ability to predict cancer risk associated with exposure to low doses of high-LET ionizing radiation (IR) remains a challenge. Epidemiological methods lack the sensitivity and power to provide detailed risk estimates for cancer and ignore individual variance in IR sensitivity. We have hypothesized that DNA repair capacity can be used as a marker to evaluate and differentiate individual radiation sensitivity. More specifically, this work is based on the concept that the combined time-dose dependence of radiation-induced foci (RIF) of p53-binding protein 1 (53BP1) following low-LET exposure contains sufficient information to infer sensitivity to any other LET. Our hypothesis was tested in 15 different mouse strains as well as in primary human immune cells. We first approached individual ionizing radiation sensitivity in a mouse model by culturing primary skin fibroblasts extracted from 76 mice of 15 different genetic backgrounds and exposing them to HZE particles and X-rays. This work is one of the most extensive studies on the kinetics and possible genetic underpinnings of radiation-induced DNA damage and repair. Our results is in agreement with a DNA repair model we previously postulated, where nearby DNA double strand breaks (DSB) in the nucleus are brought together for more efficient repair, leading to RIF clustering. Such mechanism was evidenced by a specific dose and LET dependence of RIF numbers. Briefly, RIF quantification after low-LET X-ray exposure showed an asymptotic saturation for doses between 1 Gy and 4 Gy 4 hours post-irradiation across all 15 strains. The clustering of DSB across all strains also led to more RIF/Gy for lower LET (X-ray and 350 MeV/n Ar) than for higher LET (600 MeV/n Fe) 4 hours post-exposure. Considering the fact that the number of DSB/Gy should be independent of LET, our data suggest there are more DSB in individual RIF as the LET increases. RIF numbers for 24 and 48 hours post-exposure led to the inverse trend, with more remaining RIF/Gy for higher LET (by 600 MeV/n Fe). This result suggests cells have more difficulty resolving RIF from higher LET as they the number DSB/RIF increases. Note that for most conditions, the variance of RIF/Gy was small within individual animals of the same strain and large between strains, suggesting a strong genetics component. Furthermore, we present our preliminary data from an ongoing study on human genetic associations with IR sensitivity. To address the human variability in responses to HZE particle irradiation in a maximally comprehensive manner, we are in the process of collecting and isolating primary blood mononuclear cells from 768 healthy subjects of European descent, 18-75 years of age, 50/50 male/female distribution. We have analyzed 53BP1+ RIF formation as well as oxidative stress and cell death in primary cells from 192 subjects in response to the same HZE particles as used in mice: 600 MeV/n Fe, 350 MeV/n Ar and 350 MeV/n Si, 1.1 and 3 particles/100m2, 4 and 24 hours after irradiation. We will next complete the quantification of HZE particle-induced DNA and cellular damage in the remaining subjects and compare it to their responses to low-LET irradiation. Finally, we will perform GWAS analysis to identify human genomic associations with IR sensitivity and potential targets for biomarker development.

High-LET radiation↗
DOE OSTI2026

Assessing Metal Ion Assignment Accuracy in Protein Data Bank Models via Elemental Spectroscopy

Accurate representation of metal ions in macromolecular structures is critical for chemical interpretation, computational modeling, and machine-learning methods that rely on Protein Data Bank (PDB) entries. However, the elemental identity of metals modeled in crystallographic structures is often inferred indirectly and rarely validated experimentally. Here, we combine Particle Induced X-ray Emission (PIXE) and X-ray Fluorescence Spectroscopy (XRFS) to determine the elemental composition of protein samples used to generate 70 deposited metalloprotein crystal structures. By analyzing the original protein material employed for crystallization, but before the addition of crystallization buffer solutions, we assess whether the modeled metal ions in deposited structures are consistent with experimentally detectable elemental content. We find that in a majority of cases, the metals modeled in the corresponding PDB entries are inconsistent with the metals present in the protein samples before crystallization, or that additional metals are present but not represented in the structural models. Spectroscopic results were integrated with automated crystallographic validation metrics, including real-space Z-difference (RSZD) analysis and systematic rerefinement, to evaluate atomic-number mismatch at metal sites. PIXE and XRFS show strong agreement for dominant elemental signals and provide complementary, scalable approaches for identifying suspect metal assignments. This work does not address physiological or functional metalation but instead highlights a widespread data integrity issue in deposited macromolecular structures, PDB-wide. These results establish an experimentally corroborated link between elemental identity and crystallographic validation metrics, enabling the large-scale detection of chemically inconsistent annotations in structural databases used for computational modeling and machine learning.

Crystallization↗
DOE OSTI2024

Describing patterns of familial cancer risk in subfertile men using population pedigree data

STUDY QUESTION Can we simultaneously assess risk for multiple cancers to identify familial multicancer patterns in families of azoospermic and severely oligozoospermic men? SUMMARY ANSWER Here, distinct familial cancer patterns were observed in the azoospermia and severe oligozoospermia cohorts, suggesting heterogeneity in familial cancer risk by both type of subfertility and within subfertility type. WHAT IS KNOWN ALREADY Subfertile men and their relatives show increased risk for certain cancers including testicular, thyroid, and pediatric. STUDY DESIGN, SIZE, DURATION A retrospective cohort of subfertile men (N = 786) was identified and matched to fertile population controls (N = 5674). Family members out to third-degree relatives were identified for both subfertile men and fertile population controls (N = 337 754). The study period was 1966–2017. Individuals were censored at death or loss to follow-up, loss to follow-up occurred if they left Utah during the study period. PARTICIPANTS/MATERIALS, SETTING, METHODS Azoospermic (0 × 10 6 /mL) and severely oligozoospermic (<1.5 × 10 6 /mL) men were identified in the Subfertility Health and Assisted Reproduction and the Environment cohort (SHARE). Subfertile men were age- and sex-matched 5:1 to fertile population controls and family members out to third-degree relatives were identified using the Utah Population Database (UPDB). Cancer diagnoses were identified through the Utah Cancer Registry. Families containing ≥10 members with ≥1 year of follow-up 1966–2017 were included (azoospermic: N = 426 families, 21 361 individuals; oligozoospermic: N = 360 families, 18 818 individuals). Unsupervised clustering based on standardized incidence ratios for 34 cancer phenotypes in the families was used to identify familial multicancer patterns; azoospermia and severe oligospermia families were assessed separately. MAIN RESULTS AND THE ROLE OF CHANCE Compared to control families, significant increases in cancer risks were observed in the azoospermia cohort for five cancer types: bone and joint cancers hazard ratio (HR) = 2.56 (95% CI = 1.48–4.42), soft tissue cancers HR = 1.56 (95% CI = 1.01–2.39), uterine cancers HR = 1.27 (95% CI = 1.03–1.56), Hodgkin lymphomas HR = 1.60 (95% CI = 1.07–2.39), and thyroid cancer HR = 1.54 (95% CI = 1.21–1.97). Among severe oligozoospermia families, increased risk was seen for three cancer types: colon cancer HR = 1.16 (95% CI = 1.01–1.32), bone and joint cancers HR = 2.43 (95% CI = 1.30–4.54), and testis cancer HR = 2.34 (95% CI = 1.60–3.42) along with a significant decrease in esophageal cancer risk HR = 0.39 (95% CI = 0.16–0.97). Thirteen clusters of familial multicancer patterns were identified in families of azoospermic men, 66% of families in the azoospermia cohort showed population-level cancer risks, however, the remaining 12 clusters showed elevated risk for 2-7 cancer types. Several of the clusters with elevated cancer risks also showed increased odds of cancer diagnoses at young ages with six clusters showing increased odds of adolescent and young adult (AYA) diagnosis [odds ratio (OR) = 1.96–2.88] and two clusters showing increased odds of pediatric cancer diagnosis (OR = 3.64–12.63). Within the severe oligozoospermia cohort, 12 distinct familial multicancer clusters were identified. All 12 clusters showed elevated risk for 1–3 cancer types. An increase in odds of cancer diagnoses at young ages was also seen in five of the severe oligozoospermia familial multicancer clusters, three clusters showed increased odds of AYA diagnosis (OR = 2.19–2.78) with an additional two clusters showing increased odds of a pediatric diagnosis (OR = 3.84–9.32). LIMITATIONS, REASONS FOR CAUTION Although this study has many strengths, including population data for family structure, cancer diagnoses and subfertility, there are limitations. First, semen measures are not available for the sample of fertile men. Second, there is no information on medical comorbidities or lifestyle risk factors such as smoking status, BMI, or environmental exposures. Third, all of the subfertile men included in this study were seen at a fertility clinic for evaluation. These men were therefore a subset of the overall population experiencing fertility problems and likely represent those with the socioeconomic means for evaluation by a physician. WIDER IMPLICATIONS OF THE FINDINGS This analysis leveraged unique population-level data resources, SHARE and the UPDB, to describe novel multicancer clusters among the families of azoospermic and severely oligozoospermic men. Distinct overall multicancer risk and familial multicancer patterns were observed in the azoospermia and severe oligozoospermia cohorts, suggesting heterogeneity in cancer risk by type of subfertility and within subfertility type. Describing families with similar cancer risk patterns provides a new avenue to increase homogeneity for focused gene discovery and environmental risk factor studies. Such discoveries will lead to more accurate risk predictions and improved counseling for patients and their families. STUDY FUNDING/COMPETING INTEREST(S) This work was funded by GEMS: Genomic approach to connecting Elevated germline Mutation rates with male infertility and Somatic health (Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD): R01 HD106112). The authors have no conflicts of interest relevant to this work.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Data for Physiological Controls on Carbon Fluxes and Biomass Production in Miscanthus: Insights From a Process- Based Agroecosystem Model

Biomass crops serve as essential feedstocks for renewable energy and bioproducts and play a critical role in achieving lower emissions in the transportation sector. However, dedicated perennial biomass crops such as Miscanthus × giganteus (Miscanthus) remain underrepresented in process- based agroecosystem models, limiting robust evaluation of their economic and environmental performance. In this study, we developed a data- constrained representation of the sterile triploid Miscanthus (IL clone) within the process- based model ecosys, integrating global sensitivity analysis, ensemble simulation, and parameter calibration. Planting, harvesting, and fertilization practices consistent with field management were incorporated, and phenology was constrained using PhenoCam- derived Green Chromatic Coordinate (GCC) data. Using the Morris global sensitivity analysis method, we identified 11 key physiological parameters governing plant carbon, water, and nutrient relations, particularly processes associated with CO2 assimilation. We then conducted ensemble simulations by perturbing these parameters and calibrated the model against eddy covariance fluxes and field- measured biomass. Building on the calibrated operating state, parameter- response analyses show that different photosynthetic processes influence productivity in different ways. Protein allocation determines whether productivity increases toward a higher level, whereas electron transport capacity controls additional gains once protein allocation approaches saturation. These findings demonstrate that parameter importance depends on physiological context and on which photosynthetic processes remain limiting. Calibration and validation against observations show that ecosys can reliably reproduce carbon and water fluxes, as well as both above- and belowground biomass, with post- calibration GPP R2 improving from 0.67 to 0.95 during the calibration period and remaining high during validation (R2 = 0.95). These results provide a mechanistic foundation for regional simulations and sustainable bioenergy assessments.

Carbon↗
DOE OSTI2025

Mondo: integrating disease terminology across communities

Precision medicine aims to enhance diagnosis, treatment, and prognosis by integrating multimodal data at the point of care. However, challenges arise due to the vast number of diseases, differing methods of classification, and conflicting terminological coding systems and practices used to represent molecular definitions of disease. This lack of interoperability artificially constrains the potential for diagnosis, clinical decision support, care outcome analysis, as well as data linkage across research domains to support the development or repurposing of therapeutics. There is a clear and pressing need for a unified system for managing disease entities⁠—including identifiers, synonyms, and definitions. To address these issues, we created the Mondo disease ontology—a community-driven, open-source, unified disease classification system that harmonizes diverse terminologies into a consistent, computable framework. Mondo integrates key medical and biomedical terminologies, including Online Mendelian Inheritance in Man (OMIM), Orphanet, Medical Subject Headings (MeSH), National Cancer Institute Thesaurus (NCIt), and more, to provide a comprehensive and accurate representation of disease concepts with fully provenanced and attributed links back to the sources. Mondo can be used as the handle for curation of gene–disease associations utilized in diagnostic applications, research applications such as computational phenotyping, and in clinical coding systems in clinical decision support by pointing the clinician to the numerous knowledge resources linked to the Mondo identifier. Mondo's community-centric approach, stewarded by the Monarch Initiative's expertise in ontologies, ensures that the ontology remains adaptable to the evolving needs of biomedical research and clinical communities, as well as the knowledge providers.

biomedical informatics↗