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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Systematic analysis of Baobaoqu fermentation starter for Wuliangye Baijiu by the combination of metagenomics and metabolomics

Baobaoqu (BBQ) is a traditional fermenting power, which is widely applied in Nong-flavor Baijiu brewing. There are two different types of BBQ (premium BBQ and normal BBQ) used in industrial manufacture, but the reasons for the significant differences between two kinds of BBQ have not been clearly illuminated. In this study, the combination of metagenomics and metabolomics was performed to compare the differences in the composition of microbial communities and the components of flavors between premium BBQ and normal BBQ. The results showed that the glycosidase-producing microorganisms are the biomarkers of premium BBQ, contributing a better ability of carbon source utilization than normal BBQ. In addition, several important flavors (ethyl hexanoate, phenylethanol, ethyl acetate) were rich in normal BBQ, which have a significant positive correlation with the biomarkers ( Lactobacillus and Pichia kudriavzevii ) of normal BBQ. It suggests that the microbial community has an advantage in utilizing raw materials in premium BBQ, while the community was inclined to form flavors in normal BBQ. The differences between two types of BBQ at the microbial and flavor level have theoretical and practical guiding significance in the application of premium and normal BBQ and in the further improvements of taste and quality of Baijiu.

Zhang, Qingmei↗
DOE OSTI2023

Topography of respiratory tract and gut microbiota in mice with influenza A virus infection

Introduction Influenza A virus (IAV)-induced dysbiosis may predispose to severe bacterial superinfections. Most studies have focused on the microbiota of single mucosal surfaces; consequently, the relationships between microbiota at different anatomic sites in IAV-infected mice have not been fully studied. Methods We characterized respiratory and gut microbiota using full-length 16S rRNA gene sequencing by Nanopore sequencers and compared the nasopharyngeal, oropharyngeal, lung and gut microbiomes in healthy and IAV-infected mice. Results The oropharyngeal, lung and gut microbiota of healthy mice were dominated by Lactobacillus spp., while nasopharyngeal microbiota were comprised primarily of Streptococcus spp. However, the oropharyngeal, nasopharyngeal, lung, and gut microbiota of IAV-infected mice were dominated by Pseudomonas, Escherichia , Streptococcus , and Muribaculum spp., respectively. Lactobacillus murinus was identified as a biomarker and was reduced at all sites in IAV-infected mice. The microbiota composition of lung was more similar to that of the nasopharynx than the oropharynx in healthy mice. Discussion These findings suggest that the main source of lung microbiota in mice differs from that of adults. Moreover, the similarity between the nasopharyngeal and lung microbiota was increased in IAV-infected mice. We found that IAV infection reduced the similarity between the gut and oropharyngeal microbiota. L. murinus was identified as a biomarker of IAV infection and may be an important target for intervention in post-influenza bacterial superinfections.

Chen, Qichao↗
DOE OSTI2024

Effects of photon irradiation in the presence and absence of hindlimb unloading on the behavioral performance and metabolic pathways in the plasma of Fischer rats

The space environment astronauts experience during space missions consists of multiple environmental challenges, including microgravity. In this study, we assessed the behavioral and cognitive performances of male Fisher rats 2 months after sham irradiation or total body irradiation with photons in the absence or presence of simulated microgravity. We analyzed the plasma collected 9 months after sham irradiation or total body irradiation for distinct alterations in metabolic pathways and to determine whether changes to metabolic measures were associated with specific behavioral and cognitive measures. A total of 344 male Fischer rats were irradiated with photons (6 MeV; 3, 8, or 10 Gy) in the absence or presence of simulated weightlessness achieved using hindlimb unloading (HU). To identify potential plasma biomarkers of photon radiation exposure or the HU condition for behavioral or cognitive performance, we performed regression analyses. The behavioral effects of HU on activity levels in an open field, measures of anxiety in an elevated plus maze, and anhedonia in the M&M consumption test were more pronounced than those of photon irradiation. Phenylalanine, tyrosine, and tryptophan metabolism, and phenylalanine metabolism and biosynthesis showed very strong pathway changes, following photon irradiation and HU in animals irradiated with 3 Gy. Here, 29 out of 101 plasma metabolites were associated with 1 out of 13 behavioral measures. In the absence of HU, 22 metabolites were related to behavioral and cognitive measures. In HU animals that were sham-irradiated or irradiated with 8 Gy, one metabolite was related to behavioral and cognitive measures. In HU animals irradiated with 3 Gy, six metabolites were related to behavioral and cognitive measures. These data suggest that it will be possible to develop stable plasma biomarkers of behavioral and cognitive performance, following environmental challenges like HU and radiation exposure.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Effects of 5-ion 6-beam sequential irradiation in the presence and absence of hindlimb or control hindlimb unloading on behavioral performances and plasma metabolic pathways of Fischer 344 rats

Introduction Effects and interactions between different spaceflight stressors are expected to be experienced by crew on missions when exposed to microgravity and galactic cosmic rays (GCRs). One of the limitations of previous studies on simulated weightlessness using hindlimb unloading (HU) is that a control HU condition was not included. Methods We characterized the behavioral performance of male Fischer rats 2 months after sham or total body irradiation with a simplified 5-ion 6-mixed-beam exposure representative of GCRs in the absence or presence of HU. Six months later, the plasma, hippocampus, and cortex were processed to determine whether the behavioral effects were associated with long-term alterations in the metabolic pathways. Results In the open field without and with objects, interactions were observed for radiation × HU. In the plasma of animals that were not under the HU or control HU condition, the riboflavin metabolic pathway was affected most for sham irradiation vs. 0.75 Gy exposure. Analysis of the effects of control HU on plasma in the sham-irradiated animals showed that the alanine, aspartate, glutamate, riboflavin, and glutamine metabolisms as well as arginine biosynthesis were affected. The effects of control HU on the hippocampus in the sham-irradiated animals showed that the phenylalanine, tyrosine, and tryptophan pathway was affected the most. Analysis of effects of 0.75 Gy irradiation on the cortex of control HU animals showed that the glutamine and glutamate metabolic pathway was affected similar to the hippocampus, while the riboflavin pathway was affected in animals that were not under the control HU condition. The effects of control HU on the cortex in sham-irradiated animals showed that the riboflavin metabolic pathway was affected. Animals receiving 0.75 Gy of irradiation showed impaired glutamine and glutamate metabolic pathway, whereas animals receiving 1.5 Gy of irradiation showed impaired riboflavin metabolic pathways. A total of 21 plasma metabolites were correlated with the behavioral measures, indicating that plasma and brain biomarkers associated with behavioral performance are dependent on the environmental conditions experienced. Discussion Phenylalanine, tyrosine, and tryptophan metabolism as well as phenylalanine and tryptophan as plasma metabolites are biomarkers that can be considered for spaceflight as they were revealed in both Fischer and WAG/Rij rats exposed to simGCRsim and/or HU.

Physiology↗
DOE OSTI2024

Transcriptomic and metabolomic changes in postharvest sugarbeet roots reveal widespread metabolic changes in storage and identify genes potentially responsible for respiratory sucrose loss

Endogenous metabolism is primarily responsible for losses in sucrose content and processing quality in postharvest sugarbeet roots. The genes responsible for this metabolism and the transcriptional changes that regulate it, however, are largely unknown. To identify genes and metabolic pathways that participate in postharvest sugarbeet root metabolism and the transcriptional changes that contribute to their regulation, transcriptomic and metabolomic profiles were generated for sugarbeet roots at harvest and after 12, 40 and 120 d storage at 5 and 12°C and gene expression and metabolite concentration changes related to storage duration or temperature were identified. During storage, 8656 genes, or 34% of all expressed genes, and 225 metabolites, equivalent to 59% of detected metabolites, were altered in expression or concentration, indicating extensive transcriptional and metabolic changes in stored roots. These genes and metabolites contributed to a wide range of cellular and molecular functions, with carbohydrate metabolism being the function to which the greatest number of genes and metabolites classified. Because respiration has a central role in postharvest metabolism and is largely responsible for sucrose loss in sugarbeet roots, genes and metabolites involved in and correlated to respiration were identified. Seventy-five genes participating in respiration were differentially expressed during storage, including two bidirectional sugar transporter SWEET17 genes that highly correlated with respiration rate. Weighted gene co-expression network analysis identified 1896 additional genes that positively correlated with respiration rate and predicted a pyruvate kinase gene to be a central regulator or biomarker for respiration rate. Overall, these results reveal the extensive and diverse physiological and metabolic changes that occur in stored sugarbeet roots and identify genes with potential roles as regulators or biomarkers for respiratory sucrose loss.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Relationships between Habitual Polyphenol Consumption and Gut Microbiota in the INCLD Health Cohort

While polyphenol consumption is often associated with an increased abundance of beneficial microbes and decreased opportunistic pathogens, these relationships are not completely described for polyphenols consumed via habitual diet, including culinary herb and spice consumption. This analysis of the International Cohort on Lifestyle Determinants of Health (INCLD Health) cohort uses a dietary questionnaire and 16s microbiome data to examine relationships between habitual polyphenol consumption and gut microbiota in healthy adults (n = 96). In this exploratory analysis, microbial taxa, but not diversity measures, differed by levels of dietary polyphenol consumption. Taxa identified as exploratory biomarkers of daily polyphenol consumption (mg/day) included Lactobacillus, Bacteroides, Enterococcus, Eubacterium ventriosum group, Ruminococcus torques group, and Sutterella. Taxa identified as exploratory biomarkers of the frequency of polyphenol-weighted herb and spice use included Lachnospiraceae UCG-001, Lachnospiraceae UCG-004, Methanobrevibacter, Lachnoclostridium, and Lachnotalea. Several of the differentiating taxa carry out activities important for human health, although out of these taxa, those with previously described pro-inflammatory qualities in certain contexts displayed inverse relationships with polyphenol consumption. Our results suggest that higher quantities of habitual polyphenol consumption may support an intestinal environment where opportunistic and pro-inflammatory bacteria are represented in a lower relative abundance compared to those with less potentially virulent qualities.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Multimodal Mass Spectrometry Imaging (MSI) of Archean and Jurassic Geologic Samples

Current geochemical analytical techniques such as gas chromatography-mass spectrometry (GC-MS and $G\acute{C}GC-MS$) can effectively determine the composition and structure of the organic biosignatures within a sample but cannot resolve the spatial distribution of organic biosignatures within rocks or sediments. The spatial distribution can be used to determine the character of the organic biosignatures [1], which can be indigenous (deposited with host rocks), non-indigenous (incorporated after deposition via fluid migration), or contaminant. Determining the character of organic biosignatures is critical for ancient samples [2] and will be critical if organics are observed in samples returned from Mars [3].Fs-LDPI-MS has the ability to map organic compounds across the surface of samples at 2 - 10 μm lateral resolution. Furthermore, fs-LDPI-MS can be used to carry out multiple analyses in the same location for micron-scale analysis of previously buried material [4]. We used fs-LDPI-MS to examine a ~164 million year old organic-rich mudstone from SW England (14.2 wt. % total organic carbon) [5] and determined the spatial distribution of likely indigenous components buried below potentially contaminated surface layers.We then used ToF-SIMS to re-analyze the mudstone to directly compare ToF-SIMS to fs-LDPI-MS datasets, and to analyze a series of ~2.7 billion year old geologic samples from Timmins, ON, CA [6]. Ref. [6] previously analyzed the Archean samples and observed a series of archaeal biomarkers, as well as hopanes and steranes. Using ToF-SIMS analysis, we were able to detect hopanes and steranes in the mudstone samples, but not in the Archean samples indicating that the hopanes and steranes are most likely contaminants in the Archean samples. We also did not observe the archaeal biomarkers; however, this may be from the lack of molecular ion preservation during ToF-SIMS.

Pasterski, Michael↗
DOE OSTI2025

Leveraging hyperspectral phenotyping for accurate, non-destructive prediction of metabolite profiles in poplar under drought stress

Accurately predicting drought tolerance in woody perennial bioenergy crops is critical for sustainable biomass production under fluctuating precipitation. Hyperspectral imaging (HSI) in the visible-near-infrared (VNIR) and shortwave-infrared (SWIR) ranges offers a promising approach for predicting plant biochemical traits, yet its application in metabolite profiling remains underexplored. We integrated VNIR+SWIR HSI with untargeted metabolomics to investigate drought-induced metabolic shifts in Populus leaves from eight Populus genotypes. Metabolite profiling identified 127 compounds, with 73 showing significant drought responses spanning amino acids (AA), carbohydrates (CHO), phenolic glycosides (PG), organic acids (OA), fatty acids and alcohols (FA), terpenes (T), phenolic metabolites (P), and unclassified metabolites. Spectral analysis revealed consistently higher reflectance across VNIR and SWIR wavelengths in drought-stressed plants, corresponding with increased accumulation of AA and reduced CHO and PG levels. Least absolute shrinkage and selection operator (LASSO) regression modeling identified robust spectral predictors of metabolite concentrations, associating VNIR wavelengths (500–700 nm) predominantly with AA and P, whereas SWIR wavelengths (1680–1700 nm) reliably predicted CHO, OA, and T. Several stable spectral-metabolite associations persisted across the two watering regimes (drought vs. well-watered), highlighting their potential as spectral biomarkers for non-destructive stress monitoring. Minimal genotype-specific variation suggests that observed spectral and metabolic responses were driven primarily by environmental factors, likely reflecting limited genetic diversity among the commercial Populus genotypes examined. This work establishes VNIR+SWIR hyperspectral imaging as a powerful, non-destructive phenotyping tool for precision monitoring and targeted improvement of drought resilience in bioenergy crops.

Biochemical trait prediction↗
DOE OSTI2023

Deep Learning on Multimodal Chemical and Whole Slide Imaging Data for Predicting Prostate Cancer Directly from Tissue Images

Prostate cancer is one of the most common cancers globally and is the second most common cancer in the male population in the US. Here we develop a study based on correlating the hematoxylin and eosin (H&E)-stained biopsy data with MALDI mass-spectrometric imaging data of the corresponding tissue to determine the cancerous regions and their unique chemical signatures and variations of the predicted regions with original pathological annotations. We obtain features from high-resolution optical micrographs of whole slide H&E stained data through deep learning and spatially register them with mass spectrometry imaging (MSI) data to correlate the chemical signature with the tissue anatomy of the data. We then use the learned correlation to predict prostate cancer from observed H&E images using trained coregistered MSI data. This multimodal approach can predict cancerous regions with ~80% accuracy, which indicates a correlation between optical H&E features and chemical information found in MSI. Further, we show that such paired multimodal data can be used for training feature extraction networks on H&E data which bypasses the need to acquire expensive MSI data and eliminates the need for manual annotation saving valuable time. Two chemical biomarkers were also found to be predicting the ground truth cancerous regions. This study shows promise in generating improved patient treatment trajectories by predicting prostate cancer directly from readily available H&E-stained biopsy images aided by coregistered MSI data.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Radiation exposure induces cross-species temporal metabolic changes that are mitigated in mice by amifostine

Exposure to acute, damaging radiation may occur through a variety of events from cancer therapy and industrial accidents to terrorist attacks and military actions. Our understanding of how to protect individuals and mitigate the effects of radiation injury or Acute Radiation Syndrome (ARS) is still limited. There are only a few Food and Drug Administration-approved therapies for ARS; whereas, amifostine is limited to treating low dose (0.7–6 Gy) radiation poisoning arising from cancer radiotherapy. An early intervention is critical to treat ARS, which necessitates identifying diagnostic biomarkers to quickly characterize radiation exposure. Towards this end, a multiplatform metabolomics study was performed to comprehensively characterize the temporal changes in metabolite levels from mice and non-human primate serum samples following γ-irradiation. The metabolomic signature of amifostine was also evaluated in mice as a model for radioprotection. The NMR and mass spectrometry metabolomics analysis identified 23 dysregulated pathways resulting from the radiation exposure. These metabolomic alterations exhibited distinct trajectories within glucose metabolism, phospholipid biosynthesis, and nucleotide metabolism. A return to baseline levels with amifostine treatment occurred for these pathways within a week of radiation exposure. Together, our data suggests a unique physiological change that is independent of radiation dose or species. Furthermore, a metabolic signature of radioprotection was observed through the use of amifostine prophylaxis of ARS.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Neuronally-derived tau is increased in experienced breachers and is associated with neurobehavioral symptoms

Military and law enforcement breachers are exposed to many low-level blasts during their training and occupational experiences in which they detonate explosives to force entry into secured structures. There is a concern that exposure to these repetitive blast events in career breachers could result in cumulative neurological effects. This study aimed to determine concentrations of neurofilament light (NF-L), tau, and amyloid-beta 42 (Aβ42) in serum and in neuronal-derived extracellular vesicles (EVs) in an experienced breacher population, and to examine biomarker associations with neurobehavioral symptoms. Thirty-four participants enrolled in the study: 20 experienced breachers and 14 matched military or civilian law enforcement controls. EV tau concentrations were significantly elevated in experienced breachers (0.3301 ± 0.5225) compared to controls (–0.4279 ± 0.7557; F = 10.43, p = 0.003). No statistically significant changes were observed in EV levels of NF-L or Aβ42 or in serum levels of NF-L, tau, or Aβ42 (p’s > 0.05). Elevated EV tau concentrations correlated with increased Neurobehavioral Symptom Inventory (NSI) score in experienced breachers (r = 0.596, p = 0.015) and predicted higher NSI score (F(1,14) = 7.702, p = 0.015, R 2 = 0.355). These findings show that neuronal-derived EV concentrations of tau are significantly elevated and associated with neurobehavioral symptoms in this sample of experienced breachers who have a history of many low-level blast exposures.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Understanding the structure and composition of recalcitrant oligosaccharides in hydrolysate using high-throughput biotin-based glycome profiling and mass spectrometry

Novel Immunological and Mass Spectrometry Methods for Comprehensive Analysis of Recalcitrant Oligosaccharides in AFEX Pretreated Corn Stover. Lignocellulosic biomass is a sustainable alternative to fossil fuel and is extensively used for developing bio-based technologies to produce products such as food, feed, fuel, and chemicals. The key to these technologies is to develop cost competitive processes to convert complex carbohydrates present in plant cell wall to simple sugars such as glucose, xylose, and arabinose. Since lignocellulosic biomass is highly recalcitrant, it must undergo a combination of thermochemical treatment such as Ammonia Fiber Expansion (AFEX), dilute acid (DA), Ionic Liquid (IL) and biological treatment such as enzyme hydrolysis and microbial fermentation to produce desired products. However, when using commercial fungal enzymes during hydrolysis, only 75–85% of the soluble sugars generated are monomeric sugars, while the remaining 15–25% are soluble recalcitrant oligosaccharides that cannot be easily utilized by microorganisms. Previously, we successfully separated and purified the soluble recalcitrant oligosaccharides using a combination of charcoal and celite-based separation followed by size exclusion chromatography and studies their inhibitory properties on enzymes. We discovered that the oligosaccharides with higher degree of polymerization (DP) containing methylated uronic acid substitutions were more recalcitrant towards commercial enzyme mixtures than lower DP and neutral oligosaccharides. Here, we report the use of several complementary techniques that include glycome profiling using plant biomass glycan specific monoclonal antibodies (mAbs) to characterize sugar linkages in plant cell walls and enzymatic hydrolysate, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) using structurally-informative diagnostic peaks offered by negative ion post-secondary decay spectra, gas chromatography followed by mass spectrometry (GC–MS) to characterize oligosaccharide sugar linkages with and without derivatization. Since oligosaccharides (DP 4–20) are small, it is challenging to mobilize these molecules for mAbs binding and characterization. To overcome this problem, we have applied a new biotin-coupling based oligosaccharide immobilization method that successfully tagged most of the low DP soluble oligosaccharides on to a micro-plate surface followed by specific linkage analysis using mAbs in a high-throughput system. This new approach will help develop more advanced versions of future high throughput glycome profiling methods that can be used to separate and characterize oligosaccharides present in biomarkers for diagnostic applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Preparing for the next pandemic via transfer learning from existing diseases with hierarchical multi-modal BERT: a study on COVID-19 outcome prediction

Abstract Developing prediction models for emerging infectious diseases from relatively small numbers of cases is a critical need for improving pandemic preparedness. Using COVID-19 as an exemplar, we propose a transfer learning methodology for developing predictive models from multi-modal electronic healthcare records by leveraging information from more prevalent diseases with shared clinical characteristics. Our novel hierarchical, multi-modal model ( $${\textsc {TransMED}}$$ T R A N S MED ) integrates baseline risk factors from the natural language processing of clinical notes at admission, time-series measurements of biomarkers obtained from laboratory tests, and discrete diagnostic, procedure and drug codes. We demonstrate the alignment of $${\textsc {TransMED}}$$ T R A N S MED ’s predictions with well-established clinical knowledge about COVID-19 through univariate and multivariate risk factor driven sub-cohort analysis. $${\textsc {TransMED}}$$ T R A N S MED ’s superior performance over state-of-the-art methods shows that leveraging patient data across modalities and transferring prior knowledge from similar disorders is critical for accurate prediction of patient outcomes, and this approach may serve as an important tool in the early response to future pandemics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Progress Toward a Multiomic Understanding of Traumatic Brain Injury: A Review

Traumatic brain injury (TBI) is not a single disease state but describes an array of conditions associated with insult or injury to the brain. While some individuals with TBI recover within a few days or months, others present with persistent symptoms that can cause disability, neuropsychological trauma, and even death. Understanding, diagnosing, and treating TBI is extremely complex for many reasons, including the variable biomechanics of head impact, differences in severity and location of injury, and individual patient characteristics. Because of these confounding factors, the development of reliable diagnostics and targeted treatments for brain injury remains elusive. We argue that the development of effective diagnostic and therapeutic strategies for TBI requires a deep understanding of human neurophysiology at the molecular level and that the framework of multiomics may provide some effective solutions for the diagnosis and treatment of this challenging condition. To this end, we present here a comprehensive review of TBI biomarker candidates from across the multiomic disciplines and compare them with known signatures associated with other neuropsychological conditions, including Alzheimer’s disease and Parkinson’s disease. We believe that this integrated view will facilitate a deeper understanding of the pathophysiology of TBI and its potential links to other neurological diseases.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

The human plasma lipidome response to exertional heat tolerance testing

The year of 2023 displayed the highest average global temperatures recorded in history— the duration and severity of extreme heat are projected to increase. Rising global temperatures represent a major public health threat, especially to occupations exposed to hot environments, such as construction and agricultural workers, and first responders. Despite efforts of the scientific community, there is still a need to characterize the pathophysiological processes leading to heat related illness and develop biomarkers that can predict its onset. Here, we performed a plasma lipidomic analysis on male and female subjects who underwent heat tolerance testing (HTT), consisting of a 2-h treadmill walk at 5 km/h with 2% inclination at a controlled temperature of 40oC. We identified 995 lipids from 27 classes, with nearly half of all detected lipids being responsive to HTT. Lipid classes related to substrate utilization were predominantly affected by HTT, with a downregulation of triacylglycerols and upregulation of free fatty acids and acyl-carnitines. We additionally examined correlations between changes in plasma lipids by using the even chain acyl-carnitines is physiological strain index (PSI). Here, even chain acyl-carnitines, bi-products of incomplete beta oxidation, and diacylglycerols displayed the highest correlation to PSI. PSI did not correlate with plasma lactate levels, which suggests that correlations with related to metabolic efficiency versus physical exertion. Overall, our results show that HTT has a strong impact on the plasma lipidome and that incomplete beta oxidation may underlie heat intolerance.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics

Limited therapy options due to antibiotic resistance underscore the need for optimization of current diagnostics. In some bacterial species, antimicrobial resistance can be unambiguously predicted based on their genome sequence. In this study, we sequenced the genomes and transcriptomes of 414 drug-resistant clinical Pseudomonas aeruginosa isolates. By training machine learning classifiers on information about the presence or absence of genes, their sequence variation, and expression profiles, we generated predictive models and identified biomarkers of resistance to four commonly administered antimicrobial drugs. Using these data types alone or in combination resulted in high (0.8–0.9) or very high (> 0.9) sensitivity and predictive values. For all drugs except for ciprofloxacin, gene expression information improved diagnostic performance. Our results pave the way for the development of a molecular resistance profiling tool that reliably predicts antimicrobial susceptibility based on genomic and transcriptomic markers. Finally, the implementation of a molecular susceptibility test system in routine microbiology diagnostics holds promise to provide earlier and more detailed information on antibiotic resistance profiles of bacterial pathogens and thus could change how physicians treat bacterial infections.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Data from : "Warming promotes loss of subsoil carbon through accelerated degradation of plant-derived organic matter". Blodgett warming experiment

This dataset contains data used for the paper: Warming promotes loss of subsoil carbon through accelerated degradation of plant-derived organic matter. Soil Biology and Biochemistry 156, 108185. doi:10.1016/j.soilbio.2021.108185On April 2018 (after 4.5 years of warming), we collected O-horizon (organic horizon) material and soil core down to 90 cm depth from a whole-soil warming experiment in a mixed-coniferous temperate forest, located at the University of California Blodgett experimental forest in the foothills of the Sierra Nevada, CA USA. The goal was to assess how 4.5 years of +4 °C whole-soil warming affected the quantity and quality of soil organic matter. Cores were collected from 6 experimental plots (three replicated blocks); samples were collected in 10 cm increments from 0 to 90 cm depth.This dataset contains a compressed (.zip) archive of the data used for this manuscript. The dataset includes files in .xlsx format, which can be accessed and processed using MS Excel or R. Carbon and nitrogen concentrations, as well as stable carbon isotope composition ((δ13C) and solvent extractable lipid biomarker (alkanoic acids and alkanes) data are provided as processed data files. Diffuse reflectance infrared Fourier transform (DRIFT) spectroscopy data are provided as raw output and processed data files.The dataset files "Blodgett_warming_Data" and "Blodgett_warming_raw_data.xlsx" were updated on July 7, 2021. The following updates were made: (1) specification of the units of measurements in the dataset (the unit of measurement was missing in some parameters) and (2) the column name "pulled depth" was changed to "pooled depth" in the updated data files.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A Novel DNA Repair Gene Signature for Immune Checkpoint Inhibitor-Based Therapy in Gastric Cancer

Gastric cancer is a heterogeneous group of diseases with only a fraction of patients responding to immunotherapy. The relationships between tumor DNA damage response, patient immune system and immunotherapy have recently attracted attention. Accumulating evidence suggests that DNA repair landscape is a significant factor in driving response to immune checkpoint blockade (ICB) therapy. In this study, to explore new prognostic and predictive biomarkers for gastric cancer patients who are sensitive and responsive to immunotherapies, we developed a novel 15-DNA repair gene signature (DRGS) and its related scoring system and evaluated the efficiency of the DRGS in discriminating different molecular and immune characteristics and therapeutic outcomes of patients with gastric adenocarcinoma, using publicly available datasets. The results demonstrated that DRGS high score patients showed significantly better therapeutic outcomes for ICB compared to DRGS low score patients (p < 0.001). Integrated analysis of multi-omics data demonstrated that the patients with high DRGS score were characteristic of high levels of anti-tumor lymphocyte infiltration, tumor mutation burden (TMB) and PD-L1 expression, and these patients exhibited a longer overall survival, as compared to the low-score patients. Results obtained from HPA and IHC supported significant dysregulation of the genes in DRGS in gastric cancer tissues, and a positive correlation in protein expression between DRGS and PD-L1. Therefore, the DRGS scoring system may have implications in tailoring immunotherapy in gastric cancers. A preprint has previously been published (Yuan et al., 2021).

60 APPLIED LIFE SCIENCES↗