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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

EDGE COVID-19: a web platform to generate submission-ready genomes from SARS-CoV-2 sequencing efforts

Abstract Summary Genomics has become an essential technology for surveilling emerging infectious disease outbreaks. A range of technologies and strategies for pathogen genome enrichment and sequencing are being used by laboratories worldwide, together with different and sometimes ad hoc, analytical procedures for generating genome sequences. A fully integrated analytical process for raw sequence to consensus genome determination, suited to outbreaks such as the ongoing COVID-19 pandemic, is critical to provide a solid genomic basis for epidemiological analyses and well-informed decision making. We have developed a web-based platform and integrated bioinformatic workflows that help to provide consistent high-quality analysis of SARS-CoV-2 sequencing data generated with either the Illumina or Oxford Nanopore Technologies (ONT). Using an intuitive web-based interface, this workflow automates data quality control, SARS-CoV-2 reference-based genome variant and consensus calling, lineage determination and provides the ability to submit the consensus sequence and necessary metadata to GenBank, GISAID and INSDC raw data repositories. We tested workflow usability using real world data and validated the accuracy of variant and lineage analysis using several test datasets, and further performed detailed comparisons with results from the COVID-19 Galaxy Project workflow. Our analyses indicate that EC-19 workflows generate high-quality SARS-CoV-2 genomes. Finally, we share a perspective on patterns and impact observed with Illumina versus ONT technologies on workflow congruence and differences. Availability and implementation https://edge-covid19.edgebioinformatics.org, and https://github.com/LANL-Bioinformatics/EDGE/tree/SARS-CoV2. Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Functional metal–organic frameworks as effective sensors of gases and volatile compounds

Developing efficient sensor materials with superior performance for selective, fast and sensitive detection of gases and volatile organic compounds (VOCs) is essential for human health and environmental protection, through monitoring indoor and outdoor air pollutions, managing industrial processes, controlling food quality and assisting early diagnosis of diseases. Metal–organic frameworks (MOFs) are a unique type of crystalline and porous solid material constructed from metal nodes (metal ions or clusters) and functional organic ligands. They have been investigated extensively for possible use as high performance sensors for the detection of many different gases and VOCs in recent years, due to their large surface area, tunable pore size, functionalizable sites and intriguing properties, such as electrical conductivity, magnetism, ferroelectricity, luminescence and chromism. The high porosity of MOFs allows them to interact strongly with various analytes, including gases and VOCs, thus resulting in easily measurable responses to different physicochemical parameters. Although much of the recent work on MOF-based luminescent sensors have been summarized in several excellent reviews (up to 2018), a comprehensive overview of these materials for sensing gases and VOCs based on chemiresistive, magnetic, ferroelectric, and colorimertic mechanisms is missing. In this review, we highlight the most recent progress in developing MOF sensing and switching materials with an emphasis on sensing mechanisms based on electricity, magnetism, ferroelectricity and chromism. We provide a comprehensive analysis on the MOF–analyte interactions in these processes, which play a key role in the sensing performance of the MOF-based sensors and switches. We discuss in detail possible applications of MOF-based sensing and switching materials in detecting oxygen, water vapor, toxic industrial gases (such as hydrogen sulfide, ammonia, sulfur dioxide, nitrous oxide, carbon oxides and carbon disulfide) and VOCs (such as aromatic and aliphatic hydrocarbons, ketones, alcohols, aldehydes, chlorinated hydrocarbons and N,N'-dimethylformamide). Altogether, this review serves as a timely source of information and provides insight for the future development of advanced MOF materials as next-generation gas and VOC sensors.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Improving Detection of Disease Re-emergence Using a Web-Based Tool (RED Alert): Design and Case Analysis Study

Background: Currently, the identification of infectious disease re-emergence is performed without describing specific quantitative criteria that can be used to identify re-emergence events consistently. This practice may lead to ineffective mitigation. In addition, identification of factors contributing to local disease re-emergence and assessment of global disease re-emergence require access to data about disease incidence and a large number of factors at the local level for the entire world. This paper presents Re-emerging Disease Alert (RED Alert), a web-based tool designed to help public health officials detect and understand infectious disease re-emergence. Objective: Our objective is to bring together a variety of disease-related data and analytics needed to help public health analysts answer the following 3 primary questions for detecting and understanding disease re-emergence: Is there a potential disease re-emergence at the local (country) level? What are the potential contributing factors for this re-emergence? Is there a potential for global re-emergence? Methods: We collected and cleaned disease-related data (eg, case counts, vaccination rates, and indicators related to disease transmission) from several data sources including the World Health Organization (WHO), Pan American Health Organization (PAHO), World Bank, and Gideon. We combined these data with machine learning and visual analytics into a tool called RED Alert to detect re-emergence for the following 4 diseases: measles, cholera, dengue, and yellow fever. We evaluated the performance of the machine learning models for re-emergence detection and reviewed the output of the tool through a number of case studies. Results: Our supervised learning models were able to identify 82%-90% of the local re-emergence events, although with 18%-31% (except 46% for dengue) false positives. This is consistent with our goal of identifying all possible re-emergences while allowing some false positives. The review of the web-based tool through case studies showed that local re-emergence detection was possible and that the tool provided actionable information about potential factors contributing to the local disease re-emergence and trends in global disease re-emergence. Conclusions: To the best of our knowledge, this is the first tool that focuses specifically on disease re-emergence and addresses the important challenges mentioned above.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

One Health Assessment of Bacillus anthracis Incidence and Detection in Anthrax-Endemic Areas of Pakistan

Anthrax, a severe zoonotic disease, is infrequently reported in anthrax-endemic regions of Pakistan. Despite clinical reports indicating its presence, particularly cutaneous anthrax, there is insufficient laboratory evidence regarding disease occurrence and environmental persistence. The present study aimed to confirm Bacillus anthracis presence, accountable for animal mortality and human infection, while exploring environmental transmission factors. Between March 2019 and July 2021, a total of 19 outbreaks were documented. Of these, 11 affected sheep/goats in Zhob district and 8 affected cattle/sheep in Bajour Agency. Clinical signs suggestive of Bacillus anthracis outbreak were observed in 11 animals. Blood and swab samples were collected for confirmation. The study followed a One Health approach, analyzing animal, environmental (soil/plant), and human samples. Of the 19 outbreaks, 11 were confirmed positive for anthrax based on growth characteristics, colony morphology, and PCR. Soil and plant root samples from the outbreak areas were collected and analyzed microscopically and molecularly. Cutaneous anthrax was observed in six humans, and swab samples were taken from the lesions. Human serum samples (n = 156) were tested for IgG antibodies against PA toxin and quantitative analysis of anthrax toxin receptor 1 (ANTXR1). Bacillus anthracis was detected in 65 out of 570 (11.40%) soil samples and 19 out of 190 (10%) plant root samples from the outbreak areas. Four out of six human samples from cutaneous anthrax lesions tested positive for Bacillus anthracis. Human anthrax seroprevalence was found to be 11% and 9% in two districts, with the highest rates among butchers and meat consumers. The highest ANTXR1 levels were observed in butchers, followed by meat consumers, farm employees, meat vendors, veterinarians, and farm owners. These findings highlight the persistence of anthrax in the region and emphasize the potential public health risks.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Artificial Intelligence for Autonomous Molecular Design: A Perspective

Domain-aware artificial intelligence has been increasingly adopted in recent years to expedite molecular design in various applications, including drug design and discovery. Recent advances in areas such as physics-informed machine learning and reasoning, software engineering, high-end hardware development, and computing infrastructures are providing opportunities to build scalable and explainable AI molecular discovery systems. This could improve a design hypothesis through feedback analysis, data integration that can provide a basis for the introduction of end-to-end automation for compound discovery and optimization, and enable more intelligent searches of chemical space. Several state-of-the-art ML architectures are predominantly and independently used for predicting the properties of small molecules, their high throughput synthesis, and screening, iteratively identifying and optimizing lead therapeutic candidates. However, such deep learning and ML approaches also raise considerable conceptual, technical, scalability, and end-to-end error quantification challenges, as well as skepticism about the current AI hype to build automated tools. To this end, synergistically and intelligently using these individual components along with robust quantum physics-based molecular representation and data generation tools in a closed-loop holds enormous promise for accelerated therapeutic design to critically analyze the opportunities and challenges for their more widespread application. This article aims to identify the most recent technology and breakthrough achieved by each of the components and discusses how such autonomous AI and ML workflows can be integrated to radically accelerate the protein target or disease model-based probe design that can be iteratively validated experimentally. Taken together, this could significantly reduce the timeline for end-to-end therapeutic discovery and optimization upon the arrival of any novel zoonotic transmission event. Our article serves as a guide for medicinal, computational chemistry and biology, analytical chemistry, and the ML community to practice autonomous molecular design in precision medicine and drug discovery.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Training calibration-based counterfactual explainers for deep learning models in medical image analysis

The rapid adoption of artificial intelligence methods in healthcare is coupled with the critical need for techniques to rigorously introspect models and thereby ensure that they behave reliably. This has led to the design of explainable AI techniques that uncover the relationships between discernible data signatures and model predictions. In this context, counterfactual explanations that synthesize small, interpretable changes to a given query while producing desired changes in model predictions have become popular. This under-constrained, inverse problem is vulnerable to introducing irrelevant feature manipulations, particularly when the model’s predictions are not well-calibrated. Hence, in this paper, we propose the TraCE (training calibration-based explainers) technique, which utilizes a novel uncertainty-based interval calibration strategy for reliably synthesizing counterfactuals. Given the wide-spread adoption of machine-learned solutions in radiology, our study focuses on deep models used for identifying anomalies in chest X-ray images. Using rigorous empirical studies, we demonstrate the superiority of TraCE explanations over several state-of-the-art baseline approaches, in terms of several widely adopted evaluation metrics. Our findings show that TraCE can be used to obtain a holistic understanding of deep models by enabling progressive exploration of decision boundaries, to detect shortcuts, and to infer relationships between patient attributes and disease severity.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

Lipid droplet-associated proteins in alcohol-associated fatty liver disease: A proteomic approach

The earliest manifestation of alcohol-associated liver disease (ALD) is steatosis characterized by deposition of fat in specialized organelles called lipid droplets (LDs). While alcohol administration causes a rise in LD numbers in the hepatocytes, little is known regarding their characteristics that allow their accumulation and size to increase. The aim of the present study is to gain insights into underlying pathophysiological mechanisms by investigating the ethanol-induced changes in hepatic LD proteome as a function of LD size. Adult male Wistar rats (180–200 g BW) were fed with ethanol liquid diet for 6 weeks. At sacrifice, large-, medium-, and small-sized hepatic LD subpopulations (LD1, LD2, and LD3, respectively) were isolated and subjected to morphological and proteomic analyses. Morphological analysis of LD1-LD3 fractions of ethanol-fed rats clearly demonstrated that LD1 contained larger LDs compared with LD2 and LD3 fractions. Our preliminary results from principal component analysis showed that the proteome of different-sized hepatic LD fractions was distinctly different. Proteomic data analysis identified over 2000 proteins in each LD fraction with significant alterations in protein abundance among the three LD fractions. Among the altered proteins, several were related to fat metabolism, including synthesis, incorporation of fatty acid, and lipolysis. Ingenuity pathway analysis revealed increased fatty acid synthesis, fatty acid incorporation, LD fusion, and reduced lipolysis in LD1 compared to LD3. Overall, the proteomic findings indicate that the increased level of protein that facilitates fusion of LDs combined with an increased association of negative regulators of lipolysis dictates the generation of large-sized LDs during the development of alcohol-associated hepatic steatosis. Several significantly altered proteins were identified in different-sized LDs isolated from livers of ethanol-fed rats. Ethanol-induced increases in specific proteins that hinder LD lipid metabolism led to the accumulation and persistence of large-sized LDs in the liver.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Repetitive diacetyl vapor exposure promotes ubiquitin proteasome stress and precedes bronchiolitis obliterans pathology

Bronchiolitis obliterans (BO) is a devastating lung disease seen commonly after lung transplant but also seen following severe respiratory tract infection or chemical inhalation exposure. Diacetyl (DA; 2,3-butanedione), highly reactive alpha-diketone is known to cause BO when inhaled, however, the mechanisms of how inhalation exposure leads to BO development remains poorly understood. In the current work, we combined two clinically relevant models for studying the pathogenesis of DA-induced BO: (1) an in vivo rat model of repetitive DA vapor exposures with recovery and (2) an in vitro model of primary human airway epithelial cells exposed to pure DA vapors. Rats exposed to 5 consecutive days 200 parts-per-million DA 6 hrs per day had worsening survival, persistent hypoxemia, poor weight gain, and histologic evidence of BO 14 days after DA exposure cessation. At the end of exposure, increased expression of the ubiquitin stress protein, ubiquitin-C, accumulated within DA-exposed rat lung homogenates, localized primarily to the airway epithelium, the primary site of BO development, and did not occur in air-exposed controls. Lung proteasome activity increased concurrently after DA exposure. In primary human airway cultures, global proteomics analysis identified 519 significantly modified proteins in DA-exposed samples relative to controls with the ubiquitin proteasome system, endosomal reticulum transport, and the response to unfolded protein pathways being upregulated while cell-cell adhesion and oxidation-reduction pathways being downregulated in DA-exposed samples. Altogether, repetitive DA vapor exposure resulted in abundant protein damage, accumulation of polyubiquinated proteins, and ubiquitin proteasome stress prior to the development of chemical-induced BO pathology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Genomic Analysis of Diverse Members of the Fungal Genus Monosporascus Reveals Novel Lineages, Unique Genome Content and a Potential Bacterial Associate

The genus Monosporascus represents an enigmatic group of fungi important in agriculture and widely distributed in natural arid ecosystems. Of the nine described species, two (M. cannonballus and M. eutypoides) are important pathogens on the roots of members of Cucurbitaceae in agricultural settings. The remaining seven species are capable of colonizing roots from a diverse host range without causing obvious disease symptoms. Recent molecular and culture studies have shown that members of the genus are nearly ubiquitous as root endophytes in arid environments of the Southwestern United States. Isolates have been obtained from apparently healthy roots of grasses, shrubs and herbaceous plants located in central New Mexico and other regions of the Southwest. Phylogenetic and genomic analyses reveal substantial diversity in these isolates. The New Mexico isolates include close relatives of M. cannonballus and M. ibericus, as well as isolates that represent previously unrecognized lineages. To explore evolutionary relationships within the genus and gain insights into potential ecological functions, we sequenced and assembled the genomes of three M. cannonballus isolates, one M. ibericus isolate, and six diverse New Mexico isolates. The assembled genomes were significantly larger than what is typical for the Sordariomycetes despite having predicted gene numbers similar to other members of the class. Differences in predicted genome content and organization were observed between endophytic and pathogenic lineages of Monosporascus. Several Monosporascus isolates appear to form associations with members of the bacterial genus Ralstonia (Burkholdariaceae).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Early Diagnosis of Fibromyalgia Using Surface-Enhanced Raman Spectroscopy Combined with Chemometrics

Fibromyalgia (FM) is a chronic muscle pain disorder that shares several clinical features with other related rheumatologic disorders. This study investigates the feasibility of using surface-enhanced Raman spectroscopy (SERS) with gold nanoparticles (AuNPs) as a fingerprinting approach to diagnose FM and other rheumatic diseases such as rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), osteoarthritis (OA), and chronic low back pain (CLBP). Blood samples were obtained on protein saver cards from FM (n = 83), non-FM (n = 54), and healthy (NC, n = 9) subjects. A semi-permeable membrane filtration method was used to obtain low-molecular-weight fraction (LMF) serum of the blood samples. SERS measurement conditions were standardized to enhance the LMF signal. An OPLS-DA algorithm created using the spectral region 750 to 1720 cm -1 enabled the classification of the spectra into their corresponding FM and non-FM classes (Rcv > 0.99) with 100% accuracy, sensitivity, and specificity. The OPLS-DA regression plot indicated that spectral regions associated with amino acids were responsible for discrimination patterns and can be potentially used as spectral biomarkers to differentiate FM and other rheumatic diseases. This exploratory work suggests that the AuNP SERS method in combination with OPLS-DA analysis has great potential for the label-free diagnosis of FM.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Structural and biochemical analysis of the metallo-β-lactamase L1 from emerging pathogen Stenotrophomonas maltophilia revealed the subtle but distinct di-metal scaffold for catalytic activity

Emergence of Enterobacteriaceae harboring metallo-beta-lactamases (MBL) has raised global threats due to their broad antibiotic resistance profiles and the lack of effective inhibitors against them. We have been studied one of the emerging environmental MBL, the L1 from Stenotrophomonas maltophilia K279a . We determined several crystal structures of L1 complexes with three different classes of beta-lactam antibiotics (penicillin G, moxalactam, meropenem, and imipenem), with the inhibitor captopril and different metal ions (Zn +2 , Cd +2 , and Cu +2 ). All hydrolyzed antibiotics and the inhibitor were found binding to two Zn +2 ions mainly through the opened lactam ring and some hydrophobic interactions with the binding pocket atoms. Without a metal ion, the active site is very similarly maintained as that of the native form with two Zn +2 ions, however, the protein does not bind the substrate moxalactam. When two Zn +2 ions were replaced with other metal ions, the same di-metal scaffold was maintained and the added moxalactam was found hydrolyzed in the active site. Differential scanning fluorimetry and isothermal titration calorimetry were used to study thermodynamic properties of L1 MBL compared with New Deli Metallo-beta-lactamase-1 (NDM-1). Both enzymes are significantly stabilized by Zn +2 and other divalent metals but showed different dependency. These studies also suggest that moxalactam and its hydrolyzed form may bind and dissociate with different kinetic modes with or without Zn +2 for each of L1 and NDM-1. Our analysis implicates metal ions, in forming a distinct di-metal scaffold, which is central to the enzyme stability, promiscuous substrate binding and versatile catalytic activity. Statement: The L1 metallo-beta-lactamase from an environmental multidrug-resistant opportunistic pathogen Stenotrophomonas maltophilia K279a has been studied by determining 3D structures of L1 enzyme in the complexes with several beta-lactam antibiotics and different divalent metals and characterizing its biochemical and ligand binding properties. We found that the two-metal center in the active site is critical in the enzymatic process including antibiotics recognition and binding, which explains the enzyme's activity toward diverse antibiotic substrates. This study provides the critical information for understanding the ligand recognition and for advanced drug development.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Single‐Cell Nanodroplet Processing Proteomics Pipeline for Analysis of Human‐Derived Microglia

Single-cell omics tools provide unique insights into heterogeneous cell populations and their responses to stimuli. For example, single-cell RNA sequencing has identified several transcriptionally distinct populations of microglia, which are resident immune cells of the central nervous system (CNS) that are responsive to CNS injury, infection, and neurodegeneration. To date, single-cell studies of microglia have focused on RNA-sequencing or cytometry by time of flight (CyTOF), which provide indirect readouts of protein abundance or quantification of a limited number of targets. Herein, we present a workflow based on FACS-assisted isolation, cryopreservation, and nanodroplet-based processing for single-cell mass spectrometry proteomics analysis of the postmortem human brain cortex-derived microglia. From a single microglial cell, 1039 proteins could be identified on average. As a proof-of-principle, we applied single-cell proteomics for exploring the heterogeneity of brain microglia at the cellular level. This pilot proteomics data partially recapitulates the prior microglia subtypes. Specifically, we determined that mitochondrial proteins, in particular members of NADH dehydrogenase (Complex I), cytochrome b-c1 (Complex III), cytochrome c oxidase (Complex IV), F1-ATPase (Complex V), and Na+/K+-ATPase complex, drive variation across microglia. This pipeline offers the potential for identifying functionally and analytically relevant protein targets for microglia in Alzheimer's disease and other neurological disorders.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

New insights into the natural history of bronchopulmonary dysplasia from proteomics and multiplexed immunohistochemistry

Bronchopulmonary dysplasia (BPD) is a disease of prematurity related to the arrest of normal lung development. The objective of this study was to better understand how proteome modulation and cell-type shifts are noted in BPD pathology. Pediatric human donors aged 1–3 yr were classified based on history of prematurity and histopathology consistent with “healed” BPD (hBPD, n = 3) and “established” BPD (eBPD, n = 3) compared with respective full-term born (n = 6) age-matched term controls. Proteins were quantified by tandem mass spectroscopy with selected Western blot validations. Multiplexed immunofluorescence (MxIF) microscopy was performed on lung sections to enumerate cell types. Protein abundances and MxIF cell frequencies were compared among groups using ANOVA. Cell type and ontology enrichment were performed using an in-house tool and/or EnrichR. Proteomics detected 5,746 unique proteins, 186 upregulated and 534 downregulated, in eBPD versus control with fewer proteins differentially abundant in hBPD as compared with age-matched term controls. Cell-type enrichment suggested a loss of alveolar type I, alveolar type II, endothelial/capillary, and lymphatics, and an increase in smooth muscle and fibroblasts consistent with MxIF. Histochemistry and Western analysis also supported predictions of upregulated ferroptosis in eBPD versus control. Finally, several extracellular matrix components mapping to angiogenesis signaling pathways were altered in eBPD. Despite clear parsing by protein abundance, comparative MxIF analysis confirms phenotypic variability in BPD. This work provides the first demonstration of tandem mass spectrometry and multiplexed molecular analysis of human lung tissue for critical elucidation of BPD trajectory-defining factors into early childhood.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Evaluation of co-circulating pathogens and microbiome from COVID-19 infections

Co-infections or secondary infections with SARS-CoV-2 have the potential to affect disease severity and morbidity. Additionally, the potential influence of the nasal microbiome on COVID-19 illness is not well understood. In this study, we analyzed 203 residual samples, originally submitted for SARS-CoV-2 testing, for the presence of viral, bacterial, and fungal pathogens and non-pathogens using a comprehensive microarray technology, the Lawrence Livermore Microbial Detection Array (LLMDA). Eighty-seven percent of the samples were nasopharyngeal samples, and 23% of the samples were oral, nasal and oral pharyngeal swabs. We conducted bioinformatics analyses to examine differences in microbial populations of these samples, as a proxy for the nasal and oral microbiome, from SARS-CoV-2 positive and negative specimens. We found 91% concordance with the LLMDA relative to a diagnostic RT-qPCR assay for detection of SARS-CoV-2. Sixteen percent of all the samples (32/203) revealed the presence of an opportunistic bacterial or frank viral pathogen with the potential to cause co-infections. The two most detected bacteria, Streptococcus pyogenes and Streptococcus pneumoniae , were present in both SARS-CoV-2 positive and negative samples. Human metapneumovirus was the most prevalent viral pathogen in the SARS-CoV-2 negative samples. Sequence analysis of 16S rRNA was also conducted to evaluate bacterial diversity and confirm LLMDA results.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Identification of candidate host-specificity genes in Exserohilum turcicum using comparative genomics and transcriptomics

Abstract Exserohilum turcicum causes northern corn leaf blight and sorghum leaf blight. While the same species cause disease in both crops, the strains are host-specific. Here, we report the sequence and de novo annotated assemblies of one sorghum- and one maize-specific E. turcicum strain. The strains were sequenced using the PacBio Sequel II system. The total genome length for both assemblies was between 44 and 45 Mb with N50 of ∼2.5 Mb. Ninety-eight percent of the Benchmarking Universal Single-Copy Orthologs (BUSCO) for both assemblies had complete status. The estimated number of genes was 11,762 and 12,029 in the sorghum- and maize-specific isolates, respectively. Funannotate, EffectorP, SignalP, and transcriptome data were used to create functional annotation of each genome. The whole-genome comparison identified ten large-scale inversions and three translocations between the maize- and sorghum-specific strains, along with homologous genes and gene duplications. RNA was sequenced from the maize- and sorghum-specific isolate 10 days post-inoculation in maize and sorghum and from axenic cultures. Gene expression data from planta and axenic growth experiments were compared for each strain. Candidate host-specificity genes were identified by combining results from whole-genome comparison, synteny analysis, gene annotations, and transcriptome data. Overall, this study identified several candidate host-specificity genes that provide insights into E. turcicum interaction with its hosts.

Krone, Mara J. (ORCID:0000000159006624)↗
DOE OSTI2025

Actinomycetota isolated from the sponge Hymeniacidon perlevis as a source of novel compounds with pharmacological applications: diversity, bioactivity screening, and metabolomic analysis

Abstract Aims To combat health conditions, such as multi-resistant bacterial infections, cancer, and metabolic diseases, new drugs need to be urgently found and, in this respect, marine Actinomycetota have a high potential to produce secondary metabolites with pharmacological importance. We aimed to study the cultivable Actinomycetota community associated with a marine sponge from the Portuguese coast, Hymeniacidon perlevis, and investigate the potential of the retrieved isolates to produce compounds with antimicrobial, anticancer and anti-obesity properties. Methods and results The analysis of the 16S rRNA gene revealed 79 Actinomycetota isolates affiliated with 12 genera—Brachybacterium, Dietzia, Glutamicibacter, Gordonia, Micrococcus, Micromonospora, Nocardia, Nocardiopsis, Paenoartrhobacter, Rhodococcus, Streptomyces, and Tsukamurella, most of which affiliated with the genus Streptomyces. The screening of antimicrobial activity revealed 13 strains, all belonging to the Streptomyces genus, capable of inhibiting the growth of Candida albicans, Bacillus subtilis, or Staphylococcus aureus. Forty-three extracts exhibited cytotoxic activity against at least one tested cell line (HepG2, HCT-116, and hCMEC-D3). Three extracts that were active against the two cancer cell lines tested, did not reduce the viability of the non-cancer endothelial cell line, hCMEC-D3. One Gordonia strain exhibited anti-obesity activity, revealed by its ability to reduce the neutral lipids in zebrafish larvae. Mass spectrometry-based dereplication analysis of active extracts identified several compounds associated with known Actinomycetota natural products. Nonetheless, five clusters contained metabolites that did not match any annotated natural products, suggesting they may represent new bioactive molecules. Conclusions This work contributed to increase the knowledge on the diversity and bioactive potential of Actinomycetota associated with H. perlevis.

Fonseca, Ana C.↗
DOE OSTI2023

Impaired cardiac glycolysis and glycogen depletion are linked to poor myocardial outcomes in juvenile male swine with metabolic syndrome and ischemia

Abstract Obesity continues to rise in the juveniles and obese children are more likely to develop metabolic syndrome (MetS) and related cardiovascular disease. Unfortunately, effective prevention and long‐term treatment options remain limited. We determined the juvenile cardiac response to MetS in a swine model. Juvenile male swine were fed either an obesogenic diet, to induce MetS, or a lean diet, as a control (LD). Myocardial ischemia was induced with surgically placed ameroid constrictor on the left circumflex artery. Physiological data were recorded and at 22 weeks of age the animals underwent a terminal harvest procedure and myocardial tissue was extracted for total metabolic and proteomic LC/MS–MS, RNA‐seq analysis, and data underwent nonnegative matrix factorization for metabolic signatures. Significantly altered in MetS versus. LD were the glycolysis‐related metabolites and enzymes. In MetS compared with LD Glycogen synthase 1 (GYS1)‐glycogen phosphorylases (PYGM/PYGL) expression disbalance resulted in a loss of myocardial glycogen. Our findings are consistent with the concept that transcriptionally driven myocardial changes in glycogen and glucose metabolism‐related enzymes lead to a deficiency of their metabolite products in MetS. This abnormal energy metabolism provides insight into the pathogenesis of the juvenile heart in MetS. This study reveals that MetS and ischemia diminishes ATP availability in the myocardium via altering the glucose‐G6P‐pyruvate axis at the level of metabolites and gene expression of related enzymes. The observed severe glycogen depletion in MetS coincides with disbalance in expression of GYS1 and both PYGM and PYGL. This altered energy substrate metabolism is a potential target of pharmacological agents for improving juvenile myocardial function in MetS and ischemia.

Broadwin, Mark↗