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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Emergence of nosocomial associated opportunistic pathogens in the gut microbiome after antibiotic treatment

Introduction: According to the Centers for Disease Control’s 2015 Hospital Acquired Infection Hospital Prevalence Survey, 1 in 31 hospital patients was infected with at least one nosocomial pathogen while being treated for unrelated issues. Many studies associate antibiotic administration with nosocomial infection occurrence. However, to our knowledge, there is little to no direct evidence of antibiotic administration selecting for nosocomial opportunistic pathogens. Aim: This study aims to confirm gut microbiota shifts in an animal model of antibiotic treatment to determine whether antibiotic use favors pathogenic bacteria. Methodology: We utilized next-generation sequencing and in-house metagenomic assembly and taxonomic assignment pipelines on the fecal microbiota of a urinary tract infection mouse model with and without antibiotic treatment. Results: Antibiotic therapy decreased the number of detectable species of bacteria by at least 20-fold. Furthermore, the gut microbiota of antibiotic treated mice had a significant increase of opportunistic pathogens that have been implicated in nosocomial infections, like Acinetobacter calcoaceticus/baumannii complex, Chlamydia abortus, Bacteroides fragilis, and Bacteroides thetaiotaomicron. Moreover, antibiotic treatment selected for antibiotic resistant gene enriched subpopulations for many of these opportunistic pathogens. Conclusions: Oral antibiotic therapy may select for common opportunistic pathogens responsible for nosocomial infections. In this study opportunistic pathogens present after antibiotic therapy harbored more antibiotic resistant genes than populations of opportunistic pathogens before treatment. Our results demonstrate the effects of antibiotic therapy on induced dysbiosis and expansion of opportunistic pathogen populations and antibiotic resistant subpopulations of those pathogens. Follow-up studies with larger samples sizes and potentially controlled clinical investigations should be performed to confirm our findings.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Dose optimization of an adjuvanted peptide-based personalized neoantigen melanoma vaccine

The advancements in next-generation sequencing have made it possible to effectively detect somatic mutations, which has led to the development of personalized neoantigen cancer vaccines that are tailored to the unique variants found in a patient’s cancer. These vaccines can provide significant clinical benefit by leveraging the patient’s immune response to eliminate malignant cells. However, determining the optimal vaccine dose for each patient is a challenge due to the heterogeneity of tumors. To address this challenge, we formulate a mathematical dose optimization problem based on a previous mathematical model that encompasses the immune response cascade produced by the vaccine in a patient. We propose an optimization approach to identify the optimal personalized vaccine doses, considering a fixed vaccination schedule, while simultaneously minimizing the overall number of tumor and activated T cells. To validate our approach, we perform in silico experiments on six real-world clinical trial patients with advanced melanoma. We compare the results of applying an optimal vaccine dose to those of a suboptimal dose (the dose used in the clinical trial and its deviations). Our simulations reveal that an optimal vaccine regimen of higher initial doses and lower final doses may lead to a reduction in tumor size for certain patients. Our mathematical dose optimization offers a promising approach to determining an optimal vaccine dose for each patient and improving clinical outcomes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Association of microbial dynamics with urinary estrogens and estrogen metabolites in patients with endometriosis

Endometriosis is an estrogen dependent gynecological disease associated with altered microbial phenotypes. The association among endogenous estrogen, estrogen metabolites, and microbial dynamics on disease pathogenesis has not been fully investigated. Here, we identified estrogen metabolites as well as microbial phenotypes in non-diseased patients (n = 9) and those with pathologically confirmed endometriosis (P-EOSIS, n = 20), on day of surgery (DOS) and ~1–3 weeks post-surgical intervention (PSI). Then, we examined the effects of surgical intervention with or without hormonal therapy (OCPs) on estrogen and microbial profiles of both study groups. For estrogen metabolism analysis, liquid chromatography/tandem mass spectrometry was used to quantify urinary estrogens. The microbiome data assessment was performed with Next generation sequencing to V4 region of 16S rRNA. Surgical intervention and hormonal therapy altered gastrointestinal (GI), urogenital (UG) microbiomes, urinary estrogen and estrogen metabolite levels in P-EOSIS. At DOS, 17β-estradiol was enhanced in P-EOSIS treated with OCPs. At PSI, 16-keto-17β-estradiol was increased in P-EOSIS not receiving OCPs while 2-hydroxyestradiol and 2-hydroxyestrone were decreased in P-EOSIS receiving OCPs. GI bacterial α-diversity was greater for controls and P-EOSIS that did not receive OCPs. P-EOSIS not utilizing OCPs exhibited a decrease in UG bacterial α-diversity and differences in dominant taxa, while P-EOSIS utilizing OCPs had an increase in UG bacterial α-diversity. P-EOSIS had a strong positive correlation between the GI/UG bacteria species and the concentrations of urinary estrogen and its metabolites. These results indicate an association between microbial dysbiosis and altered urinary estrogens in P-EOSIS, which may impact disease progression.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

COVID-19 biomarkers based on respiratory microbiome content

COVID-19 patient care management would greatly benefit from new tools that enable accurate assessment of disease severity and stage, potentially enabling a personalized medicine approach. Detection of the SARS-CoV-2 virus itself, or even quantitation of viral loads, is not sufficient for accurate assessment of disease state beyond diagnosis of infection [eg, doi:10.1093/cid/ciaa344]. Levels of usual suspect protein biomarkers associated with host response to infection [eg, C-reactive protein (CRP); cytokines like IL-6, TNF-alpha, and IL-10; complement proteins like C3a and C5a], and of individual blood cell types (eg, leukocytes, lymphocytes, and subsets thereof), show limited correlation with disease severity and stage, with high patient-to-patient and study-to-study variability [eg, doi:10.1093/cid/ciaa248]. High-dimensional panels of biomarkers should have greater predictive power and resilience to unavoidable sources of variability; however, their assembly from proteins and cell types is extremely difficult, due to technical limitations in analyte measurement, especially with regard to starting material requirements and detection sensitivity. Host response profiling through Next Generation Sequencing (NGS) of gene expression patterns (ie, RNA-Seq) is a promising approach, but at the time of this project there were only two publicly available datasets of relevance [doi:10.1093/cid/ciaa203, doi:10.1080/22221751.2020.1747363], and close inspection of them revealed that each had at least one major flaw that severely undermined its value in supporting robust analysis of host response to SARS-CoV- 2 infection. However, the first of these studies [doi:10.1093/cid/ciaa203] fortuitously collected NGS data not only from host cells, but also from bacteria present in bronchoalveolar lavage fluid (BALF) recovered from COVID-19 patients; and because the respiratory microbiome (in terms of bacterial species content) is far less complex than the human transcriptome, the NGS data collected were sufficient to provide coverage depth supporting robust analysis. Surprisingly, the authors of the study did not carry out a detailed analysis of these data and their potential for revealing important new information about COVID-19. Therefore, we carried out a meta-analysis of the dataset as a first step in evaluating the potential for profiling of respiratory microbiome dynamics as a means of accurately assessing COVID-19 disease state.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Ice Nucleating Particles, Aerosols and Clouds over the Higher Latitude Southern Ocean

Improved understanding of aerosol-cloud-precipitation interactions is a key goal of the DOE-ASR program. Ice Nucleating Particles (INPs) are rare aerosol particles that trigger ice formation in clouds. By doing so they may initiate precipitation in clouds, and this impacts cloud lifetime. Nowhere may the role of INPs be manifested so dramatically as in the Southern Ocean (SO). This vast region was the focus of the DOE-ARM MARCUS (Measurement of Aerosols, Radiation and CloUds over the Southern Oceans) and MICRE (Macquarie Island Cloud and Radiation Experiment) campaigns. In the SO, a deficit of INPs has been a leading hypothesis for why liquid clouds persist, which underlays the bias of global climate models in predicting excess shortwave radiation reaching the ocean surface between 55°S and Antarctica. In this study, we used the 6-month acquisition of INP data on four MARCUS cruises from Hobart, Tasmania, to Antarctica, and the single-location full annual INP cycle observed in MICRE, to gain a holistic, quantitative picture of the INP number concentrations as a function of temperature and their variability over SO latitudes from 43°S to Antarctica. Augmenting initial processing of filter collections of aerosol particles during MARCUS and MICRE, additional freezing studies following thermal treatment (to removes INPs associated with microbes) and peroxide treatment (to remove all organics), as well as ionic chemistry and total organic carbon analysis, were applied to samples to improve time and space resolution, and differentiate marine from terrestrial contributions. We also performed Next Generation Sequencing to determine that bacterial composition can tag periods of enhanced and degraded marine organic INPs and the relative lack of occurrence of land-sourced bio-particles. Through these and other analyses, INP data were categorized into representative source types (e.g., marine versus terrestrially-influenced). The products of our analyses are being used to parameterize ice formation for use in numerical modeling studies to determine if, and when, ocean-derived INPs control the microphysical composition and radiative balance of SO clouds in a manner not presently captured by global models. The specific objectives and approaches proposed were largely accomplished, including: 1) Additional and value-added chemical and biological analyses of archived samples of aerosols were completed to categorize and quantify INP types and concentrations over the SO. 2) Analyses were completed to place INP data in meteorological and aerosol context. 3) Sea spray aerosol INPs were demonstrated to dominate the SO region in all but episodic events. 4) New parameterizations for INP sources relevant to the SO were constructed. 5) Collaborative modeling studies demonstrating the crucial role of INPs in determining cloud radiative and precipitation properties were conducted with MARCUS and MICRE partners. Collaborative publication submissions in this regard ensued within the two year study. This work will advance the science of interactions of aerosols, clouds and precipitation, to improve their representation in regional and global climate models. Results have informed representation of primary ice crystal nucleation, give inference to the role of secondary processes, and improve investigations of aerosol influences on clouds via ice nucleation over SO high latitudes and similar vast ocean regions. Application will improve representation of such interactions for clouds in both regional and global climate models. The data base and analyses methods applied will continue to serve research studies in the future.

58 GEOSCIENCES↗
DOE OSTI2022

Prokaryotic and eukaryotic cell-free systems for prototyping (CRADA Final Report)

CRADA FP00008491 between Berkeley Lab and Synvitrobio, Inc. (now Tierra Biosciences) validated the use of cell-free phenotyping to conduct functional genomics. Currently, most phenotyping work occurs using cellular fermentation methods and cellular techniques. This limits functional genomics throughput, which increasingly cannot handle the wealth of genetic information developed from next-generation sequencing technologies. De-risking a cell-free phenotyping approach has the advantage of increasing multiple-fold the throughput of genetic information that can be explored and expanding the $3B market for protein synthesis and characterization, leading to the accelerated development of human therapeutics and new biologically based materials.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

High throughput single cell multiomics platform [Abstract]

In this collaborative project, PNNL and Scienion will co-develop an integrated microfluidic technology to co-measure the transcriptome and proteome in single cells. The technology will enable us to efficiently separate proteins from mRNA transcripts between two microchips, barcode the molecules, and measure them separately with next-generation sequencing and mass spectrometry, respectively. Multicellular organisms contain diverse cell types and tremendous cell-to-cell heterogeneity that dictates a multitude of biological functions in both physiological and pathological environments. Even in the case of microbes, these genetically identical organisms can randomly differentiate into many subpopulations that assume different roles for the survival of the community. Bulk-scale measurements are insufficient to resolve such complexities. The development and applications of high throughput single-cell omics technologies have transformed our understanding of cellular heterogeneities and their differential responses to internal signaling events or external stimulations. Despite these advances, most current single-cell omic technologies provide information on only one type of biomolecule. Perse, such measurements provide incomplete information because the cell phenotype is determined by multiple layers of biomolecules and the interplay between genome, epigenome, transcriptome, and proteome. For example, mRNA abundance in one cell can not be precisely referred to the corresponding DNA and protein in other cells because of the potential subtle difference in genotype (e.g., somatic mutation or copy number variation) or phenotype (external microenvironment and cell-cell interactions). As such, parallel measurement of multiple biomolecules in the same single cells can offer unique advantages compared with measuring them separately in different single cells. Scienion is a world-leading biotech company focusing on precision liquid handling and its application in single-cell whole-genome sequencing and RNA sequencing. PNNL is the leading institution in ultrasensitive mass spectrometry, microfluidics, and untargeted single-cell proteomics (scProteomics). This collaboration will facilitate a unique fusion between scTranscriptomics capability at Scienion and scProteomics capabilities at PNNL to, for the first time, perform both untargeted transcriptomics and proteomics from the same single cells.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

High throughput, accurate gene annotation through AI and HPC-enabled structural analysis

With the advances in next generation sequencing technologies, the number of sequenced genomes is growing exponentially, resulting in a technology bottleneck for the translation of sequence information into usable hypotheses about the function of each gene. We have proposed leveraging our leadership high-performance computing (HPC) resources to help break this annotation bottleneck. Here we design an HPC-based framework to infer gene function from gene sequence by incorporating information about protein structure and interactions predicted by deep learning approaches. Accurate functional prediction and gene annotation using computational methods will facilitate breakthroughs in the genomic sciences essential to understanding and harnessing life processes in bacteria, fungi and plants. The development and applications of the state-of-the-art deep neural networks to protein structural modeling, interaction prediction, sequence comparison, and quality assessment of protein structural models will be made possible by leadership computational resources. These HPC-enabled bioinformatics and molecular modeling tools will provide powerful insights into molecular functions of genes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Modularization of EDGE Workflows Using Nextflow: Improving the Efficiency and Maintainability of Bioinformatics Software

EDGE is a bioinformatics platform developed in 2016 by researchers at Los Alamos National Laboratory (LANL) to facilitate the analysis of next-generation sequencing data by researchers with varying levels of experience in bioinformatics (Li et al., 2017). Users with single-end, paired-end or long-read sequencing data can provide their reads as input to EDGE and select the combination of workflows to run that are most useful for their research (e.g., quality control of reads, genome assembly, or the taxonomic classification of input reads). Table 1 summarizes the modules available in EDGE. EDGE is available as a web platform at https://edgebioinformatics.org, as installable source code maintained on GitHub under a GPLv3 license, and as a publicly hosted Docker image.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Stability of Floodplain Subsurface Microbial Communities Through Seasonal Hydrological and Geochemical Cycles

Riparian floodplains represent an interaction zone between the terrestrial subsurface and rivers, where regional groundwater flows, infiltration, and evapotranspiration drive mixing of water and import/export of nutrients and contaminants. These dynamics create seasonally transient redox conditions that drive biogeochemical transformations, which strongly modify groundwater quality. Microbial responses to changing hydrological conditions are perhaps the critical step connecting hydrology to geochemical transformations and groundwater quality, yet are not well understood. We aimed to address this knowledge gap by monitoring seasonal transitions at the U.S. Department of Energy legacy uranium ore processing site in Riverton, WY, through spring-summer-fall hydrological transitions. Our goal was to characterize the microbial community throughout the soil profile, down to the saturated aquifer, and observe its response to wet-dry transitions across a full season and compare to changes in geochemistry and hydrology. Next-generation sequencing was employed to identify biogeochemically-relevant microbial taxa based on the 16S rRNA gene; we found a broad diversity of microbial clades including taxa involved in sulfur and metal cycling, as well as nitrification. These data were paired with measurements of soil moisture, major nutrients and cations, and trace elements. Overall microbial community composition was dependent on soil depth or type, with seasonal effects only observed in the topsoil or subsurface aquifer. This finding indicates that microbial communities in the transiently-reduced center of the soil profile at the Riverton, WY site are remarkably stable, despite moisture and redox inversions. In addition, these communities likely impact the communities in surrounding soil horizons through export of metabolites and solutes as the water table rises and falls throughout the season.

16S rRNA↗
DOE OSTI2020

FeGenie: a comprehensive tool for the identification of iron genes and iron gene neighborhoods in genomes and metagenome assemblies

Iron is a micronutrient for nearly all life on Earth. It can be used as an electron donor and electron acceptor by iron-oxidizing and iron-reducing microorganisms and is used in a variety of biological processes, including photosynthesis and respiration. While it is the fourth most abundant metal in the Earth’s crust, iron is often limiting for growth in oxic environments because it is readily oxidized and precipitated. Much of our understanding of how microorganisms compete for and utilize iron is based on laboratory experiments. However, the advent of next-generation sequencing and surge in publicly available sequence data has made it possible to probe the structure and function of microbial communities in the environment. To bridge the gap between our understanding of iron acquisition, iron redox cycling, iron storage, and magnetosome formation in model microorganisms and the plethora of sequence data available from environmental studies, we have created a comprehensive database of hidden Markov models (HMMs) based on genes related to iron acquisition, storage, and reduction/oxidation in Bacteria and Archaea. Along with this database, we present FeGenie, a bioinformatics tool that accepts genome and metagenome assemblies as input and uses our comprehensive HMM database to annotate provided datasets with respect to iron-related genes and gene neighborhood. An important contribution of this tool is the efficient identification of genes involved in iron oxidation and dissimilatory iron reduction, which have been largely overlooked by standard annotation pipelines. We validated FeGenie against a selected set of 28 isolate genomes and showcase its utility in exploring iron genes present in 27 metagenomes, 4 isolate genomes from human oral biofilms, and 17 genomes from candidate organisms, including members of the candidate phyla radiation. We show that FeGenie accurately identifies iron genes in isolates. Furthermore, analysis of metagenomes using FeGenie demonstrates that the iron gene repertoire and abundance of each environment is correlated with iron richness. While this tool will not replace the reliability of culture-dependent analyses of microbial physiology, it provides reliable predictions derived from the most up-to-date genetic markers. FeGenie’s database will be maintained and continually updated as new genes are discovered.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Modulation of Plant and Fungal Gene Expression Upon Cd Exposure and Symbiosis in Ericoid Mycorrhizal Vaccinium myrtillus

The success of Ericaceae in stressful habitats enriched in heavy metals has been ascribed to the distinctive abilities of their mycorrhizal fungal partners to withstand heavy metal stress and to enhance metal tolerance in the host plant. Whereas heavy metal tolerance has been extensively investigated in some ericoid mycorrhizal (ERM) fungi, the molecular and cellular mechanisms that extend tolerance to the host plant are currently unknown. Here, we show a reduced Cd content in Cd-exposed mycorrhizal roots of Vaccinium myrtillus colonized by a metal tolerant isolate of the fungus Oidiodendron maius as compared to non-mycorrhizal roots. To better understand this phenotype, we applied Next Generation Sequencing technologies to analyze gene expression in V. myrtillus and O. maius Zn grown under normal and Cd-stressed conditions, in the free living and in the mycorrhizal status. The results clearly showed that Cd had a stronger impact on plant gene expression than symbiosis, whereas fungal gene expression was mainly regulated by symbiosis. The higher abundance of transcripts coding for stress related proteins in non-mycorrhizal roots may be related to the higher Cd content. Regulated plant metal transporters have been identified that may play a role in reducing Cd content in mycorrhizal roots exposed to this metal.

Cd tolerance, transcriptomics↗
DOE OSTI2020

Comparative Genomic Analysis of Ochratoxin A Biosynthetic Cluster in Producing Fungi: New Evidence of a Cyclase Gene Involvement

The widespread use of Next-Generation Sequencing has opened a new era in the study of biological systems by significantly increasing the catalog of fungal genomes sequences and identifying gene clusters for known secondary metabolites as well as novel cryptic ones. However, most of these clusters still need to be examined in detail to completely understand the pathway steps and the regulation of the biosynthesis of metabolites. Genome sequencing approach led to the identification of the biosynthetic genes cluster of ochratoxin A (OTA) in a number of producing fungal species. Ochratoxin A is a potent pentaketide nephrotoxin produced by Aspergillus and Penicillium species and found as widely contaminant in food, beverages and feed. The increasing availability of several new genome sequences of OTA producer species in JGI Mycocosm and/or GenBank databanks led us to analyze and update the gene cluster structure in 19 Aspergillus and 2 Penicillium OTA producing species, resulting in a well conserved organization of OTA core genes among the species. Furthermore, our comparative genome analyses evidenced the presence of an additional gene, previously undescribed, located between the polyketide and non-ribosomal synthase genes in the cluster of all the species analyzed. The presence of a SnoaL cyclase domain in the sequence of this gene supports its putative role in the polyketide cyclization reaction during the initial steps of the OTA biosynthesis pathway. The phylogenetic analysis showed a clustering of OTA SnoaL domains in accordance with the phylogeny of OTA producing species at species and section levels. The characterization of this new OTA gene, its putative role and its expression evidence in three important representative producing species, are reported here for the first time.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Molecular evolution of human coronavirus-NL63, -229E, -HKU1 and -OC43 in hospitalized children in China

Human coronaviruses (HCoVs) HCoV-NL63, HCoV-229E, HCoV-HKU1 and HCoV-OC43 have been circulated in the human population worldwide, and they are associated with a broad range of respiratory diseases with varying severity. However, there are neither effective therapeutic drugs nor licensed vaccines available for the treatment and prevention of infections by the four HCoVs. In this study, we collected nasopharyngeal aspirates of children hospitalized for respiratory tract infection in China during 2014–2018 and conducted next-generation sequencing. Sequences of four HCoVs were then selected for an in-depth analysis. Genome sequences of 2 HCoV-NL63, 8 HCoV-229E, 2 HCoV-HKU1, and 6 HCoV-OC43 were obtained. Based on the full-length S gene, a strong temporal signal was found in HCoV-229E and the molecular evolutionary rate was 6 × 10 −4 substitutions/site/year. Based on the maximum-likelihood (ML) phylogenetic tree of complete S gene, we designated H78 as a new sub-genotype C2 of HCoV-HKU1, and the obtained P43 sequence was grouped into the reported novel genotype K of HCoV-OC43 circulating in Guangzhou, China. Based on the complete genome, potential recombination events were found to occur as two phenomena, namely intraspecies and interspecies. Moreover, we observed two amino acid substitutions in the S1 subunit of obtained HCoV-NL63 (G534V) and HCoV-HKU1 (H512R), while residues 534 and 512 are important for the binding of angiotensin-converting enzyme 2 and neutralizing antibodies, respectively. Our findings might provide a clue for the molecular evolution of the four HCoVs and help in the early diagnosis, treatment and prevention of broad-spectrum HCoV infection.

Shao, Nan↗
DOE OSTI2024

The contribution of BvgR, RisA, and RisS to global gene regulation, intracellular cyclic-di-GMP levels, motility, and biofilm formation in Bordetella bronchiseptica

Bordetella bronchiseptica is a highly contagious respiratory bacterial veterinary pathogen. In this study the contribution of the transcriptional regulators BvgR, RisA, RisS, and the phosphorylation of RisA to global gene regulation, intracellular cyclic-di-GMP levels, motility, and biofilm formation were evaluated. Next Generation Sequencing (RNASeq) was used to differentiate the global gene regulation of both virulence-activated and virulence-repressed genes by each of these factors. The BvgAS system, along with BvgR, RisA, and the phosphorylation of RisA served in cyclic-di-GMP degradation. BvgR and unphosphorylated RisA were found to temporally regulate motility. Additionally, BvgR, RisA, and RisS were found to be required for biofilm formation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Transboundary determinants of avian zoonotic infectious diseases: challenges for strengthening research capacity and connecting surveillance networks

As the climate changes, global systems have become increasingly unstable and unpredictable. This is particularly true for many disease systems, including subtypes of highly pathogenic avian influenzas (HPAIs) that are circulating the world. Ecological patterns once thought stable are changing, bringing new populations and organisms into contact with one another. Wild birds continue to be hosts and reservoirs for numerous zoonotic pathogens, and strains of HPAI and other pathogens have been introduced into new regions via migrating birds and transboundary trade of wild birds. With these expanding environmental changes, it is even more crucial that regions or counties that previously did not have surveillance programs develop the appropriate skills to sample wild birds and add to the understanding of pathogens in migratory and breeding birds through research. For example, little is known about wild bird infectious diseases and migration along the Mediterranean and Black Sea Flyway (MBSF), which connects Europe, Asia, and Africa. Focusing on avian influenza and the microbiome in migratory wild birds along the MBSF, this project seeks to understand the determinants of transboundary disease propagation and coinfection in regions that are connected by this flyway. Through the creation of a threat reduction network for avian diseases (Avian Zoonotic Disease Network, AZDN) in three countries along the MBSF (Georgia, Ukraine, and Jordan), this project is strengthening capacities for disease diagnostics; microbiomes; ecoimmunology; field biosafety; proper wildlife capture and handling; experimental design; statistical analysis; and vector sampling and biology. Here, we cover what is required to build a wild bird infectious disease research and surveillance program, which includes learning skills in proper bird capture and handling; biosafety and biosecurity; permits; next generation sequencing; leading-edge bioinformatics and statistical analyses; and vector and environmental sampling. Creating connected networks for avian influenzas and other pathogen surveillance will increase coordination and strengthen biosurveillance globally in wild birds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The Sorghum (Sorghum bicolor) Brown Midrib 30 Gene Encodes a Chalcone Isomerase Required for Cell Wall Lignification

In sorghum ( Sorghum bicolor ) and other C 4 grasses, brown midrib ( bmr ) mutants have long been associated with plants impaired in their ability to synthesize lignin. The brown midrib 30 ( Bmr30 ) gene, identified using a bulk segregant analysis and next-generation sequencing, was determined to encode a chalcone isomerase (CHI). Two independent mutations within this gene confirmed that loss of its function was responsible for the brown leaf midrib phenotype and reduced lignin concentration. Loss of the Bmr30 gene function, as shown by histochemical staining of leaf midrib and stalk sections, resulted in altered cell wall composition. In the bmr30 mutants, CHI activity was drastically reduced, and the accumulation of total flavonoids and total anthocyanins was impaired, which is consistent with its function in flavonoid biosynthesis. The level of the flavone lignin monomer tricin was reduced 20-fold in the stem relative to wild type, and to undetectable levels in the leaf tissue of the mutants. The bmr30 mutant, therefore, harbors a mutation in a phenylpropanoid biosynthetic gene that is key to the interconnection between flavonoids and monolignols, both of which are utilized for lignin synthesis in the grasses.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A scoping review of zoonotic parasites and pathogens associated with abattoirs in Eastern Africa and recommendations for abattoirs as disease surveillance sites

Abattoirs are facilities where livestock are slaughtered and are an important aspect in the food production chain. There are several types of abattoirs, which differ in infrastructure and facilities, sanitation and PPE practices, and adherence to regulations. In each abattoir facility, worker exposure to animals and animal products increases their risk of infection from zoonotic pathogens. Backyard abattoirs and slaughter slabs have the highest risk of pathogen transmission because of substandard hygiene practices and minimal infrastructure. These abattoir conditions can often contribute to environmental contamination and may play a significant role in disease outbreaks within communities. To assess further the risk of disease, we conducted a scoping review of parasites and pathogens among livestock and human workers in abattoirs across 13 Eastern African countries, which are hotspots for zoonoses. Our search results (n = 104 articles) showed the presence of bacteria, viruses, fungi, and macroparasites (nematodes, cestodes, etc.) in cattle, goats, sheep, pigs, camels, and poultry. Most articles reported results from cattle, and the most frequent pathogen detected was Mycobacterium bovis, which causes bovine tuberculosis. Some articles included worker survey and questionnaires that suggested how the use of PPE along with proper worker training and safe animal handling practices could reduce disease risk. Based on these findings, we discuss ways to improve abattoir biosafety and increase biosurveillance for disease control and mitigation. Abattoirs are a ‘catch all’ for pathogens, and by surveying animals at abattoirs, health officials can determine which diseases are prevalent in different regions and which pathogens are most likely transmitted from wildlife to livestock. We suggest a regional approach to biosurveillance, which will improve testing and data gathering for enhanced disease risk mapping and forecasting. Next generation sequencing will be key in identifying a wide range of pathogens, rather than a targeted approach.

60 APPLIED LIFE SCIENCES↗