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DOE OSTI2025

Biophysical and biochemical evidence for the role of acetate kinases (AckAs) in an acetogenic pathway in pathogenic spirochetes

Unraveling the metabolism of Treponema pallidum is a key component to understanding the pathogenesis of the human disease that it causes, syphilis. For decades, it was assumed that glucose was the sole carbon/energy source for this parasitic spirochete. But the lack of citric-acid-cycle enzymes suggested that alternative sources could be utilized, especially in microaerophilic host environments where glycolysis should not be robust. Recent bioinformatic, biophysical, and biochemical evidence supports the existence of an acetogenic energy-conservation pathway in T . pallidum and related treponemal species. In this hypothetical pathway, exogenous D-lactate can be utilized by the bacterium as an alternative energy source. Herein, we examined the final enzyme in this pathway, acetate kinase (named TP0476), which ostensibly catalyzes the generation of ATP from ADP and acetyl-phosphate. We found that TP0476 was able to carry out this reaction, but the protein was not suitable for biophysical and structural characterization. We thus performed additional studies on the homologous enzyme (75% amino-acid sequence identity) from the oral pathogen Treponema vincentii , TV0924. This protein also exhibited acetate kinase activity, and it was amenable to structural and biophysical studies. We established that the enzyme exists as a dimer in solution, and then determined its crystal structure at a resolution of 1.36 Å, showing that the protein has a similar fold to other known acetate kinases. Mutation of residues in the putative active site drastically altered its enzymatic activity. A second crystal structure of TV0924 in the presence of AMP (at 1.3 Å resolution) provided insight into the binding of one of the enzyme’s substrates. On balance, this evidence strongly supported the roles of TP0476 and TV0924 as acetate kinases, reinforcing the hypothesis of an acetogenic pathway in pathogenic treponemes.

Deka, Ranjit K.↗

DOE OSTI2025

Sequencing and analysis of 131 SARS-CoV-2 isolates in previously sampled and unsampled regions of Jordan from 2020 to 2023

The Hashemite Kingdom of Jordan remains an understudied country for next generation sequencing analysis of SARS-CoV-2 genomes collected during the 2019 pandemic. Here we provide 131 additional reference genomes collected between 2020–2023 from SARS-CoV-2-positive patients across Jordan. Phylogenetic analysis supports existing pandemic narratives of changing clade dominance over time and adds genomes in novel Jordanian locations and timepoints to make Jordan SARS-CoV-2 databases more comprehensive. Samples from the less-sequenced cities of Ajloun, Jaresh, Karak, and Madaba identified previously unreported lineages while Amman, Irbid, and Zarqa have existing sequencing efforts bolstered. Despite many incomplete patient records and a relatively small sample size, we observe interesting symptom patterns that support existing global and Jordanian pandemic narratives. We note how in-country COVID-19 pandemic genomic studies showcase Jordan’s efforts to expand next generation sequencing capabilities, especially through the leveraging of EDGE COVID-19, a bioinformatics platform for performing rapid, batched analysis of SARS-CoV-2 sequencing that streamlines sample processing prepared from a network of hospital locations.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

Transcriptome-Wide Cleavage Site Mapping on Cellular mRNAs Reveals Features Underlying Sequence-Specific Cleavage by the Viral Ribonuclease SOX

Many viruses express factors that reduce host gene expression through widespread degradation of cellular mRNA. An example of this class of proteins is the mRNA-targeting endoribonuclease SOX from the gamma-herpesvirus Kaposi’s sarcoma-associated herpesvirus (KSHV). Previous studies indicated that cleavage of messenger RNAs (mRNA) by SOX occurs at specific locations defined by the sequence of the target RNA, which is at odds with the down-regulation of a large portion of cellular transcripts. In this study, we address this paradox by using high-throughput sequencing of cleavage intermediates combined with a custom bioinformatics-based analysis pipeline to identify SOX cleavage sites across the mRNA transcriptome. These data, coupled with targeted mutagenesis, reveal that while cleavage sites are specific and reproducible, they are defined by a degenerate sequence motif containing a small number of conserved residues rather than a strong consensus sequence. This degenerate element is well represented in both human and KSHV mRNA, and its presence correlates with RNA destabilization by SOX. This represents a new endonuclease targeting strategy, in which use of a degenerate targeting element enables RNA cleavage at specific locations without restricting the range of targets. Furthermore, it shows that strong target selectivity can be achieved without a high degree of sequence specificity.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2018

A Model Integration Pipeline for the Improvement of Human Genome-Scale Metabolic Reconstructions

Abstract Metabolism has been a major field of study in the last years, mainly due to its importance in understanding cell physiology and certain disease phenotypes due to its deregulation. Genome-scale metabolic models (GSMMs) have been established as important tools to help achieve a better understanding of human metabolism. Towards this aim, advances in systems biology and bioinformatics have allowed the reconstruction of several human GSMMs, although some limitations and challenges remain, such as the lack of external identifiers for both metabolites and reactions. A pipeline was developed to integrate multiple GSMMs, starting by retrieving information from the main human GSMMs and evaluating the presence of external database identifiers and annotations for both metabolites and reactions. Information from metabolites was included into a graph database with omics data repositories, allowing clustering of metabolites through their similarity regarding database cross-referencing. Metabolite annotation of several older GSMMs was enriched, allowing the identification and integration of common entities. Using this information, as well as other metrics, we successfully integrated reactions from these models. These methods can be leveraged towards the creation of a unified consensus model of human metabolism.

Vieira, Vítor↗

DOE OSTI2016

Investigating the Role of Extensin Proteins in Poplar Biomass Recalcitrance

The biological conversion of cellulosic biomass to biofuel is hindered by cell wall recalcitrance, which can limit the ability of cellulases to access and break down cellulose. The purpose of this study was to investigate whether hydroxyproline-rich cell wall proteins (extensins) are present in poplar stem biomass, and whether these proteins may contribute to recalcitrance. Three classical extensin genes were identified in Populus trichocarpa through bioinformatic analysis of poplar genome sequences, with the following proposed names: PtEXTENSIN1 (Potri.001G019700); PtEXTENSIN2 (Potri.001G020100); PtEXTENSIN3 (Potri.018G050100). Tissue print immunoblots localized the extensin proteins in poplar stems to regions near the vascular cambium. Different thermochemical pretreatments reduced but did not eliminate hydroxyproline (Hyp, a proxy for extensins) from the biomass. Protease treatment of liquid hot water-pretreated poplar biomass reduced Hyp content by a further 16% and increased subsequent glucose yield by 20%. These data suggest that extensins may contribute to recalcitrance in pretreated poplar biomass, and that incorporating protease treatment into pretreatment protocols could result in a small but significant increase in the yield of fermentable glucose.

09 BIOMASS FUELS↗

DOE OSTI2025

Old Woman Creek Wetland Sediment and Electrochemical Sensor Microbial Community, 2023

We are developing a technique to monitor microbiological activities referred to as zero resistance ammetry, which entails the deployment of graphite electrodes in sediments. Measurement of current between electrodes of contrasting redox regimes and/or predominant terminal electron accepting processes can be used as an indicator of the extents of microbiological activity. We deployed an electrode array at depths of 2 mm, 4 mm, 76 mm, 78 mm, 152 mm, 154 mm, 227 mm, and 229 mm below the wetland sediment water interface in the Old Woman Creek National Estuarine Research Center, Huron, OH, USA (Lat. = 41.380833, Long. = -82.508889). A core was collected from adjacent sediment and subsamples were collected from depth intervals of 0 – 25 mm, 25 – 127 mm, 127 – 128 mm, and below 178 mm. To determine if the microbial communities attached to the electrodes were reflective of the adjacent sediment-associated microbial community, we conducted a 16S rRNA gene-based (V4 region) survey of these respective materials. This data package contains the results of these surveys, including metadata on the depths from which samples were collected (samples.csv), DNA extraction and sequencing information (OWC_DEPTH_AMPLICON_SEQUENCING_METADATA), sequence processing information (OWC_DEPTH_BIOINFORMATIC_METADATA.csv), an operational taxonomic unit (OTU) table (OWC_DEPTH_97OTUS_TABLE.csv), and nucleotide sequences of OTUs (OWC_DEPTH_97OTUS_SEQS.fasta). All files can be opened using a text-editing application. The fasta file is compatible with bioinformatics applications.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Soil microbial ecology and microbiome-metabolite linkages improve understanding of ecosystem states along terrestrial-aquatic interfaces

These data are from Bandopadhyay et al., "Soil microbial ecology and microbiome-metabolite linkages improve understanding of ecosystem states along terrestrial-aquatic interfaces". This study aims to understand the soil microbial ecology along terrestrial-aquatic interfaces of a freshwater and estuarine region and how it relates to organic matter. We analyzed soil microbial (16S rRNA gene) and organic matter (Fourier-transform ion cyclotron resonance mass spectrometry, FTICR-MS) composition from upland (forested), transition (stressed forest), and wetland positions at three sites in each of the Lake Erie (freshwater) and Chesapeake Bay (estuarine) regions. This dataset includes 16S rRNA gene amplicon data (only processed file types included here) and organic matter composition from FTICR-MS data (raw and processed files included here) from upland (forested), transition (stressed forest), and wetland positions at three sites in each of the Lake Erie and Chesapeake Bay regions. These sites are part of the COMPASS-FME project (https://compass.pnnl.gov/FME/COMPASSFME). File formats and software needed to access files: 16S rRNA gene amplicon data: These files follow the format reported here https://ess-dive.gitbook.io/amplicon-sequencing-reporting-format#updates-in-v1.0.1. As per this format, there are four file types reported: 1. Taxon tables (also called sequence-by-sample or OTU (operational taxonomic unit)/ESV (exact sequence variant) tables) : available in a .txt file format and accessible using TextEdit or MS Excel. 2. Representative sequences (also called consensus sequences) : available in a .fasta format and accessible using TextEdit. 3. Sequencing metadata : available in a MS Excel workbook file format and CSV file format 4. Bioinformatic metadata : available in a MS Excel workbook file format and CSV file format FTICR-MS data: 1. Raw data converted to a processed file with intensities of the peaks in the given samples : available in a MS Excel CSV file format 2. Processed file used in analyses and visualizations (appended as icr_long_) : available in a MS Excel CSV file format 3. Metadata file for ICR features (appended as icr_meta) : available in a MS Excel CSV file format

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

MyCrunchGPT: A LLM Assisted Framework for Scientific Machine Learning

Scientific machine learning (SciML) has advanced recently across many different areas in computational science and engineering. Here, the objective is to integrate data and physics seamlessly without the need of employing elaborate and computationally taxing data assimilation schemes. However, preprocessing, problem formulation, code generation, postprocessing, and analysis are still time- consuming and may prevent SciML from wide applicability in industrial applications and in digital twin frameworks. Here, we integrate the various stages of SciML under the umbrella of ChatGPT, to formulate MyCrunchGPT, which plays the role of a conductor orchestrating the entire workflow of SciML based on simple prompts by the user. Specifically, we present two examples that demonstrate the potential use of MyCrunchGPT in optimizing airfoils in aerodynamics, and in obtaining flow fields in various geometries in interactive mode, with emphasis on the validation stage. To demonstrate the flow of the MyCrunchGPT, and create an infrastructure that can facilitate a broader vision, we built a web app based guided user interface, that includes options for a comprehensive summary report. The overall objective is to extend MyCrunchGPT to handle diverse problems in computational mechanics, design, optimization and controls, and general scientific computing tasks involved in SciML, hence using it as a research assistant tool but also as an educational tool. While here the examples focus on fluid mechanics, future versions will target solid mechanics and materials science, geophysics, systems biology, and bioinformatics.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

A novel gene expression-based prognostic scoring system to predict survival in gastric cancer

Analysis of gene expression patterns in gastric cancer (GC) can help to identify a comprehensive panel of gene biomarkers for predicting clinical outcomes and to discover potential new therapeutic targets. Here, a multi-step bioinformatics analytic approach was developed to establish a novel prognostic scoring system for GC. We first identified 276 genes that were robustly differentially expressed between normal and GC tissues, of which, 249 were found to be significantly associated with overall survival (OS) by univariate Cox regression analysis. The biological functions of 249 genes are related to cell cycle, RNA/ncRNA process, acetylation and extracellular matrix organization. A network was generated for view of the gene expression architecture of 249 genes in 265 GCs. Finally, we applied a canonical discriminant analysis approach to identify a 53-gene signature and a prognostic scoring system was established based on a canonical discriminant function of 53 genes. The prognostic scores strongly predicted patients with GC to have either a poor or good OS. Our study raises the prospect that the practicality of GC patient prognosis can be assessed by this prognostic scoring system.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2017

A robust gene expression-based prognostic risk score predicts overall survival of lung adenocarcinoma patients

Identification of reliable predictive biomarkers and new therapeutic targets is a critical step for significant improvement in patient outcomes. Here, we developed a multi-step bioinformatics analytic strategy to mine large omics and clinical data to build a prognostic scoring system for predicting the overall survival (OS) of lung adenocarcinoma (LuADC) patients. In latter we first identified 1327 significantly and robustly deregulated genes, 600 of which were significantly associated with the OS of LuADC patients. Gene co-expression network analysis revealed the biological functions of these 600 genes in normal lung and LuADCs, which were found to be enriched for cell cycle-related processes, blood vessel development, cell-matrix adhesion and metabolic processes. Finally, we implemented a multiple resampling method combined with Cox regression analysis to identify a 27-gene signature associated with OS, and then created a prognostic scoring system based on this signature. This scoring system robustly predicted OS of LuADC patients in 100 sampling test sets and was further validated in four independent LuADC cohorts. In addition, in comparison to other existing prognostic gene signatures published in the literature, our signature was significantly superior in predicting OS of LuADC patients. In summary, our multiomics and clinical data integration study created a 27-gene prognostic risk score that can predict OS of LuADC patients independent of age, gender and clinical stage. This score could guide therapeutic selection and allow stratification in clinical trials.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2013

Intra-membrane molecular interactions of K+ channel proteins :

Ion channel proteins regulate complex patterns of cellular electrical activity and ionic signaling. Certain K+ channels play an important role in immunological biodefense mechanisms of adaptive and innate immunity. Most ion channel proteins are oligomeric complexes with the conductive pore located at the central subunit interface. The long-term activity of many K+ channel proteins is dependent on the concentration of extracellular K+; however, the mechanism is unclear. Thus, this project focused on mechanisms underlying structural stability of tetrameric K+ channels. Using KcsA of Streptomyces lividans as a model K+ channel of known structure, the molecular basis of tetramer stability was investigated by: 1. Bioinformatic analysis of the tetramer interface. 2. Effect of two local anesthetics (lidocaine, tetracaine) on tetramer stability. 3. Molecular simulation of drug docking to the ion conduction pore. The results provide new insights regarding the structural stability of K+ channels and its possible role in cell physiology.

Moczydlowski, Edward G.↗

DOE OSTI2013

Integrated Approach to Reconstruction of Microbial Regulatory Networks

This project had the goal(s) of development of integrated bioinformatics platform for genome-scale inference and visualization of transcriptional regulatory networks (TRNs) in bacterial genomes. The work was done in Sanford-Burnham Medical Research Institute (SBMRI, P.I. D.A. Rodionov) and Lawrence Berkeley National Laboratory (LBNL, co-P.I. P.S. Novichkov). The developed computational resources include: (1) RegPredict web-platform for TRN inference and regulon reconstruction in microbial genomes, and (2) RegPrecise database for collection, visualization and comparative analysis of transcriptional regulons reconstructed by comparative genomics. These analytical resources were selected as key components in the DOE Systems Biology KnowledgeBase (SBKB). The high-quality data accumulated in RegPrecise will provide essential datasets of reference regulons in diverse microbes to enable automatic reconstruction of draft TRNs in newly sequenced genomes. We outline our progress toward the three aims of this grant proposal, which were: Develop integrated platform for genome-scale regulon reconstruction; Infer regulatory annotations in several groups of bacteria and building of reference collections of microbial regulons; and Develop KnowledgeBase on microbial transcriptional regulation.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2013

Pathogenicity island mobility and gene content.

Key goals towards national biosecurity include methods for analyzing pathogens, predicting their emergence, and developing countermeasures. These goals are served by studying bacterial genes that promote pathogenicity and the pathogenicity islands that mobilize them. Cyberinfrastructure promoting an island database advances this field and enables deeper bioinformatic analysis that may identify novel pathogenicity genes. New automated methods and rich visualizations were developed for identifying pathogenicity islands, based on the principle that islands occur sporadically among closely related strains. The chromosomally-ordered pan-genome organizes all genes from a clade of strains; gaps in this visualization indicate islands, and decorations of the gene matrix facilitate exploration of island gene functions. A %E2%80%9Clearned phyloblocks%E2%80%9D method was developed for automated island identification, that trains on the phylogenetic patterns of islands identified by other methods. Learned phyloblocks better defined termini of previously identified islands in multidrug-resistant Klebsiella pneumoniae ATCC BAA-2146, and found its only antibiotic resistance island.

Williams, Kelly Porter↗

DOE OSTI2013

Multi-Scale Toxicology: Building the Next Generation of Tools for Toxicology

Our objective was to develop a novel set of analytical capabilities that provide quantitative measurements of molecular and pharmacokinetics endpoints useful for predicting the toxicity of nanoparticles and other compounds. These data will eventually be used to improve the accuracy of computational predictions of toxicity by applying the National Laboratories’ imaging and computational capabilities and Battelle’s bioinformatics tools and toxicology insights.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2014

Final Report for Award DE-FG02-09ER16008

The research objectives of this project was to characterize the factors that regulate the pattern, amount and quality of cellulose deposition in higher plants. We pursued this goal in four ways: (1) by searching for proteins that interact with the cellulose synthase complex using novel bioinformatics tools to identify genes which exhibit gene expression patterns that are highly correlated with cellulose synthase components, (2) by using cellulose synthase domains as bait in 2-hybrid screens, and then characterizing any proteins that are identified by both gene expression network analysis and 2-hybrid screens (3) by testing the function of all known post-translational modifications, (4) by identifying and characterizing kinases that carry out most of the known post-translational modification of cellulose synthase components.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2014

Translational Genomics for the Improvement of Switchgrass

Our objectives were to apply bioinformatics and high throughput sequencing technologies to identify and classify the genes involved in cell wall formation in maize and switchgrass. Targets for genetic modification were to be identified and cell wall materials isolated and assayed for enhanced performance in bioprocessing. We annotated and assembled over 750 maize genes into gene families predicted to function in cell wall biogenesis. Comparative genomics of maize, rice, and Arabidopsis sequences revealed differences in gene family structure. In addition, differences in expression between gene family members of Arabidopsis, maize and rice underscored the need for a grass-specific genetic model for functional analyses.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2015

Experimental Systems-Biology Approaches for Clostridia-Based Bioenergy Production

This is the final project report for project "Experimental Systems-Biology Approaches for Clostridia-Based Bioenergy Production" for the funding period of 9/1/12 to 2/28/2015 (three years with a 6-month no-cost extension) OVERVIEW AND PROJECT GOALS The bottleneck of achieving higher rates and titers of toxic metabolites (such as solvents and carboxylic acids that can used as biofuels or biofuel precursors) can be overcome by engineering the stress response system. Thus, understanding and modeling the response of cells to toxic metabolites is a problem of great fundamental and practical significance. In this project, our goal is to dissect at the molecular systems level and build models (conceptual and quantitative) for the stress response of C. acetobutylicum (Cac) to its two toxic metabolites: butanol (BuOH) and butyrate (BA). Transcriptional (RNAseq and microarray based), proteomic and fluxomic data and their analysis are key requirements for this goal. Transcriptional data from mid-exponential cultures of Cac under 4 different levels of BuOH and BA stress was obtained using both microarrays (Papoutsakis group) and deep sequencing (RNAseq; Meyers and Papoutsakis groups). These two sets of data do not only serve to validate each other, but are also used for identification of stress-induced changes in transcript levels, small regulatory RNAs, & in transcriptional start sites. Quantitative proteomic data (Lee group), collected using the iTRAQ technology, are essential for understanding of protein levels and turnover under stress and the various protein-protein interactions that orchestrate the stress response. Metabolic flux changes (Antoniewicz group) of core pathways, which provide important information on the re-allocation of energy and carbon resources under metabolite stress, were examined using 13C-labelled chemicals. Omics data are integrated at different levels and scales. At the metabolic-pathway level, omics data are integrated into a 2nd generation genome-scale model (GSM) (Maranas group). Omics data are also integrated using bioinformatics (Wu and Huang group), whereby regulatory details of gene and protein expression, protein-protein interactions and metabolic flux regulation are incorporated. The PI (Papoutsakis) facilitated project integration through monthly meeting and reports, conference calls, and collaborative manuscript preparation. The five groups collaborated extensively and made a large number of presentations in national and international meetings. It has also published several papers, with several more in the preparation stage. Several PhD, MS and postdoctoral students were trained as part of this collaborative and interdisciplinary project.

09 BIOMASS FUELS↗

DOE OSTI2016

Improved Understanding of Microbial Iron and Sulfate Reduction Through a Combination of Bottom-up and Top-down Functional Proteomics Assays

Our overall goal was to improve the understanding of microbial iron and sulfate reduction by evaluating a diverse iron and sulfate reducing organisms utilizing a multi-omics approach combining “top-down” and “bottom-up” omics methodologies. We initiated one of the first combined comparative genomics, shotgun proteomics, RTqPCR, and heterologous expression studies in pursuit of our project objectives. Within the first year of this project, we created a new bioinformatics tool for ortholog identification (“SPOCS”). SPOCS is described in our publication, Curtis et al., 2013. Using this tool we were able to identify conserved orthologous groups across diverse iron and sulfate reducing microorganisms from Firmicutes, gamma-proteobacteria and delta-proteobacteria. For six iron and sulfate reducers we also performed shotgun proteomics (“bottom-up” proteomics including accurate mass and time (AMT) tag and iTRAQ approaches). Cultures include Gram (-) and Gram (+) microbes. Gram (-) were: Geobacter sulfureducens (grown on iron citrate and fumarate), Geobacter bemidjiensis (grown on iron citrate and fumarate), Shewanella oneidiensis (grown on iron citrate and fumarate) and Anaeromyxobacter dehalogenans (grown on iron citrate and fumarate). Although all cultures grew on insoluble iron, the iron precipitates interfered with protein extraction and analysis; which remains a major challenge for researchers in disparate study systems. Among the Gram (-) organisms studied, Anaeromyxobacter dehalogenans remains the most poorly characterized. Yet, it is arguably the most versatile organisms we studied. In this work we have used comparative proteomics to hypothesize which two of the dozens of predicted c-type cytochromes within Anaeromyxobacter dehalogenans may be directly involved in soluble iron reduction. Unfortunately, heterologous expression of these Anaeromyxobacter dehalogenans ctype cytochromes led to poor protein production and/or formation of inclusion bodies, even when we co-expressed several genes known to be important for assembly of cytochrome holoenzymes. We confirmed the proteomics trends at the RNA level by designing specific primer sets for hypothesized iron reductases in Anaeromyxobacter dehalogenans, and performed Reverse Transcription-qPCR. AD_0127 was 20 fold upregulated only on iron citrate conditions. AD_0127 is described as a hypothetical protein, but Pfam predicts it to be C554 type cytochrome having a possible role in nitrification (Wang et al., in preparation).

59 BASIC BIOLOGICAL SCIENCES↗