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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

RNA methyltransferase NSUN2 promotes hypopharyngeal squamous cell carcinoma proliferation and migration by enhancing TEAD1 expression in an m5C-dependent manner

Highlights: • NSUN2 is highly expressed in hypopharyngeal squamous cell carcinoma. • NSUN2 knockdown in vitro and in vivo decreased cell proliferation and invasion. • NSUN2 promotes tumor proliferation and invasion through TEAD1. RNA methyltransferase NSUN2 is involved in cell proliferation and invasion in a variety of tumors. However, the expression, function, and mechanism of NSUN2 in hypopharyngeal squamous cell carcinoma (HPSCC) remains unknown. We used a bioinformatics database, polymerase chain reaction, cell culture and transfection, immunohistochemistry, cell proliferation assay, wound healing experiments, transwell assays, western blotting, RNA-seq detection, dual-luciferase reporter assay, in vivo experiments, and a dot blot assay to evaluate the role of NSUN2 in HPSCC. NSUN2 mRNA and protein were highly expressed in HPSCC; NSUN2 knockdown in vitro and in vivo decreased cell proliferation and invasion. Studies have shown that TEAD1, a transcription factor, may act downstream of NSUN2 in HPSCC. NSUN2 was found to promote the proliferation and invasion of HPSCC by upregulating TEAD1 in an 5-methylcytosine-dependent manner, thereby representing an oncogene and potential new target for treating HPSCC.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1983

Establishing homologies in protein sequences

Computer-based statistical techniques used to determine homologies between proteins occurring in different species are reviewed. The technique is based on comparison of two protein sequences, either by relating all segments of a given length in one sequence to all segments of the second or by finding the best alignment of the two sequences. Approaches discussed include selection using printed tabulations, identification of very similar sequences, and computer searches of a database. The use of the SEARCH, RELATE, and ALIGN programs (Dayhoff, 1979) is explained; sample data are presented in graphs, diagrams, and tables and the construction of scoring matrices is considered.

Dayhoff, M. O.↗
DOE OSTI2025

Fragme∩t: An Open‐Source Framework for Multiscale Quantum Chemistry Based on Fragmentation

Fragment-based quantum chemistry offers a means to circumvent the nonlinear computational scaling of conventional electronic structure calculations, by partitioning a large calculation into smaller subsystems then considering the many-body interactions between them. Variants of this approach have been used to parameterize classical force fields and machine learning potentials, applications that benefit from interoperability between quantum chemistry codes. However, there is a dearth of software that provides interoperability yet is purpose-built to handle the combinatorial complexity of fragment-based calculations. To fill this void we introduce “Fragme∩t”, an open-source software application that provides a tool for community validation of fragment-based methods, a platform for developing new approximations, and a framework for analyzing many-body interactions. Fragme∩t includes algorithms for automatic fragment generation and structure modification, and for distance- and energy-based screening of the requisite subsystems. Checkpointing, database management, and parallelization are handled internally and results are archived in a portable database. Interfaces to various quantum chemistry engines are easy to write and exist already for Q-Chem, PySCF, xTB, Orca, CP2K, MRCC, Psi4, NWChem, GAMESS, and MOPAC. Applications reported here demonstrate parallel efficiencies around 96% on more than 1000 processors but also showcase that the code can handle large-scale protein fragmentation using only workstation hardware, all with a codebase that is designed to be usable by non-experts. Fragme∩t conforms to modern software engineering best practices and is built upon well established technologies including Python, SQLite, and Ray. The source code is available under the Apache 2.0 license.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRSDate not supplied

An Open-Science Approach to Address Individual Response to Simulated GCR In Genetically Diverse Populations of Mice and Humans

This project addresses the challenge of understanding and predicting individual radiation sensitivity by integrating genetics, demographics and biomarker characteristics across species (mice and humans). We hypothesize that ex vivo DNA repair response to GCR components is a central determinant of cancer risk from space radiation and can serve as a biomarker of radiation risk in combination with genetics. Automated image quantification of 53BP1+ radiation-induced foci (RIF) during the first 4-48 h post-irradiation was performed as a function of dose and LET in non-immortalized primary skin fibroblasts derived from 76 mice across 15 strains (5 inbred reference strains and 10 collaborative-cross strains) exposed to X rays (0.1, 1 and 4 Gy), 350 MeV/n 40Ar and 600 MeV/n 56Fe (1.1 and 3 particles/100sq. μm), as well as in peripheral blood mononuclear cells (PBMCs) from 768 healthy donors (matched ethnicity, 50/50 male/female, 18-70 years old) exposed to gamma rays (0.1 and 1 Gy), 350 MeV/n 28Si, 350 MeV/n 40Ar and 600 MeV/n 56Fe (1.1 and 3 particles/100sq. μm). A genome-wide association study (GWAS) was performed on the mouse strains between DNA damage responses to space radiation and single nucleotide polymorphisms (SNPs). We found SNPs, which were significantly associated to the RIF phenotype, mapped to genes and pathways that are functionally linked to health hazards for deep space exploration (e.g. carcinogenesis, nervous system damage and immune dysfunction). Some of these SNPs were located within protein coding regions, potentially interfering with protein functions and providing promising genetic targets for countermeasures. We also found correlations between both spontaneous and radiation-induced DNA damage and SNPs mapped to pathways associated with cellular metabolism. GWAS is undergoing for the human data. All data have been made available via the NASA Space Biology Open-Science database (genelab.nasa.gov) and we will discuss how various genomic and transcriptomic datasets can be accessed for modeling and integrated using machine learning methods for discovering new radiation biology.

Sylvain V Costes↗
DOE OSTI2026

Isolation of genome-predicted Caldatribacterium ( Atribacterota ) reveals pervasive microbial cultivation problem due to folate precipitation

Most bacterial phyla have few or no pure cultures, including Atribacterota , comprised of ubiquitous anaerobes. Here, we report genome-guided enrichment and isolation of two Atribacterota species representing a new family, Caldatribacterium saccharofermentans from a hot spring, and Caldatribacterium inferamans from a deep aquifer. Both were co-enriched with sulfate-reducing bacteria and initially resisted isolation, which we link to inadvertent removal of precipitated folic acid by filter-sterilization of unbuffered Wolin’s vitamin solution. We then predict folate auxotrophy across the Atribacterota and ~29% of all bacteria, with extensive auxotrophy in 27% of phyla. Since ≥604 of 791 ( ≥ 76%) media with folic acid additions in the MediaDive database use unbuffered vitamin solutions in which folic acid is likely removed during filter-sterilization, we propose that folate auxotrophy limits culturability in defined media en masse. We also uncover unusual features of Caldatribacterium , including three lipid membrane-like layers (LMLs), with the inner LML surrounding the nucleoid, and a high percentage of secreted proteins, supporting a unique cell biology of Atribacterota .

Biological and medical sciences↗
DOE OSTI2025

Database of low‐temperature absorption and fluorescence spectra of native photosynthetic tetrapyrrole macrocycles

Low-temperature (77 K) absorption and fluorescence spectra of 12 naturally occurring photosynthetic tetrapyrrole macrocycles have been recorded in a frozen glass (2-methyltetrahydrofuran). The compounds encompass distinct chromophore classes: porphyrin, chlorophyll c 2 ; chlorin, chlorophylls a, b, d, f and bacteriochlorophylls c, d, e, f; and bacteriochlorin, bacteriochlorophylls a, b, g. The spectra are compared with those of the same pigment in liquid solution (predominantly 2-methyltetrahydrofuran) at room temperature (293 K). The measured Stokes shifts at 77 K across the 12 macrocycles range from ~30 to 300 cm −1 . The spectral data in digital form are made available as part of the PhotochemCAD databases. Literature searches have revealed extensive published data for Chl a (often in biological matrices) but at best rather limited data for less common macrocycles. The availability of a systematic collection of curated spectral data collected at low temperature should be useful for a variety of assessments, including reconstruction of absorption spectra of (bacterio)chlorophyll-containing protein complexes, vibrational analysis of absorption and fluorescence spectra, and calculations where knowledge of energy levels is important.

Niedzwiedzki, Dariusz M. [Washington University in↗
DOE OSTI2022

Classification of bacterial plasmid and chromosome derived sequences using machine learning

Plasmids are important genetic elements that facilitate horizonal gene transfer between bacteria and contribute to the spread of virulence and antimicrobial resistance. Most bacterial genome sequences in the public archives exist in draft form with many contigs, making it difficult to determine if a contig is of chromosomal or plasmid origin. Using a training set of contigs comprising 10,584 chromosomes and 10,654 plasmids from the PATRIC database, we evaluated several machine learning models including random forest, logistic regression, XGBoost, and a neural network for their ability to classify chromosomal and plasmid sequences using nucleotide k-mers as features. Based on the methods tested, a neural network model that used nucleotide 6-mers as features that was trained on randomly selected chromosomal and plasmid subsequences 5kb in length achieved the best performance, outperforming existing out-of-the-box methods, with an average accuracy of 89.38% ± 2.16% over a 10-fold cross validation. The model accuracy can be improved to 92.08% by using a voting strategy when classifying holdout sequences. In both plasmids and chromosomes, subsequences encoding functions involved in horizontal gene transfer—including hypothetical proteins, transporters, phage, mobile elements, and CRISPR elements—were most likely to be misclassified by the model. This study provides a straightforward approach for identifying plasmid-encoding sequences in short read assemblies without the need for sequence alignment-based tools.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Hemiptera phylogenomic resources: Tree‐based orthology prediction and conserved exon identification

Abstract High‐throughput sequencing of transcriptomes and targeted genomic regions are advancing our knowledge of The Tree of Life. Building phylogenies with regions of the genome requires 1‐to‐1 orthologue resources of genes and noncoding loci. One organismal group that has received little attention in this area is the Hemiptera, the fifth largest insect order represented by ~103,590 named species. Here, we present a set of 3,872 Hemiptera 1‐to‐1 orthogroups based on tree‐based orthology inference of eight Hemiptera species with publicly available genome sequences. We also estimate a set of 406 orthologous exons with similar mRNA splice sites that can be used for Sanger sequencing and develop enrichment probes for targeted genome sequencing for phylogenomic inference. We show this novel set of orthologues is informative at the protein, coding sequence and exon molecular levels and provides robust branch support in both gene tree–species tree methods and concatenated sequence phylogenies. In addition, we demonstrate the utility of these loci to resolve relationships in whiteflies, Bemisia tabaci , a large species complex with few phylogenomic resources. Last, we compare our Hemiptera phylogeny with previously published phylogenies and other orthologue databases, while providing suggestions on further improvement to this phylogenomic resource.

Owen, Christopher L.↗
DOE OSTI2023

Computer-aided, resistance gene-guided genome mining for proteasome and HMG-CoA reductase inhibitors

Secondary metabolites (SMs) are biologically active small molecules, many of which are medically valuable. Fungal genomes contain vast numbers of SM biosynthetic gene clusters (BGCs) with unknown products, suggesting that huge numbers of valuable SMs remain to be discovered. It is challenging, however, to identify SM BGCs, among the millions present in fungi, that produce useful compounds. One solution is resistance gene-guided genome mining, which takes advantage of the fact that some BGCs contain a gene encoding a resistant version of the protein targeted by the compound produced by the BGC. The bioinformatic signature of such BGCs is that they contain an allele of an essential gene with no SM biosynthetic function, and there is a second allele elsewhere in the genome. We have developed a computer-assisted approach to resistance gene-guided genome mining that allows users to query large databases for BGCs that putatively make compounds that have targets of therapeutic interest. Working with the MycoCosm genome database, we have applied this approach to look for SM BGCs that target the proteasome β6 subunit, the target of the proteasome inhibitor fellutamide B, or HMG-CoA reductase, the target of cholesterol reducing therapeutics such as lovastatin. Our approach proved effective, finding known fellutamide and lovastatin BGCs as well as fellutamide- and lovastatin-related BGCs with variations in the SM genes that suggest they may produce structural variants of fellutamides and lovastatin. Gratifyingly, we also found BGCs that are not closely related to lovastatin BGCs but putatively produce novel HMG-CoA reductase inhibitors.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2003

Protein Crystal Growth

In order to rapidly and efficiently grow crystals, tools were needed to automatically identify and analyze the growing process of protein crystals. To meet this need, Diversified Scientific, Inc. (DSI), with the support of a Small Business Innovation Research (SBIR) contract from NASA s Marshall Space Flight Center, developed CrystalScore(trademark), the first automated image acquisition, analysis, and archiving system designed specifically for the macromolecular crystal growing community. It offers automated hardware control, image and data archiving, image processing, a searchable database, and surface plotting of experimental data. CrystalScore is currently being used by numerous pharmaceutical companies and academic and nonprofit research centers. DSI, located in Birmingham, Alabama, was awarded the patent Method for acquiring, storing, and analyzing crystal images on March 4, 2003. Another DSI product made possible by Marshall SBIR funding is VaporPro(trademark), a unique, comprehensive system that allows for the automated control of vapor diffusion for crystallization experiments.

Source record↗
DOE OSTI2025

Changes to virus taxonomy, the international code of virus classification and nomenclature, and the ICTV statutes ratified by the International Committee on Taxonomy of Viruses (2025)

Abstract The 56th meeting of the Executive Committee (EC) of the International Committee on Taxonomy of Viruses (ICTV) was held in Bari, Italy, in July/August, 2024, and 115 submitted taxonomy proposals were reviewed. A total of 112 were subsequently ratified by the ICTV membership. An additional 9 error correction proposals were also approved in August 2025. This article lists the taxonomy proposals that have now been incorporated into release 40 version v2 of the Master Species List ( https://ictv.global/msl ), the Virus Metadata Resource ( https://ictv.global/vmr ), and associated ICTV databases. In addition to the assignments of 1,563 new virus species, 243genera, 55 families, 11 orders, and 8 classes, there were substantial additions to higher taxonomic ranks. These include the creation of a new realm ( Singelaviria ), which is based on the recognition of a separate evolutionary origin for the hallmark capsid genes of members of the kingdom Helvetiavirae. These express capsid proteins forming a single jelly-roll fold that is structurally and evolutionarily distinct from those of members of the family Bamfordvirae , assigned to the realm Varidnaviria . Furthermore, the realm Varidnaviria underwent a major reorganization, including the addition of a new kingdom, Abadenavirae . Another notable change was the classification of the vertebrate-infecting single-stranded DNA anellovirids into a new phylum Commensaviricota (kingdom Shotokuvirae , realm Monodnaviria ). Archaeal viruses infecting the hyperthermophilic Archaeoglobi were assigned to a new phylum Calorviricota , in the kingdom Trapavirae (realm Monodnaviria ), whereas RNA viruses infecting hyperthermophilic bacteria were classified into a new phylum Artimaviricota (realm Riboviria ). In recognition of his extensive and valuable contributions to virus taxonomic developments in Study Groups and over the period of his EC membership, Stuart Siddell was honoured as a new life member of the ICTV. The ICTV has created a new strategy for disseminating information on taxonomy advances through annual open-access publication of citeable taxonomy proposal summaries from each ICTV Subcommittee. A collective total of 354 co-authors of the seven summaries were drawn from members of each Subcommittee, the EC, and a very large number of contributors from the wider virology community.

Simmonds, Peter (ORCID:0000000279644700)↗
DOE OSTI2020

Accurate Identification of Deamidation and Citrullination from Global Shotgun Proteomics Data Using a Dual-Search Delta Score Strategy

While proteins with deamidated/citrullinated amino acids play critical roles in the pathogenesis of many human diseases, identifying these modifications in complex biological samples has been an ongoing challenge. Herein we present a method to accurately identify these modifications from shotgun proteomics data from a deep proteome profiling study of human pancreatic islets obtained by laser capture microdissection. All MS/MS spectra were searched against database by MSGF+ twice with or without a +0.9840 Da mass shift on amino acids asparagine, glutamine, and arginine (NQR) as a dynamic modification. Consequently, for each spectrum the resulting two peptide-to-spectrum matches (PSM) with their respective MSGF+ scores were used for Delta Score calculation. It was observed that all PSMs with positive Delta Score values were clustered with mass errors around 0 ppm, while PSMs with negative Delta Score values were distributed nearly equally within the defined mass error range (20 ppm) for database searching. To estimate false discovery rate (FDR), a “pseudo-decoy” approach was applied whether datasets were searched against a database with a “real modification” mass shift (+0.9840 Da) and a “mock modification” mass shift (+1.0227 Da). FDR was controlled to ~2% with a Delta Score filter greater than zero. Manual inspection of spectra showed that PSMs with positive Delta Score value contained deamidated/citrullinated fragments in their MS/MS spectra. Finally, the results demonstrated that in-situ deamidated/citrullinated peptides can be accurately identified from shotgun tissue proteomics data by the Dual-search Delta Score Strategy.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Predicting drug-metagenome interactions: Variation in the microbial β-glucuronidase level in the human gut metagenomes

Characterizing the gut microbiota in terms of their capacity to interfere with drug metabolism is necessary to achieve drug efficacy and safety. Although examples of drug-microbiome interactions are well-documented, little has been reported about a computational pipeline for systematically identifying and characterizing bacterial enzymes that process particular classes of drugs. The goal of our study is to develop a computational approach that compiles drugs whose metabolism may be influenced by a particular class of microbial enzymes and that quantifies the variability in the collective level of those enzymes among individuals. The present paper describes this approach, with microbial β-glucuronidases as an example, which break down drug-glucuronide conjugates and reactivate the drugs or their metabolites. We identified 100 medications that may be metabolized by β-glucuronidases from the gut microbiome. These medications included morphine, estrogen, ibuprofen, midazolam, and their structural analogues. The analysis of metagenomic data available through the Sequence Read Archive (SRA) showed that the level of β-glucuronidase in the gut metagenomes was higher in males than in females, which provides a potential explanation for the sex-based differences in efficacy and toxicity for several drugs, reported in previous studies. Our analysis also showed that infant gut metagenomes at birth and 12 months of age have higher levels of β-glucuronidase than the metagenomes of their mothers and the implication of this observed variability was discussed in the context of breastfeeding as well as infant hyperbilirubinemia. Overall, despite important limitations discussed in this paper, our analysis provided useful insights on the role of the human gut metagenome in the variability in drug response among individuals. Importantly, this approach exploits drug and metagenome data available in public databases as well as open-source cheminformatics and bioinformatics tools to predict drug-metagenome interactions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Meta-virus resource (MetaVR): expanding the frontiers of viral diversity with 24 million uncultivated virus genomes

Viruses are ubiquitous in all environments and impact host metabolism, evolution, and ecology, although our knowledge of their biodiversity is still extremely limited. Viral diversity from genomic and metagenomic datasets has led to an explosion of uncultivated virus genomes (UViGs) and the development of specialized databases to catalog this viral diversity, though many lack comprehensive integration. Here, we introduce meta-virus resource (MetaVR), the successor of the IMG/VR database, designed to overcome previous limitations such as large-scale querying and programmatic access. Drawing on the increase of publicly available genomes and metagenomes, MetaVR significantly expands viral diversity, now comprising 24,435,662 UViGs, a 57.6% increase from its predecessor, organized into over 12 million viral operational taxonomic units. Key enhancements include the integration of curated eukaryotic host information, the integration of protein clusters and predicted structures for comparative studies, and an API for programmatic data access. Furthermore, MetaVR features an updated taxonomic framework based on ICTV release 39, assignment to Baltimore classes, and enhanced host assignment through novel computational tools like iPHoP. These advancements position MetaVR as a unique resource for exploring viral diversity, evolution, and host interactions across diverse environments. MetaVR can be freely accessed at https://www.meta-virome.org/.

Fiamenghi, Mateus B↗
NASA NTRS2007

Simulated Microgravity Regulates Gene Transcript Profiles of 2T3 Preosteoblasts: Comparison of the Random Positioning Machine and the Rotating Wall Vessel Bioreactor

Microgravity of spaceflight induces bone loss due in part to decreased bone formation by osteoblasts. We have previously examined the microgravity-induced changes in gene expression profiles in 2T3 preosteoblasts using the Random Positioning Machine (RPM) to simulate microgravity conditions. Here, we hypothesized that exposure of preosteoblasts to an independent microgravity simulator, the Rotating Wall Vessel (RWV), induces similar changes in differentiation and gene transcript profiles, resulting in a more confined list of gravi-sensitive genes that may play a role in bone formation. In comparison to static 1g controls, exposure of 2T3 cells to RWV for 3 days inhibited alkaline phosphatase activity, a marker of differentiation, and downregulated 61 genes and upregulated 45 genes by more than two-fold as shown by microarray analysis. The microarray results were confirmed with real time PCR for downregulated genes osteomodulin, bone morphogenic protein 4 (BMP4), runx2, and parathyroid hormone receptor 1. Western blot analysis validated the expression of three downregulated genes, BMP4, peroxiredoxin IV, and osteoglycin, and one upregulated gene peroxiredoxin I. Comparison of the microarrays from the RPM and the RWV studies identified 14 gravi-sensitive genes that changed in the same direction in both systems. Further comparison of our results to a published database showing gene transcript profiles of mechanically loaded mouse tibiae revealed 16 genes upregulated by the loading that were shown to be downregulated by RWV and RPM. These mechanosensitive genes identified by the comparative studies may provide novel insights into understanding the mechanisms regulating bone formation and potential targets of countermeasure against decreased bone formation both in astronauts and in general patients with musculoskeletal disorders.

Patel, Mamta J.↗
DOE OSTI2024

Protein–Protein Interaction Networks Derived from Classical and Machine Learning-Based Natural Language Processing Tools

The study of protein-protein interactions (PPIs) provides insight into various biological mechanisms, including the binding of antibodies to antigens, enzymes to inhibitors or promoters, and receptors to ligands. Recent studies of PPIs have led to significant biological breakthroughs. For example, the study of PPIs involved in the human:SARS-CoV-2 viral infection mechanism aided in the development of the SARS-CoV-2 vaccines. Though several databases exist for the manual curation of PPI networks, text mining methods have been routinely demonstrated as useful alternatives for newly studied or understudied species where databases are incomplete. Here, the relationship extraction (RE) performance of several open-source classical text processing, machine learning (ML)-based natural language processing (NLP), and large language model (LLM)-based NLP tools were compared. Overall, our results indicated that networks derived from classical methods tend to have high true positive rates at the expense of having overconnected-networks, ML-based NLP methods have lower true positive rates but networks with the closest structures to the target network, and LLM-based NLP methods tend to exist in-between the two other approaches, with variable performances. Finally, the selection of a specific NLP approach should be tied to the needs of a study and text availability, as models varied in performance due to the amount of text provided.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Omics-guided metabolic pathway discovery in plants: Resources, approaches, and opportunities

Plants produce a vast array of metabolites, the biosynthetic routes of which remain largely undetermined. Genome-scale enzyme and pathway annotations and omics technologies have revolutionized research to decrypt plant metabolism and produced a growing list of functionally characterized metabolic genes and pathways. However, what is known is still a tiny fraction of the metabolic capacity harbored by plants. Here, in this work, we review plant enzyme and pathway annotation resources and cutting-edge omics approaches to guide discovery and characterization of plant metabolic pathways. We also discuss strategies for improving enzyme function prediction by integrating protein 3D structure information and single cell omics. This review aims to serve as a primer for plant biologists to leverage omics datasets to facilitate understanding and engineering plant metabolism.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2002

Genes encoding calmodulin-binding proteins in the Arabidopsis genome

Analysis of the recently completed Arabidopsis genome sequence indicates that approximately 31% of the predicted genes could not be assigned to functional categories, as they do not show any sequence similarity with proteins of known function from other organisms. Calmodulin (CaM), a ubiquitous and multifunctional Ca(2+) sensor, interacts with a wide variety of cellular proteins and modulates their activity/function in regulating diverse cellular processes. However, the primary amino acid sequence of the CaM-binding domain in different CaM-binding proteins (CBPs) is not conserved. One way to identify most of the CBPs in the Arabidopsis genome is by protein-protein interaction-based screening of expression libraries with CaM. Here, using a mixture of radiolabeled CaM isoforms from Arabidopsis, we screened several expression libraries prepared from flower meristem, seedlings, or tissues treated with hormones, an elicitor, or a pathogen. Sequence analysis of 77 positive clones that interact with CaM in a Ca(2+)-dependent manner revealed 20 CBPs, including 14 previously unknown CBPs. In addition, by searching the Arabidopsis genome sequence with the newly identified and known plant or animal CBPs, we identified a total of 27 CBPs. Among these, 16 CBPs are represented by families with 2-20 members in each family. Gene expression analysis revealed that CBPs and CBP paralogs are expressed differentially. Our data suggest that Arabidopsis has a large number of CBPs including several plant-specific ones. Although CaM is highly conserved between plants and animals, only a few CBPs are common to both plants and animals. Analysis of Arabidopsis CBPs revealed the presence of a variety of interesting domains. Our analyses identified several hypothetical proteins in the Arabidopsis genome as CaM targets, suggesting their involvement in Ca(2+)-mediated signaling networks.

NASA Discipline Plant Biology↗