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At least 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Distinct temporal changes in host cell lncRNA expression during the course of an adenovirus infection

The deregulation of cellular long non-coding RNA (lncRNA) expression during a human adenovirus infection was studied by deep sequencing. Expression of lncRNAs increased substantially following the progression of the infection. Among 645 significantly expressed lncRNAs, the expression of 398 was changed more than 2-fold. More than 80% of them were up-regulated and 80% of them were detected during the late phase. Based on the genomic locations of the deregulated lncRNAs in relation to known mRNAs and miRNAs, they were predicted to be involved in growth, structure, apoptosis and wound healing in the early phase, cell proliferation in the intermediate phase and protein synthesis, modification and transport in the late phase. The most significant functions of cellular RNA-binding proteins, previously shown to interact with the deregulated lncRNAs identified here, are involved in RNA splicing, nuclear export and translation events. We hypothesize that adenoviruses exploit the lncRNA network to optimize their reproduction. - Highlights: • The expression of 398 lncRNAs showed a distinct temporal pattern during Ad2 infection. • 80% of the deregulated lncRNAs were up-regulated during the late phase of infection. • The deregulated lncRNAs potentiallyinteract with 33 cellular RNA binding proteins. • These RBPs are involved in RNA splicing, nuclear export and translation. • Adenovirus exploits the cellular lncRNA network to optimize its replication.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

On the Rapid Calculation of Binding Affinities for Antigen and Antibody Design and Affinity Maturation Simulations

The accurate and efficient calculation of protein-protein binding affinities is an essential component in antibody and antigen design and optimization, and in computer modeling of antibody affinity maturation. Such calculations remain challenging despite advances in computer hardware and algorithms, primarily because proteins are flexible molecules, and thus, require explicit or implicit incorporation of multiple conformational states into the computational procedure. The astronomical size of the amino acid sequence space further compounds the challenge by requiring predictions to be computed within a short time so that many sequence variants can be tested. In this study, we compare three classes of methods for antibody/antigen (Ab/Ag) binding affinity calculations: (i) a method that relies on the physical separation of the Ab/Ag complex in equilibrium molecular dynamics (MD) simulations, (ii) a collection of 18 scoring functions that act on an ensemble of structures created using homology modeling software, and (iii) methods based on the molecular mechanics-generalized Born surface area (MM-GBSA) energy decomposition, in which the individual contributions of the energy terms are scaled to optimize agreement with the experiment. When applied to a set of 49 antibody mutations in two Ab/HIV gp120 complexes, all of the methods are found to have modest accuracy, with the highest Pearson correlations reaching about 0.6. In particular, the most computationally intensive method, i.e., MD simulation, did not outperform several scoring functions. The optimized energy decomposition methods provided marginally higher accuracy, but at the expense of requiring experimental data for parametrization. Within each method class, we examined the effect of the number of independent computational replicates, i.e., modeled structures or reinitialized MD simulations, on the prediction accuracy. We suggest using about ten modeled structures for scoring methods, and about five simulation replicates for MD simulations as a rule of thumb for obtaining reasonable convergence. We anticipate that our study will be a useful resource for practitioners working to incorporate binding affinity calculations within their protein design and optimization process.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Molecular architecture and functional dynamics of the pre-incision complex in nucleotide excision repair

Nucleotide excision repair (NER) is vital for genome integrity. Yet, our understanding of the complex NER protein machinery remains incomplete. Combining cryo-EM and XL-MS data with AlphaFold2 predictions, we build an integrative model of the NER pre-incision complex(PInC). Here TFIIH serves as a molecular ruler, defining the DNA bubble size and precisely positioning the XPG and XPF nucleases for incision. Using simulations and graph theoretical analyses, we unveil PInC’s assembly, global motions, and partitioning into dynamic communities. Remarkably, XPG caps XPD’s DNA-binding groove and bridges both junctions of the DNA bubble, suggesting a novel coordination mechanism of PInC’s dual incision. XPA rigging interlaces XPF/ERCC1 with RPA, XPD, XPB, and 5' ssDNA, exposing XPA’s crucial role in licensing the XPF/ERCC1 incision. Mapping disease mutations onto our models reveals clustering into distinct mechanistic classes, elucidating xeroderma pigmentosum and Cockayne syndrome disease etiology.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2012

Structural Basis and Sequence Rules for Substrate Recognition by Tankyrase Explain the Basis for Cherubism Disease

The poly(ADP-ribose)polymerases Tankyrase 1/2 (TNKS/TNKS2) catalyze the covalent linkage of ADP-ribose polymer chains onto target proteins, regulating their ubiquitylation, stability, and function. Dysregulation of substrate recognition by Tankyrases underlies the human disease cherubism. Tankyrases recruit specific motifs (often called RxxPDG hexapeptides) in their substrates via an N-terminal region of ankyrin repeats. These ankyrin repeats form five domains termed ankyrin repeat clusters (ARCs), each predicted to bind substrate. Here we report crystal structures of a representative ARC of TNKS2 bound to targeting peptides from six substrates. Using a solution-based peptide library screen, we derive a rule-based consensus for Tankyrase substrates common to four functionally conserved ARCs. This 8-residue consensus allows us to rationalize all known Tankyrase substrates and explains the basis for cherubism-causing mutations in the Tankyrase substrate 3BP2. Structural and sequence information allows us to also predict and validate other Tankyrase targets, including Disc1, Striatin, Fat4, RAD54, BCR, and MERIT40.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2011

Characterization of Metalloproteins by High-throughput X-ray Absorption Spectroscopy

High-throughput X-ray absorption spectroscopy was used to measure transition metal content based on quantitative detection of X-ray fluorescence signals for 3879 purified proteins from several hundred different protein families generated by the New York SGX Research Center for Structural Genomics. Approximately 9% of the proteins analyzed showed the presence of transition metal atoms (Zn, Cu, Ni, Co, Fe, or Mn) in stoichiometric amounts. The method is highly automated and highly reliable based on comparison of the results to crystal structure data derived from the same protein set. To leverage the experimental metalloprotein annotations, we used a sequence-based de novo prediction method, MetalDetector, to identify Cys and His residues that bind to transition metals for the redundancy reduced subset of 2411 sequences sharing <70% sequence identity and having at least one His or Cys. As the HT-XAS identifies metal type and protein binding, while the bioinformatics analysis identifies metal-binding residues, the results were combined to identify putative metal-binding sites in the proteins and their associated families. We explored the combination of this data with homology models to generate detailed structure models of metal-binding sites for representative proteins. Finally, we used extended X-ray absorption fine structure data from two of the purified Zn metalloproteins to validate predicted metalloprotein binding site structures. This combination of experimental and bioinformatics approaches provides comprehensive active site analysis on the genome scale for metalloproteins as a class, revealing new insights into metalloprotein structure and function.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

PERCEPTIVE: an R shiny $\underline{p}$ipelin$\underline{e}$ for the p$\underline{r}$edi$\underline{c}$tion of $\underline{ep}$igenetic modula$\underline{t}$ors $\underline{i}$n no$\underline{v}$el sp$\underline{e}$cies

Epigenetic processes are central to regulating gene expression, genome stability, and metabolic function across the tree of life; yet, their roles remain underexplored in microalgae, especially as new species continue to be identified and characterized. This is likely due to the cumbersome nature and species-dependent attributes of epigenetic wet-lab methodologies, which preclude the rapid identification of epigenetic modifications and modulators. However, there is high conservation of epigenetic processes from budding yeast to humans; in many cases, one may infer how behavior and function are epigenetically regulated in novel species by identifying epigenetic modulators, or the proteins responsible for conferring epigenetic modifications. Here, to this end, we have developed a graphical software package, titled PERCEPTIVE (pipeline for the prediction of epigenetic modulators in novel species). This platform solely uses the genomic sequence of an algal species, and preexisting information from other model organisms, to predict the epigenetic modulators and associated modifications in algae. Predictions are presented to the user in a graphical interface, which provides literature-based interpretation of results, enabling users to quickly understand potential epigenetic processes in their algal species of interest and plan follow-up experiments. To test PERCEPTIVE, we predicted epigenetic modulators in several feedstock candidate algae species. To validate these predictions, wet-lab studies were performed, including mass spectrometry; these results underscore the high accuracy of PERCEPTIVE predictions. Overall, PERCEPTIVE represents a powerful in silico tool for the research and manipulation of algal species, which does not require a priori knowledge of epigenetics and is accessible to a broad set of investigators.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Quantification of the transferability of a designed protein specificity switch reveals extensive epistasis in molecular recognition

Re-engineering protein–protein recognition is an important route to dissecting and controlling complex interaction networks. Experimental approaches have used the strategy of “second-site suppressors,” where a functional interaction is inferred between two proteins if a mutation in one protein can be compensated by a mutation in the second. Mimicking this strategy, computational design has been applied successfully to change protein recognition specificity by predicting such sets of compensatory mutations in protein–protein interfaces. To extend this approach, it would be advantageous to be able to “transplant” existing engineered and experimentally validated specificity changes to other homologous protein–protein complexes. Here, we test this strategy by designing a pair of mutations that modulates peptide recognition specificity in the Syntrophin PDZ domain, confirming the designed interaction biochemically and structurally, and then transplanting the mutations into the context of five related PDZ domain–peptide complexes. We find a wide range of energetic effects of identical mutations in structurally similar positions, revealing a dramatic context dependence (epistasis) of designed mutations in homologous protein–protein interactions. To better understand the structural basis of this context dependence, we apply a structure-based computational model that recapitulates these energetic effects and we use this model to make and validate forward predictions. The context dependence of these mutations is captured by computational predictions, our results both highlight the considerable difficulties in designing protein–protein interactions and provide challenging benchmark cases for the development of improved protein modeling and design methods that accurately account for the context.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Comparative Performance Evaluation of Large Language Models for Extracting Molecular Interactions and Pathway Knowledge

Understanding the interactions and regulatory relationships among biomolecules is essential for deciphering complex biological systems and elucidating the mechanisms behind diverse biological functions. Traditionally, the collection of such molecular interaction data has relied on expert curation, a process that is both time-consuming and labor-intensive. To address these limitations, this study explores the use of large language models (LLMs) to automate the genome-scale extraction of molecular interaction knowledge. Here, we evaluate the performance of various LLMs on key biological tasks, including the identification of protein-protein interactions, detection of genes associated with pathways influenced by low-dose radiation, and inference of gene regulatory relationships. Our findings demonstrate that larger LLMs tend to perform better, particularly in extracting intricate gene and protein interactions. Despite their strengths, these models face challenges in recognizing functionally diverse gene groups and highly correlated regulatory relationships. Through a comprehensive analysis using established molecular interaction and pathway databases, we show that LLMs possess the potential to identify relevant biomolecules and predict their interactions, offering valuable insights and marking a significant step toward AI-driven biological knowledge discovery.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2012

LCLS-II New Instruments Workshops Report

The LCLS-II New Instruments workshops chaired by Phil Heimann and Jerry Hastings were held on March 19-22, 2012 at the SLAC National Accelerator Laboratory. The goal of the workshops was to identify the most exciting science and corresponding parameters which will help define the LCLS-II instrumentation. This report gives a synopsis of the proposed investigations and an account of the workshop. Scientists from around the world have provided short descriptions of the scientific opportunities they envision at LCLS-II. The workshops focused on four broadly defined science areas: biology, materials sciences, chemistry and atomic, molecular and optical physics (AMO). Below we summarize the identified science opportunities in the four areas. The frontiers of structural biology lie in solving the structures of large macromolecular biological systems. Most large protein assemblies are inherently difficult to crystallize due to their numerous degrees of freedom. Serial femtosecond protein nanocrystallography, using the 'diffraction-before-destruction' approach to outrun radiation damage has been very successfully pioneered at LCLS and diffraction patterns were obtained from some of the smallest protein crystals ever. The combination of femtosecond x-ray pulses of high intensity and nanosized protein crystals avoids the radiation damage encountered by conventional x-ray crystallography with focused beams and opens the door for atomic structure determinations of the previously largely inaccessible class of membrane proteins that are notoriously difficult to crystallize. The obtained structures will allow the identification of key protein functions and help in understanding the origin and control of diseases. Three dimensional coherent x-ray imaging at somewhat lower resolution may be used for larger objects such as viruses. The chemistry research areas of primary focus are the predictive understanding of catalytic mechanisms, with particular emphasis on photo- and heterogeneous catalysis. Of particular interest is the efficient conversion of light to electrical or chemical energy, which requires understanding the non-adiabatic dynamics of electronic excited states. Ultrafast x-ray scattering presents an excellent opportunity to investigate structural dynamics of molecular systems with atomic resolution, and x-ray scattering and spectroscopy present an excellent opportunity to investigating the dynamics of the electronic charge distribution. Harnessing solar energy to generate fuels, either indirectly with photovoltaics and electrochemical catalysis or directly with photocatalysts, presents a critical technological challenge that will require the use of forefront scientific tools such as ultrafast x-rays. At the center of this technical challenge is the rational design of efficient and cost effective catalysts. Important materials science opportunities relate to information technology applications, in particular the transport and storage of information on increasingly smaller length- and faster time-scales. Of interest are the understanding of the intrinsic size limits associated with the storage of information bits and the speed limits of information or bit processing. Key questions revolve about how electronic charges and spins of materials can be manipulated by electric and magnetic fields. This requires the exploration of speed limits subject to the fundamental conservation laws of energy and linear and angular momentum and the different coupling of polar electric and axial magnetic fields to charge and spin. Of interest are novel composite materials, including molecular systems combining multi electric and magnetic functionality. Ultrafast x-rays offer the required probing speed, can probe either the charge or spin properties through polarization control and through scattering and spectroscopy cover the entire energy-time-momentum-distance phase space. In the field of atomic and molecular science, LCLS II promises to elucidate the fundamental interactions among electrons and between electrons and nuclei, and to explore the frontiers of light-matter interactions. The high x-ray intensities unique to LCLS II will allow for highly localized electron excitation and probing at different positions inside a molecule, thereby interrogating the fundamental electron correlations. At the ultimate intensities, a breakdown of our established model of x-ray - matter interaction is predicted, leading to possibly new regimes of plasma physics.

14 SOLAR ENERGY↗
DOE OSTI2025

Extending quantum-mechanical benchmark accuracy to biological ligand-pocket interactions

Predicting the binding affinity of ligands to protein pockets is key in the drug design pipeline. The flexibility of ligand-pocket motifs arises from a range of attractive and repulsive electronic interactions during binding. Accurately accounting for all interactions requires robust quantum-mechanical (QM) benchmarks, which are scarce for ligand-pocket systems. Additionally, disagreement between “gold standard” Coupled Cluster (CC) and Quantum Monte Carlo (QMC) methods casts doubt on many benchmarks for larger non-covalent systems. We introduce the “QUantum Interacting Dimer” (QUID) benchmark framework containing 170 non-covalent (non-)equilibrium systems modeling chemically and structurally diverse ligand-pocket motifs. Symmetry-adapted perturbation theory shows that QUID broadly covers non-covalent binding motifs and energetic contributions. Robust binding energies are obtained using complementary CC and QMC methods, achieving agreement of 0.5 kcal/mol. The benchmark data analysis reveals that several dispersion-inclusive density functional approximations provide accurate energy predictions, though their atomic van der Waals forces differ in magnitude and orientation. Contrarily, semiempirical methods and empirical force fields require improvements in capturing non-covalent interactions (NCIs) for out-of-equilibrium geometries. The wide span of NCIs, highly accurate interaction energies, and analysis of molecular properties take QUID beyond the “gold standard” for QM benchmarks of ligand-protein systems.

Puleva, Mirela [University of Luxembourg, Luxembou↗
DOE OSTI2016

Evidence for Osteocalcin Binding and Activation of GPRC6A in β-Cells

The possibility that G protein-coupled receptor family C member A (GPRC6A) is the osteocalcin (Ocn)-sensing G protein-coupled receptor that directly regulates pancreatic β-cell functions is controversial. In the current study, we found that Ocn and an Ocn-derived C-terminal hexapeptide directly activate GPRC6A-dependent ERK signaling in vitro. Computational models probe the structural basis of Ocn binding to GPRC6A and predict that the C-terminal hexapeptide docks to the extracellular side of the transmembrane domain of GPRC6A. Consistent with the modeling, mutations in the computationally identified binding pocket of GPRC6A reduced Ocn and C-terminal hexapeptide activation of this receptor. In addition, selective deletion of Gprc6a in β-cells (Gprc6a β-cell-cko ) by crossing Gprc6a flox/flox mice with Ins2-Cre mice resulted in reduced pancreatic weight, islet number, insulin protein content, and insulin message expression. Both islet size and β-cell proliferation were reduced in Gprc6a β-cell-cko compared with control mice. Gprc6a β-cell-cko exhibited abnormal glucose tolerance, but normal insulin sensitivity. Islets isolated from Gprc6a β-cell-cko mice showed reduced insulin simulation index in response to Ocn. Here, these data establish the structural basis for Ocn direct activation of GPRC6A and confirm a role for GPRC6A in regulating β-cell proliferation and insulin secretion.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Predicting peak spectral sensitivities of vertebrate cone visual pigments using atomistic molecular simulations

Vision is the dominant sensory modality in many organisms for foraging, predator avoidance, and social behaviors including mate selection. Vertebrate visual perception is initiated when light strikes rod and cone photoreceptors within the neural retina of the eye. Sensitivity to individual colors, i.e., peak spectral sensitivities (λ max ) of visual pigments, are a function of the type of chromophore and the amino acid sequence of the associated opsin protein in the photoreceptors. Large differences in peak spectral sensitivities can result from minor differences in amino acid sequence of cone opsins. To determine how minor sequence differences could result in large spectral shifts we selected a spectrally-diverse group of 14 teleost Rh2 cone opsins for which sequences and λ max are experimentally known. Classical molecular dynamics simulations were carried out after embedding chromophore-associated homology structures within explicit bilayers and water. These simulations revealed structural features of visual pigments, particularly within the chromophore, that contributed to diverged spectral sensitivities. Statistical tests performed on all the observed structural parameters associated with the chromophore revealed that a two-term, first-order regression model was sufficient to accurately predict λ max over a range of 452–528 nm. The approach was accurate, efficient and simple in that site-by-site molecular modifications or complex quantum mechanics models were not required to predict λ max . These studies identify structural features associated with the chromophore that may explain diverged spectral sensitivities, and provide a platform for future, functionally predictive opsin modeling.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2012

Design and Implementation of Scientific Software Components to Enable Multiscale Modeling: The Effective Fragment Potential (QM/EFP) Method

The design and development of scientific software components to provide an interface to the effective fragment potential (EFP) methods are reported. Multiscale modeling of physical and chemical phenomena demands the merging of software packages developed by research groups in significantly different fields. Componentization offers an efficient way to realize new high performance scientific methods by combining the best models available in different software packages without a need for package readaptation after the initial componentization is complete. The EFP method is an efficient electronic structure theory based model potential that is suitable for predictive modeling of intermolecular interactions in large molecular systems, such as liquids, proteins, atmospheric aerosols, and nanoparticles, with an accuracy that is comparable to that of correlated ab initio methods. The developed components make the EFP functionality accessible for any scientific component-aware software package. The performance of the component is demonstrated on a protein interaction model, and its accuracy is compared with results obtained with coupled cluster methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Chromosome-level genome assembly and transcriptome of the green alga Chromochloris zofingiensis illuminates astaxanthin production

Microalgae have potential to help meet energy and food demands without exacerbating environmental problems. There is interest in the unicellular green alga Chromochloris zofingiensis, because it produces lipids for biofuels and a highly valuable carotenoid nutraceutical, astaxanthin. Here, to advance understanding of its biology and facilitate commercial development, we present a C. zofingiensis chromosome-level nuclear genome, organelle genomes, and transcriptome from diverse growth conditions. The assembly, derived from a combination of short- and long-read sequencing in conjunction with optical mapping, revealed a compact genome of ~58 Mbp distributed over 19 chromosomes containing 15,274 predicted protein-coding genes. The genome has uniform gene density over chromosomes, low repetitive sequence content (~6%), and a high fraction of protein-coding sequence (~39%) with relatively long coding exons and few coding introns. Functional annotation of gene models identified orthologous families for the majority (~73%) of genes. Synteny analysis uncovered localized but scrambled blocks of genes in putative orthologous relationships with other green algae. Two genes encoding beta-ketolase (BKT), the key enzyme synthesizing astaxanthin, were found in the genome, and both were up-regulated by high light. Isolation and molecular analysis of astaxanthin-deficient mutants showed that BKT1 is required for the production of astaxanthin. Moreover, the transcriptome under high light exposure revealed candidate genes that could be involved in critical yet missing steps of astaxanthin biosynthesis, including ABC transporters, cytochrome P450 enzymes, and an acyltransferase. Finally, the high-quality genome and transcriptome provide insight into the green algal lineage and carotenoid production.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Quantitative differentiation of benign and misfolded glaucoma-causing myocilin variants on the basis of protein thermal stability

Accurate predictions of the pathogenicity of mutations associated with genetic diseases are key to the success of precision medicine. Inherited missense mutations in the myocilin (MYOC) gene, within its olfactomedin (OLF) domain, constitute the strongest genetic link to primary open-angle glaucoma via a toxic gain of function, and thus MYOC is an attractive precision-medicine target. However, not all mutations in MYOC cause glaucoma, and common variants are expected to be neutral polymorphisms. The Genome Aggregation Database (gnomAD) lists ~100 missense variants documented within OLF, all of which are relatively rare (allele frequency <0.001%) and nearly all are of unknown pathogenicity. To distinguish disease-causing OLF variants from benign OLF variants, we first characterized the most prevalent population-based variants using a suite of cellular and biophysical assays, and identified two variants with features of aggregation-prone familial disease variants. Next, we considered all available biochemical and clinical data to demonstrate that pathogenic and benign variants can be differentiated statistically based on a single metric: the thermal stability of OLF. Our results motivate genotyping MYOC in patients for clinical monitoring of this widespread, painless and irreversible ocular disease.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Integration of transcriptomic and proteomic data identifies biological functions in cell populations from human infant lung

Systems biology uses computational approaches to integrate diverse data types to understand cell and organ behavior. Data derived from complementary technologies, for example transcriptomic and proteomic analyses, are providing new insights into development and disease. We compared mRNA and protein profiles from purified endothelial, epithelial, immune, and mesenchymal cells from normal human lung tissue. Signatures for each cell type were identified and compared at both mRNA and protein levels. Cell specific biological processes and pathways were predicted by analysis of concordant and discordant RNA-protein pairs. Cell clustering and gene set enrichment comparisons identified shared versus unique processes associated with transcriptomic and/or proteomic data. Clear cell-cell correlations between mRNA and protein data were obtained from each cell type. Approximately 40% of RNA-protein pairs were coherently expressed. While the correlation between RNA and their protein products was relatively low (approximately 0.4), cell specific signature genes involved in functional processes characteristic of each cell type, were more highly correlated with their protein products. Consistency of cell specific RNA-protein signatures indicated an essential framework for the function of each cell type. Visualization and reutilization of the protein and RNA profiles are supported by a new web application, "LungProteomics," which is incorporated into the LGEA web portal and freely accessible at (https://research.cchmc.org/pbge/lunggens/lungProteomics/human_sorted_profiles.html).

human lung, sorted cells, integrated omics, Transc↗
DOE OSTI2023

A single amino acid change led to structural and functional differentiation of PvHd1 to control flowering in switchgrass

Abstract Switchgrass, a forage and bioenergy crop, occurs as two main ecotypes with different but overlapping ranges of adaptation. The two ecotypes differ in a range of characteristics, including flowering time. Flowering time determines the duration of vegetative development and therefore biomass accumulation, a key trait in bioenergy crops. No causal variants for flowering time differences between switchgrass ecotypes have, as yet, been identified. In this study, we mapped a robust flowering time quantitative trait locus (QTL) on chromosome 4K in a biparental F2 population and characterized the flowering-associated transcription factor gene PvHd1, an ortholog of CONSTANS in Arabidopsis and Heading date 1 in rice, as the underlying causal gene. Protein modeling predicted that a serine to glycine substitution at position 35 (p.S35G) in B-Box domain 1 greatly altered the global structure of the PvHd1 protein. The predicted variation in protein compactness was supported in vitro by a 4 °C shift in denaturation temperature. Overexpressing the PvHd1-p.35S allele in a late-flowering CONSTANS-null Arabidopsis mutant rescued earlier flowering, whereas PvHd1-p.35G had a reduced ability to promote flowering, demonstrating that the structural variation led to functional divergence. Our findings provide us with a tool to manipulate the timing of floral transition in switchgrass cultivars and, potentially, expand their cultivation range.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Functional coding and non-coding variants in human BRCA1 gene and their use in genetic screening

BRCA1 is involved in double-strand DNA damage repair pathways, and mutations in the gene are associated with hereditary breast and ovarian cancers. With great help of the development of high-throughput DNA sequencing techniques numerous single-nucleotide polymorphisms (SNPs) and insertion deletion (Indel) mutations are detected on both coding and non-coding/regulatory regions of the BRCA1. Mutations may cause pathogenic or benign changes on the protein function or affect its expression. In the last decade, use of genetic screening tests to detect mutations on such genes has become greatly popular. However, it is very important to know the effect of the detected mutations, which is mostly possible by the use of predictive softwares, and also the related family history to be able to correctly analyse the screening results and to inform the patient. Therefore, use of in silico and in vitro techniques to score the pathogenicity of detected variants on genes like BRCA1 is now of great importance. Otherwise, results obtained from screening tests and family history cannot be analysed precisely.

62 RADIOLOGY AND NUCLEAR MEDICINE↗