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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Speciation Underpinned by Unexpected Molecular Diversity in the Mycorrhizal Fungal Genus Pisolithus

The mutualistic ectomycorrhizal (ECM) fungal genus Pisolithus comprises 19 species defined to date which colonize the roots of >50 hosts worldwide suggesting that substantial genomic and functional evolution occurred during speciation. To better understand this intra-genus variation, we undertook a comparative multi-omic study of nine Pisolithus species sampled from North America, South America, Asia, and Australasia. We found that there was a small core set of genes common to all species (13%), and that these genes were more likely to be significantly regulated during symbiosis with a host than accessory or species-specific genes. Thus, the genetic “toolbox” foundational to the symbiotic lifestyle in this genus is small. Transposable elements were located significantly closer to gene classes including effector-like small secreted proteins (SSPs). Poorly conserved SSPs were more likely to be induced by symbiosis, suggesting that they may be a class of protein that tune host specificity. The Pisolithus gene repertoire is characterized by divergent CAZyme profiles when compared with other fungi, both symbiotic and saprotrophic. This was driven by differences in enzymes associated with symbiotic sugar processing, although metabolomic analysis suggest that neither copy number nor expression of these genes is sufficient to predict sugar capture from a host plant or its metabolism in fungal hyphae. Our results demonstrate that intra-genus genomic and functional diversity within ECM fungi is greater than previously thought, underlining the importance of continued comparative studies within the fungal tree of life to refine our focus on pathways and evolutionary processes foundational to this symbiotic lifestyle.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Enhancers in Plant Development, Adaptation and Evolution

Understanding plant responses to developmental and environmental cues is crucial for studying morphological divergence and local adaptation. Gene expression changes, governed by cis-regulatory modules (CRMs) including enhancers, are a major source of plant phenotypic variation. However, while genome-wide approaches have revealed thousands of putative enhancers in mammals, far fewer have been identified and functionally characterized in plants. This review provides an overview of how enhancers function to control gene regulation, methods to predict DNA sequences that may have enhancer activity, methods utilized to functionally validate enhancers and the current knowledge of enhancers in plants, including how they impact plant development, response to environment and evolutionary adaptation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

An African-Specific Variant of TP5 3 Reveals PADI4 as a Regulator of p53-Mediated Tumor Suppression

TP53 is the most frequently mutated gene in cancer, yet key target genes for p53-mediated tumor suppression remain unidentified. Here, we characterize a rare, African-specific germline variant of TP53 in the DNA-binding domain Tyr107His (Y107H). Nuclear magnetic resonance and crystal structures reveal that Y107H is structurally similar to wild-type p53. Consistent with this, we find that Y107H can suppress tumor colony formation and is impaired for the transactivation of only a small subset of p53 target genes; this includes the epigenetic modifier PADI4, which deiminates arginine to the nonnatural amino acid citrulline. Surprisingly, we show that Y107H mice develop spontaneous cancers and metastases and that Y107H shows impaired tumor suppression in two other models. We show that PADI4 is itself tumor suppressive and that it requires an intact immune system for tumor suppression. We identify a p53–PADI4 gene signature that is predictive of survival and the efficacy of immune-checkpoint inhibitors.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Sub-daily virus sampling at the Bermuda Atlantic Time Series reveals diel and depth-structured population dynamics without community-level shifts

Ocean microbes contribute to biogeochemical cycles and ecosystem function, but they do so under top-down pressure imposed by viruses. While viruses are increasingly understood spatially and beginning to be incorporated into predictive modeling, high-frequency ocean virus dynamics remain understudied due to methodological challenges. Here we sampled stratified Bermuda Atlantic Time Series (BATS) waters for 112 hours at sub-daily 4- (surface) or 12- (deep chlorophyll maximum) hour intervals, purified viral particles from these samples, sequenced their metagenomes, and used the resulting data to characterize high-frequency virus community dynamics. Aggregated community diversity metrics changed with depth, but were not statistically significant temporally at a fixed location. However, finer-scale population-level analyses revealed both depth and temporal change, including physicochemical depth-driven differences and, in surface waters, thousands of viral populations that exhibited statistically significant diel rhythms. Statistical analyses revealed three main archetypes of temporal dynamics that themselves differed in abundance patterns, host predictions, viral taxonomy, and gene functions. Among these, highlights include viruses resembling an archetype with a night peaking pattern in activity that include an over-representation of viruses that putatively infect Prochlorococcus, a phototrophic cyanobacteria. Together, these efforts provide baseline community- and population-scale short-time-frame observations relevant to future climate state modeling.

Carrillo, Alfonso [The Ohio State University, Colu↗
DOE OSTI2020

Bayesian inference of Stochastic reaction networks using Multifidelity Sequential Tempered Markov Chain Monte Carlo

Stochastic reaction network models are often used to explain and predict the dynamics of gene regulation in single cells.These models usually involve several parameters, such as the kinetic rates of chemical reactions, that are not directly measurable and must be inferred from experimental data. Bayesian inference provides a rigorous probabilistic frame-work for identifying these parameters by finding a posterior parameter distribution that captures their uncertainty.Traditional computational methods for solving inference problems such as Markov Chain Monte Carlo methods based on classical Metropolis-Hastings algorithm involve numerous serial evaluations of the likelihood function, which in turn requires expensive forward solutions of the chemical master equation (CME). We propose an alternate approach based on a multifidelity extension of the Sequential Tempered Markov Chain Monte Carlo (ST-MCMC) sampler. This algorithm is built upon Sequential Monte Carlo and solves the Bayesian inference problem by decomposing it into a sequence of efficiently solved subproblems that gradually increase both model fidelity and the influence of the observed data. We reformulate the finite state projection (FSP) algorithm, a well-known method for solving the CME, to produce a hierarchy of surrogate master equations to be used in this multifidelity scheme. To determine the appropriate fidelity, we introduce a novel information-theoretic criteria that seeks to extract the most information about the ultimate Bayesian posterior from each model in the hierarchy without inducing significant bias. This novel sampling scheme is tested with high performance computing resources using biologically relevant problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Taxonomic Re-Evaluation and Genomic Comparison of Novel Extracellular Electron Uptake-Capable Rhodovulum visakhapatnamense and Rhodovulum sulfidophilum Isolates

Rhodovulum spp. are anoxygenic phototrophic purple bacteria with versatile metabolisms, including the ability to obtain electrons from minerals in their environment to drive photosynthesis, a relatively novel process called phototrophic extracellular electron uptake (pEEU). A total of 15 strains of Rhodovulum sulfidophilum were isolated from a marine estuary to observe these metabolisms in marine phototrophs. One representative strain, Rhodovulum sulfidophilum strain AB26, can perform phototrophic iron oxidation (photoferrotrophy) and couples carbon dioxide fixation to pEEU. Here, we reclassify two R. sulfidophilum isolates, strainAB26 and strain AB19, as Rhodovulum visakhapatnamense using taxonomic re-evaluation based on 16S and pufM phylogenetic analyses. The strain AB26 genome consists of 4,380,746 base-pairs, including two plasmids, and encodes 4296 predicted protein-coding genes. Strain AB26 contains 22 histidine kinases, 20 response regulators, and dedicates ~16% of its genome to transport. Transcriptomic data under aerobic, photoheterotrophy, photoautotrophy, and pEEU reveals how gene expression varies between metabolisms in a novel R. visakhapatnamense strain. Genome comparison led by transcriptomic data under pEEU reveals potential pEEU-relevant genes both unique to R. visakhapatnamense strains and shared within the R. sulfidophilum genomes. With these data we identify potential pEEU-important transcripts and how speciation may affect molecular mechanisms of pEEU in Rhodovulum species from the same environment.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Constructing Regulatory Networks to Compare Axenic and Interspecies Microbial Gene Transcription

In this preliminary study, we constructed gene regulatory networks (GRNs) from transcriptional expression data of axenic and interspecies microbial cultures with the goal of predicting how cocultivation affected greenhouse gas respiration by these species. The specific strains of Methylotuvimicrobium alkaliphilum 20Z, a methylotroph, and Cyanobacterium stanieri HL-69, a phototroph, were chosen for their viability in industrial bioprocessing. We ranked directed interactions between gene pairs based on the ability of the input gene to predict the expression of a target gene relative to their transcriptomes. While we were able to identify topological differences between conditions, our initial findings require validation through experimental analysis and further modeling. We aimed to develop a systematic thresholding approach to optimize the accuracy of our networks. We filtered out trial networks separately from top gene interactions of the scored rankings. Parameters of unfiltered and filtered networks were used to test and develop thresholding approaches. Knee point detection of edge weight distributions was explored as an approach for separating significant interactions from insignificant interactions in unfiltered networks. While knee detection failed to produce analogous networks for broad cross-condition comparisons, the results informed us about the proportions of significant edges present in unfiltered networks. We also calculated the average mean degree for nodes in a selection of trial networks to find a thresholding value characteristic to all groups. While we did not reach a definitive conclusion, we gained insight into the coregulatory structures of our groups and made critical evaluations of systematic methods for filtering networks. We recommend an iterative process for the inference of GRNs, where the most significant results from preliminary explorations are used to improve the efficiency with which regulatory motifs are chosen for experimental characterization. Experimental results can then inform the framework of adjusted models to improve broad interpretations of GRNs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

RNA language models predict mutations that improve RNA function

Structured RNA lies at the heart of many central biological processes, from gene expression to catalysis. RNA structure prediction is not yet possible due to a lack of high-quality reference data associated with organismal phenotypes that could inform RNA function. We present GARNET (Gtdb Acquired RNa with Environmental Temperatures), a new database for RNA structural and functional analysis anchored to the Genome Taxonomy Database (GTDB). GARNET links RNA sequences to experimental and predicted optimal growth temperatures of GTDB reference organisms. Using GARNET, we develop sequence- and structure-aware RNA generative models, with overlapping triplet tokenization providing optimal encoding for a GPT-like model. Leveraging hyperthermophilic RNAs in GARNET and these RNA generative models, we identify mutations in ribosomal RNA that confer increased thermostability to the Escherichia coli ribosome. The GTDB-derived data and deep learning models presented here provide a foundation for understanding the connections between RNA sequence, structure, and function.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

TriGORank: A Gene Ontology Enriched Learning-to-Rank Framework for Trigenic Fitness Prediction

Machine learning (ML) has been gaining interest in the metabolic engineering community as a means to automate prediction tasks. In this work, we introduce and study the task of using ML to recommend high-fitness triplet mutants as candidates for wet-lab experiments. We first utilize individual fitness and digenic fitness scores as features and train machine learning models that produce a ranked list, from high to low fitness scores, for triplet gene mutants of S. cerevisiae. Then, we incorporate prior metabolic knowledge from an existing gene ontology, by designing a novel graph representation and deducing features that can capture gene similarity and gene interactions. Lastly, experimental results show that our proposed gene ontology enriched model, termed TriGORank, improves both performance and explainability.

Labhishetty, Sahiti↗
DOE OSTI2023

BayFlux: A Bayesian method to quantify metabolic Fluxes and their uncertainty at the genome scale

Metabolic fluxes, the number of metabolites traversing each biochemical reaction in a cell per unit time, are crucial for assessing and understanding cell function. 13 C Metabolic Flux Analysis ( 13 C MFA) is considered to be the gold standard for measuring metabolic fluxes. 13 C MFA typically works by leveraging extracellular exchange fluxes as well as data from 13 C labeling experiments to calculate the flux profile which best fit the data for a small, central carbon, metabolic model. However, the nonlinear nature of the 13 C MFA fitting procedure means that several flux profiles fit the experimental data within the experimental error, and traditional optimization methods offer only a partial or skewed picture, especially in “non-gaussian” situations where multiple very distinct flux regions fit the data equally well. Here, we present a method for flux space sampling through Bayesian inference (BayFlux), that identifies the full distribution of fluxes compatible with experimental data for a comprehensive genome-scale model. This Bayesian approach allows us to accurately quantify uncertainty in calculated fluxes. We also find that, surprisingly, the genome-scale model of metabolism produces narrower flux distributions (reduced uncertainty) than the small core metabolic models traditionally used in 13 C MFA. The different results for some reactions when using genome-scale models vs core metabolic models advise caution in assuming strong inferences from 13 C MFA since the results may depend significantly on the completeness of the model used. Based on BayFlux, we developed and evaluated novel methods (P- 13 C MOMA and P- 13 C ROOM) to predict the biological results of a gene knockout, that improve on the traditional MOMA and ROOM methods by quantifying prediction uncertainty.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

maslov-group/FUN-PROSE

mRNA levels of all genes in a genome is a critical piece of information defining the overall state of the cell in a given environmental condition. Being able to reconstruct such condition-specific expression in fungal genomes is particularly important to metabolically engineer these organisms to produce desired chemicals in industrially scalable conditions. Most previous deep learning approaches focused on predicting the average expression levels of a gene based on its promoter sequence, ignoring its variation across different conditions. Here we present FUN-PROSE—a deep learning model trained to predict differential expression of individual genes across various conditions using their promoter sequences and expression levels of all transcription factors. We train and test our model on three fungal species and get the correlation between predicted and observed condition-specific gene expression as high as 0.85. We then interpret our model to extract promoter sequence motifs responsible for variable expression of individual genes. We also carried out input feature importance analysis to connect individual transcription factors to their gene targets. A sizeable fraction of both sequence motifs and TF-gene interactions learned by our model agree with previously known biological information, while the rest corresponds to either novel biological facts or indirect correlations.

Liu, Simon↗
DOE OSTI2022

Disentangling direct from indirect relationships in association networks

Networks are vital tools for understanding and modeling interactions in complex systems in science and engineering, and direct and indirect interactions are pervasive in all types of networks. However, quantitatively disentangling direct and indirect relationships in networks remains a formidable task. Here, we present a framework, called iDIRECT (Inference of Direct and Indirect Relationships with Effective Copula-based Transitivity), for quantitatively inferring direct dependencies in association networks. Using copula-based transitivity, iDIRECT eliminates/ameliorates several challenging mathematical problems, including ill-conditioning, self-looping, and interaction strength overflow. With simulation data as benchmark examples, iDIRECT showed high prediction accuracies. Application of iDIRECT to reconstruct gene regulatory networks in Escherichia coli also revealed considerably higher prediction power than the best-performing approaches in the DREAM5 (Dialogue on Reverse Engineering Assessment and Methods project, #5) Network Inference Challenge. In addition, applying iDIRECT to highly diverse grassland soil microbial communities in response to climate warming showed that the iDIRECT-processed networks were significantly different from the original networks, with considerably fewer nodes, links, and connectivity, but higher relative modularity. Further analysis revealed that the iDIRECT-processed network was more complex under warming than the control and more robust to both random and target species removal ( P < 0.001). As a general approach, iDIRECT has great advantages for network inference, and it should be widely applicable to infer direct relationships in association networks across diverse disciplines in science and engineering.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Integrating functional scoring and regulatory data to predict the effect of non-coding SNPs in a complex neurological disease

Abstract Most SNPs associated with complex diseases seem to lie in non-coding regions of the genome; however, their contribution to gene expression and disease phenotype remains poorly understood. Here, we established a workflow to provide assistance in prioritising the functional relevance of non-coding SNPs of candidate genes as susceptibility loci in polygenic neurological disorders. To illustrate the applicability of our workflow, we considered the multifactorial disorder migraine as a model to follow our step-by-step approach. We annotated the overlap of selected SNPs with regulatory elements and assessed their potential impact on gene expression based on publicly available prediction algorithms and functional genomics information. Some migraine risk loci have been hypothesised to reside in non-coding regions and to be implicated in the neurotransmission pathway. In this study, we used a set of 22 non-coding SNPs from neurotransmission and synaptic machinery-related genes previously suggested to be involved in migraine susceptibility based on our candidate gene association studies. After prioritising these SNPs, we focused on non-reported ones that demonstrated high regulatory potential: (1) VAMP2_rs1150 (3′ UTR) was predicted as a target of hsa-mir-5010-3p miRNA, possibly disrupting its own gene expression; (2) STX1A_rs6951030 (proximal enhancer) may affect the binding affinity of zinc-finger transcription factors (namely ZNF423) and disturb TBL2 gene expression; and (3) SNAP25_rs2327264 (distal enhancer) expected to be in a binding site of ONECUT2 transcription factor. This study demonstrated the applicability of our practical workflow to facilitate the prioritisation of potentially relevant non-coding SNPs and predict their functional impact in multifactorial neurological diseases.

Felício, Daniela↗
DOE OSTI2024

Extending evolutionary forecasts across bacterial species

Improving evolutionary forecasting requires progressing from studying repeated evolution of a single genotype under identical conditions to formulating broad principles. These principles should enable predictions of how similar species will adapt to similar selective pressures. Evolve-and-resequence experiments with multiple species allow testing forecasts on different biological levels and elucidating the causes for failed predictions. Here, we show that forecasts for adaptation to static culture conditions can be extended to multiple species by testing previous predictions for Pseudomonas syringae and Pseudomonas savastanoi. In addition to sequence divergence, these species differ in their repertoire of biofilm regulatory genes and structural components. Consistent with predictions, both species repeatedly produced biofilm mutants with a wrinkly spreader phenotype. Predominantly, mutations occurred in the wsp operon, with less frequent promoter mutations near uncharacterized diguanylate cyclases. However, mutational patterns differed on the gene level, which was explained by a lack of conservation in relative fitness of mutants between more divergent species. The same mutation was the most frequent for both species suggesting that conserved mutation hotspots can increase parallel evolution. This study shows that evolutionary forecasts can be extended across species, but that differences in the genotype–phenotype–fitness map and mutational biases limit predictability on a detailed molecular level.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRSDate not supplied

Congruence of Clusters Defined By Whole Genome Sequencing and MALDI-TOF for Bacteria Isolated From Cleanrooms

Introduction: Oligotrophic conditions can render cleanrooms inhospitable to microbes. Despite these constraints, fungi and bacteria are frequently isolated from surfaces in astromaterials cleanrooms at the Johnson Space Center. Bacillus species are of particular concern because endospores belonging to this genus are resilient and can affect astromaterials. Current monitoring programs rely on 16S rRNA sequencing and the VITEK2 Compact system. These methods have limited power to resolve Bacillus species. Matrix-assisted laser desorption - time of flight mass spectrometry (MALDI-TOF MS), provides a rapid, low cost, method of identifying bacterial isolates and has a higher resolution than 16S rRNA sequencing, particularly for Bacillus species; however, few studies have compared this method to the industry gold standard, whole genome sequencing (WGS). Methods: Based on 16S rRNA classification, we selected 14 isolates for analysis with MALDI-TOF and WGS. Mass spectra were generated with MALDI-TOF MS and processed with custom scripts to identify clusters of closely related isolates and calculate a matrix of pairwise cosine similarity scores. Hybrid Illumina and Nanopore sequencing were used to generate draft genomes. Pairwise similarity scores were calculated from these genomes based on the average amino acid identity (AAI) predicted from single copy core genes. Congruence of clustering between these methods, was assessed by calculating adjusted Rand and Wallace coefficients. Results: Clusters of species generated from MALDI-TOF MS showed good agreement of phylotypes generated with WGS. Pairs of strains that were > 94% similar to each other in terms of predicted amino acid sequences consistently showed cosine similarities of mass spectra > 0.65 and, of the 9 clusters identified with WGS, 8 were identical with MALDI-TOF. This corresponds to an adjusted Rand index of 0.95 and a 95% confidence interval of 0.80 – 1.00 for adjusted Wallace coefficients. The only discordance was for a pair of isolates that were classified as Paenibacillus species. This pair showed relatively high similarity (0.84) in terms of MALDI-TOF MS but only 85% similarity in terms of AAI. Conclusion: This study shows that MALDI-TOF and WGS exhibit a similar ability to delineate Bacillus species isolated from cleanrooms and taxonomic units described by these two methods are consistent with one another. Since MALDI-TOF MS is low in cost and high in throughput, this approach appears to be an ideal option for routine microbial monitoring and identifying Bacillus species.

Farnaz Mazhari↗
NASA NTRS2005

Early immune response and regulation of IL-2 receptor subunits

Affymetrix oligonucleotide arrays were used to monitor expression of 8796 genes and probe sets in activated T-cells; analysis revealed that 217 genes were significantly upregulated within 4 h. Induced genes included transcription factors, cytokines and their receptor genes. Analysis by semi-quantitative RT-PCR confirmed the significant induction of IL-2, IL-2R(gamma) and IL-2R(alpha). Forty-eight of the 217 induced genes are known to or predicted to be regulated by a CRE promoter/enhancer. We found that T-cell activation caused a significant increase in CREB phosphorylation furthermore, inhibition of the PKC pathway by GF109203 reduced CREB activation by 50% and inhibition of the PKA pathway caused a total block of CREB phosphorylation and significantly reduced IFN(gamma), IL-2 and IL-2R(alpha) gene expression by approximately 40% (p<0.001). PKC(theta) plays a major role in T-cell activation: inhibition of PKC significantly reduced the expression of IFN(gamma), IL-2 and IL-2R(alpha). Since PKC blocked activation of CREB, we studied potential cross-talk between the PKC and the PKA/MAPK pathways, PMA-stimulated Jurkat cells were studied with specific signal pathway inhibitors. Extracellular signal-regulated kinase-2 (ERK2) pathway was found to be significantly activated greater than seven-fold within 30 min; however, there was little activation of ERK-1 and no activation of JNK or p38 MAPK. Inhibition of the PKA pathway, but not the PKC pathway, resulted in inhibition of ERK1/2 activation at all time points, inhibition of MEK1 and 2 significantly blocked expression of IL-2 and IL-2R(alpha). Gene expression of IL-2R(alpha) and IFN(gamma) was dependent on PKA in S49 wt cells but not in kin- mutants. Using gel shift analysis, we found that forskolin activation of T-cells resulted in activation of AP1 sites; this increase in nuclear extract AP1 was significantly blocked by MEK1 inhibitor U0126. Taken together, these results suggest that the PKA in addition to PKC and MAPK pathways plays a role in early T-cell activation and induction of IL-2, IL-2R(alpha) and IFN(gamma) gene expression.

Non-NASA Center↗