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DOE OSTI2026

CAHS: Context-Aware Homology Search

Protein homology search is foundational to bioinformatics: it supports annotation transfer, structure/function inference, and evolutionary analysis over rapidly expanding sequence repositories (e.g., UniProtKB). Profile hidden Markov models (pHMMs), as implemented in HMMER, remain the most widely trusted approach because they provide statistically calibrated E-values; however, their gap behavior is fixed once a profile is trained, despite biological evidence that insertion/deletion tolerance varies across flexible loops and intrinsically disordered regions. We present CAHS (Context-Aware Homology Search), a lightweight query-time adapter for pHMM search that incorporates learned and biologically motivated signals without changing HMMER's downstream search pipeline or its calibrated E-value reporting. Given a query sequence, CAHS computes per-residue representations from a protein language model and a disorder predictor, maps these to profile coordinates, and modulates only match-state transition rows (gap-open and gap-extension probabilities) while preserving Plan7 constraints. We comprehensively evaluate CAHS across six structurally diverse protein families and multi-domain architectures against a 570k-sequence target corpus. CAHS expands detection capability, retrieving thousands of additional remote homologs at relaxed thresholds by maintaining alignment quality through flexible regions. For multi-domain proteins, context-aware modulation resolves 94% of fragmented alignments. Crucially, CAHS preserves hit-set invariance at stringent operating points (E<10-10), demonstrating increased statistical confidence without inflating false positives. Furthermore, sharper statistical distinction between homologs and background noise during early filter stages yields up to a 3.87× acceleration in end-to-end wall-clock time on high-performance computing clusters. Overall, CAHS illustrates a practical AI-for-science design pattern: augmenting a trusted probabilistic model with query-specific learned signals to improve interpretable, reproducible inference in data-rich biology.

Bhattaram, Swethasree [Georgia Institute of Techno↗

DOE OSTI2020

Towards the Commercialization of the All-Solid-State Li-ion Battery: Local Bonding Structure and the Reversibility of Sheet-Style Si-PAN Anodes

A slurry-coated sheet-style Si-based anode is developed for use in all-solid-state Li-ion batteries. Inexpensive, mixed-conducting polyacrylonitrile (PAN) is utilized as both binder and conductive additive, enabling Si-rich electrodes (70 wt%) to attain large reversible capacities ~1,500 mAh g-1 (Si) at 1 C rates (>3 mA cm -2 ). Cross sectional analysis of a discharged all-solid-state half-cell indicates that the Si-PAN anode achieves the largest volumetric specific capacity ever reported for a Li-ion electrode (>1500 mAh cm -3 ). Ex situ Raman spectroscopic studies reveal that the anodes' reversibility is attributed to the preservation of small tetrahedrally coordinated clusters within their Si nanoparticles upon discharge. We therefore argue that careful lithiation limitations should be implemented in all Si-based Li-ion anodes to preserve this local tetrahedral atomic structure. Furthermore, the slurry-coated sheet-style Si-PAN anodes have been successfully cycled in all-solid-state full-cells against high-voltage nickel-rich NMC 811 composite cathodes to encourage future commercialization of similar sheet-style all-solid-state designs.

25 ENERGY STORAGE↗

DOE OSTI2015

Differential Effects Of Octanoate And Heptanoate On Myocardial Metabolism During Extracorporeal Membrane Oxygenation In An Infant Swine Model

Background: Nutritional energy support during extracorporeal membrane oxygenation (ECMO) should promote successful myocardial adaptation and eventual weaning from the ECMO circuit. Fatty acids (FAs) are a major myocardial energy source, and medium-chain FAs (MCFAs) are easily taken up by cell and mitochondria without membrane transporters. Oddnumbered MCFAs supply carbons to the citric acid cycle (CAC) via anaplerotic propionyl-CoA as well as acetyl-CoA, the predominant betaoxidation product for even-numbered MCFA. Theoretically, this anaplerotic pathway enhances carbon entry into the CAC, and provides superior energy state and preservation of protein synthesis. We tested this hypothesis in an immature swine model undergoing ECMO. Methods: Fifteen male Yorkshire pigs (26-45 days old) with 8-hour ECMO were received either normal saline, heptanoate (odd-numbered MCFA) or octanoate (even-numbered MCFA) at 2.3 μmol/kg body wt/min as MCFAs systemically during ECMO (n = 5 per group). The 13-Carbon (13C)-labeled substrates ([2-13C]lactate, [5,6,7-13C3]heptanoate and [U-13C6]leucine) were systemically infused as metabolic markers for the final 60 minutes before left ventricular tissue extraction. Extracted tissues were analyzed for the 13C-labeled and absolute concentrations of metabolites by nuclear magnetic resonance and gas chromatography-mass spectrometry. Results: Octanoate produced markedly higher myocardial citrate concentration, and led to a higher [ATP]/[ADP] ratio compared with other http://mc.manuscriptcentral.com/jpen Journal of Parenteral and Enteral Nutrition For Peer Review groups. Unexpectedly, octanoate increased the flux of propionyl-CoA relative to acetyl-CoA into the CAC as well as heptanoate. MCFAs promoted increases in leucine oxidation, but were not associated with a difference in fractional protein synthesis rate. Conclusion: Octanoate provides energetic advantages to the heart over heptanoate, while preserving protein synthesis.

amino acids↗

DOE OSTI2015

Parallel seed-based approach to multiple protein structure similarities detection

Finding similarities between protein structures is a crucial task in molecular biology. Most of the existing tools require proteins to be aligned in order-preserving way and only find single alignments even when multiple similar regions exist. We propose a new seed-based approach that discovers multiple pairs of similar regions. Its computational complexity is polynomial and it comes with a quality guarantee—the returned alignments have both root mean squared deviations (coordinate-based as well as internal-distances based) lower than a given threshold, if such exist. We do not require the alignments to be order preserving (i.e., we consider nonsequential alignments), which makes our algorithm suitable for detecting similar domains when comparing multidomain proteins as well as to detect structural repetitions within a single protein. Because the search space for nonsequential alignments is much larger than for sequential ones, the computational burden is addressed by extensive use of parallel computing techniques: a coarse-grain level parallelism making use of available CPU cores for computation and a fine-grain level parallelism exploiting bit-level concurrency as well as vector instructions.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Analysis of Electric Vehicle Charging Behavior Patterns with Function Principal Component Analysis Approach

This manuscript focused on analyzing electric vehicles’ (EV) charging behavior patterns with a functional data analysis (FDA) approach, with the goal of providing theoretical support to the EV infrastructure planning and regulation, as well as the power grid load management. 5-year real-world charging log data from a total of 455 charging stations in Kansas City, Missouri, was used. The focuses were placed on analyzing the daily usage occupancy variability, daily energy consumption variability, and station-level usage variability. Compared with the traditional discrete-based analysis models, the proposed FDA modeling approach had unique advantages in preserving the smooth function behavior of the data, bringing more flexibility in the modeling process with little required assumptions or background knowledge on independent variables, as well as the capability of handling time series data with different lengths or sizes. In addition to the patterns revealed in the EV charging station’s occupancy and energy consumption, the differences between EV driver’s charging time and parking time were analyzed and called for the needs for parking regulation and enforcement. The different usage patterns observed at charging stations located on different land-use types were also analyzed.

Engineering↗

DOE OSTI2022

Name Normalizer

Our name normalizer and enrichment micro-service address the shortcomings of standard text normalization. Initially the text input is split into tokens. This process takes into account some conventions of formatting. Any dashes present between two tokens preserves the relationship of those two tokens. Conversely a comma between two tokens ensures that the separation between the two tokens is maintained. The order of tokens is also preserved. Standard text normalization (conversion to lowercase, trimming extra white space, and canonicalization) is then applied to the tokens. Once normalized every unique and sequence of tokens is given confidence values by comparing the normalized values to publicly available data.

Bleeker, Amelia↗

DOE OSTI2025

Knowledge Oriented Graph Unified Transformer (KOGUT) v0.1

KOGUT — Knowledge Oriented Graph Unified Transformer KOGUT implements the Relational Graph Transformer (RelGT) architecture for knowledge graph link prediction in biological domains, with a primary focus on microbial growth media prediction. While the original RelGT (arXiv:2505.10960) targets relational tables, time series, and multi-table databases, KOGUT adapts this architecture for heterogeneous biological knowledge graphs, providing first-in-class AI predictive models for microbial cultivation. Key Adaptations Beyond Original RelGT: - Knowledge Graph Focus: Applied to biological KGs with semantic node types (taxa, chemicals, media, phenotypes, environments) versus generic relational database tables, trained on the KG-Microbe knowledge graph (1.3M entities, 2.9M edges, 24 relation types). - Multimodal Node Encoding: Integrates node labels, categories, descriptions, and synonyms from KG metadata through learned embedding layers—adapting relational column features to graph node attributes with textual semantics. - Extended K-Hop Subgraph Strategy: Optimized neighborhood sampling (3-hop default, configurable up to 200 nodes) tuned for sparse biological networks, building on the original local-global attention framework with biological relation preservation. - Biolink Predicate Preservation: Type-specific transformations for 24 biological edge semantics (occurs_in, consumes, produces, has_phenotype, subclass_of) beyond standard relational foreign keys, enabling multi-relation link prediction. - Inductive Learning Support: Enables zero-shot predictions for novel taxa through feature-based embeddings (temperature, oxygen requirements, gram stain, cell shape), extending the original transductive relational benchmark scope to uncultured microorganisms. CheapSOTA Performance Optimizations (This Distribution): - VQ-EMA Centroid Attention: Vector quantization with exponential moving average for improved global context modeling (+5-10% MRR improvement). - HDF5 Precomputed Data Loading: One-time preprocessing of k-hop subgraphs to eliminate redundant graph traversals (2-5× training speedup). - Distributed Data Parallel Training: Multi-GPU support for scaling to larger knowledge graphs (tested on 4× NVIDIA A100 GPUs at NERSC Perlmutter). - Mixed Precision Training: Automatic mixed precision (AMP) for memory efficiency and faster training. Advantages Over Standard Knowledge Graph Embedding Models: Combines RelGT's proven multi-element tokenization (features, type, hop, structure) with graph-native biological representations, enabling interpretable link prediction across heterogeneous entities that standard embedding models (TransE, RotatE, ComplEx) and table-based transformers cannot directly model. Achieves near-perfect performance on microbial growth media prediction (MRR: 0.9966, Precision@1: 0.9932, Hit@10: 1.0000) while maintaining explainability through attention-based reasoning over biological pathways. Training Data: - KG-Microbe merged knowledge graph: 1,379,337 nodes, 2,960,472 edges - 24 biological relation types including taxonomic hierarchies, metabolic interactions, phenotype associations, and environmental relationships - Primary prediction task: Growth media suitability for microbial taxa (biolink:occurs_in, 50K edges) - Multi-relation capability: Predicts links for any of the 24 relation types, including chemical consumption/production, phenotype associations, and taxonomic classification Citation: Original RelGT Architecture: Dwivedi et al., "Relational Graph Transformer", arXiv:2505.10960, 2025 KOGUT Implementation: Knowledge Oriented Graph Unified Transformer for Microbial Growth Media Prediction Developed at Lawrence Berkeley National Laboratory (LBNL) Trained on NERSC Perlmutter supercomputer

Joachimiak, Marcin [Lawrence Berkeley National Lab↗

DOE OSTI2026

AstraAI v1

AstraAI is an open-source, structure-aware AI coding agent designed for large scientific and DOE-HPC codebases such as AMReX-based applications. Unlike general-purpose coding assistants, AstraAI combines retrieval-augmented generation (RAG) with compiler-level Abstract Syntax Tree (AST) analysis to perform precise, scope-constrained code modifications. It identifies exact function spans, enforces locality of edits, and maintains cross-file invariants, enabling deterministic and build-safe transformations in complex C++/GPU environments. AstraAI is intended for developers working on large, evolving HPC frameworks where correctness, reproducibility, and structural integrity are critical. Typical use cases include modifying physics kernels, updating GPU device lambdas, and performing multi-file refactors without breaking compilation or runtime semantics. Compared to conventional LLM-based coding agents - even those with repository access - AstraAI provides structural guarantees rather than free-form text patches. It minimizes unintended diffs, prevents scope drift, preserves formatting and build stability, and reduces structural hallucinations. By integrating compiler tooling directly into the generation loop, AstraAI transforms AI-assisted coding from probabilistic text editing into deterministic, structure-preserving program transformation suitable for mission-critical scientific software.

Natarajan, Mahesh [Lawrence Berkeley National Labo↗

DOE OSTI2022

Impact of Advection Schemes on Tracer Interrelationships in Large-Eddy Simulations of Deep Convection

This study investigates the preservation of tracer interrelationships during advection in large-eddy simulations of an idealized deep convective cloud, which is particularly relevant to chemistry, aerosol, and cloud microphysics models. Employing the Cloud Model 1, advection is represented using third-, fifth-, and seventh-order weighted essentially non-oscillatory schemes. As a simplified analogy for cloud hydrometeors and aerosols, several inert passive tracers following linear and nonlinear relationships are initialized after the cloud reaches ~6-km depth. Numerical mixing in the simulated turbulent convective clouds leads to significant deviations from the initial nonlinear relationships between tracers. In these simulations, a considerable fraction of the grid points where the tracers’ nonlinear relationships are altered from advection are classified as unrealistic (e.g., ~13% for the environmental tracers on average), including errors from range-preserving unmixing and overshooting. Errors in the sum of three tracers are also relatively large, ranging between ~1% and 16% for 5% of the grid points in and near the cloud. The magnitude of unrealistic mixing and errors in the sum of three tracers generally increase with the order of accuracy of the advection scheme. These results are consistent across model grid spacings ranging from 50 to 200 m, and across three different flow realizations for each combination of grid spacing and advection scheme tested. Tests employing a previously proposed scalar normalization procedure show substantially reduced errors in the sum of three tracers with a relatively small negative impact on other tracer relationships. This analysis, therefore, suggests efficacy of the normalization procedure when applied to turbulent three-dimensional cloud simulations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Simulation as a sustainable trading zone: Aiming at intergenerational justice

The paper, drawing on the example of simulation codes used in nuclear physics and high-energy physics, seeks to call attention to the ethical implications of discontinuing support for simulation codes and the loss of knowledge embodied in them. Predicated on the concept of trading zones and actor network models, the paper addresses the problem of extinction of simulation codes and attempts to understand their evolution and development within those frameworks. We show that simulation codes of closed type develop to the level of creoles, becoming local languages and standards of scientific centers and disappearing as their few main developers leave, whereas codes of open types become universal languages, imposing problemsolving patterns on the entire community and crowding out other codes. The paper suggests that because of simulations’ reliance on tacit knowledge, practices entrenched in codes cannot be exhaustively explicated or transmitted through writing alone; on the contrary, the life cycle of a simulation code is determined by the life cycle of its trading zone. We examine the extent to which both of these phenomena pose a risk to the preservation of knowledge. Bearing upon intergenerational ethics, we draw analogies between the pure intergenerational problem (PIP) and the problem of preserving the knowledge implemented in simulation codes and transmitting it to future generations. We argue that for the complete transfer of knowledge, it is necessary to develop and maintain inhabitability and sustainability of simulation trading zones in a controllable way, at least until the demand for these codes is warranted to cease in the future.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Multiscale X-ray phase contrast imaging of human cartilage for investigating osteoarthritis formation

The evolution of cartilage degeneration is still not fully understood, partly due to its thinness, low radio-opacity and therefore lack of adequately resolving imaging techniques. X-ray phase-contrast imaging (X-PCI) offers increased sensitivity with respect to standard radiography and CT allowing an enhanced visibility of adjoining, low density structures with an almost histological image resolution. This study examined the feasibility of X-PCI for high-resolution (sub-) micrometer analysis of different stages in tissue degeneration of human cartilage samples and compare it to histology and transmission electron microscopy. Ten 10%-formalin preserved healthy and moderately degenerated osteochondral samples, post-mortem extracted from human knee joints, were examined using four different X-PCI tomographic set-ups using synchrotron radiation the European Synchrotron Radiation Facility (France) and the Swiss Light Source (Switzerland). Volumetric datasets were acquired with voxel sizes between 0.7 × 0.7 × 0.7 and 0.1 × 0.1 × 0.1 µm 3 . Data were reconstructed by a filtered back-projection algorithm, post-processed by ImageJ, the WEKA machine learning pixel classification tool and VGStudio max. For correlation, osteochondral samples were processed for histology and transmission electron microscopy. X-PCI provides a three-dimensional visualization of healthy and moderately degenerated cartilage samples down to a (sub-)cellular level with good correlation to histologic and transmission electron microscopy images. X-PCI is able to resolve the three layers and the architectural organization of cartilage including changes in chondrocyte cell morphology, chondrocyte subgroup distribution and (re-)organization as well as its subtle matrix structures. X-PCI captures comprehensive cartilage tissue transformation in its environment and might serve as a tissue-preserving, staining-free and volumetric virtual histology tool for examining and chronicling cartilage behavior in basic research/laboratory experiments of cartilage disease evolution.

3D analysis↗

DOE OSTI2026

EvoDiffMol: evolutionary diffusion framework for 3D molecular design with optimized properties

Designing molecules with specific target properties remains a fundamental challenge in computational chemistry. While existing approaches show promise, most rely on simplified representations like SMILES strings or 2D graphs that lack essential three-dimensional geometric information. We present EvoDiffMol, a computational framework that integrates evolutionary algorithms with three-dimensional diffusion models for property-driven molecular generation. The method operates through adaptive evolutionary optimization, where population-based selection guides the generation process toward desired property landscapes. EvoDiffMol supports both unconstrained molecular design and scaffold-constrained generation that preserves fixed substructures while optimizing complementary regions. Comprehensive evaluation demonstrates exceptional performance, achieving the highest drug-likeness score (0.94) among all compared state-of-the-art methods while maintaining excellent validity, uniqueness, and novelty. Beyond single property optimization, the framework demonstrates flexible multi-property optimization capabilities, simultaneously controlling multiple molecular descriptors including synthetic accessibility, lipophilicity, topological polar surface area, and clinically relevant ADMET properties such as cardiotoxicity (hERG) and intestinal permeability (Caco-2). This adaptability spans from simple descriptors to practical pharmaceutical endpoints without requiring complete model retraining. The framework achieves precise control over target property values, generating molecules with properties closely matching specified targets for both single and multiple descriptors. Scaffold-constrained experiments preserve fixed molecular cores while maintaining effective property optimization. The three-dimensional representation offers advantages in maintaining structural validity during iterative optimization, with potential for geometry-aware applications in materials science and drug discovery.

3D molecular generation↗

DOE OSTI2026

Multitask graph neural networks for elastoplastic response prediction in dual-phase polycrystals

Microstructure-sensitive prediction of elastoplastic response remains a recurring bottleneck in multiscale damage and fatigue modeling, where large ensembles of statistically distinct polycrystals are required to quantify variability and extreme-value behavior. In this work, we develop a multitask graph neural network (GNN) surrogate that maps dual-phase ferrite–martensite polycrystal microstructures to Statistical Volume Element (SVE)-level elastoplastic Quantities of Interest (QoIs). Each SVE is represented as a grain-adjacency graph, with node features encoding phase, geometry, and crystallographic orientation, and edge features encoding relative misorientation. A message-passing graph convolution generates node embeddings, which are pooled into a graph representation and passed to a multitask regression head that jointly predicts 10 scalar QoIs and vector-valued stress–strain responses in orthogonal loading directions across multiple martensite volume fractions and SVE sizes. Results show high accuracy for scalar QoIs and strong agreement for full stress–strain trajectories, with population envelopes reproducing both median behavior and finite-SVE variability across compositions and partition scales. A unified model trained on pooled volume-fraction data preserves most within-regime accuracy relative to regime-specific models while also capturing the broader cross-regime variation reflected in the pooled test set. Distributional comparisons further demonstrate that the surrogate preserves heterogeneity under SVE partitioning, enabling statistically consistent block-wise random-field construction for mesoscale analyses. Overall, the proposed grain-graph surrogate provides a practical pathway to accelerate ensemble-based studies of SVE-level constitutive variability in dual-phase polycrystals.

Crystal plasticity↗

DOE OSTI2021

Nomenclatural issues concerning cultured yeasts and other fungi: why it is important to avoid unneeded name changes

The unambiguous application of fungal names is important to communicate scientific findings. Names are critical for (clinical) diagnostics, legal compliance, and regulatory controls, such as biosafety, food security, quarantine regulations, and industrial applications. Consequently, the stability of the taxonomic system and the traceability of nomenclatural changes is crucial for a broad range of users and taxonomists. The unambiguous application of names is assured by the preservation of nomenclatural history and the physical organisms representing a name. Fungi are extremely diverse in terms of ecology, lifestyle, and methods of study. Predominantly unicellular fungi known as yeasts are usually investigated as living cultures. Methods to characterize yeasts include physiological (growth) tests and experiments to induce a sexual morph; both methods require viable cultures. Thus, the preservation and availability of viable reference cultures are important, and cultures representing reference material are cited in species descriptions. Historical surveys revealed drawbacks and inconsistencies between past practices and modern requirements as stated in the International Code of Nomenclature for Algae, Fungi, and Plants (ICNafp). Improper typification of yeasts is a common problem, resulting in a large number invalid yeast species names. With this opinion letter, we address the problem that culturable microorganisms, notably some fungi and algae, require specific provisions under the ICNafp. We use yeasts as a prominent example of fungi known from cultures. But viable type material is important not only for yeasts, but also for other cultivable Fungi that are characterized by particular morphological structures (a specific type of spores), growth properties, and secondary metabolites. We summarize potential proposals which, in our opinion, will improve the stability of fungal names, in particular by protecting those names for which the reference material can be traced back to the original isolate.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Gridded Sub-daily Climate Forcings for North America Based on Daymet and ERA5 (Daymet-ERA5)

To support high spatial and temporal resolution land surface modeling, this dataset provides hourly time step historic weather forcing at 1-km spatial resolution for the entire North America. The latest Daymet V4 data provides gridded historic daily weather observations at 1-km spatial resolution from 1980 to 2024. Using sub-daily temporal information from the ECMWF ERA5 reanalysis, Daymet was further temporally downscaled to hourly time steps and provided in the format required for land surface model simulations. The process of temporal downscaling preserves the relative magnitude in each hourly time step from ERA5 while maintaining the total and average values from Daymet for each day. This results in a blended 1980-2024 Daymet-ERA5 dataset. Available variables include surface air temperature, precipitation, specific humidity, shortwave and longwave radiation, wind speed, and pressure. These data can be used as a high-resolution meteorological forcing dataset to support high-resolution land surface modeling where accurate meteorological forcing datasets built from historic observations and/or reanalysis datasets are desirable.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Macroalgal germplasm banking for conservation, food security, and industry

Ex situ seed banking was first conceptualized and implemented in the early 20th century to maintain and protect crop lines. Today, ex situ seed banking is important for the preservation of heirloom strains, biodiversity conservation and ecosystem restoration, and diverse research applications. However, these efforts primarily target microalgae and terrestrial plants. Although some collections include macroalgae (i.e., seaweeds), they are relatively few and have yet to be connected via any international, coordinated initiative. In this piece, we provide a brief introduction to macroalgal germplasm banking and its application to conservation, industry, and mariculture. We argue that concerted effort should be made globally in germline preservation of marine algal species via germplasm banking with an overview of the technical advances for feasibility and ensured success.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Multidimensional scaling informed by F -statistic: Visualizing grouped microbiome data with inference

Multidimensional scaling (MDS) is a widely used dimensionality reduction technique in microbial ecology data analysis that captures the multivariate structure of the data while preserving pairwise distances between samples. While improvements in MDS have enhanced the ability to reveal group-specific data patterns, these MDS-based methods require prior assumptions for inference, limiting their application in general microbiome analysis. Here, in this study, we introduce a new MDS-based ordination method, “F-informed MDS,” which configures the data distribution based on the F-statistic, the ratio of dispersion between groups sharing common and different characteristics. Using semisynthetic datasets, we demonstrate that the proposed method is robust to hyperparameter selection while maintaining statistical significance throughout the ordination process. Various quality metrics for evaluating dimensionality reduction confirm that F-informed MDS is comparable to state-of-the-art methods in preserving both local and global data structures. Its application to a diatom-associated bacterial community suggests the role of this new method in interpreting the community’s response to the host. Our approach offers a well-founded refinement of MDS that aligns with statistical test results, which can be beneficial for broader multidimensional data analyses in microbiology and ecology. This new visualization tool can be incorporated into standard microbiome data analyses.

Biological and medical sciences↗

DOE OSTI2014

Electron Tomography of Cryo-Immobilized Plant Tissue: A Novel Approach to Studying 3D Macromolecular Architecture of Mature Plant Cell Walls In Situ

Cost-effective production of lignocellulosic biofuel requires efficient breakdown of cell walls present in plant biomass to retrieve the wall polysaccharides for fermentation. In-depth knowledge of plant cell wall composition is therefore essential for improving the fuel production process. The precise spatial three-dimensional (3D) organization of cellulose, hemicellulose, pectin and lignin within plant cell walls remains unclear to date since the microscopy techniques used so far have been limited to two-dimensional, topographic or low-resolution imaging, or required isolation or chemical extraction of the cell walls. In this paper we demonstrate that by cryo-immobilizing fresh tissue, then either cryo-sectioning or freeze-substituting and resin embedding, followed by cryo- or room temperature (RT) electron tomography, respectively, we can visualize previously unseen details of plant cell wall architecture in 3D, at macromolecular resolution (~2 nm), and in near-native state. Qualitative and quantitative analyses showed that wall organization of cryo-immobilized samples were preserved remarkably better than conventionally prepared samples that suffer substantial extraction. Lignin-less primary cell walls were well preserved in both self-pressurized rapidly frozen (SPRF), cryo-sectioned samples as well as high-pressure frozen, freeze-substituted and resin embedded (HPF-FS-resin) samples. Lignin-rich secondary cell walls appeared featureless in HPF-FS-resin sections presumably due to poor stain penetration, but their macromolecular features could be visualized in unprecedented details in our cryo-sections. While cryo-tomography of vitreous tissue sections is currently proving to be instrumental in developing 3D models of lignin-rich secondary cell walls, here we confirm that the technically easier method of RT-tomography of HPF-FS-resin sections could be used immediately for routine study of low-lignin cell walls. As a proof of principle, we characterized the primary cell walls of a mutant (cob-6) and wild type Arabidopsis hypocotyl parenchyma cells by RT-tomography of HPF-FS-resin sections, and detected a small but significant difference in spatial organization of cellulose microfibrils in the mutant walls.

59 BASIC BIOLOGICAL SCIENCES↗