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DOE OSTI2024

CYP76BK1 orthologs catalyze furan and lactone ring formation in clerodane diterpenoids across the mint family

The Lamiaceae (mint family) is the largest known source of furanoclerodanes, a subset of clerodane diterpenoids with broad bioactivities including insect antifeedant properties. The Ajugoideae subfamily, in particular, accumulates significant numbers of structurally related furanoclerodanes. The biosynthetic capacity for formation of these diterpenoids is retained across most Lamiaceae subfamilies, including the early-diverging Callicarpoideae which forms a sister clade to the rest of Lamiaceae. VacCYP76BK1, a cytochrome P450 monooxygenase from Vitex agnus-castus, was previously found to catalyze the formation of the proposed precursor to furan and lactone-containing labdane diterpenoids. Through transcriptome-guided pathway exploration, we identified orthologs of VacCYP76BK1 in Ajuga reptans and Callicarpa americana. Functional characterization demonstrated that both could catalyze the oxidative cyclization of clerodane backbones to yield a furan ring. Subsequent investigation revealed a total of 10 CYP76BK1 orthologs across six Lamiaceae subfamilies. Through analysis of available chromosome-scale genomes, we identified four CYP76BK1 members as syntelogs within a conserved syntenic block across divergent subfamilies. This suggests an evolutionary lineage that predates the speciation of the Lamiaceae. Functional characterization of the CYP76BK1 orthologs affirmed conservation of function, as all catalyzed furan ring formation. Additionally, some orthologs yielded two novel lactone ring moieties. The presence of the CYP76BK1 orthologs across Lamiaceae subfamilies closely overlaps with the distribution of reported furanoclerodanes. Together, the activities and distribution of the CYP76BK1 orthologs identified here support their central role in furanoclerodane biosynthesis within the Lamiaceae family. Our findings lay the groundwork for biotechnological applications to harness the economic potential of this promising class of compounds.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Machine learning to identify geologic factors associated with production in geothermal fields: a case-study using 3D geologic data, Brady geothermal field, Nevada

Abstract In this paper, we present an analysis using unsupervised machine learning (ML) to identify the key geologic factors that contribute to the geothermal production in Brady geothermal field. Brady is a hydrothermal system in northwestern Nevada that supports both electricity production and direct use of hydrothermal fluids. Transmissive fluid-flow pathways are relatively rare in the subsurface, but are critical components of hydrothermal systems like Brady and many other types of fluid-flow systems in fractured rock. Here, we analyze geologic data with ML methods to unravel the local geologic controls on these pathways. The ML method, non-negative matrix factorization with k -means clustering (NMF k ), is applied to a library of 14 3D geologic characteristics hypothesized to control hydrothermal circulation in the Brady geothermal field. Our results indicate that macro-scale faults and a local step-over in the fault system preferentially occur along production wells when compared to injection wells and non-productive wells. We infer that these are the key geologic characteristics that control the through-going hydrothermal transmission pathways at Brady. Our results demonstrate: (1) the specific geologic controls on the Brady hydrothermal system and (2) the efficacy of pairing ML techniques with 3D geologic characterization to enhance the understanding of subsurface processes.

58 GEOSCIENCES↗

DOE OSTI2024

Data for "Design of Diverse, Functional Mitochondrial Targeting Sequences Across Eukaryotic Organisms Using Variational Autoencoder"

Mitochondria play a key role in energy production and metabolism, making them a promising target for metabolic engineering and disease treatment. However, despite the known influence of passenger proteins on localization efficiency, only a few protein-localization tags have been characterized for mitochondrial targeting. To address this limitation, we leverage a Variational Autoencoder to design novel mitochondrial targeting sequences. In silico analysis reveals that a high fraction of the generated peptides (90.14%) are functional and possess features important for mitochondrial targeting. We characterize artificial peptides in four eukaryotic organisms and, as a proof-of-concept, demonstrate their utility in increasing 3-hydroxypropionic acid titers through pathway compartmentalization and improving 5-aminolevulinate synthase delivery by 1.62-fold and 4.76-fold, respectively. Moreover, we employ latent space interpolation to shed light on the evolutionary origins of dual-targeting sequences. Overall, our work demonstrates the potential of generative artificial intelligence for both fundamental research and practical applications in mitochondrial biology.

AI/ML↗

DOE OSTI2021

Machine Learning to Identify Geologic Factors Associated with Production in Geothermal Fields: A Case-Study Using 3D Geologic Data from Brady Geothermal Field and NMFk

In this paper, we present an analysis using unsupervised machine learning (ML) to identify the key geologic factors that contribute to the geothermal production in Brady geothermal field. Brady is a hydrothermal system in northwestern Nevada that supports both electricity production and direct use of hydrothermal fluids. Transmissive fuid-fow pathways are relatively rare in the subsurface, but are critical components of hydrothermal systems like Brady and many other types of fuid-fow systems in fractured rock. Here, we analyze geologic data with ML methods to unravel the local geologic controls on these pathways. The ML method, non-negative matrix factorization with k-means clustering (NMFk), is applied to a library of 14 3D geologic characteristics hypothesized to control hydrothermal circulation in the Brady geothermal field. Our results indicate that macro-scale faults and a local step-over in the fault system preferentially occur along production wells when compared to injection wells and non-productive wells. We infer that these are the key geologic characteristics that control the through-going hydrothermal transmission pathways at Brady. Our results demonstrate: (1) the specific geologic controls on the Brady hydrothermal system and (2) the efficacy of pairing ML techniques with 3D geologic characterization to enhance the understanding of subsurface processes. This submission includes the published journal article detailing this work, the published 3D geologic map of the Brady Geothermal Area used as a basis to develop structural and geological variables that are hypothesized to control or effect permeability or connectivity, 3D well data, along which geologic data were sampled for PCA analyses, and associated metadata file. This work was done using the GeoThermalCloud framework, which is part of SmartTensors (both are linked below).

15 GEOTHERMAL ENERGY↗

DOE OSTI2021

Machine learning to identify geologic factors associated with production in geothermal fields: A casestudy using 3D geologic data, Brady geothermal field, Nevada

In this paper, we present an analysis using unsupervised machine learning (ML) to identify the key geologic factors that contribute to the geothermal production in the Brady geothermal field. Brady is a hydrothermal system in northwestern Nevada that supports both electricity production and direct use of hydrothermal fluids. Transmissive fluid flow pathways are relatively rare in the subsurface but are critical components of hydrothermal systems like Brady and many other types of fluid flow systems in fractured rock. The ML method, non-negative matrix factorization with k-means clustering (NMFk), is applied to a library of fourteen 3D geologic characteristics hypothesized to control hydrothermal circulation in the Brady geothermal field. Our results indicate the macro-scale faults and a local step-over in the fault system preferentially occur along with production wells when compared to injection wells and non-productive wells. We infer that these are the key geologic characteristics that control the through-going hydrothermal transmission pathways at Brady. Our results demonstrate 1) the specific geologic controls on the Brady hydrothermal system and 2) the efficacy of pairing ML techniques with 3D geologic characterization to enhance the understanding of subsurface processes.

15 GEOTHERMAL ENERGY↗

DOE OSTI2020

Woody Feedstocks 2020 State of Technology Report

The U.S. Department of Energy promotes production of advanced liquid transportation fuels from lignocellulosic biomass by funding fundamental and applied research that advances the state of technology (SOT). As part of its involvement with this overall mission, Idaho National Laboratory completes annual SOT reports for nth-plant biomass feedstock logistics. The purpose of the SOTs is to provide the status of feedstock supply system technology development for biomass to biofuels, based on actual data and experimental results relative to technical targets and cost goals from specific design cases. The 2020 Woody Feedstock SOT is separated into three separate pathways that utilize woody feedstocks: indirect liquefaction (IDL), catalytic fast pyrolysis (CFP), and algal-blend hydrothermal liquefaction (AHTL).

09 BIOMASS FUELS↗

DOE OSTI2023

Optimizing Cell-based Antimicrobials through Pooled Genomic Libraries

DNA synthesis and assembly technologies ushered in through synthetic biology have great promise for biomanufacturing, bioremediation, and the development of living therapeutics. Unfortunately, predicting sequence to function relationships, including for biosynthetic pathways expressed in a new host organism, is difficult and often requires many iterative cycles of design, construction, and testing. We are working to develop data-driven approaches to identify the genetic determinants of growth defects and productivity for the expression of a cell-based antimicrobial. We assayed the growth, pigment production, and antimicrobial activity of a collection of over 10,000 genetic mutants of the violacein biosynthetic pathway and sequenced the genetic variation of these mutants. Through this project, we have developed an innovative codebase to automate the determination of pigmentation and antimicrobial clearing diameter for tens of thousands of genetic mutants cultivated on agar dishes. Further, we have written DNA sequence analysis code to demultiplex & provide consensus sequences from high-throughput PacBio long-read circular consensus sequencing (CCS) datasets. From this foundation, we plan to map DNA sequence to function to predict an optimal genetic design to maximize antimicrobial activity while minimizing deleterious growth effects. The workflows and algorithms developed through this project can be broadly applied to other engineered functions in microbes, uncovering sequence to function relationships for complex phenotypes where function impacts fitness.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

cTULIP: application of a human-based RNA-seq primary tumor classification tool for cross-species primary tumor classification in canine

The domestic dog, Canis familiaris, is quickly gaining traction as an advantageous model for use in the study of cancer, one of the leading causes of death worldwide. Naturally occurring canine cancers share clinical, histological, and molecular characteristics with the corresponding human diseases. In this study, we take a deep-learning approach to test how similar the gene expression profile of canine glioma and bladder cancer (BLCA) tumors are to the corresponding human tumors. We likewise develop a tool for identifying misclassified or outlier samples in large canine oncological datasets, analogous to that which was developed for human datasets. We test a number of machine learning algorithms and found that a convolutional neural network outperformed logistic regression and random forest approaches. We use a recently developed RNA-seq-based convolutional neural network, TULIP, to test the robustness of a human-data-trained primary tumor classification tool on cross-species primary tumor prediction. Our study ultimately highlights the molecular similarities between canine and human BLCA and glioma tumors, showing that protein-coding one-to-one homologs shared between humans and canines, are sufficient to distinguish between BLCA and gliomas. The results of this study indicate that using protein-coding one-to-one homologs as the features in the input layer of TULIP performs good primary tumor prediction in both humans and canines. Furthermore, our analysis shows that our selected features also contain the majority of features with known clinical relevance in BLCA and gliomas. Our success in using a human-data-trained model for cross-species primary tumor prediction also sheds light on the conservation of oncological pathways in humans and canines, further underscoring the importance of the canine model system in the study of human disease.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Relationship of Soil Microbiota to Seed Kernel Metabolism in Camellia oleifera Under Mulched

An experiment was conducted from 2016 to 2017 to assess the effect of kernel metabolism in development stages after organic mulching compared to control. Organic mulching significantly increased crop yields (higher 128% in 2016, higher 60% in 2017), oil content (the highest oil content was 27.6% higher than that of the control), and improved soil properties (SOC, SAN, AP, and AK). In this study, soil pH, SOC, AN, AP, and AK in 0–30 cm soil depth were measured. Results showed that the effect of mulching on soil pH was not significant at the harvesting stage. The greatest metabolic differences occurred during the period of high oil conversion (S2–S4), primarily involving 11 relevant metabolic pathways. This further verified that Camellia oleifera oil yield was improved after mulching. A total of 1,106 OTUs were detected by using 16S rRNA, and Venn diagram showed that there were 106 unique OTUs in control and 103 OTUs in the treatment, respectively. Correlation analysis showed that soil pH and soil temperature were two indicators with the most correlations with soil microbiota. The yield was significantly positively correlated with soil microbial Proteobacteria, Bacteroidetes, and soil nutrition indexes. Organic mulching improved the physicochemical properties of soils, caused differences in the relative abundance of dominant bacteria in soil bacteria, and improved the soil microbiological environment to promote plant growth, indicating that organic mulching is an effective measure to alleviate seasonal drought.

Ye, Honglian↗

DOE OSTI2023

Modeling the U.S. Western Electric Interconnection to Understand the Consequences of Hydrometeorological Extremes and Options for Risk Mitigation

Electricity grid operators around the world face a dual challenge; withstanding increasingly severe weather and the longer term impacts of climate change, while simultaneously decarbonizing. Extreme weather events such as heat waves and droughts are rising in both severity and frequency, which is threatening the reliability of electricity grids through increased demand, generation capacity losses, and equipment failures. Consequently, incorporating hydrometeorological stressors into computational power systems analysis is becoming an even more critical tool in long term planning and short term operations. However, there is a general lack of open-source customizable grid simulation software capable of exhaustively stress testing the grid under hydrometeorological uncertainty, and/or examining potential risk mitigation pathways. A related, persistent challenge for power system modelers is striking an appropriate balance between model fidelity (e.g. spatial scale and time resolution) and computational tractability (wall clock run-time). In this study, we are proposing a solution to this problem with open-source software that allows users to seamlessly customize the scale and track the accuracy of grid operations models. Our approach allows users to search over numerous model parameters (network topology, mathematical formulation, economic hurdle rates, and transmission line scaling) to identify model instantiations that accommodate experimental design. Further, we use this approach to demonstrate the importance of including extreme weather events in model validation and model selection. Focusing on the occurrence of heatwaves and droughts in the U.S. Western Interconnection, we examine role of extreme events in balancing tradeoffs between model fidelity and run-time at the model design stage.

Economics↗

DOE OSTI2021

Theory-Guided Inelastic Neutron Scattering of Crystalline Alkaline Aluminate Salts Bearing Principal Motifs of Solution-State Species

Aluminate salts precipitated from caustic alkaline solutions exhibit a correlation between the anionic speciation and the identity of the alkali cation in the precipitate, with the aluminate ions occurring either in monomeric (Al(OH) 4 – ) or dimeric (Al 2 O(OH) 6 2– ) forms. The origin of this correlation is poorly understood as are the roles that oligomeric aluminate species play in determining the solution structure, prenucleation clusters, and precipitation pathways. Characterization of aluminate solution speciation with vibrational spectroscopy results in spectra that are difficult to interpret because the ions access a diverse and dynamic configurational space. To investigate the Al(OH) 4 – and Al 2 O(OH) 6 2– anions within a well-defined crystal lattice, inelastic neutron scattering (INS) and Raman spectroscopic data were collected and simulated by density functional theory for K 2 [Al 2 O(OH) 6 ], Rb 2 [Al 2 O(OH) 6 ], and Cs[Al(OH) 4 ]·2H 2 O. These structures capture archetypal solution aluminate species: the first two salts contain dimeric Al 2 O(OH) 6 2– anions, while the third contains the monomeric Al(OH) 4 – anion. Here, comparisons were made to the INS and Raman spectra of sodium aluminate solutions frozen in a glassy state. In contrast to solution systems, the crystal lattice of the salts results in well-defined vibrations and associated resolved bands in the INS spectra. The use of a theory-guided analysis of the INS of this solid alkaline aluminate series revealed that differences were related to the nature of the hydrogen-bonding network and showed that INS is a sensitive probe of the degree of completeness and strength of the bond network in hydrogen-bonded materials. Results suggest that the ionic size may explain cation-specific differences in crystallization pathways in alkaline aluminate salts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Atlantic Offshore Wind Transmission Study

The Atlantic Offshore Wind Transmission Study (AOSWTS) is part of the U.S. Department of Energy's (DOE) efforts to understand and facilitate the transmission of electricity from wind in the Atlantic Ocean. It was informed by the Atlantic Offshore Wind Transmission Literature Review and Gaps Analysis (Bothwell et al. 2021) and the convening workshops hosted in 2022-2023 by DOE and the U.S. Department of the Interior's Bureau of Ocean Energy Management. The study results help to inform An Action Plan for Offshore Wind Transmission Development in the U.S. Atlantic Region (Baker et al. 2023). DOE's Wind Energy Technologies Office funded AOSWTS. The AOSWTS identifies and evaluates pathways to enable offshore wind energy deployment in the Atlantic Ocean through coordinated offshore transmission solutions in the near term (by 2030) and long term (by 2050). The study fills gaps in prior analyses by providing a multiregional planning perspective that evaluates offshore wind generation development with transmission planning. It incorporates environmental, ocean co-use, and other siting considerations into defining potential offshore transmission routes. The study also compares different multiregional offshore transmission topologies and their associated costs (using potential cable routes) and benefits (in terms of production cost savings and enhanced resource adequacy). In addition, the AOSWTS analyzes reliability impacts from a multiregional perspective. The study provides guidance for policymakers and transmission stakeholders on possible outcomes resulting from a proactive, coordinated, and interregional approach to transmission planning for offshore wind energy development in the Atlantic. While this study presents possibilities, additional work following system operator methods and procedures can help build on this analysis.

17 WIND ENERGY↗

DOE OSTI2024

Atlantic Offshore Wind Transmission Study: Executive Summary

17 WIND ENERGY↗

DOE OSTI2022

Ecosystems and Networks Integrated with Genes and Molecular Assemblies (ENIGMA). Final report

The goal of the Stahl lab is to use a combination of isotopic, genomic, and activity-based analyses in the field, of selected field isolates, and defined synthetic communities to identify sources and sinks of different nitrogen species in the field, focusing on the FRC at the ORNL. The most general research objective is to characterize variants in the pathway for denitrification among field isolates and synthetic microbial assemblies as a foundation for predicting the biotic and abiotic controls of nitrogen and carbon transformations in the field. Specifically, we have focused on characterizing environmental controls of nitrous oxide emissions, an important greenhouse gas and ozone depleting molecule. Studies have mapped the flux of nitrous oxide and controlling genetic elements within the FRC subsurface, indicating the importance of different enzyme variants in nitrous oxide production and consumption in controlling surface emissions. We have developed methods to quantify and catalog the isotopic signatures imparted on nitrous oxide by different nitrous oxide reductase variants in isolated organisms and model synthetic communities. We have developed methods to quantify and catalog the nitrous oxide affinity of field relevant isolates and their biomass yield when respiring nitrous oxide on different carbon substrates. We continue to expand the collection of field-relevant microbial isolates within ENIGMA by focused enrichment and isolation from field material. These objectives have been facilitated by the development of new technologies, including: 1) the development of a multiplexed automated optical density measurement system for quantifying growth kinetics of microorganisms dependent on gaseous substrates in monoculture or gas exchange in synthetic communities and 2) the development of a microcalorimetric analysis format to quantify the impact of environmental stress on microbial maintenance energy. Since field relevant processes are mediated by assemblies of organisms interacting with each other and their local environment, we have also collaborated with other ENIGMA investigators on the assembly and characterization of synthetic communities. These studies have evaluated the role of organisms lacking a complete pathway for denitrification in the control of field processes, measuring process rates of assemblies of organisms that individually lack the full denitrification pathway but mediate the complete denitrification when growing together. These efforts have so far resulted in the publication of 18 peer reviewed papers ranging from ecological and physiological drivers of pathway optimization to field-scale process characterization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Vision Foundation Models in Remote Sensing: A survey

Artificial intelligence (AI) technologies have profoundly transformed the field of remote sensing (RS), revolutionizing data collection, processing, and analysis. Traditionally reliant on manual interpretation and task-specific models, RS research has been significantly enhanced by the advent of foundation models (FMs)—large-scale pretrained AI models capable of performing a wide array of tasks with unprecedented accuracy and efficiency. This article provides a comprehensive survey of FMs in the RS domain. We categorize these models based on their architectures, pretraining datasets, and methodologies. Through detailed performance comparisons, we highlight emerging trends and the significant advancements achieved by those FMs. Additionally, we discuss technical challenges, practical implications, and future research directions, addressing the need for high-quality data, computational resources, and improved model generalization. Our research also finds that pretraining methods, particularly self-supervised learning (SSL) techniques like contrastive learning (CL) and masked autoencoders (MAEs), remarkably enhance the performance and robustness of FMs. This survey aims to serve as a resource for researchers and practitioners by providing a panorama of advances and promising pathways for the continued development and application of FMs in RS.

data models↗

DOE OSTI2025

Electrochemical Corrosion and Catalysis Dynamics of Tin Oxide during Water Oxidation

Metal oxide corrosion severely limits anodic electrocatalysis, particularly at high potentials in acidic environments, where degradation pathways remain poorly defined. This study establishes explicit connections between corrosion and electrocatalysis on tin oxide during water oxidation by examining the roles of lattice defects, reactive oxygen species, interfacial pH variations, and speciation of corroded tin in acid. We first demonstrate the presence of structural defects such as oxygen vacancies and substoichiometric Sn(II) species by integrating electron paramagnetic resonance spectroscopy, ultraviolet photoelectron spectroscopy, and Mott–Schottky analysis. Kohn–Sham density functional theory calculations reveal that explicit water structures thermodynamically stabilize reaction intermediates and lower reaction overpotentials. Moreover, we propose that water dissociation leads to hydrogen-bonding networks formed by H* and OH* intermediates, which may span the entire catalyst surface and decrease the interfacial pH to drive corrosion. In contrast, the electrochemical generation of reactive oxygen species is shown to play a minor role in catalyst corrosion during water oxidation using inductively coupled plasma mass spectrometry coupled with selective chemical scavengers. Square-wave voltammetry combined with rotating ring-disk electrodes is used to reveal that under open-circuit conditions, only Sn(IV) cations chemically dissolve from tin oxide, while both Sn(IV) and Sn(II) species electrochemically corrode during water oxidation. Our results unveil a dynamic and complicated interplay between corrosive and catalytic pathways on metal oxide electrocatalysts: a decrease in interfacial pH due to water oxidation exacerbates Sn(II)/Sn(IV) corrosion. Subsequently, the electrochemical corrosion of Sn(II)/Sn(IV) facilitates product formation from lattice oxygen, while the redeposition of corroded Sn(II) as Sn(IV) can enable oxygen exchange with water. By elucidating the roles of defects and interfacial chemistry, this work provides a roadmap for engineering improved electrocatalysts that balance activity and stability, a critical step toward scalable and durable energy technologies.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Nondestructive microstructural investigation of defects in 4H-SiC epilayers using a multiscale luminescence analysis approach

The development of metal oxide semiconductor field effect transistors (MOSFETs) utilizing epitaxially grown 4H-SiC has accelerated in recent years due to their favorable properties, including a high breakdown field, high saturated electron drift velocity, and good thermal conductivity. However, extended defects in epitaxial 4H-SiC can affect both device yields and operational lifetime. In this work, we demonstrate the importance of a multiscale luminescence characterization approach to studying nondestructively extended defects in epitaxial 4H-SiC semiconducting materials. Multiscale luminescence analysis reveals different aspects of excess charge carrier recombination behavior based on the scale of a particular measurement. Combining measurements of the same extended defect area at different scales tells us more about the essential nature of that defect and its microstructure. Here, we use photoluminescence imaging and cathodoluminescence spectrum imaging to investigate the recombination behavior of several different types of extended defects, including stacking faults, inclusions, and basal plane dislocations. A detailed understanding of the optoelectronic properties of extended defects in epitaxial SiC helps elucidate the microstructure of extended defects and can provide pathways to mitigate detrimental changes during device operation related to their evolution, such as the recombination enhanced dislocation glide effect that affects SiC-based MOSFETs.

3C-inclusion↗

DOE OSTI2020

Perimatrix of middle ear cholesteatoma: A granulation tissue with a specific transcriptomic signature

Objectives/Hypothesis: To establish comprehensive transcriptomic profiles of cholesteatoma perimatrix tissue and granulation tissue from chronic otitis media (COM) that did not develop cholesteatoma, which can indicate molecular pathways involved in the cholesteatoma perimatrix pathology and invasiveness. Study Design: Retrospective Case Series. Methods: Transcriptome data were obtained from cholesteatoma perimatrix tissue and COM granulation tissue by an Illumina iScan microarray. Differentially expressed genes (DEGs) were subsequently analyzed using both bioinformatical functional annotation and network analysis. Expression of candidate genes (MMP9 and LCN2) was validated by quantitative reverse transcription-polymerase chain reaction (qRT-PCR) on a larger group of samples. Results: Analysis of the transcriptome led to the identification of 169 differentially expressed genes between investigated tissues. Bioinformatic analysis suggested that most significant biological processes involving DEGs were previously described in cholesteatoma pathology. Network analysis identified ERBB2, TFAP2A, and TP63 as major hubs of the DEGs molecular network. Furthermore, it was observed that the cellular component most significantly enriched in DEGs was extracellular space containing 47 DEGs. Using qRT-PCR, it was confirmed that mRNA levels of the major extracellular hub (MMP9) are increased, whereas its interacting molecule (LCN2) mRNA levels were decreased in cholesteatoma perimatrix tissue compared to COM granulation tissue. Conclusions: The current study approach offers an overall look at molecular mechanisms that describe the cholesteatoma entity by focusing exclusively on the perimatrix processes in comparison to COM granulation tissue. The observed differences in gene expression between cholesteatoma perimatrix and COM granulation tissue could suggest novel markers potentially influenced by the perimatrix–matrix molecular interplay, which is not present in COM without cholesteatoma. Level of Evidence: NA. Laryngoscope, 130:E220–E227, 2020. © 2019 The American Laryngological, Rhinological and Otological Society, Inc.

60 APPLIED LIFE SCIENCES↗