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NASA NTRS2019

GeneLab: A Systems Biology Platform for Omics Analysis

NASA's GeneLab includes an open-access repository of some 200+ omics datasets generated by biological experiments relevant to spaceflight (including simulated cosmic radiation and microgravity). In order to maximize the intelligibility of these data, particularly for users with limited bioinformatics knowledge, GeneLab is now transforming the data in the repository into actual biological and physiological knowledge of the genetic and proteomic signatures found in these samples. This processed data is being derived by establishing standard data analysis workflows vetted by 114 scientists who are members of the four GeneLab Analysis Working Groups (Animal AWG, Plant AWG, Microbe AWG, Multi-Omics AWG). AWG members from institutes spanning the U.S. and four other countries participate on a voluntary basis. The AWGs meet monthly to discuss data mining, compare results and interpretations, and test forthcoming releases of the GeneLab Data Systems (GLDS). GLDS version 3.0 has been available to the general public since October 1st 2018, and has been providing a professional state-of-the-art bioinformatics platform for everyone in the space biology community to upload their data into a space biology omics data commons, to process their data with vetted standard workflows and to compare to existing analyses. The user interface for the platform is being designed to be accessible to a broad variety of users including those with limited bioinformatics experience, including high school and college students who can use it to learn about omics data analysis and space biology. As such, Genelab will constitute a powerful general public outreach capability of NASA and the Space Biology community at large. Data mining of the GeneLab database by the AWG has already started generating very interesting findings, including reports linking specific spaceflight conditions such as radiation, microgravity or carbon dioxide levels to molecular changes seen across various species. In this presentation, we will report on the current and future objectives for GeneLab, and review recent studies reported by the various AWGs relating molecular changes observed in various animal models and tissue with microgravity, radiation, circadian rhythm, hydration and carbon dioxide conditions.

Omics↗
NASA NTRS2019

GeneLab: A Systems Biology Platform for Omics Analysis: Disseminate and Reuse Data, Tools, and Samples Post-Project

NASA's GeneLab includes an open-access repository of some 200 plus omics datasets generated by biological experiments relevant to spaceflight (including simulated cosmic radiation and microgravity). In order to maximize the intelligibility of these data, particularly for users with limited bioinformatics knowledge, GeneLab is now transforming the data in the repository into actual biological and physiological knowledge of the genetic and proteomic signatures found in these samples. This processed data is being derived by establishing standard data analysis workflows vetted by 114 scientists who are members of the four GeneLab Analysis Working Groups (Animal AWG, Plant AWG, Microbe AWG, Multi-Omics AWG). AWG members from institutes spanning the U.S. and four other countries participate on a voluntary basis. The AWGs meet monthly to discuss data mining, compare results and interpretations, and test forthcoming releases of the GeneLab Data Systems (GLDS). GLDS version 3.0 has been available to the general public since October 1st 2018, and has been providing a professional state-of-the-art bioinformatics platform for everyone in the space biology community to upload their data into a space biology omics data commons, to process their data with vetted standard workflows and to compare to existing analyses. The user interface for the platform is being designed to be accessible to a broad variety of users including those with limited bioinformatics experience, including high school and college students who can use it to learn about omics data analysis and space biology. As such, Genelab will constitute a powerful general public outreach capability of NASA and the Space Biology community at large. Data mining of the GeneLab database by the AWG has already started generating very interesting findings, including reports linking specific spaceflight conditions such as radiation, microgravity or carbon dioxide levels to molecular changes seen across various species. In this presentation, we will report on the current and future objectives for GeneLab, and review recent studies reported by the various AWGs relating molecular changes observed in various animal models and tissue with microgravity, radiation, circadian rhythm, hydration and carbon dioxide conditions.

Omics↗
NASA NTRSDate not supplied

Understanding amyloids to prevent biofilm formation in space

There is a pressing need to search for novel approaches to combat biofilm formation, both in space and in medical applications. Many proteins have the ability to form ordered aggregates called amyloids. Amyloids are known to be an important part of biofilms. The use of anti-amyloid drugs is a novel venue for the development of antimicrobial agents. The ultrastructure of the amyloid aggregate shows a high packing of proteins, the second-order structure of which is dominated by β-sheets. The ability to form an amyloid aggregate is especially typical for proteins containing domains (protein fragments) with sufficient lability to arrange themselves in a tight β-sheet structure. Bioinformatics tools allow the prediction of such behavior of proteins in genomic data. We use GeneLab data of microbial populations identified aboard the International Space Station and other spacecraft to look for bacterial species that utilize amyloid aggregation in biofilm formation. We use a combined bioinformatic approach with a relatively high throughput molecular biology assay and biophysical assays to evaluate the anti-amyloid anti-biofilm approach. The significance of the research extends from understanding basic microbial community responses to spaceflight, to biofouling of the built environments in space as well as the long-term health of astronauts. Bioinformatics shows that onboard the ISS, bacterial species produce far more amyloid and prion proteins than are currently verified, hence their role in bacterial ecosystems is largely unknown. As we propose there is a link between amyloid formation in space and biofilm production, this research should lead to new paths for biofilm remediation in space.

Tomasz Zajkowski↗
NASA NTRSDate not supplied

NASA GeneLab: Open Science for Life in Space

The NASA GeneLab project capitalizes on multi-omic technologies to maximize the return on spaceflight experiments. To do this, GeneLab maintains a publicly accessible database (GLDS) that houses spaceflight and spaceflight relevant multi-omics data and collaborates with NASA principal investigators and projects to generate additional omics data. GeneLab houses more than 350 transcriptomic, proteomic, metabolomic and epigenomic datasets from plant, animal and microbial experiments, with a growing number of these having been produced by the GeneLab Sequencing Lab. The GLDS contains rich metadata about each experiment and has integrated radiation dosimetry data from experiments flown on the Space Shuttle, International Space Station, and Free Flying spacecrafts. With the increasing amount and complexity of omics data being generated, GeneLab utilizes community-defined, common models for metadata and terminology so that omics data and results are discoverable and reliably reproducible. GeneLab uses the ISA-Tab specification and semantic model for organizing and representing omics metadata. In addition to metadata standards, data files must be open-source file or common exchange formats to ensure accessibility and usability by all users. To ease data ingestion and transfer, the web-based submission tool allows PIs a user-friendly user interface to curate, organize, and publish their space relevant omics data. In the more recent years, data curation and submission portal has incorporated the FAIR principles making data findable, accessible, interoperable, and reusable. To increase reusability of data, GeneLab has implemented an effort to present processed data in the GLDS in addition to the raw omics data. The processed data will enable interpretation of the data by a larger group of students, scientists and the general public. Standard pipelines for the transformation of raw data into visualizations were developed by four GeneLab Analysis Working Groups (animals, plants, microbes, multi-omics) comprised of over 200 scientists from NASA, industry, and academia. To explore the data, the GLDS provides users various tools for data analysis, collaborative workspace for file storage and sharing, and a visualization portal. The analysis platform built using the Galaxy toolshed provides access to a broad variety of users including those with limited bioinformatics experience and students to learn how to analyze spaceflight omics data. The visualization portal takes GeneLab one step closer to data democratization by removing all bioinformatics requisites to interpret transcriptomics data hosted in the repository. To train the next generation of scientists, NASA offers training programs such as GeneLab 4 High School (GL4HS) and GeneLab 4 Universities. NLM Curation at a Scale Workshop 2022 | NASA GeneLab (GL4U) to teach students bioinformatics and computational biology methods to analyze omics data. Discoveries made using GeneLab have begun and will continue to deepen our understanding of biology, advance the field of genomics, and help to discover cures for diseases, create better diagnostic tools, and ultimately allow astronauts to better withstand the rigors of long-duration spaceflight.

GeneLab↗
NASA NTRS2020

Amino Acid Encoding for Deep Learning Applications

Background: The number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are available. In deep learning applications, discrete data, e.g. words or n-grams in language, or amino acids or nucleotides in bioinformatics, are generally represented as a continuous vector through an embedding matrix. Recently, learning this embedding matrix directly from the data as part of the continuous iteration of the model to optimize the target prediction – a process called ‘end-to-end learning’ – has led to state-of-the-art results in many fields. Although usage of embeddings is well described in the bioinformatics literature, the potential of end-to-end learning for single amino acids, as compared to more classical manually-curated encoding strategies, has not been systematically addressed. To this end, we compared classical encoding matrices, namely one-hot, VHSE8 and BLOSUM62, to end-to-end learning of amino acid embeddings for two different prediction tasks using three widely used architectures, namely recurrent neural networks (RNN), convolutional neural networks (CNN), and the hybrid CNN-RNN. Results: By using different deep learning architectures, we show that end-to-end learning is on par with classical encodings for embeddings of the same dimension even when limited training data is available, and might allow for a reduction in the embedding dimension without performance loss, which is critical when deploying the models to devices with limited computational capacities. We found that the embedding dimension is a major factor in controlling the model performance. Surprisingly, we observed that deep learning models are capable of learning from random vectors of appropriate dimension. Conclusion: Our study shows that end-to-end learning is a flexible and powerful method for amino acid encoding. Further, due to the flexibility of deep learning systems, amino acid encoding schemes should be benchmarked against random vectors of the same dimension to disentangle the information content provided by the encoding scheme from the distinguishability effect provided by the scheme.

Deep-learning↗
NASA NTRS2020

Amino Acid Encoding for Deep Learning Applications

Background: The number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are available. In deep learning applications, discrete data, e.g. words or n-grams in language, or amino acids or nucleotides in bioinformatics, are generally represented as a continuous vector through an embedding matrix. Recently, learning this embedding matrix directly from the data as part of the continuous iteration of the model to optimize the target prediction – a process called ‘end-to-end learning’ – has led to state-ofthe-art results in many fields. Although usage of embeddings is well described in the bioinformatics literature, the potential of end-to-end learning for single amino acids, as compared to more classical manually-curated encoding strategies, has not been systematically addressed. To this end, we compared classical encoding matrices, namely one-hot, VHSE8 and BLOSUM62, to end-to-end learning of amino acid embeddings for two different prediction tasks using three widely used architectures, namely recurrent neural networks (RNN), convolutional neural networks (CNN), and the hybrid CNN-RNN. Results: By using different deep learning architectures, we show that end-to-end learning is on par with classical encodings for embeddings of the same dimension even when limited training data is available, and might allow for a reduction in the embedding dimension without performance loss, which is critical when deploying the models to devices with limited computational capacities. We found that the embedding dimension is a major factor in controlling the model performance. Surprisingly, we observed that deep learning models are capable of learning from random vectors of appropriate dimension. Conclusion: Our study shows that end-to-end learning is a flexible and powerful method for amino acid encoding. Further, due to the flexibility of deep learning systems, amino acid encoding schemes should be benchmarked against random vectors of the same dimension to disentangle the information content provided by the encoding scheme from the distinguishability effect provided by the scheme.

Hesham ElAbd↗
DOE OSTI2023

Improving Protein–Ligand Interaction Modeling with cryo-EM Data, Templates, and Deep Learning in 2021 Ligand Model Challenge

Elucidating protein–ligand interaction is crucial for studying the function of proteins and compounds in an organism and critical for drug discovery and design. The problem of protein–ligand interaction is traditionally tackled by molecular docking and simulation, which is based on physical forces and statistical potentials and cannot effectively leverage cryo-EM data and existing protein structural information in the protein–ligand modeling process. In this work, we developed a deep learning bioinformatics pipeline (DeepProLigand) to predict protein–ligand interactions from cryo-EM density maps of proteins and ligands. DeepProLigand first uses a deep learning method to predict the structure of proteins from cryo-EM maps, which is averaged with a reference (template) structure of the proteins to produce a combined structure to add ligands. The ligands are then identified and added into the structure to generate a protein–ligand complex structure, which is further refined. The method based on the deep learning prediction and template-based modeling was blindly tested in the 2021 EMDataResource Ligand Challenge and was ranked first in fitting ligands to cryo-EM density maps. These results demonstrate that the deep learning bioinformatics approach is a promising direction for modeling protein–ligand interactions on cryo-EM data using prior structural information.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A high-throughput skim-sequencing approach for genotyping, dosage estimation and identifying translocations

The development of next-generation sequencing (NGS) enabled a shift from array-based genotyping to directly sequencing genomic libraries for high-throughput genotyping. Even though whole-genome sequencing was initially too costly for routine analysis in large populations such as breeding or genetic studies, continued advancements in genome sequencing and bioinformatics have provided the opportunity to capitalize on whole-genome information. As new sequencing platforms can routinely provide high-quality sequencing data for sufficient genome coverage to genotype various breeding populations, a limitation comes in the time and cost of library construction when multiplexing a large number of samples. Here we describe a high-throughput whole-genome skim-sequencing (skim-seq) approach that can be utilized for a broad range of genotyping and genomic characterization. Using optimized low-volume Illumina Nextera chemistry, we developed a skim-seq method and combined up to 960 samples in one multiplex library using dual index barcoding. With the dual-index barcoding, the number of samples for multiplexing can be adjusted depending on the amount of data required, and could be extended to 3,072 samples or more. Panels of doubled haploid wheat lines ( Triticum aestivum , CDC Stanley x CDC Landmark), wheat-barley ( T . aestivum x Hordeum vulgare ) and wheat-wheatgrass ( Triticum durum x Thinopyrum intermedium ) introgression lines as well as known monosomic wheat stocks were genotyped using the skim-seq approach. Bioinformatics pipelines were developed for various applications where sequencing coverage ranged from 1 × down to 0.01 × per sample. Using reference genomes, we detected chromosome dosage, identified aneuploidy, and karyotyped introgression lines from the skim-seq data. Leveraging the recent advancements in genome sequencing, skim-seq provides an effective and low-cost tool for routine genotyping and genetic analysis, which can track and identify introgressions and genomic regions of interest in genetics research and applied breeding programs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

MbnH is a diheme MauG-like protein associated with microbial copper homeostasis

Methanobactins (Mbns) are ribosomally-produced, post-translationally modified peptidic copper-binding natural products produced under conditions of copper limitation. Genes encoding Mbn biosynthetic and transport proteins have been identified in a wide variety of bacteria, indicating a broader role for Mbns in bacterial metal homeostasis. Many of the genes in the Mbn operons have been assigned functions, but two genes usually present, mbnP and mbnH, encode uncharacterized proteins predicted to reside in the periplasm. MbnH belongs to the bacterial diheme cytochrome c peroxidase (bCcP)/MauG protein family, and MbnP contains no domains of known function. Here, we performed a detailed bioinformatic analysis of both proteins and have biochemically characterized MbnH from Methylosinus (Ms.) trichosporium OB3b. We note that the mbnH and mbnP genes typically co-occur and are located proximal to genes associated with microbial copper homeostasis. Our bioinformatics analysis also revealed that the bCcP/MauG family is significantly more diverse than originally appreciated, and that MbnH is most closely related to the MauG subfamily. A 2.6 Å resolution structure of Ms. trichosporium OB3b MbnH combined with spectroscopic data and peroxidase activity assays provided evidence that MbnH indeed more closely resembles MauG than bCcPs, although its redox properties are significantly different from those of MauG. The overall similarity of MbnH to MauG suggests that MbnH could post-translationally modify a macromolecule, such as internalized CuMbn or its uncharacterized partner protein, MbnP. Our results indicate that MbnH is a MauG-like diheme protein that is likely involved in microbial copper homeostasis and represents a new family within the bCcP/MauG superfamily.

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

Diazotroph Community Characterization via a High-Throughput nifH Amplicon Sequencing and Analysis Pipeline

The dinitrogenase reductase gene ( nifH ) is the most widely established molecular marker for the study of nitrogen-fixing prokaryotes in nature. A large number of PCR primer sets have been developed for nifH amplification, and the effective deployment of these approaches should be guided by a rapid, easy-to-use analysis protocol. Bioinformatic analysis of marker gene sequences also requires considerable expertise. In this study, we advance the state of the art for nifH analysis by evaluating nifH primer set performance, developing an improved amplicon sequencing workflow, and implementing a user-friendly bioinformatics pipeline. Furthermore, the developed amplicon sequencing workflow is a three-stage PCR-based approach that uses established technologies for incorporating sample-specific barcode sequences and sequencing adapters. Based on our primer evaluation, we recommend the Ando primer set be used with a modified annealing temperature of 58°C, as this approach captured the largest diversity of nifH templates, including paralog cluster IV/V sequences. To improve nifH sequence analysis, we developed a computational pipeline which infers taxonomy and optionally filters out paralog sequences. In addition, we employed an empirical model to derive optimal operational taxonomic unit (OTU) cutoffs for the nifH gene at the species, genus, and family levels. A comprehensive workflow script named TaxADivA (TAXonomy Assignment and DIVersity Assessment) is provided to ease processing and analysis of nifH amplicons. Our approach is then validated through characterization of diazotroph communities across environmental gradients in beach sands impacted by the Deepwater Horizon oil spill in the Gulf of Mexico, in a peat moss-dominated wetland, and in various plant compartments of a sugarcane field. IMPORTANCE: Nitrogen availability often limits ecosystem productivity, and nitrogen fixation, exclusive to prokaryotes, comprises a major source of nitrogen input that sustains food webs. The nifH gene, which codes for the iron protein of the nitrogenase enzyme, is the most widely established molecular marker for the study of nitrogen-fixing microorganisms (diazotrophs) in nature. In this study, a flexible sequencing/analysis pipeline, named TaxADivA, was developed for nifH amplicons produced by Illumina paired-end sequencing, and it enables an inference of taxonomy, performs clustering, and produces output in formats that may be used by programs that facilitate data exploration and analysis. Diazotroph diversity and community composition are linked to ecosystem functioning, and our results advance the phylogenetic characterization of diazotroph communities by providing empirically derived nifH similarity cutoffs for species, genus, and family levels. Thus, the utility of our pipeline is validated for diazotroph communities in a variety of ecosystems, including contaminated beach sands, peatland ecosystems, living plant tissues, and rhizosphere soil.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

CyanoCyc cyanobacterial web portal

CyanoCyc is a web portal that integrates an exceptionally rich database collection of information about cyanobacterial genomes with an extensive suite of bioinformatics tools. It was developed to address the needs of the cyanobacterial research and biotechnology communities. The 277 annotated cyanobacterial genomes currently in CyanoCyc are supplemented with computational inferences including predicted metabolic pathways, operons, protein complexes, and orthologs; and with data imported from external databases, such as protein features and Gene Ontology (GO) terms imported from UniProt. Five of the genome databases have undergone manual curation with input from more than a dozen cyanobacteria experts to correct errors and integrate information from more than 1,765 published articles. CyanoCyc has bioinformatics tools that encompass genome, metabolic pathway and regulatory informatics; omics data analysis; and comparative analyses, including visualizations of multiple genomes aligned at orthologous genes, and comparisons of metabolic networks for multiple organisms. CyanoCyc is a high-quality, reliable knowledgebase that accelerates scientists’ work by enabling users to quickly find accurate information using its powerful set of search tools, to understand gene function through expert mini-reviews with citations, to acquire information quickly using its interactive visualization tools, and to inform better decision-making for fundamental and applied research.

59 BASIC BIOLOGICAL SCIENCES↗
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↗
DOE OSTI2019

Prosaposin is a biomarker of mesenchymal glioblastoma and regulates mesenchymal transition through the TGF‐β1/Smad signaling pathway

Abstract Mesenchymal glioblastoma (GBM) is the most aggressive subtype of GBM. Our previous study found that neurotrophic factor prosaposin (PSAP) is highly expressed and secreted in glioma and can promote the growth of glioma. The role of PSAP in mesenchymal GBM is still unclear. In this study, bioinformatic analysis, western blotting and RT‐qPCR were used to detect the expression of PSAP in different GBM subtypes. Human glioma cell lines and patient‐derived glioma stem cells were studied in vitro and in vivo , revealing that mesenchymal GBM expressed and secreted the highest level of PSAP among four subtypes of GBM, and PSAP could promote GBM invasion and epithelial–mesenchymal transition (EMT)‐like processes in vivo and in vitro . Bioinformatic analysis and western blotting showed that PSAP mainly played a regulatory role in GBM invasion and EMT‐like processes via the TGF‐β1/Smad signaling pathway. In conclusion, the overexpression and secretion of PSAP may be an important factor causing the high invasiveness of mesenchymal GBM. PSAP is therefore a potential target for the treatment of mesenchymal GBM. © 2019 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd

Jiang, Yang↗
DOE OSTI2023

Specificity determinants revealed by the structure of glycosyltransferase Campylobacter concisus PglA

Abstract In selected Campylobacter species, the biosynthesis of N‐linked glycoconjugates via the pgl pathway is essential for pathogenicity and survival. However, most of the membrane‐associated GT‐B fold glycosyltransferases responsible for diversifying glycans in this pathway have not been structurally characterized which hinders the understanding of the structural factors that govern substrate specificity and prediction of resulting glycan composition. Herein, we report the 1.8 Å resolution structure of Campylobacter concisus PglA, the glycosyltransferase responsible for the transfer of N ‐acetylgalatosamine (GalNAc) from uridine 5′‐diphospho‐ N ‐acetylgalactosamine (UDP‐GalNAc) to undecaprenyl‐diphospho‐ N , N ′‐diacetylbacillosamine (UndPP‐diNAcBac) in complex with the sugar donor GalNAc. This study identifies distinguishing characteristics that set PglA apart within the GT4 enzyme family. Computational docking of the structure in the membrane in comparison to homologs points to differences in interactions with the membrane‐embedded acceptor and the structural analysis of the complex together with bioinformatics and site‐directed mutagenesis identifies donor sugar binding motifs. Notably, E113, conserved solely among PglA enzymes, forms a hydrogen bond with the GalNAc C6″‐OH. Mutagenesis of E113 reveals activity consistent with this role in substrate binding, rather than stabilization of the oxocarbenium ion transition state, a function sometimes ascribed to the corresponding residue in GT4 homologs. The bioinformatic analyses reveal a substrate‐specificity motif, showing that Pro281 in a substrate binding loop of PglA directs configurational preference for GalNAc over GlcNAc. This proline is replaced by a conformationally flexible glycine, even in distant homologs, which favor substrates with the same stereochemistry at C4, such as glucose. The signature loop is conserved across all Campylobacter PglA enzymes, emphasizing its importance in substrate specificity.

Vuksanovic, Nemanja↗
DOE OSTI2019

MicroRNA expression profiling provides novel insights into immune-related pathways involved in gastric cancer

Gastric cancer is one of the most common cancers, and an increasing number of studies have found that microRNAs (miRNAs) play essential roles in gastric cancer progression; however, the roles of specific miRNAs involved in the immune response to this disease remain unclear. We compared the miRNA expression in tissues from primary gastric cancer patients and healthy controls to find miRNAs dysregulated in gastric cancer and used bioinformatics tools to determine potential roles of these miRNAs in the immune system. We evaluated 25 primary gastric cancer tissues and five healthy gastric tissues. Quantitative real-time polymerase chain reaction was performed for a set of miRNAs, followed by the prediction of their target genes and functional enrichment analysis of these targets. Analysis of a microarray dataset showed that the miRNA miR-196a-5p was significantly upregulated, while miR-374a-5p and miR-375 were downregulated in gastric cancer patients. In addition, miR-374-5p was significantly downregulated in patients with metastasis compared with its expression levels in non-metastatic patients (p = 0.03). Bioinformatics analysis suggested that the pathways regulated by these differentially expressed miRNAs were related to the immune response, cell adhesion, and cell migration. Most importantly, this study provides a new insight into the potential use of multiple miRNAs to find distinct pathways of immune regulation in gastric cancer.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Long non-coding RNA LUCAT1 modulates methotrexate resistance in osteosarcoma via miR-200c/ABCB1 axis

Highlights: • LncRNA LUCAT1 and ABCB1 protein are both up-regulated in osteosarcoma methotrexate-resistant cells. • LncRNA LUCAT1 knockdown suppress the expression levels drug resistance related genes, proliferation, invasion and tumor growth of osteosarcoma cells in vitro and vivo. • Bioinformatics predictive tools and luciferase assay reveal the LUCAT1/miR-200c/ABCB1 pathway. • Rescue experiments confirm the combined role of LUCAT1, miR-200c and ABCB1 on osteosarcoma methotrexate resistance. Long non-coding RNAs (lncRNAs) have been verified to participate in the tumorigenesis of multiple cancers. Nevertheless, the deepgoing role molecular mechanisms of lncRNAs on osteosarcoma chemoresistance remain unclear. In present study, we investigate the function of lncRNA LUCAT1 on osteosarcoma methotrexate (MTX) resistant phenotype and discover the potential regulatory mechanism. Results showed that LUCAT1 was up-regulated in MTX-resistant cells (MG63/MTX, HOS/MTX) compared to that in parental cells. LncRNA LUCAT1 and ABCB1 protein expression levels were both up-regulated when induced by different concentration of methotrexate. In vitro and vivo, LUCAT1 knockdown decreased the expression levels drug resistance related genes (MDR1, MRP5, LRP1), proliferation, invasion and tumor growth of osteosarcoma cells. Bioinformatics tools and luciferase assay reveled that miR-200c both targeted the 3′-UTR of LUCAT1 and ABCB1 mRNA, suggesting the modulation of LUCAT1 on ABCB1 through sponging miR-200c. Rescue experiments confirmed the combined role of LUCAT1, miR-200c and ABCB1 on osteosarcoma proliferation, invasion and methotrexate resistance. Overall, results indicate the vital role of LUCAT1 in the methotrexate resistance regulation through miR-200c/ABCB1 pathway, providing a novel insight and treatment strategy for osteosarcoma drug resistance.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Identification and characterization of human bone-derived cells

Highlights: • In non-osteogenic condition, BdCs are more correlated to MSCs than FOBs in bioinformatics result. • BdCs sustained not only high ALP, OPN and osterix expression, but differentiated to mature osteoblast. • In osteogenic condition, BdCs are similar with FOBs than MSCs in physiological changes results. • We suggest BdCs are osteoprogenitor cells or undifferentiated preosteoblasts. This study was designed to identify and characterize primary bone-derived cells (BdCs) and investigate the potential role of osteoblast differentiation. Primary BdCs were isolated from surgical bone for comparative analysis with mesenchymal stem cells (MSCs) and fetal osteoblasts (FOBs) and for potential differentiation to mature osteoblasts. Using three different cells, we successfully cultivated human osteoblast differentiation and activity which were evaluated using microarray and biochemical methods. BdCs are more correlated to MSCs in bioinformatics result and similar with FOBs in gene expression. In particular, Osterix, osteoprogenitor marker, was high expressed in BdCs, while the expression in MSCs and FOBs were very low. Furthermore, BdCs exhibited a marked alkaline phosphatase (ALP) expression, early stage of osteogenic marker, and retained osteogenic properties and physiological changes into maturation as in FOBs. BdCs also showed an increase in bone morphogenic protein 2 (BMP2), osteopontin (OPN), and osteocalcin (OCN) mRNA expressions during differentiation. This study suggests that BdCs may be osteoprogenitor cells or undifferentiated preosteoblasts with strong capacity to differentiate toward mature osteoblasts.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

The lncRNA TUG1/miR-145-5p/FGF10 regulates proliferation and migration in VSMCs of hypertension

Vascular remodeling is a characteristic pathological feature of hypertension, it can cause of increasing vascular resistance and decrease of compliance. Vascular smooth muscle cell (VSMCs) dysfunction is the important foundation of vascular remodeling. Increasing evidences have revealed that lncRNA is an important regulatory factor of VSMC function. In this paper, we explored the function of lncRNA TUG1 in vascular remodeling of hypertension. Here, we found that lncRNA TUG1 was highly expressed in aorta of spontaneously hypertensive rats (SHR) rats and promoted the proliferation and migration of VSMCs (SHR-VSMCs). Bioinformatics analyze showed that lncRNA TUG1 sequence had miR-145-5p binding sites. Luciferase reporter test, RNA pulldown and qRT-PCR showed that lncRNA TUG1 could bind miR-145-5p. Similarly, bioinformatics analyze found that FGF10 3 ′UTR contained miR-145-5p binding sites. Luciferase reporter test, qRT-PCR and Western blot were shown that miR-145-5p inhibited FGF10 expression by binding to its 3 ′UTR. MTT showed that miR-145-5p inhibited and FGF10 promoted SHR-VMSCs proliferation and migration. Overexpression of miR-145-5p or knocking down of FGF10 after overexpresion of lncRNA TUG1 could rescue the proliferation and migration promoted by lncRNA TUG1. LncRNA TUG1 and FGF10 promoted and miR-145-5p suppressed the expression of β-catenin, TCF and LEF in SHR-VSMCs. Therefore, lncRNA TUG1/miR-145-5p/FGF10 promotes the proliferation and migration of VSMCs in hypertensive state by activating the Wnt/β-catenin pathway.

60 APPLIED LIFE SCIENCES↗