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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Single-nuclei transcriptome analysis of channel catfish spleen provides insight into the immunome of an aquaculture-relevant species

The catfish industry is the largest sector of U.S. aquaculture production. Given its role in food production, the catfish immune response to industry-relevant pathogens has been extensively studied and has provided crucial information on innate and adaptive immune function during disease progression. To further examine the channel catfish immune system, we performed single-cell RNA sequencing on nuclei isolated from whole spleens, a major lymphoid organ in teleost fish. Libraries were prepared using the 10X Genomics Chromium X with the Next GEM Single Cell 3’ reagents and sequenced on an Illumina sequencer. Each demultiplexed sample was aligned to the Coco_2.0 channel catfish reference assembly, filtered, and counted to generate feature-barcode matrices. From whole spleen samples, outputs were analyzed both individually and as an integrated dataset. The three splenic transcriptome libraries generated an average of 278,717,872 reads from a mean 8,157 cells. The integrated data included 19,613 cells, counts for 20,121 genes, with a median 665 genes/cell. Cluster analysis of all cells identified 17 clusters which were classified as erythroid, hematopoietic stem cells, B cells, T cells, myeloid cells, and endothelial cells. Subcluster analysis was carried out on the immune cell populations. Here, distinct subclusters such as immature B cells, mature B cells, plasma cells, γδ T cells, dendritic cells, and macrophages were further identified. Differential gene expression analyses allowed for the identification of the most highly expressed genes for each cluster and subcluster. This dataset is a rich cellular gene expression resource for investigation of the channel catfish and teleost splenic immunome.

Science & Technology - Other Topics↗
DOE OSTI2024

PPI DataHub Project Data Package: S. elongatus PCC 7942 Circadian Control Bioproduction Transcriptomics (PB-DP3)

The purpose of this experiment was to evaluate how circadian clock regulation impacts carbon partitioning between storage, growth, and product synthesis in Synechococcus elongatus PCC 7942 in providing insights to strategies for enhanced bioproduction. Sample data was acquired using a Illumina HiSeq sequencer system and processed for RNA sequencing (RNA-Seq) expression analysis. Transcriptomic differential expression analysis revealed coordinated circadian clock-driven adjustment of the cell cycle and rewiring of energy and carbon metabolism. Processed RNA-Seq datasets are openly accessible from the PNNL DataHub project dataset download page and contain secondary processed RNA-seq results files and supporting metadata materials linked to relevant source code information supporting data transparency and reuse.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Single-nuclei transcriptome analysis of IgM+ cells isolated from channel catfish (Ictalurus punctatus) spleen

Catfish production is the primary aquaculture sector in the United States, and the key cultured species is channel catfish (Ictalurus punctatus). The major causes of production losses are pathogenic diseases, and the spleen, an important site of adaptive immunity, is implicated in these diseases. To examine the channel catfish immune system, single-nuclei transcriptomes of sorted and captured IgM + cells were produced from adult channel catfish. Three channel catfish (~1 kg) were euthanized, the spleen dissected, and the tissue dissociated. The lymphocytes were isolated using a Ficoll gradient and IgM + cells were then sorted with flow cytometry. The IgM + cells were lysed and single-nuclei libraries generated using a Chromium Next GEM Single Cell 3’ GEM Kit and the Chromium X Instrument (10x Genomics) and sequenced with the Illumina NovaSeq X Plus sequencer. The reads were aligned to theI. punctatusreference assembly (Coco_2.0) using Cell Ranger, and normalization, cluster analysis, and differential gene expression analysis were carried out with Seurat. Across the three samples, approximately 753.5 million reads were generated for 18,686 cells. After filtering, 10,637 cells remained for the cluster analysis. The cluster analysis identified 16 clusters which were classified as B cells (10,276), natural killer-like (NK-like) cells (178), T cells or natural killer cells (45), hematopoietic stem and progenitor cells (HSPC)/megakaryocytes (MK) (66), myeloid/epithelial cells (40), and plasma cells (32). The B cell clusters were further defined as different populations of mature B cells, cycling B cells, and plasma cells. The plasma cells highly expressedighmand we demonstrated that the secreted form of the transcript was largely being expressed by these cells. This atlas provides insight into the gene expression of IgM + immune cells in channel catfish. The atlas is publicly available and could be used garner more important information regarding the gene expression of splenic immune cells.

Immunology↗
DOE OSTI2024

Study on the effect of ascorbic acid on the biosynthesis of pigment and citrinin in red yeast rice based on comparative transcriptomics

Pigment is one of the most important metabolites in red yeast rice. However, citrinin may accumulate and cause quality security issues. In the present study, the effect of ascorbic acid (EAA) on the pigment and citrinin was studied, and the metabolic mechanism was discussed using comparative transcriptomics. The introduction of EAA increased the pigment by 58.2% and decreased citrinin by 65.4%. The acid protease activity, DPPH scavenging rate, and total reducing ability also increased by 18.7, 9.0, and 26.7%, respectively. Additionally, a total of 791 differentially expressed genes were identified, and 79 metabolic pathways were annotated, among which carbon metabolism, amino acid metabolism, and fatty acid metabolism were closely related to the biosynthesis of pigment and citrinin. Ethanol dehydrogenase ( M pigC ), oxidoreductase ( M pigE ), reductase ( M pigH ), and monooxygenase ( M pigN ) may be related to the increase of pigment. ctnC and pksCT contributed to the decline of citrinin.

Jiang, Zhilin↗
DOE OSTI2024

Evaluating two live-attenuated vaccines against Salmonella enterica serovar Reading in turkeys: reduced tissue colonization and cecal tonsil transcriptome responses

Vaccines that cross-protect across serovars ofSalmonella enterica(Salmonella) would be a beneficial intervention against emerging and persistentSalmonellaisolates of concern for the turkey industry. The 2017–2019 foodborne outbreak ofSalmonella entericaserovar Reading (S. Reading) revealed the need for effective control of this serovar in turkey production. This study evaluated two live-attenuatedSalmonellavaccines, an internally developed cross-protective vaccine and a commercially available vaccine, against an outbreak-associated strain ofS. Reading in turkeys. At 1 day and 3 weeks of age, male turkey poults were either mock-vaccinated with phosphate buffered saline (PBS) or given one of the vaccines by oral gavage (primary and booster) or aerosol spray (primary) then drinking water (booster). At 7 weeks of age, poults were challenged with 10 9 colony forming units (CFU) ofS. Reading; a mock-vaccinated group was mock-challenged with PBS. Colonization of the cecal contents and cecal tonsil was 1.5–3 log 10 CFU/g lower in vaccinated birds than mock-vaccinated birds at 7 and/or 14 days post-inoculation (DPI).Salmonelladissemination to the spleen was significantly reduced by both vaccines. Gene expression of intestinal transporters (such asSCNN1BandSLC10A2) and tight junction proteins was significantly decreased in the turkey cecal tonsil transcriptome at 2 DPI withS. Reading. Vaccination with either vaccine mitigated most cecal tonsil gene expression responses toS. Reading challenge. Therefore, both the internally developed vaccine and commercial vaccine were cross-protective against colonization and dissemination, and both were able to limit transcriptional changes from challenge in intestinal health-related genes in the cecal tonsil, thereby providing vaccination efficacy and impact data againstS. Reading in turkeys.

Veterinary Sciences↗
DOE OSTI2024

Transcriptomic and Proteomic Insights into Host Immune Responses in Pediatric Severe Malarial Anemia: Dysregulation in HSP60-70-TLR2/4 Signaling and Altered Glutamine Metabolism

Severe malarial anemia (SMA, Hb < 6.0 g/dL) is a leading cause of childhood morbidity and mortality in holoendemic Plasmodium falciparum transmission zones. This study explored the entire expressed human transcriptome in whole blood from 66 Kenyan children with non-SMA (Hb ≥ 6.0 g/dL, n = 41) and SMA (n = 25), focusing on host immune response networks. RNA-seq analysis revealed 6862 differentially expressed genes, with equally distributed up-and down-regulated genes, indicating a complex host immune response. Deconvolution analyses uncovered leukocytic immune profiles indicative of a diminished antigenic response, reduced immune priming, and polarization toward cellular repair in SMA. Weighted gene co-expression network analysis revealed that immune-regulated processes are central molecular distinctions between non-SMA and SMA. A top dysregulated immune response signaling network in SMA was the HSP60-HSP70-TLR2/4 signaling pathway, indicating altered pathogen recognition, innate immune activation, stress responses, and antigen recognition. Validation with high-throughput gene expression from a separate cohort of Kenyan children (n = 50) with varying severities of malarial anemia (n = 38 non-SMA and n = 12 SMA) confirmed the RNA-seq findings. Proteomic analyses in 35 children with matched transcript and protein abundance (n = 19 non-SMA and n = 16 SMA) confirmed dysregulation in the HSP60-HSP70-TLR2/4 signaling pathway. Additionally, glutamine transporter and glutamine synthetase genes were differentially expressed, indicating altered glutamine metabolism in SMA. This comprehensive analysis underscores complex immune dysregulation and novel pathogenic features in SMA.

Microbiology↗
DOE OSTI2024

Transcriptomic Analysis of the CAM Species Kalanchoë fedtschenkoi Under Low- and High-Temperature Regimes

Temperature stress is one of the major limiting environmental factors that negatively impact global crop yields. Kalanchoë fedtschenkoi is an obligate crassulacean acid metabolism (CAM) plant species, exhibiting much higher water-use efficiency and tolerance to drought and heat stresses than C 3 or C 4 plant species. Previous studies on gene expression responses to low- or high-temperature stress have been focused on C 3 and C 4 plants. There is a lack of information about the regulation of gene expression by low and high temperatures in CAM plants. To address this knowledge gap, we performed transcriptome sequencing (RNA-Seq) of leaf and root tissues of K. fedtschenkoi under cold (8 °C), normal (25 °C), and heat (37 °C) conditions at dawn (i.e., 2 h before the light period) and dusk (i.e., 2 h before the dark period). Our analysis revealed differentially expressed genes (DEGs) under cold or heat treatment in comparison to normal conditions in leaf or root tissue at each of the two time points. In particular, DEGs exhibiting either the same or opposite direction of expression change (either up-regulated or down-regulated) under cold and heat treatments were identified. In addition, we analyzed gene co-expression modules regulated by cold or heat treatment, and we performed in-depth analyses of expression regulation by temperature stresses for selected gene categories, including CAM-related genes, genes encoding heat shock factors and heat shock proteins, circadian rhythm genes, and stomatal movement genes. Our study highlights both the common and distinct molecular strategies employed by CAM and C 3 /C 4 plants in adapting to extreme temperatures, providing new insights into the molecular mechanisms underlying temperature stress responses in CAM species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Transcriptomic analysis of ZMO_0422 in Zymomonas mobilis

Deletion of the IscR homolog ZMO_0422 was performed in Zymomonas mobilis to investigate the role of Fe-S cluster biogenesis in Zymomonas. Here we perform genome-wide transcirptomics study to examine transcript chagnes in delta-ZMO_0422 compared to WT Zymomonas mobilis under both aerobic and anaerobic growth conditions. Overall design: Transcriptomic analysis of WT and a deletion of ZMO_0422 of Zymomonas mobilis ZM4 under aerobic and anaerobic growth conditions.

aerobic↗
NASA NTRS2014

Radiation Quality Effects on Transcriptome Profiles in 3-D Cultures After Charged Particle Irradiation

In this work, we evaluated the differential effects of low- and high-LET radiation on 3-D organotypic cultures in order to investigate radiation quality impacts on gene expression and cellular responses. Current risk models for assessment of space radiation-induced cancer have large uncertainties because the models for adverse health effects following radiation exposure are founded on epidemiological analyses of human populations exposed to low-LET radiation. Reducing these uncertainties requires new knowledge on the fundamental differences in biological responses (the so-called radiation quality effects) triggered by heavy ion particle radiation versus low-LET radiation associated with Earth-based exposures. In order to better quantify these radiation quality effects in biological systems, we are utilizing novel 3-D organotypic human tissue models for space radiation research. These models hold promise for risk assessment as they provide a format for study of human cells within a realistic tissue framework, thereby bridging the gap between 2-D monolayer culture and animal models for risk extrapolation to humans. To identify biological pathway signatures unique to heavy ion particle exposure, functional gene set enrichment analysis (GSEA) was used with whole transcriptome profiling. GSEA has been used extensively as a method to garner biological information in a variety of model systems but has not been commonly used to analyze radiation effects. It is a powerful approach for assessing the functional significance of radiation quality-dependent changes from datasets where the changes are subtle but broad, and where single gene based analysis using rankings of fold-change may not reveal important biological information.

Patel, Zarana S.↗
NASA NTRS2014

Radiation Quality Effects on Transcriptome Profiles in 3-d Cultures After Particle Irradiation

In this work, we evaluate the differential effects of low- and high-LET radiation on 3-D organotypic cultures in order to investigate radiation quality impacts on gene expression and cellular responses. Reducing uncertainties in current risk models requires new knowledge on the fundamental differences in biological responses (the so-called radiation quality effects) triggered by heavy ion particle radiation versus low-LET radiation associated with Earth-based exposures. We are utilizing novel 3-D organotypic human tissue models that provide a format for study of human cells within a realistic tissue framework, thereby bridging the gap between 2-D monolayer culture and animal models for risk extrapolation to humans. To identify biological pathway signatures unique to heavy ion particle exposure, functional gene set enrichment analysis (GSEA) was used with whole transcriptome profiling. GSEA has been used extensively as a method to garner biological information in a variety of model systems but has not been commonly used to analyze radiation effects. It is a powerful approach for assessing the functional significance of radiation quality-dependent changes from datasets where the changes are subtle but broad, and where single gene based analysis using rankings of fold-change may not reveal important biological information. We identified 45 statistically significant gene sets at 0.05 q-value cutoff, including 14 gene sets common to gamma and titanium irradiation, 19 gene sets specific to gamma irradiation, and 12 titanium-specific gene sets. Common gene sets largely align with DNA damage, cell cycle, early immune response, and inflammatory cytokine pathway activation. The top gene set enriched for the gamma- and titanium-irradiated samples involved KRAS pathway activation and genes activated in TNF-treated cells, respectively. Another difference noted for the high-LET samples was an apparent enrichment in gene sets involved in cycle cycle/mitotic control. It is plausible that the enrichment in these particular pathways results from the complex DNA damage resulting from high-LET exposure where repair processes are not completed during the same time scale as the less complex damage resulting from low-LET radiation.

Patel, Z. S.↗
NASA NTRS2016

Transcriptomic Response of Drosophila Melanogaster Pupae Developed in Hypergravity

The metamorphosis of Drosophila is evolutionarily adapted to Earth's gravity, and is a tightly regulated process. Deviation from 1g to microgravity or hypergravity can influence metamorphosis, and alter associated gene expression. Understanding the relationship between an altered gravity environment and developmental processes is important for NASA's space travel goals. In the present study, 20 female and 20 male synchronized (Canton S, 2 to 3day old) flies were allowed to lay eggs while being maintained in a hypergravity environment (3g). Centrifugation was briefly stopped to discard the parent flies after 24hrs of egg laying, and then immediately continued until the eggs developed into P6-staged pupae (25 - 43 hours after pupation initiation). Post hypergravity exposure, P6-staged pupae were collected, total RNA was extracted using Qiagen RNeasy mini kits. We used RNA-Seq and qRT-PCR techniques to profile global transcriptomic changes in early pupae exposed to chronic hypergravity. During the pupal stage, Drosophila relies upon gravitational cues for proper development. Assessing gene expression changes in the pupa under altered gravity conditions helps highlight gravity dependent genetic pathways. A robust transcriptional response was observed in hypergravity-exposed pupae compared to controls, with 1,513 genes showing a significant (q < 0.05) difference in gene expression. Five major biological processes were affected: ion transport, redox homeostasis, immune response, proteolysis, and cuticle development. This outlines the underlying molecular changes occurring in Drosophila pupae in response to hypergravity.

RNASeq↗
NASA NTRS2017

Network Analysis of Rodent Transcriptomes in Spaceflight

Network analysis methods leverage prior knowledge of cellular systems and the statistical and conceptual relationships between analyte measurements to determine gene connectivity. Correlation and conditional metrics are used to infer a network topology and provide a systems-level context for cellular responses. Integration across multiple experimental conditions and omics domains can reveal the regulatory mechanisms that underlie gene expression. GeneLab has assembled rich multi-omic (transcriptomics, proteomics, epigenomics, and epitranscriptomics) datasets for multiple murine tissues from the Rodent Research 1 (RR-1) experiment. RR-1 assesses the impact of 37 days of spaceflight on gene expression across a variety of tissue types, such as adrenal glands, quadriceps, gastrocnemius, tibalius anterior, extensor digitorum longus, soleus, eye, and kidney. Network analysis is particularly useful for RR-1 -omics datasets because it reinforces subtle relationships that may be overlooked in isolated analyses and subdues confounding factors. Our objective is to use network analysis to determine potential target nodes for therapeutic intervention and identify similarities with existing disease models. Multiple network algorithms are used for a higher confidence consensus.

genomics↗
NASA NTRSDate not supplied

Transcriptomic Analysis of Irradiated Mouse Retina Following Readaptation

Rodent models are used as analogs for studying the effects of spaceflight. NASA GeneLab provides access to relevant omics datasets generated from spaceflight and ground-based experiments allowing for additional retrospective analysis. In this study, we used GeneLab’s GLDS-203, a dataset generated by researchers at Loma Linda University to study the impact of prolonged unloading and/or low-dose radiation on mouse retina. We analyzed transcriptomics data from retina of mice irradiated with gamma-rays for 21 days followed by 7 days, 1 month, or 4 months of readaptation. We obtained raw gene counts from GeneLab and performed differential gene expression analysis after data normalization. For each of the three timepoints, we performed differential expression analysis to compare transcriptional profiles for retina from irradiated vs. non-irradiated (controls) mice, all exposed to gravity. We observed the highest number of differentially expressed genes at 7 days, followed by 1 month and 4 months. Enrichment analysis showed top pathways (adjusted p-value < 0.05) were related to transport along microtubule and photoreceptor cell development in the 7-day readaptation group. Fewer significantly enriched pathways were observed for the 1-month group and included mRNA metabolic processes and neuron differentiation. No significantly enriched pathways were found in the 4-month group. The Gene Ontology biological processes common between the 7 days and 1-month groups include visual perception, synapse organization, and perception of light stimulus. This analysis is part of a larger effort to characterize the molecular mechanisms involved in retinal readaptation following radiation exposure. Future analyses will include other related retina datasets in GeneLab repository to assess whether gene expression patterns are consistent across different study cohorts.

Prachi Kothiyal↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), has typically limited machine learning (ML) in space studies and further study of radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNAseq) data from 6 mouse liver GeneLab datasets (GLDS) with a total of 113 spaceflight and ground-control samples to determine top features relevant to spaceflight including the effect of radiation exposure. Data was normalized within each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. The top MRMR features were used to predict spaceflight vs. ground-control samples using a Random Forest (RF) classifier with 5-fold cross validation (CV). The ML-based gene sets were further compared against differential gene expression results from individual GLDS. CV training using the top 100 MRMR genes show averages of 86% accuracy and 0.95 AUC value on the validation set over 5 folds (Figure 1A). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 811 or 68 DEGs overlapping between at least 2 or 3 studies, respectively (Figure 1B). Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism. Set analysis between the MRMR features and the DEGs showed 60 or 8 genes overlapping with at least 1 or 2 studies, respectively. MRMR feature selection and ensemble ML methods (e.g. RF) improve performance relative to a Naïve Bayes classifier when NGS data sets are analyzed. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise ratio. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from RNASeq analysis. Non-intersecting sets introduce opportunity to explore spaceflight relevant genes and implementing ML methods across existing NGS datasets may overcome sample size limitations. ML coupled with existing analytical methods enhances understanding of disease by revealing common underlying pathways across datasets.

Machine Learning↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
NASA NTRSDate not supplied

Histological and Transcriptomic Analysis of Spaceflight-Induced Ocular Changes in the Mouse Retina

Anatomical changes have been observed in astronauts’ eyes after long duration spaceflight missions. These alterations can lead to visual impairment which in part constitutes the spaceflight-associated neuroocular syndrome (SANS), one of the top risk priorities for deep space missions. The HRP Systems Biology (SysBio) Translation Project will apply systems biology approaches utilizing current human physiological spaceflight data, molecular results from rodents, and future research with a multi-level, multi-system, and multi-species perspective to augment the existing research plan to resolve the SANS risk. Not much is known about SANS at the cellular and molecular level, but studies in mice and rats have recently begun to determine how spaceflight might affect the biology of the eye. Preliminary studies of mice that flew on the Space Shuttle, and more recently the International Space Station (ISS), have shown changes in retinal physiology as assessed by histology and gene expression analysis. The study presented here obtained samples from the CASIS sponsored Rodent Research 8 Experiment delivered to the ISS by SpaceX CRS-16 on 12/08/2018. Female BALB/cAnNTac mice flew on the ISS for 45 days, while ground controls were housed in a standard vivarium or animal enclosure module. Sacrifice and sample acquisition occurred once mice returned to Earth, possibly allowing for readaptation affecting retinal homeostasis. We applied standard transcriptomic (RNAseq) and histological approaches to characterize genes and pathways in the mouse retina affected by spaceflight or age. The differentially expressed gene (DEG) data was analyzed using Galaxy (GeneLab) and Ingenuity Pathway Analysis. Significant DEGs between flight and ground samples were relatively few but biologically meaningful. Pathways identified related to neuronal differentiation, cellular transport/movement, and wound healing. Age effects were detected between the young (10–12 weeks) and old (32 weeks) groups and between the baseline and end of experiment (~46 days). The biological relevance of specific DEGs were confirmed through immunohistochemical evaluation using fixed histological sections of the eye from four flight group mice and four habitat control mice. Staining was performed specific for synaptophysin, glial fibrillary acidic protein (GFAP), and neurofilament in the retinal periphery, equator, and peripapillary regions. For synaptophysin staining, the innerplexiform and outerplexiform layers were scored; for GFAP staining, Mueller cells and perivascular astrocytes were scored. Results show flight samples typically had more staining of GFAP and neurofilament while, conversely, the habitat control group had more staining of synaptophysin.

C. Perez↗