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[Role of membrane lipids in myocardial cytoprotection]

The cardiomyocyte capacity to regulate ATP production to face any change in energy demand is a major determinant of cardiac function. This process is based on a balanced fatty acid (FA) metabolism, because FA is the main fuel of the heart, although the most expensive one in oxygen. The pathway is, however, weakly controlled by the cardiac myocyte which can well regulate FA mitochondrial entry but not cell FA uptake. For this reason, several pathological situations often result from either harmful accumulation of FA and derivatives or excess FA-oxidation. Control of the FA/glucose balance by decreased energy production from FA would thus offer an alternative strategy in the treatment of ischaemia, providing the cardiomyocytes weak ability in handling the non-metabolised FA is controlled. The initiation and the regulation of cardiac contraction both result from membrane activity; the other major role of lipids in the heart is their contribution to membrane homeostasis through phospholipid synthesis pathways and phospholipases. The anti-anginal activity of Trimetazidine, reported as a cytoprotective effect without a haemo-dynamic component; is associated with reduced use of FA for energy. However, accumulation of FA and derivatives has never been observed. Trimetazidine is reported to increase significantly the synthesis of phospholipids without influencing the other lipid classes, thus increasing the incorporation of FA in membrane structures. This cytoprotection appears to be based on the redirection of the use of FA to phospholipid synthesis, which would decrease their availability for energy production. This class of compounds, with the same properties as Trimetazidine, offers a metabolic approach to the treatment of ischaemia.

Cytoprotection/physiology↗

Structure and mechanism of Staphylococcus aureus oleate hydratase (OhyA)

Flavin adenine dinucleotide (FAD)-dependent bacterial oleate hydratases (OhyAs) catalyze the addition of water to isolated fatty acid carbon–carbon double bonds. Staphylococcus aureus uses OhyA to counteract the host innate immune response by inactivating antimicrobial unsaturated fatty acids. Mechanistic information explaining how OhyAs catalyze regiospecific and stereospecific hydration is required to understand their biological functions and the potential for engineering new products. In this study, we deduced the catalytic mechanism of OhyA from multiple structures of S. aureus OhyA in binary and ternary complexes with combinations of ligands along with biochemical analyses of relevant mutants. The substrate-free state shows Arg81 is the gatekeeper that controls fatty acid entrance to the active site. FAD binding engages the catalytic loop to simultaneously rotate Glu82 into its active conformation and Arg81 out of the hydrophobic substrate tunnel, allowing the fatty acid to rotate into the active site. FAD binding also dehydrates the active site, leaving a single water molecule connected to Glu82. This active site water is a hydronium ion based on the analysis of its hydrogen bond network in the OhyA•PEG400•FAD complex. We conclude that OhyA accelerates acid-catalyzed alkene hydration by positioning the fatty acid double bond to attack the active site hydronium ion, followed by the addition of water to the transient carbocation intermediate. Structural transitions within S. aureus OhyA channel oleate to the active site, curl oleate around the substrate water, and stabilize the hydroxylated product to inactivate antimicrobial fatty acids.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Crystal structure of a putative 3-hydroxypimelyl-CoA dehydrogenase, Hcd1, from Syntrophus aciditrophicus strain SB at 1.78 Å resolution

Syntrophus aciditrophicus strain SB is a model syntroph that degrades benzoate and alicyclic acids. Here, the structure of a putative 3-hydroxypimelyl-CoA dehydrogenase from S. aciditrophicus strain SB (SaHcd1) was resolved at 1.78 Å resolution. SaHcd1 contains sequence motifs and structural features that belong to the short-chain dehydrogenase/reductase (SDR) family of NADPH-dependent oxidoreductases. SaHcd1 is proposed to concomitantly reduce NAD + or NADP + to NADH or NADPH, respectively, while converting 3-hydroxypimelyl-CoA to 3-oxopimeyl-CoA. Further enzymatic studies are needed to confirm the function of SaHcd1.

59 BASIC BIOLOGICAL SCIENCES↗

A Lunar Ground Truth of Microbes that are Integral to Sustaining Biological Life Support Systems

A lunar outpost such as Gateway or a Mars transit vehicle will use a semi-closed to closed loop biological life support system (BLSS) adapted for microgravity and high levels of radiation. A moon habitat will likely exist under partial gravity conditions and sustained levels of high radiation as a semi-closed loop system able to get resources from Earth via Gateway. Additionally, the moon will act as a closed-loop testbed for Mars habitat operations and will employ various elements of in situ resource utilization (ISRU). Perhaps the longest running BLSS collective study to date is that of the European MELiSSA (Micro Ecological Life Support System Alternative) project, a circular life support system, established to gain knowledge on regenerative systems aimed at the highest degree of autonomy to produce food, water and oxygen from mission wastes. This setup has evaluated the use of discrete microbial compartments and a higher plant compartment to carry out the necessary life support functions to support crew. Such compartments utilize thermophilic anaerobes to break down human and inedible plant wastes, photoheterotrophic bacteria that can further metabolize volatile fatty acids, nitrifying bacteria that can convert ammonium to plant and microalgae available nitrates, and photoautotrophic bacteria and higher plants which will convert carbon dioxide to oxygen, purify water, and provide food for human consumption. This being the most advanced BLSS model example to date -aside from the Yuegong-1, Chinese Lunar Palace- with the most defined compartment composition, and the assumption that other BLSSs will build upon such models, it becomes relevant to study the effects of the lunar environment upon the microbes that are integral to a BLSS. In ramping up to advanced stage BLSS systems, relatively simple experiments can be conducted on Artemis missions using continuous culturing and sampling of the model microbes, subsequently subjected to DNA sequencing for mutational analysis monitoring and chemical analysis to assess the sustained ability to carry out their hallmark biochemical processes efficiently. A suite of microbes should be assessed, prioritizing those with the duel capacity to be utilized in an BLSS and the ability to biochemically facilitate ISRU goals, such as the ability to transform the biogeochemistry of Moon or Martian regolith into materials that can support crop growth or extract elements of industrial significance such as aluminum or iron.

Biological Life Support Systems↗

Creating yellow seed Camelina sativa with enhanced oil accumulation by CRISPR ‐mediated disruption of Transparent Testa 8

Summary Camelina ( Camelina sativa L.), a hexaploid member of the Brassicaceae family, is an emerging oilseed crop being developed to meet the increasing demand for plant oils as biofuel feedstocks. In other Brassicas, high oil content can be associated with a yellow seed phenotype, which is unknown for camelina. We sought to create yellow seed camelina using CRISPR/Cas9 technology to disrupt its Transparent Testa 8 (TT8) transcription factor genes and to evaluate the resulting seed phenotype. We identified three TT8 genes, one in each of the three camelina subgenomes, and obtained independent CsTT8 lines containing frameshift edits. Disruption of TT8 caused seed coat colour to change from brown to yellow reflecting their reduced flavonoid accumulation of up to 44%, and the loss of a well‐organized seed coat mucilage layer. Transcriptomic analysis of CsTT8 ‐edited seeds revealed significantly increased expression of the lipid‐related transcription factors LEC1 , LEC2 , FUS3 , and WRI1 and their downstream fatty acid synthesis‐related targets. These changes caused metabolic remodelling with increased fatty acid synthesis rates and corresponding increases in total fatty acid (TFA) accumulation from 32.4% to as high as 38.0% of seed weight, and TAG yield by more than 21% without significant changes in starch or protein levels compared to parental line. These data highlight the effectiveness of CRISPR in creating novel enhanced‐oil germplasm in camelina. The resulting lines may directly contribute to future net‐zero carbon energy production or be combined with other traits to produce desired lipid‐derived bioproducts at high yields.

59 BASIC BIOLOGICAL SCIENCES↗

Longitudinal Single‐Cell Imaging of Engineered Strains with Stimulated Raman Scattering to Characterize Heterogeneity in Fatty Acid Production

Abstract Understanding metabolic heterogeneity is critical for optimizing microbial production of valuable chemicals, but requires tools that can quantify metabolites at the single‐cell level over time. Here, longitudinal hyperspectral stimulated Raman scattering (SRS) chemical imaging is developed to directly visualize free fatty acids in engineered Escherichia coli over many cell cycles. Compositional analysis is also developed to estimate the chain length and unsaturation of the fatty acids in living cells. This method reveals substantial heterogeneity in fatty acid production among and within colonies that emerges over the course of many generations. Interestingly, the strains display distinct types of production heterogeneity in an enzyme‐dependent manner. By pairing time‐lapse and SRS imaging, the relationship between growth and production at the single‐cell level are examined. The results demonstrate that cell‐to‐cell production heterogeneity is pervasive and provides a means to link single‐cell and population‐level production.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Plant unusual fatty acids: learning from the less common

The plant kingdom contains an abundance of structurally diverse fatty acids referred to as unusual fatty acids. Unusual fatty acids on plant surfaces can form polyesters that contribute to the function of cutin as a barrier for water loss and pathogen protection. Unusual fatty acids are also found as abundant components of seed oils of selected species and often confer desirable properties for industrial and nutritional applications. Here in this paper, we review recent findings on the biosynthesis and metabolism of unusual fatty acids in cutin and seed oils and use of this information for enzyme structure-function studies and seed oil metabolic engineering. We also highlight the recent discovery of unusual fatty acids that are formed from a previously undescribed variation of fatty acid elongation.

59 BASIC BIOLOGICAL SCIENCES↗

Genome-wide profiling of histone (H3) lysine 4 (K4) tri-methylation (me3) under drought, heat, and combined stresses in switchgrass

Background: Switchgrass (Panicum virgatum L.) is a warm-season perennial (C4) grass identified as an important biofuel crop in the United States. It is well adapted to the marginal environment where heat and moisture stresses predominantly affect crop growth. However, the underlying molecular mechanisms associated with heat and drought stress tolerance still need to be fully understood in switchgrass. The methylation of H3K4 is often associated with transcriptional activation of genes, including stress-responsive. Therefore, this study aimed to analyze genome-wide histone H3K4-tri-methylation in switchgrass under heat, drought, and combined stress. Results: In total, ~ 1.3 million H3K4me3 peaks were identified in this study using SICER. Among them, 7,342; 6,510; and 8,536 peaks responded under drought (DT), drought and heat (DTHT), and heat (HT) stresses, respectively. Most DT and DTHT peaks spanned 0 to + 2000 bases from the transcription start site [TSS]. By comparing differentially marked peaks with RNA-Seq data, we identified peaks associated with genes: 155 DT-responsive peaks with 118 DT-responsive genes, 121 DTHT-responsive peaks with 110 DTHT-responsive genes, and 175 HT-responsive peaks with 136 HT-responsive genes. We have identified various transcription factors involved in DT, DTHT, and HT stresses. Gene Ontology analysis using the AgriGO revealed that most genes belonged to biological processes. Most annotated peaks belonged to metabolite interconversion, RNA metabolism, transporter, protein modifying, defense/immunity, membrane traffic protein, transmembrane signal receptor, and transcriptional regulator protein families. Further, we identified significant peaks associated with TFs, hormones, signaling, fatty acid and carbohydrate metabolism, and secondary metabolites. qRT-PCR analysis revealed the relative expressions of six abiotic stress-responsive genes (transketolase, chromatin remodeling factor-CDH3, fatty-acid desaturase A, transmembrane protein 14C, beta-amylase 1, and integrase-type DNA binding protein genes) that were significantly (P < 0.05) marked during drought, heat, and combined stresses by comparing stress-induced against un-stressed and input controls. Conclusion: Our study provides a comprehensive and reproducible epigenomic analysis of drought, heat, and combined stress responses in switchgrass. Significant enrichment of H3K4me3 peaks downstream of the TSS of protein-coding genes was observed. In addition, the cost-effective experimental design, modified ChIP-Seq approach, and analyses presented here can serve as a prototype for other non-model plant species for conducting stress studies.

59 BASIC BIOLOGICAL SCIENCES↗

Symbiosis Under Stress: How Arbuscular Mycorrhizal Fungi and Sorghum Metabolism Shift Under Drought

As drought becomes more prevalent across the globe, causing billions of dollars in agricultural loss, the need to maintain crop health and productivity grows increasingly important. Out of the most important cereal crops, sorghum shows the greatest drought tolerance, and plant-microbiome interactions at the root region play a crucial role in this. A key microbial player is arbuscular mycorrhizal fungi (AMF), which deliver water and nutrients to plants in exchange for nutrients they cannot produce. Using sorghum as a model plant, we combine mass-spectrometry based proteomics and metabolomics to examine metabolic interactions between sorghum roots and AMF under drought stress. With AMF, sorghum downregulated lipid-related biological processes involving fatty acid biosynthesis and metabolism. Similarly, amino acid biosynthesis pathways were also suppressed; however, metabolite analysis revealed increased amino acid abundance related to the phenylpropanoid pathway. AMF also enhanced the upregulation of drought-protective osmolytes, such as mannitol and sorbitol, suggesting their key role in mediating sorghum’s response to drought stress. AMF hyphal biomass also had an increased abundance of key osmoprotectant amino acids, indicating similar mechanisms of drought tolerance between sorghum and AMF. Metabolomic evidence also suggested that carbohydrate exchange between sorghum and AMF shifted under stress, indicating an altered exudation pattern likely driven by drought response. Our results demonstrate the molecular mechanisms through which AMF modulate sorghum metabolism under drought conditions, highlighting their promising role in improving crop resilience. By identifying the molecular targets that can improve drought tolerance, we can begin engineering drought resistant agricultural biosystems.

60 APPLIED LIFE SCIENCES↗

Population ecology and biogeochemical implications of ssDNA and dsDNA viruses along a permafrost thaw gradient

Anthropogenic-driven climate change is accelerating permafrost thaw, threatening to release vast carbon stores through increased microbial activity. While microbial roles are increasingly studied, the contributions of viruses remain largely unexplored, in part due to soil-associated technical challenges that have hindered their detection and characterization. Here, we applied an optimized virion enrichment workflow along a permafrost thaw gradient, identifying 9,963 viral populations (vOTUs), including single- and double-stranded DNA viruses, with 99.9% novelty compared to other soils. Hosts were predicted for 38% of vOTUs, spanning nine archaeal, and 36 bacterial phyla, 22% of which were linked to metagenome-assembled genomes, including key carbon-cycling taxa. Genomic analyses revealed 811 putative auxiliary metabolic genes (AMGs) from 658 vOTUs, nearly half involved in carbon processing. These included 59 glycoside hydrolases (GH) across nine GH families, 45 for monosaccharide degradation, and seven involved in short-chain fatty acid and C1 metabolism, linking viruses to both early and late stages of carbon turnover. Additionally, six vOTUs carried racD, which may stabilize microbial necromass and promote long-term carbon storage. Viral and AMG functional diversity increased with thaw stage, indicating that viruses might participate in a broadening range of microbial metabolic processes as permafrost thaws. These findings expand our understanding of virus contributions in microbial carbon processing and suggest their important role in deciphering soil carbon fate under changing climate conditions.

Biological and medical sciences↗

Knocking out the carboxyltransferase interactor 1 (CTI1) in Chlamydomonas boosted oil content by fivefold without affecting cell growth

Summary The first step in chloroplast de novo fatty acid synthesis is catalysed by acetyl‐CoA carboxylase (ACCase). As the rate‐limiting step for this pathway, ACCase is subject to both positive and negative regulation. In this study, we identify a Chlamydomonas homologue of the plant carboxyltransferase interactor 1 (CrCTI1) and show that this protein interacts with the Chlamydomonas α‐carboxyltransferase (Crα‐CT) subunit of the ACCase by yeast two‐hybrid protein–protein interaction assay. Three independent CRISPR‐Cas9 mediated knockout mutants for CrCTI1 each produced an ‘enhanced oil’ phenotype, accumulating 25% more total fatty acids and storing up to fivefold more triacylglycerols (TAGs) in lipid droplets. The TAG phenotype of the crcti1 mutants was not influenced by light but was affected by trophic growth conditions. By growing cells under heterotrophic conditions, we observed a crucial function of CrCTI1 in balancing lipid accumulation and cell growth. Mutating a previously mapped in vivo phosphorylation site (CrCTI1 Ser108 to either Ala or to Asp), did not affect the interaction with Crα‐CT. However, mutating all six predicted phosphorylation sites within Crα‐CT to create a phosphomimetic mutant reduced this pairwise interaction significantly. Comparative proteomic analyses of the crcti1 mutants and WT suggested a role for CrCTI1 in regulating carbon flux by coordinating carbon metabolism, antioxidant and fatty acid β‐oxidation pathways, to enable cells to adapt to carbon availability. Taken together, this study identifies CrCTI1 as a negative regulator of fatty acid synthesis in algae and provides a new molecular brick for the genetic engineering of microalgae for biotechnology purposes.

Li, Zhongze [Aix‐Marseille Université, CEA, CNRS, ↗

Adaptive laboratory evolution and metabolic engineering of Cupriavidus necator for improved catabolism of volatile fatty acids

Bioconversion of high-volume waste streams into value-added products will be an integral component of the growing bioeconomy. Volatile fatty acids (VFAs) (e.g., butyrate, valerate, and hexanoate) are an emerging and promising waste-derived feedstock for microbial carbon upcycling. Cupriavidus necator H16 is a favorable host for conversion of VFAs into various bioproducts due to its diverse carbon metabolism, ease of metabolic engineering, and use at industrial scales. Here, in this study, we report that a common strategy to improve product titers in C. necator, deletion of the polyhydroxybutyrate (PHB) biosynthetic operon, results in a significant growth defect on VFA substrates. Using adaptive laboratory evolution, we identify mutations to the regulator gene phaR, the two-component response regulator-histidine kinase pair encoded by H16_A1372/H16_A1373, and the tripartite transporter assembly encoded by H16_A2296-A2298 as causative for improved growth on VFA substrates. Deletion of phaR and H16_A1373 led to significantly reduced NADH abundance accompanied by large changes to expression of genes involved in carbon metabolism, balance of electron carriers, and oxidative stress tolerance that may be responsible for improved growth of these engineered strains. These results provide insight into the role of PHB biosynthesis in carbon and energy metabolism and highlight a key role for the regulator PhaR in global regulatory networks. By combining mutations, we generated platform strains with significant growth improvements on VFAs, which can enable improved conversion of waste-derived VFA substrates to target bioproducts.

09 BIOMASS FUELS↗

Effects of Low Dose Space Radiation Exposures on the Splenic Metabolome

Future space missions will include a return to the Moon and long duration deep space roundtrip missions to Mars. Leaving the protection that Low Earth Orbit provides will unavoidably expose astronauts to higher cumulative doses of space radiation, in addition to other stressors, e.g., microgravity. Immune regulation is known to be impacted by both radiation and spaceflight and it remains to be seen whether prolonged effects that will be encountered in deep space can have an adverse impact on health. In this study, we investigated the effects in the overall metabolism of three different low dose radiation exposures (γ-rays, 16O, and 56Fe) in spleens from male C57BL/6 mice at 1, 2, and 4 months after exposure. Forty metabolites were identified with significant enrichment in purine metabolism, tricarboxylic acid cycle, fatty acids, acylcarnitines, and amino acids. Early perturbations were more prominent in the γ irradiated samples, while later responses shifted towards more prominent responses in groups with high energy particle irradiations. Regression analysis showed a positive correlation of the abundance of identified fatty acids with time and a negative association with γ-rays, while the degradation pathway of purines was positively associated with time. Taken together, there is a strong suggestion of mitochondrial implication and the possibility of long-term effects on DNA repair and nucleotide pools following radiation exposure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Plant Hosts Modify Belowground Microbial Community Response to Extreme Drought

Drought stress negatively impacts microbial activity, yet the magnitude of stress responses is likely dependent on a diversity of belowground interactions. Populus trichocarpa individuals and no-plant bulk soils were exposed to extended drought (~0.03% gravimetric water content [GWC] after 12 days), rewet, and a 12-day “recovery” period to determine the effects of plant presence in mediating soil microbiome stability to water stress. Plant metabolomic analyses indicated that drought exposure increased host investment in C and N metabolic pathways (amino acids, fatty acids, phenolic glycosides) regardless of recovery. Several metabolites positively correlated with root-associated microbial alpha-diversity, but not those of soil communities. Soil bacterial community composition shifted with P. trichocarpa presence and with drought relative to irrigated controls, whereas soil fungal composition shifted only with plant presence. However, root fungal communities strongly shifted with drought, whereas root bacterial communities changed to a lesser degree. The proportion of bacterial water-stress opportunistic operational taxonomic units (OTUs) (enriched counts in drought) was high (~11%) at the end of drying phases and maintained after rewet and recovery phases in bulk soils, but it declined over time in soils with plants present. For root fungi, opportunistic OTUs were high at the end of recovery in drought treatments (~17% abundance), although relatively not responsive in soils, particularly planted soils (<0.5% abundance for sensitive or opportunistic). These data indicate that plants modulate soil and root-associated microbial drought responses via tight plant-microbe linkages during extreme drought scenarios, but trajectories after extreme drought vary with plant habitat and microbial functional groups.

59 BASIC BIOLOGICAL SCIENCES↗

Coordinate up-regulation of low-density lipoprotein receptor and cyclo-oxygenase-2 gene expression in human colorectal cells and in colorectal adenocarcinoma biopsies

Many colorectal cancers have high levels of cyclo-oxygenase 2 (COX-2), an enzyme that metabolizes the essential fatty acids into prostaglandins. Since the low-density lipoprotein receptor (LDLr) is involved in the uptake of essential fatty acids, we studied the effect of LDL on growth and gene regulation in colorectal cancer cells. DiFi cells grown in lipoprotein-deficient sera (LPDS) grew more slowly than cells with LDL. LDLr antibody caused significant inhibition of tumor cell growth but did not affect controls. In addition, LDL uptake did not change in the presence of excess LDL, suggesting that ldlr mRNA lacks normal feedback regulation in some colorectal cancers. Analysis of the ldlr mRNA showed that excess LDL in the medium did not cause down-regulation of the message even after 24 hr. The second portion of the study examined the mRNA expression of ldlr and its co-regulation with cox-2 in normal and tumor specimens from patients with colorectal adenocarcinomas. The ratio of tumor:paired normal mucosa of mRNA expression of ldlr and of cox-2 was measured in specimens taken during colonoscopy. ldlr and cox-2 transcripts were apparent in 11 of 11 carcinomas. There was significant coordinate up-regulation both of ldlr and of cox-2 in 6 of 11 (55%) tumors compared with normal colonic mucosa. There was no up-regulation of cox-2 without concomitant up-regulation of ldlr. These data suggest that the LDLr is abnormally regulated in some colorectal tumors and may play a role in the up-regulation of cox-2. Copyright 1999 Wiley-Liss, Inc.

Non-NASA Center↗

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↗

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↗

Transcriptomics-based Machine Learning (ML) 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% 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↗