1. Cell Biology
  2. Computational and Systems Biology

Multi-omic analysis of bat versus human fibroblasts reveals altered central metabolism

  1. N Suhas Jagannathan
  2. Javier Yu Peng Koh
  3. Younghwan Lee
  4. Radoslaw Mikolaj Sobota
  5. Aaron T Irving
  6. Lin-fa Wang
  7. Yoko Itahana  Is a corresponding author
  8. Koji Itahana  Is a corresponding author
  9. Lisa Tucker-Kellogg  Is a corresponding author
  1. Cancer and Stem Cell Biology Programme, Duke-NUS Medical School, Singapore
  2. Centre for Computational Biology, Duke-NUS Medical School, Singapore
  3. Functional Proteomics Laboratory, Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research, Singapore
  4. Programme in Emerging Infectious Diseases, Duke-NUS Medical School, Singapore
  5. Zhejiang University-University of Edinburgh Institute, Zhejiang University School of Medicine, Zhejiang University, China
  6. SingHealth Duke-NUS Global Health Institute, Singapore
https://doi.org/10.7554/eLife.94007
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Cite this article
  1. N Suhas Jagannathan
  2. Javier Yu Peng Koh
  3. Younghwan Lee
  4. Radoslaw Mikolaj Sobota
  5. Aaron T Irving
  6. Lin-fa Wang
  7. Yoko Itahana
  8. Koji Itahana
  9. Lisa Tucker-Kellogg
(2024)
Multi-omic analysis of bat versus human fibroblasts reveals altered central metabolism
eLife 13:e94007.
https://doi.org/10.7554/eLife.94007
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14 figures, 6 tables and 11 additional files

Figures

Figure 1 with 2 supplements
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RNAseq data analysis of PaLung and WI-38 cells for differential expression and pathway enrichment.

(A) Workflow of bioinformatics analysis pipeline for RNAseq data from PaLung (P. alecto) and WI-38 (H. sapiens) cells (n=3). (B) Heatmap showing the expression patterns for genes that passed our differential expression thresholds in the three WI-38 samples (W1–W3) and the three PaLung samples (P1–P3). (C and D) Gene set enrichment analysis (GSEA) identifies respiratory electron transport and cellular response to hypoxia as top metabolic pathways that are differentially regulated between PaLung and WI-38 cells. Shown here are the enrichment score plots for (C) respiratory electron transport and (D) cellular response to hypoxia.

Figure 1—figure supplement 1
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Analysis of transcriptomics data from PaLung and WI-38 cells.

(A) Principal component analysis plots showing the separation of the PaLung and WI-38 transcriptomics datasets. (B) Volcano plot showing the genes that passed our differential expression (DE) thresholds (|log2 fold change [LFC]| ≥ 1 and false discovery rate (FDR) < 0.05). (C) Correlation between the gene fold changes estimated via the DESeq2 pipeline and the gene length corrected trimmed mean of M-values (GeTMM) pipeline (see Materials and methods). The points on the X-axis represent LFC of genes that passed our DE thresholds in DESeq2 but not in GeTMM. Conversely, points on the Y-axis represent genes that passed our DE thresholds in the GeTMM pipeline but not in the DESeq2 pipeline. (D) Pie chart showing that the gene set enrichment analysis (GSEA)-suggested upregulation of electron transport chain (ETC) in transcriptomics data is a result dominated by genes corresponding to Complex I subunits. C1=Complex I; C2=Complex II; C3=Complex III; C4=Complex IV of the ETC.

Figure 1—figure supplement 2
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A summary of transcriptomics log fold changes (LFC) overlaid onto key metabolic reactions from central carbon metabolism.

Shown here is a network of reactions in central carbon metabolism that includes reactions from multiple pathways (glycolysis, pentose phosphate pathway, TCA cycle, fatty acid oxidation, electron transport chain, urea cycle, and malate aspartate shuttle). The nodes of the network indicate metabolites and are shown as purple boxes with black outlines. The black arrows represent metabolic reactions. The colored boxes adjacent to the arrows represent the LFC of a gene involved in catalyzing the specific metabolic reaction (subunit/isoform/alternate genes). The number of boxes corresponds to the total number of genes participating in the reaction. The color scale indicates the extent to which the gene is upregulated in PaLung (blue) or WI-38 (red). Gray squares indicate that a particular gene/transcript implicated in the reaction was either not detected or did not pass our differential expression thresholds between PaLung and WI-38 samples.

Figure 2 with 1 supplement
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Proteomic data analysis of mitochondrial fractions of PaLung and WI-38 cells for differential expression, pathway enrichment, and electron transport chain (ETC) activity.

(A) Workflow of bioinformatics analysis pipeline for proteomics data from PaLung (P. alecto) and WI-38 (H. sapiens) cells (n=3). (B) Heatmap showing the expression patterns for the 129 differentially expressed mitochondrial proteins in the three WI-38 samples (W1–W3) and the three PaLung samples (P1–P3). (C) Differentially expressed mitochondrial proteins (nodes colored by log fold change) are overlaid on a network of mitochondrial protein-protein interactions (obtained from STRING) (W=WI-38 cells, P=PaLung cells). The nodes are then clustered with respect to reactome-annotated pathways. (D and E) Gene set enrichment analysis (GSEA) identifies citric acid cycle, oxidative phosphorylation, and Complex I biogenesis as top metabolic pathways that are differentially regulated between PaLung and WI-38 cells. Shown here are the enrichment score plots for (D) citric acid cycle and oxidative phosphorylation and (E) Complex I biogenesis. (F) Oxygen consumption rate (OCR) measurement of PaLung cells (blue) and WI-38 cells (red) plotted as mean ± SD from n>15 independent experiments. O=oligomycin, F=FCCP, R+A = rotenone+antimycin A. (G) Pie chart showing that the proteomic upregulation of the ETC, implied by GSEA, is dominated by genes for subunits of Complex I. C1=Complex I; C3=Complex III; C4=Complex IV of the ETC.

Figure 2—figure supplement 1
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Proteomic analysis of PaLung and WI-38 data.

(A) Gene ontology category cellular (GO CC) enrichment using the Enrichr tool for compartmental enrichment on the 1469 genes detected in the proteomics dataset shows enrichment for the mitochondrial compartment. (B) Volcano plot showing the differentially expressed (DE) proteins (|log2 fold change| ≥ 1 and false discovery rate (FDR) < 0.05). Gray dots represent non-mitochondrial proteins that are not differentially expressed. Black dots show non-mitochondrial proteins that are differentially expressed. Red dots show all mitochondrial proteins.

Figure 3 with 2 supplements
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Metabolomic data and model-based analysis of mitochondrial metabolism in PaLung and WI-38 cells.

(A) Schematic showing the metabolic modeling pipeline. We begin with the context-specific reconstruction of a metabolic model – the process where a generic mitochondrial model (Smith et al., 2017) is tailored specifically to PaLung and WI-38 cells using proteomic and transcriptomic expression patterns. The individual metabolic models are then simulated using constraint-based flux sampling methods to give a distribution of possible fluxes for each reaction in the model. Comparing these flux distributions between the two models allows the detection of metabolic reactions that are likely to be differentially regulated in response to user-imposed constraints on metabolism. Simulations are performed on both the PaLung and WI-38 models under no constraints or with constraints on Complex I and mitochondrial O2 intake. (B) Sample histograms showing the feasible flux distributions for electron transport chain (ETC) reactions in the unconstrained PaLung (P) and WI-38 (W) metabolic models. (C) Flux distributions of ETC reactions in PaLung (P) and WI-38 (W) cells when PaLung cells are constrained to have higher flux through Complex I of ETC but lower oxygen intake in the mitochondria. (D) Heatmap showing differentially regulated metabolites from central carbon metabolism in the three WI-38 (W1–W3) and the three PaLung (P1–P3) samples. (E) Intra-sample ratios of metabolites from the TCA cycle in either PaLung (P) or WI-38 (W) cells, plotted as mean ± SD from three independent experiments. * and *** represent p-value≤0.05 or ≤0.001 respectively (unpaired Student’s two-sided t-test with Benjamini-Hochberg correction for multiple hypothesis testing).

Figure 3—figure supplement 1
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Schematic of the workflow for metabolic flux modeling.

(A) Left is a toy network of metabolic reactions showing a few reactions from the glycolysis pathway for illustration. This network of metabolic reactions (defined by nodes and arrows) is converted from the format of reaction equations into the format of a matrix called the stoichiometric matrix. Each row of the matrix represents a single metabolite in the network and each column represents a single reaction in the network. The (i,j) element of matrix S represents the stoichiometric coefficient of metabolite i in reaction j. Stoichiometric coefficients indicate how many copies of each reactant or product are consumed or produced when the reaction is utilized (often 1 or –1). By converting all the reactions of a metabolic network into this matrix format, a single column represents a single reaction in the metabolic network, including all metabolites produced or consumed in it. For example, the first column of the matrix (corresponding to reaction R1) has the value of –1 for the Glc row and 1 for G6P row, indicating that in reaction R1, one unit of Glc is consumed and one unit of G6P is produced. Likewise, each row of the matrix represents all sources of production and consumption for a single metabolite. For example, the second row of the matrix (corresponding to the metabolite G6P) has the values of 1, –1, and –1 for the reactions R1, R2, and R3, respectively. This indicates that one unit of G6P is produced in reaction R1, while one unit of G6P is consumed in reactions R2 and R3, respectively. (B) Example of a flux vector. A flux vector contains as many elements as the number of reactions in the network, and their values are typically unknown. Flux values indicate the rate at which the corresponding reaction is utilized (a forward reaction is a positive flux, and a reverse reaction is a negative flux). The goal of flux sampling is to obtain values (or distributions) for each element of the flux vector. (C) The product of the stoichiometric matrix and the flux vector (written as the product Sv) results in a set of linear algebra equations. Each linear equation has flux variables and stoichiometric coefficients. If we assume the flux is balanced in the system, then everything produced has somewhere to go or some reaction to consume it. This steady-state assumption is described mathematically by setting the matrix product to zero (Sv = 0). (D) The process of flux sampling searches the space of possible flux vectors v to find those that satisfy Sv = 0. Sampling is typically necessary because the set of equations from (C) is usually underdetermined and does not have a unique solution for the flux variables v. The computational sampling process outputs flux vectors that satisfy the equations (i.e. feasible vectors), and that also capture a diversity of feasible behaviors for how the metabolic network could achieve steady state. From this set of flux vectors, we plot histograms (distributions) of the feasible values of the flux variable for each reaction in the metabolic network. This theoretical delineation of feasible behaviors can be used for drawing biological inferences by assuming that the actual biological pathway activity falls within the feasible range of the theoretical model. When comparing two models such as bat and human, inferences arise from reactions or pathways where feasible behaviors become infeasible or vice versa. This can be seen by identifying reactions, whose flux histograms show large differences between the human and bat models.

Figure 3—figure supplement 2
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Analysis of the metabolomics data from PaLung and WI-38 cells.

(A) Principal component analysis (PCA) plot shows the separation between the three PaLung samples and the three WI-38 samples in the metabolomics data. (B–D) Bar plots of AMP (B), the ratios of Glu/Gln (C), and Asp/Asn (D) in PaLung cells (P, blue) and WI-38 cells (W, red) are shown. Bars are the mean ± SD from three independent experiments (n=3). ** or *** represents p-value≤0.01 or ≤0.001 respectively (unpaired Student’s two-sided t-test). Note that PaLung cells have a higher glutamate-to-glutamine ratio and a lower aspartate-to-asparagine ratio than WI-38 cells. This is also supported by the high expression of the gene ASNS (asparagine synthetase) in PaLung cells (Supplementary file 1). ASNS is a cytoplasmic enzyme that can convert aspartate to asparagine concomitantly with the hydrolysis of glutamine to glutamate, allowing for glutamate to be used as an alternative source for energy, or as a precursor metabolite for the synthesis of glutathione.

Figure 4
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PaLung cells show basal metabolism that resembles an ischemic-like state.

(A) Fold changes (mean PaLung/mean WI-38) of metabolite abundances, obtained through targeted metabolomics profiling of central carbon metabolites in PaLung and WI-38 cells. (B and C) Amino acid and metabolite changes in TCA cycle (PaLung/WI-38) in PaLung cells and WI-38 cells. (D) Bar plots of AMP/(AMP+ADP+ATP), ATP/ADP ratio, and lactate amounts in PaLung (P) and WI-38 (W) cells plotted as mean ± SD from three independent experiments. ** and *** represent p-value≤0.01 or ≤0.001 respectively (unpaired Student’s two-sided t-test with Benjamini-Hochberg correction for multiple hypothesis testing). (E) Extracellular acidification rate (ECAR) measurement of PaLung (blue) and WI-38 (red) cells plotted as mean ± SD from n>15 independent experiments (2-DG=2-deoxy-D-glucose). (F) Phase contrast images of PaLung and WI-38 cells with or without glucose deprivation for 96 hr. Scale bar, 100 µm.

Figure 5 with 1 supplement
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Reactive oxygen species (ROS) and antioxidant system measurements in PaLung and WI-38 cells.

(A) MitoSOX measurement of PaLung and WI-38 cells with or without antimycin A treatment for 1 hr. Antimycin A is an electron transport chain (ETC) inhibitor known to induce superoxide generation. (B) Bar charts showing the expression levels of antioxidant genes that passed our differential expression thresholds (as transcripts per million [TPM]) in PaLung (blue) and WI-38 (red) cells. Genes have been sorted in increasing order of P/W fold change. (C) Bar plots show the ratio of reduced to oxidized glutathione (GSH/GSSG), total glutathione normalized to protein content (GSH+GSSG), and the ratio of NADPH/NADP in PaLung (P) and WI-38 (W) cells. For all panels, bars are the mean ± SD from three independent experiments (n=3). *, **, or *** represents p-value<0.05, ≤0.01, or ≤0.001 respectively (unpaired Student’s two-sided t-test with Benjamini-Hochberg correction for multiple hypothesis testing).

Figure 5—figure supplement 1
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Increased phosphorylation of NAD cofactors in PaLung cells.

(A) Ratio of NADP/NAD in PaLung (P) and WI-38 (W) cells. (B) Bar graphs show the expression levels of the genes involved in NAD synthesis and phosphorylation (as transcripts per million [TPM]) in PaLung (blue) and WI-38 (red) cells. *, **, or *** represents p-value≤0.05, ≤0.01, or ≤0.001 respectively (unpaired Student’s two-sided t-test with Benjamini-Hochberg correction for multiple hypothesis testing).

Figure 6
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PaLung cells display high resistance to ferroptosis.

(A and B) WI-38 or PaLung cells were treated with 2.5 µM erastin and/or 1 µM ferrostatin-1. Representative images were taken at 6 hr using phase contrast microscopy (A). Propidium iodide (PI) exclusion assay was performed 24 hr after erastin treatment (B). (C and D) WI-38 or PaLung cells were cultured in media with or without cystine. Ferrostatin-1 (1 µM) was treated simultaneously. Representative images were taken at 8 hr using phase contrast microscopy (C). PI exclusion assay was performed at 24 hr after cystine deprivation (D). (E–H) WI-38 or PaLung cells were cultured in media with or without cystine for 6 hr. Intracellular glutathione levels were measured. Reduced glutathione (GSH)/oxidized glutathione (GSSG) ratio (E), total glutathione (the sum of GSH and GSSG) (F), GSH (G), and GSSG (H) levels were measured. Scale bars, 50 μm. The mean ± SD of three independent experiments is shown.

Appendix 1—figure 1
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Gene set enrichment analysis (GSEA) enrichment plots of the same gene sets as shown in Figure 2D and E of the main text, after removing the outlier proteomic samples P1 and W1 from the analysis (n=2).
Appendix 2—figure 1
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Gene set enrichment analysis (GSEA) enrichment plots of the gene sets that are biological equivalents of the gene sets shown in Figure 2D and E of the main text.

This analysis was performed using the MitoCarta 3.0 gene set list instead of the Gene Ontology Biological Process (GO BP) gene set list used to generate Figure 2D and E.

Appendix 3—figure 1
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Flux sampling histograms of the P and W metabolic models in the unconstrained control (left column) and the constrained (right column) cases.

The P fluxes are in blue and the W fluxes in red. The first two rows show the constrained reactions (Complex I and O2), while the third row shows the flux histograms of the Complex II reaction.

Appendix 3—figure 2
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Flux sampling histograms of the P and W metabolic models under different threshold values for constraints.

Each row corresponds to a constraint scheme (unconstrained, 20-80, 30-70, 40-60, and 50-50), and was obtained by running our script with different minFrac values. Within each panel, the P model fluxes are in blue and the W model fluxes in red. The leftmost column shows the flux histograms of the Complex I reaction, the middle column shows flux histograms of the mitochondrial O2 transport reaction, and the rightmost column shows the flux histograms of the Complex II reaction.

Author response image 1
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Histograms of coefficient-of-variation (CV) for the four protein subsets (defined by p-value and log fold change).

Each row corresponds to a different subset indicated by the title above. Within each row, the blue histogram corresponds to the CV from the 3 PaLung samples, and the red histogram corresponds to the CV from the 3 WI-38 samples. The one major discrepancy is that the histogram for the WI-38 samples in subset 1 (Row one right panel), contains much higher values than the other 7 histograms.

Author response image 2
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Histograms of log(abundance) for the four protein subsets (defined by p-value and log fold change) for WI-38 samples.

Each panel corresponds to a different subset indicated by the title above.

Author response image 3
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Scatter plot of Log fold change of proteomic data vs Log fold change of transcriptomics data.

Each point represents a protein/gene that was detected in both proteomics and transcriptomics experiments. Log fold change was computed in each case as log2(PaLung /WI-38).

Author response image 4
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Flux histograms showing the feasible flux distributions for the Complex I (CI) and the mitochondrial oxygen transport (O2) reactions in the P (PaLung) and W (WI-38) metabolic models.

Each column corresponds to setting constraints as per one of the four experiments described above.

Tables

Appendix 1—table 1
Table showing the top 35 gene sets enriched in the PaLung mitochondrial proteomics samples, after removing the outlier samples P1 and W1.

Columns indicate the name of the gene set, size (number of genes in gene set), normalized enrichment score, and the false discovery rate (FDR) value.

NAMESIZENESFDR q-val
MUSCLE CONTRACTION GOBP GO:000693635–2.3010.002
NICOTINIC ACETYLCHOLINE RECEPTOR SIGNALING PATHWAY PANTHER PATHWAY P0004415–2.1910.012
REGULATION OF CELL JUNCTION ASSEMBLY GOBP GO:190188815–2.1630.011
THE CITRIC ACID (TCA) CYCLE AND RESPIRATORY ELECTRON TRANSPORT REACTOME R-HSA-1428517.164–2.1540.011
HALLMARK_OXIDATIVE_PHOSPHORYLATION MSIGDB_C2 HALLMARK_OXIDATIVE_PHOSPHORYLATION91–2.1420.010
COLLAGEN FORMATION REACTOME DATABASE ID RELEASE 71 147429031–2.0960.015
RESPIRATORY ELECTRON TRANSPORT, ATP SYNTHESIS BY CHEMIOSMOTIC COUPLING, AND HEAT PRODUCTION BY UNCOUPLING PROTEINS. REACTOME R-HSA-163200.132–2.0740.017
RESPIRATORY ELECTRON TRANSPORT REACTOME R-HSA-611105.332–2.0590.019
NADH DEHYDROGENASE COMPLEX ASSEMBLY GOBP GO:001025716–2.0590.017
EPH-EPHRIN SIGNALING REACTOME DATABASE ID RELEASE 71 268233430–2.0570.016
COMPLEX I BIOGENESIS REACTOME R-HSA-6799198.116–2.0360.018
MUSCLE SYSTEM PROCESS GOBP GO:000301240–2.0330.017
RHO GTPASES ACTIVATE PKNS REACTOME DATABASE ID RELEASE 71 562574017–2.0300.016
ACTIN FILAMENT-BASED MOVEMENT GOBP GO:003004817–2.0220.017
ACTOMYOSIN STRUCTURE ORGANIZATION GOBP GO:003103221–2.0210.016
MITOCHONDRIAL RESPIRATORY CHAIN COMPLEX I ASSEMBLY GOBP GO:003298116–1.9990.019
INTEGRIN SIGNALLING PATHWAY PANTHER PATHWAY P0003444–1.9940.019
KERATINIZATION GOBP GO:003142415–1.9760.021
MITOCHONDRIAL ATP SYNTHESIS COUPLED ELECTRON TRANSPORT GOBP GO:004277526–1.9710.021
MITOCHONDRIAL ELECTRON TRANSPORT, NADH TO UBIQUINONE GOBP GO:000612015–1.9680.021
MITOCHONDRIAL RESPIRATORY CHAIN COMPLEX ASSEMBLY GOBP GO:003310819–1.9660.020
CORNIFICATION GOBP GO:007026815–1.9630.019
SYSTEM PROCESS GOBP GO:000300877–1.9530.021
MIDBRAIN DEVELOPMENT GOBP GO:003090115–1.9530.020
COLLAGEN BIOSYNTHESIS AND MODIFYING ENZYMES REACTOME R-HSA-1650814.325–1.9490.020
KERATINIZATION REACTOME DATABASE ID RELEASE 71 680556715–1.9360.022
ELECTRON TRANSPORT CHAIN (OXPHOS SYSTEM IN MITOCHONDRIA) WIKIPATHWAYS_20191210 WP111 HOMO SAPIENS28–1.9330.022
NABA_CORE_MATRISOME MSIGDB_C2 NABA_CORE_MATRISOME34–1.9210.025
CELLULAR RESPIRATION GOBP GO:004533352–1.9050.028
HALLMARK_ESTROGEN_RESPONSE_EARLY MSIGDB_C2 HALLMARK_ESTROGEN_RESPONSE_EARLY16–1.9020.028
EXTRACELLULAR MATRIX ORGANIZATION REACTOME DATABASE ID RELEASE 71 147424459–1.8950.029
RHO GTPASES ACTIVATE PAKS REACTOME R-HSA-5627123.216–1.8630.038
INFLAMMATION MEDIATED BY CHEMOKINE AND CYTOKINE SIGNALING PATHWAY PANTHER PATHWAY P0003130–1.8480.043
ELECTRON TRANSPORT CHAIN GOBP GO:002290031–1.8420.044
ATP SYNTHESIS COUPLED ELECTRON TRANSPORT GOBP GO:004277327–1.8320.048
Appendix 2—table 1
Table showing the top gene sets enriched in the PaLung mitochondrial proteomics samples, when gene set enrichment analysis (GSEA) was performed using the MitoCarta 3.0 gene set list instead of the Gene Ontology Biological Process (GO BP) gene set.

Columns indicate the name of the gene set, size (number of genes in gene set), normalized enrichment score, and the false discovery rate (FDR) value.

NAMESIZENESFDR q-val
OXPHOS_SUBUNITS34–2.1860.002
OXPHOS41–2.1660.001
CARBOHYDRATE_METABOLISM36–2.0040.005
TRANSLATION16–1.9870.005
COMPLEX_I17–1.9660.005
CI_SUBUNITS15–1.8920.007
FATTY_ACID_OXIDATION20–1.8640.007
METALS_AND_COFACTORS30–1.7940.011
METABOLISM153–1.7820.011
AMINO_ACID_METABOLISM33–1.7630.011
MITOCHONDRIAL_CENTRAL_DOGMA24–1.6090.030
TCA_CYCLE15–1.5100.053
LIPID_METABOLISM43–1.4800.057
PROTEIN_IMPORT_SORTING_AND_HOMEOSTASIS24–1.2000.223
PROTEIN_HOMEOSTASIS18–1.0530.377
Appendix 3—table 1
Minimum and maximum flux values possible for the Complex I and mitochondrial O2 transport reaction when different constraints are applied to the P and W metabolic models.
Constraint description (all follow the 30-70 protocols)Complex 1Oxygen transport
Bat (P model)Human (W model)Bat (P model)Human (W model)
MinMaxMinMaxMinMaxMinMax
Control simulation – no constraints041.43041.44019.8019.8
Ideal target flux range expected with the 30-70 protocol, when CI and O2 are independent of each other2941.43012.4305.9413.8619.8
Constraining only the Complex I reaction2941.43012.4313.5819.8019.8
Constraining only the O2 reaction013.74041.4305.9413.8619.8
Constraining CI first, then constraining O2, without avoiding flux range overlap2932.72012.4313.5815.4513.8619.8
Constraining CI first, then constraining O2, avoiding flux range overlap2932.72012.4313.5815.4515.4519.8
Constraining O2 first, then constraining CI, without avoiding flux range overlap9.6113.73012.433.885.9413.8619.8
Constraining O2 first, then constraining CI, avoiding flux range overlap9.6113.7309.613.885.9413.8619.8
Author response table 1
NAMESIZENESFDR
L13A-MEDIATED TRANSLATIONAL SILENCING OF CERULOPLASMIN EXPRESSION REACTOME DATABASE ID RELEASE 71 156827102-1.864590
CAP-DEPENDENT TRANSLATION INITIATION REACTOME DATABASE ID RELEASE 71 72737110-1.852110
TRANSLATIONAL INITIATION GOBP GO:0006413114-1.84460
REGULATION OF EXPRESSION OF SLITS AND ROBOS REACTOME DATABASE ID RELEASE 71 9010553153-1.842660
NONSENSE MEDIATED DECAY (NMD) ENHANCED BY THE EXON JUNCTION COMPLEX (EJC) REACTOME R-HSA-975957.1107-1.837960
GTP HYDROLYSIS AND JOINING OF THE 60S RIBOSOMAL SUBUNIT REACTOME R-HSA-72706.2103-1.829110
NUCLEAR-TRANSCRIBED MRNA CATABOLIC PROCESS GOBP GO:0000956179-1.821340
MRNA CATABOLIC PROCESS GOBP GO:0006402191-1.818240
EUKARYOTIC TRANSLATION INITIATION REACTOME DATABASE ID RELEASE 71 72613110-1.816180
EUKARYOTIC TRANSLATION ELONGATION REACTOME R-HSA-156842.285-1.816110
SRP-DEPENDENT COTRANSLATIONAL PROTEIN TARGETING TO MEMBRANE REACTOME R-HSA-1799339.2103-1.815780
SELENOCYSTEINE SYNTHESIS REACTOME DATABASE ID RELEASE 71 240855784-1.814450
NONSENSE-MEDIATED DECAY (NMD) REACTOME R-HSA-927802.2107-1.812510
TRANSLATION REACTOME DATABASE ID RELEASE 71 72766278-1.81210
RNA CATABOLIC PROCESS GOBP GO:0006401214-1.810420
PEPTIDE BIOSYNTHETIC PROCESS GOBP GO:0043043313-1.80980
FORMATION OF A POOL OF FREE 40S SUBUNITS REACTOME DATABASE ID RELEASE 71 7268992-1.80980
INFLUENZA LIFE CYCLE REACTOME DATABASE ID RELEASE 71 168255129-1.809350
RESPONSE OF EIF2AK4 (GCN2) TO AMINO ACID DEFICIENCY REACTOME DATABASE ID RELEASE 71 963301292-1.808560
VIRAL MRNA TRANSLATION REACTOME DATABASE ID RELEASE 71 19282381-1.808410
PEPTIDE CHAIN ELONGATION REACTOME R-HSA-156902.281-1.807430
NONSENSE MEDIATED DECAY (NMD) INDEPENDENT OF THE EXON JUNCTION COMPLEX (EJC) REACTOME R-HSA-975956.187-1.804990
INFLUENZA VIRAL RNA TRANSCRIPTION AND REPLICATION REACTOME DATABASE ID RELEASE 71 168273121-1.803810
PROTEIN LOCALIZATION TO ENDOPLASMIC RETICULUM GOBP GO:0070972119-1.801930
NUCLEAR-TRANSCRIBED MRNA CATABOLIC PROCESS, NONSENSE-MEDIATED DECAY GOBP GO:0000184107-1.799580
SIGNALING BY ROBO RECEPTORS REACTOME R-HSA-376176.4193-1.796570
INFLUENZA INFECTION REACTOME R-HSA-168254.2139-1.794220
TRANSLATION GOBP GO:0006412296-1.792320
EUKARYOTIC TRANSLATION TERMINATION REACTOME R-HSA-72764.485-1.790570
SELENOAMINO ACID METABOLISM REACTOME DATABASE ID RELEASE 71 2408522105-1.77470
MAJOR PATHWAY OF RRNA PROCESSING IN THE NUCLEOLUS AND CYTOSOL REACTOME R-HSA-6791226.3170-1.773210
ESTABLISHMENT OF PROTEIN LOCALIZATION TO ENDOPLASMIC RETICULUM GOBP GO:0072599100-1.769310
COTRANSLATIONAL PROTEIN TARGETING TO MEMBRANE GOBP GO:000661390-1.76820
RRNA PROCESSING IN THE NUCLEUS AND CYTOSOL REACTOME R-HSA-8868773.3180-1.763632.64E-05
PROTEIN TARGETING TO MEMBRANE GOBP GO:0006612134-1.760382.57E-05
CYTOPLASMIC RIBOSOMAL PROTEINS WIKIPATHWAYS_20191210 WP477 HOMO SAPIENS82-1.757834.97E-05
PROTEIN TARGETING TO ER GOBP GO:004504797-1.754467.25E-05
VIRAL GENE EXPRESSION GOBP GO:0019080122-1.753517.06E-05
SRP-DEPENDENT COTRANSLATIONAL PROTEIN TARGETING TO MEMBRANE GOBP GO:000661485-1.750316.88E-05
VIRAL TRANSCRIPTION GOBP GO:0019083105-1.747016.71E-05
ACTIVATION OF THE MRNA UPON BINDING OF THE CAP-BINDING COMPLEX AND EIFS, AND SUBSEQUENT BINDING TO 43S REACTOME R-HSA-72662.356-1.743448.72E-05
AMIDE BIOSYNTHETIC PROCESS GOBP GO:0043604383-1.740848.51E-05
NUCLEOBASE-CONTAINING COMPOUND CATABOLIC PROCESS GOBP GO:0034655322-1.735961.04E-04
CYTOPLASMIC TRANSLATION GOBP GO:000218154-1.734361.02E-04
RRNA PROCESSING REACTOME DATABASE ID RELEASE 71 72312189-1.733649.96E-05
TRANSLATION INITIATION COMPLEX FORMATION REACTOME DATABASE ID RELEASE 71 7264955-1.731839.75E-05
RIBOSOMAL SCANNING AND START CODON RECOGNITION REACTOME R-HSA-72702.355-1.714262.11E-04
PROTEIN TARGETING GOBP GO:0006605283-1.709042.62E-04
FORMATION OF THE TERNARY COMPLEX, AND SUBSEQUENTLY, THE 43S COMPLEX REACTOME DATABASE ID RELEASE 71 7269548-1.703254.03E-04
CELLULAR NITROGEN COMPOUND CATABOLIC PROCESS GOBP GO:0044270345-1.697844.66E-04
ESTABLISHMENT OF PROTEIN LOCALIZATION TO ORGANELLE GOBP GO:0072594325-1.693916.33E-04
AROMATIC COMPOUND CATABOLIC PROCESS GOBP GO:0019439347-1.69256.90E-04
PEPTIDE METABOLIC PROCESS GOBP GO:0006518392-1.691966.94E-04
CELLULAR RESPONSES TO STRESS REACTOME DATABASE ID RELEASE 71 2262752456-1.675910.001561
HETEROCYCLE CATABOLIC PROCESS GOBP GO:0046700343-1.668490.00217
ESTABLISHMENT OF PROTEIN LOCALIZATION TO MEMBRANE GOBP GO:0090150215-1.667140.00226
CELLULAR RESPONSES TO EXTERNAL STIMULI REACTOME DATABASE ID RELEASE 71 8953897459-1.667080.002236
ORGANIC CYCLIC COMPOUND CATABOLIC PROCESS GOBP GO:1901361365-1.648690.004763
CALNEXIN CALRETICULIN CYCLE REACTOME R-HSA-901042.223-1.604740.022972
N-GLYCAN TRIMMING IN THE ER AND CALNEXIN CALRETICULIN CYCLE REACTOME DATABASE ID RELEASE 71 53266832-1.599810.026341
RIBOSOMAL LARGE SUBUNIT BIOGENESIS GOBP GO:004227364-1.585470.041181
OXYGEN-DEPENDENT PROLINE HYDROXYLATION OF HYPOXIA-INDUCIBLE FACTOR Α REACTOME DATABASE ID RELEASE 71 123417661-1.582140.045084
ER QUALITY CONTROL COMPARTMENT (ERQC) REACTOME DATABASE ID RELEASE 71 90103218-1.575590.054216
RIBOSOME ASSEMBLY GOBP GO:004225549-1.565630.071338
AMINO ACID AND DERIVATIVE METABOLISM REACTOME R-HSA-71291.6282-1.564870.071759
CITRIC ACID CYCLE (TCA CYCLE) REACTOME DATABASE ID RELEASE 71 7140322-1.561280.078243
TRANSLATION FACTORS WIKIPATHWAYS_20191210 WP107 HOMO SAPIENS48-1.559850.079794
REGULATION OF TP53 DEGRADATION REACTOME R-HSA-6804757.131-1.556490.086393
VIRAL PROCESS GOBP GO:0016032259-1.55530.08777
SIGNALING BY FGFR4 REACTOME R-HSA-5654743.227-1.554240.088823
REGULATION OF CALCIUM-MEDIATED SIGNALING GOBP GO:005084847-1.550840.095609
NEGATIVE REGULATION OF G0 TO G1 TRANSITION GOBP GO:007031736-1.546150.106841
CYCLIN D ASSOCIATED EVENTS IN G1 REACTOME R-HSA-69231.740-1.545340.107785
G1 PHASE REACTOME DATABASE ID RELEASE 71 6923640-1.544810.107951
INSULIN PROCESSING REACTOME R-HSA-264876.220-1.542260.114012
ERROR-PRONE TRANSLESION SYNTHESIS GOBP GO:004227619-1.537410.126991
TRANSLESION SYNTHESIS BY POLH REACTOME R-HSA-110320.118-1.535060.132554
SMOOTH MUSCLE CONTRACTION REACTOME R-HSA-445355.329-1.533570.136112
CELLULAR RESPONSE TO HYPOXIA REACTOME R-HSA-1234174.269-1.530840.143644
INTERSPECIES INTERACTION BETWEEN ORGANISMS GOBP GO:0044419322-1.530110.144136
DUAL INCISION IN TC-NER REACTOME R-HSA-6782135.162-1.530110.142368
TCA CYCLE (AKA KREBS OR CITRIC ACID CYCLE) WIKIPATHWAYS_20191210 WP78 HOMO SAPIENS18-1.527320.149964
SYMBIONT PROCESS GOBP GO:0044403316-1.526340.151093
REGULATION OF TP53 EXPRESSION AND DEGRADATION REACTOME R-HSA-6806003.132-1.52620.149924
ERROR-FREE TRANSLESION SYNTHESIS GOBP GO:007098719-1.525720.149766
TRANSCRIPTIONAL REGULATION BY E2F6 REACTOME DATABASE ID RELEASE 71 895375034-1.525150.150193
MITOPHAGY REACTOME DATABASE ID RELEASE 71 520564725-1.524020.152274
GAP-FILLING DNA REPAIR SYNTHESIS AND LIGATION IN TC-NER REACTOME DATABASE ID RELEASE 71 678221062-1.521250.161169
ER-PHAGOSOME PATHWAY REACTOME DATABASE ID RELEASE 71 123697469-1.521070.159943
SYNTHESIS OF ACTIVE UBIQUITIN: ROLES OF E1 AND E2 ENZYMES REACTOME R-HSA-8866652.229-1.520270.160681
FCERI MEDIATED NF-ΚB ACTIVATION REACTOME R-HSA-2871837.271-1.518810.164231
SIGNALING BY Fgfr1 REACTOME DATABASE ID RELEASE 71 565473632-1.518360.164082
CENTRAL NERVOUS SYSTEM NEURON DEVELOPMENT GOBP GO:002195429-1.518080.163292
REGULATION OF TP53 ACTIVITY THROUGH METHYLATION REACTOME DATABASE ID RELEASE 71 680476017-1.516840.166234
AUF1 (HNRNP D0) BINDS AND DESTABILIZES MRNA REACTOME DATABASE ID RELEASE 71 45040850-1.515930.167798
B CELL ACTIVATION REACTOME DATABASE ID RELEASE 71 98370592-1.51380.173984
CYTOPLASMIC PATTERN RECOGNITION RECEPTOR SIGNALING PATHWAY GOBP GO:000275332-1.513650.172791
IKK COMPLEX RECRUITMENT MEDIATED BY RIP1 REACTOME DATABASE ID RELEASE 71 93704118-1.513320.172421
VIRION ASSEMBLY GOBP GO:001906835-1.512490.173743
ENDOSOMAL SORTING COMPLEX REQUIRED FOR TRANSPORT (ESCRT) REACTOME R-HSA-917729.127-1.51170.174884
INFECTIOUS DISEASE REACTOME DATABASE ID RELEASE 71 5663205379-1.510830.176457
CHONDROITIN SULFATE METABOLIC PROCESS GOBP GO:003020426-1.509090.181727
PROTEIN LOCALIZATION TO MEMBRANE GOBP GO:0072657357-1.50880.18113
I-KAPPAB KINASE/NF-KAPPAB SIGNALING GOBP GO:000724951-1.508170.181788
TICAM1, RIP1-MEDIATED IKK COMPLEX RECRUITMENT REACTOME R-HSA-168927.317-1.507220.183755
RIBOSOME BIOGENESIS GOBP GO:0042254227-1.506720.183798
VESICLE-MEDIATED TRANSPORT BETWEEN ENDOSOMAL COMPARTMENTS GOBP GO:009892721-1.504470.190799
DEGRADATION OF GLI2 BY THE PROTEASOME REACTOME R-HSA-5610783.156-1.503870.191333
CITRATE METABOLIC PROCESS GOBP GO:000610129-1.503820.189784
ATP METABOLIC PROCESS GOBP GO:0046034136-1.500160.202786
PRADER-WILLI AND ANGELMAN SYNDROME WIKIPATHWAYS_20191210 WP3998 HOMO SAPIENS31-1.498510.20778
MYD88-INDEPENDENT TOLL-LIKE RECEPTOR SIGNALING PATHWAY GOBP GO:000275627-1.498410.206341
ANTIGEN PROCESSING AND PRESENTATION OF PEPTIDE ANTIGEN VIA MHC CLASS I GOBP GO:000247480-1.497620.207835
STABILIZATION OF P53 REACTOME R-HSA-69541.553-1.495150.216427
DOWNSTREAM TCR SIGNALING REACTOME R-HSA-202424.376-1.494420.217854
THE ROLE OF GTSE1 IN G2 M PROGRESSION AFTER G2 CHECKPOINT REACTOME DATABASE ID RELEASE 71 885227657-1.490620.233181
ABC TRANSPORTER DISORDERS REACTOME DATABASE ID RELEASE 71 561908464-1.490370.232277
GAP-FILLING DNA REPAIR SYNTHESIS AND LIGATION IN GG-NER REACTOME R-HSA-5696397.124-1.489480.234394
TNFR2 NON-CANONICAL NF-ΚB PATHWAY REACTOME R-HSA-5668541.280-1.489340.233057
CLEC7A (DECTIN-1) SIGNALING REACTOME R-HSA-5607764.189-1.48910.232317
REGULATION OF G0 TO G1 TRANSITION GOBP GO:007031638-1.487920.235624
NEGATIVE REGULATION OF FGFR4 SIGNALING REACTOME DATABASE ID RELEASE 71 565473319-1.487380.236268
VIF-MEDIATED DEGRADATION OF APOBEC3G REACTOME DATABASE ID RELEASE 71 18058550-1.487110.235552
G1 S DNA DAMAGE CHECKPOINTS REACTOME R-HSA-69615.263-1.487080.233782
GLI3 IS PROCESSED TO GLI3R BY THE PROTEASOME REACTOME R-HSA-5610785.156-1.486930.232652
RESPIRATORY ELECTRON TRANSPORT, ATP SYNTHESIS BY CHEMIOSMOTIC COUPLING, AND HEAT PRODUCTION BY UNCOUPLING PROTEINS. REACTOME R-HSA-163200.1107-1.486720.231731
REGULATION OF TP53 ACTIVITY THROUGH PHOSPHORYLATION REACTOME DATABASE ID RELEASE 71 680475685-1.486420.231313
BUDDING AND MATURATION OF HIV VIRION REACTOME DATABASE ID RELEASE 71 16258824-1.485940.231623
SIGNALING BY FGFR REACTOME R-HSA-190236.261-1.485740.230753
PHOSPHODIESTERASES IN NEURONAL FUNCTION WIKIPATHWAYS_20191210 WP4222 HOMO SAPIENS29-1.483960.237066
P53-DEPENDENT G1 DNA DAMAGE RESPONSE REACTOME DATABASE ID RELEASE 71 6956361-1.483330.237846
PINK PARKIN MEDIATED MITOPHAGY REACTOME R-HSA-5205685.220-1.482280.24089
MFAP5 EFFECT ON PERMEABILITY AND MOTILITY OF ENDOTHELIAL CELLS VIA CYTOSKELETON REARRANGEMENT WIKIPATHWAYS_20191210 WP4560 HOMO SAPIENS16-1.482050.240132
FC EPSILON RECEPTOR (FCERI) SIGNALING REACTOME DATABASE ID RELEASE 71 2454202108-1.482030.238453
OXIDATIVE STRESS INDUCED SENESCENCE REACTOME R-HSA-2559580.465-1.480990.241159
Author response table 2
NAMESIZENESFDR
MUSCLE CONTRACTION GOBP GO:000693635-2.290.008034
NICOTINIC ACETYLCHOLINE RECEPTOR SIGNALING PATHWAY PANTHER PATHWAY P0004415-2.179470.018238
REGULATION OF CELL JUNCTION ASSEMBLY GOBP GO:190188815-2.177820.012159
THE CITRIC ACID (TCA) CYCLE AND RESPIRATORY ELECTRON TRANSPORT REACTOME R-HSA-1428517.164-2.136970.015138
HALLMARK_OXIDATIVE_PHOSPHORYLATION MSIGDB_C2 HALLMARK_OXIDATIVE_PHOSPHORYLATION91-2.123750.013852
COLLAGEN FORMATION REACTOME DATABASE ID RELEASE 71 147429031-2.112950.013374
MUSCLE SYSTEM PROCESS GOBP GO:000301240-2.075220.017172
EPH-EPHRIN SIGNALING REACTOME DATABASE ID RELEASE 71 268233430-2.065530.016753
ACTIN FILAMENT-BASED MOVEMENT GOBP GO:003004817-2.057730.017006
RESPIRATORY ELECTRON TRANSPORT REACTOME R-HSA-611105.332-2.05150.016612
RHO GTPASES ACTIVATE PKNS REACTOME DATABASE ID RELEASE 71 562574017-2.05040.015369
COLLAGEN BIOSYNTHESIS AND MODIFYING ENZYMES REACTOME R-HSA-1650814.325-2.030230.018264
RESPIRATORY ELECTRON TRANSPORT, ATP SYNTHESIS BY CHEMIOSMOTIC COUPLING, AND HEAT PRODUCTION BY UNCOUPLING PROTEINS. REACTOME R-HSA-163200.132-2.02530.017967
COMPLEX I BIOGENESIS REACTOME R-HSA-6799198.116-2.023790.016892
MITOCHONDRIAL RESPIRATORY CHAIN COMPLEX I ASSEMBLY GOBP GO:003298116-2.017050.01673
INTEGRIN SIGNALLING PATHWAY PANTHER PATHWAY P0003444-2.007580.017239
NADH DEHYDROGENASE COMPLEX ASSEMBLY GOBP GO:001025716-1.999190.017902
MITOCHONDRIAL ATP SYNTHESIS COUPLED ELECTRON TRANSPORT GOBP GO:004277526-1.994970.01756
ACTOMYOSIN STRUCTURE ORGANIZATION GOBP GO:003103221-1.985120.018578
KERATINIZATION GOBP GO:003142415-1.962720.024099
Author response table 3
Gene sets enriched in phenotype P (3 samples).
GS follow link to MSigDBGS DETAILSSIZEESNESNOM p-valFOR q-valFWER p-val
OXPHOS SUBUNITSDetails.34-0.55-2.19o.ooo0.0020.001
2OXPHOSDetails.41-0.53-2.17o.ooo0.0010.001
3CARBOHYDRATE METABOLISMDetails.36-0.50-2.00o.ooo0.0050.009
4TRANSLATIONDetails.16-0.62-1.990.0020.0050.011
5COMPLEX IDetails.17-0.60-1.970.0040.0050.013
6Cl SUBUNITSDetails.15-0.62-1.890.0040.0070.024
7FATTY ACID OXIDATIONDetails.20-0.55-1.860.0080.0070.030
8METALS AND COFACTORSDetails.30-0.471.790.0060.0110.051
9METABOLISMDetails.153-0.341.780.0020.0110.058
10AMINO ACID METABOLISMDetails.33-0.461.760.0110.0110.066
11MITOCHONDRIAL CENTRAL DOGMADetails.-0.45-1.610.0120.030o. 181
12TCA CYCLEDetails.15-0.48-1.510.0520.0530.310
13LIPID METABOLISMDetails.43-0.36-1.480.0320.0570.359
14PROTEIN IMPORT SORTING AND HOMEOSTASISDetails.24-0.34-1.200.2050.2230.846

Additional files

Supplementary file 1

Genes that pass our differential expression cutoffs (false discovery rate [FDR] < 0.05; |log fold change| > 1) in PaLung vs WI-38 samples from whole-cell transcriptomics data.

Differential expression analysis was performed using the DESeq2 pipeline. Fold changes are indicated as PaLung/WI-38.

https://cdn.elifesciences.org/articles/94007/elife-94007-supp1-v1.xlsx
Supplementary file 2

Biological pathways upregulated in PaLung cells from gene set enrichment analysis (GSEA) of transcriptomics data.

GSEA was performed on the transcriptomics data using (PI) value as a metric. The table below lists the pathways detected as upregulated in PaLung cells (compared to WI-38 cells) and associated enrichment metrics.

https://cdn.elifesciences.org/articles/94007/elife-94007-supp2-v1.xlsx
Supplementary file 3

Biological pathways upregulated in WI-38 cells from gene set enrichment analysis (GSEA) of transcriptomics data.

GSEA was performed on the transcriptomics data using (PI) value as a metric. The table below lists the pathways detected as upregulated in WI-38 cells (compared to PaLung cells) and associated enrichment metrics.

https://cdn.elifesciences.org/articles/94007/elife-94007-supp3-v1.xlsx
Supplementary file 4

Differentially expressed (DE) mitochondrial proteins in PaLung vs WI-38 samples from mitochondrial proteomics data.

405 DE proteins were first identified using a Student’s t-test on median-corrected protein abundances from the mitochondrial samples of PaLung and WI-38. Of the 405 DE proteins, 127 were identified to be core mitochondrial proteins (as defined by MitoCarta and IMPI datasets) and are listed in this sheet. Fold changes are indicated as PaLung/WI-38.

https://cdn.elifesciences.org/articles/94007/elife-94007-supp4-v1.xlsx
Supplementary file 5

Biological pathways upregulated in PaLung cells from gene set enrichment analysis (GSEA) of proteomics data.

GSEA was performed on the proteomics data using protein abundances as input. The table below lists the pathways detected as upregulated in PaLung cells (compared to WI-38 cells) and associated enrichment metrics.

https://cdn.elifesciences.org/articles/94007/elife-94007-supp5-v1.xlsx
Supplementary file 6

Biological pathways upregulated in WI-38 cells from gene set enrichment analysis (GSEA) of proteomics data.

GSEA was performed on the proteomics data using protein abundances as input. The table below lists the pathways detected as upregulated in WI-38 cells (compared to PaLung cells) and associated enrichment metrics.

https://cdn.elifesciences.org/articles/94007/elife-94007-supp6-v1.xlsx
Supplementary file 7

Metabolic model for PaLung cells.

A metabolic flux model was constructed for the central carbon metabolism of PaLung cells by overlaying proteomic and transcriptomic information onto the existing mitocore model from literature.

https://cdn.elifesciences.org/articles/94007/elife-94007-supp7-v1.xlsx
Supplementary file 8

Metabolic model for WI-38 cells.

A metabolic flux model was constructed for the central carbon metabolism of WI-38 cells by overlaying proteomic and transcriptomic information onto the existing mitocore model from literature.

https://cdn.elifesciences.org/articles/94007/elife-94007-supp8-v1.xlsx
Supplementary file 9

Flux sampling results comparing flux distributions in the constrained PaLung and WI-38 models.

Flux sampling was performed with 5000 flux vectors for the PaLung and WI-38 metabolic models each. The flux histograms for each reaction were compared across the two models and the following statistics were extracted from the histograms.

https://cdn.elifesciences.org/articles/94007/elife-94007-supp9-v1.xlsx
Supplementary file 10

Absolute metabolite quantification in PaLung and WI-38 cells.

Absolute concentrations of metabolites detected in PaLung and WI-38 cells by Human Metabolome Technologies (HMT).

https://cdn.elifesciences.org/articles/94007/elife-94007-supp10-v1.xlsx
MDAR checklist
https://cdn.elifesciences.org/articles/94007/elife-94007-mdarchecklist1-v1.docx

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  1. N Suhas Jagannathan
  2. Javier Yu Peng Koh
  3. Younghwan Lee
  4. Radoslaw Mikolaj Sobota
  5. Aaron T Irving
  6. Lin-fa Wang
  7. Yoko Itahana
  8. Koji Itahana
  9. Lisa Tucker-Kellogg
(2024)
Multi-omic analysis of bat versus human fibroblasts reveals altered central metabolism
eLife 13:e94007.
https://doi.org/10.7554/eLife.94007