Skip to main content
NCBI home page
American Journal of Respiratory Cell and Molecular Biology logoLink to American Journal of Respiratory Cell and Molecular Biology
. 2026 Mar 13;74(5):604–616. doi: 10.1093/ajrcmb/aanag010

Reduction of palmitoleic acid due to changes in pulmonary microbiota contributes to acute lung injury after single-lung ventilation

Zhizhi Wang 1,†, Zhiming Chen 2,†, Tianlan Ye 3,†, Jiayang Fan 4, Yikuan Cai 5, Hu Zhou 6, Tongyin Ou 7, Di Lu 8,✉, Kaican Cai 9,✉
PMCID: PMC13147449  PMID: 42089307

Abstract

Background

Single-lung ventilation (SLV), commonly used in thoracic surgery, carries a risk of acute lung injury and acute respiratory distress syndrome. Our previous studies suggest that pulmonary microbiota may play a role in SLV-induced lung injury, but the precise mechanisms remain unclear. Palmitoleic acid (PoA), a crucial metabolite, is associated with protective physiological processes. This study explores the variations in pulmonary microbiota and metabolites during SLV-induced lung injury and investigates the potential protective role of PoA.

Results

The development of SLV-induced lung injury correlated with the changes of pulmonary microbiota and alterations of certain microbial genera and species. Through metabolomics and correlation analysis of pulmonary metabolites and microbiota, several metabolites exhibited a negative association with SLV-induced lung injury, with PoA being particularly noteworthy. Changes in pulmonary microbiota appeared to contribute to the reduction of PoA levels, ultimately leading to lung injury. Supplementation with PoA significantly attenuated the severity of lung injury, both in vivo and in vitro, by upregulating PPARγ expression and its downstream signaling pathways.

Conclusions

The dysregulation of pulmonary microbiota contributes to SLV-induced lung injury, with PoA reduction playing a role in this process. PoA supplementation offers a protective effect by activating the PPAR pathway, suggesting its therapeutic potential in mitigating SLV-induced lung injury.

Keywords: lung injury, palmitoleic acid, PPAR, pulmonary microbiota, single-lung ventilation

Clinical Relevance.

Single-lung ventilation (SLV) is widely used in thoracic surgery but carries a risk of acute lung injury. Our findings suggest that pulmonary microbiota dysregulation and reduced palmitoleic acid (PoA) levels may contribute to this process. Modulating PoA levels and related metabolic pathways may offer a potential strategy to reduce SLV-associated lung injury.

Introduction

Acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) arise from direct lung injury or systemic inflammation due to sepsis, trauma, or major surgery,1,2 and are key causes of death after thoracic surgery, with ARDS showing up to 20% mortality.3,4 Single-lung ventilation (SLV) is a commonly employed technique whereby 1 lung is excluded from ventilation while perfusion to the non-ventilated lung is maintained.5 It is a unique technique in thoracic surgery as it is favored by the increasing use of minimally invasive techniques.5,6 SLV can induce lung injury through mechanisms such as shear stress after relapse, cytokine release, neutrophil recruitment, and ischemia-reperfusion (IR) injury.7 Our previous research has demonstrated that pulmonary microbiota significantly contribute to lung collapse and reflation injury ­during SLV.8

In 2010, the airway microbiota was identified,9 breaking the long-held belief that the lung was sterile during health.10 Over the last decade, researchers have demonstrated that host bacterial colonizers play a key role in maintaining normal metabolism and supporting immune system development.11-14 Dysbiosis of the pulmonary microbiome has an impact on the disease occurrence, progression, and prognosis.15,16 In the field of ALI, the pulmonary microbiota of sepsis and mechanical ventilation-induced lung injury will undergo significant alterations, indicating its involvement in both ALI and the associated inflammatory response, which is partly mediated by interleukin-6.17 In our earlier research, we observed similar microbial imbalances in SLV-induced ALI.8 To our knowledge, few studies have explored the mechanism linking the imbalance of pulmonary microbiota and SLV-induced lung injury.

Palmitoleic acid (PoA), a monounsaturated fatty acid (MUFA), is released from adipose tissue and exerts various biological effects. PoA is considered to be a lipid factor that affects the metabolism of distant organs, decreases and fatty acid desaturation in adipocytes, and suppresses the generation of cytokines.18,19 PoA could enhance the expression and activity of enzymes and transporters by activating the peroxisome proliferators-activated receptors (PPAR) pathway, mediating lipid ingestion and mitochondrial fatty acid oxidation, thereby protecting catecholamine-induced cardiac injury.20

However, it remains unclear if PoA protects against SLV-induced lung injury or if pulmonary microbiota is involved. This study aims to investigate the role of pulmonary microbiota and PoA in SLV-induced lung injury and seeks to identify potential strategies for perioperative critical care treatment in thoracic surgery.

Materials and methods

Animals and treatments

Male Sprague Dawley (SD) rats aged 7-8 weeks and weighing 250-350 g were obtained from the Laboratory Animal Center of Southern Medical University. They were housed in standard laboratory conditions with a 12 h light-dark cycle, temperature maintained at 22 ± 1 °C, and humidity at 60 ± 10%, with ad libitum access to food and water. All animal procedures were approved by the Animal Care Committee of Nanfang Hospital, Southern Medical University of China.

SLV model

For the SLV model, rats were randomly assigned to 2 groups (n = 6 each): the SLV group and the Sham group. SLV was conducted following the protocol described by Kentaro et al.21 Rats were anesthetized with sodium pentobarbital (50 mg/kg, i.p.), followed by tracheotomy and insertion of a rubber cannula into the trachea.22,23 SLV was initiated with specific settings: tidal volume 4 mL/kg; ventilation frequency 80/min. Left posterolateral thoracotomy from the fifth intercostal space was performed to observe lung collapse and confirm cannula placement. Rats in the SLV group underwent SLV for 1 h, while those in the Sham group did not undergo pretreatment.24 Afterward, double lung ventilation continued for 3 h, while the settings were the same as those mentioned previously except the tidal volume was 8 mL/kg. Upon completion of mechanical ventilation, lungs were exposed through bilateral thoracotomies, blood was collected from the apex of the heart, and rats were euthanized, followed by specimen retrieval.25

AntiB intervention and FMT model construction

For the AntiB intervention and FMT model construction, broad-spectrum antibiotics (ampicillin 1 g/L, vancomycin 500 mg/L, metronidazole 1 g/L, and ciprofloxacin 20 mg/L) were added to the drinking water of rats for 14 consecutive days before the creation of the Sham or SLV model to obtain the AntiB group (n = 6). To counteract the impact of antibiotics on gut microbiota, fecal microbiota transplantation (FMT) was performed on rats. For fecal transplants, fresh fecal pellets (50 mg/mL) from healthy rats were homogenized, centrifuged at 1000 rpm to remove large particles, and the supernatant was used for FMT treatment. On the 10th day of antibiotic treatment, healthy rat fecal bacterial solution (1 mL, once per day) was orally administered to the AntiB group rats for 5 consecutive days to obtain the FMT group (n = 6). Subsequently, SLV was performed on rats in the FMT group to obtain the FMT + SLV group (n = 6).

POA model construction

For the POA model construction, 0.5 g/kg PoA (Aladdin) was orally gavaged to rats every day, resulting in the Sham + PoA (n = 6), SLV + PoA (n = 6), and FMT + SLV + PoA (n = 6) groups, to investigate the role of PoA in SLV-induced lung injury.

Cell culture

Both human umbilical vein endothelial cells (HUVECs) and human adenocarcinoma alveolar basal epithelial cells (A549) were cultured in 1640 medium (Gibco) supplemented with 10% fetal bovine serum (Gibco) in a humidified incubator with 5% carbon dioxide at 37 °C. Upon reaching 80%-90% confluence on Petri dishes, experiments were initiated. Details of the cell culture conditions, in vitro SLV model, and PoA treatment protocols are provided in the Supplementary Methods.

Due to space constraints, additional methodological details, including specific experimental procedures and data analysis approaches, are provided in the Supplementary Methods.

Results

Validation of in vivo and in vitro models of single-lung ventilation-induced lung injury

As shown in Figure 1A, an in vivo model of SLV-induced lung injury was constructed in rats. Evaluation parameters including histological scoring, lung W/D ratio, and levels of IL-6 and MDA in serum were employed to assess the severity of lung injury. Results demonstrated significantly elevated histological scores (P < .0001), increased lung W/D ratio (P < .01), as well as heightened levels of IL-6 (P < .05) and MDA (P < .01) in the SLV group compared to the Sham group, indicating the successful construction of SLV-induced lung injury (Figure 1B and C).

Figure 1.

For image description, please refer to the figure legend and surrounding text.

Open in a new tab

Construction and identification of SLV-induced lung injury in vivo and in vitro models. SLV-induced lung injury in vivo model construction pattern (A). Lung histopathological alterations in rats in the Sham group and SLV group, in which the rats underwent SLV for 1 h (right lung ventilation and left lung collapsed) and double lungs ventilation for 3 h (B). The lung injury score of H&E staining, lung W/D ratio, IL-6, and MDA in plasma were shown by ELISA in rats in each group (C). Ischemia-reperfusion injury in vitro model construction pattern (D). Quantitative analysis of IL-1β, IL-6, and TNF-α in A549 and HUVECs culture supernatant in different groups by ELISA (E). Data are presented as the mean ± standard error of the mean. *P < .05, **P < .01, ***P < .001, ****P < .0001. Abbreviations: NS, not significantly different; Sham, sham group (n = 6); SLV, single-lung ventilation group (n = 6); Control, control group; IR, ischemia-reperfusion group; H&E, hematoxylin and eosin; W/D, wet/dry ratio; IL-6, interleukin-6; MDA, malondialdehyde; ELISA, enzyme-linked immunosorbent assay; IL-1β, interleukin-1β, TNF-α, tumor necrosis factor-α.

Concurrently, an in vitro IR model was utilized to replicate hypoxia-reoxygenation injury in HUVECs and human A549 cells. Elevated levels of inflammatory cytokines, including IL-1β (P < .001), IL-6 (P < .0001), and TNF-α (P < .001), were observed in the cell supernatant of the IR group compared to the control group, indicative of the successful construction of the simulated SLV-induced lung injury in vitro model (Figure 1D).

Collectively, the above results confirmed the successful establishment of both in vivo and in vitro models of SLV-induced lung injury, providing valuable platforms for further investigation.

Characterization and changes of pulmonary microbiota in response to single-lung ventilation-induced lung injury

The pulmonary microbiota in bronchoalveolar lavage fluid (BALF) of 2 groups, Sham and SLV, underwent OTU clustering, followed by statistical analysis at 7 classification levels. At the genus level, the top 3 genera in the SLV group were Pseudomonas, Acinetobacter, and Sphingomonas, while in the Sham group, they were Pseudomonas, Escherichia-Shigella, and Bacteroides (Figure 2A). Principal component analysis (PCA) results showed distinct different clusters of microbiotas between the 2 groups (Figure 2B). Circos plots further illustrated species proportions, aiding in indicator species identification (Figure 2C). At the species level, the SLV group was dominated by Acinetobacter_baumannii, Pasteurella_sp_V6, and gut_metagenome, whereas the Sham group showed Kosakonia_cowanii, Pediococcus_acidilactici, and Bacteroides_vulgatus (Figure 2D-F), demonstrating significant shifts in microbiota composition.

Figure 2.

For image description, please refer to the figure legend and surrounding text.

Open in a new tab

The pulmonary microbiota composition and variance of the two groups in SLV-induced lung injury. Stacked graph of the structures of pulmonary microbiota at genus level in Sham and SLV groups (A). PCA of the pulmonary microbiota between the two groups at the genus level (B). Circos plot of the relative abundance of pulmonary microbiota at the genus level (C). Stacked graph of the structure of pulmonary microbiota at the species level (D). PCA of the pulmonary microbiota between the two groups at the species level (E). Circos plot of the relative abundance of pulmonary microbiota at the species level (F). The cladograms at both genus and species levels of target differential bacteria between the Sham and SLV group (G, H). Histograms of the LDA scores of differential bacteria between groups (I, J). Heatmap of related pathways of pulmonary microbiota (K). *P < .05. Abbreviations: PCA, principal component analysis; Sham, sham group (n = 6); SLV, single-lung ventilation group (n = 6).

To further determine the exact differential pulmonary microbiota, LEfSe analysis method was employed. Differential microbiota with a P < .05 was selected and visualized in a cladogram (Figure 2G and H). The LDA score histograms identified microbiota with significant differences in abundance among groups (Figure 2I and J). Notably, 37 differential genera were identified, with the top 3 being Staphylococcus, Planctomycetales_bacterium_Ellin7224, and Paucimonas (Table S1). At the species level, 42 differential species were identified, led by Staphylococcus_sciuri_subsp_sciuri, Planctomycetales_bacterium_Ellin7224, and Comamonas_aquatica (Table S2). These indicated changes in pulmonary microbiota throughout the SLV process.

Further Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed key pathways associated with these microbiota changes, particularly in fatty acid metabolism and linoleic acid metabolism (Figure 2K) (Table S3). The analysis above revealed the impact of SLV-induced lung injury on the pulmonary microbiota composition.

Elucidating palmitoleic acid reduced through metabolomics analysis and correlation analysis of pulmonary metabolites and microbiota in single-lung ventilation-induced lung injury

To clarify the changes in metabolites in rat lung tissue during SLV-induced lung injury, Liquid chromatography-mass spectrometry technology was employed to conduct untargeted metabolomics on BALF. Base peak chromatogram reflected the types and relative abundances of metabolites in the Sham and SLV groups under positive ion (POS) mode (Figure S1A, see online supplementary material for a color version of this figure). Multivariate analysis using PCA, partial least squares discriminant analysis and orthogonal projection to latent structures-discriminant analysis showed clear distinctions in metabolite profiles between the groups in both POS and negative ion (NEG) modes (Figure 3A and B and Figure S1B-F, see online supplementary material for a color version of this figure). Model validation was confirmed through the permutation test (Figure S1G and H, see online supplementary material for a color version of this figure).

Figure 3.

For image description, please refer to the figure legend and surrounding text.

Open in a new tab

The pulmonary metabolites composition and variance of the two groups in SLV-induced lung injury. OPLS-DA score scatter plot of differential metabolites between the Sham group and the SLV group under POS mode (A). OPLS-DA score scatter plot of differential metabolites between Sham and SLV group under NEG mode (B). Differential metabolite heatmap of each group under POS and NEG modes (C-D). Histogram of the quantity of up-and down-regulated metabolites in the two modes (E). Loading plots of the most variable metabolites between two groups under POS and NEG mode (F-G). KEGG pathway enrichment analysis of metabolism (H). Bubble chart from the top 20 pathways enriching with the most metabolites (I). Correlations between pulmonary microbiota at genus level and metabolites (J). Correlations between pulmonary microbiota at species level and metabolites (K). Heatmap of targeted metabolites in the Sham group and SLV group (L). PoA levels under targeted metabolomics analysis (M). Data are presented as the mean ± standard error of the mean. *P < .05. Abbreviations: OPLS-DA, orthogonal projection to latent structures-discriminant analysis; POS, positive ion; NEG, negative ion; Sham, sham group (n = 6); SLV, single-lung ventilation group (n = 6); KEGG, Kyoto Encyclopedia of Genes and Genomes; PoA, palmitoleic acid.

Based on the relative abundance of different metabolites between the 2 groups, heatmaps were constructed (Figure 3C and D). In POS mode, of the 116 identified metabolites, 84 were upregulated, 32 were downregulated compared to the Sham group, and 32 were previously known metabolites (Figure 3E) (Table S4). Top VIP scores included 2-Methylbutyrylcarnitine (VIP = 6.55), Isobutyryl carnitine (VIP = 5.54), and N6-(1,2-Dicarboxyethyl)-AMP (VIP = 4.21). Meanwhile, in NEG mode, 72 different metabolites were detected, with 24 upregulated, 48 downregulated, and 23 of them being previously known metabolites (Figure 3E) (Table S5). Notably, PoA (VIP = 23.27) had the highest score and was significantly reduced after SLV (P = .026), suggesting extensive reduction during SLV-induced lung injury. Other significant metabolites included Isoleucine, Aminomalote, Palmitic amide, Linoleamide, Oleamide in POS mode, and 9Z , 12Z-Linoleic acid, oleic acid, and palmitic acid (PA) in NEG mode (Figure 3F and G). Furthermore, 39 pathways related to the above metabolites were identified through KEGG pathway enrichment analysis (Figure 3H and I) (Table S6).

To narrow in on specific molecular connections between pulmonary microbiota and metabolites mentioned above, joint multi-omics was conducted. Pearson correlation assessed correlations between pulmonary microbiota at genus and species and metabolites, identifying 119 genus-level and 76 species-level associations potentially linked to SLV (Figure 3J and K) (Tables S7 and S8). At genus level, there was a significant negative correlation between PoA and Planctomycetales_bacterium_Ellin7224, as well as PoA and Candidatus_Planktophila. And at species level, the relationship between PoA and Planctomycetales_bacterium_Ellin7224 was significantly negative.

Subsequently, to further investigate the high VIP-value metabolites obtained in previous studies, targeted metabolomics further examined 37 free fatty acids, revealing clustering consistent with untargeted analysis (Figure 3L) (Table S9). Specifically, compared to the Sham group, a decrease in PoA level was observed in the SLV group (P < .01), indicating extensive reduction of PoA during SLV-induced lung injury (Figure 3M).

Pulmonary microbiota changes led to a reduction of metabolite palmitoleic acid and its supplementation alleviated single-lung ventilation-induced lung injury

To clarify the impact of pulmonary microbiota on lung injury following SLV, broad-spectrum antibiotics were applied to suppress pulmonary microbiota. To reduce the influence of the lack of gut microbiota, we used broad-spectrum antibiotics to deplete the symbiotic microbiota, followed by FMT to restore gut microbiota. Therefore, rats were categorized into 3 groups: Sham, broad-spectrum antibiotics group (AntiB), and broad-spectrum antibiotics with FMT group (FMT) (Figure 4A). Following the establishment of the models above, microbiological analysis was conducted on the pulmonary and gut microbiota of all 3 groups. Microbiological analysis of the pulmonary microbiota at genus and species levels showed distinct compositions across the groups (Figure 4B and C). PCA, rank abundance curves, α-diversity boxplots, and rarefaction curves based on Shannon diversity index illustrated reduced diversity of the pulmonary microbiota in both the AntiB and FMT groups compared to Sham (P = .031), with similar distributions and significant differences from Sham (Figure 4D-F) (Figure S2G, see online supplementary material for a color version of this figure). The clear separation between the Sham group and both the AntiB and FMT groups, suggesting that antibiotic treatment significantly altered the pulmonary microbiota in the AntiB and FMT groups. Gut microbiota analysis revealed that the AntiB group had a significantly different composition from the Sham and FMT groups (P = .0004), indicating that FMT restored the gut microbiota affected by antibiotics (Figure S2A-F, see online supplementary material for a color version of this figure).

Figure 4.

For image description, please refer to the figure legend and surrounding text.

Open in a new tab

The reduction of metabolite PoA was due to the changes of pulmonary microbiota and its supplementation can alleviate SLV-induced lung injury. Schematic illustration of in vivo model construction for the Sham group (①), AntiB group (②), and FMT group (③) (A). Stacked graph of the structures of pulmonary microbiota at genus and species levels in Sham, AntiB, and FMT groups (B-C). Scatter plot of PCA analysis of pulmonary microbiota in the Sham, AntiB, and FMT groups (D). α-diversity boxplot of the pulmonary microbiota of the Sham, AntiB, and FMT groups (E). Rarefaction curve based on Shannon diversity index of the pulmonary microbiota of the Sham, AntiB, and FMT groups (F). Differential metabolites based on targeted metabolome between the SLV group and the FMT + SLV group (G). The levels of PoA between the Sham group, the SLV group, FMT group, and the FMT + SLV group (H). Lung histopathological alterations in rats after the supplementation of PoA in the Sham group, SLV group, and FMT + SLV group (I). The lung injury score of hematoxylin and eosin staining, lung W/D ratio, and IL-6 in plasma were shown by ELISA in rats in each group (J-L). Data are presented as the mean ± standard error of the mean. NS: not significantly different. *P < .05, **P < .01, ***P < .001, ****P < .0001. Abbreviations: Sham, sham group (n = 6); AntiB, broad-spectrum antibiotics group (n = 6); FMT, broad-spectrum antibiotics with fecal microbiota transplantation group (n = 6); SLV, single-lung ventilation group (n = 6); FMT + SLV, FMT group underwent single-lung ventilation (n = 6); PoA, palmitoleic acid; Sham + PoA, sham group supplemented PoA in advance (n = 6); SLV + PoA, single-lung ventilation group supplemented PoA in advance (n = 6); FMT + SLV + PoA, FMT + SLV group supplemented PoA in advance (n = 6); H&E, hematoxylin and eosin; W/D, wet/dry ratio; IL-6, interleukin-6; ELISA, enzyme-linked immunosorbent assay.

SLV modeling was then applied to the FMT group, establishing the FMT + SLV group, with the procedure performed in the same manner as shown in Figure 1A. Next, targeted measurement was performed on the free fatty acids between SLV group and the FMT + SLV group, depicted in a scatter plot (Figure 4G). There was no significant difference of PoA level between FMT and the Sham group (Figure 4H). Moreover, the level of PoA in the FMT + SLV group was significantly higher than that in the SLV group (P < .05). This suggests that pulmonary microbiota changes during SLV accelerate the reduction of PoA.

Further analysis involved applying PoA to existing models. The SLV + PoA group showed significantly lower lung injury scores (P < .01) and serum IL-6 levels (P < .001) compared to SLV, with a non-significant but downward trend in the W/D ratio (Figure 4I-L). After removing pulmonary microbiota, the W/D ratio (P < .01) and serum IL-6 levels (P < .5) in the FMT + SLV group decreased compared to the SLV group (Figure 4K and L). Notably, despite undergoing SLV, no significant differences were observed in lung injury score, W/D ratio, and serum IL-6 levels between SLV + PoA and Sham + PoA groups (Figure 4I-L).

The findings above indicated that PoA supplementation can alleviate the SLV-induced lung injury by changes in pulmonary microbiota.

Reactivation of PPAR pathway by palmitoleic acid supplementation mitigates single-lung ventilation-induced lung injury

In the previous section, we obtained insights into the pulmonary microbiota and its related pathways following SLV (Figure 2K), which hinted a few downstream pathways such as fatty acid metabolism and linoleic acid metabolism regulated by PPAR pathway. Next, the role of PPAR pathway in the process of PoA reduction leading to lung injury was investigated.

RNA-seq analysis was conducted on lung tissue from the Sham, SLV, and PoA + SLV groups. The heatmap shown in Figure 5A displays differential gene expression across these 3 groups. PPARγ showed strong expression in the PoA + SLV group, whereas it was expressed at a low level in the SLV group (Figure S2H, see online supplementary material for a color version of this figure). The volcano plot in Figure 5B presents the differential gene expression between the SLV + PoA and SLV groups, highlighting a significant downregulation of PPARγ in the SLV group. GSEA was employed to compare the genes of the 3 groups and generate enriched score plots for further analysis (Figure 5C) (Figure S2I and J, see online supplementary material for a color version of this figure).

Figure 5.

For image description, please refer to the figure legend and surrounding text.

Open in a new tab

Lung injury ensued due to the inhibition of the PPAR pathway caused by the reduction of PoA. Heatmap showing the differential gene expression across the Sham, SLV, and PoA + SLV groups (A). Volcano plot displaying differential gene expression between the SLV + PoA and SLV groups, highlighting significantly downregulated genes, including PPARγ (B). GSEA enrichment results showing the relevant pathways in the SLV group and PoA + SLV group (C). qPCR showing mRNA levels of genes related to PPAR pathway (CPT1A, HMGCS2, PDK4, ASCL1, and FASN) in mice between the SLV and the PoA + SLV groups (D). PPARγ protein and CPT1A protein levels in the in vitro model were validated through WB (E). PPARγ protein and CPT1A protein levels in the in vivo model were validated through WB (F). IHC revealing PPAR pathway expression among Sham group, SLV group, and FMT + SLV group (G). Volcano plot revealing upregulated and downregulated genes in ARDS patients (H). Bubble chart showing the top 10 pathways in ARDS patient enriching by KEGG (I). Histogram showing the top KEGG pathways in ARDS patient (J). Data are presented as the mean ± standard error of the mean. *P < .05, **P < .01, ****P < .0001. Abbreviations: NS, not significantly different. PoA, palmitoleic acid; Sham, sham group (n = 6); SLV, single-lung ventilation group (n = 6); PoA + SLV, single-lung ventilation group supplemented PoA in advance (n = 6); GSEA, gene set enrichment analysis; qPCR, quantitative real-time PCR; WB, western blotting; IR, ischemia-reperfusion group; Control, control group; Control + PoA, control group supplemented PoA in advance; IR + PoA, ischemia reperfusion group supplemented PoA in advance; FMT + SLV, broad-spectrum antibiotics with fecal microbiota transplantation group underwent single-lung ventilation (n = 6); FMT + SLV + PoA, FMT + SLV group supplemented PoA in advance (n = 6); IHC, immunohistochemistry; KEGG, Kyoto Encyclopedia of Genes and Genomes.

To further validate the PPAR pathway, qPCR was conducted to detect the mRNA expression levels of key genes involved in the PPAR pathway, including PPARγ, as well as its downstream molecules such as CPT1A, HMGCS2, PDK4, ASCL1, and FASN of both the SLV and the PoA + SLV groups (Figure 5D). The findings revealed that the mRNA levels of PPARγ (P < .0001) and its downstream molecules CPT1A (P < .01) and FASN (P < .05) significantly increased following PoA supplementation.

Subsequently, WB was performed in both in vitro and in vivo models. In the in vitro model, the addition of PoA resulted in a significant increase in the expression of PPARγ in the IR + PoA group compared to the IR group (P < .0001). Similarly, the expression of PPARγ and CPT1A was significantly increased in the Control + PoA group compared to the Control group (P < .0001) (Figure 5E). In the in vivo model, compared with the group without adding PoA, the expression of PPARγ was significantly increased in both the SLV + PoA group (P < .001) and the FMT + SLV + PoA group (P < .001) (Figure 5F). IHC exhibited a higher positive staining of PPARγ protein in both the Sham group and SLV group lung tissue, especially in the SLV group (Figure 5G). Within the FMT + SLV group, the application of PoA did not result in any notable alteration in the PPARγ positive rate (Figure 5G).

Finally, the gene expression of a cohort of ARDS patients was depicted in a volcano plot (Figure 5H) (Table S10). The KEGG results showed the top 10 metabolic pathways in ARDS patients, including fatty acid metabolism and PPAR signaling pathways, with q-value higher than 0.05 (Figure 5I and J) (Table S11).

It suggested that the decrease of PoA can inhibit the PPAR pathway, ultimately exacerbating lung injury. Conversely, PoA supplementation may restore PPAR pathway activation and alleviate SLV-induced lung injury.

Discussion

In this study, we found that the SLV caused lung collapse and reflation injury in vivo and in vitro, consistent with previous findings from our team.8 SLV was associated with alterations in the specific composition and function of pulmonary microbiota and metabolites. After SLV, there was a decrease of PoA due to the changes in pulmonary microbiota. These changes caused suppression of the PPAR pathway, resulting in an inflammatory response and ultimately leading to ALI. Furthermore, the use of broad-spectrum antibiotics not only eliminated the pulmonary microbiota but also mitigated the reduction of PoA. Additionally, supplementation of PoA was found to activate the PPAR pathway and alleviate ALI. Together, these findings suggest a potential relationship between pulmonary microbiota, PoA metabolism, and lung injury. While our data point to PoA supplementation as a promising preventive or therapeutic strategy, further mechanistic studies are needed to confirm these observations.

SLV has become increasingly prevalent due to the development of clinical minimally invasive techniques and the increasing complexity of thoracic surgery.26 However, a significant concern in SLV procedures is the IR injury that occurs upon reoxygenation of the collapsed lung, which can lead to ALI with potentially catastrophic consequences.27 Over the past decade, the pulmonary microbiome has been reported to be associated with health and disease.28 Investigations have consistently demonstrated alterations in the pulmonary microbiome across various lung conditions, encompassing asthma, cystic fibrosis, and pneumonia.29-31 Our previous research has also highlighted the pivotal role of pulmonary microbiota in SLV-induced lung injury.8

In this paper, our 16S rRNA sequencing analysis revealed significant changes in the composition and abundance of pulmonary microbiota following SLV. Notably, some of their protective and harmful effects have been reported previously. Murine commensal bacterium Clostridium_sensu_stricto_1 was potentially correlated with the severity of allergic airway inflammation.32 Reduction in Clostridium abundance has been linked to impairments in microbial fatty acid metabolism,33 tryptophan metabolism34 and sorbitol catabolism. Furthermore, the restoration of Clostridium abundance, possibly through stimulation of PPAR-γ signaling, may contribute to alleviating epithelial hypoxia in the colon.35  Collinsella was associated with the induction of T helper 17 cells, which modulated the immune system,36 and Collinsella species may protect some patients against developing immune-mediated colitis.37 The genus Collinsella could utilize N-acetylgalactosamine (GalNAc) associated with the host’s cardiometabolic health.38 Our findings indicate a reduction in the abundance of Clostridium and Collinsella, as well as other beneficial pulmonary microbiota such as Alloprevotella, Oscillospira, and Glutamicibacter following SLV.39 And most of them were associated with fat acid metabolism,33,39 these results suggest that SLV-induced lung injury may be linked to alterations in microbial abundance. However, direct causal relationships between these changes and metabolite depletion remain to be demonstrated. As current techniques for direct manipulation of the lung microbiome are still limited,40 our findings provide correlative evidence linking pulmonary microbiota perturbations with metabolic alterations during SLV, which lays the groundwork for future mechanistic validation.

Normally the pulmonary microbiome regulates host metabolic homeostasis via microbial metabolites or co-metabolites, but previous studies have focused on short-chain fatty acids41 and glycans.42 However, recent research has shed light on the significant impact of dietary long-chain unsaturated fatty acids (LCFAs) in modulating inflammatory processes.43 PA is the most common saturated fatty acid and metabolized to oleic acid, stearic acid, sphingolipids as well as PoA.44 PoA is an omega-7 MUFA with a double bond, which might have anti-inflammatory capabilities.45 In addition, PoA treatment can protect the liver from lipid accumulation, enhance fatty acid oxidation through AMPK phosphorylation, and reduce fatty acid uptake by downregulating CD36.46 In the circulatory system, PoA can enhance the expression and activity of enzymes and transporters by activating the PPAR pathway, mediating lipid uptake and mitochondrial fatty acid oxidation, ultimately offering protection against catecholamine-induced cardiac injury.20 Our data demonstrated that the changes of pulmonary microbiota decreased PoA during SLV, which could be mitigated upon the removal of the pulmonary microbiota effects. Moreover, our work demonstrated the in vivo and in vitro effects of PoA on alleviating SLV-induced lung injury caused by the changes of the pulmonary microbiota. These results support a potential causal chain linking microbiome alterations, PoA depletion, and lung injury. Future studies will be required to directly test whether modulation of the lung microbiome can influence PoA levels and, in turn, impact lung injury outcomes. However, whether and how other LCFAs interact with the pulmonary microbiota and underlying mechanisms of SLV injury remains unknown.

PPAR gamma (PPAR-γ), is a member of the PPAR subgroup within the nuclear receptor superfamily.35 Activation of the PPAR-γ pathway can enhance the response of vascular endothelial cells to stress,47 and has a clear protective effect on organ failure and myocardial IR damage caused by sepsis.48,49 In addition, serving as a ligand-activated transcription factor, PPAR assumes a crucial role in regulating the metabolism of organisms.50 In this paper, it has been found that the blockage of the PPAR pathway is closely related to the occurrence of ALI and even ARDS in patients through bioinformatics analysis. However, it should be noted that the enrichment of the PPAR signaling pathway observed in the human dataset (Figure 5I) reached nominal significance (P < .05) but did not remain significant after multiple testing correction (q-value > 0.05). This indicates that the association should be interpreted cautiously and considered as a hypothesis-generating finding rather than definitive evidence. Given the complexity of lipid metabolism, where multiple pathways interact, further validation in independent human cohorts and functional studies will be needed to determine whether PPAR signaling plays a central role in the observed microbiome–metabolite–injury relationships. Furthermore, transcriptomics and subsequent results show PoA therapy could reprogram the lung microbial ecology, activate the PPAR pathway, and alleviate SLV-induced ALI. Previous studies have shown that PoA can facilitate the biological therapeutic efficacy in treating inflammatory bowel disease through reprogrammed gut microbiota.51 Thus, PoA and PoA-reprogrammed pulmonary microbiota may exhibit broader spectrum roles in enhancing efficacy of other biological therapies against SLV-induced ALI and other inflammatory disorders.

Taken together, the significant changes in lung metabolism observed in rats with ALI might be coupled with inflammatory response and metabolic disorders. Additionally, the results of serum markers also proved the potential association between these altered microbiota/metabolites and inflammation. This study reveals the potential role of PoA in regulating the molecular and chemical network of pulmonary microbiota and lung metabolites, which may influence the abundance and diversity of beneficial lung microbial ecosystems. Moreover, our findings provide supportive evidence that targeting pulmonary microbiota or metabolites could represent a promising strategy to mitigate SLV-induced lung injury, although definitive causal proof will require additional mechanistic and clinical studies.

Conclusions

In conclusion, following SLV, the reduction of the metabolite PoA appears to be associated with the dysregulation of the pulmonary microbiota, which may contribute to increased inflammatory stress and lung injury, potentially through inhibition of the PPAR pathway (Figure 6).

Figure 6.

For image description, please refer to the figure legend and surrounding text.

Open in a new tab

Mechanism linking pulmonary microbiota dysregulation, palmitoleic acid (PoA) reduction, and SLV-induced lung injury via the PPAR pathway.

Supplementary Material

aanag010_Supplementary_Data
aanag010_supplementary_data.zip (31.9MB, zip)

Contributor Information

Zhizhi Wang, Department of Thoracic Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China.

Zhiming Chen, Department of Thoracic Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China.

Tianlan Ye, Department of Thoracic Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China.

Jiayang Fan, Department of Thoracic Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China.

Yikuan Cai, Department of Thoracic Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China.

Hu Zhou, Department of Thoracic Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China.

Tongyin Ou, School of Public Health, Southern Medical University, Guangzhou, China.

Di Lu, Email: david_lu1989@163.com, Department of Thoracic Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China.

Kaican Cai, Email: doc_cai@163.com, Department of Thoracic Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China.

Author contributions

Zhizhi Wang, Zhiming Chen, and Kaican Cai designed the study and performed the experiments. Zhizhi Wang, Tianlan Ye, and Di Lu performed the data analysis. Tianlan Ye, Jiayang Fan, Hu Zhou, and Tongyin Ou assisted in statistical analysis of the metadata. Zhizhi Wang, Tianlan Ye, and Hu Zhou performed the metabolomic analysis. Zhiming Chen, Yikuan Cai, and Tianlan Ye wrote the manuscript. Di Lu, Zhizhi Wang, and Kaican Cai revised the manuscript. Zhizhi Wang, Zhiming Chen, and Tianlan Ye contributed to this study equally. All authors read and approved the final manuscript.

Supplementary material

Supplementary material is available at American Journal of Respiratory Cell and Molecular Biology online.

Conflicts of interest

Please see the ICMJE disclosure forms, which have been provided as supplementary material. The authors declare not to have any conflicts of interest that may be considered to influence directly or indirectly the content of the manuscript.

Funding

This project was supported by the funding by Science and Technology Projects in Guangzhou (2024A04J5190) and (2024A04J5106) and the Guang Dong Basic and Applied Basic Research Foundation (2023A1515110168) and (2025A1515010570) and the President Foundation of Nanfang Hospital, Southern Medical University (2022A028).

Data availability

All raw data have been deposited in the NCBI database under accession code PRJNA707375. The other datasets supporting the conclusions of this article are included within the article and supplementary files.

Ethics approval and consent to participate

Animal experimental procedures in this work were authorized by the Animal Care and Use Committee of Nanfang Hospital, Southern Medical University, and followed the guidelines of the Laboratory Animal Center of Southern Medical University.

References

  • 1. Li N, Liu B, Xiong R, Li G, Wang B, Geng Q.  HDAC3 deficiency protects against acute lung injury by maintaining epithelial barrier integrity through preserving mitochondrial quality control. Redox Biol. 2023;63:102746. 10.1016/j.redox.2023.102746 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2. Fan EKY, Fan J.  Regulation of alveolar macrophage death in acute lung inflammation. Respir Res. 2018;19:50. 10.1186/s12931-018-0756-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3. Vlastos D, Zeinah M, Ninkovic-Hall G, et al.  The effects of ischaemic conditioning on lung ischaemia-reperfusion injury. Respir Res. 2022;23:351. 10.1186/s12931-022-02288-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4. Bellani G, Laffey JG, Pham T, et al. ; ESICM Trials Group. Epidemiology, patterns of care, and mortality for patients with acute respiratory distress syndrome in intensive care units in 50 countries. JAMA. 2016;315:788-800. 10.1001/jama.2016.0291 [DOI] [PubMed] [Google Scholar]
  • 5. Ferrando C, Carramiñana A, Piñeiro P, et al. ; iPROVE-OLV Research Network Group  Individualised, perioperative open-lung ventilation strategy during one-lung ventilation (iPROVE-OLV): a multicentre, randomised, controlled clinical trial. Lancet Respir Med. 2024;12:195-206. 10.1016/s2213-2600(23)00346-6 [DOI] [PubMed] [Google Scholar]
  • 6. Cho RL, Lin WN, Wang CY, et al.  Heme oxygenase-1 induction by rosiglitazone via PKCalpha/AMPKalpha/p38 MAPKalpha/SIRT1/PPARgamma pathway suppresses lipopolysaccharide-mediated pulmonary inflammation. Biochem Pharmacol. 2018;148:222-237. 10.1016/j.bcp.2017.12.024 [DOI] [PubMed] [Google Scholar]
  • 7. Lohser J, Slinger P.  Lung injury after one-lung ventilation: a review of the pathophysiologic mechanisms affecting the ventilated and the collapsed lung. Anesth Analg. 2015;121:302-318. 10.1213/ANE.0000000000000808 [DOI] [PubMed] [Google Scholar]
  • 8. Lu D, Wang Z, Chen Z, et al.  Olmesartan attenuates single-lung ventilation induced lung injury via regulating pulmonary microbiota. Front Pharmacol. 2022;13:822615. 10.3389/fphar.2022.822615 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9. Hilty M, Burke C, Pedro H, et al.  Disordered microbial communities in asthmatic airways. PLoS One. 2010;5:e8578. 10.1371/journal.pone.0008578 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10. Di Simone SK, Rudloff I, Nold-Petry CA, Forster SC, Nold MF.  Understanding respiratory microbiome-immune system interactions in health and disease. Sci Transl Med. 2023;15:eabq5126. 10.1126/scitranslmed.abq5126 [DOI] [PubMed] [Google Scholar]
  • 11. Man WH, de Steenhuijsen Piters WA, Bogaert D.  The microbiota of the respiratory tract: gatekeeper to respiratory health. Nat Rev Microbiol. 2017;15:259-270. 10.1038/nrmicro.2017.14 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12. Yin Y, Hountras P, Wunderink RG.  The microbiome in mechanically ventilated patients. Curr Opin Infect Dis. 2017;30:208-213. 10.1097/qco.0000000000000352 [DOI] [PubMed] [Google Scholar]
  • 13. Fagundes CT, Amaral FA, Teixeira AL, Souza DG, Teixeira MM.  Adapting to environmental stresses: the role of the microbiota in controlling innate immunity and behavioral responses. Immunol Rev. 2012;245:250-264. 10.1111/j.1600-065X.2011.01077.x [DOI] [PubMed] [Google Scholar]
  • 14. Perdijk O, Azzoni R, Marsland BJ.  The microbiome: an integral player in immune homeostasis and inflammation in the respiratory tract. Physiol Rev. 2024;104:835-879. 10.1152/physrev.00020.2023 [DOI] [PubMed] [Google Scholar]
  • 15. Whiteside SA, McGinniss JE, Collman RG.  The lung microbiome: progress and promise. J Clin Invest. 2021;131:e150473. 10.1172/jci150473 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16. Li R, Li J, Zhou X.  Lung microbiome: new insights into the pathogenesis of respiratory diseases. Signal Transduct Target Ther. 2024;9:19. 10.1038/s41392-023-01722-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17. Poroyko V, Meng F, Meliton A, et al.  Alterations of lung microbiota in a mouse model of LPS-induced lung injury. Am J Physiol Lung Cell Mol Physiol. 2015;309:L76-83. 10.1152/ajplung.00061.2014 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18. Frigolet ME, Gutiérrez-Aguilar R.  The role of the novel lipokine palmitoleic acid in health and disease. Adv Nutr. 2017;8:173s-181s. 10.3945/an.115.011130 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19. Bermúdez MA, Pereira L, Fraile C, Valerio L, Balboa MA, Balsinde J.  Roles of palmitoleic acid and its positional isomers, hypogeic and sapienic acids, in inflammation, metabolic diseases and cancer. Cells. 2022;11:2146. 10.3390/cells11142146 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20. Betz IR, Qaiyumi SJ, Goeritzer M, et al.  Cardioprotective effects of palmitoleic acid (C16:1n7) in a mouse model of catecholamine-induced cardiac damage are mediated by PPAR activation. Int J Mol Sci. 2021;22:12695. 10.3390/ijms222312695 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21. Tojo K, Goto T, Kurahashi K.  Protective effects of continuous positive airway pressure on a nonventilated lung during one-lung ventilation: a prospective laboratory study in rats. Eur J Anaesthesiol. 2016;33:776-783. 10.1097/eja.0000000000000460 [DOI] [PubMed] [Google Scholar]
  • 22. Dumas ME, Wilder SP, Bihoreau MT, et al.  Direct quantitative trait locus mapping of mammalian metabolic phenotypes in diabetic and normoglycemic rat models. Nat Genet. 2007;39:666-672. 10.1038/ng2026 [DOI] [PubMed] [Google Scholar]
  • 23. Zhang B, Zhang X, Li Q, Ma F, Sun L, Wang M.  Dexmedetomidine attenuates ventilator-induced lung injury in rats by up-regulating NLRC3. Ann Palliat Med. 2020;9:2474-2484. 10.21037/apm-19-375 [DOI] [PubMed] [Google Scholar]
  • 24. Tekinbas C, Ulusoy H, Yulug E, et al.  One-lung ventilation: for how long?  J Thorac Cardiovasc Surg. 2007;134:405-410. 10.1016/j.jtcvs.2007.05.003 [DOI] [PubMed] [Google Scholar]
  • 25. Matute-Bello G, Fau—Downey G, Downey G, et al. An official American Thoracic Society workshop report: features and measurements of experimental acute lung injury in animals (1535-4989 (Electronic)). [DOI] [PMC free article] [PubMed]
  • 26. Karzai W, Schwarzkopf K.  Hypoxemia during one-lung ventilation: prediction, prevention, and treatment. Anesthesiology. 2009;110:1402-1411. 10.1097/ALN.0b013e31819fb15d [DOI] [PubMed] [Google Scholar]
  • 27. Ferrari RS, Andrade CF.  Oxidative stress and lung ischemia-reperfusion injury. Oxid Med Cell Longev. 2015;2015:590987. 10.1155/2015/590987 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28. Wu BG, Segal LN.  Lung microbiota and its impact on the mucosal immune phenotype. Microbiol Spectr. 2017;5. 10.1128/microbiolspec.BAD-0005-2016 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29. Dickson RP, Erb-Downward JR, Martinez FJ, Huffnagle GB.  The microbiome and the respiratory tract. Annu Rev Physiol. 2016;78:481-504. 10.1146/annurev-physiol-021115-105238 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30. Teo SM, Mok D, Pham K, et al.  The infant nasopharyngeal microbiome impacts severity of lower respiratory infection and risk of asthma development. Cell Host Microbe. 2015;17:704-715. 10.1016/j.chom.2015.03.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31. Dickson RP, Erb-Downward JR, Prescott HC, et al.  Analysis of culture-dependent versus culture-independent techniques for identification of bacteria in clinically obtained bronchoalveolar lavage fluid. J Clin Microbiol. 2014;52:3605-3613. 10.1128/jcm.01028-14 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32. Yang X, Feng H, Zhan X, et al.  Early-life vancomycin treatment promotes airway inflammation and impairs microbiome homeostasis. Aging. 2019;11:2071-2081. 10.18632/aging.101901 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33. Zhang Y, Tu S, Ji X, et al.  Dubosiella newyorkensis modulates immune tolerance in colitis via the L-lysine-activated AhR-IDO1-Kyn pathway. Nat Commun. 2024;15:1333. 10.1038/s41467-024-45636-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34. Wang G, Fan Y, Zhang G, et al.  Microbiota-derived indoles alleviate intestinal inflammation and modulate microbiome by microbial cross-feeding. Microbiome. 2024;12:59. 10.1186/s40168-024-01750-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35. Lee JY, Tiffany CR, Mahan SP, et al.  High fat intake sustains sorbitol intolerance after antibiotic-mediated clostridia depletion from the gut microbiota. Cell. 2024;187:1191-1205.e15. 10.1016/j.cell.2024.01.029 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36. Zheng D, Liwinski T, Elinav E.  Interaction between microbiota and immunity in health and disease. Cell Res. 2020;30:492-506. 10.1038/s41422-020-0332-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37. Halsey TM, Thomas AS, Hayase T, et al.  Microbiome alteration via fecal microbiota transplantation is effective for ­refractory immune checkpoint inhibitor-induced colitis. Sci Transl Med. 2023;15:eabq4006. 10.1126/scitranslmed.abq4006 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38. Zhernakova DV, Wang D, Liu L, et al.  Lifelines Cohort Study. Host genetic regulation of human gut microbial structural variation. Nature. 2024;625:813-821. 10.1038/s41586-023-06893-w [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39. Peña-Cearra A, Castelo J, Lavín JL, et al.  Mitochondrial dysfunction-associated microbiota establishes a transmissible refractory response to anti-TNF therapy during ulcerative colitis. Gut Microbes. 2023;15:2266626. 10.1080/19490976.2023.2266626 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40. Carney SM, Clemente JC, Cox MJ, et al. Methods in Lung Microbiome Research (1535-4989 (Electronic)).
  • 41. Dalile B, Van Oudenhove L, Vervliet B, Verbeke K.  The role of short-chain fatty acids in microbiota-gut-brain communication. Nat Rev Gastroenterol Hepatol. 2019;16:461-478. 10.1038/s41575-019-0157-3 [DOI] [PubMed] [Google Scholar]
  • 42. Park SY, Rao C, Coyte KZ, et al.  Strain-level fitness in the gut ­microbiome is an emergent property of glycans and a single metabolite. Cell. 2022;185:513-529.e21. 10.1016/j.cell.2022.01.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43. Adolph TE, Meyer M, Schwärzler J, Mayr L, Grabherr F, Tilg H.  The metabolic nature of inflammatory bowel diseases. Nat Rev Gastroenterol Hepatol. 2022;19:753-767. 10.1038/s41575-022-00658-y [DOI] [PubMed] [Google Scholar]
  • 44. Hosomi K, Kiyono H, Kunisawa J.  Fatty acid metabolism in the host and commensal bacteria for the control of intestinal immune responses and diseases. Gut Microbes. 2020;11:276-284. 10.1080/19490976.2019.1612662 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45. Yang B, Kallio HP.  Fatty acid composition of lipids in sea buckthorn (Hippophaë rhamnoides L.) berries of different origins. J Agric Food Chem. 2001;49:1939-1947. 10.1021/jf001059s [DOI] [PubMed] [Google Scholar]
  • 46. Souza CO, Teixeira AAS, Biondo LA, et al.  Palmitoleic acid reduces high fat diet-induced liver inflammation by promoting PPAR-γ-independent M2a polarization of myeloid cells. Biochim Biophys Acta Mol Cell Biol Lipids. 2020;1865:158776. 10.1016/j.bbalip.2020.158776 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47. Ali F, Ali NS, Bauer A, et al.  PPARdelta and PGC1alpha act cooperatively to induce haem oxygenase-1 and enhance vascular endothelial cell resistance to stress. Cardiovasc Res. 2010;85:701-710. 10.1093/cvr/cvp365 [DOI] [PubMed] [Google Scholar]
  • 48. Kapoor A, Collino M, Castiglia S, Fantozzi R, Thiemermann C.  Activation of peroxisome proliferator-activated receptor-beta/delta attenuates myocardial ischemia/reperfusion injury in the rat. Shock. 2010;34:117-124. 10.1097/SHK.0b013e3181cd86d6 [DOI] [PubMed] [Google Scholar]
  • 49. Kapoor A, Shintani Y, Collino M, et al.  Protective role of peroxisome proliferator-activated receptor-β/δ in septic shock. Am J Respir Crit Care Med. 2010;182:1506-1515. 10.1164/rccm.201002-0240OC [DOI] [PubMed] [Google Scholar]
  • 50. Liu Y, Colby JK, Zuo X, Jaoude J, Wei D, Shureiqi I.  The role of PPAR-δ in metabolism, inflammation, and cancer: many characters of a critical transcription factor. Int J Mol Sci. 2018;19:. 10.3390/ijms19113339 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51. Chen Y, Mai Q, Chen Z, et al.  Dietary palmitoleic acid reprograms gut microbiota and improves biological therapy against colitis. Gut Microbes. 2023;15:2211501. 10.1080/19490976.2023.2211501 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

aanag010_Supplementary_Data
aanag010_supplementary_data.zip (31.9MB, zip)

Data Availability Statement

All raw data have been deposited in the NCBI database under accession code PRJNA707375. The other datasets supporting the conclusions of this article are included within the article and supplementary files.

Articles from American Journal of Respiratory Cell and Molecular Biology are provided here courtesy of American Thoracic Society

RESOURCES