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<journal-id journal-id-type="publisher-id">Front. Cell. Infect. Microbiol.</journal-id>
<journal-title-group>
<journal-title>Frontiers in Cellular and Infection Microbiology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Cell. Infect. Microbiol.</abbrev-journal-title>
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<issn pub-type="epub">2235-2988</issn>
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<publisher-name>Frontiers Media S.A.</publisher-name>
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<article-id pub-id-type="doi">10.3389/fcimb.2026.1661639</article-id>
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<subj-group subj-group-type="heading">
<subject>Original Research</subject>
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<title-group>
<article-title>Modified Dachengqi Decoction ameliorates sepsis-induced lung injury via the gut microbiota-bile acid axis</article-title>
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<name><surname>Wu</surname><given-names>Lulu</given-names></name>
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<name><surname>Peng</surname><given-names>Weihang</given-names></name>
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<name><surname>Li</surname><given-names>Ya</given-names></name>
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<name><surname>Yu</surname><given-names>Liyuan</given-names></name>
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<name><surname>Huang</surname><given-names>Peiying</given-names></name>
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<name><surname>Feng</surname><given-names>Yuchao</given-names></name>
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<name><surname>Chen</surname><given-names>Bojun</given-names></name>
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<name><surname>Chen</surname><given-names>Li</given-names></name>
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<aff id="aff1"><label>1</label><institution>The Second Clinical School of Medicine, Guangzhou University of Chinese Medicine</institution>, <city>Guangzhou</city>,&#xa0;<country country="cn">China</country></aff>
<aff id="aff2"><label>2</label><institution>Guangdong Provincial Academy of Traditional Chinese Medicine, Clinical Research Team of Prevention and Treatment of Cardiac Emergencies with Traditional Chinese Medicine</institution>, <city>Guangzhou</city>,&#xa0;<country country="cn">China</country></aff>
<aff id="aff3"><label>3</label><institution>Emergency Department, Guangdong Provincial Hospital of Traditional Chinese Medicine</institution>, <city>Guangzhou</city>,&#xa0;<country country="cn">China</country></aff>
<author-notes>
<corresp id="c001"><label>*</label>Correspondence: Yuchao Feng, <email xlink:href="mailto:speeters@126.com">speeters@126.com</email>; Bojun Chen, <email xlink:href="mailto:gzcbj@gzucm.edu.cn">gzcbj@gzucm.edu.cn</email>; Li Chen, <email xlink:href="mailto:chenliyisheng@gzucm.edu.cn">chenliyisheng@gzucm.edu.cn</email></corresp>
<fn fn-type="equal" id="fn003">
<label>&#x2020;</label>
<p>These authors have contributed equally to this work and share first authorship</p></fn>
</author-notes>
<pub-date publication-format="electronic" date-type="pub" iso-8601-date="2026-02-06">
<day>06</day>
<month>02</month>
<year>2026</year>
</pub-date>
<pub-date publication-format="electronic" date-type="collection">
<year>2026</year>
</pub-date>
<volume>16</volume>
<elocation-id>1661639</elocation-id>
<history>
<date date-type="received">
<day>08</day>
<month>07</month>
<year>2025</year>
</date>
<date date-type="accepted">
<day>05</day>
<month>01</month>
<year>2026</year>
</date>
<date date-type="rev-recd">
<day>04</day>
<month>01</month>
<year>2026</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2026 Wu, Peng, Li, Yu, Huang, Ye, Feng, Chen and Chen.</copyright-statement>
<copyright-year>2026</copyright-year>
<copyright-holder>Wu, Peng, Li, Yu, Huang, Ye, Feng, Chen and Chen</copyright-holder>
<license>
<ali:license_ref start_date="2026-02-06">https://creativecommons.org/licenses/by/4.0/</ali:license_ref>
<license-p>This is an open-access article distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License (CC BY)</ext-link>. The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p>
</license>
</permissions>
<abstract>
<sec>
<title>Background</title>
<p>Sepsis-induced acute lung injury (SI-ALI) is associated with high mortality. The gut microbiota-bile acid axis plays a critical role in regulating host inflammatory responses; however, the mechanism of action of traditional Chinese medicine (TCM) compounds targeting this axis remains unclear.</p>
</sec>
<sec>
<title>Aim</title>
<p>This study aimed to systematically evaluate the protective effects of Modified DaChengqi Decoction (MDD) against lipopolysaccharide (LPS)-induced SI-ALI and to elucidate its underlying mechanism in modulating inflammation and neutrophil extracellular traps (NETs) through the regulation of gut microbiota and bile acid metabolism.</p>
</sec>
<sec>
<title>Methods</title>
<p>An LPS-induced mouse model of SI-ALI was established. Mice were orally administered MDD, and 72&#x2212;h survival rate, lung function, histopathology, and inflammatory cytokine levels were assessed. Fecal 16S rRNA sequencing and targeted bile acid metabolomics were combined to analyze changes in the microbiota and metabolites. Network pharmacology was employed to screen key targets, followed by experimental validation using Western blotting, immunohistochemistry, and ELISA to confirm candidate pathways.</p>
</sec>
<sec>
<title>Results</title>
<p>Compared with the model group, MDD significantly improved survival and lung function, alleviated pulmonary inflammation and vascular permeability. Microbiomic analysis revealed that MDD downregulated the abundance of <italic>Parabacteroides</italic> and <italic>Bacteroides</italic>. Targeted metabolomics showed that MDD markedly altered the levels of several primary and secondary bile acids, mainly including glycoursodeoxycholic acid (GUDCA), taurochenodesoxycholic acid (TCDCA), chenodeoxycholic acid (CDCA), and taurocholic acid (TCA). Molecular validation demonstrated that the nuclear receptor FXR was significantly upregulated, while the TLR4 and downstream MYD88-NF&#x2212;&#x3ba;B/JNK signaling pathways were inhibited. Additionally, the expression of PAD4 and CitH3 as well as NETs formation were reduced.</p>
</sec>
<sec>
<title>Conclusion</title>
<p>MDD can alleviate LPS-induced SI-ALI by modulating the gut microbiota-bile acid metabolism, activating FXR, and thereby suppressing the TLR4/MYD88&#x2212;mediated inflammatory cascade and NETs generation.</p>
</sec>
</abstract>
<kwd-group>
<kwd>bile acid metabolism</kwd>
<kwd>FXR/TLR4/MYD88</kwd>
<kwd>gut microbiota</kwd>
<kwd>Modified Dachengqi Decoction</kwd>
<kwd>neutrophil extracellular traps</kwd>
<kwd>sepsis-induced acute lung injury</kwd>
</kwd-group>
<funding-group>
<funding-statement>The author(s) declared that financial support was received for this work and/or its publication. This work was supported by the 2024 Beijing Union Medical Foundation - Rui E Emergency Medicine Research Fund (PUMF01010010-2024-09), and the Special Project of Guangdong Provincial Hospital of Chinese Medicine (YN2024GZRPY066).</funding-statement>
</funding-group>
<counts>
<fig-count count="11"/>
<table-count count="2"/>
<equation-count count="0"/>
<ref-count count="93"/>
<page-count count="26"/>
<word-count count="11966"/>
</counts>
<custom-meta-group>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Intestinal Microbiome</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
<body>
<sec id="s1" sec-type="intro">
<label>1</label>
<title>Introduction</title>
<p>Sepsis is a severe condition that poses a significant threat to global health, with persistently high incidence and mortality rates. The excessive inflammatory response triggered by sepsis can lead to multiple organ failure (<xref ref-type="bibr" rid="B61">Singer et&#xa0;al., 2016</xref>). Notably, the lungs are among the organs most susceptible to septic injury, ultimately resulting in sepsis-induced acute lung injury(SI-ALI). Approximately 45% of sepsis patients progress to acute respiratory distress syndrome (ARDS), characterized by disruption of the pulmonary endothelial barrier and diffuse lung injury (<xref ref-type="bibr" rid="B37">Li N, et&#xa0;al., 2023</xref>). Despite decades of research on SI-ALI, outstanding breakthroughs in clinical treatment remain lacking, highlighting an urgent need to develop novel pharmaceuticals and innovative therapeutic strategies.</p>
<p>Recent studies have increasingly emphasized the concept of &#x201c;gut-derived sepsis&#x201d;, underlining the critical role of the gut in driving multi-organ dysfunction during sepsis through complex organ crosstalk mechanisms (<xref ref-type="bibr" rid="B39">Li D, et&#xa0;al., 2024</xref>).Gut dysbiosis is a pivotal contributor to sepsis progression by promoting pathogenic bacterial proliferation, triggering excessive pro-inflammatory immune responses, and reducing the production of beneficial metabolites, thereby increasing host susceptibility (<xref ref-type="bibr" rid="B7">Charitos et&#xa0;al., 2025</xref>). This dysbiosis also profoundly disrupts host bile acid metabolic homeostasis. Dysregulation of bile acid metabolism plays a crucial role in sepsis-induced lung injury. Recent reviews emphasize the central role of bile acids and their receptors, especially FXR and TGR5, in coordinating metabolic homeostasis and inflammatory responses, and highlight that modulation of the bile acid-FXR signaling axis is a promising therapeutic strategy to suppress systemic inflammation and treat metabolic disorders (<xref ref-type="bibr" rid="B18">Fleishman and Kumar, 2024</xref>; <xref ref-type="bibr" rid="B39">Li et&#xa0;al., 2024</xref>).Mechanistically, bile acids exert broad regulatory effects on host metabolism, immunity, and inflammation through activation of nuclear receptors such as FXR and PXR as well as the G protein&#x2013;coupled receptor TGR5 (<xref ref-type="bibr" rid="B65">Thomas, 2017</xref>; <xref ref-type="bibr" rid="B38">Li and Chiang, 2024</xref>). Sepsis itself exacerbates dysbiosis and bile acid imbalance (<xref ref-type="bibr" rid="B24">Haak and Wiersinga, 2017</xref>), forming a vicious cycle of dysbiosis-metabolic disorder-sepsis aggravation. The cohort observations and animal experiments have further confirmed that following the onset of sepsis, the composition of the gut microbiota and the profile of bile acids undergo rapid alterations. These changes are closely associated with elevated systemic inflammatory markers and adverse clinical outcomes. This suggests a potential causal pathway in which infection may induce dysbiosis, subsequently leading to disturbances in bile acid metabolism, and ultimately exacerbating the systemic inflammatory response (<xref ref-type="bibr" rid="B41">Li Z. et&#xa0;al., 2024</xref>; <xref ref-type="bibr" rid="B60">Shang et&#xa0;al., 2025</xref>). Therapeutic strategies targeting gut microbiota and bile acid metabolism modulation urgently require further exploration. In this context, traditional Chinese medicine (TCM), with its long-standing history, has gradually demonstrated unique advantages in the clinical treatment of sepsis. TCM has been observed to induce corresponding changes in gut microbiota while ameliorating organ damage caused by sepsis (<xref ref-type="bibr" rid="B17">Fan et&#xa0;al., 2023</xref>), exerting its effects through multiple mechanisms, encompassing the regulation of immune responses and anti-inflammatory effects. Although the significance of gut microbiota and their metabolites in sepsis and multiple organ dysfunction has been revealed by previous studies, the mechanism by which gut microbiota&#x2212;associated bile acid imbalance disrupts vascular endothelial structure in distant organs through interactions between nuclear receptors and pattern recognition receptors remains incompletely untangled. Moreover, there is a lack of systematic experimental evidence on how multi&#x2212;component traditional Chinese medicine formulas can simultaneously modulate the microbiome and bile acid metabolism to alleviate SI&#x2212;ALI.</p>
<p>Furthermore, in the context of ALI and sepsis triggered by bacterial infection and LPS stimulation, TLR4 recognizes LPS and activates downstream pro-inflammatory cascades via the adaptor MYD88, leading to I&#x3ba;B degradation and NF-&#x3ba;B translocation. This process upregulates inflammatory cytokines such as IL-1&#x3b2;, IL-6, and TNF-&#x3b1;, resulting in alveolar epithelial and vascular endothelial injury, increased permeability, and inflammatory exudation (<xref ref-type="bibr" rid="B83">Zhang et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B23">Guo et&#xa0;al., 2024</xref>). Concurrently, TLR4 activation also induces phosphorylation of JNK, a member of the MAPK family, which plays a critical role in LPS-induced inflammatory responses and neutrophil function. The TLR4-JNK axis has been demonstrated to determine the occurrence and extent of neutrophil NETosis upon LPS stimulation (<xref ref-type="bibr" rid="B33">Khan et&#xa0;al., 2017</xref>). The reviews and experimental evidence have extended and confirmed this mechanism. LPS activates TLR4, which subsequently triggers NADPH oxidase 2 (NOX2)-dependent oxidative stress via c-Jun N-terminal kinase (JNK) signaling(<xref ref-type="bibr" rid="B68">Wang H, et&#xa0;al., 2024</xref>; <xref ref-type="bibr" rid="B15">Espiritu and O&#x2019;Sullivan, 2025</xref>), thereby promoting the release of neutrophil extracellular traps (NETs). In sepsis models, blocking TLR4 or inhibiting JNK significantly reduces NET formation and tissue injury (<xref ref-type="bibr" rid="B75">Xia et&#xa0;al., 2024</xref>).Studies have shown that MYD88 drives detrimental intravascular NET formation in sepsis and exacerbates disease progression; MYD88 deficiency significantly reduces lethal NET formation and improves outcomes, indicating that MYD88 is a key upstream regulator of excessive NET activation (<xref ref-type="bibr" rid="B14">Englert et&#xa0;al., 2025</xref>). Therefore, in the pathogenesis of SI-ALI, TLR4/MYD88-mediated NF-&#x3ba;B and JNK signaling not only amplify the pro-inflammatory cytokine storm but also promote NET generation and oxidative stress, collectively contributing to endothelial dysfunction and lung injury. Blocking this pathway effectively alleviates inflammation and lung injury, underscoring its significance as a therapeutic target.</p>
<p>The reviews and methodological evaluations have indicated that although existing clinical studies on TCM for sepsis have limitations in randomization and sample size, a growing body of animal model research and increasingly standardized clinical trials are demonstrating the potential effects of TCM compound formulations in improving sepsis&#x2212;related organ function, alleviating inflammation, and restoring the intestinal barrier. This suggests that this field is entering a phase of more rigorous clinical validation (<xref ref-type="bibr" rid="B20">Fu et&#xa0;al., 2024</xref>; <xref ref-type="bibr" rid="B31">Ji et&#xa0;al., 2025</xref>).Preclinical studies suggest that the therapeutic effect of MDD on sepsis may be achieved through synergistic regulation of pulmonary and gastrointestinal functions (<xref ref-type="bibr" rid="B10">Chen et&#xa0;al., 2016</xref>, <xref ref-type="bibr" rid="B9">2018</xref>). This finding provides modern clinical evidence for the classical theory of TCM that &#x201c;the lung and intestine are interiorly-exteriorly related.&#x201d; It indicates that this formula can exert favorable comprehensive therapeutic effects via the approach of &#x201c;simultaneous treatment of the lung and intestine,&#x201d; as demonstrated in years of clinical practice at Guangdong Provincial Hospital of Chinese Medicine and Guizhou Provincial Hospital of Traditional Chinese Medicine, with its protective effect on gastrointestinal function being particularly notable (<xref ref-type="bibr" rid="B10">Chen et&#xa0;al., 2016</xref>, <xref ref-type="bibr" rid="B9">2018</xref>). The composition of MDD includes <italic>Rheum palmatum L</italic> (Dahuang), Na<sub>2</sub>SO<sub>4</sub>&#xb7;10H<sub>2</sub>O (Mangxiao), <italic>Magnolia officinalis</italic> var. <italic>biloba</italic> (Houpo), <italic>Forsythia suspensa</italic> (Lianqiao), <italic>Scutellaria baicalensis Georgi</italic> (Huangqin), <italic>Prunus armeniaca</italic> var. <italic>armeniaca</italic> (Xingren), <italic>Bletilla striata</italic> (Baiji), and <italic>Panax notoginseng</italic> (Sanqi). The plant names have been checked with <ext-link ext-link-type="uri" xlink:href="http://www.worldfloraonline.org">http://www.worldfloraonline.org</ext-link> (04/09/2025).</p>
<p>The major constituents of MDD have been reported to be involved in sepsis, acute lung injury, and the regulation of intestinal microbiota. Further microbiome and metabolome studies have demonstrated that baicalin alleviates intestinal inflammation by modulating the composition of the gut microbiota and enhancing intestinal barrier function, which complements its effects on inhibiting inflammatory pathways and promoting the polarization of macrophages toward an anti-inflammatory phenotype (<xref ref-type="bibr" rid="B67">Wan et&#xa0;al., 2024</xref>; <xref ref-type="bibr" rid="B86">Zhang et&#xa0;al., 2025</xref>). Emodin has been reported to alleviate LPS-induced lung injury by inhibiting the JNK/Nur77/c-Jun pathway and to ameliorate sepsis-associated lung injury both <italic>in vitro</italic> and <italic>in vivo</italic> through modulation of SIRT1 (<xref ref-type="bibr" rid="B45">Liu et&#xa0;al., 2022</xref>; <xref ref-type="bibr" rid="B78">Xie et&#xa0;al., 2022</xref>). Furthermore, honokiol, a major active component of <italic>magnolia officinalis</italic>, significantly alleviates pulmonary edema and histopathological damage, and improves survival rates in LPS- and CLP-induced acute lung injury or sepsis models by inhibiting inflammation and oxidative stress, protecting the pulmonary microvascular endothelial barrier, and suppressing NLRP3-mediated pyroptosis (<xref ref-type="bibr" rid="B74">Weng et&#xa0;al., 2011</xref>; <xref ref-type="bibr" rid="B48">Liu et&#xa0;al., 2021</xref>, <xref ref-type="bibr" rid="B44">2023</xref>).Ginsenoside-related metabolites are associated with the regulation of immune homeostasis (<xref ref-type="bibr" rid="B92">Zhuang et&#xa0;al., 2021</xref>).It is interesting that <italic>Rheum palmatum L</italic>(Dahuang), Na<sub>2</sub>SO<sub>4</sub>&#xb7;10H<sub>2</sub>O(Mangxiao), <italic>Magnolia officinalis</italic> var. <italic>biloba</italic>(Houpo) improve ALI by regulating the expression of HIF-1&#x3b1;, downregulating glycolysis, and reducing inflammation (<xref ref-type="bibr" rid="B59">Shang et&#xa0;al., 2024</xref>). Besides, multiple major components in MDD have been reported to modulate the gut microbiota. Baicalin can alleviate intestinal inflammation and improve intestinal barrier function by inhibiting inflammatory pathways and remodeling the gut microbiota composition (<xref ref-type="bibr" rid="B87">Zhang S, et&#xa0;al., 2024</xref>).Emodin significantly alters the structure of the gut microbial community, reduces the Firmicutes/Bacteroidetes ratio, and promotes the production of beneficial metabolites, thereby ameliorating inflammation and intestinal permeability (<xref ref-type="bibr" rid="B50">Mabwi et&#xa0;al., 2023</xref>).Ginsenosides simultaneously increased short-chain fatty acids (SCFAs) and significantly modulated bile acids in the blood and tissues, with changes in the microbiota being correlated with alterations in bile acids (<xref ref-type="bibr" rid="B40">Li et&#xa0;al., 2025</xref>). The study demonstrated that ginsenoside Rk3 enriched butyrate-producing bacteria and elevated butyrate levels. As a key microbial metabolite, butyrate mediated the anti-inflammatory and neuroprotective effects (<xref ref-type="bibr" rid="B90">Zhou S, et&#xa0;al., 2025</xref>).Honokiol has been shown to ameliorate non-alcoholic fatty liver disease in mice, accompanied by remodeling of the ileal gut microbiota and alterations in the serum bile acid profile; notably, significant correlations between microbial taxa and bile acid levels suggest that honokiol exerts its protective effects through coordinated regulation of gut microbiota and bile acid metabolism (<xref ref-type="bibr" rid="B82">Zhai et&#xa0;al., 2023</xref>). Therefore, the multi-component combination of MDD possesses the potential to simultaneously target the gut microbiota ecosystem, bile acid metabolism, and pulmonary cell signaling pathways, which constitutes the theoretical basis for selecting MDD as an interventional agent.</p>
<p>This study systematically analyzed the gut microbiota characteristics and associated bile acid profiles in the control group, model group, and MDD group, aiming to investigate how gut microbiota and its mediated bile acid metabolic disorders influence systemic inflammation and SI-ALI. The results are intended to elucidate the microbiological and metabolomic basis of the TCM principle of &#x201c;simultaneous treatment of lung and gut&#x201d; in SI-ALI. We hypothesized that MDD alleviates SI-ALI by modulating the gut microbiota&#x2013;bile acid metabolism, activating the FXR, thereby inhibiting the TLR4/MYD88 pathway, reducing NETs formation, and protecting the pulmonary vascular endothelium. These findings provide a theoretical foundation for developing novel therapeutic strategies targeting the microbiota-bile acid axis.</p>
</sec>
<sec id="s2" sec-type="materials|methods">
<label>2</label>
<title>Materials and methods</title>
<sec id="s2_1">
<label>2.1</label>
<title>MDD formulation preparation</title>
<p>The constituents of MDD were obtained from the Traditional Chinese Medicine pharmacy of Guangdong Provincial Hospital of Traditional Chinese Medicine, with Dahuang dosed at 20g and all other herbs at 10g each. Detailed information can be found in <xref ref-type="table" rid="T1"><bold>Table&#xa0;1</bold></xref>. To prepare the decoction, 500 mL of water was added to the herbs and boiled until the solution was concentrated to 50 mL, yielding a final crude herb concentration of 1.8 g/mL. In the clinical protocol, a patient weighing 60 kg received a dose corresponding to 90g of raw herbs. The herbs were decocted with 500 mL of water and concentrated to yield 4.5 g of extract. Therefore, the clinical dose was equivalent to 4.5g/60 kg. First, based on the body surface area conversion principle, this human dose was converted to the mouse equivalent dose, resulting in 0.92 g/kg. This dose was subsequently designated as the medium dose. On this basis, 0.5 times (0.46 g/kg) and 2.0 times (1.84 g/kg) of this dose were defined as the low and high doses, respectively. For the positive control group, dexamethasone (DEX) tablets (Xianju, LB2426) were used to prepare a solution with a concentration of 0.5 mg/mL.</p>
<table-wrap id="T1" position="float">
<label>Table&#xa0;1</label>
<caption>
<p>Component herbs of MDD.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="center">Latin name</th>
<th valign="middle" align="center">Chinese name</th>
<th valign="middle" align="center">Place of origin</th>
<th valign="middle" align="center">Manufacturers</th>
<th valign="middle" align="center">Batch number</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="left">Rheum palmatum L</td>
<td valign="middle" align="left">Da Huang</td>
<td valign="middle" align="left">Gansu Province, China</td>
<td valign="middle" align="left">Sinopsin Group Feng Liaoxing (Foshan) Pharmaceutical Co., LTD</td>
<td valign="middle" align="left">C12405083</td>
</tr>
<tr>
<td valign="middle" align="left">&#x2013;</td>
<td valign="middle" align="left">Mangxiao</td>
<td valign="middle" align="left">Sichuan Province,China</td>
<td valign="middle" align="left">Kangmei Pharmaceutical Co., LTD</td>
<td valign="middle" align="left">240200961</td>
</tr>
<tr>
<td valign="middle" align="left">Houpoea officinalis</td>
<td valign="middle" align="left">Houpo</td>
<td valign="middle" align="left">Guangxi Province,China</td>
<td valign="middle" align="left">Yulin Bencaotang Chinese Medicine Co., LTD</td>
<td valign="middle" align="left">240501</td>
</tr>
<tr>
<td valign="middle" align="left">Forsythia suspensa</td>
<td valign="middle" align="left">Lianqiao</td>
<td valign="middle" align="left">Shanxi Province,China</td>
<td valign="middle" align="left">Sinopsin Group Feng Liaoxing (Foshan) Pharmaceutical Co., LTD</td>
<td valign="middle" align="left">C22403120</td>
</tr>
<tr>
<td valign="middle" align="left">Scutellaria baicalensis Georgi</td>
<td valign="middle" align="left">Huangqin</td>
<td valign="middle" align="left">Shanxi Province,China</td>
<td valign="middle" align="left">Lingnan Traditional Chinese Medicine Tablets Co. LTD</td>
<td valign="middle" align="left">2405001</td>
</tr>
<tr>
<td valign="middle" align="left">Semen Armeniacae Amarum</td>
<td valign="middle" align="left">Xingren</td>
<td valign="middle" align="left">Hebei Province, China</td>
<td valign="middle" align="left">Kangmei Pharmaceutical Co., LTD</td>
<td valign="middle" align="left">240502301</td>
</tr>
<tr>
<td valign="middle" align="left">Bletilla striata</td>
<td valign="middle" align="left">Baiji</td>
<td valign="middle" align="left">Yunnan Province, China</td>
<td valign="middle" align="left">Sinopsin Group Feng Liaoxing (Foshan) Pharmaceutical Co., LTD</td>
<td valign="middle" align="left">C12312080</td>
</tr>
<tr>
<td valign="middle" align="left">Panax notoginseng</td>
<td valign="middle" align="left">Sanqi</td>
<td valign="middle" align="left">Yunnan Province, China</td>
<td valign="middle" align="left">Lingnan Traditional Chinese Medicine Tablets Co. LTD</td>
<td valign="middle" align="left">2403003</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p><bold>MDD,</bold> Modified Dachengqi Decoction.</p></fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s2_2">
<label>2.2</label>
<title>Ultra performance liquid chromatography mass spectrometry analysis of MDD</title>
<p>A total of 6 mL of the prepared MDD solution was aliquoted into three samples of 2 mL each for triplicate testing. The analysis was conducted using a Vanquish ultra-high-performance liquid chromatography (UHPLC) system from Thermo Fisher Scientific, employing a Phenomenex Kinetex C18 column (2.1 mm &#xd7; 100 mm, 2.6 &#x3bc;m) for chromatographic separation of the target compounds. The mobile phase A consisted of water with 0.01% acetic acid, while phase B was a mixture of isopropanol and acetonitrile (1:1, v/v). The sample tray temperature was maintained at 4&#xb0;C, and the injection volume was set at 2 &#x3bc;L. The Orbitrap Exploris 120 mass spectrometer was operated under the control of the Xcalibur software (version 4.4, Thermo Fisher Scientific) for the acquisition of both MS and MS/MS data. The detailed parameters were as follows: Sheath gas flow rate: 50 Arb, Aux gas flow rate: 15 Arb, Capillary temperature: 320 &#xb0;C, Full ms resolution: 60000, MS/MS resolution: 15000, Collision energy: SNCE 20/30/40, Spray Voltage: 3.8 kV (positive) or -3.4 kV (negative).</p>
</sec>
<sec id="s2_3">
<label>2.3</label>
<title>Animal experiments</title>
<sec id="s2_3_1">
<label>2.3.1</label>
<title>Dosage of treatment</title>
<p>Eight-week-old male <italic>C57BL/6</italic> mice (n=190) were housed at the Guangdong Provincial Academy of Chinese Medical Sciences, and all experimental procedures were conducted in accordance with the ARRIVE 2.0 guidelines (<xref ref-type="bibr" rid="B58">Percie et&#xa0;al., 2020</xref>)under certificate number No. 2024017 (obtained March 18, 2024). After a one-week acclimatization period, the C57BL/6 mice were randomly grouped using a random number table into the control group (CON), lipopolysaccharide group (LPS), low-dose of MDD group (MDD-L), medium-dose of MDD group (MDD-M), high-dose of MDD group (MDD-H), and dexamethasone group (DEX). Two days before establishing the model, we administered preventive treatment. In addition to CON and LPS groups, the remaining groups received the corresponding intervention. The administered doses of MDD were 0.46 g/kg (low dose), 0.92 g/kg (intermediate dose), and 1.84 g/kg (high dose), while the dose of dexamethasone was 5 mg/kg (<xref ref-type="bibr" rid="B62">Son et&#xa0;al., 2022</xref>).</p>
</sec>
<sec id="s2_3_2">
<label>2.3.2</label>
<title>Sepsis mortality</title>
<p>In this study, 100 mice were initially used to observe the 72-hour mortality rate of sepsis. The experimental animals were randomly assigned to the following five groups (n=20 per group): the LPS group, MDD-L group, MDD-M group, MDD-H group, and the DEX group. Two days after continuous MDD and DEX gavage, the mice were intraperitoneally injected with LPS (Sigma, L2880-100MG) to establish a sepsis model. LPS was diluted with physiological saline to a concentration of 1 mg/mL and injected into the abdominal cavity at a dosage of 10 mg/kg (<xref ref-type="bibr" rid="B57">Park et&#xa0;al., 2019</xref>).Subsequently, the mouse mortality was recorded every 12 hours for 72 hours. Surviving mice received a daily oral gavage during this period. The final mortality rate for each group is shown in <xref ref-type="fig" rid="f1"><bold>Figure&#xa0;1A</bold></xref>.</p>
<fig id="f1" position="float">
<label>Figure&#xa0;1</label>
<caption>
<p>MDD can reduce mortality in mice with SI-ALI and alleviate inflammation and respiratory function impairment. <bold>(A)</bold> Experimental protocol for SI-ALI model and treatment to 72h survival and <bold>(B)</bold> 24h-treatment; <bold>(C)</bold> Survival Rate of SI-ALI(n=10); <bold>(D)</bold> peak inspiratory flow(mL/s); <bold>(E)</bold> respiratory frequency(BPM)(n=5-7); <bold>(F)</bold> tidal volume(mL)(n=5); <bold>(G)</bold> inspiration time(s)(n=5); <bold>(H)</bold> expiratory flow 50(mL/s)(n=5-7); <bold>(I)</bold> Wet/Dry (W/D) ratio of the lungs(n=5-6); <bold>(J-L)</bold>The concentrations of TNF-&#x3b1;, IL-6, and protein in BALF(n=5-7). These results were presented as mean &#xb1; SD. *<italic>p</italic> &lt; 0.05, **<italic>p</italic> &lt; 0.01,***<italic>p</italic> &lt; 0.001, compared with the CON group. #<italic>p</italic> &lt; 0.05, ##<italic>p</italic> &lt; 0.01, ###<italic>p</italic> &lt; 0.001, compared with the LPS group.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fcimb-16-1661639-g001.tif">
<alt-text content-type="machine-generated">Diagrams and graphs showing experimental procedures and results in mice. Panels A and B depict the setup and timeline for the administration of substances and sample collection. Panel C presents a survival rate graph over 72 hours for different groups. Panels D to L display bar graphs with error bars showing various physiological and biochemical parameters, such as peak inspiratory flow, respiratory frequency, tidal volume, inspiration time, expiratory flow, wet-to-dry weight ratio, and levels of TNF-alpha, IL-6, and protein in BALF across different experimental groups.</alt-text>
</graphic></fig>
</sec>
<sec id="s2_3_3">
<label>2.3.3</label>
<title>Study on the mechanism of MDD</title>
<p>To assess disease severity, we selected the critical time point of 24 hours post-LPS injection for observation, with a sample size of 15 mice per group across six groups: the CON group, LPS, MDD-L, MDD-M, MDD-H, and DEX groups. Consistent with the above experiment, we pretreated mice in the corresponding groups with MDD and DEX for 2 consecutive days before LPS injection. At 24 hours post-LPS injection, we examined the lung function of the mice and collected tissues for subsequent analysis (<xref ref-type="fig" rid="f1"><bold>Figure&#xa0;1B</bold></xref>).</p>
</sec>
</sec>
<sec id="s2_4">
<label>2.4</label>
<title>Assessment of respiratory function</title>
<p>Pulmonary function assessments were conducted 24 hours after LPS injection to evaluate the severity of lung injury. Mice were placed in the EMMSlink Whole Body Plethysmography system (EMMSlinks WBP) and allowed to acclimate before measurements commenced. Each mouse underwent a 5-minute assessment period.</p>
</sec>
<sec id="s2_5">
<label>2.5</label>
<title>Tissue collection</title>
<p>Following the respiratory function assessments, three fecal pellets were collected from each mouse. Subsequently, the mice were anesthetized for blood collection, and bronchoalveolar lavage was performed on the right lung with saline to obtain bronchoalveolar lavage fluid (BALF). The remaining lung tissue alongside intestinal tissue was harvested and stored at -80&#xb0;C for future analysis.</p>
</sec>
<sec id="s2_6">
<label>2.6</label>
<title>Lung wet-to-dry weight ratio</title>
<p>The wet-to-dry weight ratio of lung tissues is commonly utilized to assess the severity of pulmonary edema. In brief, the right lung lobes were weighed immediately after surface fluids were removed to obtain the wet weight. Subsequently, the lung tissues were baked at 60&#xb0;C until a constant weight was achieved to determine the dry weight. Ultimately, the wet-to-dry lung weight ratio was calculated to evaluate the severity of edema.</p>
</sec>
<sec id="s2_7">
<label>2.7</label>
<title>Histopathological staining</title>
<p>Lung and intestinal tissues were sectioned into 4 &#xb5;m-thick slices after fixation in tissue fixative, dehydration and embedding. The sections were then stained with hematoxylin and eosin (H&amp;E), and examined under a microscope. H&amp;E stained slides were scored as previously describe (<xref ref-type="bibr" rid="B52">Matute-Bello et&#xa0;al., 2011</xref>; <xref ref-type="bibr" rid="B8">Chassaing et&#xa0;al., 2015</xref>).</p>
</sec>
<sec id="s2_8">
<label>2.8</label>
<title>BALF preparation and analysis</title>
<p>The previously collected BALF was centrifuged at 1500 rpm for 10 minutes, and the supernatant was obtained for protein content analysis using the BCA kit (P0011, Beyotime). The levels of TNF-&#x3b1; and IL-6 in BALF were detected by using the Mouse TNF-&#x3b1; ELISA kit (ZC-39024, Zhuocai) and Mouse IL-6 ELISA Kit (ZC-37988, Zhuocai) in accordance with the manufacturer&#x2019;s instructions. Finally, the concentrations of TNF-&#x3b1; and IL-6 were calculated through standard curves.</p>
</sec>
<sec id="s2_9">
<label>2.9</label>
<title>16S rRNA microbial community analysis</title>
<p>Fecal samples were collected from mice in the CON, LPS, and MDD-M groups, with each group comprising six specimens, for 16S rRNA analysis.</p>
<sec id="s2_9_1">
<label>2.9.1</label>
<title>DNA extraction and amplification</title>
<p>Total genomic DNA was extracted using MagPure Soil DNA LQ Kit (Magan) following the manufacturer&#x2019;s instructions. DNA concentration and integrity were measured with NanoDrop 2000 (Thermo Fisher Scientific, USA) and agarose gel electrophoresis. Extracted DNA was stored at -20 &#xb0;C until further processing. The extracted DNA was used as a template for PCR amplification of bacterial 16S rRNA genes with the barcoded primers and Takara Ex Taq (Takara). For bacterial diversity analysis, V3-V4 variable regions of 16S rRNA genes were amplified with universal primers 343F (5&#x2019;-TACGGRAGGCAGCAG-3&#x2019;) and 798R (5&#x2019;-AGGGTATCTAATCCT-3&#x2019;) and 907R (5&#x2019;-CCGTCAATTCMTTTRAGTTT-3&#x2019;) (<xref ref-type="bibr" rid="B79">Xiong et&#xa0;al., 2012</xref>).</p>
</sec>
<sec id="s2_9_2">
<label>2.9.2</label>
<title>Library construction and sequencing</title>
<p>The Amplicon quality was visualized using agarose gel electrophoresis. The PCR products were purified with AMPure XP beads (Agencourt) and amplified for another round of PCR. After being purified with the AMPure XP beads again, the final amplicon was quantified using Qubit dsDNA Assay Kit (Thermo Fisher Scientific, USA). The concentrations were then adjusted for sequencing. Sequencing was performed on an Illumina NovaSeq 6000 with 250 bp paired-end reads (Illumina Inc., San Diego, CA; OE Biotech Company; Shanghai, China).</p>
</sec>
<sec id="s2_9_3">
<label>2.9.3</label>
<title>16S rRNA amplicon sequencing analysis process</title>
<p>The library sequencing and data processing were conducted by OE Biotech Co., Ltd. (Shanghai, China). Raw sequencing data were in FASTQ format. Paired-end reads were then preprocessed using Cutadapt software to detect and cut off the adapter. After trimming, paired-end reads were filtering low quality sequences, denoised, merged and detect and cut off the chimera reads using DADA2 (<xref ref-type="bibr" rid="B4">Callahan et&#xa0;al., 2016</xref>)with the default parameters of QIIME2 (<xref ref-type="bibr" rid="B2">Bolyen et&#xa0;al., 2019</xref>) (2020.11). At last, the software output the representative reads and the ASV abundance table. The representative read of each ASV was selected using the QIIME2 package. All representative reads were annotated and blasted against the SILVA database (Version 138) using q2-feature-classifier with the default parameters.</p>
<p>QIIME2 software was used for alpha and beta diversity analysis. The microbial diversity in samples was estimated using alpha diversity metrics, including the Chao1 index (<xref ref-type="bibr" rid="B6">Chao and Bunge, 2002</xref>) and Shannon index (<xref ref-type="bibr" rid="B27">Hill et&#xa0;al., 2003</xref>). The unweighted Unifrac distance matrix performed by R package was used for unweighted Unifrac Principal coordinates analysis (PCoA) to estimate the beta diversity. Next, the R package was used to analyze the significant differences between different groups using the Kruskal-Wallis statistical test. The linear discriminant analysis effect size (LEfSe) method was used to compare the taxonomy abundance spectrum.</p>
</sec>
</sec>
<sec id="s2_10">
<label>2.10</label>
<title>Targeted bile acid metabolism</title>
<p>Fecal samples were collected from the CON, LPS, and MDD-M groups for the assessment of targeted bile acid metabolism, with each group comprising six samples. An analytical balance was used to accurately measure the required amount of the standard compound, which was then dissolved in water to prepare the standard stock solution at a concentration of 1 mg/mL. The standard working solution was subsequently prepared from the standard stock solution, and a standard curve was generated through serial dilution of the mixed standard solution. Standard information and methodological Data are provided in <xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary File 1-2</bold></xref>.</p>
<sec id="s2_10_1">
<label>2.10.1</label>
<title>Sample pretreatment</title>
<p>An appropriate amount of fecal samples was taken, accurately weighed, and recorded. The samples were then loaded into pre-cooled 1.5 mL Eppendorf (EP) tubes (pre-cooled at -20 &#xb0;C). Subsequently, 10 &#x3bc;L of a mixed internal standard solution (comprising CA-d4, Lyso PC17, GCA-C13, and L-2-chlorophenylalanine) was added, followed by the addition of 590 &#x3bc;L of methanol containing 1 mM butylated hydroxytoluene (BHT). Two steel balls were added to each sample, and the samples were swirled for 30 seconds. The samples were then placed in a -20 &#xb0;C refrigerator for 2 minutes, after which they were ground in a grinder operating at 60 Hz for 1 minute. The samples were subjected to ultrasonic extraction in an ice bath for 10 minutes and subsequently centrifuged for 10 minutes at 4 &#xb0;C and 12,000 rpm. Following centrifugation, 200 &#x3bc;L of the supernatant was carefully aspirated using a syringe and filtered through a 0.22 &#x3bc;m organic phase pinhole filter. Finally, the filtered samples were transferred to brown sample bottles and stored at -80 &#xb0;C until further machine analysis.</p>
</sec>
<sec id="s2_10_2">
<label>2.10.2</label>
<title>Chromatography and mass spectrometry</title>
<p>In this study, the UPLC-ESI-MS/MS analytical method was employed for both the qualitative and quantitative detection of target metabolites. The specific analytical conditions and methodologies utilized are detailed as follows:</p>
</sec>
<sec id="s2_10_3">
<label>2.10.3</label>
<title>Chromatographic methods</title>
<p>The sample volume utilized was 5 &#x3bc;L, with a flow rate of 0.45 mL/min. The mobile phases employed were as follows: Phase A consisted of a 0.1% formic acid aqueous solution, while Phase B comprised a mixture of methanol, acetonitrile, and isopropanol in a 1:1:1 ratio, also containing 0.1% formic acid. The gradient elution procedure was executed according to the following schedule: 0 min, A/B (80:20, v/v); 0.5 min, A/B (80:20, v/v); 1.5 min, A/B (62:38, v/v); 12 min, A/B (50:50, v/v); 17.5 min, A/B (5:95, v/v); 19 min, A/B (5:95, v/v); 19.01 min, A/B (80:20, v/v); and 20 min, A/B (80:20, v/v).</p>
</sec>
<sec id="s2_10_4">
<label>2.10.4</label>
<title>Mass spectrometry</title>
<p>The mass spectrometry analysis was conducted under the following conditions: the gas curtain was maintained at a pressure of 35 psi, and the collision-activated dissociation (CAD) parameters were set to medium. The negative ion spray voltage was -4500 V, while the positive ion spray voltage was 5500 V. The ion source temperature was regulated at 450&#xb0;C, and the column temperature was maintained at 45 &#xb0;C. Besides, the spray gas (Gas1) and the auxiliary heating gas (Gas2) were both set to a pressure of 55 psi.</p>
</sec>
<sec id="s2_10_5">
<label>2.10.5</label>
<title>Qualitative and quantitative analysis</title>
<p>Targeted metabolites were analyzed in multiple reaction monitoring (MRM) mode. The MRM pairs, declustering potentials (DP), and collision energies (CE) were optimized for each analyte. Data acquisitions and further analysis were conducted using Analyst software. SCIEX OS-MQ software was used to quantify all metabolites.</p>
</sec>
</sec>
<sec id="s2_11">
<label>2.11</label>
<title>Correlation analysis between gut microbiota and bile acid metabolite</title>
<p>The data on microbial diversity and the metabolome were initially screened based on the original differential criteria. Subsequently, the correlation coefficients among all differential features were calculated using the Spearman correlation algorithm.</p>
</sec>
<sec id="s2_12">
<label>2.12</label>
<title>Immunofluorescence staining</title>
<p>After fixation, the lung tissues were embedded and then sectioned. The sections were deparaffinized, dehydrated, and restored with a citrate-EDTA antigen retrieval solution. The primary antibodies, VCAM-1 (1:200, ab134047, Abcam), VE-cadherin (1:200, 27956-1-AP, Proteintech), CitH3 (1:200, 97272S, CST), MPO (1:200, AF3667, R&amp;D),phospho-SAPK/JNK(1:200,9255,CST), and phospho-p65(1:500,3033,CST)were utilized for overnight incubation with the lung sections at 4&#xb0;C. Afterward, the sections were exposed to fluorescent-conjugated secondary antibodies for 2 h at room temperature in darkness. After three washes with PBS, the sections were stained with DAPI solution (Beyotime, Shanghai, China). Ultimately, images were captured employing a fluorescence microscope.</p>
</sec>
<sec id="s2_13">
<label>2.13</label>
<title>ELISA assay</title>
<p>The concentration of MPO-DNA complexes in mouse serum was measured using the Mouse MPO-DNA ELISA Kit (ZC-56424, Zhuocai).</p>
</sec>
<sec id="s2_14">
<label>2.14</label>
<title>Immunohistochemical staining</title>
<p>Lung and intestinal tissue samples were fixed in 4% paraformaldehyde, paraffin-embedded, and sectioned (4 &#x3bc;m). After deparaffinization and rehydration, antigen retrieval was performed in a citrate buffer at 95 &#xb0;C for 20 minutes. Endogenous peroxidase was blocked with 3% H<sub>2</sub>O<sub>2</sub> for 10 minutes, and nonspecific binding was blocked with 5% BSA for 30 minutes. Sections were incubated with primary FXR (1:200, ab155124, Abcam) antibody at 4 &#xb0;C overnight, followed by HRP-conjugated secondary antibody at room temperature for 1 hour. DAB was used for chromogenic development, and hematoxylin was applied for counterstaining. After dehydration and mounting, FXR expression and localization were observed under a microscope. The positive area was quantitatively analyzed and calculated using ImageJ software.</p>
</sec>
<sec id="s2_15">
<label>2.15</label>
<title>Western blotting analysis</title>
<p>Lung tissue lysates with approximately 90 &#x3bc;g of proteins were resolved on 10% SDS-PAGE and were subjected to western blot assay utilizing the anti-CitH3 (1:1000, 97272S, CST), PAD4 (1:1000, HA721657, HUABIO), TLR4 (1:1000, 66350-1-Ig, Proteintech), GAPDH(1:3000,60004-1-Ig,Proteintech), &#x3b2;-actin(1:3000,66009-1-Ig,Proteintech), &#x3b2;-tubulin(1:500,ab18207,Abcam), MYD88 (1:1000, ab219413, Abcam). After incubation with the appropriate secondary antibody, the bands were visualized with the Molecular Imager System (BIO-RAD, Hercules, USA) with an enhanced chemiluminescence substrate (Thermo Fisher Scientific, Massachusetts, USA). Subsequently, the intensities of these bands were quantitatively analyzed and calculated using ImageJ software for precise and accurate measurements.</p>
</sec>
<sec id="s2_16">
<label>2.16</label>
<title>Network pharmacological analysis</title>
<sec id="s2_16_1">
<label>2.16.1</label>
<title>Screening of core drug active ingredients and targets</title>
<p>Based on data mining, rhubarb, mirabilitum, magnolia bark, forsythia, scutellaria, almond, bletilla striata, and pseudo-ginseng were selected as the core drugs. Their chemical components were retrieved from the TCMSP database, and active ingredients with an oral bioavailability (OB) greater than 30% and a drug-likeness index (DL) of 0.18 or higher were screened. Effective protein targets were obtained using TCMSP, and the target names were standardized through the UniProt database.</p>
</sec>
<sec id="s2_16_2">
<label>2.16.2</label>
<title>Acquisition of sepsis targets</title>
<p>Using &#x201c;sepsis&#x201d; as the keyword, genes related to sepsis were searched in the TTD, OMIM, DrugBank, and GeneCards databases. The gene names were then normalized and deduplicated through the UniProt database.</p>
</sec>
<sec id="s2_16_3">
<label>2.16.3</label>
<title>Construction of PPI network and &#x201c;drug-ingredient-target&#x201d; network</title>
<p>The intersection of genes related to core drug targets and sepsis targets was obtained using Jvenn. These genes were imported into the String database for protein-protein interaction (PPI) analysis (confidence level &gt; 0.9, species &#x201c;<italic>Homo sapiens</italic>&#x201d;), and the results were visualized using Cytoscape 3.9.1. A &#x201c;core drug-active ingredient-intersecting target&#x201d; network was constructed, and the network topology parameters were analyzed using the CytoNCA plugin to screen for key hub targets (DC, BC, CC greater than the median).</p>
</sec>
<sec id="s2_16_4">
<label>2.16.4</label>
<title>Functional enrichment analysis</title>
<p>Metascape (<ext-link ext-link-type="uri" xlink:href="http://metascape.org">http://metascape.org</ext-link>) was employed to perform Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on the intersecting targets (<italic>P</italic> &lt; 0.01). The GO analysis included three categories: Biological Process (BP), Cellular Component (CC), and Molecular Function (MF). Enriched terms with <italic>P</italic> &lt; 0.01 and containing at least three genes were selected for further analysis. The significance threshold for enrichment was set as a <italic>P</italic>-value &lt; 0.01 and a minimum enrichment factor of 1.5. Redundant terms were removed using Metascape&#x2019;s built-in redundancy reduction algorithm with a similarity cutoff of 0.3. The top 10 significantly enriched GO terms and KEGG pathways were visualized using an online microbiology tool (<ext-link ext-link-type="uri" xlink:href="https://www.bioinformatics.com.cn">https://www.bioinformatics.com.cn</ext-link>). The criteria for selecting representative pathways were based on both statistical significance (<italic>P</italic> &lt; 0.01) and biological relevance to sepsis and lung injury mechanisms.</p>
</sec>
</sec>
<sec id="s2_17">
<label>2.17</label>
<title>Statistical analysis</title>
<p>All data were expressed as mean &#xb1; standard deviation. Multivariate differences were evaluated by one-way analysis (ANOVA), followed by Fisher&#x2019;s LSD test. A <italic>p-</italic>value&lt;0.05 was considered statistically significant. All statistical analyses were performed using GraphPad Prism 10.0 (GraphPad Software Inc.).</p>
</sec>
</sec>
<sec id="s3" sec-type="results">
<label>3</label>
<title>Results</title>
<sec id="s3_1">
<label>3.1</label>
<title>Chemical composition of MDD</title>
<p>The chemical composition of MDD was analyzed applying UPLC-MS to establish quality control by identifying the ingredients. A total of 1259 components were identified (<xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary File 3</bold></xref>), including Baicalin, 4-aminobutyric acid (GABA), quercetin, emodin, and ginsenoside Rg3, among others. They mainly belong to Anthraquinones, anthrones, Flavones and fatty acids. The results of the negative ion flow chromatography for the MDD formula are presented in <xref ref-type="fig" rid="f2"><bold>Figure&#xa0;2</bold></xref>, while <xref ref-type="table" rid="T2"><bold>Table&#xa0;2</bold></xref> lists the 13 compounds along with their basic formulas and structures. Among the identified compounds, 12 components achieved a qualitative level of Level 1, while 1 is Level 2, signifying a high degree of confidence in the identification of these compounds.</p>
<fig id="f2" position="float">
<label>Figure&#xa0;2</label>
<caption>
<p>Total ion chromatogram of Modified Dachengqi Decoction (MDD) in negative ion mode of UPLC-MS. 1, 4-Aminobutyric acid (GABA); 2, Amygdalin; 3, Forsythiaside; 4, Scutellarin; 5, Baicalin; 6, Quercetin; 7, Emodin-8-glucoside; 8, batatasin III; 9, Rhein; 10, Emodin; 11, Ginsenoside Rg3; 12, Myristic acid; 13, Linoleic acid.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fcimb-16-1661639-g002.tif">
<alt-text content-type="machine-generated">Chromatogram showing intensity versus time with multiple peaks labeled from 1 to 13. The highest peak occurs at approximately 5 minutes, indicating significant intensity. Smaller peaks are distributed throughout the timeline, with labels marking key points.</alt-text>
</graphic></fig>
<table-wrap id="T2" position="float">
<label>Table&#xa0;2</label>
<caption>
<p>Results of UPLC-MS analysis of MDD.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="center">Peak name</th>
<th valign="middle" align="center">Retention time(min)</th>
<th valign="middle" align="center">m/zmed</th>
<th valign="middle" align="center">ms2Adduct</th>
<th valign="middle" align="center">Formula</th>
<th valign="middle" align="center">Subclass</th>
<th valign="middle" align="center">ms2 name</th>
<th valign="middle" align="left">Level</th>
<th valign="middle" align="center">Chemical structure</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="left">1</td>
<td valign="middle" align="left">0.716666667</td>
<td valign="middle" align="left">102.056</td>
<td valign="middle" align="left">[M-H]-</td>
<td valign="middle" align="left">C4H9NO2</td>
<td valign="middle" align="left">Amino acids</td>
<td valign="middle" align="left">4-Aminobutyric acid (GABA)</td>
<td valign="middle" align="left">1</td>
<td valign="top" align="center"><inline-graphic mimetype="image" mime-subtype="tiff" xlink:href="fcimb-16-1661639-i001.tif"><alt-text content-type="machine-generated">Chemical structure of a molecule with a six-carbon backbone featuring a carboxyl group at one end and an amine group at the other. Oxygen atoms are marked in red, and the nitrogen atom is marked in blue.</alt-text></inline-graphic></td>
</tr>
<tr>
<td valign="middle" align="left">2</td>
<td valign="middle" align="left">4.041666667</td>
<td valign="middle" align="left">456.1506</td>
<td valign="middle" align="left">[M-H]-</td>
<td valign="middle" align="left">C20H27NO11</td>
<td valign="middle" align="left">Cyanogenic glycosides</td>
<td valign="middle" align="left">Amygdalin</td>
<td valign="middle" align="left">1</td>
<td valign="top" align="center"><inline-graphic mimetype="image" mime-subtype="tiff" xlink:href="fcimb-16-1661639-i002.tif"><alt-text content-type="machine-generated">Chemical structure diagram displaying the molecular configuration of Burosumab. The molecule includes multiple hexagonal rings, oxygen atoms indicated by red, and a nitrogen atom highlighted in blue.</alt-text></inline-graphic></td>
</tr>
<tr>
<td valign="middle" align="left">3</td>
<td valign="middle" align="left">4.603333333</td>
<td valign="middle" align="left">623.1975</td>
<td valign="middle" align="left">[M-H]-</td>
<td valign="middle" align="left">C29H36O15</td>
<td valign="middle" align="left">Cinnamic acids and derivatives</td>
<td valign="middle" align="left">Forsythiaside</td>
<td valign="middle" align="left">1</td>
<td valign="top" align="center"><inline-graphic mimetype="image" mime-subtype="tiff" xlink:href="fcimb-16-1661639-i003.tif"><alt-text content-type="machine-generated">Chemical structure diagram displaying a complex organic molecule with multiple hexagonal rings. Various functional groups, including hydroxyl and ether linkages, are attached, indicated by black, red, and gray lines.</alt-text></inline-graphic></td>
</tr>
<tr>
<td valign="middle" align="left">4</td>
<td valign="middle" align="left">4.963333333</td>
<td valign="middle" align="left">461.0725</td>
<td valign="middle" align="left">[M-H]-</td>
<td valign="middle" align="left">C21H18O12</td>
<td valign="middle" align="left">Flavones</td>
<td valign="middle" align="left">Scutellarin</td>
<td valign="middle" align="left">1</td>
<td valign="top" align="center"><inline-graphic mimetype="image" mime-subtype="tiff" xlink:href="fcimb-16-1661639-i004.tif"><alt-text content-type="machine-generated">Chemical structure diagram of curcumin, featuring two aromatic rings connected by a linear chain of carbon atoms. Various hydroxyl and methoxy groups are attached to the rings and chain in specific positions, indicated by red and black bonds.</alt-text></inline-graphic></td>
</tr>
<tr>
<td valign="middle" align="left">5</td>
<td valign="middle" align="left">5.266666667</td>
<td valign="middle" align="left">445.0768</td>
<td valign="middle" align="left">[M-H]-</td>
<td valign="middle" align="left">C21H18O11</td>
<td valign="middle" align="left">Flavones</td>
<td valign="middle" align="left">Baicalin</td>
<td valign="middle" align="left">1</td>
<td valign="top" align="center"><inline-graphic mimetype="image" mime-subtype="tiff" xlink:href="fcimb-16-1661639-i005.tif"><alt-text content-type="machine-generated">Chemical structure of Quercetin, a flavonoid, showing a three-ring backbone with hexagonal shapes. Various hydroxyl (OH) groups are attached to the rings. The diagram highlights molecular bonds and positions of different functional groups.</alt-text></inline-graphic></td>
</tr>
<tr>
<td valign="middle" align="left">6</td>
<td valign="middle" align="left">5.526666667</td>
<td valign="middle" align="left">301.0352</td>
<td valign="middle" align="left">[M-H]-</td>
<td valign="middle" align="left">C15H10O7</td>
<td valign="middle" align="left">Flavonols</td>
<td valign="middle" align="left">Quercetin</td>
<td valign="middle" align="left">1</td>
<td valign="top" align="center"><inline-graphic mimetype="image" mime-subtype="tiff" xlink:href="fcimb-16-1661639-i006.tif"><alt-text content-type="machine-generated">Chemical structure diagram depicting quercetin, a flavonoid with three benzene rings and several hydroxyl groups attached.</alt-text></inline-graphic></td>
</tr>
<tr>
<td valign="middle" align="left">7</td>
<td valign="middle" align="left">5.915</td>
<td valign="middle" align="left">431.0981</td>
<td valign="middle" align="left">[M-H]-</td>
<td valign="middle" align="left">C21H20O10</td>
<td valign="middle" align="left">Anthraquinones and anthrones</td>
<td valign="middle" align="left">Emodin-8-glucoside</td>
<td valign="middle" align="left">1</td>
<td valign="top" align="center"><inline-graphic mimetype="image" mime-subtype="tiff" xlink:href="fcimb-16-1661639-i007.tif"><alt-text content-type="machine-generated">Chemical structure diagram of a compound with multiple hexagonal carbon rings and various functional groups, including hydroxyl and ketone groups. Bonds are represented in black and red lines, indicating different bond types.</alt-text></inline-graphic></td>
</tr>
<tr>
<td valign="middle" align="left">8</td>
<td valign="middle" align="left">6.285</td>
<td valign="middle" align="left">243.1026</td>
<td valign="middle" align="left">[M-H]-</td>
<td valign="middle" align="left">C15H16O3</td>
<td valign="middle" align="left">Monomeric stilbenes</td>
<td valign="middle" align="left">batatasin III</td>
<td valign="middle" align="left">2</td>
<td valign="top" align="center"><inline-graphic mimetype="image" mime-subtype="tiff" xlink:href="fcimb-16-1661639-i008.tif"><alt-text content-type="machine-generated">Chemical structure of adipic acid, showing two hexagonal carbon rings connected by a linear chain. Each ring has an attached oxygen and hydrogen group.</alt-text></inline-graphic></td>
</tr>
<tr>
<td valign="middle" align="left">9</td>
<td valign="middle" align="left">7.005</td>
<td valign="middle" align="left">283.0245</td>
<td valign="middle" align="left">[M-H]-</td>
<td valign="middle" align="left">C15H8O6</td>
<td valign="middle" align="left">Anthraquinones and anthrones</td>
<td valign="middle" align="left">Rhein</td>
<td valign="middle" align="left">1</td>
<td valign="top" align="center"><inline-graphic mimetype="image" mime-subtype="tiff" xlink:href="fcimb-16-1661639-i009.tif"><alt-text content-type="machine-generated">Chemical structure of acetylsalicylic acid (aspirin) shown with hexagonal benzene rings and various functional groups, including hydroxyl and acetyl, connected by bonds.</alt-text></inline-graphic></td>
</tr>
<tr>
<td valign="middle" align="left">10</td>
<td valign="middle" align="left">7.61</td>
<td valign="middle" align="left">269.0453</td>
<td valign="middle" align="left">[M-H]-</td>
<td valign="middle" align="left">C15H10O5</td>
<td valign="middle" align="left">Anthraquinones and anthrones</td>
<td valign="middle" align="left">Emodin</td>
<td valign="middle" align="left">1</td>
<td valign="top" align="center"><inline-graphic mimetype="image" mime-subtype="tiff" xlink:href="fcimb-16-1661639-i010.tif"><alt-text content-type="machine-generated">Chemical structure diagram of paracetamol, showing a benzene ring with hydroxyl and amide groups attached. The structure is depicted in a skeletal formula style with labels for each functional group.</alt-text></inline-graphic></td>
</tr>
<tr>
<td valign="middle" align="left">11</td>
<td valign="middle" align="left">7.833333333</td>
<td valign="middle" align="left">783.4899</td>
<td valign="middle" align="left">[M-H]-</td>
<td valign="middle" align="left">C42H72O13</td>
<td valign="middle" align="left">Dammarane and Protostane triterpenoids</td>
<td valign="middle" align="left">Ginsenoside Rg3</td>
<td valign="middle" align="left">1</td>
<td valign="top" align="center"><inline-graphic mimetype="image" mime-subtype="tiff" xlink:href="fcimb-16-1661639-i011.tif"><alt-text content-type="machine-generated">Chemical structure of vitamin D, consisting of a series of hexagonal and pentagonal rings connected by linear chains, with marked points representing atoms or functional groups.</alt-text></inline-graphic></td>
</tr>
<tr>
<td valign="middle" align="left">12</td>
<td valign="middle" align="left">9.083333333</td>
<td valign="middle" align="left">227.2017</td>
<td valign="middle" align="left">[M-H]-</td>
<td valign="middle" align="left">C14H28O2</td>
<td valign="middle" align="left">Branched fatty acids</td>
<td valign="middle" align="left">Myristic acid</td>
<td valign="middle" align="left">1</td>
<td valign="top" align="center"><inline-graphic mimetype="image" mime-subtype="tiff" xlink:href="fcimb-16-1661639-i012.tif"><alt-text content-type="machine-generated">Chemical structure diagram of caprylic acid, a carboxylic acid with an eight-carbon atom chain, featuring a carboxyl group at one end. The structure shows alternating single and double bonds.</alt-text></inline-graphic></td>
</tr>
<tr>
<td valign="middle" align="left">13</td>
<td valign="middle" align="left">9.22</td>
<td valign="middle" align="left">279.233</td>
<td valign="middle" align="left">[M-H]-</td>
<td valign="middle" align="left">C18H32O2</td>
<td valign="middle" align="left">Unsaturated fatty acids</td>
<td valign="middle" align="left">Linoleic acid</td>
<td valign="middle" align="left">1</td>
<td valign="top" align="center"><inline-graphic mimetype="image" mime-subtype="tiff" xlink:href="fcimb-16-1661639-i013.tif"><alt-text content-type="machine-generated">Chemical structure of oleic acid, showing a long chain hydrocarbon with a carboxylic acid group on one end and a cis double bond near the middle of the chain.</alt-text></inline-graphic></td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Level 1: Indicates that the sample material&#x2019;s MS/MS spectrum (all fragment ions of the material) and Retention time match the database material with a score of 0.7 or above.</p></fn>
<fn>
<p>Level 2: Represents a match between the sample material&#x2019;s MS/MS spectrum (including all fragment ions) and Retention time with the database material, scoring between 0.5 and 0.7.</p></fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3_2">
<label>3.2</label>
<title>MDD lowered sepsis mortality</title>
<p>Sepsis is characterized by a high mortality rate, and survival rates serve as a direct indicator of therapeutic efficacy. Following LPS injection, the survival rate at 72h was 50% (<xref ref-type="fig" rid="f1"><bold>Figure&#xa0;1C</bold></xref>). After MDD intervention, the mortality rate markedly decreased, with survival rates of 75%, 90%, and 70% observed in the low, intermediate, and high-dose MDD groups, respectively. Notably, the survival rate in the MDD-M group was comparable to that of the DEX group (90%).</p>
</sec>
<sec id="s3_3">
<label>3.3</label>
<title>MDD alleviated SI-ALI and improved pulmonary function</title>
<p>Pulmonary function tests reflect the health condition of the lungs, and lung injury typically induces a decline in these indicators. We utilized the EMMSlinks WBP system to assess general parameters, volume indicators, conductance metrics, airway obstruction indicators and ventilation parameters. Compared to the CON group, the LPS group demonstrated a reduction in tidal volume, a decrease in respiratory frequency, and a slowdown in peak inspiratory flow. Besides, general parameters such as inspiration time were prolonged (<xref ref-type="fig" rid="f1"><bold>Figures&#xa0;1D&#x2013;G</bold></xref>). These findings indicate that LPS injection successfully induced SI-ALI, which could be reversed following MDD intervention, with the MDD-M exhibiting the most significant improvement.</p>
<p>However, expiratory flow at 50% (EF50), an indicator of airway obstruction, was significantly reduced in the LPS group. Although MDD intervention showed a trend toward improvement, the difference was not statistically significant (<italic>p</italic> &gt; 0.05) (<xref ref-type="fig" rid="f1"><bold>Figure&#xa0;1H</bold></xref>). These findings suggested that MDD can mitigate lung injury caused by sepsis by improving lung volume, gas exchange and ventilation; however, it does not effectively relieve airway obstruction.</p>
<p>Pulmonary edema reflects the extent of lung injury. To assess the pulmonary edema, we measured the W/D ratio of the lungs and found that the LPS group exhibited an increased ratio, while MDD reduced the severity of pulmonary edema (<xref ref-type="fig" rid="f1"><bold>Figure&#xa0;1I</bold></xref>). After collecting BALF, we measured the levels of inflammatory factors, such as TNF-&#x3b1; and IL-6, using ELISA. The LPS group showed elevated levels of TNF-&#x3b1; and IL-6, whereas MDD intervention decreased these pro-inflammatory cytokines (<xref ref-type="fig" rid="f1"><bold>Figures&#xa0;1J,K</bold></xref>). Moreover, BCA quantification revealed an increase in protein content in the BALF of the LPS group, which was reduced by MDD treatment (<xref ref-type="fig" rid="f1"><bold>Figure&#xa0;1L</bold></xref>). In summary, MDD effectively alleviates pulmonary edema, reduces inflammation, and promotes the recovery of respiratory function, with the MDD-M group showing the most significant improvement compared to the DEX group.</p>
</sec>
<sec id="s3_4">
<label>3.4</label>
<title>MDD reduced inflammatory infiltration in the lungs and intestines, and alleviated endothelial dysfunction in the lungs</title>
<p>To further assess pulmonary inflammatory infiltration, we performed H&amp;E staining to observe the extent of inflammatory cell infiltration in the lungs. The LPS group exhibited significant exudation, while the infiltration of inflammatory cells was notably reduced after MDD treatment (<xref ref-type="fig" rid="f3"><bold>Figure&#xa0;3A</bold></xref>). Similarly, the LPS group showed marked intestinal injury characterized by extensive inflammatory cell infiltration and destruction of the intestinal wall (<xref ref-type="fig" rid="f3"><bold>Figure&#xa0;3B</bold></xref>). However, MDD intervention resulted in marked improvement in both lung and intestinal conditions (<xref ref-type="fig" rid="f3"><bold>Figures&#xa0;3C,D</bold></xref>).</p>
<fig id="f3" position="float">
<label>Figure&#xa0;3</label>
<caption>
<p>MDD reduced inflammatory infiltration in the lungs and intestine, and alleviated endothelial dysfunction in the lungs. <bold>(A)</bold> H&amp;E staining in the lung and (n=6,bar:100&#xb5;m)<bold>(B)</bold> gut(n=6,bar:100&#xb5;m). <bold>(C)</bold> The injury score of lung and <bold>(D)</bold> gut. <bold>(E)</bold> The VCAM-1 and <bold>(F)</bold> VE-cadherin expression in lung tissue(bar:10&#xb5;m). <bold>(G)</bold> Relative Fluorescence of VCAM-1(n=6) and <bold>(H)</bold> VE-cadherin(n=6) in lung tissue. These results were presented as mean &#xb1; SD. *<italic>p</italic> &lt; 0.05, **<italic>p</italic> &lt; 0.01,***<italic>p</italic> &lt; 0.001, compared with the CON group. #<italic>p</italic> &lt; 0.05, ##<italic>p</italic> &lt; 0.01, ###<italic>p</italic> &lt; 0.001, compared with the LPS group.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fcimb-16-1661639-g003.tif">
<alt-text content-type="machine-generated">Histological and immunofluorescence analysis images show lung and gut tissue samples under various treatments (CON, LPS, MDD-L, MDD-M, MDD-H, DEX), with HE staining. Bar charts (C, D) compare lung and gut inflammation scores across treatments. Immunofluorescence panels (E, F) display VCAM-1 and VE-cadherin expression with DAPI staining. Corresponding graphs (G, H) quantify expression levels as percentages, indicating statistical significance between groups.</alt-text>
</graphic></fig>
<p>One of the hallmarks of sepsis is endothelial dysfunction (<xref ref-type="bibr" rid="B84">Zhang et&#xa0;al., 2023</xref>). Given that MDD significantly improved pulmonary exudation, we hypothesized that this effect may be associated with the amelioration of pulmonary vascular endothelial dysfunction. We next assessed the expression of vascular cell adhesion molecule-1(VCAM-1) and vascular endothelial cadherin (VE-cadherin), and found that in the LPS group, the endothelial integrity marker VE-cadherin (<xref ref-type="bibr" rid="B21">Giannotta et&#xa0;al., 2013</xref>) was reduced (<xref ref-type="fig" rid="f3"><bold>Figure&#xa0;3E</bold></xref>), while the endothelial injury marker VCAM-1 (<xref ref-type="bibr" rid="B70">Wang L. et&#xa0;al., 2024</xref>) was elevated (<xref ref-type="fig" rid="f3"><bold>Figure&#xa0;3F</bold></xref>). Notably, MDD treatment alleviated the severity of endothelial dysfunction (<xref ref-type="fig" rid="f3"><bold>Figures&#xa0;3G, H</bold></xref>). Collectively, these findings suggest that MDD alleviates SI-ALI by enhancing endothelial function.</p>
</sec>
<sec id="s3_5">
<label>3.5</label>
<title>MDD improved gut dysbiosis</title>
<p>TCM emphasizes the connection between the lungs and intestine, which has attracted significant interest in recent years (<xref ref-type="bibr" rid="B72">Wang et&#xa0;al., 2022</xref>, <xref ref-type="bibr" rid="B71">2023</xref>). Our results demonstrated that MDD confers protection against intestinal injury, potentially through the restoration of gut microbiota dysbiosis. Analysis of 16S rRNA sequencing data revealed that as the sampling depth increased, the Chao1 and phylogenetic diversity (PD) whole tree curves plateaued, indicating sufficient sequencing depth for reliable downstream analysis (<xref ref-type="fig" rid="f4"><bold>Figures&#xa0;4A, B</bold></xref>).</p>
<fig id="f4" position="float">
<label>Figure&#xa0;4</label>
<caption>
<p>MDD improved the dysbiosis of the gut microbiota. <bold>(A)</bold> The Chao1 and <bold>(B)</bold> PD whole tree curves. <bold>(C)</bold> &#x3b1;-diversity analysis based on the Shannon index(n=6) and Chao1 index(n=6). <bold>(D)</bold> PCoA plots of &#x3b2;-diversity(n=6). <bold>(E)</bold> Bacterial taxa profiling of gut microbiota at the phylum level. <bold>(F)</bold> Bacterial taxa profiling of gut microbiota at the genus level. *<italic>p</italic> &lt; 0.05, **<italic>p</italic> &lt; 0.01,***<italic>p</italic> &lt; 0.001, compared with the CON group. #<italic>p</italic> &lt; 0.05, ##<italic>p</italic> &lt; 0.01, ###<italic>p</italic> &lt; 0.001, compared with the LPS group.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fcimb-16-1661639-g004.tif">
<alt-text content-type="machine-generated">Two-part figure related to microbial diversity and abundance. (A) Chao1 and (B) PD whole tree diversity indices, comparing groups CON, LPS, and MDD by sequences per sample. (C) Boxplots for the Shannon index and Chao1 metric, showing differences among groups. (D) PCoA plot with PERMANOVA p-value of 0.001, illustrating distinct clustering of groups. (E) Bar graphs of microbial abundance at the phylum level and (F) genus level across the groups, with a legend indicating various taxa.</alt-text>
</graphic></fig>
<p>The &#x3b1;-diversity analysis validated that the Chao1 and Shannon indices in the LPS were significantly lower than those in the CON group, suggesting that LPS markedly reduced the gut microbiota diversity. In contrast, these indices were higher in the MDD group than in the LPS group. However, no significant difference was observed between the two groups (<xref ref-type="fig" rid="f4"><bold>Figure&#xa0;4C</bold></xref>).</p>
<p>The PCoA analysis revealed distinct &#x3b2;-diversity clustering patterns among the CON, LPS and MDD groups (<xref ref-type="fig" rid="f4"><bold>Figure&#xa0;4D</bold></xref>). Notably, the clustering trend of the MDD was similar to that of the CON group, indicating shared characteristics in their gut microbiota. These results suggest that the improvement of SI-ALI by MDD may be attributed to some extent to the modulation of gut microbiota composition. Next, we investigated the specific taxonomic alterations in gut microbiota induced by MDD.</p>
<p>At the phylum level, <italic>Actinobacteriota</italic>, <italic>Firmicutes</italic> and <italic>Bacteroidota</italic> were found to play significant regulatory roles (<xref ref-type="fig" rid="f4"><bold>Figure&#xa0;4E</bold></xref>). Compared to the CON group, the abundance of <italic>Actinobacteriota</italic> and <italic>Firmicutes</italic> in the LPS was comparatively reduced, while MDD reversed this trend. On the contrary, the abundance of <italic>Bacteroidota</italic> was relatively higher in the LPS group, but MDD markedly reversed this increase in abundance.</p>
<p>Next, we investigated the differences in microbial communities at the genus level. The results revealed that compared to the CON group, the abundance of <italic>Alistipes</italic>, <italic>Rikenellaceae_RC9_gut_group</italic>, <italic>Parabacteroides</italic> and <italic>Bacteroides</italic> was increased in the LPS group, while MDD reversed this trend (<xref ref-type="fig" rid="f4"><bold>Figure&#xa0;4F</bold></xref>). These findings confirm that MDD plays an important role in multiple levels of the gut microbiota within the SI-ALI model.</p>
</sec>
<sec id="s3_6">
<label>3.6</label>
<title>MDD regulated bile acid metabolism</title>
<p>Bile acids, as crucial metabolites of gut microbiota, can regulate the severity of SI-ALI based on their concentration (<xref ref-type="bibr" rid="B26">He et&#xa0;al., 2023</xref>). Studies have demonstrated that bile acids exhibit concentration-dependent immunomodulatory properties. In acute pancreatitis models, conjugated bile acids such as GCDCA significantly suppress NLRP3 inflammasome activation by activating the TGR5 receptor pathway (<xref ref-type="bibr" rid="B87">Zhang Z. Y. et&#xa0;al., 2024</xref>). However, at high concentrations, bile acids exhibit cytotoxicity, Deoxycholic acid (DCA) and CDCA were found to induce robust secretion of pro-inflammatory cytokines IL-1&#x3b1; and IL-1&#x3b2; from bone marrow-derived dendritic cells in ex vivo experiments (<xref ref-type="bibr" rid="B55">Oleszycka et&#xa0;al., 2023</xref>).</p>
<p>We next employed targeted bile acid metabolism to investigate changes in bile acid profiles in feces, ultimately identifying 53 distinct bile acids. The PCA plot revealed marked differences across the three groups of metabolites (<xref ref-type="fig" rid="f5"><bold>Figure&#xa0;5A</bold></xref>), with significant differences between the CON and LPS groups. Subsequently, heat maps(<xref ref-type="fig" rid="f5"><bold>Figure&#xa0;5B</bold></xref>) and barplots (<xref ref-type="fig" rid="f5"><bold>Figures&#xa0;5C&#x2013;H</bold></xref>) were generated to illustrate the expression levels of all metabolites. The results revealed that, compared to the CON group, Cholic Acid 7 Sulfate (CA7S) was significantly decreased in the LPS but showed an increase in the MDD group. Notably, CA7S does not conform strictly to the conventional classifications of primary or secondary bile acids; instead, it is recognized as a metabolite or derivative of bile acid. Conversely, the abundance of Glycoursodeoxycholic Acid (GUDCA), Taurochenodesoxycholic Acid (TCDCA), Chenodeoxycholic Acid (CDCA), Taurocholic Acid (TCA) were significantly increased in LPS group and MDD decreased them. For Ursodeoxycholic Acid (UDCA), there were no change between LPS group and CON group, while MDD still decreased it. This suggests that MDD has a significant impact on both bile acids and bile acid metabolites. Overall, LPS significantly increased primary and secondary bile acids, whereas MDD markedly reduced their levels, which may be closely associated with alterations in gut microbiota.</p>
<fig id="f5" position="float">
<label>Figure&#xa0;5</label>
<caption>
<p>MDD regulates bile acid metabolism. <bold>(A)</bold> PCA plots of samples from each group(n=5-6). <bold>(B)</bold> Heatmap analysis of bile acid metabolism. <bold>(C-H)</bold> Abundance of cholic acid 7 sulfate (CA7S), Glycoursodeoxycholic Acid(GUDCA),Taurochenodesoxycholic Acid (TCDCA),Chenodeoxycholic Acid(CDCA),Taurocholic Acid(TCA),Ursodeoxycholic Acid(UDCA)(n=5-6). These results were presented as mean &#xb1; SD. *<italic>p</italic> &lt; 0.05, **<italic>p</italic> &lt; 0.01,***<italic>p</italic> &lt; 0.001, compared with the CON group. #<italic>p</italic> &lt; 0.05, ##<italic>p</italic> &lt; 0.01, ###<italic>p</italic> &lt; 0.001, compared with the LPS group.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fcimb-16-1661639-g005.tif">
<alt-text content-type="machine-generated">PCA plot, heatmap, and bar graphs depict data analysis across control (CON), LPS, and MDD groups. The PCA shows distinct group clustering. The heatmap visualizes metabolite abundance variations, with color gradients. Bar graphs (C-H) display abundance of specific acids: cholic acid 7 sulfate, glycoursodeoxycholic acid, taurochenodeoxycholic acid, chenodeoxycholic acid, taurocholic acid, and ursodeoxycholic acid across groups, with statistical significance indicated by asterisks and hash symbols.</alt-text>
</graphic></fig>
</sec>
<sec id="s3_7">
<label>3.7</label>
<title>MDD improved the gut microbiota and inhibited levels of bile acids</title>
<p>Gut microbiota and bile acids are closely correlated, influencing each other&#x2019;s generation and composition (<xref ref-type="bibr" rid="B11">Collins et&#xa0;al., 2023</xref>). <italic>Bacteroides</italic> and <italic>Parabacteroides distasonis</italic> alleviates obesity and metabolic dysfunctions via production of secondary bile acids (<xref ref-type="bibr" rid="B76">Xiang et&#xa0;al., 2022</xref>; <xref ref-type="bibr" rid="B73">Wei et&#xa0;al., 2023</xref>). The correlation analysis revealed that <italic>Parabacteroides</italic>, <italic>Bacteroides, Alistipes</italic>, and <italic>Rikenellaceae_RC9_gut_group</italic> exhibited a positive correlation with GUDCA, CDCA, TCDCA, and TCA (<xref ref-type="fig" rid="f6"><bold>Figure 6</bold></xref>).</p>
<fig id="f6" position="float">
<label>Figure&#xa0;6</label>
<caption>
<p>Correlation analysis between phylum and genus level of gut microbiota and bile acid metabolite. The correlation analysis between genus level of gut microbiota and bile acid metabolite.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fcimb-16-1661639-g006.tif">
<alt-text content-type="machine-generated">Heatmap showing correlations between various bile acids and bacterial taxa. The vertical axis lists bile acids, while the horizontal axis lists bacterial taxa. Color intensity ranges from red for positive correlations to blue for negative correlations, with varying shades representing correlation strengths. Specific labels and correlation values are marked with stars for significance.</alt-text>
</graphic></fig>
</sec>
<sec id="s3_8">
<label>3.8</label>
<title>MDD alleviated endothelial dysfunction by promoting the reduction of NETs</title>
<p>Based on our research on gut microbiota and bile acid metabolism, we found that MDD could improve gut microbiota structure and alter primary bile acids and their derivatives. However, the mechanisms remain unclear. Previous studies indicated that NETs may promote inflammation, activate endothelial cells, or directly damage the endothelial glycocalyx, impairing endothelial function and facilitating sepsis ultimately (<xref ref-type="bibr" rid="B49">Ma et&#xa0;al., 2019</xref>; <xref ref-type="bibr" rid="B30">Iba et&#xa0;al., 2024</xref>).</p>
<p>It is reported that NETs is a significant mechanism contributing to endothelial dysfunction (<xref ref-type="bibr" rid="B56">Papayannopoulos, 2018</xref>). The citrullinated histone H3 (CitH3) serves as a biomarker for the formation of NETs (<xref ref-type="bibr" rid="B51">Masuda et&#xa0;al., 2016</xref>). Immunofluorescence experiments demonstrated a predominant growth in the co-expression of CitH3 and the MPO in the LPS, which was reversed by MDD (<xref ref-type="fig" rid="f7"><bold>Figures&#xa0;7A, B</bold></xref>). Myeloperoxidase (MPO) is an enzyme predominantly localized in neutrophils and monocytes. The MPO-DNA complex, which signifies the co-localization of neutrophil-derived proteins and extracellular DNA, is an indicator of NETs formation (<xref ref-type="bibr" rid="B51">Masuda et&#xa0;al., 2016</xref>). Accordingly, we measured the concentration of the MPO-DNA complex in mouse serum and found a remarkable increase in the LPS group, which could be reduced by MDD (<xref ref-type="fig" rid="f7"><bold>Figure&#xa0;7C</bold></xref>). Western blot analysis also shows MDD decrease the CitH3 of lung. (<xref ref-type="fig" rid="f7"><bold>Figure&#xa0;7D</bold></xref>). It has been reported that peptidyl arginine deiminase 4 (PAD4) citrullinates histones, leading to the loosening of chromatin structure and promoting DNA release, which is a key step in the formation of NETs (<xref ref-type="bibr" rid="B47">Liu et&#xa0;al., 2024</xref>). By analyzing its expression in lung tissue, we observed results consistent with those of CitH3. LPS significantly increased its expression, whereas MDD treatment effectively reversed this effect. These findings suggest that MDD may reduce SI-ALI by mitigating excessive NETs to improve endothelial dysfunction.</p>
<fig id="f7" position="float">
<label>Figure&#xa0;7</label>
<caption>
<p>MDD alleviated endothelial dysfunction by promoting the reduction of NETs. <bold>(A, B)</bold> The co-expression of CitH3 and MPO in lung tissue(n=6,bar:50&#xb5;m). <bold>(C)</bold> The MPO-DNA complex in serum. <bold>(D)</bold> The relative protein expression of CitH3 and PAD4 in lung tissue(n=5). These results were presented as mean &#xb1; SD. *<italic>p</italic> &lt; 0.05, **<italic>p</italic> &lt; 0.01,***<italic>p</italic> &lt; 0.001, compared with the CON group. #<italic>p</italic> &lt; 0.05, ##<italic>p</italic> &lt; 0.01, ###<italic>p</italic> &lt; 0.001, compared with the LPS group.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fcimb-16-1661639-g007.tif">
<alt-text content-type="machine-generated">Panel A displays fluorescence microscopy images showing CitH3, MPO, and DAPI staining, with a merged view, comparing control (CON), LPS, and MDD groups. Panel B is a bar graph illustrating the percentage of NETs-releasing cells, while Panel C shows serum MPO-DNA levels in ng/mL for each group. Panel D includes Western blot analysis of CitH3, PAD4, and loading controls β-actin and GAPDH, with corresponding quantification graphs. Significant differences are indicated by asterisks and hashes.</alt-text>
</graphic></fig>
</sec>
<sec id="s3_9">
<label>3.9</label>
<title>MDD primarily acted on the FXR/TLR4/MYD88 signaling pathway</title>
<p>We obtained 4,478 sepsis-related gene targets and 227 MDD-associated target genes through database searching. Using Jvenn online software analysis, we identified 154 overlapping genes between the core drug target genes and sepsis targets (<xref ref-type="fig" rid="f8"><bold>Figure&#xa0;8A</bold></xref>). Subsequently, these 154 overlapping genes were imported into the STRING database to construct a PPI network (<xref ref-type="fig" rid="f8"><bold>Figure&#xa0;8B</bold></xref>). The results of GO functional analysis revealed that these genes or proteins were primarily involved in biological processes such as tumor necrosis factor response, neuronal apoptosis, and metal ion response. They were localized in cellular components, including membrane rafts, cytoplasmic vesicles, and granular lumina, and possess molecular functions like DNA-binding transcription factor binding, cytokine activity, and kinase activator binding, suggesting their critical roles in signal transduction, immune regulation, and gene expression control (<xref ref-type="fig" rid="f8"><bold>Figure&#xa0;8C</bold></xref>). Moreover, KEGG pathway enrichment analysis indicated that the core targets were significantly enriched in the Toll-like receptor signaling pathway, further untangling their potential regulatory mechanisms in immune and inflammatory responses (<xref ref-type="fig" rid="f8"><bold>Figure&#xa0;8D</bold></xref>). The key upregulated genes in the TLR signaling pathway were visualized using the multiprotein Profiler 1.0 (MPP) web server (<ext-link ext-link-type="uri" xlink:href="https://mproteinprofiler.microbiologyandimmunology.dal.ca/">https://mproteinprofiler.microbiologyandimmunology.dal.ca/</ext-link>)(<xref ref-type="bibr" rid="B59a">Sganzerla Martinez et&#xa0;al., 2024</xref>), and their protein physicochemical characteristics were displayed, as shown in <xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary file 9-10</bold></xref>.</p>
<fig id="f8" position="float">
<label>Figure&#xa0;8</label>
<caption>
<p>Network pharmacological analysis of MDD and sepsis. <bold>(A)</bold> Intersection targets of sepsis and MDD; <bold>(B)</bold> PPI network diagram of intersection targets of sepsis and MDD; <bold>(C)</bold> Bar charts of biological processes, cellular components, and molecular functions from GO enrichment analysis; <bold>(D)</bold> Bubble chart of the top 10 KEGG enrichment analyses.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fcimb-16-1661639-g008.tif">
<alt-text content-type="machine-generated">(A) Venn diagram showing overlap of gene expression between sepsis and MDD with 4324 sepsis-specific, 73 MDD-specific, and 154 common genes. (B) Protein-protein interaction network with red nodes highlighting central proteins like ESR1, HIF1A, and AKT1. (C) Bar chart categorizing gene counts into biological processes (BP), cellular components (CC), and molecular functions (MF). (D) Dot plot of pathways such as Toll-like receptor signaling and NF-kappa B signaling, with color and size indicating p-value and gene count.</alt-text>
</graphic></fig>
<p>FXR serves as a crucial sensor for bile acids and can be inhibited by <italic>Parabacteroides distasonis</italic> (<xref ref-type="bibr" rid="B88">Zhao et&#xa0;al., 2023</xref>). According to research findings, mice overexpressing FXR exhibit enhanced tolerance to sepsis (<xref ref-type="bibr" rid="B25">Hao et&#xa0;al., 2017</xref>; Li et&#xa0;al., 2023). In this study, we examined the expression of FXR in intestinal and lung tissues. The results indicated a significant reduction in both the lungs and intestines of the LPS group compared to the CON group (<xref ref-type="fig" rid="f9"><bold>Figures&#xa0;9A, B</bold></xref>). Conversely, MDD treatment increased FXR expression, primarily in lung epithelial cells (<xref ref-type="fig" rid="f9"><bold>Figure&#xa0;9B</bold></xref>). Furthermore, the toll-like receptor signaling pathway identified through enrichment analysis in network pharmacology has been experimentally validated. Toll-like receptor 4(TLR4) is a crucial member of the toll-like receptor family, while myeloid differentiation primary response 88(MYD88) serves as a key adaptor protein downstream. The TLR4/MYD88 signaling pathway has been identified as a critical regulator of NETs in sepsis. Studies have demonstrated that MYD88-deficient mice exhibit reduced formation of lethal intravascular NETs, consequently attenuating septic progression, which underscores the pivotal role of MYD88 in NET hyperactivation (<xref ref-type="bibr" rid="B28">Hu and Mao, 2016</xref>).Our results indicated a significant upregulation of TLR4 and MYD88 expression in the LPS group, whereas a downregulation was observed in the MDD group (<xref ref-type="fig" rid="f9"><bold>Figure&#xa0;9C</bold></xref>). Furthermore, the TLR4 signaling pathway is closely associated with the expression of other inflammatory mediators, including NF-&#x3ba;B and IL-1&#x3b2;, which also participate in the formation and modulation of NETs (<xref ref-type="bibr" rid="B14">Englert et&#xa0;al., 2025</xref>). The immunofluorescence detection of phosphorylated p65 (p-P65) and phosphorylated JNK (p-JNK) in lung tissues was performed. The results indicated that LPS significantly increased the positive signals of p-P65 and p-JNK in lung tissues, whereas MDD treatment markedly attenuated the expression of these phosphorylation markers (<xref ref-type="fig" rid="f9"><bold>Figure&#xa0;9D, E</bold></xref>). Therefore, the FXR/TLR4/MYD88 signaling pathway may serve as a crucial mechanism through which MDD modulates SI-ALI, and this mechanism is closely associated with <italic>Parabacteroides distasonis</italic>.</p>
<fig id="f9" position="float">
<label>Figure&#xa0;9</label>
<caption>
<p>MDD primarily acted on the FXR/TLR4/MYD88 signaling pathway. <bold>(A)</bold> FXR expression in lung tissue(n=6,bar:100&#xb5;m); <bold>(B)</bold> FXR expression in gut(n=6,bar:50&#xb5;m); <bold>(C)</bold> Expression levels of TLR4/MYD88 in lung tissue(n=6). <bold>(D)</bold>The p-JNK and <bold>(E)</bold> p-p65 expression in lung tissue(n=6,bar:50&#xb5;m)These results were presented as mean &#xb1; SD. *<italic>p</italic> &lt; 0.05, **<italic>p</italic> &lt; 0.01,***<italic>p</italic> &lt; 0.001, compared with the CON group. #<italic>p</italic> &lt; 0.05, ##<italic>p</italic> &lt; 0.01, ###<italic>p</italic> &lt; 0.001, compared with the LPS group.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fcimb-16-1661639-g009.tif">
<alt-text content-type="machine-generated">Histology and immunofluorescence images depict FXR expression in the lung and gut (A, B), with bar charts showing quantification of positive areas. Western blots in (C) display TLR4 and MYD88 expression across conditions, with quantitative analysis. Panels (D, E) exhibit DAPI staining and p-JNK/p-p65 expression in lung tissues, combining images with corresponding bar charts of expression levels. Each set is labeled with CON, LPS, and MDD, indicating distinct experimental groups. The scale is provided for cell size reference.</alt-text>
</graphic></fig>
</sec>
</sec>
<sec id="s4" sec-type="discussion">
<label>4</label>
<title>Discussion</title>
<p>In this study, we employed a comprehensive approach combining 16S rRNA sequencing and targeted bile acid metabolomics to investigate the gut-lung axis correlation and the therapeutic role of anti-SI-ALI. Our mechanistic findings demonstrated that MDD exerts protective effects against LPS-induced SI-ALI through multiple pathways. Specifically, MDD downregulates NETs activation primarily via modulation of the FXR/TLR4/MYD88 signaling pathway (<xref ref-type="fig" rid="f10"><bold>Figures&#xa0;10</bold></xref>, <xref ref-type="fig" rid="f11"><bold>11</bold></xref>). Furthermore, our results show that MDD significantly decreases fecal levels of primary and secondary bile acids and modulates gut microbiota composition in SI-ALI mice, particularly by reducing the abundance of <italic>Parabacteroides</italic> and <italic>Bacteroides</italic> genus. These combined effects contribute to SI-ALI alleviation through two primary mechanisms: mitigation of intrapulmonary NETs formation and reduction of pulmonary endothelial dysfunction. Importantly, our findings establish that the therapeutic efficacy of MDD in SI-ALI is significantly associated with its dual regulatory effects on gut microbiota composition and bile acid metabolism.</p>
<fig id="f10" position="float">
<label>Figure&#xa0;10</label>
<caption>
<p>The potential mechanism of MDD in alleviating SI-ALI partially through influencing bile acids metabolism and gut microbiota.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fcimb-16-1661639-g010.tif">
<alt-text content-type="machine-generated">Diagram illustrating the effects of MDD oral administration on a mouse model. The process involves LPS injection and monitoring survival rates. Pathway analysis shows impacts on the gut-lung axis, including fecal 16s rRNA sequencing, changes in bile acid metabolism, and downregulation of certain bacteria. It highlights improved pulmonary and endothelial function with reduced lung weight ratios and inflammatory markers, leading to ameliorated SI-ALI.</alt-text>
</graphic></fig>
<fig id="f11" position="float">
<label>Figure&#xa0;11</label>
<caption>
<p>Methodology of this study.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fcimb-16-1661639-g011.tif">
<alt-text content-type="machine-generated">Flowchart depicting a study on mice divided into two parts. Part 1 involves 100 mice divided into groups receiving LPS, MDD-L, MDD-M, MDD-H, and DEX treatments, monitored for 72-hour survival. Part 2 involves 90 mice, similarly grouped, undergoing a 24-hour post-LPS injection analysis. Procedures include serum ELISA, lung tissue (H and E, IHC, Western blot), intestine (H and E, IHC), and fecal sample analysis for RNA and bile acid metabolism.</alt-text>
</graphic></fig>
<p>In this study, MDD significantly improved the 72-hour survival rate, indicating its comprehensive protective effect against acute inflammatory outburst and its downstream complications. The increase in survival was consistent with reductions in inflammatory factors, decreased NETs, and recovery of pulmonary function, suggesting that MDD alleviates SI-ALI not through a single pathway but via multi-target synergistic mechanisms. Improvements in respiratory parameters such as tidal volume and peak inspiratory flow reflected the restoration of gas exchange and lung compliance, which likely resulted from reduced pulmonary interstitial edema and inflammatory cell infiltration, thereby lowering alveolar&#x2013;capillary barrier permeability. Decreases in the W/D weight ratio and BALF protein further demonstrated the alleviation of microvascular permeability and pulmonary edema, supporting its protective effect on the microvascular barrier. Meanwhile, downregulation of TNF-&#x3b1; and IL-6 indicated suppression of the early inflammatory amplification loop. This anti-inflammatory effect corresponded with improvements in endothelial markers&#x2014;MDD reversed the LPS-induced upregulation of VCAM-1 and downregulation of VE-cadherin, demonstrating that it not only reduces pro-inflammatory stimuli but also preserves cell&#x2013;cell adhesion and endothelial integrity. To explore the upstream factors underlying these outcomes, the following section will focus on alterations in gut microbiota and bile acid metabolism, as well as evidence supporting their roles as potential mediating mechanisms. A recent study has identified that phenylpyruvate, secreted by intestinal <italic>Candida albicans</italic>, may target SIRT2 in the context of sepsis (<xref ref-type="bibr" rid="B22">Gu et&#xa0;al., 2023</xref>). Moreover, the production of hyodeoxycholic acid (HDCA) functions as an endogenous inhibitor of TLR4, potentially mitigating systemic inflammation associated with sepsis (<xref ref-type="bibr" rid="B36">Li J. et&#xa0;al., 2023</xref>). A recent study revealed that rhamnose, a bacterial carbohydrate metabolite, could enhance the phagocytic capacity of macrophages by directly binding to and activating SLC12A4 within these cells, which protect the host against sepsis (<xref ref-type="bibr" rid="B35">Li D. et&#xa0;al., 2024</xref>). The above findings overlap in their assertion of the significant role of gut microbiota and their associated metabolites in the pathogenesis of sepsis. Nonetheless, the application of fecal microbiota transplantation(FMT) in patients presents considerable challenges (<xref ref-type="bibr" rid="B1">Biemond et&#xa0;al., 2023</xref>), emphasizing the need to investigate straightforward and practical approaches for modulating gut microbiota within the framework of sepsis treatment. To address this challenge, the present study employed a TCM formulation to evaluate its effects on gut microbiota, aiming to untangle the mechanism underlying lung-gut co-therapy and develop an alternative rapid strategy for FMT.</p>
<p>Previous studies have demonstrated that MDD exhibits promising efficacy in clinical disease treatment (<xref ref-type="bibr" rid="B10">Chen et&#xa0;al., 2016</xref>, <xref ref-type="bibr" rid="B9">2018</xref>). However, its specific clinical mechanisms remain unclear, and direct evidence for the role of traditional Chinese medicine in alleviating pulmonary and intestinal complications associated with sepsis is relatively limited. In this study, we found that MDD could improve <italic>Parabacteroides</italic> and <italic>Bacteroides</italic> on genus level. Studies have revealed that <italic>Parabacteroides</italic> exhibits a dual role. Lower abundance is showed in patients with obesity, inflammatory bowel disease (IBD), and metabolic syndrome, whereas it is increased in individuals with psoriasis, neonatal cholestasis, alopecia areata, hypertension, and polycystic ovary syndrome (PCOS) (<xref ref-type="bibr" rid="B16">Ezeji et&#xa0;al., 2021</xref>). This suggests that <italic>Parabacteroides</italic> may display both probiotic and pathogenic properties depending on virous host. Notably, it promotes the synthesis of secondary bile acids (<xref ref-type="bibr" rid="B69">Wang et&#xa0;al., 2019</xref>), which aligns with the findings of this study.</p>
<p>Similarly, <italic>Bacteroides</italic>, a prominent genus within the gut microbiota, functions as an opportunistic pathogen like <italic>Parabacteroides</italic> (<xref ref-type="bibr" rid="B81">Zafar and Saier, 2021</xref>). It is now understood that <italic>Bacteroides</italic> play a crucial role in maintaining gut health and facilitating digestion in healthy individuals. However, under compromised immune function, broad-spectrum antibiotic employment, or other adverse conditions, <italic>Bacteroides</italic> may overgrow, increasing the risk of sepsis (<xref ref-type="bibr" rid="B66">van der Poll et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B54">Mu et&#xa0;al., 2022</xref>). Such over-proliferation can lead to bacterial translocation and toxin release, triggering a systemic inflammatory response that may ultimately result in multi-organ dysfunction, posing a serious threat to patient survival. In this respect, a high abundance of <italic>Bacteroides</italic> has been associated with increased likelihood of achieving a Sequential Organ Failure Assessment (SOFA) score of 10 or more during hospitalization, a higher proportion of patients with ICU stays exceeding 30 days, and elevated mortality rates (<xref ref-type="bibr" rid="B63">Sun et&#xa0;al., 2023</xref>). Given that our results indicated that MDD could reduce the abundance of <italic>Bacteroides</italic> in mice, we hypothesize that the alleviation of SI-ALI by MDD may be associated with <italic>Parabacteroides</italic> and <italic>Bacteroides</italic>.</p>
<p>The role of bile acids in lung injury exhibits a double-edged effect. On one hand, certain bile acids demonstrate significant therapeutic potential. For instance, GCDCA significantly suppress NLRP3 inflammasome activation by activating the TGR5 receptor pathway (<xref ref-type="bibr" rid="B85">Zhang Z. Y. et&#xa0;al., 2024</xref>). On the other hand, bile acids can induce lung injury through various mechanisms, including interaction with the secretory phospholipase A2 (sPLA2) pathway, disruption of alveolar surfactant function, modulation of inflammatory responses and local immunity, as well as direct cytotoxic effects (<xref ref-type="bibr" rid="B12">De Luca et&#xa0;al., 2022</xref>).A high concentrations-bile acids exhibit cytotoxicity, Deoxycholic acid (DCA) and CDCA were found to induce robust secretion of pro-inflammatory cytokines IL-1&#x3b1; and IL-1&#x3b2; from bone marrow-derived dendritic cells (<xref ref-type="bibr" rid="B55">Oleszycka et&#xa0;al., 2023</xref>). Our findings demonstrate that MDD reduces the concentrations of both primary and secondary bile acids, particularly the pathogenic bile acid CDCA. This reduction in concentration markedly attenuates the cytotoxic effects of bile acids on cells.</p>
<p>Significant alterations in gut microbiota composition were observed in the SI-ALI, characterized by a marked increase in the abundance of opportunistic pathogens such as <italic>Parabacteroides</italic> and <italic>Bacteroides</italic>. These microbiota harbor abundant BSH and 7&#x3b1;-dehydroxylase, which accelerate the conversion of primary bile acids to secondary bile acids. Notably, at physiological concentrations, bile acids exert anti-inflammatory and metabolic regulatory roles by activating receptors such as FXR and TGR5. However, elevated bile acid levels can trigger the explosive release of pro-inflammatory cytokines IL-1&#x3b2; and TNF-&#x3b1;, directly damaging tissue barriers. This study demonstrated that increased secondary bile acids in the intestinal tract of the LPS group were positively correlated with pulmonary inflammatory infiltration and endothelial injury, suggesting that microbiota-mediated excessive bile acid accumulation may be a key driver of SI-ALI.</p>
<p>Notably, it was observed that the protective effect of MDD against lung injury was not limited to intestinal microenvironment regulation. Further investigations revealed that MDD significantly suppressed the formation of NETs in the lungs-a key effector mediating endothelial injury and pulmonary microcirculatory dysfunction. The NETs markers CitH3 and MPO-DNA complexes were markedly elevated in the LPS group but were substantially inhibited following MDD intervention. This change was probably associated with MDD-mediated reduction in <italic>Parabacteroides</italic>, activation of FXR expression in both lung and intestinal tissues, and suppression of the TLR4/MYD88 signaling pathway. Previous studies have demonstrated that FXR, functioning as a bile acid nuclear receptor, can attenuate excessive inflammatory responses by antagonizing the TLR4 pathway (<xref ref-type="bibr" rid="B77">Xiao et&#xa0;al., 2025</xref>). Importantly, TLR4/MYD88 activation has been identified as the core driver of NETs formation (<xref ref-type="bibr" rid="B64">Suprewicz et&#xa0;al., 2025</xref>).</p>
<p>NETs, released by neutrophils, are web-like structures primarily composed of DNA, histones, and granular proteins. They play a pivotal role in defending against infections by physically entrapping and chemically killing pathogens (<xref ref-type="bibr" rid="B3">Brinkmann et&#xa0;al., 2004</xref>). However, excessive formation or clearance impairment of NETs can lead to tissue damage and inflammatory responses, contributing to pathological processes such as autoimmune diseases, thrombosis, and cancer progression (<xref ref-type="bibr" rid="B56">Papayannopoulos, 2018</xref>). Research indicated that NETs play a vital part in developing sepsis and the associated endothelial dysfunction (<xref ref-type="bibr" rid="B84">Zhang et&#xa0;al., 2023</xref>). Although a moderate level of NETs is conducive to immune function, excessive and prolonged NETs release can amplify inflammatory processes (<xref ref-type="bibr" rid="B13">Doring et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B80">Yang et&#xa0;al., 2020</xref>). The primary components of NETs, including extracellular DNA, histones, neutrophil elastase (NE), and MPO, can directly damage pulmonary microvascular endothelium, increase vascular permeability, and induce cell death (<xref ref-type="bibr" rid="B91">Zhou et&#xa0;al., 2024</xref>). Concurrently, they upregulate adhesion molecules such as VCAM-1 (<xref ref-type="bibr" rid="B19">Folco et&#xa0;al., 2018</xref>; <xref ref-type="bibr" rid="B84">Zhang et&#xa0;al., 2023</xref>), recruit inflammatory cells, and provide a scaffold for platelets and coagulation factors, thereby promoting immunothrombosis (<xref ref-type="bibr" rid="B53">Middleton et&#xa0;al., 2020</xref>).This cascade leads to impaired microcirculatory perfusion and tissue hypoxia. Clinical and experimental data indicate that NETs levels are positively correlated with disease severity and prognosis in severe lung injuries such as ARDS and COVID-19 (<xref ref-type="bibr" rid="B53">Middleton et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B5">Cesta et&#xa0;al., 2023</xref>).Inhibition of NETs formation or acceleration of their clearance has been shown to mitigate endothelial injury and improve lung function. NETs contribute to endothelial dysfunction, further worsening sepsis-induced lung injury.</p>
<p>FXR, a nuclear receptor belonging to the nuclear receptor superfamily, is primarily expressed in the liver, intestine, and kidney, although studies have confirmed its expression in lung tissue as well (<xref ref-type="bibr" rid="B42">Li Z. et&#xa0;al., 2023</xref>). This process can be inhibited by the highly abundant <italic>Parabacteroides</italic> (<xref ref-type="bibr" rid="B88">Zhao et&#xa0;al., 2023</xref>), which may represent the core mechanism through which MDD modulates FXR signaling. As a critical regulatory factor in bile acid metabolism, FXR plays a significant role in inflammation and metabolic regulation. Research indicates that mice overexpressing FXR demonstrate significant resistance to endotoxemia (<xref ref-type="bibr" rid="B25">Hao et&#xa0;al., 2017</xref>), suggesting a potential role of FXR in inhibiting systemic inflammatory responses. TLR4, a pattern recognition receptor, primarily recognizes LPS, a component of the outer membrane of Gram-negative bacteria. TLR4 binds to the adaptor protein MYD88 through its Toll/IL-1 Receptor homologous domain, initiating downstream signaling pathways and inducing the expression of proinflammatory cytokines such as TNF-&#x3b1; and IL-6 (<xref ref-type="bibr" rid="B32">Kawai and Akira, 2011</xref>). Overactivation of TLR4 is closely associated with a high incidence of sepsis (<xref ref-type="bibr" rid="B34">Kondo et&#xa0;al., 2012</xref>). Studies have shown that FXR can effectively inhibit inflammatory responses by suppressing the TLR4/MYD88 signaling pathway (<xref ref-type="bibr" rid="B77">Xiao et&#xa0;al., 2025</xref>). Furthermore, previous research has confirmed that the TLR4-dependent pathway can significantly enhance neutrophil migration and promote the formation of NETs (<xref ref-type="bibr" rid="B64">Suprewicz et&#xa0;al., 2025</xref>).This suggests that the FXR/TLR4/MYD88 pathway serves as a critical axis through which MDD modulates NETs and their downstream responses.</p>
<p>Based on the above findings, this study proposes that MDD alleviates SI-ALI through a dual synergistic mechanism: first, by directly reshaping the gut microbiota to reduce the accumulation of pro-inflammatory and cytotoxic bile acids, thereby lowering the systemic inflammatory burden at its source; second, by indirectly restoring FXR activity, which suppresses the TLR4/MYD88 signaling pathway, reduces PAD4-mediated NETs release, and consequently protects pulmonary microvascular endothelium, decreases permeability, and improves lung function. The composition of MDD is diverse. We detected the Anthraquinones, anthrones, Flavones and fatty acids, which are closely related to sepsis and ALI. Studies have shown that the delivery of baicalin in a sepsis mouse model via nanomaterials could significantly promote the polarization of macrophages from the proinflammatory M1 phenotype to the anti-inflammatory M2 phenotype, thereby effectively inhibiting inflammatory responses and exerting a therapeutic effect (<xref ref-type="bibr" rid="B89">Zhou M. et&#xa0;al., 2025</xref>). Emodin was also found to significantly alleviate LPS-induced ALI and exert a protective effect by regulating the JNK/Nur77/c-Jun signaling pathway (<xref ref-type="bibr" rid="B78">Xie et&#xa0;al., 2022</xref>). Besides, it was reported that emodin regulate the level of severe acute pancreatitis-related exosomes and their proinflammatory components, inhibit the proinflammatory polarization of alveolar macrophages, and thereby alleviate ALI (<xref ref-type="bibr" rid="B29">Hu et&#xa0;al., 2022</xref>).MDD may exhibit its unique therapeutic advantages based on multi-component synergistic interactions by regulating the gut-lung axis function through multiple targets and intervening in the pathological process of SI-ALI.</p>
<p>However, this study has certain limitations that should be acknowledged. Firstly, MDD, being a multi-component traditional Chinese medicine, comprises a complex mixture of bioactive compounds. Although some components have been identified through UPLC-MS analysis, the interactions among these components and their specific contributions to the overall therapeutic effect remain incompletely understood, requiring further investigation. Secondly, this study represents a preliminary exploration primarily aimed at untangling the potential mechanisms through which MDD alleviates SI-ALI by modulating gut microbiota and bile acid metabolism. Therefore, the findings of this study need to be further validated through larger-scale clinical studies and more in-depth experimental verification to confirm its efficacy and mechanisms. It should be noted that 16S rRNA sequencing is inherently a relative quantification method, which may introduce limitations in interpreting changes in community abundance. Subsequent studies could further employ qPCR to perform targeted quantification of key genera and species to strengthen the conclusions. Besides, the MDD-M group exhibited the optimal therapeutic efficacy, while the high- and low-dose groups showed relatively weaker effects. This observation suggests that the action of MDD may not follow a strictly dose-dependent pattern. Similar phenomena are not uncommon in multi-component TCM formulations (<xref ref-type="bibr" rid="B43">Lin et&#xa0;al., 2025</xref>; <xref ref-type="bibr" rid="B46">Liu et&#xa0;al., 2025</xref>), which may be attributed to their multi-target, multi-pathway regulatory characteristics and saturation effects <italic>in vivo</italic> metabolism. On the one hand, certain components at higher doses may lead to competition among pharmacological pathways or increased metabolic burden, thereby attenuating the overall synergistic effect. On the other hand, insufficient exposure to active constituents at lower doses may also limit efficacy. From the perspective of TCM theory, a high dose of MDD may exert overly strong purgative effects, potentially impairing the body&#x2019;s healthy qi (zhengqi), whereas a low dose may provide insufficient purgative action to effectively eliminate the pathogenic factors. In summary, while this study primarily focused on the therapeutic effects of MDD, it did not systematically evaluate the safety and potential side effects of high doses, which represents an important direction for future research. More detailed histopathological examinations and monitoring of biochemical indicators such as liver function in the high-dose group will help clarify its safety window.</p>
</sec>
<sec id="s5" sec-type="conclusions">
<label>5</label>
<title>Conclusions</title>
<p>The study demonstrates that MDD can significantly alleviate SI-ALI, as evidenced by improved histopathology, reduced inflammatory cytokines, and attenuated pulmonary edema. The remodeling of the intestinal microbiota and alterations in targeted bile acids, combined with molecular biology validation, suggest that MDD may modulate the gut microbiota&#x2013;bile acid axis, activate FXR, and inhibit the TLR4/MYD88 inflammatory pathway, thereby reducing PAD4-mediated NETs formation and ultimately ameliorating endothelial dysfunction.</p>
<p>However, this study has several limitations. First, the interactions among the components of MDD and their respective contributions to therapeutic efficacy remain unclear, necessitating further research on the interactions between individual compounds and the complete formula. Second, more rigorous mechanistic validation is required regarding dose-response relationships, safety assessments, and the translation of findings from animal models to clinical applications. Third, the limitations of the relative quantification method using 16S rRNA sequencing suggest that key bacterial genera or species should be further validated using targeted approaches such as quantitative polymerase chain reaction (qPCR) or metagenomics.</p>
</sec>
</body>
<back>
<sec id="s6" sec-type="data-availability">
<title>Data availability statement</title>
<p>The original contributions presented in the study are included in the article/<xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary Material</bold></xref>. Further inquiries can be directed to the corresponding authors.</p></sec>
<sec id="s7" sec-type="ethics-statement">
<title>Ethics statement</title>
<p>The animal study was approved by Guangdong Provincial Hospital of Chinese Medicine. The study was conducted in accordance with the local legislation and institutional requirements.</p></sec>
<sec id="s8" sec-type="author-contributions">
<title>Author contributions</title>
<p>LW: Writing &#x2013; review &amp; editing, Writing &#x2013; original draft, Visualization, Methodology, Formal analysis, Data curation. WP: Writing &#x2013; original draft, Visualization, Validation, Investigation. YL: Writing &#x2013; review &amp; editing, Formal analysis, Data curation, Investigation. LY: Writing &#x2013; review &amp; editing, Visualization, Validation. PH: Writing &#x2013; review &amp; editing, Validation. YY: Writing &#x2013; review &amp; editing, Supervision. YF: Writing &#x2013; original draft, Validation, Supervision. BC: Writing &#x2013; review &amp; editing, Supervision, Project administration, Funding acquisition. LC: Writing &#x2013; review &amp; editing, Supervision, Project administration, Conceptualization.</p></sec>
<sec id="s10" sec-type="COI-statement">
<title>Conflict of interest</title>
<p>The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec>
<sec id="s11" sec-type="ai-statement">
<title>Generative AI statement</title>
<p>The author(s) declared that generative AI was not used in the creation of this manuscript.</p>
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<title>Publisher&#x2019;s note</title>
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<sec id="s13" sec-type="supplementary-material">
<title>Supplementary material</title>
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<fn-group>
<fn id="n1" fn-type="custom" custom-type="edited-by">
<p>Edited by: <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/370547">Nagaraju Marka</ext-link>, UCONN Health, United States</p></fn>
<fn id="n2" fn-type="custom" custom-type="reviewed-by">
<p>Reviewed by: <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/238105">Anuj Kumar</ext-link>, Dalhousie University, Canada</p>
<p><ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/3160802">Sanjeev Kumar Yadav</ext-link>, UCONN Health, United States</p></fn>
</fn-group>
<fn-group>
<fn fn-type="abbr" id="abbrev1">
<label>Abbreviations:</label>
<p>ARDS, acute respiratory distress syndrome; CA7S, Cholic Acid 7 Sulfate; CitH3, citrullinated histone H3; DEX, dexamethasone; FXR, Farnesoid X receptor; LPS, lipopolysaccharide; MDD, Modified Dachengqi Decoction; MPO, myeloperoxidase; MYD88, myeloid differentiation primary response 88; NETs, neutrophil extracellular traps; PAD4, Peptidyl arginine deiminase 4; SI-ALI, sepsis-induced acute lung injury; TCDCA, Taurochenodesoxycholic Acid; TLR4, Toll-like receptor 4; VCAM-1, vascular cell adhesion molecule-1; VE-cadherin, vascular endothelial cadherin; W/D, wet/dry.</p>
</fn>
</fn-group>
</back>
</article>