
==== Front
eBioMedicine
EBioMedicine
eBioMedicine
2352-3964
Elsevier

S2352-3964(24)00332-3
10.1016/j.ebiom.2024.105296
105296
Articles
Gut bacterial type III secretion systems aggravate colitis in mice and serve as biomarkers of Crohn’s disease
Xu Jun ah
Li Peijie ah
Li Zhenye a
Liu Sheng a
Guo Huating a
Lesser Cammie F. bcd
Ke Jia kjia@mail.sysu.edu.cn
efg∗∗∗
Zhao Wenjing zhaowj29@ms.sysu.edu.cn
a∗∗
Mou Xiangyu mouxy5@ms.sysu.edu.cn
af∗
a Shenzhen Key Laboratory of Systems Medicine for Inflammatory Diseases, School of Medicine, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong 518107, China
b Center for Bacterial Pathogenesis, Division of Infectious Diseases, Department of Medicine, Massachusetts General Hospital, Boston, MA 02115, USA
c Department of Microbiology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
d Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
e Department of General Surgery (Intestinal Surgery), The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510655, China
f Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510655, China
g Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510655, China
∗ Corresponding author. Shenzhen Key Laboratory of Systems Medicine for Inflammatory Diseases, School of Medicine, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong 518107, China. mouxy5@ms.sysu.edu.cn
∗∗ Corresponding author. zhaowj29@ms.sysu.edu.cn
∗∗∗ Corresponding author.Department of General Surgery (Intestinal Surgery), The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou 510655, China. kjia@mail.sysu.edu.cn
h Authors contributed equally.

30 8 2024
9 2024
30 8 2024
107 10529618 2 2024
3 8 2024
6 8 2024
© 2024 The Author(s)
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Summary

Background

Mesenteric adipose tissue (mAT) hyperplasia, known as creeping fat, is a pathologic characteristic of Crohn’s disease (CD). In our previously reported cohort, we observed that Achromobacter pulmonis was the most abundant and prevalent bacteria cultivated from creeping fat.

Methods

A whole genomic sequencing and identification of T3SS orthologs of mAT-derived A. pulmonis were used. A functional type III secretion system (T3SS) mediated the pathogenic potential of A. pulmonis in vitro and in mouse colitis model. Furthermore, a T3SS Finder pipeline was introduced to evaluate gut bacterial T3SS orthologs in the feces of CD patients, ulcerative colitis and colorectal cancer patients.

Findings

Here, we reveal that mAT-derived A. pulmonis possesses a functional T3SS, aggravates colitis in mice via T3SS, and exhibits T3SS-dependent cytotoxicity via a caspase-independent mechanism in macrophages and epithelial cells, which demonstrated the pathogenic potential of the T3SS-harboring A. pulmonis. Metagenomic analyses demonstrate an increased abundance of Achromobacter in the fecal of Crohn's disease patients compared to healthy controls. A comprehensive comparison of total microbial vT3SS abundance in various intestine diseases demonstrated that the specific enrichment of vT3SS genes was shown in fecal samples of CD, neither ulcerative colitis nor colorectal cancer patients, and ten T3SS gene-based biomarkers for CD were discovered and validated in a newly recruited CD cohort. Furthermore, treatment with exclusive enteral nutrition (EEN), an intervention that improves CD patient symptomatology, was found associated with a significant reduction in the prevalence of T3SS genes in fecal samples.

Interpretation

These findings highlight the pathogenic significance of T3SSs in the context of CD and identify specific T3SS genes that could potentially function as biomarkers for diagnosing and monitoring the clinical status of CD patients.

Funding

This work is supported by the 10.13039/501100012166 National Key Research and Development Program of China (2020YFA0907800 ), the 10.13039/501100002858 China Postdoctoral Science Foundation (2023M744089 ), the 10.13039/501100001809 National Natural Science Foundation of China (32000096 ), the Shenzhen Science and Technology Programs (KQTD2020082014582202 3 , RCIC20231211085944057 , and ZDSYS20220606100803007), National Key Clinical Discipline, Guangdong Provincial Clinical Research Center for Digestive Diseases (2020B1111170004 ), Qingfeng Scientific Research Fund of the China Crohn’s & Colitis Foundation (CCCF) (CCCF-QF-2022B71-1 ), and the Sixth Affiliated Hospital, 10.13039/501100002402 Sun Yat-sen University Clinical Research 1010 Program 1010CG(2023)-08. These funding provided well support for this research work, which involved data collection, analysis, interpretation, patient recruitment and so on.

Keywords

Crohn’s disease
Achromobacter pulmonis
Type III secretion system
Cytotoxicity
Exclusive enteral nutrition
==== Body
pmc Research in context

Evidence before this study

In the past decade, gut microbiota dysbiosis has consistently been associated with Crohn’s disease (CD). Microbial-driven mesenteric adipose tissue expansion in CD patients may be relevant to the etiopathogenesis of CD broadly. Achromobacter pulmonis, a bacterium isolated from the hyperplastic mesenteric adipose tissue of CD patients, was reported to enhance colitis in mice, however, the etiological mechanism and critical microbial factors contributing to its pathogenicity have yet to be fully elucidated.

Added value of this study

In vivo, A. pulmonis enhanced dextran sulfate sodium (DSS)-induced colitis in mice via its type III secretion system (T3SS). Bacteria and cell coculture confirmed that A. pulmonis infection exhibits T3SS-dependent cytotoxicity via a caspase-independent mechanism in macrophages and non-myeloid cells. Furthermore, metagenomic analyses reveal an increased abundance of Achromobacter in the feces of CD patients compared to healthy controls. A comprehensive comparison of total microbial vT3SS abundance in various intestine diseases demonstrated that total bacterial T3SS genes were enriched in the feces of CD patients, but not in ulcerative colitis or colorectal cancer patients.

Implications of all the available evidence

These findings highlight the pathogenic significance of T3SS, a notorious bacterial virulence factor, in the context of CD, and identify specific T3SS genes that could serve as biomarkers for diagnosing and monitoring the clinical status of CD patients.

Introduction

Crohn’s disease (CD) is a chronic inflammatory disease of the gastrointestinal tract that is currently increasing in prevalence worldwide.1 Mesenteric adipose tissue (mAT) hyperplasia, known as creeping fat, is a pathologic characteristic of CD. The formation of creeping fat is a protective response that prevents systemic bacterial dissemination by blocking translocation across the dysfunctional gut barrier,2 although when resecting large regions of the intestines of patients with CD, the inclusion of inflammatory mesentery, which contains creeping fat, reduces post-operative disease recurrence.3 These observations suggest that bacteria within the fatty tissue, mAT-resident bacteria, play a role in the pathogenesis of CD.

Previously, we found that A. pulmonis is the most abundant and prevalent bacteria cultivated from creeping fat samples grown under aerobic conditions. In the cohort studied, A. pulmonis was detected in ∼30% of CD patients from Southern China.4 Achromobacter is closely related to Bordetella, a bacterial genus that includes pathogens such as Bordetella pertussis and Bordetella bronchiseptica.5 Achromobacter species are widespread in humid outdoor and indoor environments in rural and urban areas.6 While considered aerobes, Achromobacter strains also grow under anaerobic conditions.7 Clinical isolates of Achromobacter are often multi-drug resistant.8 Achromobacter species are increasingly being recognized as opportunistic pathogens that infect immunocompromised patients and those with chronic diseases. These infections are associated with various infections, including bacteremia, urinary tract infections, pneumonia, bronchitis, and cystic fibrosis exacerbations.8, 9, 10 Furthermore, A. pulmonis was demonstrated to promote colitis in mice,4 however, the virulence mechanism underlying infection by A. pulmonis strains infection remains unclear.

A clinical isolate of Achromobacter xylosoxidans AXX-A was previously predicted to encode a type III secretion system (T3SS) related to one from Bordetella.11 T3SSs are complex nanomachines that assemble in the outer membrane of Gram-negative bacteria. T3SSs are divided into two categories. Flagellar T3SSs (fT3SSs) promote flagella formation, while virulence T3SSs (vT3SSs) enable the direct translocation of bacterial proteins, referred to as effectors, from bacteria into the cytosol of host cells.12 These effectors manipulate host cell pathways to promote the pathogenesis of bacteria.13 vT3SSs are limited to bacterial pathogens of plants and animals, i.e., Yersinia, Salmonella, Shigella, pathogenic Escherichia coli, Pseudomonas, Burkholderia, and Chlamydia species, and insect endosymbionts. fT3SSs are more broadly found. Many of the core components of the nanomachines of flagella and virulence T3SSs are highly conserved. Unless otherwise noted, the term T3SS in this article refers to vT3SS.

Here, we have characterized A. pulmonis mAT1, a variant of A. pulmonis strains isolated from the mAT of one of the Crohn’s disease patients.4 We have found that A. pulmonis mAT1 harbors a functional T3SS, which is required for A. pulmonis-enhanced colitis in mice. Moreover, the mAT-isolated A. pulmonis induces T3SS-dependent cytotoxicity of macrophages and epithelial cells via a caspase-independent manner, demonstrating the pathogenic potential of the T3SS-harboring A. pulmonis. Pan-screening of all T3SS genes in a previously published metagenomic data of the microbiota of three significant gastrointestinal diseases revealed that the vT3SS abundance was specifically enriched in the fecal microbiome of CD, neither UC nor colorectal cancer (CRC) patients. Ten T3SS gene-based biomarkers for CD were discovered and validated in a newly recruited CD cohort. Furthermore, treatment with exclusive enteral nutrition (EEN), an intervention that improves CD patient symptomatology, was found associated with a significant reduction in the prevalence of T3SS genes in fecal samples, supporting a connection between bacterial T3SS genes and CD disease state. Taken together, our study, for the first effort, reveals the pathogenic potential of T3SS in the context of CD and identifies potential T3SS gene-based biomarkers for CD diagnostics and therapy.

Methods

Bacteria strains and plasmids

All bacteria strains and plasmids are listed in Supplementary Table S1. All A. pulmonis strains were cultured in BHI (Brain heart infusion) media (HuanKai Microbial; #028360) unless otherwise noted. All E. coli strains were cultured in Luria–Bertani media (HuanKai Microbial; #028324). Yersinia pseudotuberculosis CMCC(B) 53504 (CMCC; #53504) was cultured in BHI media at 28 °C. The arabinose-inducible GFP plasmid was generated using a modified version of the In-Fusion® Cloning strategy.14 Briefly, the gfpmut3 gene from pUC18T-mini-T7nT-Zeo-gfpmut3 was introduced into the pBAD24 vector. The arabinose PBAD promoter and gfpmut3 gene fragment were amplified to replace the Lac operon cassette of pBBR1-MCS1. The resulting vector, pBBR1-PBAD-gfpmut3, was transferred into Achromobacter via conjugation. On the day of the assays, overnight cultures of Achromobacter and Yersinia were back diluted into the fresh medium at a ratio of 1:50 or 1:40, respectively. The OD600 of each bacterial culture was measured, and then all desired CFU of bacteria for each assay were pelleted. Antibiotics were added at the following final concentrations: chloramphenicol (Aladdin; #C100331) 50 μg/mL, ampicillin sodium salt (Aladdin; #A105483) 100 μg/mL, streptomycin (Aladdin; #S276470) 50 μg/mL, and gentamicin sulfate (Aladdin; #G100392) 200 μg/mL.

Identification and quantification of T3SS gene orthologs in bacterial genomes

The analysis pipeline is illustrated in Supplementary Fig. S1. Twenty T3SS core structure protein orthologs including SctA, SctB, SctC, SctD, SctE, SctF, SctI, SctJ, SctK, SctL, SctN, SctO, SctP, SctQ, SctR, SctS, SctT, SctU, SctV, SctW15 were analyzed. Protein/gene names that belong to each ortholog, excluding flagellar ones, were collected in Supplementary Table S2. Genes in bacterial genomes were identified by Prokka (v1.14.6). Non-redundant gene catalogs of each cohort were generated by CD-HIT (v4.8.1). Non-redundant genes were annotated by Diamond (v2.0.2)16 with NR database (Non-redundant protein sequence database). Next, the annotated genes in each genome were searched for names listed in Supplementary Table S2 and counted by a script. Any gene with a matched name was defined as a designated T3SS gene ortholog. The result was plotted as a heatmap by the “pheatmap” package of R software (4.2.1).

Whole genome sequencing and assembly of A. pulmonis mAT1

Bacteria harvested from the culture plate were lysed for high molecular weight gDNA extraction (Quick-DNA HMW MagBead Kit; Zymo Research; #D6060) according to the manufacturer’s protocol. The quality of the gDNA was determined using a Nanodrop (Thermo Fisher Scientific; #ND-ONE-W) to ensure an optical density 260/280 ratio of 1.8–2.0 for library input. Genomic DNA quantified by Qubit™ dsDNA HS and BR Assay Kits (Thermo Fisher Scientific; #Q32850) was used to prepare two libraries. An Illumina Nextera XT kit (Illumina; #FC-131-1024) was used to generate short-read libraries, which were sequenced with an Illumina NovaSeq 6000. An Oxford Nanopore Technologies Ligation Sequencing kit (#SQK-LSK109) was used to prepare long-reads libraries, which were performed using an ONT MinION (Flow cell R9.4.1; #FLO-MIN106D) to generate fast5 files that were subsequently base-called using ONT’s Guppy (version [v] 2.3.1) with default parameter values. These files were assembled using SMRT Link (version [v] 5.1.0).17 Long-read fastq files and short-read fastq files of A. pulmonis mAT1 were assembled into a complete genome by Unicycler (v0.4.9b).18

Generation of the phylogenetic tree of 36 Achormobacter genomes and T3SS ATPase SctN

Achromobacter genomes were aligned by GTDB-tk (v2.1.0)19 with the GTDB (release 207),20 and IQ-TREE2 (v2.1.2)21 was used to find the best-fit model of the phylogenetic tree. For the phylogenetic tree of T3SS ATPase SctN, protein sequences from NCBI were aligned by the Clustal W plugin of MEGA v10.2.6. The phylogenetic tree was generated by the neighbor-joining method, and the reliability of the tree was evaluated by bootstrap analysis22 with 1000 replicates. Trees were visualized using interactive ChiPlot.23 The Genebank accession numbers of each ATPase (from top to bottom) were CAE32125.1, NP 880888.1, YP 009159.1, NP250388.1, NP798047.1, ABO92550.1, CFV35788.1, BAB552652.1, CAD18021.1, NP791227.1, NP220188.1, AAG58832.1, and NP858282.1). The Genebank accession numbers of each ATPase (from top to bottom) were CAE32125.1, NP 880888.1, YP 009159.1, NP250388.1, NP798047.1, ABO92550.1, CFV35788.1, BAB552652.1, CAD18021.1, NP791227.1, NP220188.1, AAG58832.1, and NP858282.1.

In-frame deletion and complement of ascN

In-frame deletion of T3SS ATPase, encoded by the ascN gene, was carried out by homologous recombination. Briefly, the ∼600 bp upstream and downstream of the ascN gene were amplified by PCR using primers listed in Supplementary Table S3, respectively. The resulting two DNA fragments were sewed together by PCR and then cloned into the suicide plasmid pDS132. The resulting vector was transferred into A. pulmonis mAT1 through conjugation. Candidate colonies were selected on media containing 50 μg/mL streptomycin and 50 μg/mL chloramphenicol. The deletion strains were verified by PCR and Sanger sequencing. For complement, the ∼100bp upstream included with ascN gene was amplified by PCR and cloned into pBBR1-MCS1 under the control of a constitutive promoter. The resulting pBBR1-pAscN plasmid was transferred into ΔascN stain through conjugation. The resulting ΔascN/pAscN strain was selected on BHI agar containing 50 μg/mL streptomycin and 50 μg/mL chloramphenicol, and verified by PCR and Sanger sequencing (Supplementary Fig. S2A and B).

Generation of rabbit anti-AopD polyclonal antibodies

The rabbit anti-AopD polyclonal antibody was prepared by Sino Biological Company (Beijing, China) as previously described.24 Briefly, the translocon protein AopD (GeneBank: MPT27054.1) of A. pulmonis mAT1 was analyzed. AopD159-273 and AopD41-129 peptides were recombined with human hyaluronic acid binding protein (HABP), respectively, and were synthesized as antigens combined with Freund’s complete adjuvant (Beyotime; #P2036) to subcutaneously immunize New Zealand rabbits (Female, 3 months old) at a dose of 600 μg per dose. Booster immunizations are administered using antigens mixed with Freund’s incomplete adjuvant (Beyotime; #P2031) starting from the 4th week, and subsequent doses are given at 3-week intervals. After the fifth immunization, the rabbits were bled, and serum was prepared from whole blood. Rabbit anti-AopD polyclonal antibodies were purified via HABP and antigen-specific affinity purification.

Secretion assay

WT, ΔascN, and ΔascN/pAscN A. pulmonis mAT1 were cultured overnight in Stainer and Scholte (SS) liquid medium (TOPBIO; #M2343B). 16 h post back dilution into fresh modified SS medium25 with Mg2+ depleted respectively, the OD600 of each bacterial culture was measured, and then equivalent numbers (normalized to OD600 = 1.0) of bacteria from each culture were pelleted. The proteins in the supernatant fraction were precipitated in 10% (v/v) trichloroacetic acid.26 Proteins in both fractions were separated via 12% SDS-PAGE and analyzed by immunoblotting with a rabbit anti-AopD polyclonal antibody and a rabbit anti-DnaK polyclonal antibody (CUSABIO; #CSB-PA633459HA01EGW).

Cell lines

The gsdmd−/− THP-1 and HeLa cell lines are generous gifts from Professor Feng Shao at National Institute of Biological Sciences, Beijing, China. Other cell lines were obtained from the American Type Culture Collection (ATCC). Murine pre-adipocytes 3T3-L1 (ATCC; #CL-173), murine monocyte/macrophage RAW264.7 (ATCC; #TIB-71), human cervical adenocarcinoma cells HeLa (ATCC; #CCL-2), and rat small intestine epithelial cells IEC-6 (ATCC; #CRL-1592) were maintained in Dulbecco’s Modified Eagle Medium (Gibco; #11965092); human carcinoma cells HCT116 (ATCC; #CCL-247) were maintained in Modified McCoy's 5A Medium (Gibco; #16600082) supplemented with 10% FBS (MIKX; #MK1123) at 37 °C with 5% CO2. Human acute monocytic leukemia cells THP-1 (ATCC; #TIB-202) and gsdmd knockout THP-1 cells were maintained in RPMI 1640 Medium (Gibco; #11875093) supplemented with 10% FBS at 37 °C with 5% CO2. Mouse BMDMs were obtained from the bone marrow of unstimulated mice using a strategy described previously.27 The human mesenchymal stem cells (hMSCs) used for this experiment were isolated from mesentery adipose tissues of CD as described previously28 and were maintained in Human Mesentery Stem Cell Bases Medium (Cyagen; #HUXMA-90011) supplemented with 10% FBS plus glutamine, penicillin (100 U/ml), and streptomycin (100 U/ml) (Gibco; #15140122) at 37 °C with 5% CO2.

Cell infection conditions

One day before infection, IEC-6, HCT116, BMDM, RAW264.7, HeLa, and 3T3L1 cells were seeded in tissue culture plate wells as described below. The following day, the cells were infected with bacteria in high-glucose DMEM media plus 1% FBS. The plates were centrifuged at 400×g for 5 min to promote bacteria-cell contact. When indicated, 1% arabinose (Macklin; #A6312) was added to the culture media.

Cytotoxicity assay

The cytotoxicity assay was performed as previously described with modifications.29 IEC-6, HCT116, 3T3L1 cells, BMDM, RAW264.7, or PMA-induced THP-1 macrophages seeded in 96-well plates (2 × 104 cells/well) were infected as described in the Cell infection conditions section at a multiplicity of infection (MOI) of 10 or 100 as indicated. At designated time points, cells were incubated with 1 μg/mL Hoechst33342 (Beyotime, #C1002) plus 5 μg/mL propidium iodide (Yeasen; #40711ES10) for 10 min before being imaged with a 10 × objective on a Nikon TE2000 fluorescent microscope. The stained cell nuclei were identified and quantified using CellProfiler 3.0.30 Cell nuclei labeled only blue were classified as live, while those labeled both blue and red as dead. When indicated, 25 μM Z-VAD-FMK (APExBIO; #A1902) or 20 μM Necrostatin-1 (APExBIO; #A4213) was used.

Infectivity rate measured with flow cytometry

Rat IEC6 cells seeded in 12-well plates (105 cells/well) were infected with bacteria that carry an arabinose-inducible GFP-expressing plasmid. As invasive and non-invasive bacterial controls, Y. pseudotuberculosis and E. coli were studied respectively. At 2 h post-infection, the cells were treated with gentamicin (200 μg/mL) to kill extracellular bacteria, and 1% arabinose was added to induce GFP expression in intracellular bacteria for an additional 2 h. Then, cells were washed three times with sterile phosphate-buffered saline (PBS), harvested, and analyzed on a Beckman CytoFLEX instrument (Beckman; #B53000). A minimum of 10,000 events/samples were collected and analyzed using FlowJo v10.

Animal experiments

Male C57BL/6J mice (6 weeks old, 22 g ± 0.5 g) were acquired from Guangdong Medical Experimental Animal Center and housed in a specific pathogen-free (SPF) facility. 24 SPF mice were randomly allocated into four groups (n = 6). All mice were treated with 3% dextran sodium sulfate (DSS, MP Biomedicals; #160110) in drinking water for three days before they were gavaged with wild-type (WT), ΔascN or ΔascN/pAscN A. pulmonis mAT1 (1 × 109 CFU/dose), or PBS solution (100 μL/dose) as control, respectively. The animals were monitored for weight loss during experiments and euthanized under isoflurane anesthesia on day 7. Colon tissues (n = 6) were fixed in 4% paraformaldehyde for hematoxylin and eosin (HE) staining as previously described.4 The colon of the mouse was collected and stored at −20 °C for inflammation cytokines analysis (n = 6). Histological scores were assigned blindly by a trained pathologist evaluating the following set of modified variables as described4: severity of inflammation (0, none; 1, low density confined to the mucosa; 2, moderate or higher density in the mucosa and/or low to moderate density in mucosa and submucosa; 3, high density in submucosa and/or extension to muscular; 4, high density with frequent transmural extension), and extent of epithelial/crypt damage (0, none; 1, basal 1/3; 2, basal 2/3; 3, crypt loss; 4, crypt and surface epithelial destruction). Each variable was multiplied by a factor reflecting the percentage of the colon involved (1, 0%–25%; 2, 26%–50%; 3, 51%–75%; 4, 76%–100%). Lymphoid aggregates (0, none; 1, occasional scattered lymphoid aggregates, 2, scattered lymphoid aggregates with occasional mild infiltrates; 3, scattered lymphoid aggregates with patchy moderate infiltrates; 4, multiple extensive areas with lymphoid aggregates). An overall score was obtained by summing the scores assigned to each variable. All procedures have complied with the National Institute of Health Guide for the Care and Use of Laboratory Animals. This study was approved by the Institutional Animal Care and Use Committee at School of Medicine, Sun Yat-sen University, with the ethical number SYSU-IACAU-MED-2023-B114.

Quantification of levels of cytokines in mouse colon tissue

Mouse colons were homogenized in pre-cold PBS solutions. Then total proteins were extracted from the supernatant and measured via bicinchoninic acid (BCA) at OD562. The levels of IL-6, TNF-α, and IL-1β in mice colon tissue were quantified with ELISA Kits (RUIXIN BIOTECH; #RX203049M, # RX202412M, and # RX203063M) following the manufacturer’s instructions, respectively.

RNA extraction and quantitative PCR (qPCR)

Samples were collected and snap-frozen in liquid nitrogen. Total RNA was extracted using a Trizol reagent kit (Invitrogen; #15596026) using standard chloroform extraction. RNA quality was assessed on an Agilent 2100 Bioanalyzer (Agilent Technologies) and checked using RNase-free agarose gel electrophoresis. For qPCR analysis, one microgram of total RNA was used to generate cDNA (Accurate Biotechnology; #AG11706). qPCR was performed using a 2x SYBR Green qPCR Master Mix kit (Selleck; #B21203) on QuantStudio 7 Pro instrument (Applied Biosystems). PCR conditions were 95 °C for 30 s, followed by 40 cycles of 95 °C for 15 s and 60 °C for 30 s. Data were analyzed using the 2-ΔΔCt method with gyrB or Actb serving as the reference housekeeping gene.

RNA sequencing analysis

After total RNA was extracted, eukaryotic mRNA was enriched by Oligo (dT) beads. Then the enriched mRNA was fragmented into short fragments using fragmentation buffer and reversely transcribed into cDNA by using NEB Next Ultra RNA Library Prep Kit for Illumina (New England Biolabs, NEB; #7530). The purified double-stranded cDNA fragments were end-repaired, a base added, and ligated to Illumina sequencing adapters. The ligation reaction was purified with the AMPure XP Beads (1.0X). The resulting cDNA library was sequenced using the Illumina Novaseq 6000 System by Gene Denovo Biotechnology Co. (Guangzhou, China). Reads containing adapters and low-quality reads were removed by fastp (v. 0.23.0).31 An index of the reference genome was built, and paired-end clean reads were mapped to the reference genome using HISAT2. 2.432 and other parameters are set as a default. Alignments of each sample were assembled by using StringTie v1.3.133 in a reference-based approach. For each transcription region, a TPM (Transcripts per Kilobase of exon model per million mapped reads) value was calculated to quantify its expression abundance and variations, using RSEM34 software.

Differentially expressed genes analysis was performed by DESeq235 software between two different groups (and by edgeR36 between two samples). Transcripts with the parameter of false discovery rate (FDR) below 0.05 and absolute fold change ≥1.5 were considered as differentially expressed transcripts. Heat maps were created using the regularized log transform (rlog) function of the Deseq2 package. Gene set enrichment analysis was done using software GSEA and MSigDB37 to identify whether a set of genes in specific GO terms∖KEGG pathways shows significant differences in the two groups. Briefly, we input the gene expression matrix and rank genes by normalized Signal-to-Noise method. Enrichment scores and FDR adjusted p-values were calculated in default parameters.

Study participants and collection of fecal samples

31 CD patients and 30 healthy controls were recruited at the Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China in 2021 (Supplementary Table S4). Subjects were excluded if they were administered with antibiotics, antifungals, or chronic immunosuppressive medication in the three months before participation. Clinical information was collected according to standard procedures, including age, gender, and body mass index (BMI). Fecal samples from 61 subjects were collected by the researchers according to the standard operating procedures, then were immediately transported to the laboratory and stored at −80 °C until DNA extraction and metagenomic sequencing. This study complied with all relevant ethical regulations and was approved by the ethics committee of the Sixth Affiliated Hospital of Sun Yat-sen University with the ethical number 2021ZSLYEC-513. Written informed consent was obtained from all subjects.

Fecal DNA extraction and metagenome sequencing

Fecal DNA was extracted with the standard protocol for genomic DNA extraction with CTAB (Cetyl Trimethyl Ammonium Bromide). DNA concentration was measured using Qubit dsDNA Assay Kit in Qubit 2.0 Fluorometer (Life Technologies, CA, USA). For shotgun sequencing, Illumina libraries were prepared using a NEBNext Ultra DNA Library Prep Kit for Illumina (NEB, USA) following the manufacturer’s protocol and sequenced using the Illumina NovaSeq 6000 platform. Finally, 2 × 150 bp paired-end reads were generated.

Identification and quantification of T3SS orthologs in metagenomic data

For the published PRJEB15371 cohort and the EEN cohort, the metagenomic data of PRJEB15371 were downloaded and low-quality sequences were removed by Fastp (v0.23.0).31 For the validation cohort, the metagenomics raw reads were filtered to remove the adapter and low-quality sequences using Readfq (v8, https://github.com/cjfields/readfq). Human reads were removed by mapping the reads to the human reference genome (hg38) with Kneaddata (v0.10.0). Clean reads were assembled by MEGAHIT (v1.2.9).38 Coding sequence/genes present in assembled contigs were identified by Prokka (v1.14.6). Non-redundant gene catalogs of all samples were generated by CD-HIT (v4.8.1).39 Functional annotations of non-redundant genes were used Diamond (v2.0.2) with the Non-Redundant Protein Sequence Database (NR) (release 2022-05), resulting in a gene annotation table, which was searched for T3SS protein/gene names listed in Supplementary Table S2. Any gene with a matched name was defined as a designated T3SS ortholog. On the other hand, the gene abundance was calculated by Salmon (v1.6.0).40 In this way, an index was generated from the non-redundant gene catalogs fasta file by the program Salmon. Then gene reads were aligned to the index for all metagenomic samples. We elect to calculate gene abundance in TPM (transcripts per kilobase of exon model per million mapped reads), a method used and recommended by previous studies,41, 42, 43 due to its advantage over CPM (Counts per million mapped reads) in correcting gene lengths, and its advantage over FPKM (Fragments per kilo base of transcript per million mapped fragments) in comparing same genes in various samples. Taken together, T3SS ortholog tables with the abundance information were generated. The heatmap was plotted by the “pheatmap” package of R software (4.2.1). The scale function in pheatmap was used for each sample to better present data.

Random forest classifier for IBD

To discriminate samples of CD patients from controls, a classifier based on a random forest model with sixteen individual T3SS orthologs, six categories that were enriched in patients (Fold change >1) as well as Total Sct. Five trials of 10-fold cross-validation were applied to assess the performance of the prediction model. The average error of cross-validation was plotted as the number of variables increased. The mean decrease accuracy (MDA) score, which shows the importance of each variable in the model, was assigned to each variable based on the increase in error caused by removing that variable from the predictors. The receiver operator characteristic curve (ROC) was plotted by the “pROC” package in R software to evaluate the accuracy of selected variables.

Statistics

Statistical analyses were carried out using the R package or GraphPad Prism version 9.0. Unless otherwise noted, data are expressed as mean ± standard error of the mean (SEM). For gene expression level assay, the Kruskal–Wallis test with Dunne’s multiple comparations was performed and adjusted by the Benjamini-Hochberg method. For the animal experiments, the Kruskal–Wallis test with multiple comparations was applied and adjusted by the Benjamini-Hochberg method. For the cell cytotoxicity assay, one-way analysis of variance (ANOVA) with Tukey’s multiple comparisons was applied and adjusted with the Benjamini-Hochberg method. For metagenomic data analysis, statistical significance was tested by permutational multivariate analysis of variance (PERMANOVA) analysis and adjusted by the Benjamini-Hochberg correction for multiple tests. For comparing two groups, the Two-tailed Wilcoxon rank-sum test was used. Statistical significance was assigned as ∗∗∗p ≤ 0.001; ∗∗p ≤ 0.01; ∗p ≤ 0.05; ns, p > 0.05.

Role of funders

The funders had no role in study design, data collection, data analyses, interpretation, or writing of report.

Results

A. pulmonis mAT1 harbors a functional T3SS

As previously reported,4 A. pulmonis was the most abundant bacterial species isolated from the “creeping fat” mAT from 14 CD patients. Specifically, 32 out of 154 representative creeping fat-isolated colonies are A. pulmonis (Supplementary Fig. S3A). These bacteria were not from contamination during the surgery process, as only Staphplococcus were isolated from normal non-CD mAT from CRC patients who received surgery in the same operation room (Supplementary Fig. S3B). One creeping fat-isolated A. pulmonis strain, named as A. pulmonis mAT1 in this study, was demonstrated to enhance DSS-induced colitis in mice.4 A. pulmonis mAT1 was classified into the species of Achromobacter pulmonis based on the 16S rRNA sequence.4 To better study its taxonomy, we obtained the complete genome sequence of the strain by Shotgun and Nanopore sequencing and uploaded it to the Type (Strain) Genome Server (TYGS) to enable genome-to-genome comparisons with an extensive database by digital DNA–DNA hybridization.44 The phylogenic tree of A. pulmonis mAT1 and its closely related species determined by TYGS are shown in Fig. 1A and Supplementary Table S5. The results indicate that A. pulmonis mAT1 is closely related to A. xylosoxidans, Achromobacter ruhlandii, Achromobacter insuavis, Achromobacter dolens, and A. deley. Each of the six species has been reported to be associated with human infections.8,10 Clinical, but not environmental, isolates of A. xylosoxidans have been reported to encode T3SS-related genes, suggesting a role for the T3SS in its host-adaption.45 Therefore, we screened for evidence of T3SS gene clusters in the genome of A. pulmonis mAT1 and 35 other Achromobacter species. The information of the 36 genomes were listed in Supplementary Table S6 and a phylogenetic tree was generated (Supplementary Fig. S2). As outlined in Supplementary Fig. S1, genomes annotated by the NR database were searched by a computer script for vT3SS gene names that belong to each of the twenty core T3SS gene orthologs, which include SctN, SctV, SctR, SctS, SctU, SctT, SctC, SctJ, SctW, SctF, SctQ, SctD, SctI, SctO, SctP, SctE, SctL, SctB, SctK, SctA.15 Because fT3SS genes were excluded during this step, the term T3SS mentioned below refers to vT3SS only (Supplementary Table S2). Although all 20 core T3SS genes are essential for a functional T3SS, we did not observe any of Achromobacter genomes containing all 20 core T3SS genes (Fig. 1B). No SctA, SctF, or SctP genes were annotated in any Achromobacter genomes. We hypothesize that this is due to the inaccuracy of the annotation or nomenclature of the NR database. We observed that the annotated T3SS gene ortholog numbers fall into two ranges: 12–17 and 0–1 (Supplementary Fig. S4A). We assume that bacteria containing 12–17 T3SS gene orthologs are putative T3SS-containing bacteria while others are not. Host-isolated Achromobacter were more likely (17/23; 74%) than environmental isolates (5/13; 38%) to harbor a putative T3SS (Fig. 1B). The putative T3SS genes of A. pulmonis mAT1 are most closely related to those of Bordetella. T3SSs fall into seven families as indicated by the phylogenetic analyses based on the sequences of their essential T3SS ATPases (Fig. 1C). Both the A. pulmonis mAT1 and Bordella T3SSs belong to the YSC (Yersinia Secretion Complex) family. The T3SS gene cluster of A. pulmonis mAT1 aligns well with that of B. bronchiseptica RB50, which is referred to as the Bordetella secretion complex (bsc) gene cluster46 (Fig. 1D and Supplementary Table S7). Hence, we named the T3SS operons encoded by A. pulmonis mAT1 as the asc (Achromobacter secretion complex) gene cluster.Fig. 1 A. pulmonis mAT1 harbors a functional T3SS. (A) Phylogenetic tree of A. pulmonis mAT1 (red), compared to A. dolens LMG 26841 strain and 14 closest type strains automatically picked by the Type Strain Genome Server (TYGS). The phylogenetic tree was generated by TYGS with default settings. (B) 35 online-available Achromobacter genomes were re-annotated by NR and searched for genes of T3SS proteins that belong to each T3SS ortholog. The quantities of each T3SS ortholog were plotted in the heatmap. Achromobacter strains were classified into host-isolated and environmental-isolated groups. Achromobacter strains in each group were organized by phylogenic tree generated by GTDB-Tk and IQ-TREE2 software. Achromobacter strains that contain more than 10 T3SS orthologs annotated by NR are considered T3SS-harboring strains and labeled red. (C) Neighbor-joining phylogenetic tree T3SS ATPase SctN of representative T3SS-harboring bacteria strains. Bacteria names are color-coded to indicate designated T3SS families. (D) T3SS gene cluster of A. pulmonis mAT1 compared with that of B. bronchiseptica RB50. Genes are color-coded to indicate designated components of the T3SS apparatus. (E) The relative expression level of T3SS genes of A. pulmonis mAT1 grown in designated media, measured by qRT-PCR. All data are expressed as the mean ± SEM of at least three experimental repeats, each with two technical replicates. The Kruskal–Wallis test with Dunne’s multiple comparisons was performed and adjusted by the Benjamini-Hochberg method. For Dunn’s multiple comparisons test, the mean of each group was compared with the mean of the control group. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001. (F) Immunoblotting of secreted AopD by designated strains. Each bacterial culture was centrifuged to yield the “bacteria” fraction and the “supernatant” fractions. The fractions were separated by SDS/PAGE and transferred to polyvinylidene fluoride membranes. Membranes were immunoblotted with designated antibodies. DnaK served as a non-secreting and loading control. The data is representative of three experimental repeats.

The expression of T3SSs is usually tightly regulated, as they are only needed when the pathogens encounter host cells. We found that magnesium (Mg2+) depletion from Stainer and Scholte (SS) medium promoted expression of the asc gene cluster (Fig. 1E). In addition, we detected the presence of AopD within whole cell lysates of bacteria grown under these conditions (Fig. 1F). AopD is a homolog of Bordetella BopD which is a secreted component of the T3SS. We observed evidence of secretion of AopD in the supernatant fractions of WT but not ΔascN, a gene encoding T3SS ATPase which is required for secretion (Fig. 1F). In addition, the complement of ascN partially restored AopD secretion in the ΔascN/pAscN strain. DnaK, a highly abundant cytosolic protein was also immunoblotted as a cytosolic and loading control. As expected, the deletion of ascN did not impair the growth of A. pulmonis mAT1 (Supplementary Fig. S5A), indicating that the T3SS is not required for bacterial growth in culture. The ΔascN/pAscN strain grew slower than WT, likely due to the expression burden of the highly transcribed ascN (Supplementary Fig. S5B) which was driven by a strong constitutive promoter (Supplementary Fig. S5B). These observations suggest that A. pulmonis mAT1 encodes a functional T3SS.

T3SS is required for the exacerbating effect of A. pulmonis on DSS-induced colitis in mice

We next investigate whether the T3SS of A. pulmonis promotes colitis using a DSS-induced preclinical mouse model of inflammatory bowel disease. C57BL/6 mice were administered 3% DSS in their drinking water for 3 days before they were gavaged with wild-type (WT) or ΔascN or ΔascN/pAscN A. pulmonis mAT1, or PBS solution (Fig. 2A). Two days post-administration of DSS, the body weight of mice began to decrease, suggesting the presence of colitis (Fig. 2B). The body weight of the mice continued to fall through day 5, after which those that received PBS or ΔascN began to recover, while those that received WT A. pulmonis mAT1, as previously reported, continued to decline. In addition, the complement strain, ΔascN/pAscN, exhibited a similar phenotype as WT (Fig. 2B). On day 7, we sacrificed the mice, excised colon tissue for length measuring and histological staining, and assessed cytokine levels associated with colonic tissue. As shown in Fig. 2C and D, colon length was shorter in mice gavaged with WT or ΔascN/pAscN compared to mice gavaged with ΔascN A. pulmonis or PBS. Additionally, both the WT- and ΔascN/pAscN-gavaged groups exhibited pathologic evidence of inflammatory colitis characterized by increased leukocyte infiltration, microvillus damage, and disruption of mucosa barrier (Fig. 2E and F). Consistent with the histological alteration, colonic IL-6 and TNF-α levels of WT- and ΔascN/pAscN-gavaged groups were significantly elevated (Fig. 2G and H). This data demonstrates that the T3SS is required for A. pulmonis-enhanced colitis in the mouse DSS model. Notably, the uninfected mice itself had a basal level of IL-1β (Fig. 2I). We speculated that this basal level of IL-1β is trigged by gut commensal bacteria after the mucosal barrier was compromised by DSS. The level of IL-1β was lower in the WT- or the ΔascN/pAscN-gavaged mice compared to that in the ΔascN- or PBS-gavaged mice (Fig. 2I), suggesting A. pulmonis may have the ability to limit the production of IL-1β via their T3SS. Interestingly, our observations are consistent with a previous study in Bordetella that reported significantly lower IL-1β levels in the lungs of mice after challenge with WT B. pertussis compared to that with ΔbscN.47Fig. 2 T3SS is required for the exacerbating effect of A. pulmonis on DSS-induced colitis in mice. (A) Mice were provided with 3% DSS water for 3 days, followed by regular water until the end of the experiment. On day 4, mice were gavaged with A. pulmonis mAT1 (WT, n = 6), ΔascN (n = 6), ΔascN/pAscN (n = 6), or PBS (n = 6) as a non-infection control. (B) Daily weight change percentage of mice gavaged with designated strains. (C) Representative images of mice colon gavaged with designated strains on day 7. (D) Colon length of mice gavaged with designated strains on day 7. (E) Representative images of hematoxylin-eosin staining of colon Swiss rolls of mice on day 7. (F) Histological score of hematoxylin-eosin staining images of colon Swiss rolls of mice. Colonic levels of IL-6 (G), TNF-α (H), and IL-1β (I) in mice gavaged with designated strains on Day 7. Data are expressed as the mean ± SEM. Each dot indicates an individual mouse (n = 6). The Kruskal–Wallis test with multiple comparisons was applied for statistical analyses and adjusted with the Benjamini-Hochberg method. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001.

A. pulmonis exhibits T3SS-dependent cytotoxicity via a caspase-independent mechanism in macrophages and epithelial cells

Given the intrinsic potent virulence of functional T3SS involved in many notorious pathogens such as Yersinia, Salmonella, Shigella, pathogenic E. coli, Pseudomonas aeruginosa, Burkholderia pseudomallei, and Chlamydial species. Next, we investigated the fate of human cell lines infected with WT vs ΔascN A. pulmonis mAT1. To assess for host cell death, we used fluorescent microscopy to image infected cells exposed to Hoechst 33342 (blue), a dye that diffuses into both live and dead cells, and propidium iodide (PI) (red), a membrane-impermeable dye (Fig. 3A), which only enters dead or dying cells. We then determined the percentage of dead cells in acquired images using Cell Profiler 3.0 to quantify the ratio of PI+/(Hoechst+) cells. At an MOI of 10, at 2 h post-infection, ∼40% of RAW264.7 macrophages infected with WT A. pulmonis mAT1 were PI-positive. ΔascN A. pulmonis mAT1 infected cells exhibited much lower levels of cytotoxicity (∼15%) (Fig. 3B and Supplementary Fig. S6A). Cells infected with the complemented ΔascN A. pulmonis mAT1 strain (ΔascN/pAscN) exhibited similar levels of cytotoxicity as those infected with WT bacteria. Thus, A. pulmonis promotes PI-uptake of macrophages via a T3SS-dependent process. Consistently with RAW264.7 macrophages, we observed similar results on cell death with mouse bone marrow-derived macrophages (BMDMs) and PMA-induced human THP-1 cells (Fig. 3C and D), although the complement strain restored only partial cytotoxicity in THP-1 cells.Fig. 3 A. pulmonis induced macrophages and non-myeloid cells cell death via T3SS. Cells were infected with designated strains at an MOI of 10. (A) Representative images were obtained with a 10 × objective of infected RAW264.7 cells at 2 h post-infection, followed by staining with Hoechst (blue) and PI (red). (B) Quantification of cytotoxicity percentage (PI + percentage) based on microscopic images at 1 h and 2 h. Cytotoxicity percentage of mouse bone marrow-derived macrophages (BMDM) (C) and PMA-induced THP-1 cells (D) infected with designated strains at 2 h post-infection. (E) Cytotoxicity percentage of rat small intestine epithelial crypt cells IEC6 at 6 h, 8 h, and 10 h post-infection with an MOI of 100. (F) Cytotoxicity percentage of human mesenchymal stem cells (hMSC) at 16 h post-infection with an MOI of 100. Data are expressed as the mean ± SEM of six experimental repeats. One-way analysis of variance (ANOVA) with Tukey’s multiple comparisons was applied, and the Benjamini-Hochberg method was used for adjustment. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001.

Besides macrophages, A. pulmonis may encounter non-phagocytic cells including epithelial cells, mesenchymal stem cells, and pre-adipocytes during its infection in the creeping fat. We next investigated whether A. pulmonis triggers cell death of these non-phagocytic cell lines. First, we examined the fate of infected IEC-6 cells, a rat intestinal epithelial cell line. In this case, cells infected with A. pulmonis exhibited PI-uptake at much later time points, even when infecting at a higher MOI of 100 (Fig. 3E and Supplementary Fig. S6B). WT A. pulmonis mAT1-induced cell death was first observed at 8 h post-infection and reached 40% at 10 h. In contrast, ΔascN A. pulmonis mAT1 induced much lower levels of cell death. Meanwhile, we used the flow cytometry-based assay to test if A. pulmonis invades IEC-6 cells with Y. pseudotuberculosis and E. coli as invasive and non-invasive bacterial controls respectively. Cells were infected with bacteria that carry an arabinose-inducible GFP-expressing plasmid. At 2 h post-infection, the cells were treated with membrane-impermeable gentamicin to kill extracellular bacteria, and arabinose was added to induce GFP expression within live intracellular bacteria. After another 2 h, at 4 h post-infection, no GFP+ IEC-6 cells were detected (Supplementary Fig. S7A and B), suggesting that A. pulmonis does not invade these cells. Consistently with IEC-6 cells, we observed similar results on cell death with the human epithelial-like cell line HCT116 (Supplementary Fig. S7C), as well as mesenchymal stem cells (MSC) isolated from the creeping fat28 (Fig. 3F) and the mouse pre-adipocyte 3T3L1 (Supplementary Fig. S7D), although the complement strain restored only partial cytotoxicity in MSC and 3T3L1 cells. Thus, A. pulmonis showed T3SS-mediated cytotoxicity against macrophages and non-phagocytic cells including epithelial cells, MSCs, and pre-adipocytes, while not invading these non-phagocytic cells.

We next investigated whether A. pulmonis-triggered cell death of macrophage and epithelial cells is via programmed cell death pathways, i.e., pyroptosis and apoptosis, which are mediated by host cell caspases.48 Thus, we infected cells in the presence of Z-VAD, a pan-caspase inhibitor. As a control, we exposed cells to nigericin, a Streptomyces toxin known to trigger caspase-1 activation.49 The addition of Z-VAD did not inhibit A. pulmonis-induced cell death of macrophages (Fig. 4A) or epithelial cells (Fig. 4B), but did block cell death when both cell lines were exposed to nigericin. Furthermore, by using gsdmd knockout THP-1 and HeLa cells we demonstrated that the A. pulmonis-induced cell death is not dependent on GSDMD (Fig. 4C and D), which indicates that the A. pulmonis mAT1-induced cell death is not pyroptosis. These results suggest the T3SS of A. pulmonis mAT1 mediated cytotoxicity against macrophages and epithelial cells through a caspase-independent manner. We next investigate whether A. pulmonis-induced cell death is RIPK1 dependent. Receptor-interacting protein kinase 1 (RIPK1) is a key mediator that promotes TNFα-induced necroptosis and PANoptosis.50 Thus, we infected macrophages and epithelial cells in the presence of necrostatin-1, a specific small molecule inhibitor of RIPK1. The presence of necrostatin-1 did not suppress WT- or ΔascN/pAscN A. pulmonis mAT1-induced cell death of macrophages (Fig. 4E). In contrast, we observed a decrease in A. pulmonis-induced cell death of epithelial cells from ∼40% to ∼30% (Fig. 4F), suggesting a potential role for RIPK1 in A. pulmonis-induced death of epithelial cells.Fig. 4 Characterization of A. pulmonis-induced cell death. Cytotoxicity percentage of mouse BMDM cells (A) and IEC6 cells (B) infected with designated strains with/without the addition of 25 μM Z-VAD-FMK at 2 h post-infection. Cytotoxicity percentage of THP-1 (C) and HeLa cells (D) infected with designated strains with/without gsdmd knockout at 2 h and 12 h post-infection, respectively. Cytotoxicity percentage of BMDM (E) and IEC6 cells (F) infected with designated strains with/without the addition of 20 μM Necrostatin-1. The MOI is 10 for BMDM (A and C) and THP-1 (E), and the MOI is 100 for IEC6 (B and F) and HeLa (D). Data are expressed as the mean ± SEM of at least three experimental repeats. One-way analysis of variance (ANOVA) with Tukey’s multiple comparisons was applied, and the Benjamini-Hochberg method was used for adjustment. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001. ns: p > 0.05.

IL-1β is a pro-inflammatory cytokine that is secreted during pyroptosis or necroptosis. To confirm whether A. pulmonis induces pyroptosis or necroptosis in macrophages and epithelial cells, we examined the secreted IL-1β level by ELISA. Our data indicate that the lL-1β levels in WT or ΔascN A. pulmonis-infected RAW264.7 or PMA-treated THP-1 cells were not higher than the LPS-stimuli control (Supplementary Fig. S8), which confirmed the necrosis that we observed in these macrophages are not pyroptosis or necroptosis. Additionally, WT A. pulmonis reduced the secreted IL-1β levels compared to the LPS-stimuli control and ΔascN A. pulmonis, suggesting that WT A. pulmonis may limit the production of IL-1β by macrophage by lysing them via T3SS, which is concurred with our in vivo observations in mice (Fig. 2I). Intriguingly, no IL-1β was detected in WT or ΔascN A. pulmonis-infected IEC-6 or normal human colon epithelial cell CCD841 CoN even at 12 h post-infection (Data not shown), suggesting that the necrosis we observed in epithelial cells were unlikely to be necroptosis. Why cell death levels were reduced from ∼40% to ∼30% (Fig. 4F) by necrostatin-1 remains to be explored.

Notably, our data concord with the observations that B. bronchiseptica RB50 also causes caspase-independent necrosis in macrophage and epithelial cells.51 B. bronchiseptica lyses host cells via a T3SS-secreted effector BteA that targets mammalian cell membrane,52 and BteA has a conserved lipid raft targeting (LRT) domain which is shared by a putative T3SS effector in Photorhabdus luminescens and a few RTX-type toxins in P. luminescens, Vibrio splendidus and P. asymbiotica53; however, we found no LRT-containing genes in all 36 Achromobacter genomes, although we were able to find the LRT-containing genes in P. luminescens, V. splendidus and P. asymbiotica by psi-BLAST (Supplementary Table S8). We suspect that A. pulmonis may have a undiscovered cell-lysing effector that is structurally distinct from BteA. Future studies will focus on uncovering the T3SS effector of A. pulmonis and investigating how this pathogen causes host cell necrosis by T3SS.

Transcription profiles of A. pulmonis-infected macrophages

Next, we characterized the transcriptional profiles of macrophages infected with WT vs ΔascN A. pulmonis mAT1 by RNA sequencing, and sought to clarify cellular phenotypes across cells and disease. The transcriptional gene expression profile was calculated by the differential expression analysis package DESeq2,35 and genes that showed statistically significant changes in the expression level by at least 1.5-fold were considered. Of 45,659 profiled host cell transcripts, 4224 were significantly altered in abundance within WT A. pulmonis-infected macrophages compared to the uninfected group (Fig. 5A and B and Supplementary Table S9). Although 1473 transcripts were more abundant as a response to the WT A. pulmonis infection, 2751 were less abundant (Referred to as infection-repressed genes). As a consequence of A. pulmonis infection, significant alterations in the host transcriptome were evident, with many of the highly induced genes associated with inflammatory and acute-phase responses, which are among the top enriched pathways (Fig. 5B–E and Supplementary Table S9). DESeq2 estimated fold change responses of selected host transcripts were confirmed by quantitative RT-PCR (Supplementary Fig. S9).Fig. 5 Analysis on the transcription profiles of A. pulmonis infected RAW264.7 cells. (A) Three-way Venn diagram of differentially expressed genes (Fold change ≥1.5; adjusted p-value ≤0.05) in the comparisons among WT vs uninfected, ΔascN vs uninfected, and WT vs ΔascN infected groups. WT, WT A. pulmonis infected cells; ΔascN, ΔascN A. pulmonis infected cells; uninfected, non-infected cells as a control. (B) Volcano plot obtained from DESeq2 analysis of WT vs uninfected comparison. (C) Heat map of the 30 most enriched and depleted host transcripts in the comparisons of WT vs uninfected and ΔascN vs uninfected, respectively. Color-coding is based on log2 (Fold change) values. (D) Top 50 most significantly enriched gene-ontology pathways of host transcripts in the WT vs uninfected comparison, determined by gene set enrichment analysis (GSEA). (E) Enriched KEGG pathways in the WT vs uninfected comparison by GSEA analysis. (F) Significantly differentially expressed genes of the WT vs ΔascN comparison. (G) Enriched KEGG pathways in the WT vs ΔascN comparison by GESA analysis. For GESA analysis, absolute normalized-enrichment score (NES) value > 1, p-value < 0.05, and q-value < 0.25 were recognized as significantly enriched conditions.

The patterns of upregulated expression of inflammatory cytokines and receptor genes highlight the pro-inflammation immune response during A. pulmonis infection (Fig. 5B–E and Supplementary Table S9). Among the upregulated genes are genes that encode proinflammatory cytokines including IL-1α, IL-1β, TNF-α, and IFN-β, as well as chemokines including MCP-1/CCL2, CCL3, CCL7, CXCL2, CXCL3, and CXCL10 (Fig. 5B, Supplementary Fig. S9 and Table S9). They were previously shown to be strongly upregulated in mice infected with Yersinia enterocolitica or Yersinia pestis, and contribute to the pathology of Yersiniosis and plague.54 Although the transcription level of IL-1β was upregulated by A. pulmonis infection, we observed a decreased level of secreted IL-1β in macrophages infected by WT A. pulmonis compared to non-infection control (Supplementary Fig. S8). It worth pointing out that the RNA sample was harvested at 30 min post infection when no cell death occurred yet, while the assessment of IL-1β levels secreted by cells was conducted at 1 h post infection when the cell death began (Fig. 3A).

Additionally, the transcription levels of genes that encode NLRP3 (NOD-like receptor family, pyrin domain-containing 3) and TLR2 (Toll-like receptor 2), which are both activated by extracellular and intracellular PAMP sensing during infection, encode proteins of inflammatory signaling cascades.55 The activation of these proinflammatory factors is classically linked to the M1 polarization program of macrophages,56 which was confirmed again with RAW264.7 cells after 12 h exposure to the supernatant from WT A. pulmonis mAT1 lysates, formalin-killing pallets, and LPS by qRT-PCR (Supplementary Fig. S10). The lymphotoxin α that signals through enriched TNF signaling pathway and the canonical NF-κB pathway was upregulated (Fig. 5D and E). It is crucial for lymphoid cell development by affecting chemokine expression and plays important roles in immune responses to pathogens, particularly in inducing chronic inflammatory infiltrates.57 Meanwhile, C-type lection receptor pathway that plays crucial roles in pathogen recognition, phagocytosis, and immune response was upregulated (Fig. 5E). Overall, the polarization towards M1 macrophages and upregulation of TNF, IL-1, EGR2/3, COX-2, and IL-23 genes suggest that A. pulmonis infection induced Th1 and Th17-related signaling pathways in macrophages (Fig. 5E, Supplementary Figs. S9 and S10).

The transcription profiles of macrophages infected by WT and ΔascN A. pulmonis are very similar. Only 15 genes (Fig. 5F, Supplementary Fig. S11A–C and Table S11) and 7 KEGG pathways (Fig. 5G) were differently expressed, and 9 differently expressed genes were confirmed by qRT-PCR (Supplementary Fig. S11D). Notably, Chac1 (ChaC Glutathione Specific Gamma-Glutamylcyclotransferase 1) and Ddit3 (DNA Damage Inducible Transcript 3), two genes that response to endoplasmic reticulum (ER) stress58 were upregulated in WT-infected cells compared to ΔascN-infected cells, suggesting that the T3SS of A. pulmonis induced ER stress in RAW246.7 cells.

Achromobacter and bacterial virulence T3SS genes are enriched in the gut microbiome of CD patients

Our previous study4 reported that Achromobacter is enriched in the feces of CD patients compared to controls based on 16S rRNA sequencing data of the RISK cohort (n = 304) the largest available set of pediatric patients with newly diagnosed Crohn's disease.59 To examine whether Achomobacter is enriched in the feces of adult CD patients, we re-visited PRJEB15371 cohort60 which contains metagenomics data from the feces of CD patients (n = 49) and healthy controls (n = 53) (Fig. 6A and Supplementary Table S12A and B). As expected, the abundance of Achromobacter was significantly higher in CD patients’ feces. Specifically, the fold-change (CD/Control) for Achromobacter is 1.94 with an FDR-adjusted p-value of less than 0.05 (Fig. 6B). Notably, there are other patient-enriched T3SS-harboring bacteria including Proteus, Salmonella, Citrobacter, Yersinia, and Burkholderia, present enriched in the fecal microbiota of CD patients. Type III secretion systems have been found widely distributed in pathogenic bacteria and mainly play detrimental effects on inducing inflammation in infection on hosts. Meanwhile, the observation and experimental data above have strongly encouraged us to further evaluate the profile of total microbial T3SS genes in the fecal microbiome of Crohn’s disease patients. To achieve this, we developed a novel bioinformatics pipeline, named as T3Finder and as outlined in Supplementary Fig. S12, following annotation of the metagenomic data using NR, we searched for vT3SS genes that belong to 20 core T3SS gene orthologs (Supplementary Table S2) with fT3SS genes excluded in PRJEB15371 cohort (Supplementary Table S12C and D). As shown in Fig. 6C and D, we found that the abundance of majority of (12 out of 20) T3SS gene orthologs, including SctC, SctD, SctE, SctI, SctJ, SctK, SctQ, SctR, SctS, SctT, SctU, and SctV, were significantly higher in fecal microbiome of CD patients compared to healthy controls. The 20 core T3SS gene orthologs can be classified into 6 categories based on the subunits based on the components of the nanomachines that they encode.12,15 Our analyses demonstrated that 3 out of 6 categories, including ATPase complex, cytoplasmic ring, and export apparatus, were significantly enriched in samples from CD patients. In addition, the sum of all 20 T3SS orthologs, referred as to Total Sct, was also significantly higher in the fecal microbiome of patients (Fig. 6D and Supplementary Fig. S13). Taken together, total bacterial vT3SS genes were enriched in the gut microbiome of CD patients, which further supports our hypothesis that bacterial vT3SS might be essential for the inflammation of Crohn’s disease.Fig. 6 vT3SS genes are specifically enriched in the fecal microbiota of CD patients, rather than other gastrointestinal disorders. (A) Details regarding the previously published PRJEB15371 cohort, which includes 49 CD patients and 53 healthy controls. (B) Volcano plot illustrating the abundance of genera within the PRJEB15371 cohort. (C) A heat map showing the normalized abundance of 20 individual T3SS orthologs, 6 categories of, and total T3SS orthologs in each sample in the PRJEB15371 cohort. Normalization for each sample was carried out by the scale function in pheatmap to better present data. CAS: categories. (D) Boxplots of relative abundance of T3SS orthologs in the CD or control group. Boxes represent the median and interquartile ranges between the first and third quartiles. (E) Bacterial T3SS orthologs in two previously published UC cohorts were tested. The PRJNA429990 cohort contains 25 UC patients and 15 healthy controls. The HMP2 cohort contains 24 UC patients and 23 healthy controls. Boxplots of the abundance of Total T3SS orthologs in the PRJNA429990 cohort (F) and the HMP2 cohort (G). (H) Bacterial T3SS orthologs in two previously published colorectal cancer cohorts were tested. The PRJNA763023 cohort contains 100 CRC patients and 100 healthy controls. The PRJEB10878 cohort contains 75 UC patients and 53 healthy controls. Boxplots of the abundance of Total T3SS orthologs in the PRJNA763023 cohort (I) and the PRJEB10878 cohort (J). Statistical significance was tested by the Two-tailed Wilcoxon rank-sum test. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001, ns: p > 0.05.

The vT3SS genes are specifically enriched in the fecal microbiota of CD patients, rather than other gastrointestinal disorders

In light of the results presented above, it became evident that the abundance of core T3SS orthologs and categories associated with Total Sct were enriched in fecal samples from CD patients. We are interested in determining whether the prevalence of T3SS-associated genes is observed in other primary intestinal disorders. Ulcerative colitis (UC) is one form of inflammatory bowel disease that shares similar clinical features with Crohn’s disease.61 We utilized T3Finder to access the metagenomic data of fecal samples from two previously reported cohorts, PRJNA42999062 and HMP2,63 which comprised the information regarding both UC patients and healthy controls. In the PRJNA429990 cohort, we revisited its metagenomic sequencing data obtained from 25 to 15 fecal samples of Chinese UC patients and healthy controls, respectively (Fig. 6E and Supplementary Table S13A). Interestingly, there were no significant differences observed between UC patients and healthy controls in terms of all T3SS gene orthologs, categories, and Total Sct in the PRJNA429990 cohort (Fig. 6F, Supplementary Fig. S14 and Table S13B and C), the fold-change (UC/Control) of Total Sct is 2.04, the FDR-adjusted p-value is 0.38. In the HMP2 cohort, there are 24 UC fecal samples and 23 healthy controls (Fig. 6E and Supplementary Table S14A). In the HMP2 cohort, the abundance of only two out of twenty T3SS gene orthologs, SctD and SctE, and one category, the ATPase complex exhibited significant differences between cases and controls. No significant differences were observed in the rest majority of T3SS gene orthologs, categories, and Total Sct (Fig. 6G, Supplementary Fig. S15 and Table S14B and C), the fold-change (UC/Control) of Total Sct is 1.10, the FDR adjusted p-value is 0.16. This strongly indicates that T3SS is specifically present within the fecal microbiome of CD patients, compared with UC, another form of IBD.

We next evaluated its abundance in the fecal microbiome of CRC patients, which represents another significant gastrointestinal disorder with severe health implications. Before this study, no assessment of bacterial type III secretion systems in CRC was reported. We re-analyzed the fecal metagenomic data of the PRJNA763023 and PRJEB10878 cohorts via T3Finder. The PRJNA763023 cohort64 comprised 100 CRC patients’ samples and 100 age-matched healthy controls (Fig. 6H and Supplementary Table S15A), while the PRJEB10878 cohort65 included 75 fecal samples from CRC patients and 53 samples from healthy controls (Fig. 6H and Supplementary Table S16A). Notably, we had an unexpected observation in PRJNA763023 cohorts. The mean abundance values of Total Sct in healthy controls were slightly higher than CRC group, and the fold-change (CRC/Control) of Total Sct is 0.98, p-value is 0.021 (Fig. 6I). This similar trend was also revealed in PRJEB10878 cohorts, but there was no significant difference between CRC group and controls (Fig. 6J), the fold-change (CRC/Control) of Total Sct is 0.82, p-value is 0.95. The results for all T3SS gene orthologs and categories from the aforementioned two cohorts are shown in Supplementary Figs. S16 and S17 and Table S15B and C S16 B and C. In summary, this study is the first effort to evaluate the abundance of vT3SS across different gastrointestinal disorders. We found that the Total Sct of vT3SS was specifically enriched in the fecal microbiome of CD patients, neither in the healthy controls nor other significant gastrointestinal disease patients (Fig. 6 and Supplementary Table S17). This piece of new knowledge is vital for further identification of biomarkers and the development of novel therapeutic interventions specifically targeting T3SS of gut microbiota in CD patients.

Identification of T3SS-based biomarkers for CD diagnostics

To investigate whether any core T3SS orthologs can serve as fecal sample biomarkers for CD, we employed the random forest disease classifier for the PRJEB15371 cohort to analyze the abundance of 16 T3SS orthologs and 6 categories that are enriched in patients (Fold change >1), as well as Total Sct. After feature selection based on five trials of 10-fold cross-validation, ten features, including 7 orthologs (SctC, SctE, SctK, SctN, SctR, SctT, and SctV), 2 categories (ATPase complex and export apparatus), and Total Sct were retained as markers of CD (Fig. 7A). Together they yielded an area under the receiver operating curve (AUC) value of 0.8 in the sensitivity-specificity plot (Fig. 7B). To further validate the results above, we recruited a validation cohort including 31 CD patients and 30 healthy subjects (Fig. 7C and Supplementary Table S4). None of these participants had taken any antibiotics in the three months before collecting the stool samples. Similarly, Total Sct was significantly higher in the fecal microbiome of CD patients (Supplementary Fig. S18 and Table S18A–C). When using the ten T3SS ortholog-based features as markers in the validation cohort, we observed an AUC value of 0.82 in the sensitivity-specificity plot (Fig. 7D). Thus, the ten T3SS ortholog-based markers generated from the PRJEB15371 cohort were successfully validated.Fig. 7 Identification of T3SS-based biomarkers for CD diagnostics. (A) The mean decrease accuracy (MDA) score of the optimized 10 variables. CAS: categories. (B) Receiver operating characteristic (ROC) curve with the optimized 10 variables of the PRJEB15371 cohort. (C) The Validation cohort comprises 31 CD patients and 30 healthy controls. (D) Receiver operating characteristic (ROC) curve with the optimized 10 variables of the validation cohort. (E) The EEN cohort in this study consisted of twelve CD patients treated with exclusive enteral nutrition (EEN) reported previously and 53 controls from the PRJEB15371 cohort. (F) Boxplots of relative abundance of Total Sct in each group. Statistical significance was tested by a two-tailed Kruskal–Wallis test. (G) Paired dot plot of relative abundance of Total Sct in pre/post-EEN groups. Each line represents a patient. Statistical significance was tested by a two-tailed Wilcoxon paired-samples signed rank test. (H) Principal component analysis (PCA) based on the relative abundance of the ten T3SS-based markers identified in (A). Statistical significance was tested by permutational multivariate analysis of variance (PERMANOVA) analysis with 999 permutations, and adjusted by the Benjamini-Hochberg correction for multiple tests. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001, ns: p > 0.05.

To investigate whether our conclusion can be extended to reflect the remission status of CD patients, we employed a previously reported cohort,60 consisting of twelve CD adult patients treated by the exclusive enteral nutrition (EEN) therapy using a single kind of formulated, branded nutrition powder chosen from ENSURE, Fresubin, or Nutrison Powder. After treatment, all 12 patients had their symptoms relieved. The data of the 53 healthy subjects from the PRJEB15371 cohort were used as controls (Fig. 7E and Supplementary Table S19A and B). EEN is the first-line therapy for pediatric CD, whereby patients receive a formulated liquid diet for weeks. For adults, the first line treatment is corticosteroid therapy and EEN is the alternative treatment for patients who refused corticosteroid therapy, as an adjunctive therapy or where other treatment options have failed.66 Strikingly, we found that the abundance of Total Sct in the post-EEN group was significantly reduced to levels similar to the control group (Fig. 7F-G, and Supplementary Table S19C). In a dimension reduction plot (Fig. 7H) based on the ten T3SS ortholog-based markers, we observed that the control group was clustered in the center, the pre-EEN group was scattered, and all samples but one in the post-EEN group moved towards the control group. Our observations with the EEN cohort support our hypothesis that vT3SS genes are strongly correlated with the severity of Crohn’s Disease, which further indicates that to dampen the activity of gut microbial vT3SS might be a novel avenue for CD therapy.

Discussion

Accumulating evidence supports that gut-associated microbes play roles in the pathogenesis of Crohn’s disease, including our previous report that human mAT-isolated A. pulmonis promotes colitis in mice. The mechanisms by which Achromobacter promotes pathogenesis are poorly understood despite the genus having been reported to be associated with multiple infections, including pneumonia, bronchitis, and cystic fibrosis. In this study, we demonstrate that A. pulmonis encodes a functional T3SS, which promotes colitis in mice and cell death of macrophage, epithelial cells, human mesenchymal stem cells, and pre-adipose cells, and thus provides new clues regarding how it enhances colitis in patients with CD.

Besides mAT, A. pulmonis can be isolated from cystic fibrosis patients (A. pulmonis LMG 26696 and 26788) and soil (A. pulmonis ANB-1; Supplementary Table S6). However, the classification of the soil-derived A. pulmonis ANB-1 into the species of A. pulmonis may be incorrect, as it is very distant from all other A. pulmonis and is close to Achromobacter denitrificans and Achromobacter veterisilvae in the phylogenic tree based on whole genome analysis (Supplementary Fig. S3B). If the classification of A. pulmonis ANB-1 is incorrect, human samples would be the only reported source of A. pulmonis, suggesting that this species has adapted to the niches inside human hosts.

In this study, our data also demonstrated that A. pulmonis triggered upregulations on genes in Th1 and Th17-related signaling pathways (Fig. 5B–E, Supplementary Figs. S9–S11) in macrophage cells during early infection. Moreover, activation of macrophage cells occurred with inflammation response and anti-microbiota activity including upregulation of S100A4 transcripts, and enriched linoleic acid metabolism pathway in Achromobacter-infected groups (Fig. 5B–E). S100A4 protein, upregulated linoleic acid (LA), and LA metabolism pathway activation could induce multiple types of reactive oxygen species (ROS), which can effectively clear intracellular bacterium.67,68 Additionally, overexpression of mitochondrial enzyme aconitate decarboxylase 1 (also known as immune-responsive gene 1, IRG1) (Fig. 5B and Supplementary Fig. S9) could catalyze cis-aconitate that accumulates following the first break in the TCA cycle to itaconate as an endogenous antimicrobial immunometabolite that functions as a lysosomal inducer in macrophages and exerts its bactericidal effects in bacterial metabolism in response to bacterial infection.69,70 Nevertheless, A. pulmonis infection eventually induced caspase-independent death of macrophages and epithelial cells via T3SS (Fig. 3, Fig. 4). Over expression of RhoB (Fig. 5 and Supplementary Fig. S11D–F) in a SopB (a T3SS effector with phosphate phosphatase activity) dependent manner and altered phospholipase D signaling pathway of host cell play an important role in transcellular bacteria survival.71,72 Future studies will focus on investigating whether the phosphate phosphatase of this pathogen triggers host cell cytotoxicity.

Given the importance of T3SS observed in A. pulmonis and that T3SSs are widely distributed among pathogenic bacteria, we speculated that other T3SS-harboring bacteria could contribute to the development or recurrence of CD. Consistent with this hypothesis, we found that 12 out of 20 T3SS core protein orthologs were enriched in patients in two cohorts of patients with CD (Fig. 6 and Supplementary Fig. S18). In addition, the level of the sum of all T3SS orthologs (Total Sct) was also enriched in patients. Taken together, our data support that bacterial T3SS gene clusters are enriched in CD patients. While further bioinformatics analyses are required to dissect which bacterial species are contributing these T3SS orthologs, we demonstrated ten T3SS ortholog-based features, including 7 individual orthologs, 2 categories, and Total Sct, can be used together as biomarkers for CD (Fig. 7D–G).

It remains unclear which T3SS-encoding bacteria are responsible for the increased level of T3SS homologs in CD patients. A previous study identified 85 species based on metagenomics that are enriched in patients with CD, none of which is predicted to encode a T3SS.15 However, 31 of these strains are unclassified bacteria, one or more of which could potentially encode a T3SS. This will be a focus of future analyses.

To summarize, we report the discovery that an mAT-associated pathogen, A. pulmonis, common to patients with CD, promotes colitis in mice and cell death via T3SS. Furthermore, we demonstrated bacterial vT3SS genes are enriched in the gut microbiome of symptomatic CD patients, and tightly correlated with disease status and remission (Supplementary Fig. S19). To the best of our knowledge, for the first time, we revealed the pathogenic potential of T3SS in the context of CD and identified T3SS gene-based biomarkers for CD, which provide a novel target for CD therapy.

Contributors

Study conceptualization: Jun Xu, Peijie Li, Wenjing Zhao, and Xiangyu Mou. Data curation: Jun Xu, Peijie Li, Zhenye Li, Sheng Liu, Huating Guo, and Jia Ke. Funding acquisition: Jia Ke, Wenjing Zhao, and Xiangyu Mou. Supervision: Jia Ke, Wenjing Zhao, and Xiangyu Mou. Writing—original draft: Jun Xu, Peijie Li, and Xiangyu Mou. Writing—review and editing: Jun Xu, Peijie Li, Cammie F. Lesser, Jia Ke, Wenjing Zhao, and Xiangyu Mou. Jun Xu, Peijie Li, Wenjing Zhao and Xiangyu Mou had verified the underlying data reported in the work. All authors reviewed and approved the final version of the manuscript.

Data sharing statement

All data relevant to the study are included in the article or uploaded as supplementary information. The Nanopore sequencing data, RNAseq data, and metagenomic data generated in this study are available in the Genome Sequence Archive (GSA) at the National Genomics Data Center, China National Center for Bioinformation (https://ngdc.cncb.ac.cn/gsa/), and the accession numbers are CRA013193, CRA013192, and CRA013239, respectively. The code needed to reproduce the results in the article is available upon request. Correspondence and requests for materials should be addressed to Xiangyu Mou (mouxy5@ms.sysu.edu.cn) and Wenjing Zhao (zhaowj29@ms.sysu.edu.cn).

Declaration of interests

The authors declare no competing interests.

Appendix A Supplementary data

Supplementary Figs. S1–S19

Supplementary Tables S1–S7

Supplementary Table S8

Supplementary Table S9

Supplementary Table S10

Supplementary Table S11

Supplementary Table S12

Supplementary Table S13

Supplementary Table S14

Supplementary Table S15

Supplementary Table S16

Supplementary Table S17

Supplementary Table S18

Supplementary Table S19

Acknowledgements

We are grateful to all patients and volunteers participating in the study. We thank Professor Feng Shao at National Institute of Biological Sciences, Beijing, China for the gifts of GSDMD knockout THP-1 and HeLa cell lines. We thank Weicong Zhong, Gan Lin, and Junrui Mai for their help with animal experiments.

Appendix A Supplementary data related to this article can be found at https://doi.org/10.1016/j.ebiom.2024.105296.
==== Refs
References

1 Roda G. Chien Ng S. Kotze P.G. Crohn's disease Nat Rev Dis Prim 6 1 2020 22 32242028
2 Ha C.W.Y. Martin A. Sepich-Poore G.D. Translocation of viable gut microbiota to mesenteric adipose drives formation of creeping fat in humans Cell 183 3 2020 666 683.e17 32991841
3 Coffey C.J. Kiernan M.G. Sahebally S.M. Inclusion of the mesentery in ileocolic resection for Crohn's disease is associated with reduced surgical recurrence J Crohns Colitis 12 10 2018 1139 1150 29309546
4 He Z. Wu J. Gong J. Microbiota in mesenteric adipose tissue from Crohn's disease promote colitis in mice Microbiome 9 1 2021 228 34814945
5 Miguelena Chamorro B. De Luca K. Swaminathan G. Longet S. Mundt E. Paul S. Bordetella bronchiseptica and Bordetella pertussis: similarities and differences in infection, immuno-modulation, and vaccine considerations Clin Microbiol Rev 36 3 2023 e0016422
6 Sorlin P. Brivet E. Jean-Pierre V. Prevalence and variability of siderophore production in the Achromobacter genus Microbiol Spectr 12 3 2024 e0295323
7 Esposito S. Pisi G. Fainardi V. Principi N. What is the role of Achromobacter species in patients with cystic fibrosis? Front Biosci 26 12 2021 1613 1620
8 Swenson C.E. Sadikot R.T. Achromobacter respiratory infections Ann Am Thoracic Soc 12 2 2015 252 258
9 Patra P.K. Banday A.Z. Sadanand R. Achromobacter xylosoxidans pneumonia in a young child with chronic granulomatous disease-a case-based review J Clin Immunol 41 7 2021 1686 1692 34263392
10 Isler B. Kidd T.J. Stewart A.G. Harris P. Paterson D.L. Achromobacter infections and treatment options Antimicrob Agents Chemother 64 11 2020 e01025-20
11 Traglia G.M. Almuzara M. Merkier A.K. Achromobacter xylosoxidans: an emerging pathogen carrying different elements involved in horizontal genetic transfer Curr Microbiol 65 6 2012 673 678 22926720
12 Deng W. Marshall N.C. Rowland J.L. Assembly, structure, function and regulation of type III secretion systems Nat Rev Microbiol 15 6 2017 323 337 28392566
13 Galán J.E. Common themes in the design and function of bacterial effectors Cell Host Microbe 5 6 2009 571 579 19527884
14 Sleight S.C. Bartley B.A. Lieviant J.A. Sauro H.M. In-Fusion BioBrick assembly and re-engineering Nucleic Acids Res 38 8 2010 2624 2636 20385581
15 Portaliou A.G. Tsolis K.C. Loos M.S. Zorzini V. Economou A. Type III secretion: building and operating a remarkable nanomachine Trends Biochem Sci 41 2 2016 175 189 26520801
16 Buchfink B. Xie C. Huson D.H. Fast and sensitive protein alignment using DIAMOND Nat Methods 12 1 2015 59 60 25402007
17 Tyson J. Bead-free long fragment LSK109 library preparation. Protocols IO 2020
18 Wick R.R. Judd L.M. Gorrie C.L. Holt K.E. Unicycler: resolving bacterial genome assemblies from short and long sequencing reads PLoS Comput Biol 13 6 2017 22
19 Chaumeil P.A. Mussig A.J. Hugenholtz P. Parks D.H. GTDB-Tk: a toolkit to classify genomes with the genome taxonomy database Bioinformatics 36 6 2020 1925 1927
20 Parks D.H. Chuvochina M. Chaumeil P.A. Rinke C. Mussig A.J. Hugenholtz P. A complete domain-to-species taxonomy for Bacteria and Archaea (vol 58, pg 561, 2020) Nat Biotechnol 38 9 2020 1098
21 Nguyen L.T. Schmidt H.A. von Haeseler A. Minh B.Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies Mol Biol Evol 32 1 2015 268 274 25371430
22 Felsenstein J. Confidence limits on phylogenies: an approach using the Bootstrap Evol Int J Organ Evol 39 4 1985 783 791
23 Xie J. Chen Y. Cai G. Cai R. Hu Z. Wang H. Tree visualization by one table (tvBOT): a web application for visualizing, modifying and annotating phylogenetic trees Nucleic Acids Res 51 W1 2023 W587 W592 37144476
24 Leenaars M. Hendriksen C.F. Critical steps in the production of polyclonal and monoclonal antibodies: evaluation and recommendations ILAR J 46 3 2005 269 279 15953834
25 Lothe R.A. Frøholm L.O. Westre G. Kjennerud U. Stainer and Scholte's pertussis medium with an alternative buffer J Biol Stand 13 2 1985 129 134 2860114
26 Link A.J. LaBaer J. Trichloroacetic acid (TCA) precipitation of proteins Cold Spring Harb Protoc 2011 8 2011 993 994 21807853
27 Assouvie A. Daley-Bauer L.P. Rousselet G. Growing murine bone marrow-derived macrophages Methods Mol Biol 1784 2018 29 33 29761385
28 Soontararak S. Chow L. Johnson V. Mesenchymal stem cells (MSC) derived from induced pluripotent stem cells (iPSC) equivalent to adipose-derived MSC in promoting intestinal healing and microbiome normalization in mouse inflammatory bowel disease model Stem Cells Transl Med 7 6 2018 456 467 29635868
29 Mou X. Souter S. Du J. Reeves A.Z. Lesser C.F. Synthetic bottom-up approach reveals the complex interplay of Shigella effectors in regulation of epithelial cell death Proc Natl Acad Sci USA 115 25 2018 6452 6457 29866849
30 McQuin C. Goodman A. Chernyshev V. CellProfiler 3.0: next-generation image processing for biology PLoS Biol 16 7 2018 e2005970
31 Chen S.F. Zhou Y.Q. Chen Y.R. Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor Bioinformatics 34 17 2018 884 890 29126246
32 Kim D. Langmead B. Salzberg S.L. HISAT: a fast spliced aligner with low memory requirements Nat Methods 12 4 2015 357 360 25751142
33 Pertea M. Kim D. Pertea G.M. Leek J.T. Salzberg S.L. Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown Nat Protoc 11 9 2016 1650 1667 27560171
34 Li B. Dewey C.N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome BMC Bioinf 12 2011 323
35 Love M.I. Huber W. Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 Genome Biol 15 12 2014 550 25516281
36 Robinson M.D. McCarthy D.J. Smyth G.K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data Bioinformatics 26 1 2010 139 140 19910308
37 Subramanian A. Tamayo P. Mootha V.K. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles Proc Natl Acad Sci USA 102 43 2005 15545 15550 16199517
38 Li D. Liu C.M. Luo R. Sadakane K. Lam T.W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph Bioinformatics 31 10 2015 1674 1676 25609793
39 Fu L. Niu B. Zhu Z. Wu S. Li W. CD-HIT: accelerated for clustering the next-generation sequencing data Bioinformatics 28 23 2012 3150 3152 23060610
40 Patro R. Duggal G. Love M.I. Irizarry R.A. Kingsford C. Salmon provides fast and bias-aware quantification of transcript expression Nat Methods 14 4 2017 417 28263959
41 Tintelnot J. Xu Y. Lesker T.R. Microbiota-derived 3-IAA influences chemotherapy efficacy in pancreatic cancer Nature 615 7950 2023 168 174 36813961
42 Delannoy-Bruno O. Desai C. Raman A.S. Evaluating microbiome-directed fibre snacks in gnotobiotic mice and humans Nature 595 7865 2021 91 95 34163075
43 Han N.D. Cheng J. Delannoy-Bruno O. Microbial liberation of N-methylserotonin from orange fiber in gnotobiotic mice and humans Cell 185 14 2022 2495 2509.e11 35764090
44 Meier-Kolthoff J.P. Göker M. TYGS is an automated high-throughput platform for state-of-the-art genome-based taxonomy Nat Commun 10 1 2019 2182 31097708
45 Veschetti L. Sandri A. Patuzzo C. Melotti P. Malerba G. Lleo M.M. Genomic characterization of Achromobacter species isolates from chronic and occasional lung infection in cystic fibrosis patients Microb Genom 7 7 2021 e000606
46 Ahuja U. Shokeen B. Cheng N. Differential regulation of type III secretion and virulence genes in Bordetella pertussis and Bordetella bronchiseptica by a secreted anti-σ factor Proc Natl Acad Sci USA 113 9 2016 2341 2348 26884180
47 Fennelly N.K. Sisti F. Higgins S.C. Bordetella pertussis expresses a functional type III secretion system that subverts protective innate and adaptive immune responses Infect Immun 76 3 2008 1257 1266 18195025
48 Mariathasan S. Weiss D.S. Newton K. Cryopyrin activates the inflammasome in response to toxins and ATP Nature 440 7081 2006 228 232 16407890
49 Wang K. Sun Q. Zhong X. Structural mechanism for GSDMD targeting by autoprocessed caspases in pyroptosis Cell 180 5 2020 941 955.e20 32109412
50 Wang Y. Kanneganti T.D. From pyroptosis, apoptosis and necroptosis to PANoptosis: a mechanistic compendium of programmed cell death pathways Comput Struct Biotechnol J 19 2021 4641 4657 34504660
51 Stockbauer K.E. Foreman-Wykert A.K. Miller J.F. Bordetella type III secretion induces caspase 1-independent necrosis Cell Microbiol 5 2 2003 123 132 12580948
52 French C.T. Panina E.M. Yeh S.H. Griffith N. Arambula D.G. Miller J.F. The Bordetella type III secretion system effector BteA contains a conserved N-terminal motif that guides bacterial virulence factors to lipid rafts Cell Microbiol 11 12 2009 1735 1749 19650828
53 Panina E.M. Mattoo S. Griffith N. Kozak N.A. Yuk M.H. Miller J.F. A genome-wide screen identifies a Bordetella type III secretion effector and candidate effectors in other species Mol Microbiol 58 1 2005 267 279 16164564
54 Comer J.E. Sturdevant D.E. Carmody A.B. Transcriptomic and innate immune responses to Yersinia pestis in the lymph node during bubonic plague Infect Immun 78 12 2010 5086 5098 20876291
55 Keestra-Gounder A.M. Tsolis R.M. Bäumler A.J. Now you see me, now you don't: the interaction of Salmonella with innate immune receptors Nat Rev Microbiol 13 4 2015 206 216 25749454
56 Lawrence T. Natoli G. Transcriptional regulation of macrophage polarization: enabling diversity with identity Nat Rev Immunol 11 11 2011 750 761 22025054
57 Borelli A. Irla M. Lymphotoxin: from the physiology to the regeneration of the thymic function Cell Death Differ 28 8 2021 2305 2314 34290396
58 Oh-Hashi K. Nomura Y. Shimada K. Koga H. Hirata Y. Kiuchi K. Transcriptional and post-translational regulation of mouse cation transport regulator homolog 1 Mol Cell Biochem 380 1–2 2013 97 106 23615711
59 Gevers D. Kugathasan S. Denson L.A. The treatment-naive microbiome in new-onset Crohn’s disease Cell Host Microbe 15 3 2014 382 392 24629344
60 He Q. Gao Y. Jie Z. Two distinct metacommunities characterize the gut microbiota in Crohn’s disease patients GigaScience 6 7 2017 1 11
61 Schirmer M. Garner A. Vlamakis H. Xavier R.J. Microbial genes and pathways in inflammatory bowel disease Nat Rev Microbiol 17 8 2019 497 511 31249397
62 Weng Y.J. Gan H.Y. Li X. Correlation of diet, microbiota and metabolite networks in inflammatory bowel disease J Digest Dis 20 9 2019 447 459
63 Lloyd-Price J. Arze C. Ananthakrishnan A.N. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases Nature 569 7758 2019 655 662 31142855
64 Yang Y. Du L. Shi D. Dysbiosis of human gut microbiome in young-onset colorectal cancer Nat Commun 12 1 2021 6757 34799562
65 Yang J. Li D. Yang Z. Establishing high-accuracy biomarkers for colorectal cancer by comparing fecal microbiomes in patients with healthy families Gut Microb 11 4 2020 918 929
66 Wall C.L. Day A.S. Gearry R.B. Use of exclusive enteral nutrition in adults with Crohn's disease: a review World J Gastroenterol 19 43 2013 7652 7660 24282355
67 Yan B. Fung K. Ye S. Linoleic acid metabolism activation in macrophages promotes the clearing of intracellular Staphylococcus aureus Chem Sci 13 42 2022 12445 12460 36382278
68 Senizza A. Rocchetti G. Callegari M.L. Lucini L. Morelli L. Linoleic acid induces metabolic stress in the intestinal microorganism Bifidobacterium breve DSM 20213 Sci Rep 10 1 2020 5997 32265475
69 Zhang Z. Chen C. Yang F. Itaconate is a lysosomal inducer that promotes antibacterial innate immunity Mol Cell 82 15 2022 2844 2857.e10 35662396
70 Rosenberg G. Riquelme S. Prince A. Avraham R. Immunometabolic crosstalk during bacterial infection Nat Microbiol 7 4 2022 497 507 35365784
71 Kirchenwitz M. Halfen J. von Peinen K. RhoB promotes Salmonella survival by regulating autophagy Eur J Cell Biol 102 4 2023 151358
72 Yang J. Pei G. Sun X. RhoB affects colitis through modulating cell signaling and intestinal microbiome Microbiome 10 1 2022 149 36114582
