Back

Microbial Signal Recognition & Neuronal Mimicry (SRNM) axis in IBD

Anand, A. A.; Mishra, P.; Srivathsa, V. S.; Yadav, V.; Samanta, S. K.

2026-03-23 bioinformatics
10.64898/2026.03.20.713231 bioRxiv
Show abstract

BackgroundInflammatory bowel disease (IBD) is a chronic inflammatory disorder characterized by gut microbial dysbiosis and immune dysregulation. While compositional changes in the microbiome are well studied, the functional mechanisms through which microbes influence host signalling remain poorly understood. PurposeThis study aimed to investigate microbial-host molecular mimicry in IBD and to elucidate its role in modulating immune and neuronal pathways through a newly proposed Microbial Signal Recognition and Neuronal Mimicry (SRNM) axis. MethodsShotgun metagenomic datasets from IBD patients and healthy controls were analyzed using a custom Molecular Mimicry In Silico Pipeline (MMIP). Reads were assembled, annotated, and subjected to protein homology mapping, Gene Ontology enrichment, PFAM domain analysis, and taxonomic profiling to identify microbial proteins mimicking human functional pathways. ResultsIBD-associated microbiomes exhibited significantly higher functional complexity and enrichment of eukaryote-like proteins compared to healthy controls. Microbial proteins mimicking host pathways involved in neuron projection development, signal recognition particle (SRP)-mediated protein targeting, immune signaling, and stress responses were markedly enriched in IBD. Key human-like targets included TRPV1, CAMK2D, SNCA, MTCP1, TCL1B, and PEAK3. PFAM analysis revealed overrepresentation of kinase domains, zinc-finger motifs, ankyrin repeats, and ABC transporters. These signatures were predominantly contributed by IBD-enriched taxa such as Gammaproteobacteria, Fusobacteria, and Betaproteobacteria. ConclusionThis study identifies a previously unrecognized SRNM axis in IBD, revealing how microbial molecular mimicry may influence neuroimmune signaling and disease pathogenesis, and highlight potential targets for microbiome-based therapeutic intervention.

Matching journals

The top 12 journals account for 50% of the predicted probability mass.

1
mSystems
361 papers in training set
Top 0.6%
10.3%
2
Computational and Structural Biotechnology Journal
216 papers in training set
Top 0.4%
7.0%
3
Frontiers in Immunology
586 papers in training set
Top 0.9%
7.0%
4
Scientific Reports
3102 papers in training set
Top 33%
3.7%
5
Cell Communication and Signaling
35 papers in training set
Top 0.1%
3.7%
6
PLOS ONE
4510 papers in training set
Top 41%
3.3%
7
Journal of Proteomics
27 papers in training set
Top 0.1%
3.1%
8
Microbiome
139 papers in training set
Top 1%
2.9%
9
Gut Microbes
70 papers in training set
Top 0.4%
2.8%
10
Frontiers in Microbiology
375 papers in training set
Top 3%
2.7%
11
International Journal of Molecular Sciences
453 papers in training set
Top 4%
2.7%
12
PeerJ
261 papers in training set
Top 4%
2.4%
50% of probability mass above
13
Frontiers in Genetics
197 papers in training set
Top 3%
2.1%
14
npj Biofilms and Microbiomes
56 papers in training set
Top 0.8%
2.1%
15
Advanced Science
249 papers in training set
Top 9%
1.9%
16
Brain, Behavior, and Immunity
105 papers in training set
Top 1%
1.8%
17
BMC Microbiology
35 papers in training set
Top 0.5%
1.8%
18
Nature Communications
4913 papers in training set
Top 50%
1.7%
19
Microorganisms
101 papers in training set
Top 0.7%
1.7%
20
BMC Medical Genomics
36 papers in training set
Top 0.5%
1.7%
21
Frontiers in Veterinary Science
30 papers in training set
Top 0.5%
1.4%
22
mSphere
281 papers in training set
Top 4%
1.4%
23
Frontiers in Cellular and Infection Microbiology
98 papers in training set
Top 4%
1.3%
24
Immunology
29 papers in training set
Top 0.8%
0.9%
25
Frontiers in Medicine
113 papers in training set
Top 5%
0.9%
26
Journal of Medical Virology
137 papers in training set
Top 4%
0.8%
27
Glia
74 papers in training set
Top 0.5%
0.8%
28
Aging
69 papers in training set
Top 3%
0.8%
29
PLOS Computational Biology
1633 papers in training set
Top 25%
0.7%
30
The Journal of Infectious Diseases
182 papers in training set
Top 5%
0.7%