DOI: 10.5061/dryad.8931zcs53
title: Antimicrobial efficacy of <em>Bifidobacterium longum</em> FB1-4 cell-free supernatant against MRSA: Insights into mechanisms and food matrix application

description: The food safety landscape faces a significant challenge from
 methicillin-resistant Staphylococcus aureus (MRSA), a virulent
 and antibiotic-resistant foodborne pathogen. This study investigates the
 antimicrobial properties and mechanisms of cell-free supernatant (CFS)
 from Bifidobacterium longum FB1-4 against MRSA. The CFS
 exhibited robust antibacterial activity, achieving a minimum inhibitory
 concentration (MIC) of 5.56 mg/ml and maintaining a bacterial elimination
 rate of over 80 % for 8 hours at 2× MIC, demonstrating superior sustained
 bactericidal efficacy compared to vancomycin. Antibacterial
 component analysis revealed that while the initial effect is partly
 pH-dependent, the activity is largely attributable to heat-stable
 molecules, retaining 90 % efficacy after high-temperature treatment. In a
 food matrix, FB1-4 CFS exhibited impressive bactericidal effects against
 three MRSA strains, leading to elimination rates of 94.64 % at 2 hours,
 98.02 % at 4 hours, 99.90 % at 6 hours, and nearly complete eradication
 (99.99 %) by 8 hours. Furthermore, FB1-4 CFS significantly suppressed all
 three tested MRSA strains (MRSA-43300, MRSA-337371, and MRSA-361194); P
 &lt; 0.05) mecA expression, increased antibiotic
 susceptibility, and diminished biofilm formation by 70 %. Transcriptomic
 and metabolomic analyses revealed that FB1-4 CFS modulates two-component
 systems and quorum sensing, leading to reduced virulence factor
 expression. In vivo studies confirmed a 90 % reduction in MRSA
 colonization in mouse intestines. These findings provide critical insights
 into how probiotic metabolites inhibit MRSA, underscoring their potential
 as natural therapeutic agents in food safety.
description: # Antimicrobial efficacy of *Bifidobacterium longum* FB1-4 cell-free
 supernatant against MRSA: Insights into mechanisms and food matrix
 application Dataset DOI:
 [10.5061/dryad.8931zcs53](https://doi.org/10.5061/dryad.8931zcs53) ##
 Description of the data and file structure
 **Comprehensive Transcriptomics Analysis Documentation in each zip
 files.** # **1. Assemble: Novel Gene and sRNA Annotation** ##
 **File Descriptions** ### ***_novel.gtf**
 Novel gene and sRNA annotation file in GTF format: 
 • V1 seqname: Chromosome number
 • V2 source: Type (novel gene and sRNA), assembled by Rockhopper software
 • V3 feature: Structural type of annotation information, e.g., exon • V4
 start: Start coordinate of annotation region on chromosome
 • V5 end: End coordinate of annotation region on chromosome
 • V6 score: Annotation score indicating likelihood (dot means empty)
 • V7 strand: Strand information (+/-) on chromosome
 • V8 frame: Only valid for CDS annotations, values 0/1/2
 • V9 attributes: List containing multiple attributes, mainly gene ID, transcript ID, etc. ### ***_novel.fa** Novel gene and sRNA sequences in FASTA format: ### ***_novel_gene.xls** Novel gene Pfam functional annotation file in Excel format: • gene_id: Novel gene identifier • gene_name: Gene name (uses '-' for novel genes without names) • gene_chr: Chromosome number where gene is located • gene_start: Start coordinate on chromosome • gene_end: End coordinate on chromosome • gene_strand: Strand information (+/-) • gene_length: Gene length • gene_biotype: Gene type • gene_description: Functional description ### ***_novel_go.xls** Novel gene GO annotation file in Excel format: • gene_id: Novel gene identifier • go_id: GO ID corresponding to gene • go_ontology: GO database subclass • go_term: GO term name ### ***_novel_kegg.xls** Novel gene KEGG annotation file in Excel format: • gene_id: Novel gene identifier • pathway_gene_id: Gene ID in KEGG pathway • pathway_id: KEGG pathway ID • pathway_name: KEGG pathway name # **2. Enrichment: Functional Enrichment Analysis** ## **Analysis Methods** We use clusterProfiler software for GO functional enrichment analysis and KEGG pathway enrichment analysis based on hypergeometric distribution principle.  ## **Key Metrics** • GeneRatio: Ratio of differential genes enriched in a pathway to total differential genes • BgRatio: Ratio of background genes enriched in a pathway to total background genes ## **GO Enrichment Analysis** File: • Category: GO database classification (BP, CC, MF) • GOID: GO identifier • Description: Functional description of GO term • pvalue: Significance test p-value • padj: Adjusted p-value after multiple hypothesis testing • geneID: IDs of differential genes annotated to GO term • geneName: Names of differential genes • Count: Number of differential genes annotated to GO term ## **KEGG Pathway Enrichment** File: • KEGGID: KEGG pathway identifier • Description: Functional description of KEGG pathway • pvalue: Significance test p-value • padj: Adjusted p-value after multiple hypothesis testing • geneID: IDs of differential genes annotated to KEGG pathway • geneName: Names of differential genes • keggID: KEGG IDs of differential genes • Count: Number of differential genes annotated to KEGG pathway ## **Protein-Protein Interaction (PPI) Analysis** Uses STRING database to analyze protein interaction networks of differential genes. # **3. GSEA: Gene Set Enrichment Analysis** ## **Analysis Workflow** 1. Calculate differential degree (signal2noise) for all genes 2. Sort genes by differential degree 3. Calculate Enrichment Score (ES) for gene sets 4. Determine significance level using permutation test 5. Normalize ES and calculate FDR to control false positives ## **Key Files** : GSEA results in TSV format NAME: Gene set ID in GO/KEGG/Reactome databases ◦ SIZE: Number of genes in gene set ◦ ES: Enrichment Score ◦ NES: Normalized Enrichment Score ◦ NOM p-val: Nominal p-value ◦ FDR q-val: False discovery rate q-value ◦ FWER p-val: Family-wise error rate p-value # **4. Mapping: Alignment Results** ## **Reference Genome Alignment** Uses Bowtie2 for genome localization analysis. Results should show &gt;70% mapped reads with &lt;10% multiple mappings. ## **Mapping Statistics** File: • Total reads: Number of clean reads after filtering • Total mapped: Number of reads mapped to genome • Multiple mapped: Number of reads with multiple mapping positions • Uniquely mapped: Number of reads with unique mapping position • Reads map to '+'/'-': Reads mapped to positive/negative strands ## **Visualization** • Distribution of reads in different genomic regions (Exon/Intergenic) • Density distribution of reads on chromosomes # **5. QC: Quality Control** ## **Quality Metrics** File:  • Raw reads: Number of raw sequencing reads • Clean reads: Number of reads after filtering • Clean bases: Total base pairs in clean data (Gigabases) • Error rate: Average base sequencing error rate • Q20/Q30: Percentage of bases with Phred score &gt;20/30 • GC content: Percentage of G/C bases ## **Data Filtering Steps** 1. Remove reads with adapters 2. Remove reads with &gt;10% N bases 3. Remove low-quality reads (≥50% bases with sQ ≤5) # **6. Quantification: Gene Expression Analysis** ## **Quantification Method** Uses featureCounts tool from subread software. Gene expression levels are calculated using FPKM (Fragments Per Kilobase of transcript per Million mapped reads). ## **Expression Matrices** : Raw read count matrix : FPKM expression matrix : Group-averaged FPKM matrix ## **Visualization** : Sample correlation coefficient plot : 2D PCA clustering plot : 3D PCA clustering plot : Gene expression level distribution Violin plot of gene expression levels Box plot of gene expression levels # **7. SNP: Single Nucleotide Polymorphism Analysis** ## **SNP Calling** Uses GATK for SNP and Indel calling after processing alignment results with picard-tools. ## **SNP Files** : Genotype information of SNP sites ◦ CHROM: Reference sequence name ◦ POS: Position of variant site ◦ ID: Variant identifier (from dbSNP if available) ◦ REF: Reference base ◦ ALT: Alternate base ◦ QUAL: Quality score of variant ◦ GT: Genotype of samples ◦ AD: Allelic depths for REF and ALT ◦ DP: Total sequencing depth at variant site ## **Annotation** : Functional annotation of SNP sites ◦ GeneID: Gene containing variant ◦ GeneName: Name of gene ◦ FeatureID: Transcript ID ◦ Biotype: Transcript type ◦ HGVS_C: HGVS annotation at DNA level ◦ HGVS_P: HGVS annotation at protein level ◦ EFFECT: Effect of variant ◦ IMPACT: Impact level of variant # **8. Structure: Gene Structure Analysis** ## **UTR Analysis** : 3'UTR sequences : 5'UTR sequences : Shine-Dalgarno sequence prediction results : Rho-independent terminator prediction results  ## **Operon Prediction** File: • Start: Start coordinate of first gene in operon • Stop: End coordinate of last gene in operon • Strand: Strand information (+/-) • Number of Genes: Number of genes in operon • Genes: Names of genes in operon ## **Antisense Transcript Prediction** File:  • plus_transcript_id: Sense transcript ID • minus_transcript_id: Antisense transcript ID • types: Antisense transcript type (enclosed, convergent, divergent) • overlap_start: Start position of overlapping region • overlap_end: End position of overlapping region • overlap_length: Length of overlapping region ## **sRNA Analysis** : sRNA sequences : sRNA target gene prediction results : sRNA expression levels (FPKM) : BSRD database annotation of sRNAs : Rfam database annotation of sRNAs # **9. SupFile: Supplementary Files** ## **Gene Annotation** : Gene sequences in FASTA format : Gene annotation information : Differential expression analysis results for all comparison groups ## **Functional Annotation** : GO annotation of genes : KEGG pathway annotation : KO (KEGG Orthology) annotation ## **GO Classification** • : GO classification statistics plot • : Detailed GO classification results