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. 2023 Mar 29;89(3):e0008223.
doi: 10.1128/aem.00082-23. Epub 2023 Feb 21.

Novel Insights into the Molecular Mechanisms Underlying Robustness and Stability in Probiotic Bifidobacteria

Marie Schöpping  1   2 , Anisha Goel  3 , Kristian Jensen  1 , Ricardo Almeida Faria  4 , Carl Johan Franzén  2 , Ahmad A Zeidan  1
Affiliations

Novel Insights into the Molecular Mechanisms Underlying Robustness and Stability in Probiotic Bifidobacteria

Marie Schöpping et al. Appl Environ Microbiol. .

Abstract

Some probiotic bifidobacteria are highly robust and shelf-stable, whereas others are difficult to produce, due to their sensitivity to stressors. This limits their potential use as probiotics. Here, we investigate the molecular mechanisms underlying the variability in stress physiologies of Bifidobacterium animalis subsp. lactis BB-12 and Bifidobacterium longum subsp. longum BB-46, by applying a combination of classical physiological characterization and transcriptome profiling. The growth behavior, metabolite production, and global gene expression profiles differed considerably between the strains. BB-12 consistently showed higher expression levels of multiple stress-associated genes, compared to BB-46. This difference, besides higher cell surface hydrophobicity and a lower ratio of unsaturated to saturated fatty acids in the cell membrane of BB-12, should contribute to its higher robustness and stability. In BB-46, the expression of genes related to DNA repair and fatty acid biosynthesis was higher in the stationary than in the exponential phase, which was associated with enhanced stability of BB-46 cells harvested in the stationary phase. The results presented herein highlight important genomic and physiological features contributing to the stability and robustness of the studied Bifidobacterium strains. IMPORTANCE Probiotics are industrially and clinically important microorganisms. To exert their health-promoting effects, probiotic microorganisms must be administered at high counts, while maintaining their viability at the time of consumption. In addition, intestinal survival and bioactivity are important criteria for probiotics. Although bifidobacteria are among the most well-documented probiotics, the industrial-scale production and commercialization of some Bifidobacterium strains is challenged by their high sensitivity to environmental stressors encountered during manufacturing and storage. Through a comprehensive comparison of the metabolic and physiological characteristics of 2 Bifidobacterium strains, we identify key biological markers that can serve as indicators for robustness and stability in bifidobacteria.

Keywords: Bifidobacteria longum subsp. longum; Bifidobacterium animalis subsp. lactis; amino acids utilization and synthesis; cell membrane fatty acid profile; cell surface hydrophobicity; metabolite production; probiotic bacteria; robustness; stability; transcriptomic.

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Conflict of interest statement

The authors declare a potential conflict of interest. M.S., K.J., A.G., R.A.F., and A.A.Z. were employed by Chr. Hansen A/S, a global supplier of probiotics, at the time of writing this manuscript. The authors' views presented in this manuscript, however, are solely based on scientific grounds and do not reflect the commercial interests of their employer.

Figures

FIG 1
FIG 1
Growth and metabolite profiles of B. animalis BB-12 (A) and B. longum BB-46 (B). Cultivations were conducted under anaerobic conditions (80% N2 and 20% CO2) at 37°C and pH 6.5. Each data point represents the mean of 3 biological replicates ± standard deviation. The arrows indicate the sample time point in the exponential and stationary phase for stability assessment, fatty acid, and transcriptomic analyses.
FIG 2
FIG 2
Comparison of amino acid consumption and release by B. animalis BB-12 and B. longum BB-46 during growth in chemically defined medium. The chemically defined medium contained all proteinogenic amino acids, except for cysteine in a concentration of 0.04 g L−1. Cysteine that served as amino acid, reducing agent and sulfur source was added in a concentration of 0.5 g L−1. (A) Relative changes of amino acid concentrations at the exponential and stationary phase. Exp, relative change in concentration between inoculation and exponential phase (around OD600 = 1.3). Stat, relative change in concentration between inoculation and stationary phase. Each field represents the relative difference between the mean of 3 biological replicates. Cystine represents the oxidized form of cysteine, presumably formed when cysteine reduces remaining oxygen in the medium before inoculation (concentration at time of inoculation: 0.06–0.07 g L−1). (B) Concentrations of L-aspartate, l-glutamate, l-alanine, and l-glutamine during fermentations of BB-12 and BB-46. Each data point represents the mean of 3 biological replicates ± standard deviation.
FIG 3
FIG 3
Viability assessment of B. animalis BB-12 and B. longum BB-46 during short-term storage. Cells were stored under aerobic conditions for 7 to 28 days at 8 to 10°C at different pH (pH 6.5, pH 5.5, and pH 4.5), and their viability was assessed by CFU counts before and after storage. Each data point represents the mean of 3 biological replicates ± standard deviation. (A) Effect of harvesting time. Viability of BB-12 and BB-46 harvested in the exponential and stationary phase before and after storage for 7 days at pH 6.5. (B) Effect of storage pH. Viability of BB-12 and BB-46 harvested in the stationary phase before and after storage at pH 6.5, pH 5.5, and pH 4.5 for 7 days. (C) Effect of storage time. Viability of BB-12 and BB-46 harvested in the stationary phase before and after storage at pH 6.5 for 7 days or 28 days.
FIG 4
FIG 4
COG classification of significantly differentially expressed genes. (A and B) Differentially expressed genes between the exponential and stationary phase in BB-12 and BB-46, respectively. (A) 43 genes, and (B) 151 genes not assigned to COG groups. Green, upregulated; red, downregulated. (C and D) Differentially expressed genes between BB-12 and BB-46 in the exponential and stationary phase, respectively. (C) 13 genes, and (D) 21 genes not assigned to COG groups. Green, higher expressed in BB-12; red, higher expressed in BB-46. COG Categories: Information Storage and Processing: J, translation, ribosomal structure, and biogenesis; K, transcription; L, replication, recombination, and repair; B, chromatin structure and dynamics. Cellular Processes and Signaling: D, Cell cycle control, cell division, and chromosome partitioning; V, defense mechanisms; T, signal transduction mechanisms; M, cell wall/membrane/envelope biogenesis; N, cell motility; U, intracellular trafficking, secretion, and vesicular transport; O, posttranslational modification, protein turnover, and chaperones. Metabolism: C, energy production and conversion; G, carbohydrate transport and metabolism; E, amino acid transport and metabolism; F, nucleotide transport and metabolism; H, coenzyme transport and metabolism; I, lipid transport and metabolism; P, inorganic ion transport and metabolism; Q, secondary metabolites biosynthesis, transport, and catabolism. Poorly Characterized: S, function unknown.
FIG 5
FIG 5
Differential gene expression analysis of B. animalis BB-12 and B. longum BB-46 using RNA-seq. (A) Schematic representation of differentially expressed genes associated with methionine biosynthesis in BB-12 between the exponential (Exp) and stationary phase (Stat). The locus tags of all differentially expressed genes are given in bold type next to their potential functionalities. The log2(FC) values are given next to the locus tags. All log2(FC) values have an adjusted P value ≤ 0.01. L-Aspartyl-4-P, L-aspartyl-4-phosphate; MTHFS, 5-formethyltetrahydrofolate cyclo-ligase; MetE, 5-methyltetrahydropteroyltriglutamate–homocysteine methyltransferase; MetF, 5,10-methylenetetrahydrofolate reductase; 5-CHO-THF, 5-formyltetrahydrofolate; THPTG, tetrahydropteroyltri-l-glutamate; h2s, hydrogen sulfide; 5,10-CH=THF, 5,10-Methenyltetrahydrofolate; ME-THPTG, 5-methyltetrahydropteroyltri-l-glutamate; 5,10-CH2-THF, 5,10-methylenetetrahydrofolate; MsrAB, (peptide)-l-methionine-(R/S)-sulfoxide reductase; ROS, reactive oxygen species; trdox, oxidized thioredoxin; trdrd, reduced thioredoxin. (B, C, and D) Differentially expressed genes previously implicated in the stress response of bifidobacteria (3, 20) or commonly known to be involved in defense mechanism of bacteria. (B) In BB-12 between the exponential and stationary phase. |Log2(FC)| ≥ 2 and P value ≤ 0.01. (C) In BB-46 between the exponential and stationary phase. |Log2(FC)| ≥ 2 and P value ≤ 0.01. (D) Between BB-12 and BB-46 in the exponential and stationary phase. |Log2(FC)| ≥ 2 and P value ≤ 0.01. Log2(FC) > 0, higher transcription level of gene in BB-12. White fields, no significant difference in the expression level of the gene between the strains.
FIG 6
FIG 6
Membrane fatty acid profiles of B. animalis BB-12 and B. longum BB-46 showing the main components in exponential (Exp) and stationary phase (Stat). Each data point represents the mean of 3 biological replicates ± standard deviation. UFA/SFA, unsaturated:saturated fatty acid ratio. The significance of the differences between means was assessed in t-tests. Means with the same superscript letters show differences at P ≤ 0.05 before multiple testing, while means with the same number of superscript asterisks (*) within the same group show differences at Padj ≤ 0.05, after correcting for multiple testing. When superscripts are omitted, no significant difference was observed. The complete fatty acid profile is shown in Table S3.
FIG 7
FIG 7
Interfacial adhesion curves of B. animalis BB-12 and B. longum BB-46 in 100 mM sodium phosphate buffer at pH = 7. Bacterial cell surface hydrophobicity (BCSH) of BB-12 (formula image) and BB-46 (formula image) over the volume ratio (ϕ) of the hexadecane. The volume ratio is calculated as VH/VB, where VH and VB are the volumes of the hexadecane and the buffer phase for each sample, respectively. Each sample point represents the mean of a technical triplicate ± standard deviation. The interfacial adhesion curves were fitted to the data using a previously published equation (59).
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