Next Article in Journal
Changes in Quality Characteristics and Metabolite Composition of Low-Temperature and Nitrogen-Modified Atmosphere in Indica Rice during Storage
Previous Article in Journal
Role of Heat-Shock Proteins in the Determination of Postmortem Metabolism and Meat Quality Development of DFD Meat
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Foods
Volume 13
Issue 18
10.3390/foods13182966
foods-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Lactobacilli and Bifidobacteria: A Parapostbiotic Approach to Study and Explain Their Mutual Bioactive Influence

by
Clelia Altieri
1,
Alfonso Filippone
1,2,
Antonio Bevilacqua
1,*,
Maria Rosaria Corbo
1 and
Milena Sinigaglia
1
1
Department of Agriculture, Food, Natural Resources and Engineering, University of Foggia, 71122 Foggia, Italy
2
Department of Psychology and Education, Pegaso University, 80143 Napoli, Italy
*
Author to whom correspondence should be addressed.
Foods 2024, 13(18), 2966; https://doi.org/10.3390/foods13182966
Submission received: 8 August 2024 / Revised: 12 September 2024 / Accepted: 16 September 2024 / Published: 19 September 2024
(This article belongs to the Section Food Microbiology)
Browse Figures
Versions Notes

Abstract

:
Three strains of Lactiplantibacillus plantarum and three bifidobacteria (Bifidobacterium animalis subsp. lactis, Bifidobacterium breve, and Bifidobacterium subtile) were used as target strains; in addition, for each microorganism, the cell-free supernatant (CFS) was produced and used as an ingredient of the growth medium. Namely CFSs from lactobacilli were used on bifidobacteria and CFSs from bifidobacteria were used on lactobacilli. The viable count was assessed, and the data were modelled through a reparametrized Gompertz equation cast both in the positive and negative form to evaluate the parameters t-7log, which is the time after which the viable count was 7 log CFU/mL, and the t-7log*, which is the time after which the viable count was below 7 log CFU/mL; the difference between the t-7log* and t-7log defines the stability time. Statistics through a multiparametric ANOVA (analysis of variance) provided evidence for the presence of a bifidogenic and/or bioactive factor produced by bifidobacteria and active on lactobacilli, and vice versa (bioactive factor of lactobacilli with a functional effect on bifidobacteria), although further studies are required to better explain the mechanisms beyond the positive effects. In addition, the influence on the target strains can be found during the growth phase (stimulation), as well as during senescence and death phase (protective effect), with a strong strain/species dependence on both CFS production and target strain.

1. Introduction

Probiotics are “live microorganisms that, when administered in adequate amounts, confer a health benefit on the host” [1]. Probiotics have several health benefits, including the modulation of the gut microbiome; however, techno-functional limitations such as viability controls have hampered their full potential applications in the food and pharmaceutical sectors [2,3].
The actual focus is gradually shifting from viable probiotic bacteria towards non-viable paraprobiotics and/or probiotic-derived biomolecules, so-called postbiotics, defined as a “preparation of inanimate microorganisms and/or their components that confers a health benefit on the host” [4], with possible variations in the definition itself (inanimate microorganisms, deliberately inactivated cells, etc.). Generally, postbiotics may be considered a complex mixture of metabolic products secreted by probiotics in cell-free supernatants (CFSs), such as enzymes, secreted proteins, short chain fatty acids, vitamins, secreted biosurfactants, amino acids, peptides, organic acids, etc., although purified components from microbial metabolism and vaccines are not considered postbiotics [4,5,6]. Postbiotics elicit several advantages over probiotics, including the following: (i) availability in their pure form, (ii) ease in production and storage, (iii) availability of production process for industrial-scale-up, (iv) specific mechanism of action, (v) better accessibility of Microbe-Associated Molecular Patterns (MAMPs) during the recognition of and interaction with Pattern Recognition Receptors (PRRs), and (vi) being more likely to trigger only the targeted responses by specific ligand–receptor interactions [6,7,8].
The outcome of a recent literature survey by Piqué et al. [9] underscores that postbiotics exert several pharmacodynamic features over live bacteria as follows: (i) no risk of bacterial translocation from the gut lumen to blood; (ii) no chance of the acquisition and transfer of antibiotic resistance genes; (iii) more natural to extract, standardize, transport, and store; (iv) further beneficial effects produced by a loss of viability due to cell lysis; and (v) an enhanced direct interaction between the released molecule from the disrupted cells and the epithelial cells [9,10].
Lactobacilli and Bifidobacterium spp. are among the most studied probiotic and/or functional genera, and the European Food Safety Authority (EFSA) has granted the Qualifed Presumption of Safety (QPS) status to them for human and animal applications, considering their safety perspectives [11,12,13].
Lactobacilli play a crucial role in the production of fermented foods, including vegetables, meats, and fermented dairy products. Over the last decade, the scientific understanding of lactobacilli (e.g., their metabolism and functions) has expanded considerably, opening the way to more reliable process control in production and in increasing the range of industrial applications as starters and adjunct starters/cultures (including probiotics) [14,15,16,17].
Bifidobacteria are considered a part of desirable microbiota, necessary for the proper functionality of the gastrointestinal tract. They colonize the gut and constitute the predominant microbiota of mucous membranes in the colon of breast milk-fed infants. In adult people, the bifidobacteria population varies depending on age, life condition, medical treatment, as well as eating habits [18,19].
When focusing on beneficial bacteria, there is a key factor to consider: the interplays and the connections among microorganisms belonging to different groups. In this context, the interplays between lactobacilli and bifidobacteria could play a significant role for their survival in complex matrices, like foods and gut microbiota. The interplays could be different and could include metabolic cooperation and cross-feeding, the production of stimulating molecules, and the release of signalling molecules able to promote an over-expression of some genes [20].
Amongst these different ways, the focus of this paper was on possible growth-promoting factors that are able to stimulate beneficial interplays between lactobacilli and bifidobacteria, generally known as the bifidogenic growth stimulator factor (BGS), firstly reported by Kaneko et al. [21] as a result of the metabolism of Propionibacterium freudenreichii.
The BGS is probably a mixture of 1,4-dihydroxy-2-napthoic acid (DNHA), 2-amino-3-carboxy-1,4-napthoquinone (ACQN), and other still unknown compounds [22,23], present in periplasm or in cytoplasm and released in cell-free extract. In this regard, BGS contained in CFSs might be considered as potential pre-postbiotic substances that are able to stimulate the growth/survival of the bifidobacteria of the intestinal microbiota [24], or nevertheless, it may be regarded as a parapostbiotic active mix.
The CFS produced by many probiotic microorganisms contains many bioactive compounds such as biosurfactants, bacteriocins, exopolysaccharide, active peptides, etc., and, to some extent, can be referred to, along with other soluble factors (products or metabolic byproducts) secreted by live bacteria, as postbiotic [25]. Some authors have in the past reported some evidence on CFSs produced by lactobacilli on bifidobacteria and vice versa [26]. However, to the best of the authors’ knowledge, it is not clear which effects of CFSs on these groups or which modulation of the growth/death kinetic was assessed, while the exact definition of the growth/survival profile of a probiotic microorganism in complex matrices is the main requisite to assess their effectiveness.
Thus, the main aim of this paper is to focus on the CFSs of some food-grade Lactiplantibacillus plantarum and Bifidobacterium strains as a way to promote positive interactions amongst these microorganisms, achieved by assessing the effects on the growth/death curve in a laboratory medium. A secondary goal was the shift from a qualitative to a quantitative evaluation of the effects, focusing on some fitting parameters that are able to measure the beneficial action in terms of survival and growth promotion. Finally, the strain influence was studied.

2. Materials and Methods

2.1. Microorganisms

Lactiplantibacillus plantarum c3, L. plantarum c4, and L. plantarum c15 were examined throughout this study for the bioactivity of their CFS towards bifidobacteria. All strains were isolated from Italian table olives “Bella di Cerignola” [27], identified and characterized [28], and stocked at −80 °C in appropriate media.
The bifidobacteria strains studied under the influence of L. plantarum CFS were the following: Bifidobacterium animalis DSM 10140, Bifidobacterium subtile DSM 20096, and Bifidobacterium breve DSM 20213, purchased from DSMZ (Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH, Germany), and stocked at −80 °C in appropriate media, as recommended by the producer (https://www.dsmz.de/).
Similarly, bifidobacteria were examined throughout this study for the bioactivity of their CFS towards lactobacilli.

2.2. Preparation of Cell-Free Supernatants

L. plantarum strains were grown in MRS broth (Oxoid, Milan, Italy) at 30 °C for 48 h, while Bifidobacterium spp. strains were grown in MRS broth (Oxoid, Milan, Italy) with cysteine (0.05% w/v) (cMRS) (Sigma-Aldrich, Milan, Italy) at 37 °C for 48 h. Each strain was grown separately.
After this step, the cells were harvested from the broths by centrifugation at 3000× g at 5 °C for 15 min and then washed twice with sterile saline solution (NaCl 9 g/L). The supernatants were filtered through a 0.22 µm Millipore filter (Whatman, Dassel, Germany), and the cell-free hydrosoluble fractions were recovered [21]. As a result, 6 different CFSs were produced, one for each strain, as follows: CFS1, from L. plantarum c3; CFS2, from L. plantarum c4; CFS3, from L. plantarum c15; CFS4, from B. animalis subsp. lactis DSM 10140; CFS5, from B. subtile DSM 20096; and CFS6, from B. breve DSM 20213.

2.3. Assays

Frozen cultures of B. animalis, B. subtile, and B. breve were thawed and pre-cultured in cMRS. The assays were performed in 250 mL Erlenmeyers, containing 100 mL of cMRS broth. The extracts produced from lactobacilli (CFS1, CFS2, CFS3) were separately added as a medium ingredient (1% v/v). The samples were inoculated with each strain of Bifidobacterium (102 CFU/mL). Aliquots of cMRS, inoculated with the target strains but not containing CFSs, were used as controls. Thus, for each microorganism, 4 different experiments were carried out (control, medium + CFS1, CFS2, and CFS3). The samples were incubated under the optimal conditions of bifidobacteria (at 37 °C in anaerobic condition), and the viable count of bifidobacteria was periodically evaluated on cMRS agar, incubated at 37 °C for 48 h.
Frozen cultures of L. plantarum strains were thawed and pre-cultured in MRS broth and used to inoculate MRS (102 CFU/mL), containing CFSs from bifidobacteria (CFS4, CFS5, CFS6) as an ingredient (1% v/v). MRS broth, inoculated with lactobacilli but not containing the extracts, was used as the control; each microorganism was analyzed individually assessing separately the effect of each CFS, thus focusing on 4 different batches for each strain (control, medium + CFS4, CFS5, and CFS6).
The samples were incubated at 37 °C (optimal temperature for lactobacilli, at least for the functional traits; 28) in anaerobic conditions, and the viable count of L. plantarum was periodically evaluated on MRS agar, incubated at 30 °C for 48 h.

2.4. Modelling

The viable count of each strain, with and without CFSs, was modelled with two different functions: a positive Gompertz equation for the growth part (lag phase, exponential, and steady state) and a negative Gompertz equation (for this function, the growth part of the kinetic was assumed as a shoulder length, followed by a liner death kinetic and finally by a tail).
For the growth part, the Gompertz equation reparameterized by Corbo et al. [29] was used as follows:
Log (CFU) = [log(CFU)]max − A × exp{−exp{[(µmax × 2.7182) × (λ − t-7log)/A] + 1}}
+ A × exp{−exp{[(µmax × 2.7182) × (λ − t)/A] + 1}}
where [log(CFU)]max is the standard limit for the microbial population (7 log CFU/mL), A is the maximum increase in the bacterial load attained at stationary phase, μ max is the maximal growth rate [Δlog (CFU/mL)/h], λ is the lag time (hours), and t* is the t-7log; that is, the verge time (hours) after which the cell load attained a value higher than 107 CFU/mL.
For the negative part of the growth curve, the reparameterized Gompertz equation was cast as follows:
Log (CFU) = [log(CFU)]max − {−Δ × exp{−exp{[(dmax × 2.7182) × (λ − t-7log*)/Δ] + 1}}
+Δ × exp{−exp{[(dmax × 2.7182) × (α − t)/Δ] + 1}}}
where [log(CFU)]max is the standard limit for the microbial population (7 log CFU/mL), Δ is the maximum decrease in the bacterial load attained in the death phase, d max is the maximal death rate [Δlog (CFU/mL)/h], α is the shoulder length, after which the decrease in cell load started (hours), t is the time, and t-7log* is the time limit, after which the cell load attained a value lower than 107 CFU/mL (h).
The difference between t-7log* and t-7log was defined as the stability time; that is, the time when the target strain addressed the basic requisite of a probiotic microorganism in a matrix (viable count at least 7 log CFU/mL). The reparameterized Gompertz equation was originally developed for the mathematical definition of the shelf life (intended as the time before a critical threshold is attained); its main difference, compared to the classical approach for shelf-life definition, is that it allows for the evaluation of the variability of shelf life itself, as it is a primary fitting parameter for the model and it is not calculated as a ratio or through an indirect formula. In this research, the shelf-life parameter was used to evaluate t-7log and t7-log*, along with a robust measure of their variability.
In addition, considering the classical Gompertz equation, reparameterized by Zwietering et al. [30], the model has a sigmoidal shape and allows for the estimation of all the phases of a growth kinetic; moreover, it is simple in its linear form, and a simple sign change could be used to model both growth and death kinetics, while this approach is quite difficult for other primary functions (for example, Baranyi or logistic models).
An indirect parameter was then evaluated as the difference between t-7log* and t-7log as it provides a measure of the time when the concentration of the target strain was >7 log CFU/mL (stability time).
After the primary modelling, a secondary modelling approach was tested through the multifactorial analysis of variance (ANOVA) for 3 parameters (t-7log, t-7log*, and stability time) to analyze the effect of two independent variables; that is, the kind of microorganisms (the target strain of lactobacilli or bifidobacteria) and the kind of extracts (CFS1, CFS2, CFS3 for the first experiment or CFS4, CFS5, and CFS6 for the second experiment).
However, an issue to face initially was the variability connected to the growth/death kinetic of each strain, which could mask the statistic effect of the two predictors; thus, the control was used as a baseline to standardize t-7log, t-7log*, and the stability time, which were then expressed as an increase or decrease in the control. After this standardization step, the parameters were analyzed. ANOVA was performed first in the classical form of evaluating the variability within each group and the variability among groups; then, a post hoc and multiple comparison approach was carried out through Tukey’s test (p < 0.05).
Statistics were performed through the software Statistica for Windows, ver. 7.0 (Statsoft, Tulsa, OK, USA).

3. Results

The viable count of the target strains (L. plantarum in the presence of CFSs from bifidobacteria and Bifidobacterium spp. in the presence of CFSs from L. plantarum) were fitted through the Gompertz equation, cast both in the positive form for the first part of the death kinetic (from lag phase to the steady state) and the negative form for the second part (steady state or shoulder phase, death kinetic), as a complete model with all the phases was used in a preliminary phase and led to an overestimation of some fitting parameters, mainly on the growth and death rates. The choice of the target strains relied upon the promising results found in the preliminary phase when the extracts of propionibacteria had been added to laboratory media [24]. Moreover, the technological and functional properties of the strains used in this research are well known, as are their growth kinetic under optimal conditions.
For the aim of this research, two parameters were further studied and analyzed for both L. plantarum and Bifidobacterium spp.; that is, the t-7log (the time to attain a 7 log CFU/mL viable count in the exponential phase) and t-7-log* (the time after which the viable count was reduced below 7 log CFU/mL in the death phase). The difference between t-7log* and t-7 log defined the stability time (ST); that is, the time when the viable count of the target strains was >7 log CFU/mL.
The analysis of the fitting parameters revealed that the addition of a CFS determined a reduction in the t-7log of bifidobacteria and L. plantarum due to a stimulation in the exponential phase, along with an increase in the stability time, due to a possible protective effect.
An issue when analyzing data of different species or strains is that they could not be simply compared, as they are referred to as different biological systems; therefore, a preliminary standardization to delete the “biological variability” is necessary.
In this paper, the standardization was achieved by reporting the data concerning decreases in t-7log (difference between t-7log in the control and t-7 log in the samples with CFSs) to point out the stimulation and increases in the stability time to point out the protective effect.
These new standardized parameters were used as dependent variables for a two-way ANOVA, where the categorical predictors were the target strains and the kind of CFS.
The first output of ANOVA was a table of standardized effects (Table 1), which shows the statistical weight of the individual terms of predictors as well as of their interaction.
The t-7log reduction in Bifidobacterium, when the CFS from L. plantarum was used, was affected by the individual terms, although the most significant was the kind of CFS. On the other hand, t-7 log reduction in L. plantarum was influenced by the individual term “microorganism” (kind of target strain) and secondarily by the interactive term.
Finally, the ST increase in L. plantarum was significantly linked to the predictors (kind of CFS, microorganisms, and interactive term).
A table of standardized effects cannot offer quantitative results, but only qualitative (if a predictor is significant or not). Quantitative outputs can be gained through the decomposition of the statistical hypothesis, which does not show an actual trend. It is a mathematical function showing the contribution of each variable to the dataset.
Figure 1 shows the contribution of the kind of microorganisms for the assay Bifidobacterium +CFS from L. plantarum (CFS1, CFS2 or CFS3); as reported above, the addition of CFSs generally had a positive and stimulating effect with a decrease in t7-log. The strain most affected was B. animalis subsp. lactis DSM 10140, which experienced a reduction of 5.8 h, while for the other two strains, t-7 log was reduced by ca. 3 h.
For the same experiment, the contribution of the predictor CFS is in Figure 2. The most effective extract was CFS2 (from L. plantarum c4), which caused a t-7log reduction of ca. 7.2 h.
For the second experiment (L. plantarum + CFS from Bifidobacterium), Figure 3 shows the contribution of the predictor microorganism, with a slight effect on the strains c3 and c15 (mean reduction in t-7log by 1 h) and a higher effect on the strain c4 (reduction of 2.5 h), thus suggesting a general positive effect of CFS4, CFS5, and CFS6, independently from the kind of extract.
For this second experiment, the most important effect was on the stability time, as it was strongly increased in the presence of CFSs from Bifidobacterium, with a mean increase of 9 h for the strains c15 and c4 and 15 h for the strain c3 (Figure 4), independently from the kind of extracts.
Figure 5 shows the contribution of the kind of extracts from bifidobacteria, with a higher effect for the CFS5 and CFS6 (14 h, from B. subtile and B. breve) and a lower effect for B. animalis subsp. lactis (CFS4, 7.5 h). Finally, Figure 6 shows the importance of the interaction “kind of microorganism” × “kind of CFS”, as it highlights that some combinations were not or less effective (strains c4 and c15 with CFS4 from B. animalis), while others had generally higher performances (for example, those where CFS5 and CFS6, from B. subtile and B. breve, were added).

4. Discussion

Gut microbiota composition can play an important role in the host’s health status, and microbiota disruption has been linked to diarrhea [31], irritable bowel syndrome (IBS) [32], obesity [33,34], allergies [35], behavioural and developmental disorders, including autism [36], and neurological diseases [37]. Altered levels of microbial metabolites have also been associated with many conditions, including depression [38], colorectal cancer, cardiovascular disease, obesity, and type 2 diabetes [39]. Strategies designed to influence microbiota composition include the ingestion of probiotics, prebiotics, and synbiotics (a combination of selected substrates and live bacteria that provide a health benefit) [40].
In some cases, the direct use of probiotics can be dangerous for some people; for example, those with reduced immunity, an impaired intestinal barrier, sepsis, and premature babies [41,42]. Hence, the concept of administering inactivated bacterial strains, bacterial fragments, and products of their metabolism to obtain similar effects is highly useful [25].
Metabolites/CFSs and soluble factors (products or metabolic byproducts) secreted by living bacteria may be considered postbiotics, also referred to as metabiotics; that is, components or metabolites produced by probiotics able to exert positive effects on host functions and microbiota [43]. This is a novel approach in food microbiology, as in the past, CFS preparations were extensively studied as antimicrobial preparations towards a wide variety of pathogens [44,45,46,47], but their use as promoting factors or as ingredients to gain positive effects in the gut microbiota was not fully explored at least to the authors’ knowledge.
The present study demonstrated a possible mutual protective and/or stimulating effect between the tested bacteria and a species/specific variability. In our conditions, L. plantarum c4 showed the best performances, while the differences among bifidobacteria were less pronounced, and further investigations could explain the specific mechanisms of action able to enhance the probiotic potentials by using a parapostbiotic approach in microbial applications in food and pharma.
It is well known that various bifidobacterial species with complementary metabolic abilities have been reported to cooperate in the use of complex carbohydrates in vitro [48] and to enhance the in vivo persistence levels of each strain when they co-occur, and, in addition to such a mutualism within the genus, bifidobacterial strains can expand the availability of carbohydrates for other gut symbionts, i.e., members of Bacteroidetes [49].
A positive effect was also recorded for CFSs produced by lactobacilli, thus also suggesting that the species L. plantarum could produce a bifidogenic factor with a promoting effect, as also evidenced by Tan et al. [50] for the interaction CFS from L. plantarum and Enterococcus faecium. The nature of these compounds is not known, but some evidence suggests the presence of the supernatant of amino acids, including glutamic acid and proline [43]; in addition, lactobacilli and bifidobacteria can produce short chain fatty acids (SCFAs), which are known to promote growth, or can release peptides (as endopeptidases) and/or cationic species into the medium, which could then enter cells or interact with the external layers [51,52].
Amino acids and SCFAs, along with other unknown complex metabolites, could be responsible for the stimulation evidenced in the growth phase for both lactobacilli and bifidobacteria, as highlighted by the reduction in t-7log. In addition, another positive effect found in this paper is the protective effect in the steady state and senescence, as pointed out by the increase in the stability time. Some data from the literature suggest that this effect could be linked to antioxidant activity, counteracting the action of free radicals, and probably due to the presence of pyruvic, linolenic, lactic, and indole lactic acids [53].
More strong evidence emerging from the results of this paper concerns the potentiality of L. plantarum and bifidobacteria, but also the strong strain-dependence of the positive effects, linked to both the strain-producing CFS and the target strain, thus stressing the importance of the correct combination for good mutualistic growth enhancement or protective effects in the death phase.
From a practical point of view, the results could provide a background for the design of new synbiotic strategies (combination of prebiotics and probiotics), where CFSs act as prebiotic compounds that are able to prolong the survival of probiotic microorganisms in food matrices. In particular, the use of the reparameterized Gompertz equation allowed for a quantitative estimation of the beneficial effect in terms of the stability time. This approach could be used in the future to define combinations of CFSs and probiotics for functional foods with a prolonged shelf life. However, the experiments of this research were conducted at 37 °C, while it is safe to assume that the effects could have been enhanced under refrigeration or at lower temperatures; that is, under the real storage conditions of probiotic foods. Thus, an effective validation in foods and under the real storage conditions is required.
Another possibility is the design of a new combination of functional strains able to stimulate each other through release into the medium of various metabolites, combining a multi-strain cocktail and the presence of stimulating ingredients that are able to promote the functionality of probiotics. However, the strain-specific effects highlight the importance of personalized combinations of strains/species as the cross-feeding of probiotic bacteria represents a valuable strategy both for food and for the restoration of a eubiotic status in the gut microbiota [20], but it is necessary to highlight the exact mode of action of these interactions and of the differences amongst the species of a genus (as in the case of Bifidobacterium spp.) or of the strains inside the species (as for L. plantarum). Future research in this context could be focused on the definition of the metabolic pathways beyond the positive effects of CFSs.

5. Conclusions

This research contributed to the ongoing debate on the mutualistic interactions and interplays between lactobacilli and bifidobacteria, using a simple laboratory medium as the substrate and containing the extracts produced by the microorganisms as active ingredients. The use of the reparameterized Gompertz equation and the evaluation of the growth and death kinetic highlighted a general positive effect during both the growth and death steps, with an increase in the stability time for all lactobacilli and bifidobacteria, thus suggesting the potentialities of extracts as cross-feeding approaches for probiotic microorganisms. However, an important point was the strong strain dependence, concerning both the target strain and the microorganism producing the extract, with some bifidobacteria (B. breve and B. subtile) producing CFSs more effective than others or some L. plantarum (e.g., strain c4) being more affected by the presence of active ingredients.
The study was conducted under strictly controlled conditions and in a simple medium; thus, a confirmation step on a wide variety of strains, complex matrices, and also under the conditions that mimic the gut are necessary, as well clarifying the mechanisms beyond the positive effects.

Author Contributions

Conceptualization, C.A. and A.F.; methodology, C.A. and A.F.; formal analysis, A.F.; investigation, C.A., A.F. and A.B.; data curation, A.B.; writing—original draft preparation, C.A., A.F. and A.B.; writing—review and editing, C.A., A.F. and A.B.; visualization, M.R.C. and M.S.; supervision, C.A., A.B., M.R.C. and M.S.; project administration, C.A., M.R.C. and M.S.; funding acquisition, C.A., M.R.C. and M.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Hill, C.; Guarner, F.; Reid, G.; Gibson, G.R.; Merenstein, D.J.; Pot, B.; Morelli, L.; Canani, R.B.; Flint, H.J.; Salminen, S.; et al. The international Scientifc Association for Probiotics and Prebiotics consensus statement on the scope and appropriate use of the term probiotic. Nat. Rev. Gastroenterol. Hepatol. 2014, 11, 506–514. [Google Scholar] [CrossRef] [PubMed]
  2. Kisan, B.S.; Kumar, R.; Ashok, S.P.; Sangita, G. Probiotic foods for human health A review. J. Pharmacogn. Phytochem. 2019, 8, 967–971. [Google Scholar]
  3. Kwoji, I.D.; Aiyegoro, O.A.; Okpeku, M.; Adeleke, M.A. Multi-Strain Probiotics: Synergy among Isolates Enhances Biological Activities. Biology 2021, 10, 322. [Google Scholar] [CrossRef] [PubMed]
  4. Salminen, S.; Collado, M.C.; Endo, A.; Hill, C.; Lebeer, S.; Quigley, E.M.M.; Sanders, M.E.; Shamir, R.; Swann, J.R.; Szajewska, H.; et al. The International Scientific Association of Probiotics and Prebiotics (ISAPP) consensus statement on the definition and scope of postbiotics. Nate. Rev. Gastroenterol. Hepatol. 2021, 18, 649–667. [Google Scholar] [CrossRef]
  5. Zendeboodi, F.; Khorshidian, N.; Mortazavian, A.M.; da Cruz, A.G. Probiotic Conceptualization from a new approach. Curr. Op. Food Sci. 2020, 32, 103–123. [Google Scholar] [CrossRef]
  6. Teame, T.; Wang, A.; Xie, M.; Zhang, Z.; Yang, Y.; Ding, Q.; Gao, C.; Olsen, R.E.; Ran, C.; Zhou, Z. Paraprobiotics and Postbiotics of Probiotic Lactobacilli, Their Positive Effects on the Host and Action Mechanisms A Review. Front. Nutr. 2020, 7, 570344. [Google Scholar] [CrossRef]
  7. Vinderola, G.; Sanders, M.E.; Salminen, S.; Szajewska, H. Postbiotics: The concept and their use in healthy populations. Front. Nutr. 2022, 9, 1002213. [Google Scholar] [CrossRef]
  8. Wei, L.; Wang, B.; Bai, J.; Zhang, Y.; Liu, C.; Suo, H.; Wang, C. Postbiotics are a candidate for new functional foods. Food Chem. X 2024, 23, 101650. [Google Scholar] [CrossRef] [PubMed]
  9. Piqué, N.; Berlanga, M.; Miñana-Galbis, D. Health benefits of heat-killed (Tyndallized) probiotics an overview. Int. J. Mol. Sci. 2019, 20, 2534. [Google Scholar] [CrossRef]
  10. Nataraj, B.H.; Ali, S.A.; Behare, P.V.; Yadav, H. Postbiotics-parabiotics the new horizon in microbial biotherapy and functional foods. Microb. Cell Fact. 2020, 19, 168. [Google Scholar] [CrossRef]
  11. EFSA Panel on Additives and Products or Substances used in Animal Feed (FEEDAP); Bampidis, V.; Azimonti, G.; Bastos, M.D.L.; Christensen, H.; Dusemund, B.; Fasmon Durjava, M.; Kouba, M.; Lopez-AAlonso, M.; Lopez Puente, S.; et al. Assessment of the feed additive consisting of Lactiplantibacillus plantarum (previously Lactobacillus plantarum) DSM 19457 for all animal species for the renewal of its authorisation (Biomin GmbH). EFSA J. 2023, 21, 07697. [Google Scholar] [CrossRef]
  12. EFSA Panel on Biological Hazards (BIOHAZ); Koutsoumanis, K.; Allende, A.; Alvarez-Ordóñez, A.; Bolton, D.; Bover-Cid, S.; Chemaly, M.; Davies, R.; De Cesare, A.; Hilbert, F.; et al. Update of the list of QPS-recommended biological agents intentionally added to food or feed as notified to EFSA 15 Suitability of taxonomic units notified to EFSA until September 2021. EFSA J. 2022, 20, e07045. [Google Scholar] [CrossRef] [PubMed]
  13. Chen, J.F.; Hsia, K.C.; Kuo, Y.W.; Chen, S.H.; Huang, Y.Y.; Li, C.M.; Hsu, Y.C.; Tsai, S.Y.; Ho, H.H. Safety Assessment and Probiotic Potential Comparison of Bifidobacterium longum subsp. infantis BLI.02, Lactobacillus plantarum LPL28, Lactobacillus acidophilus TYCA06, and Lactobacillus paracasei ET-66. Nutrients 2024, 16, 126. [Google Scholar] [CrossRef]
  14. Kanawjia, S.K.; Nageswara Rao, K.; Singh, S.; Sabikhi, L. Role of Lactobacilli in cheese. Ind. J. Dairy. Sci. 1993, 46, 187–197. [Google Scholar]
  15. Beresford, T.P.; Fitzimons, N.A.; Brennan, N.L.; Cogan, T.M. Recent advances in cheese microbiology. Int. Dairy. J. 2001, 11, 259–274. [Google Scholar] [CrossRef]
  16. Wouters, J.T.M.; Ayad, E.H.E.; Hugenholtz, J.; Smit, G. Microbes from raw milk for fermented dairy products. Int. Dairy J. 2002, 12, 91–109. [Google Scholar] [CrossRef]
  17. Kieronczyk, A.; Skeie, S.; Langsrud, T.; Yvon, M. Cooperation between Lactococcus lactis and non-starter lactobacilli in the formation of cheese aroma from amino acids. Appl. Environ. Microbiol. 2003, 69, 734–739. [Google Scholar] [CrossRef]
  18. Ballongue, J. Bifidobacteria and probiotic action. In Probiotics the Scientific Basis; Fuller, R., Ed.; Chapman & Hall: London, UK, 1992; pp. 357–413. [Google Scholar]
  19. Fondén, R.; Mongensen, G.; Tanaka, R.; Salminen, S. Effect of culture–containing dairy products on intestinal microflora, human nutrition and health–Current knowledge and future perspectives. Bull. IDF 2000, 352, 5–30. [Google Scholar]
  20. Xiao, M.; Zhang, C.; Duan, H.; Narbad, A.; Zhao, J.; Chen, W.; Zhai, Q.; Yu, L.; Tian, F. Cross-feeding of bifidobacteria promotes intestinal homeostasis A lifelong perspective on the host health. Npj Biofilms Microbes 2024, 10, 47. [Google Scholar] [CrossRef]
  21. Kaneko, T.; Mori, H.; Iwata, M.; Meguro, S. Growth stimulator for bifidobacteria produced by Propionibacterium freudenreichii and several intestinal bacteria. J. Dairy Sci. 1994, 77, 393–404. [Google Scholar] [CrossRef]
  22. Mori, H.; Sato, Y.; Taketomo, N.; Kamiyama, T.; Yoshiyama, Y.; Meguro, S.; Sato, H.; Kaneko, T. Isolation and structural identification of bifidogenic growth stimulator produced by Propionibacterium freudenreichii. J. Dairy Sci. 1997, 80, 1959–1964. [Google Scholar] [CrossRef] [PubMed]
  23. Furuichi, K.; Hojo, K.; Katakura, Y.; Ninomiya, K.; Shioya, S. Aerobic culture of Propionibacterium freudenreichii ET-3 can increase producton ratio of 1,4-dihydroxy-2-naphthoic acid to menaquinone. J. Biosci. Bioeng. 2006, 101, 464–470. [Google Scholar] [CrossRef] [PubMed]
  24. Filippone, A.; Sinigaglia, M.; Altieri, C. Study on bioactivity of cell-free filtrates from dairy propionibacteria. Anaerobe 2014, 30, 137–145. [Google Scholar] [CrossRef] [PubMed]
  25. De Almada, C.N.; de Almada, C.N.; de Sant’Ana, A.S. Paraprobiotics as potential agents for improving animal health. In Probiotics and Prebiotics in Animal Health and Food Safety; Springer International Publishing: Berlin/Heidelberg, Germany, 2018; pp. 247–268. [Google Scholar]
  26. Cheng, C.C.; Nagasawa, T. Associative relationships between bifidobacteria and lactobacilli in milk. Jpn. J. Zootech. Sci. 1983, 54, 740–747. [Google Scholar]
  27. Campaniello, D.; Bevilacqua, A.; D’Amato, D.; Corbo, M.R.; Altieri, C.; Sinigaglia, M. Microbial characterization of table olives processed accordino to spanish and natural style. Food Technol. Biotechnol. 2005, 43, 289–294. [Google Scholar]
  28. Bevilacqua, A.; Altieri, C.; Corbo, M.R.; Sinigaglia, M.; Ouoba, L.I.I. Characterization of Lactic Acid Bacteria Isolated from Italian Bella di Cerignola Table Olives: Selection of Potential Multifunctional Starter Cultures. J. Food Sci. 2010, 75, M536–M544. [Google Scholar] [CrossRef]
  29. Corbo, M.R.; Del Nobile, M.A.; Sinigaglia, M. A novel approach for calculating shelf life of minimally processed vegetables. In. J. Food Microb. 2006, 106, 69–73. [Google Scholar] [CrossRef]
  30. Zwietering, M.H.; Jongenburger, I.; Rombouts, F.M.; van′t Riet, K. Modeling of the bacterial growth curve. Appl. Environ. Microbiol. 1990, 56, 1875–1881. [Google Scholar] [CrossRef]
  31. Gibson, D.L.; Vallance, B.A. Intestinal microbiota are transiently altered during Salmonella-induced gastroenteritis. Expert. Rev. Gastroenterol. Hepatol. 2008, 2, 525–529. [Google Scholar] [CrossRef]
  32. Kelly, J.R.; Kennedy, P.J.; Cryan, J.F.; Dinan, T.G.; Clarke, G.; Hyland, N.P. Breaking down the barriers The gut microbiome, intestinal permeability and stress-related psychiatric disorders. Front. Cell Neurosci. 2015, 9, 392. [Google Scholar] [CrossRef]
  33. Ussar, S.; Griffin, N.W.; Bezy, O.; Fujisaka, S.; Vienberg, S.; Softic, S.; Deng, L.; Bry, L.; Gordon, J.I.; Kahn, C.R. Interactions between gut microbiota, host genetics and diet modulate the predisposition to obesity and metabolic syndrome. Cell Metab. 2015, 22, 516–530. [Google Scholar] [CrossRef] [PubMed]
  34. Walters, W.A.; Xu, Z.; Knight, R. Meta-analyses of human gut microbes associated with obesity and IBD. FEBS Lett. 2014, 588, 4223–4233. [Google Scholar] [CrossRef] [PubMed]
  35. Huang, Y.J.; Marsland, B.J.; Bunyavanich, S.; O′Mahony, L.; Leung, D.Y.; Muraro, A.; Fleisher, T.A. The microbiome in allergic disease Current understanding and future opportunities-2017 PRACTALL document of the American Academy of Allergy, Asthma & Immunology and the European Academy of Allergy and Clinical Immunology. J. Allergy Clin. Immunol. 2017, 139, 1099–1110. [Google Scholar] [CrossRef] [PubMed]
  36. De Angelis, M.; Piccolo, M.; Vannini, L.; Siragusa, S.; De Giacomo, A.; Serrazzanetti, D.I.; Cristofori, F.; Guerzoni, M.E.; Gobbetti, M.; Francavilla, R. Fecal microbiota and metabolome of children with autism and pervasive developmental disorder not otherwise specified. PLoS ONE. 2013, 8, e76993. [Google Scholar] [CrossRef]
  37. Thakur, P.; Baraskar, P.; Shrivastava, V.K.; Medhi, B. Crosstalk between adipose tissue and microbiota-gut-brain-axis in brain development and neurological disorder. Brain Res. 2024, 1844, 149176. [Google Scholar] [CrossRef]
  38. Caspani, G.; Kennedy, S.; Foster, J.A.; Swann, J. Gut microbial metabolites in depression Understanding the biochemical mechanisms. Microb. Cell. 2019, 6, 454–481. [Google Scholar] [CrossRef]
  39. Postler, T.S.; Ghosh, S. Understanding the holobiont How microbial metabolites affect human health and shape the immune system. Cell. Metab. 2017, 26, 110–130. [Google Scholar] [CrossRef] [PubMed]
  40. Martyniak, A.; Medynska-Przeczek, A.; Wedrychowicz, A.; Skoczen, S.; Tomasik, P.J. Prebiotics, Probiotics, Synbiotics, Paraprobiotics and Postbiotic Compounds in IBD. Biomolecules 2021, 11, 1903. [Google Scholar] [CrossRef]
  41. Floch, M.H. Probiotic Safety and Risk Factors. J. Clin. Gastroenterol. 2013, 47, 375–376. [Google Scholar] [CrossRef]
  42. Vahabnezhad, E.; Mochon, A.B.; Wozniak, L.J.; Ziring, D.A. Lactobacillus Bacteremia Associated with Probiotic Use in a Pediatric Patient with Ulcerative Colitis. J. Clin. Gastroenterol. 2013, 47, 437–439. [Google Scholar] [CrossRef]
  43. Oleskin, A.V.; Shenderov, B.A. Microbial Communication and Microbiota-Host Interactivity Neurophysiological, Biotechnological, and Biopolitical Implications; Nova Science Publishers: Hauppauge, NY, USA, 2020. [Google Scholar]
  44. Pompilio, A.; Kaya, E.; Lupetti, V.; Catelli, E.; Bianchi, M.; Maisetta, G.; Esin, S.; Di Bonaventura, G.; Batoni, G. Cell-free supernatants from Lactobacillus strains exert antibacterial, antibiofilm, and antivirulence activity against Pseudomonas aeruginosa from cystic fibrosis patients. Microbes Infect. 2024, 26, 105301. [Google Scholar] [CrossRef] [PubMed]
  45. Wang, J.; Fan, D.C.; Wang, R.S.; Chang, Y.; Ji, X.M.; Li, X.Y.; Zhang, Y.; Liu, J.M.; Wang, S.; Wang, J. Inhibitory Potential of Bifidobacterium longum FB1-1 Cell-Free Supernatant against Carbapenem-Resistant Klebsiella pneumoniae Drug Resistance Spread. Microorganisms 2024, 12, 1203. [Google Scholar] [CrossRef]
  46. Kumar, S.A.S.; Krishnan, D.; Jothipandiyan, S.; Durai, R.; Hari, B.N.V.; Nithyanand, P. Cell-free supernatants of probiotic consortia impede hyphal formation and disperse biofilms of vulvovaginal candidiasis causing Candida in an ex-vivo model. Antonie Van Leeuwenhoek 2024, 117, 37. [Google Scholar] [CrossRef] [PubMed]
  47. Sornsenee, P.; Surachat, K.; Wong, T.; Kaewdech, A.; Saki, M.; Romyasamit, C. Lyophilized cell-free supernatants of Limosilactobacillus fermentum T0701 exhibited antibacterial activity against Helicobacter pylori. Sci. Rep. 2024, 14, 13632. [Google Scholar] [CrossRef]
  48. Rivière, A.; Selak, M.; Geirnaert, A.; Van den Abbeele, P.; De Vuyst, L. Complementary Mechanisms for Degradation of Inulin-Type Fructant and Arabinoxylan Oligosaccharides among Bifidobacterial Strains Suggest Bacterial Cooperation. Appl. Environ. Microbiol. 2018, 84, e02893-17. [Google Scholar] [CrossRef]
  49. Turroni, F.; Milani, C.; Duranti, S.; Mancabelli, L.; Mangifesta, M.; Vippiani, A.; Lugli, G.A.; Ferrario, C.; Gioiosa, L.; Ferrarini, A.; et al. Deciphering bifidobacterial-mediated metabolic interactions and their impact on gut microbiota by a multi-omics approach. ISME J. 2016, 10, 1656–1668. [Google Scholar] [CrossRef] [PubMed]
  50. Tan, X.; Zhang, S.; Liu, J.; Xiang, W.; Zhang, Q.; Tang, J. Effect of Lactiplantibacillus plantarum cell-free supernatant on the physiology, quorum sensing, transcription, and enhanced GABA production of Enterococcus faecium. LWT 2024, 198, 115986. [Google Scholar] [CrossRef]
  51. Matera, M. Bifidobacteria, Lactobacilli when, how and why to use them. Glob. Pediatr. 2024, 8, 100139. [Google Scholar] [CrossRef]
  52. Bevilacqua, A.; Costabile, A.; Bergillos-Meca, T.; Gonzalez, I.; Landriscina, L.; Ciuffreda, E.; D’Agnello, P.; Corbo, M.R.; Sinigaglia, M.; Lamacchia, C. Impact of gluten-friendly bread on the metabolism and function of in vitro gut microbiota in healthy humans and coeliac subjects. PLoS ONE 2016, 11, e0162770. [Google Scholar] [CrossRef]
  53. Gu, X.; Wang, H.; Wang, L.; Zhang, K.; Tian, Y.; Wang, X.; Xu, G.; Guo, Z.; Ahmad, S.; Egide, H.; et al. The antioxidant activity and metabolomic analysis of the supernatant of Streptococcus alactolyticus strain FGM. Sci. Rep. 2024, 14, 8413. [Google Scholar] [CrossRef]
Foods 13 02966 g001
Figure 1. Decomposition of the statistical hypothesis for the individual effect of the predictor “target microorganism” on the assay Bifidobacterium spp. and CFS of L. plantarum on the reduction in t-7 log (that is, the reduction in time to attain a viable count of 7 log CFU/mL by the target strain). Bars denote 95% confidence interval, while letters indicate significant differences (Tukey’s test, p < 0.05). The picture shows the cumulative results for the aforementioned extracts.
Figure 1. Decomposition of the statistical hypothesis for the individual effect of the predictor “target microorganism” on the assay Bifidobacterium spp. and CFS of L. plantarum on the reduction in t-7 log (that is, the reduction in time to attain a viable count of 7 log CFU/mL by the target strain). Bars denote 95% confidence interval, while letters indicate significant differences (Tukey’s test, p < 0.05). The picture shows the cumulative results for the aforementioned extracts.
Foods 13 02966 g001
Foods 13 02966 g002
Figure 2. Decomposition of the statistical hypothesis for the individual effect of the predictor “kind of CFS” on the assay Bifidobacterium spp. and CFS of L. plantarum (CFS1, L. plantarum c3; CFS2, L. plantarum c4; CFS3, L. plantarum c15) on the reduction in t-7 log (that is, the reduction in the time taken to attain a viable count of 7 log CFU/mL by the target strain). Bars denote 95% confidence interval, while letters indicate significant differences (Tukey’s test, p < 0.05). This figure shows the cumulative effects of the different extracts on all bifidobacteria.
Figure 2. Decomposition of the statistical hypothesis for the individual effect of the predictor “kind of CFS” on the assay Bifidobacterium spp. and CFS of L. plantarum (CFS1, L. plantarum c3; CFS2, L. plantarum c4; CFS3, L. plantarum c15) on the reduction in t-7 log (that is, the reduction in the time taken to attain a viable count of 7 log CFU/mL by the target strain). Bars denote 95% confidence interval, while letters indicate significant differences (Tukey’s test, p < 0.05). This figure shows the cumulative effects of the different extracts on all bifidobacteria.
Foods 13 02966 g002
Foods 13 02966 g003
Figure 3. Decomposition of the statistical hypothesis for the individual effect of the predictor “target microorganism” on the assay L. plantarum and CFS of Bifidobacterium spp. on the reduction in t-7 log (that is, the reduction in the time taken to attain a viable count of 7 log CFU/mL by the target strain). Bars denote 95% confidence interval, while letters indicate significant differences (Tukey’s test, p < 0.05). The picture shows the cumulative results for the aforementioned extracts.
Figure 3. Decomposition of the statistical hypothesis for the individual effect of the predictor “target microorganism” on the assay L. plantarum and CFS of Bifidobacterium spp. on the reduction in t-7 log (that is, the reduction in the time taken to attain a viable count of 7 log CFU/mL by the target strain). Bars denote 95% confidence interval, while letters indicate significant differences (Tukey’s test, p < 0.05). The picture shows the cumulative results for the aforementioned extracts.
Foods 13 02966 g003
Foods 13 02966 g004
Figure 4. Decomposition of the statistical hypothesis for the individual effect of the predictor “target microorganism” on the assay L. plantarum and CFS of Bifidobacterium spp. on the increase in the stability time (that is, the time when the viable count of the target strain was at least 7 log CFU/mL). Bars denote 95% confidence interval, while letters indicate significant differences (Tukey’s test, p < 0.05). The picture shows the cumulative results for the aforementioned extracts.
Figure 4. Decomposition of the statistical hypothesis for the individual effect of the predictor “target microorganism” on the assay L. plantarum and CFS of Bifidobacterium spp. on the increase in the stability time (that is, the time when the viable count of the target strain was at least 7 log CFU/mL). Bars denote 95% confidence interval, while letters indicate significant differences (Tukey’s test, p < 0.05). The picture shows the cumulative results for the aforementioned extracts.
Foods 13 02966 g004
Foods 13 02966 g005
Figure 5. Decomposition of the statistical hypothesis for the individual effect of the predictor “kind of CFS” on the assay L. plantarum and CFE of Bifidobacterium spp. (CFS4, B. animalis subsp. lactis; CFS5, B. subtile DSM 20096; CFS6, B. breve DSM 20213) on the increase in the stability time (that is, the time when the viable count of the target strain was at least 7 log CFU/mL). Bars denote 95% confidence interval, while letters indicate significant differences (Tukey’s test, p < 0.05). The figure shows the cumulative effects of the different extracts on all bifidobacteria.
Figure 5. Decomposition of the statistical hypothesis for the individual effect of the predictor “kind of CFS” on the assay L. plantarum and CFE of Bifidobacterium spp. (CFS4, B. animalis subsp. lactis; CFS5, B. subtile DSM 20096; CFS6, B. breve DSM 20213) on the increase in the stability time (that is, the time when the viable count of the target strain was at least 7 log CFU/mL). Bars denote 95% confidence interval, while letters indicate significant differences (Tukey’s test, p < 0.05). The figure shows the cumulative effects of the different extracts on all bifidobacteria.
Foods 13 02966 g005
Foods 13 02966 g006
Figure 6. Decomposition of the statistical hypothesis for the interactive effect “kind of CFS × target strain” on the assay L. plantarum and CFS of Bifidobacterium spp. on the increase in the stability time (that is, the time when the viable count of the target strain was at least 7 log CFU/mL). Bars denote 95% confidence interval, while letters indicate significant differences (Tukey’s test, p < 0.05). CFS4, B. animalis subsp. lactis; CFS5, B. subtile DSM 20096; CFS6, B. breve DSM 20213.
Figure 6. Decomposition of the statistical hypothesis for the interactive effect “kind of CFS × target strain” on the assay L. plantarum and CFS of Bifidobacterium spp. on the increase in the stability time (that is, the time when the viable count of the target strain was at least 7 log CFU/mL). Bars denote 95% confidence interval, while letters indicate significant differences (Tukey’s test, p < 0.05). CFS4, B. animalis subsp. lactis; CFS5, B. subtile DSM 20096; CFS6, B. breve DSM 20213.
Foods 13 02966 g006
Table 1. Individual standardized and interactive effects of microorganisms, kind of CFS on t7-log reduction (that is, the reduction in the time taken to attain a viable count of 7 log CFU/mL by the target strain), and the stability time increase (ST; that is, the time when the viable count of the target strain was at least 7 log CFU/mL).
Table 1. Individual standardized and interactive effects of microorganisms, kind of CFS on t7-log reduction (that is, the reduction in the time taken to attain a viable count of 7 log CFU/mL by the target strain), and the stability time increase (ST; that is, the time when the viable count of the target strain was at least 7 log CFU/mL).
SS *DfMSF-Test
t-log reduction, Bifidobacterium ssp. and CFS from L. plantarum
Microorganism31.09215.5485.90
CFS133.49266.75368.87
Interaction/**///
t-7 log reduction, L. plantarum and CFS from Bifidobacterium spp.
Microorganism14.7327.36323122.99
CFS////
Interaction1.8340.467.67
ST increase, L. plantarum and CFS from Bifidobacterium spp.
Microorganism232.252116.1319.18
CFS258.202129.1021.32
Interaction114.93428.734.75
* SS, sum of square; df, degree of freedom; MS, mean residual square; F-test, Fisher test. **, not significant.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Altieri, C.; Filippone, A.; Bevilacqua, A.; Corbo, M.R.; Sinigaglia, M. Lactobacilli and Bifidobacteria: A Parapostbiotic Approach to Study and Explain Their Mutual Bioactive Influence. Foods 2024, 13, 2966. https://doi.org/10.3390/foods13182966

AMA Style

Altieri C, Filippone A, Bevilacqua A, Corbo MR, Sinigaglia M. Lactobacilli and Bifidobacteria: A Parapostbiotic Approach to Study and Explain Their Mutual Bioactive Influence. Foods. 2024; 13(18):2966. https://doi.org/10.3390/foods13182966

Chicago/Turabian Style

Altieri, Clelia, Alfonso Filippone, Antonio Bevilacqua, Maria Rosaria Corbo, and Milena Sinigaglia. 2024. "Lactobacilli and Bifidobacteria: A Parapostbiotic Approach to Study and Explain Their Mutual Bioactive Influence" Foods 13, no. 18: 2966. https://doi.org/10.3390/foods13182966

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop