Diverse impact of a probiotic strain, Lacticaseibacillus paracasei Shirota, on peripheral mononuclear phagocytic cells in healthy Japanese office workers: a randomized, double-blind, controlled trial

Tomoaki NAITO; Masatoshi MORIKAWA; Mayuko YAMAMOTO-FUJIMURA; Akira IWATA; Ayaka MAKI; Noriko KATO-NAGAOKA; Kosuke OANA; Junko KIYOSHIMA-SHIBATA; Yumi MATSUURA; Rumi KAJI; Osamu WATANABE; Kan SHIDA; Satoshi MATSUMOTO; Tetsuji HORI

doi:10.12938/bmfh.2022-043

. 2022 Oct 11;42(1):65–72. doi: 10.12938/bmfh.2022-043

Diverse impact of a probiotic strain, Lacticaseibacillus paracasei Shirota, on peripheral mononuclear phagocytic cells in healthy Japanese office workers: a randomized, double-blind, controlled trial

Tomoaki NAITO ^1,^*, Masatoshi MORIKAWA ¹, Mayuko YAMAMOTO-FUJIMURA ¹, Akira IWATA ¹, Ayaka MAKI ¹, Noriko KATO-NAGAOKA ¹, Kosuke OANA ¹, Junko KIYOSHIMA-SHIBATA ¹, Yumi MATSUURA ², Rumi KAJI ¹, Osamu WATANABE ², Kan SHIDA ¹, Satoshi MATSUMOTO ¹, Tetsuji HORI ¹

PMCID: PMC9816042 PMID: 36660595

Abstract

Mononuclear phagocytic cells (MPCs) are classified into monocytes (Mos)/macrophages and dendritic cells (DCs) based on their functions. Cells of MPCs lineage act as immune modulators by affecting effector cells, such as NK cells, T cells, and B cells. This study aimed to investigate the effects of Lacticaseibacillus paracasei strain Shirota (LcS) ingestion on peripheral MPCs, particularly on their expression of functional cell-surface molecules enhanced in healthy adults. Thus, twelve healthy office workers consumed a fermented milk drink containing 1.0 × 10¹¹ cfu of LcS (LcS-FM) or a control unfermented milk drink (CM) once a day for 6 weeks. Peripheral blood mononuclear cells (PBMCs) were prepared from blood samples, and immune cells and functional cell-surface molecules were analyzed. We observed remarkable differences in the expression of HLAABC, MICA, CD40, and GPR43 in plasmacytoid DCs (pDCs) between the LcS-FM and CM groups, whereas no difference was found in CD86 or HLADR expression. The LcS-FM group exhibited higher CD40 expression in both conventional DCs (cDCs) and Mos, especially in type 2 conventional DCs (cDC2s) and classical monocytes (cMos); higher percentages of cMos, intermediate monocytes (iMos), and nonclassical monocytes; and higher numbers of cMos and iMos in PBMCs than the CM group. LcS ingestion increased the expression of HLAABC, MICA, CD40, and GPR43 in pDCs and CD40 in cDCs and Mos, particularly cDC2s and cMos. These results suggest that LcS modulates the function of MPCs that may lead to the regulation of immune effector functions in healthy adults.

Keywords: Lacticaseibacillus paracaseistrain Shirota (LcS), dendritic cell, monocyte, CD40

INTRODUCTION

Based on the definition of probiotics by the Food and Agricultural Organization of the United Nations and World Health Organization in 2001 [1], the International Scientific Association for Probiotics and Prebiotics (ISAPP) provided the following definition for probiotics in 2013: “probiotics are live microorganisms that, when administrated in adequate amounts confer a health benefit on the host” [2]. Lacticaseibacillus paracasei strain Shirota (LcS; formerly Lactobacillus casei strain Shirota) is a representative probiotic among the numerous species of lactic acid bacteria. In recent decades, LcS has continuously provided various beneficial effects, not only effects on intestinal microbiota but also effects on immune functions and daily physical condition. For instance, LcS can increase or maintain natural killer (NK) cell activity [3] and immunoglobulin A (IgA) secretion [4]. Such effects have also been observed for other lactic acid bacteria [5]. Furthermore, some of these beneficial bacteria have been shown to ameliorate symptoms of upper respiratory tract infection (URTI) [5,6,7]. Randomized clinical trials with healthy adults showed that LcS ingestion also had positive effects on URTI, including subjective symptoms of sore throat, runny nose, and stuffy nose [6, 7]. As mentioned above, it has been shown that LcS affects immune effector function and shows clinical benefits; however, the underlying mechanism and the immune conductor cells involved have not been studied in vivo.

In the human periphery, mononuclear phagocytic cells (MPCs) play a pivotal role in immune responses against pathogens, as well as in priming immune effector cells. MPCs have generally been categorized into dendritic cells (DCs), monocytes (Mos), and macrophages (MFs). Although their phenotypes often overlap, each cell subset has its own unique characteristics. DCs comprise at least three subsets: type 1 conventional DCs (cDC1s), type 2 conventional DCs (cDC2s), and plasmacytoid DCs (pDCs). cDC1s specialize in the uptake of dying cells, cross-presentation, activation of CD8⁺ T cells via major histocompatibility gene complex (MHC) class I, and promotion of T helper type I (Th1) and natural killer response [8, 9]. Type 2 conventional DCs stimulate naïve CD4⁺ T cells and activate Th2, Th17, Th22, Treg, and B cells [8, 9]. pDCs are specialized to sense and respond to viral infection through several mechanisms via type I and type III interferon production, and they activate CD4⁺ and CD8⁺ T cells in response to influenza virus [8, 10, 11]. Generally, the function of these three DC subsets is to bridge the innate immune system, which protects against pathogenic microorganisms, and adaptive immune system, which presents the acquired information of pathogens to immune effector cells. Mos are the definitive precursors of MFs and DCs. Phenotypically, Mos are classified into three subsets, classical monocytes (cMos), intermediate monocytes (iMos), and nonclassical monocytes (ncMos), depending on the differences in CD14 and CD16 expression [9, 12,13,14]. Each Mo subset contributes to immune defense, clearing pathogens and dead cells, tissue repair, and priming of adaptive immunity [15,16,17]. MFs consist of a heterogeneous population of cells, and their diversity is influenced by the variety of tissues in which they reside. Generally, they are well known for their ability to phagocytize, eliminate pathogens, signal the immune system, and repair tissue, and they are limited to the periphery because their development depends on the local tissue [12].

In recent years, some lactic acid bacteria that affect MPCs have been reported in in vivo human studies. Tavares-Silva et al. indicated that 30 days of supplementation with five strains of probiotics, including two Lactobacillus spp., one Lactococcus sp., and two Bifidobacterium spp., in marathoners ameliorated URTI symptoms and that this was accompanied by lower IL-6 production from Mos immediately after a race [18]. Lactococcus lactis JCM 5805 was reported to reduce symptoms of the common cold and influenza in healthy adults by stimulating pDCs [19, 20]. In the case of LcS, an ex vivo study with peripheral blood mononuclear cells (PBMCs) from healthy adults demonstrated that LcS stimuli promoted the expression of surface CD40 and other maturation markers in DCs and induced T-cell activation [21]. However, in vivo evidence showing LcS effects on human MPCs has not yet been elucidated.

In this study, a randomized controlled trial was conducted in healthy office workers to determine the effects of LcS intake on MPCs.

MATERIALS AND METHODS

Study design

Twelve male office workers between 30 and 40 years of age were recruited and divided into two groups: one group consumed a fermented milk drink containing LcS (LcS-FM), and the other group consumed a control milk drink (CM), with each group consuming one bottle per day for 6 weeks. Blood samples (18 mL) were collected from the subjects before and on days 3, 10, 28, and 43 after they began consumption. Blood samples were collected after a medical interview by a doctor to confirm the health status of the test subjects at the clinic and then transported to the Yakult Central Institute (Kunitachi, Tokyo, Japan) for blood treatment. A participant flow diagram for this study is shown in Supplementary Fig. 1. The study was contracted to CPCC Co., Ltd. (Chuo, Tokyo, Japan) to maintain blinding and was reviewed by the ethical review board of Chiyoda Para Medical Care Clinic (IRB No.: 15000088).

The study protocol was registered with the University Hospital Medical Information Network (UMIN-CTR: 000044809).

Composition of the test and control beverages

The compositions of the LcS-FM and CM are listed in Table 1. The CM was unfermented milk. The LcS-FM was produced at the Miki Plant of Yakult Honsha Co., Ltd. (Miki, Hyogo, Japan), and the CM was produced in the Product Process Research section, Development Department, Yakult Honsha Co., Ltd. (Kunitachi, Tokyo, Japan). Both were transported to Yakult Central Institute under refrigeration. Once a week, the test and control beverages were transported from Yakult Central Institute to CPCC Co., Ltd. under refrigeration for delivery to the test subjects.

Table 1. Compositions of test and control beverages (per 100 mL bottle).

	LcS-FM	CM
	(Test beverage)	(Control beverage)
Energy (kcal)	63	63
Protein (g)	1.5	1.5
Lipid (g)	0.1	0.1
Carbohydorate (g)	14.1	14.1
LcS (cfu/bottle)	1×10¹¹	-

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cfu: colony forming unit.

LcS-FM: the fermented milk drink containing Lacticaseibacillus paracasei strain Shirota (LcS); CM: control milk drink.

Blood treatment

Each 18 mL blood sample was mixed with an equal volume of saline (Otsuka Pharmaceutical, Tokyo, Japan) at room temperature (RT; between 20–25°C). One half of the resulting blood diluent (18 mL) was layered onto 15 mL of Lymphoprep (STEMCELL Technologies, AS, Oslo, Norway; two 50 mL centrifuge tubes per sample). After centrifugation at 800 × g at room temperature and slow deceleration for 30 min, the middle layer was collected as PBMCs. The collected cells were centrifuged with Dulbecco’s phosphate buffered saline (D-PBS; Nacalai Tesque, Kyoto, Japan) containing with 2% fetal bovine serum (FBS; 2% FBS/PBS) at 460 × g for 10 min at 4°C, and the supernatant was removed. The PBMC sediment was suspended in D-PBS (Nacalai Tesque), and the cells were counted. PBMCs were subjected to flow cytometry (FCM) and cytometry by time-of-flight (CyTOF) analysis. The clinical samples were handled by following the guidelines of the Biosafety Committee of Yakult Central Institute (Approval No. 312).

Sample processing and analysis for FCM

Fluorescently labeled antibody staining

Approximately 1.1 × 10⁷ PBMCs were washed with D-PBS, suspended in 275 µL of Zombie Aqua (BioLegend, San Diego, CA, USA) diluted 100-fold in D-PBS, and kept on ice for 15 min. In addition, 275 µL of TruStain FcX (BioLegend) diluted 10-fold in D-PBS was added, and the mixture was gently vortexed and kept on ice for 15 min. After the reaction, 50 µL (approximately 1.0 × 10⁶ cells) of the cell suspension was dispensed into 96-well V-bottom plates (Thermo Fisher Scientific, Waltham, MA, USA) for mean fluorescence intensity (MFI) analysis, and the same volume of antibody diluent was added to each plate and mixed with the cell suspension. The combinations of antibodies and dilution factors are shown in Supplementary Table 1. Brilliant Stain Buffer Plus (BD Biosciences, San Jose, CA, USA) was added to the antibody mixture. The plates were then incubated on ice for 30 min. After washing with 2% FBS/PBS, the cells were fixed with 200 µL FluoroFix Buffer (BioLegend) and allowed to react for 30 min under light shielding at RT. After centrifugation (460 × g, 5 min, 20°C), 200 µL of 2% FBS/PBS was added to each well, and the cells were stored at 4°C until FCM analysis.

FCM gating strategy

The antibody-stained cell suspension was applied to a CytoFLEX S flow cytometer (Beckman Coulter, Miami, FL, USA). The gating strategy was partially modified from that described previously and is shown in Supplementary Fig. 2 [14, 16, 22,23,24,25]. In brief, after excluding dead cells (live cell gating), we gated all marker-negative T cells (CD3), B cells (CD19 and CD20), NK cells (CD56), granulocytes (CD66b), and HLADR⁺ cell populations. We then expanded CD16 and CD14 to separate Mos into three subsets: cMos (CD14⁺CD16⁻), iMos (CD14⁺CD16⁺), and ncMos (CD14^loCD16⁺). To separate the DC subsets, we gated the CD14⁻CD16⁻ cell population and re-expanded the lineage and HLADR to strictly select the lineage⁻HLADR⁺ cell population. We then expanded the cells on CD11c and CD303 and gated them on pDCs (CD11c⁻CD303⁺). The CD11c⁺CD303⁻ cell population was expanded with CD141 and CD1c and separated into cDC1s (CD141⁺CD1c^−/+) and cDC2s (CD141⁻CD1c⁺). FCM data were accumulated until the lineage⁻HLADR⁺ gates reached 30,000 events or 180 sec of acquisition time under high running flow conditions (60 µL/min). FlowJo software v10 (BD Biosciences, Ashland, OR, USA) was used to analyze the acquired data. Percentages of MPC subsets (pDC%, cDC%, and Mo%) in PBMCs and of MPC subsets in HLADR⁺ cells were calculated based on the FCM gating strategy. The percentages of MPC subsets in PBMCs indicate the composition of each MPC subset among all viable PBMCs. The percentages of MPC subsets in HLADR⁺ cells indicate the composition of each MPC subset among the lineage⁻HLADR⁺ cells. Cell numbers were calculated for each MPC subset by multiplying the percentage of the MPC subset in PBMCs by actual cell counts in 1 mL blood obtained with a TC20 automated cell counter (Bio-Rad Laboratories, Inc. Hercules, CA, USA). The ratios of the percentages of MPC subsets in PBMCs, ratios of the percentages of MPC subsets in HLADR⁺ cells, and ratios of MPC subset cell numbers were calculated using the following formulas. The ratio of the percentage of the MPC subset in PBMCs at day X = (percentage of the MPC subset in PBMCs at day X) / (percentage of the MPC subset in PBMCs at day 0). The ratio of the percentage of the MPC subset in HLADR⁺ cells at day X = (percentage of the MPC subset in HLADR⁺ cells at day X) / (percentage of the MPC subset in HLADR⁺ cells at day 0). The ratio of the cell number for the MPC subset at day X = (cell number for the MPC subset at day X) / (cell number for the MPC subset at day 0). The MFIs of activation markers and isotype controls were measured by the FCM gating strategy. They were used to calculate MFI ratios with the formula illustrated below. The calculation for MPC activation marker A on day X is used as an example. MFI ratio of marker A at day X = [(MFI of marker A at day X) / (MFI of isotype at day X)] / [(MFI of marker A at day 0) / (MFI of isotype at day 0)].

Sample processing and analysis for CyTOF

Metal-labeled antibody staining

The methods and reagents used for CyTOF analysis were in accordance with the manufacturer’s protocol. A list of 16 metal-labeled antibodies is shown in Supplementary Table 2. In brief, the design for the combinations of metals and antibodies was prepared based on suggestions from Standard BioTools K.K. (f.k.a. Fluidigm K.K.) (Chuo, Tokyo, Japan). Among them, MICA, GPR43, and GPR109a were custom-labeled using a Maxpar antibody labeling kit [Standard BioTools K.K. (f.k.a. Fluidigm), San Francisco, CA, USA], and their titers were preliminarily checked with human PBMCs (data not shown). Cells (3.0 × 10⁶) were incubated with 1 μM Cell-ID Cisplatin-198Pt (Standard BioTools) for dead cell staining. After centrifugation (500 × g, 5 min, 20°C), the cells were incubated with TruStain FcX (BioLegend) diluted 10-fold in Maxpar Cell Staining Buffer (CSB; Standard BioTools), and the antibody mixture was added to the cell suspension. The cells were then stained for 30 min at RT. After centrifugal washing, the cells were fixed with 1.6% (w/v) formaldehyde (methanol free) for 10 min at RT and kept at 4°C until CyTOF analysis. Mass cytometry analysis was performed at Standard BioTools laboratory (Edogawa, Tokyo, Japan). After the cells were washed and stained with Cell-ID intercalator-Ir (Standard BioTools) as a marker of cell viability, they were applied to a Helios^® mass cytometer (Standard BioTools) within 48 hr. CyTOF data were acquired for 300,000 events in total. The obtained raw fcs data were analyzed using FlowJo software v10 (BD Biosciences).

CyTOF gating strategy

The basic concept of the CyTOF gating strategy for pDCs was similar to that of the FCM gating strategy described in Supplementary Fig. 3. After excluding debris, normalization beads, doublets, and dead cells (live cell gating), we gated lineage marker-negative cells, such as CD3, CD19, CD20, CD56, CD66b, and HLADR⁺ cell populations. We then expanded CD16 and CD14 to separate Mos, gated the CD14⁻CD16⁻ cell population, and expanded CD11c and CD303 for pDCs discrimination. Finally, the CD11c⁻CD303⁺ cell population was defined as the pDCs (Supplementary Fig. 3). The expression of activation markers was evaluated by calculating two values, mean metal intensity (MMI) and percentage positivity (PP), as mentioned in the Supplementary Fig. 4.

Statistical analysis

After entering the values of each test and measurement, the basic statistics (means and standard deviations) of the LcS-FM and CM groups were calculated. Next, the basic statistics (mean and standard deviation) were calculated for each test value by calculating the amount of change from the baseline value before consuming the drinks. For statistical analysis of differences in the amount of change between the groups, p<0.05 was considered significant, and analyses were conducted using the Mann–Whitney U test with GraphPad Prism version 8 for Windows (GraphPad Software, San Diego, CA, USA).

RESULTS

Study population

A flow diagram for the participants of this study is shown in Supplementary Fig. 1. Eligibility was assessed for 25 male office workers. Five of them were excluded based on infectious disease checks (hepatitis B virus, hepatitis C virus, human immunodeficiency virus, Treponema pallidum), blood biochemical test results, and blood pressure results. From the remaining 20 participants, 12 participants were recruited for the study and allocated to either the LcS-FM or CM group. One subject in the control group was unable to come to the clinic on day 28 due to work but was able to come on the other blood collection days (Supplementary Fig. 1). Data from all six participants in the LcS-FM group and six participants in the CM group were used for analysis.

The baseline characteristics of the participants in the two groups are summarized in Table 2. There were no substantial differences in age, BMI, frequency of defecation, sleep time, blood pressure, pulse rate, smoking habits, alcohol drinking habits, and exercise habits between the groups (Table 2). Both groups showed good compliance with regard to test drink consumption (Table 2).

Table 2. Baseline chracteristics of the participants.

	LcS-FM group (n=6)		CM group (n=6)		p value

	Mean ± SD	n (%)	Mean ± SD	n (%)
Age (years)	42.7 ± 3.6		44.0 ± 6.1		0.653^a
BMI (kg/m²)	24.9 ± 3.2		24.4 ± 3.9		0.819^a
Frequency of defecation per week (times/week)	8.0 ± 3.0		9.5 ± 2.8		0.389^a
Sleep time per day (hr/day)	7.2 ± 1.0		6.3 ± 0.8		0.141^a
Systolic blood pressure (mmHg)	122.0 ± 6.8		118.5 ± 13.9		0.592^a
Diastolic blood pressure (mmHg)	79.0 ± 4.8		75.5 ± 1.1		0.496^a
Pulse rate (bpm)	61.8 ± 10.1		63.8 ± 8.5		0.717^a
Workers over 40 hr per week		5 (83.3)		5 (83.3)	1.000^b
Smokers		1 (16.7)		0 (0)	0.399^b
Participants with drinking alcohol		3 (50.0)		4 (66.7)	0.575^b
Participants with exercise habits		2 (33.3)		3 (50.0)	0.575^b
Product compliance (%)	100 ± 0.0		100 ± 0.0		−^a

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BMI: Body mass index.

LcS-FM: the fermented milk drink containing Lacticaseibacillus paracasei strain Shirota (LcS); CM: control milk drink.

^a p-values analyzed by the unpaired Student’s t-test.

^b p-values analyzed by the χ² test.

There was no difference in the incidence rate of adverse events or health status between the two groups (data not shown).

Effect of LcS-FM on pDCs in PBMCs

To confirm the effect of consumption of the LcS-FM on representative MPCs, we first focused on pDCs because of their multiple functions, such as their anti-viral response and priming of immune effector cells. There was no difference in the ratio of cell numbers between the LcS-FM and CM groups; however, the pDC% in the LcS-FM group tended to be higher than that in the CM group on the 3rd day of consumption (Fig. 1A, 1B).

Fig. 1. — Effect of ingesting a fermented milk drink containing *Lacticaseibacillus paracasei* strain Shirota (LcS) on plasmacytoid DCs (pDCs) in peripheral blood mononuclear cells (PBMCs).

(A) Ratio of the pDC% in PBMCs by flow cytometry (FCM). (B) Ratios of the cell numbers of pDCs by FCM. (C) Ratios of the mean fluorescence intensities (MFIs) of cell-surface markers, HLAABC and MICA, on pDCs by FCM. (D) Ratios of the percentage positivity (PP) values of cell-surface markers, CD40 and GPR43, in pDCs by cytometry by time-of-flight (CyTOF). For statistical analysis of comparisons between the fermented milk drink containing *Lacticaseibacillus paracasei* strain Shirota (LcS) (LcS-FM) and control milk drink (CM) groups, ratios were calculated by dividing the values at 3, 10, 28, and 43 days after consuming the drinks by the values before consuming the drinks. The Mann–Whitney U test was used for the statistical analysis (*p<0.05, **p<0.01, ^Ɨp<0.1). The averaged ratios of subset percentages, cell numbers, MFIs, and PPs of pDCs are shown by the bar graph, and the individual changes in them are shown by the line graph.

In terms of the expression of functional cell-surface molecules and activation markers, the MFIs and PP ratios of HLAABC, MICA, CD40, and GPR43 in the LcS-FM group were substantially higher than those in the CM group (MICA and GPR43 on the 3rd and 43rd days of consumption; HLAABC and CD40 on the 3rd day of consumption; Fig. 1C, 1D). These results indicate that the intake of LcS-containing fermented milk affects the population and expression of cell-surface molecules on pDCs. This implies that LcS can modulate the expression of various molecules in pDCs and interact with the immune effector cells.

Effect of consuming LcS-FM on cDC subsets in PBMCs

Next, the impact of consuming the LcS-FM was investigated in DCs, but not in pDCs. There was no difference in the cDC1%, cDC2%, and cell number ratio between the LcS-FM and CM groups (Fig. 2A, 2B). Although there were no differences in the ratios of CD86, HLAABC, MICA, GPR43, and HLADR expression on cDCs between the two groups (data not shown), the ratio of CD40 expression on cDCs in the LcS-FM group was substantially higher than that in the CM group on the 3rd day of consumption (Fig. 2C). Furthermore, in the cDC population, the ratio of the CD40-expressing cDC2 subset in the LcS-FM group showed a remarkable increase compared with that of the CM group (Fig. 2D), suggesting that the ingestion of LcS-FM affected not only pDCs but also cDCs, especially the expression of CD40 in cDC2s.

Fig. 2. — Effect of ingesting a fermented milk drink containing *Lacticaseibacillus paracasei* strain Shirota (LcS) on conventional DC (cDC) subsets in peripheral blood mononuclear cells (PBMCs).

(A) Ratios of the cDC1% and cDC2% in PBMCs. (B) Ratios of cell number of cDC1 and cDC2. (C) Ratios of CD40 expression on cDCs. (D) Ratios of CD40 expression on cDC1 and cDC2. cDC, conventional DC. All data were obtained by flow cytometry (FCM) analysis. For statistical analysis of comparisons between the fermented milk drink containing *Lacticaseibacillus paracasei* strain Shirota (LcS) (LcS-FM) and control milk drink (CM) groups, ratios were calculated by dividing the values at 3, 10, 28, and 43 days after consuming the drinks by the values before consuming the drinks. The Mann–Whitney U test was used for the statistical analysis (*p<0.05, ^Ɨp<0.1). The averaged ratios of MFIs are shown by the bar graph, and the individual changes in them are shown by the line graph.

Effect of consuming LcS-FM on the expression of surface activation markers of Mo subsets in PBMCs

Subsequently, we investigated the effect of ingesting the LcS-FM on Mos as described in a previous report [21, 26]. The percentages of iMos and ncMos in the LcS-FM group were remarkably higher than those in the CM group (iMos on the 3rd and 10th days of consumption and ncMos on the 3rd day of consumption; Fig. 3A). The same change was observed for cMos in the LcS-FM group, with the percentage tending to be higher than that in the control group on the 3rd day of consumption. The ratio of the iMo% in HLADR⁺ cells of the LcS-FM group was substantially higher than that of the control group on the 3rd day of (Fig. 3B). Looking at the cell numbers, the ratio of iMo in the LcS-FM group was considerably higher than that in the CM group on the 10th day of consumption (Fig. 3C).

Fig. 3. — Effect of ingesting a fermented milk drink containing *Lacticaseibacillus paracasei* strain Shirota (LcS) on monocyte (Mo) subsets in peripheral blood mononuclear cells (PBMCs).

(A) Ratios of the percentages of Mo subsets (cMo%, iMo%, and ncMo%) in PBMCs. (B) Ratios of the percentages of Mo subsets in HLADR⁺ cells. (C) Ratios of the cell numbers of Mo subsets. (D) Ratios of CD40 expression on Monocytes (Mos). (E) Ratios of CD40 expression on cMos, iMos, and ncMos. cMos, classical monocytes; iMos, intermediate monocytes; ncMos, nonclassical monocytes. All data were obtained by flow cytometry (FCM) analysis. For statistical analysis of comparisons between the fermented milk drink containing *Lacticaseibacillus paracasei* strain Shirota (LcS) (LcS-FM) and control milk drink (CM) groups, ratios were calculated by dividing the values at 3, 10, 28, and 43 days after consuming the drinks by the values before consuming the drinks. The Mann–Whitney U test was used for the statistical analysis (*p<0.05, **p<0.01, ^Ɨp<0.1). The averaged ratios of subset percentages and cell numbers are shown by the bar graph, and the individual changes in them are shown by the line graph.

In addition, ingestion of LcS-FM also affected the expression of a functional cell-surface molecule, CD40, on Mos in PBMCs. The ratio of CD40 expression on Mos in the LcS-FM group was substantially higher than that in the CM group on the 3rd day of consumption and the ratio of CD40 expression on cDCs in the LcS-FM group was also higher than that in the CM group on the 3rd day of consumption (Fig. 2C and 3D). Furthermore, in the Mos population, the ratio of CD40 expression on the ncMos and iMos subsets did not differ between the groups; however, that on cMos in the LcS-FM group was considerably higher than that in the CM group (Fig. 3E). Therefore, these results indicate that the percentage or cell number of Mos in PBMCs increases and that CD40 expression on Mo subsets is enhanced within 10 days after consuming LcS-FM. Considering the results for CD40 in this study, the increase in CD40 expression on pDCs, cDCs, and Mos in the LcS-FM group indicates that LcS can impact various MPCs and is likely to modulate an immune network.

DISCUSSION

Recently, some lactic acid bacteria have been reported to exert beneficial effects on MPCs, including Mos and pDCs, in vivo. L. lactis JCM 5805 influences the entire immune system by enhancing the expression of CD86 and HLADR on human pDCs [19, 20]. However, the involvement of LcS in MPCs in an in vivo human study has not been reported. Therefore, we conducted a randomized controlled study to explore the dynamics and activation state of MPCs after the ingestion of a LcS-FM. This study did not detected any effects of LcS on CD86 and HLADR expression in pDCs (data not shown). However, ingestion of the LcS-FM increased the pDC% in PBMCs and affected the expression of other functional molecules, such as HLAABC, MICA, CD40, and GPR43. HLAABC, an MHC class Ia molecule, is involved in antigen presentation to CD8⁺ T cells and their activation [27]. MICA, a stress-inducible MHC class Ib molecule, activates NK cells [28]. CD40 is known to be involved in the acquired immune response by inducing Th1-type immune and antibody production responses by binding to CD40L on T and B cells [29]. GPR43 is mainly a receptor for acetate and propionate and is known to contribute to intestinal mucosal homeostasis via metabolites derived from intestinal bacteria [30]. These findings raise the possibility that LcS could act on both NK cell cytotoxicity and IgA production through the activation of MPCs such as pDCs in vivo.

According to previous ex vivo and in vitro LcS studies, LcS has been reported to stimulate Mo-derived DCs, stimulate MFs, and enhance the production of some cytokines, such as IL-12, by means of its specific cell-surface structure [21, 26, 31,32,33]. These previous findings raise the possibility that LcS affects not only pDCs but also other MPCs. Among several costimulatory molecules, we focused on CD40 on MPCs because CD40 signaling is known to induce changes in antigen-presenting cells, such as upregulation of HLADR and CD80/CD86 [29, 34, 35]. In this study, the expression intensity of CD40 in cDCs and Mos (i.e., cDC2s and cMos) was increased by ingesting a fermented milk drink containing LcS (Fig. 2C, 2D, 3D, and 3E). Additionally, significant positive correlation between the ratios of CD40 and CD86 expression on cDCs at day 10 suggests that CD40 expression may be involved in the regulation of downstream molecules (Supplementary Fig. 6). CD40 expressed on DCs is thought to be involved in acquired immune responses by inducing Th1-type immune responses and antibody production responses by binding to CD40L on T and B cells [29]. As previously mentioned, the cMos is one of the precursor cells for monocyte-derived DCs. You et al. reported that CD40 expression on DCs derived from PBMCs was increased by the addition of LcS in vitro, and similar results were obtained in vivo [21].

Consuming LcS-FM affected the populations and cell numbers of the Mo subsets. In this study, we found that consuming LcS-FM induced an increase in the percentages of the three Mo subsets and increased the cell numbers of both cMos and iMos on day 3 (Fig. 2 and Supplementary Table 3). These rapid proliferative responses of Mo subsets raise the new concept that LcS may have an influence on primary lymphoid organs. In view of the niches for the development of Mos, LcS could stimulate bone marrow (BM) to supply Mos to the periphery in the early periods immediately after consuming LcS. Although there have been no reports that probiotics affect BM, the concept of probiotics affecting BM could be meaningful in efforts to comprehend the homeostasis of host immunity. Indeed, it has reported that gut bacterial metabolites and components can act on hematopoietic and granulopoietic events in BM and that BM can serve as a reservoir for immune cell subsets, such as naïve B cells and Mos, in response to caloric restriction and fasting in short periods [17, 36, 37].

The present study confirmed that LcS affected Mos as well as DCs at least 3 days after ingesting the LcS-FM and that LcS influenced T and B cells thereafter (Supplementary Fig. 5 and 7). A previous study showed that 6 weeks of LcS intake prevented a reduction in NK cell activity [6]. The results indicating MICA upregulation on T cells and pDCs on the 43rd day of LcS-FM ingestion are assumed to be related to NK cell activation (Supplementary Fig. 7A and 7B). Therefore, we think that this phenomenon could be meaningful in terms of capturing aspects of connections between the innate- and acquired- immune systems. To consolidate these hypothesis, further studies are needed.

Finally, we considered what component of LcS-FM caused this effect on MPCs. Our previous reports have shown that LcS cells and their components have the potential to activate Mos and MFs via cytokine production [26, 38]. Furthermore, continuous intake of LcS-FM by the elderly increased the fecal acetic acid concentration [39]. These findings suggest that LcS-derived cell components and some metabolites produced in the intestine affect MPCs. In addition, fermentation products included in LcS-FM could play some roles. We will pursue these issues in future studies.

In conclusion, the present study suggests that the probiotic strain LcS affects various immune cells, such as pDCs, cDCs, and Mos (ncMos, iMos, cMos), in diverse manners and influences the innate and acquired immune systems of the host through CD40 expression. This research represents the first step for comprehension of the involvement of LcS with immune systems in vivo and how LcS contributes to maintain host health.

AUTHOR CONTRIBUTIONS

Conceptualization, T.N., M.M., M.Y., O.W., K.S., S.M., and T.H.; methodology, T.N., M.M., M.Y., A.I., and T.H.; sample collection, T.N., M.M., M.Y., A.I., A.M., K.O., N.K., J.K., R. K., and T.H.; formal analysis, T.N., M.M., M.Y., and T.H.; writing – original draft, T.N. and T.H.; writing – review and editing, T.N. and T.H.; resources, Y.M., O.W., K.S., S.M., and T.H.; supervision, K.S. and S. M. All authors have approved the final version of this manuscript.

FUNDING

This study received no specific grant from any funding agency.

CONFLICT OF INTEREST

All the authors were employed by Yakult Honsha, which produces fermented dairy products using the probiotic strain LcS.

Supplementary Material

Supplement Files

bmfh-42-065-s001.pdf^{(1,017.5KB, pdf)}

Acknowledgments

We thank the staff of CPCC Co., Ltd. (Chuo, Tokyo, Japan) for their help with participant management and sample collection. We would also like to thank the participants of this study. Finally, we would like to thank Dr. Masanobu Nanno from Yakult Central Institute and Editage (www.editage.com) for English language editing.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement Files

bmfh-42-065-s001.pdf^{(1,017.5KB, pdf)}

[r1] 1.Perdigón G, Fuller R, Raya R. 2001. Lactic acid bacteria and their effect on the immune system. Curr Issues Intest Microbiol 2: 27–42. [PubMed] [Google Scholar]

[r2] 2.Hill C, Guarner F, Reid G, Gibson GR, Merenstein DJ, Pot B, Morelli L, Canani RB, Flint HJ, Salminen S, Calder PC, Sanders ME. 2014. Expert consensus document. The International Scientific Association for Probiotics and Prebiotics consensus statement on the scope and appropriate use of the term probiotic. Nat Rev Gastroenterol Hepatol 11: 506–514. [DOI] [PubMed] [Google Scholar]

[r3] 3.Olivares M, Díaz-Ropero MP, Sierra S, Lara-Villoslada F, Fonollá J, Navas M, Rodríguez JM, Xaus J. 2007. Oral intake of Lactobacillus fermentum CECT5716 enhances the effects of influenza vaccination. Nutrition 23: 254–260. [DOI] [PubMed] [Google Scholar]

[r4] 4.Ericson D, Hamberg K, Bratthall G, Sinkiewicz-Enggren G, Ljunggren L. 2013. Salivary IgA response to probiotic bacteria and mutans streptococci after the use of chewing gum containing Lactobacillus reuteri. Pathog Dis 68: 82–87. [DOI] [PubMed] [Google Scholar]

[r5] 5.Hishiki H, Kawashima T, Tsuji NM, Ikari N, Takemura R, Kido H, Shimojo N. 2020. A double-blind, randomized, placebo-controlled trial of heat-killed Pediococcus acidilactici k15 for prevention of respiratory tract infections among preschool children. Nutrients 12: 1–10. [DOI] [PMC free article] [PubMed] [Google Scholar]

[r6] 6.Shida K, Sato T, Iizuka R, Hoshi R, Watanabe O, Igarashi T, Miyazaki K, Nanno M, Ishikawa F. 2017. Daily intake of fermented milk with Lactobacillus casei strain Shirota reduces the incidence and duration of upper respiratory tract infections in healthy middle-aged office workers. Eur J Nutr 56: 45–53. [DOI] [PMC free article] [PubMed] [Google Scholar]

[r7] 7.Gleeson M, Bishop NC, Oliveira M, Tauler P. 2011. Daily probiotic’s (Lactobacillus casei Shirota) reduction of infection incidence in athletes. Int J Sport Nutr Exerc Metab 21: 55–64. [DOI] [PubMed] [Google Scholar]

[r8] 8.Collin M, Bigley V. 2018. Human dendritic cell subsets: an update. Immunology 154: 3–20. [DOI] [PMC free article] [PubMed] [Google Scholar]

[r9] 9.Guilliams M, Ginhoux F, Jakubzick C, Naik SH, Onai N, Schraml BU, Segura E, Tussiwand R, Yona S. 2014. Dendritic cells, monocytes and macrophages: a unified nomenclature based on ontogeny. Nat Rev Immunol 14: 571–578. [DOI] [PMC free article] [PubMed] [Google Scholar]

[r10] 10.Reizis B. 2019. Plasmacytoid dendritic cells: development, regulation, and function. Immunity 50: 37–50. [DOI] [PMC free article] [PubMed] [Google Scholar]

[r11] 11.Vogel K, Thomann S, Vogel B, Schuster P, Schmidt B. 2014. Both plasmacytoid dendritic cells and monocytes stimulate natural killer cells early during human herpes simplex virus type 1 infections. Immunology 143: 588–600. [DOI] [PMC free article] [PubMed] [Google Scholar]

[r12] 12.Coillard A, Segura E. 2019. In vivo differentiation of human monocytes. Front Immunol 10: 1907. [DOI] [PMC free article] [PubMed] [Google Scholar]

[r13] 13.Sampath P, Moideen K, Ranganathan UD, Bethunaickan R. 2018. Monocyte subsets: phenotypes and function in tuberculosis infection. Front Immunol 9: 1726. [DOI] [PMC free article] [PubMed] [Google Scholar]

[r14] 14.Ożańska A, Szymczak D, Rybka J. 2020. Pattern of human monocyte subpopulations in health and disease. Scand J Immunol 92: e12883. [DOI] [PubMed] [Google Scholar]

[r15] 15.Merah-Mourah F, Cohen SO, Charron D, Mooney N, Haziot A. 2020. Identification of novel human monocyte subsets and evidence for phenotypic groups defined by interindividual variations of expression of adhesion molecules. Sci Rep 10: 4397. [DOI] [PMC free article] [PubMed] [Google Scholar]

[r16] 16.Tallone T, Turconi G, Soldati G, Pedrazzini G, Moccetti T, Vassalli G. 2011. Heterogeneity of human monocytes: an optimized four-color flow cytometry protocol for analysis of monocyte subsets. J Cardiovasc Transl Res 4: 211–219. [DOI] [PubMed] [Google Scholar]

[r17] 17.Jordan S, Tung N, Casanova-Acebes M, Chang C, Cantoni C, Zhang D, Wirtz TH, Naik S, Rose SA, Brocker CN, Gainullina A, Hornburg D, Horng S, Maier BB, Cravedi P, LeRoith D, Gonzalez FJ, Meissner F, Ochando J, Rahman A, Chipuk JE, Artyomov MN, Frenette PS, Piccio L, Berres ML, Gallagher EJ, Merad M. 2019. Dietary intake regulates the circulating inflammatory monocyte pool. Cell 178: 1102–1114.e17. [DOI] [PMC free article] [PubMed] [Google Scholar]

[r18] 18.Tavares-Silva E, Caris AV, Santos SA, Ravacci GR, Thomatieli-Santos RV. 2021. Effect of multi-strain probiotic supplementation on URTI symptoms and cytokine production by monocytes after a marathon race: a randomized, double-blind, placebo study. Nutrients 13: 1478. [DOI] [PMC free article] [PubMed] [Google Scholar]

[r19] 19.Sugimura T, Jounai K, Ohshio K, Tanaka T, Suwa M, Fujiwara D. 2013. Immunomodulatory effect of Lactococcus lactis JCM5805 on human plasmacytoid dendritic cells. Clin Immunol 149: 509–518. [DOI] [PubMed] [Google Scholar]

[r20] 20.Shibata T, Kanayama M, Haida M, Fujimoto S, Oroguchi T, Sata K, Mita N, Kutsuzawa T, Ikeuchi M, Kondo M, Naito K, Tsuda M, Nishizaki Y, Ishii N. 2016. Lactococcus lactis JCM5805 activates anti-viral immunity and reduces symptoms of common cold and influenza in healthy adults in a randomized controlled trial. J Funct Foods 24: 492–500. [Google Scholar]

[r21] 21.You J, Dong H, Mann ER, Knight SC, Yaqoob P. 2014. Probiotic modulation of dendritic cell function is influenced by ageing. Immunobiology 219: 138–148. [DOI] [PMC free article] [PubMed] [Google Scholar]

[r22] 22.Breton G, Zheng S, Valieris R, Tojal da Silva I, Satija R, Nussenzweig MC. 2016. Human dendritic cells (DCs) are derived from distinct circulating precursors that are precommitted to become CD1c⁺ or CD141⁺ DCs. J Exp Med 213: 2861–2870. [DOI] [PMC free article] [PubMed] [Google Scholar]

[r23] 23.Breton G, Lee J, Liu K, Nussenzweig MC. 2015. Defining human dendritic cell progenitors by multiparametric flow cytometry. Nat Protoc 10: 1407–1422. [DOI] [PMC free article] [PubMed] [Google Scholar]

[r24] 24.Basit F, Mathan T, Sancho D, de Vries IJM. 2018. Human dendritic cell subsets undergo distinct metabolic reprogramming for immune response. Front Immunol 9: 2489. [DOI] [PMC free article] [PubMed] [Google Scholar]

[r25] 25.Dutertre CA, Becht E, Irac SE, Khalilnezhad A, Narang V, Khalilnezhad S, Ng PY, van den Hoogen LL, Leong JY, Lee B, Chevrier M, Zhang XM, Yong PJA, Koh G, Lum J, Howland SW, Mok E, Chen J, Larbi A, Tan HKK, Lim TKH, Karagianni P, Tzioufas AG, Malleret B, Brody J, Albani S, van Roon J, Radstake T, Newell EW, Ginhoux F. 2019. Single-cell analysis of human mononuclear phagocytes reveals subset-defining markers and identifies circulating inflammatory dendritic cells. Immunity 51: 573–589.e8. [DOI] [PubMed] [Google Scholar]

[r26] 26.Shida K, Suzuki T, Kiyoshima-Shibata J, Shimada S, Nanno M. 2006. Essential roles of monocytes in stimulating human peripheral blood mononuclear cells with Lactobacillus casei to produce cytokines and augment natural killer cell activity. Clin Vaccine Immunol 13: 997–1003. [DOI] [PMC free article] [PubMed] [Google Scholar]

[r27] 27.Chang SH, Kim HJ, Park CG. 2020. Allogeneic ADSCs induce the production of alloreactive memory-CD8 T cells through HLA-ABC antigens. Cells 9: 1246. [DOI] [PMC free article] [PubMed] [Google Scholar]

[r28] 28.Kloss M, Decker P, Baltz KM, Baessler T, Jung G, Rammensee HG, Steinle A, Krusch M, Salih HR. 2008. Interaction of monocytes with NK cells upon Toll-like receptor-induced expression of the NKG2D ligand MICA. J Immunol 181: 6711–6719. [DOI] [PubMed] [Google Scholar]

[r29] 29.Ma DY, Clark EA. 2009. The role of CD40 and CD154/CD40L in dendritic cells. Semin Immunol 21: 265–272. [DOI] [PMC free article] [PubMed] [Google Scholar]

[r30] 30.Tan J, McKenzie C, Potamitis M, Thorburn AN, Mackay CR, Macia L. 2014. The role of short-chain fatty acids in health and disease. Adv Immunol 121: 91–119. [DOI] [PubMed] [Google Scholar]

[r31] 31.Takeda K, Suzuki T, Shimada SI, Shida K, Nanno M, Okumura K. 2006. Interleukin-12 is involved in the enhancement of human natural killer cell activity by Lactobacillus casei Shirota. Clin Exp Immunol 146: 109–115. [DOI] [PMC free article] [PubMed] [Google Scholar]

[r32] 32.Dong H, Rowland I, Tuohy KM, Thomas LV, Yaqoob P. 2010. Selective effects of Lactobacillus casei Shirota on T cell activation, natural killer cell activity and cytokine production. Clin Exp Immunol 161: 378–388. [DOI] [PMC free article] [PubMed] [Google Scholar]

[r33] 33.Nagahama K, Kumano T, Nakata T, Tsuji H, Moriyama K, Shida K, Nomoto K, Chiba K, Koumoto K, Matsui J. 2015. Synthesis and immunestimulating activity of lactobacilli-originated polysaccharide-polymeric microparticle conjugates. Langmuir 31: 1489–1495. [DOI] [PubMed] [Google Scholar]

[r34] 34.Pinchuk LM, Polacino PS, Agy MB, Klaus SJ, Clark EA. 1994. The role of CD40 and CD80 accessory cell molecules in dendritic cell-dependent HIV-1 infection. Immunity 1: 317–325. [DOI] [PubMed] [Google Scholar]

[r35] 35.Caux C, Massacrier C, Vanbervliet B, Dubois B, Van Kooten C, Durand I, Banchereau J. 1994. Activation of human dendritic cells through CD40 cross-linking. J Exp Med 180: 1263–1272. [DOI] [PMC free article] [PubMed] [Google Scholar]

[r36] 36.Negi S, Das DK, Pahari S, Nadeem S, Agrewala JN. 2019. Potential role of gut mirobiota in induction and regulation of innate immune memory. Front Immunol 10: 2441. [DOI] [PMC free article] [PubMed] [Google Scholar]

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Diverse impact of a probiotic strain, Lacticaseibacillus paracasei Shirota, on peripheral mononuclear phagocytic cells in healthy Japanese office workers: a randomized, double-blind, controlled trial

Tomoaki NAITO

Masatoshi MORIKAWA

Mayuko YAMAMOTO-FUJIMURA

Akira IWATA

Ayaka MAKI

Noriko KATO-NAGAOKA

Kosuke OANA

Junko KIYOSHIMA-SHIBATA

Yumi MATSUURA

Rumi KAJI

Osamu WATANABE

Kan SHIDA

Satoshi MATSUMOTO

Tetsuji HORI

Abstract

INTRODUCTION

MATERIALS AND METHODS

Study design

Composition of the test and control beverages

Table 1. Compositions of test and control beverages (per 100 mL bottle).

Blood treatment

Sample processing and analysis for FCM

Fluorescently labeled antibody staining

FCM gating strategy

Sample processing and analysis for CyTOF

Metal-labeled antibody staining

CyTOF gating strategy

Statistical analysis

RESULTS

Study population

Table 2. Baseline chracteristics of the participants.

Effect of LcS-FM on pDCs in PBMCs

Fig. 1.

Effect of consuming LcS-FM on cDC subsets in PBMCs

Fig. 2.

Effect of consuming LcS-FM on the expression of surface activation markers of Mo subsets in PBMCs

Fig. 3.

DISCUSSION

AUTHOR CONTRIBUTIONS

FUNDING

CONFLICT OF INTEREST

Supplementary Material

Acknowledgments

REFERENCES

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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