You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


Introduction
============

Inflammatory bowel disease (IBD), including Crohn's disease and ulcerative colitis, is a multi-factorial chronic inflammatory intestinal disorder with the highest prevalence in western countries ([@B19]; [@B27]; [@B31]). The etiology of IBD is currently incompletely understood. Both the host genetic factors and the gut microbiota contribute to the pathogenesis of IBD ([@B15]). In experimental colitis models, it has been shown that the microbiota plays important roles ([@B33]; [@B25]; [@B30]). More importantly, changes of gut microbiota, i.e., dysbiosis, are frequently found in human IBD patients ([@B9]; [@B38]). However, whether dysbiosis is a cause or a consequence of IBD is still debatable. Western diet, which is high in fat, can induce dysbiosis and is considered as a risk factor for the development of IBD ([@B12]; [@B2]). Strategies for manipulating microbiota composition and metabolism can potentially be utilized to treat gut inflammation, but many practical and theoretical challenges remain to be overcome.

Probiotics, which are defined as "live microorganisms which when administered in adequate amounts confer a health benefit on the host" ([@B13]), are thought to be able to actively modulate the gut microbiota and provide therapeutic benefits across a broad range of disorders including IBD ([@B23]; [@B14]; [@B17]). Probiotics are in fact often recommended by physicians as adjunctive therapy to induction or maintenance of remission in IBD ([@B3]). Despite their popularity, the current evidence to support extensive use of probiotics in IBD is limited. Results from clinical trials with probiotics in IBD are mixed, with some studies showing an improvement in maintenance or induction of remission while other trials have failed to show any beneficial effect ([@B5]; [@B39]). The reason behind the various outcomes of probiotic effectiveness in IBD is not clear.

Gut microbiota not only affects disease pathology, but also plays many other important roles, including preventing pathogen colonization ([@B11]), shaping the immune system ([@B26]; [@B21]), stimulating the production of gastrointestinal hormones ([@B28]), regulating brain behavior through the production of neuroactive substances, and fermentation of non-digestible carbohydrates producing short-chain fatty acids (SCFAs) ([@B6]; [@B10]). Most recently, the microbiota is also emerging as a contributing factor to interindividual variability in disease ([@B16]). However, whether the gut microbiota contributes to the person-to-person differences in response to probiotic therapy remains largely unknown.

*B. longum* JDM 301 is a widely used commercial probiotic strain in China ([@B37]). Our previous study demonstrated that *B. longum* JDM 301 can prevent *Clostridium difficile* infection (CDI) in mice ([@B36]). In the present study, we used *B. longum* JDM 301 as a model probiotic to test factors that could potentially influence the therapeutic effect of probiotics in experimental colitis. Our data demonstrated an association of the gut microbiota to interindividual variability in probiotic biotherapeutic responses. Our results suggest that personalized strategies are needed for the success of probiotic biotherapies in IBD.

Materials and Methods {#s1}
=====================

Animals
-------

Five- to six-week-old male C57Bl/6J mice were purchased from Shanghai Laboratory Animal Research Center, Shanghai, or Beijing Vital River Laboratory Animal Technology Co., Ltd., Beijing. Animals were maintained under specific pathogen-free (SPF) conditions with a 12-h light phase and 12-h dark phase and had free access to diet and drinking water. All animal experiments were done following the guidelines of the National Laboratory Animal Ethics Committee of China and were approved by the Animal Care and Use Committee of Xuzhou Medical University (Permit Number 2013-AN-65).

Diet
----

Mice were fed with standard chow diet for the first week of arrival to the laboratory. Then the mice were randomly divided into two groups. One group of mice was fed with standard chow diet (ND) and the other group of mice was fed with high-fat diet (HFD), 60% energy from fat (Ke Ao Xie Li Co., Ltd., Beijing, China). HFD feeding was continued for a total of 6 weeks.

Colitis Induction
-----------------

Colitis was induced using dextran sulfate sodium (DSS) (Molecular Weight = 36,000∼50,000) (MP Biomedicals, Santa Ana, CA, United States). For mice in the ND group (5--8 mice), DSS treatment was started when the mice became 8 weeks of age. Whereas, for mice in the HFD group (5--8 mice), acute colitis was induced with DSS after 6 weeks of HFD feeding. Drinking water of both groups was replaced with 3% DSS (w/v) for 7 days during DSS challenge, followed by 3 days of recovery period during which sterilized drinking water was supplied. The control group (3--5 mice) fed with either ND or HFD received only sterilized drinking water throughout the experiment.

Preparation of *B. longum* JDM 301 for Inoculation to Mice
----------------------------------------------------------

*B. longum* JDM 301 was originally isolated from a commercial probiotic product from China ([@B37]). The frozen glycerol stock of *B. longum* JDM 301 was thawed and plated on MRS agar plate. The plate was incubated overnight at 37°C in an anaerobic incubator (Whitley Workstation DG250; Frederick, MD, United States) containing an atmosphere of 10% hydrogen, 10% carbon dioxide, and 80% nitrogen. The next day, a single bacterial colony was inoculated in a tube containing 3--5 ml of MRS broth and was incubated anaerobically for 16--24 h. The concentration of *B. longum* JDM 301 was evaluated according to OD600 and adjusted to 1 × 10^9^ colony forming unit (cfu) per 100 μl in sterile 1 × PBS. One week prior to DSS challenge, mice in appropriate groups received altogether three doses of 1 × 10^9^ cfu *B. longum* JDM 301 on three alternate days by oral gavage.

Fecal Material Transplantation
------------------------------

Eight-week-old male C57Bl/6 mice maintained under SPF conditions were treated with a broad-spectrum antibiotic cocktail containing ampicillin (1 g/l), metronidazole (1 g/l), neomycin (1 g/l), and vancomycin (0.5 g/l) in their drinking water for 1 week. For fecal transplantation, fecal samples were suspended in sterile 1xPBS (10 mg/100 μl) and homogenized using Bio-Gen PRO200 Homogenizer (PRO Scientific Inc.). Homogenized feces were filtered through a 40 μm nylon cell strainer. Each antibiotic-treated mouse was orally gavaged with 200 μl of the filtrate daily for five consecutive days.

Colonic Tissue Collection and Processing
----------------------------------------

The colonic tissues were collected and fixed in 4% (w/v) paraformaldehyde (pH 7.0), dehydrated by increasing concentrations of ethanol, and embedded in paraffin for histological studies. For histological grading three different parameters were considered, severity of inflammation (based on polymorphonuclear neutrophil infiltration; 0--3: none, slight, moderate, and severe), depth of injury (0--3: none, mucosal, mucosal and submucosal, and transmural), and crypt damage (0--4: none, basal one-third damaged, basal two-thirds damaged, only surface epithelium intact, entire crypt, and epithelium lost). The histological score for each mouse was a sum of the score of neutrophil infiltration, depth of injury and crypt damage.

Gut Microbiota Analysis
-----------------------

Fresh fecal pellets were collected in a clean sterile Eppendorf tube, immediately frozen into liquid nitrogen, and then stored at -80°C. DNA was isolated using the E.Z.N.A. Stool DNA Kit (Omega Bio-Tek) according to the manufacturer's instructions. Fecal DNA samples were amplified by PCR using barcoded primer pairs targeting the V3-V4 region of bacterial 16S rRNA gene using 341F/R806 primer sets. The PCR reaction mixture (20 μl) contained 10 ng genomic DNA, 0.25 μM of each primer and one-unit TransStart Fastpfu DNA Polymerase (TransGen, Beijing, China). The PCR reaction conditions for amplification of DNA were as follows: initial denaturation at 95°C for 3 min, followed by 27 cycles of denaturation at 95°C for 30 s, annealing at 55°C for 30 s and extension at 72°C for 45 s, and final ending at 72°C for 10 min. The PCR products were subsequently pooled and cleaned by agarose gel purification. PCR amplicons were then sequenced using a paired-end, 2 × 250-bp cycle run on an Illumina MiSeq sequencing system. Bioinformatic analysis was done by Vazyme Biotech Co., Ltd., Nanjing, China using the QIIME analysis pipeline. In brief, the paired-end reads were joined together after demultiplexing, fasta quality files and a mapping file indicating the barcode sequence corresponding to each sample were used as inputs, reads were split by samples according to the barcode, a taxonomical classification was performed using the RDP-classifier, and an OTU table was created. Closed-reference OTU mapping was employed using the Greengenes database. Sequences sharing 97% nucleotide sequence identity were binned into the same OTUs. Bacterial taxa summarization and rarefaction analyses of microbial diversity or compositional differences (dissimilarity value indicated by Unweighted UniFrac Distance) were then calculated and PCoA plots indicating compositional difference were generated accordingly with the Vegan package in R software.

Data Availability
-----------------

The 16S rRNA sequencing data have been deposited in the NCBI SRA database. The accession number is [SRP149682](SRP149682).

Statistical Analysis
--------------------

The data are shown as mean values ± standard error of the mean (SEM). Differences between multiple groups were compared using one-way ANOVA with *post hoc* Turkey's multiple comparison test and two-way ANOVA with *post hoc* Bonferroni posttests. A Student's *t*-test was used for comparisons between two groups. Mantel-Cox test was used for survival analysis. Wilcoxon Signed Rank Test and Kruskal-Wallis (KW) sum-rank test were used as significance test in microbiota analysis. A *P*-value \< 0.05 was considered significant.

Results
=======

The Effectiveness of the Probiotic *B. longum* JDM 301 in Colitis Is Correlated With Host Microbiota
----------------------------------------------------------------------------------------------------

The effectiveness of probiotics in IBD remains unclear ([@B5]; [@B39]). We posited that one of the reasons might be related to the highly varied host microbiota. To test this hypothesis, we actively looked for C57Bl/6 wild-type (WT) mice that harbored different microbiota by screening their fecal bacterial 16S rRNA sequences. We found two cohorts of mice, which were purchased from the same vendor in a different time, turned out to have significant differences in their gut microbial communities based upon unweighted UniFrac principal coordinate analysis (PCoA) (Figure [1A](#F1){ref-type="fig"}).

![The effectiveness of the probiotic *B. longum* JDM 301 in colitis is correlated with host microbiota. **(A)** PCoA analysis based on OTU abundance of each mouse via fecal bacterial 16S rRNA sequencing. Each symbol represents one individual mouse. The fecal samples were collected from mice fed on a normal diet (ND) before probiotic and DSS treatment. **(B)** Schematic diagram of experimental design. **(C)** The body weight changes during DSS treatment. **(D)** Mean colon length in cm. Colons were collected on day 11 post DSS initiation. **(E)** Representative images of H&E stained distal colon tissues from indicated mice (magnification: 200x). **(F)** Histologic scores. All data are given as means ± SEMs. ns, no statistical significance. ^∗^*P* \< 0.05; ^∗∗∗^*P* \< 0.001.](fmicb-09-02675-g001){#F1}

We used *B. longum* JDM 301 ([@B37]), a widely used commercial probiotic strain in China, to test whether there is any effect of this specific probiotic in tempering colitis established in mice. Each mouse was orally gavaged with *B. longum* JDM 301 (1 × 10^9^ cfu in 100 μl PBS/mouse) for 3 alternate days, colitis was then induced by replacing drinking water with 3% DSS for another 7 consecutive days and then switched back to normal drinking water (Figure [1B](#F1){ref-type="fig"}).

Before DSS treatment, the mean initial body weight of mice belonging to both cohorts was similar (around 23.5--23.8 g). At the end of experimental period (i.e., 10 days post-infection), in *B. longum* JDM 301 non-treated colitic mice, the loss in body weight was more pronounced in cohort B mice (around 30%) compared to cohort A mice (around 15%) (Figure [1C](#F1){ref-type="fig"}). Furthermore, all mice in cohort A survived the intestinal injury-induced wasting disease; while 7 out of 8 mice in cohort B died within the experimental period.

To determine whether the different colitis sensitivity of the two cohorts of mice was related to their microbiota, we transferred the fecal contents from cohort A or B mice to the third batch of mice that were pretreated with antibiotics. We then challenged the third batch of mice with DSS. The mice receiving feces from cohort A mice experienced less severe colitis than those receiving feces from cohort B mice (Supplementary Figure [S1](#SM1){ref-type="supplementary-material"}). Thus, the differences in sensitivity toward gut epithelial injury-induced colitis in these two cohorts were indeed related to their microbiota.

Pretreatment with *B. longum* JDM 301 on colitic mice resulted in improvement of body weight loss and recovery of colon length in cohort A mice compared to *B. longum* JDM 301 non-treated colitic mice. However, mice in cohort B did not show obvious sign of colitis improvement with *B. longum* JDM 301 pretreatment (Figures [1C,D](#F1){ref-type="fig"}). Microscopically, colonic epithelial damage and inflammatory cell infiltration were reduced in cohort A mice that were pretreated with *B. longum* JDM 301, while severe epithelial damage and inflammation remained in cohort B mice (Figures [1E,F](#F1){ref-type="fig"}). Furthermore, all the mice in cohort A with probiotic therapy survived the wasting disease; while 9 out of 9 mice in cohort B with probiotic pretreatment failed to survive within the experimental period. In aggregate, these data indicate that the effectiveness of the probiotic *B. longum* JDM 301is correlated with host microbiota.

The Effectiveness of the Probiotic *B. longum* JDM 301 in Colitis Is Correlated With Neither Disease Severity nor a High-Fat Diet
---------------------------------------------------------------------------------------------------------------------------------

The effectiveness of *B. longum* JDM 301 in relieving colitis in cohort A but not cohort B mice could be due to milder colitis in cohort A relative to cohort B mice. To rule out this possibility, we used a HFD to exacerbate colitis. Consumption of HFD is considered as one of the risk factors of IBD and HFD can exacerbate DSS-induced colitis in animals ([@B24]; [@B4]). We, therefore, fed 5- to 6-week-old C57Bl/6 mice from cohort A and B with HFD.

We first checked the similarity in microbiota composition in the two cohorts of mice fed with HFD. Six weeks after HFD, fecal pellets were collected, bacterial DNA was extracted and 16S rRNA gene sequences were analyzed. Based upon PCoA and Bray-Curtis distances analyses, HFD changed the microbiota landscape dramatically in cohort B, while it had limited impact on the microbial communities in cohort A mice (Figure [2A](#F2){ref-type="fig"} and Supplementary Figure [S2](#SM1){ref-type="supplementary-material"}). However, the microbiota differences between cohort A and B mice still existed (Figure [2A](#F2){ref-type="fig"}).

![The effectiveness of the probiotic *B. longum* JDM 301 is not related to colitis severity or high-fat diet. **(A)** PCoA analysis illustrating the presence of different microbial community between HFD-fed mice from cohort **(A**,**B)**. Each symbol represents one individual mouse. The HFD fecal bacterial samples were collected 6 weeks after HFD and before probiotic and DSS treatment. **(B)** Schematic diagram for experimental design. **(C)** Body weight changes during DSS treatment in indicated mice. **(D)** Survival curve. **(E)** Representative images of H&E stained distal colon tissues from indicated mice (magnification: 200×). **(F)** Histologic scores. All data are given as means ± SEMs. ^∗^*P* \< 0.05; ^∗∗^*P* \< 0.01; ^∗∗∗^*P* \< 0.001. The number of mice per group was 3∼6.](fmicb-09-02675-g002){#F2}

We then challenged the HFD-fed mice with DSS (Figure [2B](#F2){ref-type="fig"}). Consistent with the previous report ([@B34]), HFD exacerbated colitis especially in cohort A mice. The body weight of HFD-fed cohort A mice was reduced by more than 25% on day 10 after the DSS challenge (Figure [2C](#F2){ref-type="fig"}). Half of the HFD-fed mice in cohort A died after the DSS challenge (Figure [2D](#F2){ref-type="fig"}). On HFD-feeding, the intestinal inflammation and epithelial damage in cohort A mice became similar to that in cohort B mice (Figures [2E,F](#F2){ref-type="fig"}).

To examine whether the probiotics still had effect in this more severe form of colitis condition, the HFD-fed mice were pretreated with *B. longum* JDM 301 1 week before DSS challenge. Our data indicated that *B. longum* JDM 301 pretreatment still tempered DSS-induced colitis in HFD-fed cohort A mice, as it did in the ND condition. The body weight loss after DSS challenge was significantly relieved in mice from cohort A when pretreated with *B. longum* JDM 301 (Figure [2C](#F2){ref-type="fig"}). All the mice from cohort A receiving *B. longum* JDM 301 pretreatment survived, while half of those not receiving *B. longum* JDM 301 pretreatment died (Figure [2D](#F2){ref-type="fig"}). Also, the colonic inflammation became less severe on pretreatment with *B. longum* JDM 301 (Figures [2E,F](#F2){ref-type="fig"}). Hence, these results indicate that although HFD exacerbated colitis in cohort A mice, the probiotic effectiveness still remained.

In contrast to its effectiveness in cohort A mice, *B. longum* JDM 301 still failed to relieve IBD in cohort B mice fed on HFD and even promoted body weight loss (Figure [2C](#F2){ref-type="fig"}). There was still 100% mortality even after probiotic therapy (Figure [2D](#F2){ref-type="fig"}). There were no signs of improvement in epithelial integrity and colonic inflammation (Figures [2E,F](#F2){ref-type="fig"}). Altogether, the data indicated that the effectiveness of the probiotic *B. longum* JDM 301 in colitis was not correlated with either disease severity or HFD.

Ecological Characteristics of the Gut Microbiota That Are Correlated With *B. longum* JDM 301 Efficacy
------------------------------------------------------------------------------------------------------

To look for the ecological characteristics of the gut microbiota that are correlated with probiotic effectiveness, we compared the overall microbial community configurations in probiotic-sensitive cohort A and probiotic-insensitive cohort B mice at both ND- and HFD-feeding conditions. The comparison was done at the baseline level, i.e., before pretreatment with *B. longum* JDM 301and challenge with DSS. Significant differences in both species richness represented by Chao1 index and species evenness represented by Shannon's index were observed between cohort A and cohort B mice (Figure [3A](#F3){ref-type="fig"}). Both indices were bigger in cohort B mice compared to those in cohort A mice, irrespective of the diet used. HFD-feeding reduced total species richness (Chao1) in both cohorts, but species evenness (Shannon index) was not disturbed by HFD (Figure [3A](#F3){ref-type="fig"}). At the phyla level, cohort A mice had more Firmicutes, Actinobacteria, Saccharibacteria and less Bacteroidetes compared to cohort B at both ND- or HFD-feeding conditions (Figure [3B](#F3){ref-type="fig"}). Shifting diet from ND to HFD resulted in an increase in phylum Proteobacteria in both cohorts (from 1 and 5% to 14%, respectively) (Figure [3B](#F3){ref-type="fig"}). This is consistent with the notion that Proteobacteria expansion is an indicator of colon epithelial dysfunction and correlates to the increase of sensitivity to DSS-induced colitis at HFD condition ([@B20]). However, Proteobacteria was not a good indicator for the *B. longum* JDM 301's efficacy, as the relative abundance of Proteobacteria were not different between cohort A and B at both ND- or HFD-feeding conditions (Figure [3B](#F3){ref-type="fig"}). Furthermore, the relative abundance of 18 specific genera varied consistently between the two cohorts of mice irrespective of the diet used (Figure [3C](#F3){ref-type="fig"}). Among them, *Alistipes* and *Parabacteroides*, two genera that have been implied participating in IBD pathogenesis ([@B8]), were increased significantly in cohort B compared with those in cohort A mice fed with either ND or HFD. How these 18 genera might be related to *B. longum* JDM 301's efficacy need further investigation.

![Ecological characteristics of the gut microbiota that are correlated with *B. longum* JDM 301 efficacy. The fecal bacterial samples were collected before probiotic and DSS treatment and bacterial 16S rRNA sequence data were analyzed accordingly. **(A)** α-diversity indicated by Chao1 (species richness) and Shannon index (species evenness). The line drawn in the middle of the box represents the median value and the box represents the range of values. **(B)** Taxonomic composition at the phyla level in the indicated mice. **(C)** Taxonomic composition at genera level in the indicated mice under ND and HFD. The top 18 genera that were significantly different (*P* \< 0.05) between the two cohorts in both ND and HFD conditions were shown. ^∗^*P* \< 0.05; ^∗∗^*P* \< 0.01.](fmicb-09-02675-g003){#F3}

*B. longum* JDM 301 Has Limited Ability to Change the Taxonomic Composition of the Gut Microbiota
-------------------------------------------------------------------------------------------------

The mechanism behind different probiotic efficacy in colitis in the same strain of mice with different microbiota is not clear. We first questioned how pretreatment with *B. longum* JDM 301 effectively alleviated colitis in cohort A mice. One possibility is that *B. longum* JDM 301 might be able to change the host microbiota so that the harmful members of microbes participating in IBD declined in cohort A mice. To test this, we collected feces from *B. longum* JDM 301 untreated and treated cohort A mice one day prior to DSS challenge and then performed high-throughput gene-sequencing analysis of fecal bacterial16S rRNA. We used rarefaction analysis to compare bacterial diversity within individual mice of a group (α diversity) in both ND- and HFD-fed conditions. *B. longum* JDM 301 treatment did not change species richness (Chao1) (Figures [4A,B](#F4){ref-type="fig"}) and species evenness (Shannon index) significantly (Figures [4C,D](#F4){ref-type="fig"}). PCoA analysis of the microbiota composition in *B. longum* JDM 301-treated mice did not show a different community composition relative to that of *B. longum* JDM 301-untreated mice at both ND- and HFD-fed conditions (Figure [4E](#F4){ref-type="fig"}). Thus, the impact of *B. longum* JDM 301on the taxonomic composition of the fecal microbiota in cohort A mice was very limited even though it succeeded in the induction of colitis remission in those mice.

![*B. longum* JDM 301 has limited ability to change the taxonomic composition of the gut microbiota. **(A--D)** High-throughput sequencing of 16S rRNA in fecal bacterial DNA from WT mice in batch A fed with ND or HFD before or 6 days after *B. longum* JDM 301 treatment. Chao1, indicative of bacterial species richness (a and b), Shannon, indicative of bacterial species evenness **(C**,**D)**. **(E)** PCoA analysis of the microbiota composition in indicated mice. Each symbol represents one individual mouse. **(F)** The relative abundance of fecal *Bifidobacterium* spp. in indicated mice before or 6 days after *B. longum* JDM 301 treatment. ^∗^*P* \< 0.05; ^∗∗∗^*P* \< 0.001.](fmicb-09-02675-g004){#F4}

Another possible factor that can potentially influence the probiotic effectiveness is related to its colonization abilities in the host gut. To determine whether oral delivery of *B. longum* JDM 301 could result in its engraftment in the gut, we checked fecal *Bifidobacterium* spp. enrichment 6 days after *B. longum* JDM 301 treatment and before DSS challenge in both cohorts of mice under ND condition via 16S rRNA gene sequence data analysis. The relative abundance of *Bifidobacterium* spp. was increased in both cohorts 6 days after *B. longum* JDM 301 treatment, however, the amplitude of increment was bigger in cohort A than that in cohort B mice (Figure [4F](#F4){ref-type="fig"}). The varied probiotic engraftment efficacy could at least partially explain the different efficacies of the probiotic *B. longum* JDM 301 in colitis in these two cohorts of mice.

Discussion
==========

In this work, we measured the ability of gut microbiota and HFD to influence the host response to a model probiotic, *B. longum* JDM 301, in a DSS-induced mouse colitis model. We demonstrated that the probiotic effectiveness in preventing colitis can be varied across individual mice even when they have the same genetic background. We further showed different microbiome features were highly correlated with different probiotic response. Consumption of the diet rich in fat can change the host sensitivity to mucosal injury-induced colitis but may not necessarily change the host responsiveness to probiotic therapy. Thus, it is likely that the gut microbiota utilizes different mechanisms in regulating colitis development and modulating probiotic colonization and functions.

Our results of mice with the same genetic background but different microbiota having different sensitivity to colitis induction are very similar to a previous report, which showed that C57Bl/6 from the UC Berkeley and JAX colony had different intestinal microbiota and had also significantly different sensitivity to DSS-induced wasting diseases ([@B29]). Furthermore, our data are also consistent with the notion that the intestinal microbiota contributes to the immunopathogenesis of IBD ([@B15]).

Although probiotics are defined as beneficial microorganisms to the host, exact mechanisms of how probiotics function inside the host remain incompletely understood. Possible pathways that have been suggested for how probiotics works include: (1) restoring microbial imbalances, (2) enhancing the epithelial barrier function, and/or (3) modulating the immune responses ([@B1]). The bacterial species that can be called probiotics are still expanding ([@B7]), but whether one type of probiotic fits for all people at the same or different disease conditions is currently not known. Our data suggested that the individual host gut microbiome can probably influence whether a given probiotic can have beneficial effects on the specific host or not.

How personalized microbiota influence probiotic effectiveness requires further investigation. One possible way is to influence the probiotic engraftment efficacy as our data and others have suggested ([@B32]; [@B18]; [@B22]). Another possible way is to influence probiotic's functions. For example, different microbiota might have different ability to influence probiotics in generating short-chain fatty acids, which are microbiota-derived metabolites that play important roles in mucosal protection and wound healing ([@B35]). Furthermore, one earlier study indicated that when certain gut microbes translocated to the internal tissue, they can induce disease tolerance ([@B29]). Many more other possible mechanisms remain to be determined.

Our data further suggested that it might be possible to predict probiotic efficacy via analysis of the host microbial and genetic features. Personalized measurements including gut microbiome have been shown to be able to more accurately predict postprandial glycemic response for each unique person ([@B40]), they might also help for personalized probiotic therapies.

In aggregate, this study demonstrated a correlation between host microbiota and the probiotic therapeutic efficacy in colitis. Therefore, carefully monitoring personal and microbiome features might be needed for a successful probiotic biotherapy for clinical IBD patients.

Author Contributions
====================

YW and KZ conceived and designed the experiments. SS performed most of the experiments and contributed to the manuscript preparation. QW helped the probiotics preparation. WL, QL, HS, ML, JG, and BZ conducted histopathology assessment. YK, ZL, and YXW contributed to the high-throughput fecal bacterial 16S rRNA gene sequence analysis. YW and SS wrote the paper.

Conflict of Interest Statement
==============================

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

**Funding.** Project support was provided in part by the National Natural Science Foundation of China (Grant No. 81770853 to YW), the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) in the year of 2014 (Grant No. KYLX14-1448), Natural Science Foundation of the Higher Education Institutions of Jiangsu Province, China (Grant No. 16KJB310016), the Starting Foundation for Talents of Xuzhou Medical University (Grant No. D2016029 to YW).

Supplementary Material
======================

The Supplementary Material for this article can be found online at: <https://www.frontiersin.org/articles/10.3389/fmicb.2018.02675/full#supplementary-material>

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[^1]: Edited by: Eugenia Bezirtzoglou, Democritus University of Thrace, Greece

[^2]: Reviewed by: Maria de los Angeles Serradell, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina; Mikkel Bentzon-Tilia, Technical University of Denmark, Denmark

[^3]: This article was submitted to Food Microbiology, a section of the journal Frontiers in Microbiology