Nayar Prize II Year 2 Progress Report - October 2018 Project: Microfluidic Drug-Microbiota Interaction Platform Team: Abhinav Bhushan, Genoveva Murillo, Rajendra Mehta Postdoctoral Scholar: Sonali Karnik, Students: Chengyao Wang, Rongfei Wu, Kihwan Kim, Andrea Cancino, Thao Dang, Adithya Subramanian. In the second year of the Nayar Prize II, we have accomplished our year 2 milestones as well as formulated clear strategies for making an impact on the lives of patients. Our overall project goals are to study the role microbiota play in influencing drug metabolism. We are developing a microfluidic platform to facilitate study of interactions between the drugs, intestine, and microbiota. We have made extraordinary progress to advance rapidly towards the milestones including strengthening collaborations with Dr. Ali Keshavarzian, who leads one of the leading groups on microbiome/IBD research. Challenged by the reviewers at the end of year 1 to define how the microfluidic drug-microbiota interaction platform can help patients, we talked to several key opinion leaders and collaborated with the business school to develop strategies that will directly benefit patients. We came up with several solutions. One builds upon the concepts of a drug-microbiota interaction platform towards a patient-specific (personalized) microfluidic bioassay; in other words, we will generate an IBD patient-based intestinal platform to test microbiota reprograming that can be used to query prebiotics, probiotics, and IBD therapeutics, potentially to determine the effects of these interventions before prescribing to the IBD patients. The other plan will give individuals the opportunity to generate a personal score that will represent the drug metabolizing capability of their own microbiome. The support from the Nayar Prize was the catalyst that made this possible. Phase II summary 1) Establish intestinal culture in the microfluidic device and characterize drug interactions. We

developed a novel microfluidic device that incorporated micrometer resolution membranes which are synthesized out of extracellular matrix. As our results show, cells on the microfluidic device with biomembranes had excellent viability for extended periods of time than the cells in a single layer device. More importantly, the biomembranes induced expression of tight junction between the intestinal cells, which is an important factor in the cells mimicking in vivo phenotypes. This work was published in ACS Biomaterials Science and Engineering and the journal highlighted the science in a special feature (“More realistic and accurate organs-on-chips”). The intellectual property concerning the collagen membrane device was filed by the university and granted a provisional patent. We built upon this work in four directions.

A. Establish primary colon cells in the device (Phase II Aim 1). Development of the primary colon culture is quite challenging as the microenvironment external to the in vivo gut environment does not have all the factors to support the growth and function of the cells. We have carried out a large number of experiments to grow the primary colon on a variety of extracellular matrices – fibronectin, collagen type I, collagen type IV, and Matrigel. We have conducted these experiments in tissue culture plates as well in the microfluidic devices. Our results indicate that Matrigel with a collagen coat provided the best results in the microfluidic devices. The colon cells express tight barrier junctions and also e-cadherin, which is one of the important cellular proteins (Figure 1). These results are being finalized in collaboration with the clinical collaborators.

Figure 1. Expression of e-cadherin protein in primary colon cells.

B. Characterize role drugs play in

regulating bacterial metabolites (Phase II, Aim 2). It has been shown that metformin boosts the capacity of gut microbiota to produce certain short-chain fatty acids (butyric acid and propionic acid). We have exposed the several strains of bacteria to drugs and their selected metabolites and characterized their effects on the bacteria.

C. Characterize role bacteria play in

regulating activity of the colon drug metabolizing enzymes (Phase II, Aim 3). Objective 3. We systematically studied the interactions (genes and specific activity of phase I metabolism) between colon cells and bacteria. The major findings are that (1) species-specific differences exist in the regulation and activity of these enzymes, (2) regulation of the various enzymes appeared to be unique, and (3) communication between the bacteria and the colon cells is required for modulation of enzymatic activity.

The results of Aims 2 and 3 are part of a manuscript that is under peer-review, which is provided with this report. The complexity of the gut environment includes a biphasic oxygen environment – normal oxygen for the epithelial cells and anaerobic environment for the microbiota. Creating such an environment in vitro is very challenging, and does not currently exist, especially in microfluidics. We have created a representative gas transport model and used it to create prototype microfluidic devices. The results are being finalized for a publication as well as invention disclosure to the university (Figure 2).

D. We initiated work towards (see Appendix and below)

a. IBD patient-based intestinal platform to test microbiota reprograming and b. microfluidic model of microbiota-intestine-brain to study microbiota mediated neural

inflammation which is implicated in neurodegenerative disorders (Alzheimer’s).

2) Market research. We carried out interviews of several technical leaders from the pharmaceutical industry on the use of the microfluidic drug-microbiota platform in drug discovery. The overwhelming response we received was that the industry is aware of the influence of bacteria but does not have assays to test for these interactions – any device or platform, such as ours, that can measure these interactions will be hugely beneficial. This work was corroborated by a study conducted in parallel by the students from the Stuart School of Business. During our research, we discovered another possible opportunity – the evaluation of an individual’s propensity for metabolizing drugs. The reports are attached as Appendix. The outcome of these studies is that our Nayar project has three possible market opportunities. Some of this work is included in the disclosure filed with the university. Plans to commercialize are

Figure 2. Oxygen transport model (not drawn to scale). A is the concentration of oxygen in the middle-chamber after cellular respiration. B is the oxygen concentration in the top-chamber. C is the oxygen concentration outside the device (disclosure pending).

PDMS top cover

Top-chamber

Membrane

Middle-chamber

Oxygen concentrationA C

B

being explored. a. drug-microbiota interaction platform, b. IBD patient-based IBD bioassay, and c. personalized score of drug metabolizing capability by microbiome.

3) Training. The project has been instrumental in exciting students about research and has helped

tremendously in recruiting students. #4 disciplines integrated (engineering, biology, clinical, business)

#12 students directly supported/educated #2 Armour college PURE scholarships #1 PhD fellowship (Starr Fieldhouse), #2 travel awards #5 presentations at national & local meetings

4) Journal papers Wang C, Tanataweethum N, Karnik S, ML, Bhushan A, A Novel Microfluidic Colon with An Extracellular Matrix Membrane, ACS Biomaterials Science and Engineering, 2018, doi: 10.1021/acsbiomaterials.7b00883. Wang C, Cancino A, Dang T, Bhushan A, Regulation of drug metabolizing enzymes by microbiota, under review. Wang C, Dang T, Bhushan A, Tunable multiphasic oxygen environments for coculture of anaerobic and aerobic bacteria with intestinal cells, under preparation. Presentations Chengyao Wang, Chengyao Wang, Maliha W Shaikh, Christopher Frosyth, Ali Keshavarzian, Abhinav Bhushan, “Intestinal Monolayers on a Novel Microfluidic Device with an Extracellular Matrix Membrane”, BMES Annual Meeting, 2018. Chengyao Wang, Nida Tanataweethum, Sonali Karnik, Abhinav Bhushan, “Novel Microfluidic Colon with an Extracellular Matrix Membrane”, Rainin Foundation Innovation Symposium, 2018. Chengyao Wang, Andrea Cancino, Abhinav Bhushan, “Role of Bacteria in Regulating Activity of Enzymes Responsible for Metabolism of Drugs”, Rainin Foundation Innovation Symposium, 2018. Role of Bacteria and Bacterial Products in Regulating Activity of Enzymes Responsible for Metabolism of Drugs in Microfluidic Devices, MIT-Harvard Microbiome Symposium. Chengyao Wang, Nida Tanataweethum, Genoveva Murillo, Rajendra Mehta, Abhinav Bhushan, “A novel microfluidic device with an extracellular matrix-based membrane”, Experimental Biology, 2017.

5) Collaborations Each of the collaborators has been excited by the strengths of our group, IIT, and the support from the Nayar Prize. It is needless to say that without the Nayar Prize, venturing into any of these areas would not have been possible. Dr. Ali Keshavarzian, Rush University - Role of microbiota along the gut-brain axis in the development of Parkinson’s disease.

Dr. Gokhan Dalgin, University of Chicago - Development of mini-brains for modeling neurodegenerative disorders.

6) Grant funding

a) Dynamics of microbial interactions in Alzheimer's using a microfluidic platform, NIH R03 under review.

b) Patient-based intestinal platform to test microbiota reprograming in IBD, NIH R21. Scored, to be resubmitted in October 2018.

c) A microfluidic platform to generate functionally reproducible human stem cell derived mini-

brains, NIH R21. Scored, to be resubmitted in July 2019.

d) Microfluidic platform to discover interactions between intestine, liver, and bacteria, NSF CAREER (positive feedback from Program Officer, to be resubmitted in 2019)

7) Intellectual property

a) Microfluidic Device with Extracellular Matrix Support Membrane, Provisional awarded.

b) Modulation of drug metabolism by bacteria, Disclosure filed with the Office of Technology Development, Illinois Institute of Technology.

Plans for Phase III

We have a clear understanding of the path to translate this research. We are on track to accomplish our original objectives as well as some of the new directions that we have identified (Figure 3). We will explore the commercialization of the “personalized score of microbiome’s drug metabolizing capability” (MyBioScore). Our office of technology development has recommended that we participate in the NSF I-Corps program. We will work with them and possibly MATTER Chicago to develop a strategy and participate for commercialization. Our objectives for Phase III are: Objective 1. Create a biphasic oxygen environment in the microfluidic devices. Several bacteria such as VSL#3, akkermansia muciniphila, and Bifidobacterium require an anaerobic environment for growth. Although anaerobic chambers exist at the macroscale, the challenge is to culture the bacteria with the intestinal cells. We have carried out feasibility studies to model and create a microfluidic device that can simultaneously support two oxygen concentrations. In this objective, we will finish characterization of cells and bacteria in these devices and develop the devices for routine fabrication. Objective 2. Characterize intestine-bacteria influence on drug metabolism in microfluidic devices. As the results from this year indicate, bacterial strains have tremendous effects on a wide range of cellular processes, including drug metabolizing capability. We know that cells in a microfluidic environment are more physiological than those in the tissue culture plates (we had shown this in year 1). In this objective, we will carry out the next phase of our studies to characterize the role of bacteria on drug metabolism in microfluidic devices. Objective 3. Develop a personalized intestinal bioassay to test therapeutics. Inflammatory bowel diseases (IBD), affect millions of Americans and carry an economic burden of hundreds of billions of dollars. There are several experimental and human observational studies to support the current notion that IBD is a consequence of abnormal mucosal immune response to intestinal microbiota due to abnormal microbiota composition, increased exposure of bacterial product to mucosal immune cells [disrupted intestinal barrier integrity] and exaggerated mucosal immunity. Thus, microbiota

reprograming is a promising therapeutic approach to prevent/treat IBD. But, in spite of success of microbiota reprograming by diet/pro and prebiotics and stool transplantation in animal models of IBD, the result in patients is disappointing. This failure could be due to lack of universal response of microbiota to any given intervention and immune response to change in microbiota. For example, it is known that not all IBD patients respond to TNF antibodies or anti-IL12/23 antibody suggesting that immune pathways leading to intestinal inflammation and tissue damage is different in different patients. This limitation could explain why result of animal studies and in vitro cell line studies are not universally translatable to human studies. Thus, availability of a robust and reliable patient-based “bioassay” is a major unmet need to overcome highly individualize and complex intestinal microenvironment in order to develop new precision and personalize therapeutic intervention including microbiota reprograming in IBD. To address this critical gap, we propose to develop a discovery platform that will enable colon-microbe-immune cultures and allow query of factors that can result in favorable/unfavorable outcomes. In this objective, we will test pre-/probiotics to regulate intestinal response to inflammation or differential drug metabolism activity. We will carry out this study at Rush University. Mindful of the additional logistics involved with patient studies, we will conduct a pilot study with all the proper controls (patient +/- Crohn’s, a number of bacteria and pre-/pro-biotics). The outcome will be a personalized patient-based assay that can be queried with therapeutics that can inform the physician on the best suitable line of therapy. In later phases, we could use patient’s own microbiota in the model and be able to carry out interesting experiments by cross culturing samples from patients and controls. Examples include= - Crohn’s intestinal cells+ healthy microbiota + probiotics; Crohn’s intestinal cells + healthy microbiota+ probiotics; Healthy intestinal cells + healthy microbiota+ microbiota; Healthy intestinal cells + Crohn’s microbiota+ probiotics.

Figure 3. Timeline for the proposed project.

10/2016 2017 2018 2019 2020

Phase I: Customizable microfluidic platform

Topological biomembranes

Microfluidic devices with biomembranes

Phase III: Drug interactions

Microfluidic devices with dual-O2 env

Intestine-microbiota drug interactions in devices

Commercialize:personalized score of microbiome’s drug metabolizing capability

Phase II: Microfluidic intestine-on-chip

Primary colon in microfluidic device

Role of drugs in regulating bacterial metabolites

Bacteria modulation of intestine activity

Personalized intestinal bioassay

Confidential

Confidential

Confidential

10/15/2018 Proceedings of the National Academy of Sciences

https://www.pnascentral.org/cgi-bin/main.plex?form_type=view_ms&j_id=1&ms_id=451782&ms_rev_no=0&ms_id_key=ftdZvUogMIm1tWmQUKvzSB… 1/1

Manuscript # 2018-17445 Current Revision # 0Submission Date 2018-10-15Manuscript Title Bacteria Regulate Activity of Enzymes Responsible for Metabolism of DrugsManuscript Type Direct SubmissionSpecial Features and Colloquia N/AClassification Biological Sciences/Applied Biological SciencesDatabase depositionCorresponding Author Abhinav Bhushan (Illinois Institute of Technology)

Co-Authors Chengyao Wang , Andrea Cancino , Rongfei Fu , Thao Dang , Qifan He , AbhinavBhushan (corr-auth)

AbstractThe trillions of microbes that inhabit our intestines and that are present inprobiotics affect health and regulate important biochemical factors. One of theimportant ... View full abstract

Significance StatementThe microbes that inhabit our intestines and that are present in probiotics affecthealth and regulate important biochemical factors including metabolism of drugs.Due to ... View full Significance Statement

Suggested Editorial Board MemberJames Collins (Massachusetts Institute of Technology)

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Bacteria Regulate Activity of Enzymes Responsible for Metabolism of Drugs

Chengyao Wang, Andrea Cancino, Rongfei Fu, Thao Dang, Qifan He, Abhinav Bhushan

Department of Biomedical Engineering, Illinois Institute of Technology,

Chicago, IL 60616

Abstract

The trillions of microbes that inhabit our intestines and that are present in probiotics affect

health and regulate important biochemical factors. One of the important factors that bacteria,

both gut microbes and probiotics, can regulate is the activity of enzymes that are important

for drug metabolism. Due to the challenges of an experimental system that can study

intestinal-bacterial interactions, studies that can report these effects are lacking. We report a

carefully designed study that systematically looks at different levels the interactions between

the colon cells and different bacteria (gut microbes and probiotics). We find that paracrine

signaling between the bacteria and colon cells is critical in regulating the activity of important

enzymes responsible for drug metabolism. Pathway analysis revealed that the bacteria cause

changes in diverse set of biological pathways, especially in those related to drug metabolism.

This was confirmed by measuring specific activities of important cytochrome P450s. The

major findings are that (1) species-specific differences exist in the regulation and activity of

these enzymes, (2) regulation of the various enzymes appeared to be unique, and (3)

communication between the bacteria and the colon cells is required for modulation of

enzymatic activity. This knowledge can help a priori design of therapeutics.

Significance statement

The microbes that inhabit our intestines and that are present in probiotics affect health and

regulate important biochemical factors including metabolism of drugs. Due to the challenges

of an experimental system that can study intestinal-bacterial interactions, studies that report

these effects are lacking. This gap has limited our ability to a priori use this knowledge in drug

development. We systematically studied the interactions (genes and specific activity of phase

I metabolism) between colon cells and bacteria. The major findings are that (1)

species-specific differences exist in the regulation and activity of these enzymes, (2)

regulation of the various enzymes appeared to be unique, and (3) communication between

the bacteria and the colon cells is required for modulation of enzymatic activity.

Introduction

The intestine is the site for processing of food, drugs, absorption of nutrients, and generation

of waste; chronic intestinal ailments affect our overall physiological health. Microbiota that

colonizes the intestinal microenvironment contribute to all these functions. The microbiota is

perturbed by change in diet, onset of diseases, and consumption of drugs (1, 2). Almost 24%

of the common drugs modulated the growth of 40 representative human gut bacterial strains

(3). The microbiota themselves are causally linked to metabolic disorders (4) and to the

metabolism of drugs (5). In fact, the microbiota may contribute to the metabolism of at least

fifty drugs (6, 7). Recent studies have shown the effect gut microbes have on the activity of

drugs such as acetaminophen and digoxin. For example, metronidazole, a drug used to treat

Crohn’s disease, has antimicrobial properties which puts selective pressure on the intestinal

microbiota that leads to inactivation of the drug and hence reduced efficiency (8, 9). Bacteria

can inactivate a drug as in the case of Actinobacterium Eggerthella lenta which converts the

cardiac drug digoxin into an inactive metabolite dihydrodigoxin (10). In another example,

the drug sulfasalazine is hydrolyzed by an intestinal bacterium to 5-aminosalicylic acid, the

active compound, and sulfapyridine. The parent drug, sulfasalazine, can inhibit the NF-κB

inflammatory pathway whereas sulfapyridine cannot, illustrating a situation where the parent

drug and its bacterial metabolites can have different mechanisms of action and presumably

different targets (11). With the trillions of bacteria in the microbiome, the inter-individual

diversity in the microbiome composition can have broad impact on the potential variation in

PK/PD of drugs. Even probiotics, the usage of which is on the rise (4), modify the

pharmacokinetics of orally administered drugs (12–14).

Cytochrome P450 enzymes (CYPs) are proteins that are used as substrates for phase I

drug metabolism and account for 75% of the metabolism of drugs (15). They are a major

source of variability in drug pharmacokinetics and response (15). Fifty-seven human CYPs,

belonging to the CYP1, 2, and 3 families, have been identified to be responsible for the

biotransformation of most foreign substances including 70–80% of all drugs in clinical use

and involved in over 90% of the reactions in drug metabolism (16). It has been shown that

approximately 70% of human drug metabolism and pharmacokinetics can be attributed to six

major CYP450s, which include CYP1A2, 2C9, 2C19, 2D6, 2E1, 3A4 and 3A5 . The CYP3A4

in the intestine and the liver is involved in the metabolism of majority of the drugs on the

market (23–27). The ability of gut bacteria to metabolize drugs is comparable to that of the

liver (28). CYP450 3A4 enzyme is abundant in the human intestine (29) and its activity is

markedly lower in patients with Crohn's disease (30); enterocytic CYP3A4 influences

coeliac disease and cystic fibrosis in pediatric populations (31). CYP1A2, another major

cytochrome in the intestine, influences the oxidative metabolism of the exogenous and

endogenous molecules including caffeine (32) as well as is involved in several drug

interactions (33).

CYP activity is modulated by many factors such as inflammation (34, 35), food (36), and

bacteria (37–39). Blockade of pro-inflammatory cytokines and lipopolysaccharide (LPS) with

aminoguanidine both extended down-regulation of CYP2A5 to the high dose range while not

affecting LPS-induced depression of CYP1A and CYP2B (34, 40). The anti-inflammatory

effect and modulation of cytochrome P450 activities by Artemisia annua tea infusions

inhibited the activity of CYP3A4 and of CYP1A1 (36). Probiotics can also affect drug

metabolism, for example, gut microbiota can influence the metabolism of nifedipine through

affecting CYP3A in the intestinal mucosa (12). VSL#3, a common probiotic (41–44), which

is a combination of eight strains, contains facultative or obligate, anaerobic, probiotic strains

including L. acidophilus, L. plantarum, L. paracasei, L. delbrueckii subsp. bulgaricus, B. breve,

B. longum, B. infantis, and Streptococcus thermophiles, modifies CYP activity (45–47).

VSL#3 probiotic regulates the intestinal epithelial barrier in vivo and in vitro (42), induces

mucin expression and secretion in colonic epithelial cells (48), and modulates human

dendritic cell phenotype and function (49). Lactobacillus and Bifidobacterium have effects on

gut disorders and drug metabolism where they can affect the metabolism of acetaldehyde

and ethanol (45–47). All the Lactobacillus and Bifidobacterium strains from VSL#3 revealed

a modification to oxidize ethanol and acetaldehyde which suggested a beneficial impact on

high gastrointestinal acetaldehyde levels following alcohol intake (45).

As we discover more and more the causative roles of bacteria on human health (4), the

need for an assay to study the role of bacteria on the potential metabolism of drugs has never

been more. However, there are limited experimental tools to screen drugs for potential

metabolism by the gut microbiome. The major bottleneck to understanding impact of

microbes (both intestinal and probiotics) on drug metabolism is the lack of fundamental

analytical approaches to quantifying the enzymes that are responsible for metabolizing the

drugs. Although the interactions between several drugs and intestinal bacterial strains were

recently reported (3), such studies ignore the role of intestinal cells, which we know play an

important role in regulating the activity of the microbes. Thus, there remains a need for

efficient approach for characterizing the role of bacteria on potential regulation of drug

metabolism. In this paper, we have systematically studied the role of three different bacteria

on the regulation of intestinal drug metabolizing enzymes. We have selected a probiotic

(VSL#3), and two intestinal microbes E-coli Nissle 1917 and Bifidobacterium adolescentis (B.

adolescentis). E-coli Nissle 1917, an anaerobic bacteria, is predominantly found in the human

gut is another bacteria, has been shown to regulate drug metabolism (49–53). E. coli Nissle

1917 administration has been shown responsible for better drug absorption from the

gastrointestinal tract (54) as well as for maintaining tight epithelial junction and repair of the

barrier function (55, 56). B. adolescentis, is a bacterial component seen in the gut

microbiota and plays a significant role in influencing the prevention or/and treatment of

different gastrointestinal infections, intestinal disorders such as Crohn’s disease (57), which

could be attributed to the effects of mechanisms of B. adolescentis consumption of lactate

and acetate (58) . Due to the challenges of in vitro culture of intestinal cells, we used Caco2

cells because of their ability to form stable monolayers with the expression of relevant human

drug transporters and CYP activity (59, 60). When grown on semi-permeable membranes,

these cells can grow into 3D structures with tight barrier junctions as others and we have

shown (59, 61) . When grown on semi-permeable membranes, these cells can grow into 3D

structures with tight barrier junctions as others and we have shown (59, 61) . We studied

more than 80 genes related to different aspects of drug metabolism under different culture of

Caco2 cells and bacteria (Figure S2). We quantified not only the changes in relative mRNA

but also the activity of important CYPs.

Materials and Methods

Cell Culture

Human intestinal epithelial Caco2 cells were obtained from ATCC (Manassas, VA) and grown

in 1X Eagle’s Modified Eagle Medium (EMEM) (VWR) containing 4.5 g/l glucose and 25 mM

HEPES supplemented with 20% fetal bovine serum (FBS) (Gibco), 1% pen-strep (Gibco).

The cells were cultured at 37°C in a humidified incubator with 5% CO2. Cells were cultured in

a 75cm2 cell culture flask until the cells were 80% confluent. The cells were then seeded in

well plates and on Transwell inserts (Corning, NY) and grown until confluent.

Bacteria Culture

Three different types of bacteria strands were used which included VSL#3 (Alphasigma USA,

Covington, LA), Bifidobacterium adolescentis (B. adolescentis ) (Manassas, VA), and E-Coli

Nissle 1917 (Ardeypharm GmbH, Germany). All three bacteria strains were grown following

the manufacturer’s instructions. The respective mediums included 1:1 mixture of Difco

Lactobacilli MRS Broth (BD) and Reinforced Clostridial Medium (BD), Reinforced Clostridial

Medium, and LB which contains 1% (W/V) Tryptone, 0.5% (W/V) yeast extract, and 1% (W/V)

sodium hloride. The bacteria were first plated on agar plates to determine the standard curve

of bacteria colonies and optical density (OD). The gradient concentrations of original bacteria

suspensions (½, 1/5, 1/10, 1/25, 1/50, 1/100, 1/250, 1/500, 1/1000) were used to determine

the standard curve. Twenty μl of bacteria suspension and a control of the medium were

seeded at agar plate. The OD of each concentration was measured using the Spectra Max

M2 (Molecular Devices) by measuring the absorbance at 600nm. After the bacteria colonies

were visible, an image was taken on the microscope and the number of colonies was counted

using Image J (NIH, Bethesda, MD). Once that information was obtained, a standard curve

was made (Figure S1). The standard curve was used to quantify the MOI for the cocultures.

Experimental Design

We carried out three sets of experiments (Figure S2). (1) Caco2 cells and bacteria were

cocultured (a) in well plates and (b) in (0.4 um) Transwell inserts. In well plates, the cells and

bacteria were in contact with each other. In Transwell inserts, cells were seeded on the

inserts in the top chamber and bacteria were seeded at bottom where caused the interactions

to be paracrine. Three different Multiplicity of Infection (MOI) of bacteria and Caco2 cells were

tested - 1:1, 10:1, and 100:1. The viability of Caco2 cells seeded with the bacteria for 2, 4, 6,

and 8 hours was estimated. Live/dead images were used to calculate the cell viability.

(2) We also used the bacterial supernatant to test its effect on intestinal enzymatic activity.

The bacteria supernatant was added to the Caco2 cell culture media in three different ratios -

1:1, 1:10, and 1:100. The conditioned media was introduced into (a) well plates and (b)

bottom well of Transwell inserts. The bacteria supernatant was filtered by 0.22 um syringe

filter (Corning) to clear all the bacteria effect. The viability of Caco2 cells seeded with the

different ratio of bacteria supernatant for 8 hours was estimated.

(3) We then used a few of the specific metabolites that the bacteria are known to produce.

Three metabolites (butyrate (Sigma), propionate (Sigma), p-cresol (Sigma)) were selected.

Bacterial metabolic products, such as short chain fatty acids and their relative ratios, may be

causal in disturbed immune and metabolic signaling (62). Metabolites were mixed with cell

culture media to result appropriate concentration - 2 mM butyrate (63, 64), 3 mM propionate

(64, 65), and 1 mM p-cresol (66, 67). The conditioned metabolites were introduced into (a)

tissue culture plates and (b) bottom chamber of Transwell inserts. The viability of Caco2 cells

seeded with the different metabolites for 8 hours was estimated.

cDNA Synthesis and Quantitative Polymerase Chain Reaction (qPCR)

Caco2 cells and the bacteria co-cultures were prepared as described above. After a 4 hour

incubation, the cells were washed with sterile 1X PBS (Sigma) and 1X trypsin (Gibco) was

added to detach the cells from the walls. The RNA was isolated using the RNeasy Mini Kit

(QIAGEN, Germantown, MD). RNA concentration of each sample was quantified, and cDNA

samples prepared by using the First Stand Kit (Qiagen) and SYBR Green (Qiagen) using

Thermo Cycler C1000 Touch (BIO-RAD, Hercules, CA). qPCR was performed using the RT2

Profiler PCR Arrays (QIAGEN) on the ABI ViiA7 (Thermo Fisher, Skokie, IL).

Pathway analysis

Heatmap was generated using the log2 (fold change) of the different genes by FunRich (68).

Data were also analyzed with FunRich, Gene Ontology (69, 69), and Reactome (70) to study

biological pathways with the following analysis settings: the fold change that larger than 3 and

less than 0.3 were selected. Fold change was calculated as below: first, the ΔCt value, which

is the difference between Ct of target gene and of HKG genes of each treatment and control,

was calculated. By subtracting the ΔCt values of experimental group from the control, ΔΔCt

was calculated for each gene which was used to calculate the fold change = 2 (-ΔΔCt). Heatmap

was generated from 28 genes with the p-value < 0.05. In the heatmap, log2 fold change was

used to represent gene expression and the color map indicates the downregulation of gene

expression (log2 fold change < 0) as blue and up regulation as red (log2 fold change > 0).

The Ct values of seven important CYP 450s were normalized to same housekeeping genes

(ΔCt = Ct of target gene - Ct of HKG gene). Fold change was calculated following the Qiagen

protocol based on the difference between control group and experimental group and log2 fold

change was plotted to show the genes that were up and down regulated. (n>=3)

CYP 450 Analysis

Caco2 cells were seeded in the 48-well plate and a 48 Transwell inserts for 2-3 days,

following which the bacteria was added as described above and cultured together in a 37°C,

5% CO2 incubator. P450-Glow CYP3A4 Assay with Luciferin-IPA, P450-Glo™ CYP1A2 and

CYP3A4 were purchased from Promega (Madison, WI) and were used according to the

manufacturer’s instructions. The luminescence of each experiment was read using the

Spectra Max M2.

Data analysis

Unless otherwise indicated, data are expressed as means ± SEM. Differences between

experimental groups were compared using t test. Statistical analyses were done with Excel

and FunRich. Differences with a p < 0.05 were considered statistically significant.

Results and Discussions

Cell viability in coculture

We used VSL#3 as model bacteria to determine the time for which the co-culture could

be carried out without significant loss of Caco2 cell viability. Viability of Caco2 cells

co-cultured with VSL#3 bacteria in a well plate was stable at first 4 hours, which stayed at

least at 97%. When the coculture was taken to 8 hours, the viability of Caco2 cells decreased

to 65% (Figure 1A, D). In contrast, the coculture in Transwell insert could be taken beyond 8

hours without significant drop in viability (Figure 1B, E). Similar high viability was observed

when the cells were cultured under different ratios of bacterial supernatant and cell culture

media (Figure 1C, F). Based on these results, we selected the four-hour time point for the

subsequent co-culture studies.

Gene expression analysis

The heatmap shows that in the selected gene set, the upregulation of genes by B.

adolescentis was higher than that by VSL#3 and E.coli Nissle1917 (Figure 2A, Figure S3).

These genes belong to CYP450, aldehyde dehydrogenases, and flavin containing

monooxygenases. Surprisingly, B. adolescentis upregulated most of the genes whereas the

other two bacteria led to a downregulation of the same genes. We discovered that for VSL#3,

CYP1A2, 3A4, 2C9, and 2E1 expression was highly upregulated, whereas 2C19, 2D6 and

3A5 was downregulated. E.coli Nissle 1917 upregulated CYP2C9, 2E1, 3A4, and 3A5 and

downregulated CYP1A2, 2C19 and 2D6. B. adolescentis downregulated CYP 2D6 and 3A5

while upregulating the other five. If we look at just the ΔCt values (lower ΔCt value reports

higher gene expression), we find that while B. adolescentis and VSL#3 upregulated several

of the CYP450 genes significantly (p < 0.05), E.coli Nissle 1917 did not (Figure 2C). VSL#3

and B. adolescentis significantly upregulated the expression of CYP 1A2 and 3A4, whereas

only B. adolescentis had an effect (downregulation) on the expression of CYP2D6. The data

suggests that B. adolescentis and VSL#3 will potentially affect the pharmacokinetics of drugs,

at least of those that rely on phase I metabolism which could be significant as these include

the CYPs that contribute to almost 70% of human drug metabolism and pharmacokinetics

(16).

Pathway analysis

We carried out analysis to identify the major pathways that corresponded to the

upregulated and downregulated genes for each of the bacteria (Figure 3). The data suggest

that all three bacteria regulate drug metabolism and inflammatory signals. The analysis

showed that for VSL#3 the upregulated genes contributed primarily to processes for

metabolism of xenobiotics and synthesis of HETE whereas the downregulated genes to

modification of inert chemicals into biologically reactive species (69) (Figure 3A). Synthesis

of HETE is associated with inflammatory signals and cancers as well as with the ability to

facilitate tumor cell motility (70). E.coli Nissle 1917 contributed to pathways important for

xenobiotics, endogenous sterols, fatty acids, and the synthesis of epoxy,

dihydroxyeicosatrienoic acids, and HETE (Figure 3B). Excessive storage of fatty acids can

lead to obesity related dyslipidemia, hypertension, and hypertriglyceridemia and also alters

the lipid metabolism (71). EET and DHET are involved in by formation and metabolism of

CYP450s (71). The genes up- and down- regulated by B. adolescentis contributed to

pathways similar to E.coli Nissle 1917 (Figure 3C). The pathways affected by E.coli Nissle

1917 and B. adolescentis were primarily related to the modification of xenobiotic metabolism

and inflammatory signals, but the proportion that the genes contribute to each pathway was

less than VSL#3. The differences among VSL#3 and the other two bacteria may occur

because VSL#3 comprises eight different strains. While it may be interesting to study the

effect of each of these stains, our results are important because consumers take VSL#3 as a

compound-probiotic. The knowledge can help a priori design of therapeutics.

CYP 450 activity

The bacteria we tested modify the activity of drug metabolizing enzymes CYP3A4 and

1A2. Three different Multiplicity of Infection (MOI) of bacteria and Caco2 cells were used - 1:1,

10:1, and 100:1 which were derived from the standard curve (Figure S1). Bringing the

bacteria in contact with the cells suppressed the CYP activity for in all three cases (Figure 4).

Both VSL#3 and its supernatant up-regulate CYP3A4 and 1A2 in Transwell inserts, but not in

well plate (Figure 4A). We observed that for MOI =1:1, 10:1 and 100:1, the activity of

CYP3A4 was more than 3-fold over the control and more than 2-fold over control for CYP1A2.

However, culturing the cells with different amounts of the bacteria supernatant conditioned

media recapitulated only partial activity of the two CYP enzymes (Figure 4B), suggesting that

signaling between the bacteria and colon cells in an important factor in determining the

activities. Paracrine signaling between VSL#3 and Caco2 cells upregulated activity of both

CYP enzymes CYP3A4 and 1A2. The modification of CYP3A4 activity was higher than that of

CYP1A2. VSL#3 supernatant also upregulated the activities although at a lower level than the

co-culture, again, suggesting the important of bidirectional paracrine signaling. In well plate

cultures, surprisingly, the supernatant did not affect CYP3A4 activity but downregulated

CYP1A2 activity. E.coli Nissle 1917 modified the activity of CYP3A4 and 1A2 in the well plate

as well as the Transwell inserts (Figure 4C). In plates, CYP3A4 activity was upregulated

when for MOIs 1:1 and 100:1, whereas there were no changes for MOI=10:1. E.coli Nissle

1917 did not affect the activity of CYP1A2 in the well plate. In the Transwell insert, CYP3A4

activity went up and the highest fold change was 1.9 that occurred at MOI=10:1 while the fold

change for MOI=1:1 and 100:1 was 1.4 and 1.5. However, the E.coli’s effect on CYP1A2 was

the opposite. The highest reduction in fold change, which was 0.75 compared to control, was

at MOI=10:1 (Figure 4C). E.coli Nissle 1917 supernatant did not change CYP3A4 activity but

slightly decreased the activity of CYP1A2 when the ratio of supernatant and media was 1:100

(Figure 4D). By decreasing the bacterial supernatant from 1:1 to1:100, the CYP1A2 activity

fold change decreased from 1.1 to 0.85 compare to control. E.coli Nissle 1917 supernatant

caused the activity of CYP3A4 to increase, while decreasing that of CYP1A2 (Figure 4D).

When the ratio of E.coli Nissle 1917 supernatant to cell culture media was 1:10, the

upregulation on CYP3A4 activity was the highest. The other two treatments, ratio of 1:10 and

1:100, just showed upregulation in the activity of CYP3A4. Unlike CYP3A4, the activity of

CYP1A2 was downregulated under all three ratios. B. adolescentis slightly lowered CYP3A4

activity at all MOIs in cell culture plate (Figure 4E). The CYP1A2 activity was also slightly

downregulated. In the Transwell insert, CYP3A4 activity was increased only at MOIs 10:1 and

100:1. When the MOI decreased to 1:1, there was no influence on 3A4 activity any more. In

contrast, CYP1A2 activity was slightly upregulated at MOI of 10:1. There was no influence on

activity when the MOIs were 1:1 and 100:1. The effect of B. adolescentis supernatant on the

activity of both CYPs was similar to that in the tissue culture plate – higher ratio of the

supernatant corresponded to lower activity. In Transwell inserts, B. adolescentis supernatant

slightly increased the CYP3A4 activity (Figure 4F). There was no significant change in the

activity of CYP1A2 at 1:1; however, when the ratio decreased to 1:10 and 1:100, the fold

change value of CYP1A2 activity was higher. Our data suggests that the bacteria have

significant effect on CYP activity when the cells and bacteria are not in contact. Bacterial

communication with the colon cells appears to be important in modulating the CYP activity.

Although bacterial supernatant can modify CYP activity as well, the levels are not the same

as those by the bacteria. In contrast, tests with individual bacterial metabolites (2mM butyrate,

3mM propionate and 2mM p-cresol) indicated no significant influence on CYP3A4 activity and

a downregulation of CYP1A2 activity (Figure 4G), although at levels lower than the other

cases. The results indicate that the individual metabolites do not significantly regulate CYP

activity as compared to the co-culture and the supernatant.

Conclusions

Despite advances in our understanding a bacteria’s (gut microbe and probiotics)

contribution to human health, one factor of the host-microbe interactions that remains

understudied is the influence of bacteria on drugs. These interactions could have an

important role in explaining the variation in human responses to drugs or the variable

response to therapy. However, there is a serious lack of available data that one could use to

determine how bacteria would potentially regulate a drug’s metabolism. As a first step to fill

that gap, we have established and thoroughly characterized a simple co-coculture between

colon cells and different types of bacteria. We have systematically dissected the role of

colon-bacteria interactions versus paracrine signaling through co-cultures of human colon

cells with three different strains of gut bacteria. We have used this setup to study alterations

in general phase I enzymes and more importantly, measured the activity of human CYP450.

Our findings indicate that the bacteria and their supernatant both modify the activity of drug

metabolizing enzymes. We found species specific differences in the regulation and activity of

the phase I enzymes uniquely. Our results indicate that communication between the bacteria

and the colon cells is required for modulation of metabolic activity. The regulation of the

various CYP450’s appeared to be different, which suggested that studies are needed to

uncover the regulatory mechanisms.

Supplementary

Figures are included in the supplementary material.

Abbreviations

TV: the colon cells co-cultured with VSL#3 in Transwell inserts, MOI=1:10

TN: the colon cells co-cultured with E.coli Nislle 1917 in Transwell inserts, MOI=1:10

TB: the colon cells co-cultured with B. adolescentis in Transwell inserts, MOI=1:10

TC: the colon cells cultured in Transwell inserts, without bacteria.

MOI: Multiplicity of Infection

P.MOI: the Multiplicity of Infection bacteria and cells tissue culture plate, ranging from 1:1 to

100:1

T.MOI: the Multiplicity of Infection bacteria and cells Transwell inserts, ranging from 1:1 to

100:1

P.CM: the bacteria supernatant conditioned media in tissue culture plate with different ratio

range from 1:1 to 1:100

P.CM: the bacteria supernatant conditioned media in Transwell inserts with different ratio

range from 1:1 to 1:100.

Acknowledgments

This project was partly funded by the Nayar Prize II and student scholarships from the Armor

College of Engineering. We acknowledge technical help from Dr. Hong lab.

Author contributions

CW designed the study, conducted experiments, and wrote the manuscript. AC conducted

experiment and wrote the manuscript. RF, TD, and QH conducted experiment. AB designed

the study and wrote the manuscript.

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List of figures and tables

Figure 1. Viability of colon cells co-cultured with VSL#3 bacteria in (A) well plate and (B)

Transwell inserts, in (C) different proportion of VSL#3 bacteria supernatant and cell culture

media mixture in tissue culture plate for 4 hr. Live/dead stain of colon cells co-cultured with

VSL#3 for 4 hr and 8 hr in (D) well plate and (E) Transwell inserts; (F) Live/dead stain of colon

cells cocultured with VSL#3 supernatant conditioned media for 4 hr, in ratio of 1:1 and 1:10. S:

VSL3 supernatant, M: cell culture media; Green live, Red dead.

Figure 2. qPCR analysis of the colon cells co-cultured with VSL#3, E.coli Nissle 1917 and B.

adolescentis bacteria in Transwell inserts. (A) Heatmap generated from 28 genes (p-value <

0.05). The gene expression is plotted as log2 fold change. (B) Log2 fold change plot of seven

CYPs. Fold change was calculated based on the control group. (n>=3). (C) Ct values of

seven primary CYP450s genes normalized to same housekeeping gene. Values represent

mean ± SEM (Student’s t test; *p < 0.05). TV: colon cells co-cultured with VSL#3 in Transwell

inserts, MOI=1:10; TN: colon cells co-cultured with E.coli Nissle 1917 in Transwell inserts,

MOI=1:10; TV: colon cells co-cultured with B. adolescentis in Transwell inserts, MOI=1:10;

TC: colon cells cultured in Transwell inserts, without bacteria.

Figure 3. (A) – (C) Biological pathway analysis of colon cells co-cultured with VSL#3, E.coli

Nissle 1917 and B. adolescentis in Transwell inserts for upregulated (fold change >3) and

downregulated genes (fold change

cells Transwell inserts, ranging from 1:1 to 100:1; P.CM: bacterial supernatant conditioned

media in well plate with different ratios ranging from 1:1 to 1:100; P.CM: bacterial supernatant

conditioned media in Transwell inserts in different ratios ranging from 1:1 to 1:100.

Figure S1. Bacteria standard curve of OD and density (CFU/mL)

Figure S2. Schematic diagram of co-culture between colon cells and bacteria in well plate

and Transwell inserts.

Figure S3. qPCR analysis of the colon cells co-cultured with VSL#3, E.coli Nissle 1917 and B.

adolescentis bacteria in Transwell inserts. Heatmap generated from 84 genes.

Figure S4. Log2 Fold Change plot of the all 84 genes in the kit. Fold change was calculated

based on the control group and separated to 6 different enzymes group. CYP450:

Cytochrome P450s; ADH:Alcohol Dehydrogenases; ALDH: Aldehyde Dehydrogenases; FMO:

Flavin Containing Monooxygenases.

Figure S5. Biological pathways analysis of colon cells co-cultured with VSL#3 bacteria in

Transwell inserts based on genes fold change >3 and 3 and 3 was considered as upregulated and < 0.3 as downregulated.

A

Figure 1. Viability of colon cells co-cultured with VSL#3 bacteria in (A) well plate and (B) Transwell inserts, in(C) different proportion of VSL#3 bacteria supernatant and cell culture media mixture in tissue culture platefor 4 hr. Live/dead stain of colon cells co-cultured with VSL#3 for 4 hr and 8 hr in (D) well plate and (E)Transwell inserts; (F) Live/dead stain of colon cells cocultured with VSL#3 supernatant conditioned media for4 hr, in ratio of 1:1 and 1:10. S: VSL3 supernatant, M: cell culture media; Green live, Red dead.

B C

0

25

50

75

100

Ctrl 2 4 8

% V

iab

ility

Coculture time (h)

0

25

50

75

100

Ctrl 2 4 8

% V

iab

ility

Coculture time (h)

D E F

4 hrs 4 hrs S:M=1:18 hrs 8 hrs S:M=1:10

0

25

50

75

100

0 0.5 0.1 0.04 0.02 0.01

% V

iab

ility

proportion of VSL#3 bacteria supernatant

Figure 2. qPCR analysis of the colon cells co-cultured with VSL#3, E.coli Nissle 1917 and B.adolescentis bacteria in Transwell inserts. (A) Heatmap generated from 28 genes (p-value < 0.05).The gene expression is plotted as log2 fold change. (B) Log2 fold change plot of seven CYPs. Foldchange was calculated based on the control group. (n>=3). (C) Ct values of seven primaryCYP450s genes normalized to same housekeeping gene. Values represent mean ± SEM(Student’s t test; *p < 0.05). TV: colon cells co-cultured with VSL#3 in Transwell inserts, MOI=1:10;TN: colon cells co-cultured with E.coli Nissle 1917 in Transwell inserts, MOI=1:10; TV: colon cellsco-cultured with B. adolescentis in Transwell inserts, MOI=1:10; TC: colon cells cultured inTranswell inserts, without bacteria.

ΔCt

0

2

4

6

8

10

12

14

16

18

20

TV

TN

TB

TC

*

** * *

-3

-2

-1

0

1

2

3

4

5

TV

TN

TBLog2

(Fo

ld C

han

ge)

AB

C

Figure 3. (A) – (C) Biological pathway analysis of colon cells co-cultured with VSL#3,E.coli Nissle 1917 and B. adolescentis in Transwell inserts for upregulated (foldchange >3) and downregulated genes (fold change

Figure 4. Enzymatic activity of CYP3A4 andCYP1A2 in colon cells co-cultured with (A)VSL3; (C) E.coli Nissle 1917; (E) B.adolescentis; and (B, D, F) their respectivesupernatant conditioned media in tissueculture plate and Transwell inserts. (G)Enzymatic activity of CYP3A4 and CYP1A2activity of colon cells when cultured withspecific bacterial metabolites (butyrate,propionate and p-cresol). P.MOI: theMultiplicity of Infection bacteria and cellstissue culture plate, ranging from 1:1 to 100:1;T.MOI: the Multiplicity of Infection bacteria andcells Transwell inserts, ranging from 1:1 to100:1; P.CM: bacterial supernatantconditioned media in well plate with differentratios ranging from 1:1 to 1:100; P.CM:bacterial supernatant conditioned media inTranswell inserts in different ratios rangingfrom 1:1 to 1:100.

0

1

2

3

4

VSL#3 3A4

1A2

0

1

2

3

4

VSL#3 supernatant 3A4

1A2

0

1

2

3

4

E.coli Nissle 19173A4

1A2

0

1

2

3

4

E.coli Nissle 1917 supernatant 3A4

1A2

0

1

2

3

4

B. adolescentis3A4

1A2

0

1

2

3

4

B. Adolescentis supernatant3A4

1A2

0

1

2

3

4

caco2+2mMbutyrate

caco2+3mMpropionate

caco2+2mMp-cresol

Metabolites

3a4

1a2

Fold

ch

ange

Fold

ch

ange

Fold

ch

ange

Fold

ch

ange

Fold

ch

ange

Fold

ch

ange

Fold

ch

ange

C

BA

D

EF

G

Table 1. Up- and down- regulated genes influenced by VSL#3, E.coli Nissle 1917, and B.adolescentis. Fold change >3 was considered as upregulated and < 0.3 as downregulated.

TV TN TB

Upregulated (fold change > 3) CYP1A2, CYP3A4, CYP4F2, AADAC

CYP1A1, CYP1B1, CYP2A13, CYP4F2, ADH4,

FMO1, FMO2

CYP11A1, CYP11B2, CYP17A1, CYP19A1, CYP1A1, CYP1B1, CYP21A2, CYP26A1, CYP26C1, CYP2A13, CYP2C8, CYP2C9, CYP2F1, CYP3A4,

CYP3A43, CYP4A11, CYP4A22, CYP4B1, CYP4F2, CYP4F8, CYP7A1, CYP7B1,

CYP8B1, ADH1A, ADH4, ADH7, AADAC, GZMB,

UCHL1, ALDH1A2, ALDH1A3, ALDH3A1, ALDH8A1, FMO1, FMO2, FMO3, FMO4, MAOB,

PTGS2

Downregulated (fold change < 1/3)CYP11B1, CYP2C18,

CYP4F11, ADH1B, GZMA, MAOA, PTGS1

CYP11B1, CYP2C19, CYP4F11, ADH1B

CYP2B6, CYP2D6, CYP3A5, CYP4F11, CYP4F12, ADH1C,

ADH6, HSD17B10, PTGS1

Figure S1. Bacteria standard curve of OD and density (CFU/mL)

y = 3.8371x + 0.0413R² = 0.9549

0

0.05

0.1

0.15

0.2

0 0.01 0.02 0.03 0.04C

FU/m

l (*1

06

)OD

E.coli Nissle 1917

y = 126.7x - 0.1084R² = 0.9754

0

0.5

1

1.5

2

2.5

3

3.5

0 0.01 0.02 0.03

CFU

/ml (

*10

6)

OD

VSL#3

y = 1.7638x - 0.5133R² = 0.9842

0

0.5

1

1.5

2

2.5

0 0.5 1 1.5 2

CFU

/ml (

*10

6 )

B. adolescentis

OD1 Linear (OD1)

Figure S2. Schematic diagram of co-culture between colon cells andbacteria in well plate and Transwell inserts.

Cell culture media

Colon cells

Colon cells

Colon cells

Microbiome

Cell culture media

Colon cells

Cell culture media

Colon cells

Conditioned media

Cell culture media

Colon cells

Microbiome

Cell culture media

Conditioned media

Figure S3. qPCR analysis of the colon cellsco-cultured with VSL#3, E.coli Nissle 1917 andB. adolescentis bacteria in Transwell inserts.Heatmap generated from 84 genes.

-4.0

-2.0

0.0

2.0

4.0

6.0

8.0

10.0C

YP

11

A1

CY

P1

1B

1

CY

P1

1B

2

CY

P1

7A

1

CY

P1

9A

1

CY

P1

A1

CY

P1

A2

CY

P1

B1

CY

P2

1A

2

CY

P2

4A

1

CY

P2

6A

1

CY

P2

6B

1

CY

P2

6C

1

CY

P2

7A

1

CY

P2

7B

1

CY

P2

A1

3

CY

P2

B6

CY

P2

C1

8

CY

P2

C1

9

CY

P2

C8

CY

P2

C9

CY

P2

D6

CY

P2

E1

CY

P2

F1

CY

P2

R1

CY

P2

S1

CY

P2

W1

CY

P3

A4

CY

P3

A4

3

CY

P3

A5

CY

P3

A7

CY

P4

A1

1

CY

P4

A2

2

CY

P4

B1

CY

P4

F11

CY

P4

F12

CY

P4

F2

CY

P4

F3

CY

P4

F8

CY

P7

A1

CY

P7

B1

CY

P8

B1

CYP450

TV

TN

TB

-4

-2

0

2

4

6

8

10

AD

H1

A

AD

H1

B

AD

H1

C

AD

H4

AD

H5

AD

H6

AD

H7

DH

RS2

HSD

17

B1

0

ADH

TV

TN

TB

-4

-2

0

2

4

6

8

10

Esterases

TV

TN

TB

-4

-2

0

2

4

6

8

10

ALD

H1

A1

ALD

H1

A3

ALD

H2

ALD

H3

A2

ALD

H3

B2

ALD

H5

A1

ALD

H7

A1

ALD

H9

A1

ALDH

TV

TN

TB

-4

-2

0

2

4

6

8

10

FMO

1

FMO

2

FMO

3

FMO

4

FMO

5

FMO

TV

TN

TB

Figure S4. Log2 Fold Change plot of the all 84 genes in the kit. Fold change was calculated based on the control group and separated to 6 different enzymes group. CYP450: Cytochrome P450s; ADH:AlcoholDehydrogenases; ALDH: Aldehyde Dehydrogenases; FMO: FlavinContaining Monooxygenases.

50.0

50.0

33.3

33.3

33.3

33.3

33.3

16.7

16.7

16.7

16.7

16.7

16.7

16.7

16.7

16.7

16.7

16.7

16.7

16.7

16.7

16.7

16.7

16.7

16.7

16.7

16.7

16.7

16.7

16.7

16.7

16.7

100 80 60 40 20 0 20 40 60 80 100

Xenobiotics

Synthesis of (16-20)-hydroxyeicosatetraenoic…

Aflatoxin activation and detoxification

Synthesis of epoxy (EET) and…

Biosynthesis of protectins

Biosynthesis of maresin-like SPMs

Miscellaneous substrates

Phase I - Functionalization of compounds

PPARA activates gene expression

Synthesis of Leukotrienes (LT) and Eoxins (EX)

Methylation

Aromatic amines can be N-hydroxylated or N-…

Fatty acids

Eicosanoids

Glucocorticoid biosynthesis

Ethanol oxidation

Endogenous sterols

Synthesis of Prostaglandins (PG) and…

Interleukin-4 and Interleukin-13 signaling

Metabolism of serotonin

Biogenic amines are oxidatively deaminated to…

Norepinephrine Neurotransmitter Release Cycle

Enzymatic degradation of dopamine by COMT

Enzymatic degradation of Dopamine by…

Defective MAOA causes Brunner syndrome…

Defective CYP11B1 causes Adrenal hyperplasia 4…

COX reactions

TV (%)

Bio

logi

cal P

ath

way

s

UP DOWN

Figure S5. Biological pathways analysis of colon cells co-cultured withVSL#3 bacteria in Transwell inserts based on genes fold change >3 and

14.3

14.3

14.3

14.3

14.3

14.3

14.3

14.3

14.3

14.3

14.3

28.6

28.6

28.6

28.6

42.9

16.7

33.3

16.7

16.7

16.7

16.7

33.3

16.7

16.7

16.7

16.7

16.7

16.7

16.7

16.7

16.7

100 80 60 40 20 0 20 40 60 80 100

PPARA activates gene expression

Ethanol oxidation

Aflatoxin activation and detoxification

RA biosynthesis pathway

Endogenous sterols

Synthesis of Leukotrienes (LT) and Eoxins (EX)

Biosynthesis of protectins

Defective CYP1B1 causes Glaucoma

CYP2E1 reactions

Miscellaneous substrates

Eicosanoids

Xenobiotics

Synthesis of epoxy (EET) and…

Fatty acids

FMO oxidises nucleophiles

Synthesis of (16-20)-hydroxyeicosatetraenoic…

Glucocorticoid biosynthesis

Synthesis of bile acids and bile salts via 7alpha-…

Synthesis of bile acids and bile salts via 24-…

Synthesis of bile acids and bile salts via 27-…

Defective CYP11B1 causes Adrenal hyperplasia…

Defective CYP27A1 causes Cerebrotendinous…

TN (%)

Bio

logi

cal P

ath

way

s

UP DOWN

Figure S6. Biological pathways analysis of colon cells co-cultured with E.coli Nissle1917 bacteria in Transwell inserts based on genes fold change >3 and

21.1 18.4 18.4 18.4

15.8 15.8

13.2 13.2

10.5 10.5

7.9 7.9 7.9 7.9 7.9 7.9 7.9

5.3 5.3 5.3 5.3 5.3 5.3 5.3

2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6

11.1 33.3

44.4

22.2 11.1

22.2

22.2

22.2 11.1

11.1 11.1

11.1 11.1 11.1 11.1 11.1

100 80 60 40 20 0 20 40 60 80 100

Endogenous sterols

Xenobiotics

Synthesis of (16-20)-hydroxyeicosatetraenoic acids (HETE)

Synthesis of Leukotrienes (LT) and Eoxins (EX)

Synthesis of epoxy (EET) and dihydroxyeicosatrienoic acids…

Glucocorticoid biosynthesis

Synthesis of bile acids and bile salts via 27-hydroxycholesterol

Ethanol oxidation

FMO oxidises nucleophiles

Phase I - Functionalization of compounds

Synthesis of Prostaglandins (PG) and Thromboxanes (TX)

Vitamins

Androgen biosynthesis

Abacavir metabolism

Pregnenolone biosynthesis

Estrogen biosynthesis

Biogenic amines are oxidatively deaminated to aldehydes by…

Defective CYP11B2 causes Corticosterone methyloxidase 1…

Defective CYP17A1 causes Adrenal hyperplasia 5 (AH5)

Biosynthesis of protectins

Defective CYP21A2 causes Adrenal hyperplasia 3 (AH3)

Defective CYP7B1 causes Spastic paraplegia 5A, autosomal…

NOTCH2 intracellular domain regulates transcription

Synthesis of 15-eicosatetraenoic acid derivatives

Biosynthesis of electrophilic ω-3 PUFA oxo-derivatives

Biosynthesis of DPAn-3 SPMs

tRNA modification in the mitochondrion

rRNA processing in the mitochondrion

TB (%)

Bio

logi

cal P

ath

way

s

UP DOWN

Supplementary Figure S5. Biological pathways analysis of colon cells co-cultured with B. adolescentis bacteria in Transwell inserts based on genes fold change >3 and