Multi-Stage Probabilistic Bipartite Graph Algorithm - Effect of Herbal Medicines on the Gut Ecosystem

Presenter: Suganya Chandrababu

03/06/2020 University of Nebraska at Omaha GRACA 2019 Straightforward Overview

n Purpose/value of the study n Effect of Herbs on the Gut Ecosystem n Our Contribution n Multi-stage Graph Probabilistic Algorithm n Dataset Used n Results and Discussion n Conclusion n Limitations n Future Work

03/06/2020 University of Nebraska at Omaha GRACA 2019 Why do we care?

n Such imbalance could result in intestinal and extra-intestinal disorders, including inflammatory bowel disease (IBD), diabetes mellitus (DM), obesity, etc.

03/06/2020 University of Nebraska at Omaha GRACA 2019 Pharmacological Effects of Natural Products

03/06/2020 University of Nebraska at Omaha GRACA 2019 Herbs Modulate Gut Bacterial Metabolism

03/06/2020 University of Nebraska at Omaha GRACA 2019 Bibliographic DB Limitation Structure DB MEDLINE, Microarray DB Interoperability PubMed NDB, GEO, PDB Data HUGE

Unstructured Metabolic DB

Enzyme DB Applicability KEGG, MetaCyc BRENDA, Scope of data LimitationsMPBA ExPasy coverage

Inconsistent DB Ontology SWISS Genome DB Prot,

Data EMBL GIB, NCBI

Organization OMIM PubChem Chemical DB Disease DB

03/06/2020 University of Nebraska at Omaha GRACA 2019 Why Hear the Talk?

To n understand a comprehensive method that integrates knowledge from multiple sources. n know how to characterize the microbial dysbiosis. n evaluate the pharmacological value of herbal medicine. n learn how and which herbs can modulate the gut microbial functions.

03/06/2020 University of Nebraska at Omaha GRACA 2019 What is our contribution?

We n provide a systems-level characterization of the effects of the small molecules in herbs on the bacterial genes and metabolic pathways.

n propose a computational network-based approach- the Multi-stage Probabilistic Bipartite graph Algorithm (MPBA), which integrates the complex heterogeneous metabolism/pathway data from herbs to gut .

03/06/2020 University of Nebraska at Omaha GRACA 2019 Background Work u The human colon modulates of gut immune function, protects the host against pathogens and diseases. [D. C. Savage, et al., 1997, R. E. Ley, et al., 2010] u Variations in products of bacterial metabolism have been implicated as a causative agent in serious diseases, including cancer (example: Helicobacter pylori). [Flint, Harry J., et al., 2012] u Herbal medicine are useful in the treatment of dysbiosis and serve as a multi-targeting complementary/alternative medicine/probiotic in the management of colon health, [M. Sałaga, et al., 2014, F. Ke, et al., 2012]

03/06/2020 University of Nebraska at Omaha GRACA 2019 Multi-Stage Probabilistic Bipartite Graph Algorithm

03/06/2020 University of Nebraska at Omaha GRACA 2019 Our Prediction Model

Bacterial Bacteria Enzymes Pathways metabolism MPBA Bacteria Pathways

Herbs Compounds Targets Herbal MPBA pharmacology Herbs Targets Pathways MPBA Herbs Pathways

Herbs modulating bacterial functions Herbs Pathways Bacteria

03/06/2020 University of Nebraska at Omaha GRACA 2019 The MPBA Pathways B1 P1 B1 H1 H1

B2 Bacteria Graph Approximation B2 Bacteria Herbs Herbs Hi Pq Hi Bj Bj , where

Pk Hm Bn Hm Bn

iq x yjq zij (a) (b)

It denotes the degree of the pathway nodes. Matrix Form

, where ∑ is a diagonal matrix , with

03/06/2020 University of Nebraska at Omaha GRACA 2019 The Divergence Loss Function

u To evaluate how well the algorithm models the data, we use the divergence loss function

u The approximation , can be done minimizing

Divergence Loss

, which equal zero when

03/06/2020 University of Nebraska at Omaha GRACA 2019 Probabilistic Graph Model u We can interpret the same equation using Markov random walks on graphs as done using PLSA [T. Hoffman, 200]. u The weight zij is essentially a quantity proportional to the stationary probability of direct transitions between hi and bj, denoted by p(hi , bj).

zij = p(hi , bj)

u In a tripartite graph there is no direct edge between the hi node and the bj node, and they can only be connected through the pathway nodes pq

03/06/2020 University of Nebraska at Omaha GRACA 2019 Probabilistic Graph Model

Transition probability

Tripartite Network

Back Substitution

03/06/2020 University of Nebraska at Omaha GRACA 2019 Model Fitting Using EM Algorithm

03/06/2020 University of Nebraska at Omaha GRACA 2019 Bayesian Formula

u For the E-step we apply Bayes’ formula to obtain

u And in the M-step one has to maximize the expected complete data log-likelihood given by,

u where is the frequency or the number of paths from herb to bacteria through the pathway node.

03/06/2020 University of Nebraska at Omaha GRACA 2019 Dataset Used

03/06/2020 University of Nebraska at Omaha GRACA 2019 Differential Abundant Pathways in IBD

Nitrogen metabolism Fructose and mannose metabolism Riboflavin metabolism Sulfur metabolism Bacterial secretion system Cysteine and methionine metabolism Butanoate metabolism UC CD without illeal Lysine biosynthesis CD with illeal Healthy Pentose phosphate pathway 2.X. C. Morgan, T. L. Tickle, H. Sokol, D. Gevers, K. L. Devaney, 3.D. V. Ward, J. A. Reyes, S. A. Shah, N. LeLeiko, S. B. Snapper, et al., “Dysfunction of the intestinal microbiome in inflammatory bowel disease and treatment,” Genome biology, vol. 13, no. 9, p. R79, 2012.

03/06/2020 University of Nebraska at Omaha GRACA 2019 Dataset and Source Herbal Bacteria compounds target harbors 273 262 bacterial enzymes enzymes belonging to belonging to the 8 Most the pathways 235 pathways 24 Culinary affected bacterial herbs, 132 Pathways species bioactive and and 273 compounds enzymes bacterial and 262 in IDB enzymes compound Provides Targets target enzymes Source: Source: KEGG Uniprot, Source: NCBI PhytoChem Genome NAL, PubChem

03/06/2020 University of Nebraska at Omaha GRACA 2019 Results and Discussion

03/06/2020 University of Nebraska at Omaha GRACA 2019 Case Study n Most Affected Microbial Metabolic Pathways and Enzymes In Both UC And CD

n Routes were traced through the metabolic model of the gut microbiota, and the organism(s) harboring the necessary genes identified.

03/06/2020 University of Nebraska at Omaha GRACA 2019 Bacterial metabolism Bacteria Enzymes Pathways

Veillonella dispar ATCC 17748 Lactobacillus delbrueckii subsp. bulgaricus Veillonella sp. oral taxon 780 str. F0422 vestibularis F0396 PB2003/044-T3-4 sp. SS2/1 Peptoniphilus lacrimalis 315-B Lactobacillus crispatus 125-2-CHN 1 Dialister microaerophilus1 UPII 345-E Streptococcus sp. 2_1_36FAA 1 Coprococcus eutactus1 ATCC 27759 Lactobacillus parafarraginis F0439 1 magna ACS-171-V-Col3 1 EC 2.5.1.78 Eubacterium saphenum ATCC 49989 LactobacillusErysipelotrichaceae iners DSM 13335 bacterium 3_1_53 1 Anaerococcus tetradius ATCC 35098 1 1 1 1 1 1 u The network 1 1 Lactobacillus gasseri 224-1 EC 4.3.1.17 Faecalibacterium cf. prausnitzii KLE1255 EC 2.5.1.9 EC 2.1.1.10 Streptococcus sanguinis 1SK115Veillonella sp. oral taxon 158 str. F0412 ATCC 17745 1 Peptoniphilus harei ACS-146-V-Sch2b1 1 grayi DSM 20601 EC 3.2.2.9Streptococcus mitis SK1073 1 Coprobacillus sp. 8_2_54BFAA EC 3.5.4.25 1 1 map00740 1 1 1 EC 2.1.1.141 1 1 consists of 8 Streptococcus sanguinis1 VMC66 1 Selenomonas artemidis F0399 Roseburia inulinivorans DSM 16841 1 EC 2.7.7.21 1 1 1 1 Lachnospiraceae bacterium oral taxonEC 082 2.7.1.26 1 1 1 EC 2.1.1.37 1 str. F0431 Lactobacillus iners LactinV 01V1-a EC 5.3.1.23 Streptococcus peroris ATCC 700780 1 1 1 EC 2.5.1.16Streptococcus sanguinis1 SK408 1 1 1 EC 4.1.99.12 Pseudoramibacter alactolyticus ATCC 23263 1 EC 2.5.1.6 Streptococcus parasanguinis SK236 1 1 1 1 1sp. 2_A_57_CT2 1 1 1 pathways, 273 Leuconostoc mesenteroidesClostridium subsp. sp. 7_3_54FAA EC 2.3.1.31 EC 1.2.1.11 1 Veillonella sp. 6_1_27 1 Streptococcus anginosus F0211 EC 1.13.11.53cremoris1 ATCC 19254 Streptococcus infantis ATCC 700779 1 1 1 1 11 1 Streptococcus1 vestibularis ATCC 49124 1 1 1 Oribacterium sp. oral taxon 108 str. F0425 1 1 1 Lactobacillus1 kisonensis F0435EC 1.1.1.193 1 EC 1.17.1.8 1 EC 1.1.1.3 1 1 EC 3.5.4.31 1 Lactobacillus ultunensis1 DSM 16047 1 map00270 1 1 1Paenibacillus sp. oral taxon 786 str. D14 Lactobacillus amylolyticus DSM 11664 1 1 bacterial Streptococcus sanguinis1 SK72 1 1 1 1 1 1 1 1 Streptococcus sanguinis SK1087EC 2.5.1.48 Streptococcus oralis SK313 EC 6.3.2.3 1 EC 2.6.1.42 1 Coprococcus1 comes ATCC 27758 Paenibacillus sp. HGF5 1 1 EC1 2.6.1.83 map00300 1 1 1 1 Streptococcus cristatus ATCC 51100 Dorea longicatena DSM 138141 1 1 EC 2.7.2.4 Erysipelotrichaceae bacterium 6_1_45 Faecalibacterium prausnitzii A2-165 Lactobacillus antri DSM 16041 1 1 Eggerthia catenaformis OT 569 = DSM 1 1 EC 2.6.1.1 1 1 enzymes, 235 1 20559 1 1 1 1 1 1 1 Streptococcus sanguinis SK1056EC 2.3.1.89 EC 6.3.2.2 1 1 Coprobacillus1 sp. 3_3_56FAA Paenibacillus sp. HGF7 1 Fusobacterium periodonticum ATCC 336931 1 EC 2.3.1.30 Streptococcus oralis ATCC 35037 EC 4.4.1.21 1 1 EC 6.3.2.10 1 1 Streptococcus sanguinis SK330 1 1 1 1 1 1 Streptococcus infantis SK1076 Subdoligranulum variabile DSM 15176 Enterobacteriaceae bacterium 9_2_54FAA 1 1 1 1 1 1 1 1 1 Centipeda periodontii DSMEC 5.3.1.92778 bacterial species, 1 1 Granulicatella adiacens ATCC 49175 1 EC 4.1.1.5 Anaerococcus1 prevotii ACS-065-V-Col13 EC 4.1.1.20 1 EC 5.4.2.7Bacillus smithii 7_3_47FAA 1 Streptococcus sanguinis SK1057 Streptococcus equinus ATCC 9812 1 EC 2.5.1.491 map00920 1 1 1 1 1 Anaerococcus hydrogenalis Leptotrichia hofstadii F0254 1 EC 1.1.1.27 ACS-025-V-Sch4 1 EC 1.1.99.3 1 EC 2.5.1.47 1 EC 4.2.1.17 and 18060 Lachnospiraceae bacterium 2_1_46FAA 1 Clostridium celatum DSM 1785 1 Selenomonas sp. F0473 EC 1.1.1.44 1 1 Fusobacterium mortiferum ATCC 9817 1 EC 1.4.1.16 1 EC 3.1.1.31 ATCC 6249 1 Lactobacillus crispatus CTV-05 EC 4.1.2.13 1 Lactobacillus buchneri ATCC 11577 1 1 Streptococcus parasanguinis ATCC 903 1 1 1 1 1 1 EC 1.3.8.1 EC 4.1.2.14 1 Streptococcus1 sanguinis SK150 1 EC 2.7.7.41 subsp. aureus 1 1 1 distinct 1 Ruminococcaceae bacterium D16 Streptococcus oralis SK255EC 2.2.1.2 EC 5.3.1.51 USA300_TCH959 EC 5.4.2.2 1 Lachnospiraceae bacterium 3_1_46FAA 1 1 1 1 1 1 1 1 map00650 1 EC 2.3.1.9 EC 2.7.1.151 1 1 1 1 1 Erysipelothrix rhusiopathiae ATCC 19414 1 EC 2.3.3.10 Oribacterium sinus F0268 1 1 Shuttleworthia satelles DSM 14600 Anaerococcus hydrogenalis DSM 7454 1 1 Streptococcus mitis SK1080 1 map00030 EC 2.2.1.1 1 EC 2.8.3.5 interactions. 1 Streptococcus1 infantis SK970 1 1 EC 2.2.1.6 1 Sporosarcina newyorkensis 2681 EC 2.7.2.7 1 1 1 Peptoniphilus sp. oral taxon 836 str. F0141 Selenomonas sp. oral taxon 149 str. 1 1 Anaerococcus lactolyticus ATCC 51172 1 1 67H29BP 1 EC 3.1.3.11 1 1 EC 1.1.1.4 1 1 1 Clostridium sp. 7_2_43FAA 1 EC 1.1.1.47Staphylococcus epidermidis1 W23144 EC 2.7.1.4 1 1 1 1 1 1 Bacterial species 1 EC 1.1.1.49 1 EC 5.3.1.6 map000511 1 1 Pseudoflavonifractor capillosus ATCC 29799Anaerostipes hadrus DSM 3319Dorea formicigenerans ATCC 27755 1 1 EC 4.1.2.4 1 Enzyme EC 2.7.1.5 Solobacterium moorei F0204 Streptococcus sp. oral taxon 071 str. 1 EC 5.1.3.1 1 1 73H25AP 1 1 1 Finegoldia magna SY403409CC001050417 Streptococcus australis ATCC 700641 1 1 EC 2.7.1.11 1 Streptococcus sanguinis SK355 1 1 Bacillus sp. 7_6_55CFAA_CT2 Pediococcus acidilactici DSM 20284 1 EC 2.7.1.90 1 Pathway 1 u EC 1.2.1.11- 1 1 1 1 Peptoniphilus sp. oral taxon1 375 str. F0436 StreptococcusSelenomonas constellatus flueggei subsp. ATCC 43531 pharyngis SK1060 = CCUG 46377 1 Streptococcus sp. C150 1 1 map00051 Fructose and Mannose metabolism Lactobacillus brevis subsp. gravesensis 1 Oribacterium sp. oral taxon 078 str. F0262 ATCC 27305 1 EC 5.3.1.1 Lachnoanaerobaculum saburreum DSM aspartate- Staphylococcus aureus subsp. aureus MN83986 EC 5.3.1.8 EC 4.1.2.19 map00650 Butanoate metabolism Lactobacillus jensenii JV-V16 1 1 Erysipelotrichaceae bacterium 5_2_54FAA1 Anaerofustis1 stercorihominis DSM 17244 1 1 Butyrivibrio crossotus DSM 2876 map00030 Pentose Phosphate metabolism 1 EC 1.7.7.1 Eubacterium infirmum F0142 Streptococcus sp. oral taxon 056 str. F0418 1 semialdehyde map00910 Nitrogen metabolism Lactobacillus ruminis ATCC Fusobacterium25644 nucleatum subsp. animalis EC 1.7.1.4 ATCC 51191 1 Parvimonas sp.1 oral taxon 110 str. F0139 map00910 1 EC 1.4.1.2 map00740 Riboflavin metabolism 1 1 EC 1.18.6.1 Anaerococcus vaginalis ATCC 51170 1 dehydrogenase map00270 Cysteine and Methionine metabolism EC 1.7.99.1 1 Selenomonas infelix ATCC 43532 map00920 Sulphur metabolism 1 Roseburia intestinalis L1-82

03/06/2020 University of Nebraska at Omaha GRACA 2019 Uniquely Encoded Bacterial Enzymes Potential Drug Targets

03/06/2020 University of Nebraska at Omaha GRACA 2019 Bacterial metabolism Bacteria Enzymes Pathways

Streptococcus constellatus subsp. pharyngis SK1060 = CCUG 46377 0.14 MPBA

Eremococcus coleocola 0.18ACS-139-V-Col8map00650 Streptococcus0.14 infantis X map00650 Bacteria Pathways

0.22 0.22 0.14 Streptococcus sanguinis SK72 0.3 Bacterial species 0.22 0.14 0.22 0.18 0.14 0.22 0.18 0.22 0.22 Subdoligranulum variabile DSM 15176 0.14 Streptococcus sp. oral taxon 071 str. Lactobacillus oris PB013-T2-3 Pathway Peptoniphilus Weissellasp. oral taxon paramesenteroides 836Anaerotruncus str. F0141 ATCC colihominis 33313 DSM 17241 73H25AP Anaerococcus tetradius ATCC 35098 Anaerococcus vaginalis ATCC 51170 0.18 Streptococcus sanguinis SK1087 0.14

0.14 0.14 Selenomonas flueggei ATCC 43531 Coprococcus eutactus ATCC 27759 0.18 Streptococcus sanguinis SK1057 Streptococcus parasanguinis ATCC 903 0.18 0.14 0.22 0.140.18 0.30.14 0.26 0.3 0.18 0.22 0.18 0.220.22 Streptococcus sanguinis SK1150.18 u Paenibacillus sp. HGF7, 0.18 0.38 0.18 Lactobacillus rhamnosus LMS2-1 StreptococcusFusobacterium oralis mortiferum SK313 ATCC 9817 0.26 Lachnospiraceae bacterium 5_1_57FAA 0.22 0.3 0.14 map00051 0.18 0.38 0.3 Streptococcus infantis ATCC 700779 Selenomonas noxia F0398 Erysipelotrichaceae bacterium 3_1_53 0.26 0.26 0.18 Streptococcus sp. oral taxon 056 str. F0418 map00051 0.18 0.34 0.22 0.54 Staphylo- coccus epidermidis 0.34 0.18 Selenomonas infelix ATCC 43532 Peptoniphilus harei ACS-146-V-Sch2b 0.46 0.54 0.14 0.22 Selenomonas noxia ATCC 43541 Lactobacillus buchneri ATCC 11577 Streptococcus oralis ATCC 35037 0.22 0.3 Streptococcus australis ATCC 700641 Faecalibacterium prausnitzii A2-165 0.18 0.18 0.26 0.26 0.22 BacillusOribacterium smithii 7_3_47FAA sp. oral taxon 078 str. F02620.38 0.18 0.3 0.14 0.54 0.3 0.22 Clostridiales bacterium 1_7_47FAA 0.3 0.22 0.14 0.62 0.18 Streptococcus cristatus ATCC 51100 Anaerococcus hydrogenalis 0.140.14 0.140.22 Staphylococcus aureus subsp. aureus Ruminococcaceae bacterium D16 Streptococcus0.14 oralis SK255 0.22Lactobacillus iners LactinV 01V1-a ACS-025-V-Sch4 map00740 0.18 0.42 USA300_TCH959 0.34 0.26 0.5 0.3 StreptococcusFaecalibacterium sanguinis cf. VMC66 prausnitzii KLE1255 Lachnospiraceae bacterium 1_4_56FAA W23144, Streptococcus Lachnoanaerobaculum saburreum DSM 0.54 0.42 Staphylococcus epidermidis3986 W231440.46 0.62 Centipeda periodontii DSM 2778 0.42 0.3 Lactobacillus crispatus 125-2-CHN 0.46 Streptococcus infantis SK1076Paenibacillus sp. HGF5 0.54 Streptococcus mitis ATCC 6249 0.3 map00740Clostridium0.26 sp. 7_3_54FAA 0.94 0.42 0.7 0.62 PeptoniphilusListeria grayi sp. DSM oral 20601 taxon 386 str. F0131 0.58 0.14 Streptococcus sanguinis SK1056 0.260.3 0.14 0.260.58 0.3 0.14Mitsuokella multacida DSM 20544 0.66 0.98 0.26 0.7 sanguinis SK1087, 0.26 Pseudoflavonifractor capillosus ATCC 29799 Lactobacillus fermentum 28-3-CHN 0.3 0.22 0.3 0.34 Dialister microaerophilus UPII 345-E Lachnospiraceae 0.22bacterium 7_1_58FAAClostridium sp. HGF2 0.26 0.18 0.5 0.78 0.34 0.26 0.30.30.26 0.5 0.62 0.14 0.38 Fusobacterium varium ATCC 27725Granulicatella 0.18elegans0.26 ATCC 700633 Lactobacillus kisonensis F0435 0.34 0.22 Selenomonas sp. oral taxon 149 str. 0.26 0.26 0.38 0.26 0.34 0.3 67H29BP0.14 0.26 0.7 0.18 0.14Lactobacillus crispatus MV-3A-US 0.26 Lachnospiraceae bacterium 9_1_43BFAA Anaerostipes hadrus DSM 3319 Lactobacillus buchneri ATCC 0.3 0.18 0.18 0.38 0.180.26 0.380.46 0.34 0.14 0.26 0.3 Anaerococcus lactolyticus ATCC 51172 0.26 0.18Anaerofustis stercorihominis DSM 17244 0.22 0.26 0.220.38 0.26 0.26 0.38 0.22 0.3 0.26 Streptococcus mitis0.54 SK1073 map00270 0.5Megasphaera0.22 genomosp. type_10.7 str. 28L 0.18 Streptococcus sanguinis SK678 0.26 0.34 0.22 0.18 0.3 0.3 0.22 0.3 0.42 0.18 0.46 Anaerococcus hydrogenalis0.18 DSM 7454 0.34 0.3 0.18 Staphylococcus aureus subsp. aureus MN8 0.3 0.26 0.14 0.18 0.26 0.3 0.3 0.3 0.46 0.3 0.5 0.34 0.3 0.18 0.140.14 0.26 Eubacterium saphenum ATCC 49989 Lactobacillus0.26 parafarraginis F0439 stomatis DSM 17678 Stomatobaculum longum 0.22 0.26 0.22 0.3 11577 Anaerococcus prevotii ACS-065-V-Col13 map00270 0.18 0.14 0.38 0.3 0.22 0.82 0.18 0.220.26 0.66 0.5 0.38 0.34 0.260.26 0.34 Lactobacillus ruminis ATCC 25644 Veillonella sp. 6_1_27 0.3 0.46 0.18 0.26 0.3 Streptococcus sanguinis SK353Blautia hansenii DSM 20583 0.14 map00910 0.340.42 0.26 0.26 Eubacterium sp. 3_1_31 Sporosarcina newyorkensis 2681 Dorea longicatena DSM 13814 0.18 0.42 Dialister micraerophilus DSM 19965 0.38 0.22 0.660.380.46 0.42 Selenomonas sp. F0473 0.38 0.58 Burkholderiales0.46 bacterium0.22 1_1_47 Peptoniphilus sp. oral taxonLactobacillus 375 str. F0436 antri DSM 16041Bacteroidetes oral taxon 274 str. F0058 0.38 0.66 0.46 Peptoniphilus indolicus ATCC 29427 0.74 0.78 0.62 0.98 Lactobacillus delbrueckii subsp. lactis DSM 0.42 0.46 Lactobacillus johnsonii ATCC0.42 33200 0.38 0.34 0.3 Peptoniphilus duerdenii ATCC BAA-1640 0.74 0.14 map00910 20072 0.78 StreptococcusGranulicatella0.22 gallolyticus adiacens 0.34subsp. ATCC 49175 0.5 0.3 0.620.5 Peptoniphilus lacrimalis 315-B 0.34 Faecalibacterium0.42Streptococcus prausnitzii sanguinis M21/2 SK160 0.14 0.3 0.34 0.34 gallolyticus TX20005 Streptococcus0.54 sanguinis SK408Erysipelothrix rhusiopathiaeCoprobacillus ATCC sp. 8_2_54BFAA 19414 Phascolarctobacterium succinatutens YIT 0.42 0.42 0.3 Bacillus0.38 sp. 7_6_55CFAA_CT2 0.26 12067 Fusobacterium nucleatum subsp. animalis 0.46 0.62 0.66 0.14 0.26 Clostridium sp. M62/1 F0419 Veillonella sp. oral taxon 1580.5 str. F0412 0.18 0.38 0.58 0.62 0.42 Finegoldia magnaPeptostreptococcus ACS-171-V-Col3 anaerobius 653-L 0.42 Lactobacillus jensenii JV-V16 0.42 map00920 0.14Paenibacillus sp. oral taxon 786 str. D14 ClostridiumEnterobacteriaceae sp. SS2/1 bacterium 9_2_54FAA 0.34 Pediococcus acidilactici DSM 20284 0.62 u Sulphur and Nitrogen Fusobacterium nucleatum subsp. animalis 0.34 0.42 LactobacillusStreptococcus amylolyticus sanguinis DSMLeptotrichia0.18 11664 SK355 goodfellowii F0264 0.14 0.42 Lactobacillus jensenii 269-3 0.58 Paenibacillus sp. HGF7 0.58 Streptococcus sanguinis SK49 ATCC 51191 0.18 Megamonas funiformis YIT 11815 0.5 0.38 Streptococcus sp. M143 Lactobacillus crispatus CTV-05 0.30.34 Finegoldia magna ATCC 53516 Johnsonella ignava ATCC 51276 0.34 Streptococcus vestibularis ATCC 49124 Dialister succinatiphilus YIT 11850Veillonella sp. 3_1_440.46 Bacillus0.38 sp. 2_A_57_CT2 Roseburia intestinalis L1-82 0.18 Streptococcus sanguinis SK330 Selenomonas artemidis F0399 Clostridium sp. D5Lactobacillus ultunensis DSM 16047 0.18 0.340.38 0.38 0.38 Streptococcus sanguinis SK150 0.5 map00920 0.34 0.18 Lachnospiraceae bacterium 3_1_46FAA 0.3 0.3 0.34 Clostridium sp.0.46 L2-50 0.54 0.46 0.3 0.34 0.3 Finegoldia magna SY403409CC001050417 Dolosigranulum pigrum0.34 ATCC 51524 0.58 0.34 metabolism has enzymes 0.3 Veillonella sp. oral taxon 780 str.0.42 F0422 Streptococcus sp. C300 0.14 Streptococcus sp. M334 Streptococcus0.58 sp. C150 0.340.3 0.3 0.34 Streptococcus mitis SK1080 Lactobacillus crispatus FB077-07 0.3 0.38 0.3 0.34 Erysipelotrichaceae bacterium 6_1_450.26 0.58 0.38 0.42 0.5 0.3 0.34 Finegoldia magnaStreptococcus BVS033A4 equinus ATCC 9812 0.3 0.380.260.5 Butyrivibrio0.3 crossotus DSM 2876 0.22 0.3 0.3 0.7 Lactobacillus fermentum ATCC 14931 0.3 0.18 0.54 0.380.54 0.26 Lactobacillus paracasei0.34 subsp. paracasei 0.42 0.5 0.34 Streptococcus salivarius SK126 0.38 Clostridium0.5 sp. 7_2_43FAA Streptococcus0.38 perorisPseudoramibacter ATCC 700780 alactolyticus ATCC 23263 0.26 ATCC 25302 0.54 Selenomonas sp. oral taxon 137 str. F0430 0.38 0.34 0.34 0.26 0.42 0.5 Lachnospiraceae bacterium0.26 2_1_46FAA map00300 Streptococcus0.26 parasanguinis SK236 0.58 Shuttleworthia satelles DSM 14600 0.66 0.54 0.42 0.62 0.42 0.22 0.42 0.54 0.38 0.38 0.3 0.3 uniquely encoded by bacterial 0.26 0.3 0.54 0.22 0.3 0.46Lachnospiraceae bacterium 2_1_58FAA 0.46 0.34 0.34 Veillonella parvula 0.54ATCC 17745 0.22 0.34 Lactobacillus delbrueckii subsp. bulgaricus 0.42 0.26 Holdemania filiformis DSM 12042 Ruminococcus lactaris ATCC 29176 Dorea formicigenerans 4_6_53AFAA Streptococcus mitis bv. 2 str. F0392 PB2003/044-T3-4 0.46 0.38 0.46 0.42 0.34 Streptococcus infantis SK9700.38 Lactobacillus iners DSM 13335 0.3 0.42 0.340.34 0.3 0.38 0.62 0.46 0.22 0.42 0.380.3 Erysipelotrichaceae bacterium 5_2_54FAAListeria innocua ATCC 33091 0.46 0.34 0.34 Streptococcus anginosus F0211 0.26 Eggerthia catenaformis OT 569 = DSM 0.42 0.38 20559 species 0.26 0.3 0.3 Coprobacillus sp. 3_3_56FAA 0.3 0.34 Helcococcus kunzii ATCC 51366 0.42 0.38 Facklamia hominis CCUG0.42 36813 0.34 0.22 0.46 Parvimonas sp. oral taxon 393 str. F0440 0.38 Facklamia ignava CCUG 37419 0.22 0.3 0.34 0.38 Veillonella atypica ACS-049-V-Sch6 0.34 0.3 0.42 Dorea formicigeneransStreptococcus ATCC 27755 sp. F0442 map00300 Ruminococcus sp.0.22 5_1_39BFAA Oribacterium sp. oral taxon 108 str. F0425Leuconostoc mesenteroides subsp. 0.34 map00030Veillonella parvulaLachnospiraceae0.42 ACS-068-V-Sch120.3 bacterium 5_1_63FAA cremoris ATCC 19254 0.26 0.3 Megasphaera micronuciformis F0359 0.3 Streptococcus mitis0.42 bv. 2 str. SK95 0.3 0.46 Dialister invisus DSM 15470 0.34 0.26 0.18 Alloiococcus otitis ATCC 51267 Streptococcus vestibularis F0396 0.3 map00051 Fructose and Mannose metabolism Fusobacterium periodonticum D10 0.38 0.26 0.18 Eubacterium ventriosum ATCC 27560 0.22 0.18 0.3 Lachnospiraceae bacterium0.62 6_1_63FAA Erysipelotrichaceae bacteriumOribacterium 2_2_44A sinus F0268 Fusobacterium necrophorum subsp. 0.34 0.34 funduliforme 1_1_36S map00650 Butanoate metabolism 0.26 0.42 Pediococcus acidilactici 7_4 0.38 0.38

0.18 Veillonella atypica ACS-134-V-Col7a 0.34 0.22 map00030 Pentose Phosphate metabolism 0.3 Roseburia inulinivorans DSM 16841 0.34 0.22 0.34 Coprococcus comes ATCC 27758 0.46 map00910 Nitrogen metabolism StreptococcusLachnospiraceae mitis SK569 bacterium map00030 3_1_57FAA_CT1 map00740 Riboflavin metabolism Anaeroglobus geminatus F0357 Subdoligranulum sp. 4_3_54A2FAA Leptotrichia hofstadii F0254 Lachnospiraceae bacterium 8_1_57FAA map00270 Cysteine and Methionine metabolism Streptococcus parasanguinis F0405 [Bacteroides] pectinophilus ATCC 43243 Lachnospiraceae oral taxon 107 str. F0167 map00300 Lysine biosynthesis Anaerostipes caccae DSM 14662

03/06/2020 University of Nebraska at Omaha GRACA 2019 Herbal Pharmacology Herbs Compounds Targets

EC 1.13.11.31 EC 1.1.1.184

1 EC 3.5.1.98 EC 2.7.11.26 EC 3.2.1.45 Herb Estradiol EC 1.1.1.- u The network 5623 Compound EC 2.7.11.30 1 1 unique herb-target Harman EC 1.1.1.100 1EC 3.1.3.48 1 Target Enzymes Diallyl-Trisulfide EC 3.1.2.14 1 Lemon Balm 1 1 Beta-Sitosterol Curcumin 1 1 interactions. Methyl-Chavicol Estragole 1 1 1 EC 1.8.3.2 1 1Trans-Nerolidol1 1 1 EC 3.4.22.- EC 1.14.13.- EC 3.1.4.11 1 Ionol 1 Ginseng 1 1 Oleanolic-Acid1 EC 2.7.11.22 1 Ursolic-Acid 1 Ferulic-Acid1 1 1 1 Garlic EC 3.4.21.26 Isothymonin 1 1 EC 3.4.21.4 TryptophanEriodictyol EC 3.5.1.2 1 1 EC 5.3.99.31 Tyrosine1 Ginger 1 1 EC1 3.3.2.6 EC 1.13.11.- 1 1 1 EC 2.6.1.7 1 1 EC1 2.7.12.11 Eo EC 3.6.4.121 1 1 1 1 Basil 1 1 1 u Dill, fennel, parsley, 1 1 1 1 1 1 1 1 EC 3.1.1.56 1 EC 3.4.21.- 1 1 EC 2.7.11.13 1 EC 4.2.1.1 1 1 Apigenin 1 EC 1.14.14.51 1 EC 2.7.-.-1 1 1 1 1 EC 2.7.10.1 1 Chervil 1 1 1 1 Serine 1EC 11.1.1.62EC 2.3.2.23 P-Coumaric-Acid 1 1 1 ginseng, french tarragon, 1 1 1 1 Rosemary EC 3.5.4.4 EC 1.14.11.4 Thyme 1 1 1 1 1 1 EC 1.14.11.30 1 EC 3.1.13.4 1 1 1 EC1 3.1.1.1 Borage 1 1 EC 1.14.14.55 1 1 1 EC 2.7.1.6 1 1 EC 1.2.3.11 1 1 1 1 basil, coriander, mint, and 1 1 EC1 2.3.2.- EC 1.4.3.3 1 1 1 Esculetin 1 1 1 1 EC 2.7.1.153 EC 2.6.1.191 Quercetin EC11 1.14.14.531 1 EC 3.4.23.46 1 1 1 EC 1.6.5.5 EC 2.7.11.31 EC 2.4.1.17 1 1 1 Xanthotoxin 1 1 EC 2.7.11.17 1 1 11 1 1 1 EC 3.5.4.3 1 Gallic-Acid 1 1 1 thyme, were capable of 1 1 1 EC 2.3.2.27 1 EC 1.1.1.44 1 EC 2.7.7.7 1 1 1 Coriander EC 1.17.1.4 Apiole EC 3.1.3.21 Threonine 1EC 2.7.4.3 1 Oleic-Acid1 1 1 Riboflavin 1 1 1 1 1 1 1 1 1 1 1 1 targeting 262 enzymes. 1 EC 3.4.19.12 1 Cynaroside 1 EC 2.7.1.91 EC 5.99.1.2 1 Scopoletin1 1 1 1 1 EC 1.1.1.239 Phenylalanine1 1 1 EC 3.1.1.47 1 1 Chives 1 1 1 1 1 EC 13.4.24.- EC 1.8.5.1 1 1 1 1 1 EC 2.6.99.- 1EC 3.1.1.31 1 1 Kaempferol1 1 1 1 1 Oregano EC 2.3.1.- 1 1 1 1 1 1 Glycine Parsley 1 1 1 1 1 1 1 1 EC 1.13.11.331 Zinc EC1 3.4.24.65 1 EC 2.3.1.48 u aldehyde reductase – French Tarragon 1 1 1 EC 1.14.14.1 Caffeic-Acid 1 1 1 1 Fennel 1 1 EC 1.1.1.21 EC 2.1.1.37 EC 1.14.14.52 Rutin 1 1 1 1 1 EC 3.4.24.35 Cardamom 1 1 1 most targeted enzyme, EC 1.2.1.36 1 1 1 1 EC 3.1.1.7 1 1 1 EC 1.14.14.14 EC 4.2.1.59 EC 1.14.18.1 1 1 1 EC 4.2.99.- Sage Ascorbic-Acid 1 EC 5.3.1.8 EC 1.2.1.121 EC 2.7.11.21 1 1 1 Cloves 1 Dill 1 causes imbalances in 5- 1 EC 2.7.11.1 EC 2.7.10.2 EC1 2.3.1.38 Luteolin 1 1 1 11 Mint 1 1 1 1 Imperatorin 1 1 Anisaldehyde 1 1 1 6-Methoxyluteolin1 HT levels. [D. Keszthelyi, et Linoleic-Acid 1 Rosmarinic-Acid Cinnamon 1 1 1 Luteolin-7-Glucoside EC 1.17.3.2 1 1 EC 1.1.1.141 Majoram 1 Cumin 1 Benzaldehyde Ellagic-Acid al., 2009] 1 Cinnamaldehyde EC 3.1.3.11 Salvin 1 1 Nepetin 1

EC 1.2.1.- 1 1 EC 1.8.1.7

EC 3.6.-.- EC 1.14.14.- EC 1.20.4.2

03/06/2020 University of Nebraska at Omaha GRACA 2019 Compounds Targeting Enzymes

03/06/2020 University of Nebraska at Omaha GRACA 2019 Herb-Target Analysis Herbs Compounds Targets MPBA Majoram Herbs Targets Target Enzyme EC 3.4.11.- EC 3.6.-.- EC 2.7.10.1 0.14 Herb 0.14 EC 1.13.11.34

0.33 EC 3.5.1.98 EC 2.7.11.27 EC 3.4.21.117 u The HT network has 5623 EC 1.4.3.4 0.14 0.24 0.14 EC 3.4.22.- 0.19 EC 3.6.3.49 unique herb-target interactions. Basil EC 1.8.3.20.14 EC 3.6.4.12 0.24 0.240.19 0.24 EC 1.8.1.4 EC 1.1.1.141 0.14 0.19 0.19 0.14 Borage EC 1.-.-.- EC 2.7.10.2 Lovage0.19 0.14 0.24 0.140.480.14 0.14 0.33 0.14 EC 1.1.1.21 u The average degree 0.14 0.14 0.33 0.140.24 Rosemary0.240.19 0.14 Dill0.29 0.380.14 0.14 0.24 0.19 0.48 EC 3.3.2.10 0.14 EC 2.3.2.27 EC 3.1.3.10.24 0.14 0.29 0.14 EC 1.14.14.-0.19 0.14 0.14 (connectivity) of the HT network 0.14 0.33 0.14 Oregano 0.24 0.33 0.240.71 0.14 EC 2.6.99.- 0.140.29 0.29 0.19 0.19 0.14 0.33 EC 3.4.24.24 0.14 0.24 0.57 0.14 0.67 0.33 0.24 (39.32) and most herbs targeted 0.14 0.14 0.480.14 0.29 0.19 0.19EC 1.14.14.1 0.33 0.33EC 2.7.11.22 EC 3.2.1.20 EC 1.14.13.- 0.24 EC 2.7.7.7 0.670.19 EC 2.7.11.10.38 0.43 0.19 0.190.24 0.29 0.14 0.38 0.14 0.19 0.29 0.43 EC 4.2.99.- on an average of 23.3 enzymes 0.43 0.140.29 0.140.57 0.430.14 0.14 0.430.52 0.48 0.290.24 0.14 Cloves EC 1.14.14.55 0.29 0.14 0.29 0.24Mint 0.380.52 0.62 0.24 0.24 0.19 EC 2.3.1.48 0.19 0.14 0.19 0.24 0.19 Fennel 0.33 EC 2.7.11.31 0.24 0.24 Thyme0.33 which is significantly higher than 0.14 0.14 0.29 EC 2.1.1.37 0.19 0.19 0.62 Parsley 0.14 Chives 0.38EC 1.14.14.56 EC 1.14.14.14 French Tarragon0.14 0.14 the modern synthetic drugs (1.8) 0.38 0.38 EC 3.1.-.- 0.19 0.380.71 Coriander 0.19 0.19 0.19 0.33 0.48 0.43 0.67 0.33 Garlic 0.19 0.14 0.14 0.290.95 0.19 0.24 0.190.38 [M.AY, et al., 2017]. EC 2.5.1.18 0.14 0.52 0.48 0.14 0.19 0.29 0.19 0.520.33 EC 1.13.11.31 0.24 EC 2.7.11.26 EC 4.2.1.1 0.33 0.33 0.29 0.38 0.19 EC 3.1.1.7 0.19 0.14 EC 2.7.12.10.19 0.29 0.14 0.14EC 1.13.11.-0.480.33 Sage 0.380.19 0.190.19 0.38 0.52 0.52 0.19 EC 1.17.3.2 0.43 0.570.14 EC0.24 1.2.1.36 0.38 0.24 0.14 0.29 EC 4.4.1.50.19 0.14 0.33 0.24 EC 1.14.14.53 Cardamom 0.29 Ginger 0.24 0.19 Ginseng 0.19 0.52 EC 1.8.5.1 0.38 0.24 EC 1.2.1.12 0.19 EC 1.13.11.33 0.24 0.240.14 0.29 0.14 EC 1.2.1.- 0.19 EC 3.1.4.- 0.14 EC 1.14.14.51 EC 2.7.1.149 0.14 EC 1.14.14.52 0.14 EC 3.1.1.47 Cinnamon

EC 1.17.1.4 EC 3.4.21.- EC 1.14.18.1 EC 2.7.11.24 EC 3.4.24.- EC 1.20.4.2 EC 2.7.11.21

03/06/2020 University of Nebraska at Omaha GRACA 2019 Herb-Target-Pathway Net Herbs Targets Pathways

map00051 Herb Fennel Ginseng Pathway 1.0 map00650 u Herb-target-pathway

1.0 Thyme EC 4.1.2.13 (HTP) network was Target Enzyme 1.0 1.0 0.57 constructed with 24 Chervil Basil 0.14 1.0 1.0 0.52EC 2.6.1.19 Coriander herbs, 262 target Cloves 1.0 0.14 0.48 1.0 map00030 0.29 0.38EC 2.7.1.1 Parsley enzymes, 8 metabolic 0.880.48 0.7 EC 1.2.1.24 EC 1.1.1.210.110.48 pathways, and 5893 Oregano 0.43 0.7 Garlic Lovage 0.43 1.0 distinct interactions. 0.190.19 0.52 0.57 EC0.380.52 2.6.1.5 Chives 0.05 0.43 EC 1.1.1.30 EC 5.3.1.8 Dill 0.7 0.64 0.57 0.43 0.33 0.52EC 1.1.1.44 Rosemary 0.52 EC 4.4.1.13 0.38 Cumin 0.05 0.29 EC 4.2.1.1 0.14 0.7 1.0 0.95 0.52 0.38 Mint French0.19 Tarragon0.19 Ginger 1.0 EC 3.1.3.2 Sage Cinnamon0.05 0.19 0.14 0.14 Cardamom EC 2.1.1.37 1.0 1.0 0.11 map00270 Majoram 1.0Borage map00051 Fructose and Mannose metabolism map00650 Butanoate metabolism map00030 Pentose Phosphate metabolism map00910 Nitrogen metabolism Lemon Balm map00740 Riboflavin metabolism map00910 map00740 map00270 Cysteine and Methionine metabolism

03/06/2020 University of Nebraska at Omaha GRACA 2019 Herbs Compounds Targets

MPBA Herb-Pathway Analysis Herbs Targets Pathways

MPBA Herbs Pathways

u Fructose and Mannose metabolism (map00051) is the pathway targeted by most herbs including, coriander, chives, thyme, parsley, ginger, garlic, mint, basil, etc.

u Butanoate and Pentose Phosphate metabolism are targeted by fewer no. of herbs.

03/06/2020 University of Nebraska at Omaha GRACA 2019 Herb-Pathway-Bacteria Net. Herbs Pathways Bacteria u What are the potential herbs which can regulate bacterial functions and compositions? u What are the bacterial map00051 species a given herb can target? map00740 u And what are the map00910 metabolic functions the herbs can regulate? map00030

map00270 map00650

map00920 Sulphur metabolism

03/06/2020 University of Nebraska at Omaha GRACA 2019 Herb-Bacteria Analysis Herbs Bacteria

• Species including Sporosarcina newyorkensis 2681, Centipeda periodontii DSM 2778, Enterobacteriaceae bacterium 9 2 54FAA, Staphylococcus epidermidis W23144, are targeted by various herbs - mint, garlic, ginger, thyme, and, basil.

• Alternatively, species including Helcococcus kunzii ATCC 51366, Facklamia ignava CCUG 37419, Eubacterium infirmum F0142, and Fusobacterium necrophorum subsp. funduliforme 1 1 36S are found to harbor unique enzymes which are not commonly encoded by the bacterial genes and are targeted by fewer herbs like sage, borage, etc

03/06/2020 University of Nebraska at Omaha GRACA 2019 Discussion u Herbal medicines, when exposed to gut microbes, metabolizes into active or absorbable compounds which are accomplished by the secreted enzymes of intestinal microflora. u Results show ginseng has as an effect on 6 out of the 8 given pathways such as, Nitrogen metabolism, Fructose, and mannose metabolism, Riboflavin metabolism, Cysteine and methionine metabolism, Butanoate metabolism, and Pentose phosphate pathway, and can target 237 enzymes belonging to these pathways which can be harbored by 235 bacterial species. u Ginseng exerted a weight loss effect (antiobesity) on gut microbiota in all participants, which differed depending on the composition of gut microbiota. [Mi-YoungSong, et at., 2014]

03/06/2020 University of Nebraska at Omaha GRACA 2019 Conclusion MPBA allows us to:

Identify what herbal compounds can affect the bacterial composition u Understand the gut bacterial metabolism under normal or diseased condition. u Functionally classify the gut microbiome through their metabolic pathways and the secreted enzymes. u Understand the pharmacology and mechanism of action of the herbs. u Model the effect of natural drug mechanism on the gut microbiome and in turn the host health. u Integrate heterogeneous data and predict critical associations. u Mathematically model complex biological systems like the herbs and gut ecosystem and provides a systems approach for characterizing the functions of the system.

03/06/2020 University of Nebraska at Omaha GRACA 2019 Limitations

n The potential limitation that we encounter lies in the incompleteness of the networks due to unavailable compound-target knowledge. n Difficulty in differentiating the therapeutic effects from the side effects, in spite of the large volume of data that currently exist.

n The proposed method could not identify the type of activity of the bioactive compounds on the target enzymes, except for some supporting literatures.

03/06/2020 University of Nebraska at Omaha GRACA 2019 Future Work

u Include abundance information from clinical samples. u Include other factors like, age, gender, environment, lifestyle, etc., which also play a vital role in shaping the microbiome of an individual.

03/06/2020 University of Nebraska at Omaha GRACA 2019 Thank you

03/06/2020 University of Nebraska at Omaha GRACA 2019 Questions

Contact:

Suganya Chandrababu Email: [email protected] University of Nebraska at Omaha

03/06/2020 University of Nebraska at Omaha GRACA 2019