This document discusses computational methods for integrating different types of omics data and discovering relationships between biomedical entities. It begins with an introduction to systems biology and analyzing biological systems using various omics data like genomics, epigenomics, transcriptomics, proteomics, and metabolomics. It then describes data-driven and knowledge-driven methods for integrating paired omics data measured from the same samples to identify networks of associations and generate novel hypotheses.

1. Computa(onal methods for
integra(ve omics and rela(on
discovery between biomedical
en((es
Feb 11, 2016
Karan Uppal (PhD) and Sophia A. Banton (MS, PhD
candidate)
Clinical Biomarkers Laboratory
Emory University School of Medicine

2. Learning Objec(ves
• Data-driven methods for integra(ng paired –
omics data and visualizing associa(ons (Karan
Uppal)
• Knowledge-driven methods for integra(ng
paired –omics data (Sophia Banton)

3. • Pa(ent
medical
records
• Clinical notes
• Lab results,
etc.
A) Electronic
Health Records
(EHR)
C) Omics data
Metabolo
mics
GWAS Epigenomics
Transcriptomi
cs
Proteomi
cs
B) Scien(fic
literature
Cochrane
Library
PubMed
Pa(ent
(disease/symptoms)
Clinician
Data sources to
support healthcare
decision making and
facilitate precision
medicine
Other
“omes”
Systems Biology

4. Introduc6on: A Systems Biology Framework
• The goal of Systems Biology:
– Systems-level understanding of biological systems
– Analyze not only individual components, but their interac(ons as well and
emergent behavior
4
Internal measurements
Exposures
Disease states
Systems Biology
“Integrative approach in which scientists study pathways and
networks will touch all areas of biology, including drug
discovery”
C. Henry and C. Washington

5. Dissec6ng the Biological system via -omics
5
Ritchie MD, Holzinger ER, Li R, Pendergrass SA, Kim D. 2015. Methods of integrating data to uncover genotype-phenotype interactions. Nat Rev Genet
16:85-97.

6. Dissec6ng the Biological system via -omics
6
Ritchie MD, Holzinger ER, Li R, Pendergrass SA, Kim D. 2015. Methods of integrating data to uncover genotype-phenotype interactions. Nat Rev Genet
16:85-97.
“Informa6on Overload”: >10,000 variables per –omics experiment

7. Ritchie MD, Holzinger ER, Li R, Pendergrass SA, Kim D. 2015. Methods of integrating data to uncover genotype-phenotype interactions. Nat Rev
Genet 16:85-97.
Meta-dimensional analysis can be divided into three categories. a | Concatenation-based integration involves combining data sets
from different data types at the raw or processed data level before modelling and analysis. b | Transformation-based integration
involves performing mapping or data transformation of the underlying data sets before analysis, and the modelling approach is
applied at the level of transformed matrices. c | Model-based integration is the process of performing analysis on each data type
independently, followed by integration of the resultant models to generate knowledge about the trait of interest. miRNA, microRNA;
SNP, single-nucleotide polymorphism.
Methods of omics integration
7

8. Data-driven methods for
integra(on

9. Paired integra(ve –omics analysis
• Discover networks of associa(ons or correlated
variables (genes, proteins, metabolites, microbiome,
epigene(c altera(ons, clinical variables, etc.) from
paired –omics data measured across same samples
– Univariate or mul(variate regression
– Example: explaining protein abundance with respect to
gene expression
• Determine if different –omics data point to same
disease mechanism
• Generate novel hypotheses for further inves(ga(on

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9_543.6806912126.3625688_552.3434156351.4421175_549.0735189488.8326192_554.7346832125.5528099_551.878674102.2253997_547.272390985.94128663_548.859660186.70025866_551.324386112.3030863_551.3134802134.3979716_549.9271324208.3209038_548.5451249260.4272927_548.3488236370.6144124_548.7596569187.7708371_533.2935842485.7288577_560.0602596349.6206031_539.819177699.5287192_511.9771877199.6839632_543.104557793.17536575_521.2370705325.327837_545.5500437438.9944861_539.314654297.51034036_522.9406677353.62927_541.1363778267.6457446_548.9696353466.3890355_562.6403935391.1842171_492.2880889420.3144177_532.4332384464.367479_305.1039439491.3874572_296.6305536256.1187295_181.4803187338.386929_546.9473348353.4906434_535.188477336.4443089_549.4880877385.4191338_538.4837321245.2227432_520.7017795314.1414433_520.153437798.3185302_532.5864604322.5652481_547.9137286181.158246_515.6547753344.7426304_521.0499971412.0755116_528.0586679263.5238442_551.6493063263.9830589_549.2916723490.870276_565.4666289329.8999903_59.72157486413.414247_485.1831589523.0270903_117.6200968329.9905618_120.508997331.9872453_122.73413186.9911755_482.6019609422.0890492_113.9474153249.0431519_486.2370062192.979825_491.452196165.9867164_497.1107771203.0044658_502.4767888289.0739376_502.2989443303.0893668_508.6798471162.9894455_508.407185166.9852896_478.3354479323.0580544_479.6284024408.3474272_583.4301896197.5586331_553.6175498565.243082_530.0170963139.2923952_554.0173574139.2947209_535.641420896.43160283_541.100560695.37530707_540.7606348188.8391828_540.2543666142.2169867_541.0755826222.8545158_542.125718192.5504976_540.2282397145.1814437_540.4626305141.5983783_541.1808672146.99859_541.0060272419.7209541_540.318356145.1369038_538.4526193137.0973052_541.5194704144.5994059_538.832334995.49226951_541.2334808175.9098196_540.4774537111.2033398_540.2888052112.3305363_540.760309795.25867631_541.1183825221.2044163_539.5605388114.365631_541.436486795.60948651_541.0676006112.6810091_541.1648675183.4538061_541.1966246112.6431062_541.0885907134.6655107_540.4921052179.0602279_537.5340346112.7070771_540.9009705115.7299491_540.8696097184.8291528_541.2578951188.4889416_541.3478834112.4803681_541.3175907111.0194442_541.5314928499.8593285_541.3599866184.3220716_540.9893703142.4165104_541.09023187.9282728_541.2587812109.242_541.2182118175.4504445_541.1402887184.2887271_541.0885418141.8562885_541.2517208142.4971854_541.2904838303.4899124_541.7160921141.8147378_541.1007423184.899641_541.0555891110.7040759_541.058484190.84402321_541.2259919115.5177241_541.10083109.0890252_541.1171007179.5385289_541.4479105212.9907994_167.4094644214.986324_514.4174391490.3388548_499.0265952296.899411_482.6459159200.9460848_505.1258227209.9933949_505.7950767309.9183156_482.5367116211.9934518_495.5066855308.9197962_501.3177222312.9158319_507.3554122318.9095314_538.6709653322.904534_502.6266582249.0060127_492.4533173263.0217332_480.359289194.9954437_531.2993118322.9156697_494.3881598334.3326442_573.9846405317.252732_557.5128375334.2818047_559.9986259631.3232155_559.9594873360.3757852_546.8031337444.337368_548.8606592380.2262221_509.9258186344.2420142_492.0830161291.1962622_516.5433879349.1937892_503.9262023259.1913203_549.7215572362.2545994_503.4179088330.3357462_535.5255334358.3697721_545.1908655359.3727681_546.4205352402.3958724_545.5652235228.2329301_585.5287901172.6130521_448.2184981527.0672073_504.3529426
−5 0 5
Value
Color Key
X0750irrdd2.2.cdf
X0816irrdd2.2.cdf
X0877iddrd2.2.cdf
X0960irrdd2.2.cdf
X0750irrdd3.2.cdf
X1162irrdd3.2.cdf
X0986irrdd2.2.cdf
X0877irrdd3.2.cdf
X1162irrdd2.2.cdf
X0750cntd7.2.cdf
X0986cntd7.2.cdf
X0960cntd7.2.cdf
X1162cntd7.2.cdf
X0960cntd2.2.cdf
X1162cntd3.2.cdf
X0816cntd7.2.cdf
X0816cntd2.2.cdf
X0816cntd3.2.cdf
X0877cntd3.2.cdf
X0877cntd7.2.cdf
X0986cntd3.2.cdf
X0986cntd2.2.cdf
X0960cntd3.2.cdf
X0750cntd3.2.cdf
X0750cntd2.2.cdf
X0877cntd2.2.cdf
X1162cntd2.2.cdf
X0986irrdd3.2.cdf
X0816irrdd3.2.cdf
X0816irrdd7.2.cdf
X0877irrdd7.2.cdf
X0986irrdd7.2.cdf
X1162irrdd7.2.cdf
X0750irrdd7.2.cdf
X0960irrdd7.2.cdf
X0877cntd17.2.cdf
X1162cntd17.2.cdf
X0816cntd14.2.cdf
X0986cntd14.2.cdf
X0816cntd28.2.cdf
X0877cntd28.2.cdf
X1162cntd28.2.cdf
X0960cntd28.2.cdf
X0986cntd28.2.cdf
X0750cntd21.2.cdf
X1162cntd21.2.cdf
X0960cntd17.2.cdf
X0986cntd17.2.cdf
X0750cntd17.2.cdf
X0816cntd17.2.cdf
X0750cntd10.2.cdf
X0877cntd21.2.cdf
X0960cntd21.2.cdf
X0750cntd14.2.cdf
X0816cntd10.2.cdf
X0877cntd10.2.cdf
X0986cntd10.2.cdf
X1162cntd14.2.cdf
X0960cntd10.2.cdf
X1162cntd10.2.cdf
X0877cntd14.2.cdf
X0960cntd14.2.cdf
X0750cntd35.2.cdf
X0877cntd35.2.cdf
X0986cntd21.2.cdf
X0816cntd21.2.cdf
X0750cntd28.2.cdf
X0960cntd35.2.cdf
X1162cntd35.2.cdf
X0816cntd35.2.cdf
X0986cntd35.2.cdf
X0986irrd35.2.cdf
X0816irrd35.2.cdf
X1162irrd35.2.cdf
X0877irrd35.2.cdf
X0960irrd35.2.cdf
X0816irrd14.2.cdf
X0986irrd14.2.cdf
X0816ittd21.2.cdf
X0986irrd21.2.cdf
X0750irrd28.2.cdf
X0877irrd28.2.cdf
X0986irrd28.2.cdf
X0877irrd17.2.cdf
X0986irrd17.2.cdf
X0816irrd28.2.cdf
X0750irrd35.2.cdf
X0960irrd28.2.cdf
X1162irrd28.2.cdf
X0877irrd21.2.cdf
X0960irrd21.2.cdf
X1162irrd21.2.cdf
X0750irrd21.2.cdf
X0816irrd17.2.cdf
X1162irrd17.2.cdf
X0750irrd17.2.cdf
X0960irrd17.2.cdf
X0750irrd14.2.cdf
X0960irrd10.2.cdf
X0960irrd14.2.cdf
X1162irrd14.2.cdf
X0877irrd14.2.cdf
X0986irrd10.2.cdf
X0877irrd10.2.cdf
X1162irrd10.2.cdf
X0750irrd10.2.cdf
X0816irrd10.2.cdf
551.5859951_471.9759025529.4376461_539.8285224546.4243019_523.1556065547.4271663_526.2363159233.0689917_490.7579052535.4505804_532.1500798348.1773101_535.1897514354.2294185_536.2349615365.1953471_527.4346883471.2737825_555.4923569594.4653686_525.0570538228.1139203_465.8183376551.0849288_491.8148044431.3368909_562.3868326404.2809145_551.3156691547.4479183_529.9512275212.0897938_477.736422316.1656468_537.3225129307.1591566_531.3062747253.7276241_448.6706638506.4422163_499.4587144682.4089338_571.3186564455.5302829_469.1895487590.4153904_527.8768707323.185583_516.8517473354.2162392_537.3378765353.2029908_548.1090893373.1907353_541.0360446353.1573289_540.5154177298.1559911_546.9382199455.5937805_44.17214808320.2274219_555.2525695337.2592043_554.215962363.2738762_543.401315430.3330383_554.0649703446.3276835_553.0300436323.1633642_550.9715589432.313578_552.5961041283.1305615_525.3281571346.2293277_525.2951014289.1809268_548.5812503305.1758526_544.9037518369.1961826_540.6358688333.1976024_541.6896398351.2168739_520.87455303.1967781_543.4467123323.2219958_549.0190239406.8179962_540.9221163438.3435472_443.9449503353.1733614_537.7456659548.4598401_524.2762655361.2210255_552.6408999532.4645874_545.6798553533.4684048_546.0769454534.4725917_546.3351778313.0682688_493.6267954314.1886946_528.190614383.2735553_551.0826629365.2159934_534.1014981545.4322986_536.8188169549.4630213_543.4436076402.2908794_547.7217445528.4314123_553.2860452347.2423119_533.8969493339.1305178_531.9654868315.1961298_556.9598907347.2132825_548.7013793448.2502347_549.6484329329.2123517_530.1333469629.3886093_558.5096133630.3925047_559.364221348.2180326_525.0291541364.2136963_516.1065997360.2092777_538.6483324552.3134385_569.054043333.2266335_557.9049279336.2548713_553.7501974105.0676709_556.7167396363.3119304_583.2702098407.3380474_584.9716496329.1024272_535.0043574513.3237167_564.7458241541.4180794_585.5436013628.471463_583.0780646313.2174201_561.489423314.2210041_571.4016718316.2001448_547.7994493299.2015583_554.3473771311.2017295_538.4397432491.254069_557.2946268535.2876623_559.7423431536.2896206_563.4825832312.2012513_520.5068429630.3658231_561.996786533.4297917_553.5609892532.4455858_559.9500556533.4493758_552.2661325301.2175382_556.965358300.204322_541.9357178301.1808788_542.3492052343.2282279_538.3697426346.1844654_510.7241538353.1647166_521.695479104.4059677_562.2693329652.3926679_538.3595097319.1914464_546.0601276317.2033418_549.7560074319.2279067_554.8232913299.1287715_530.2906821285.1773133_523.7371816285.1820164_537.6685565433.2512959_548.3785162415.3048761_573.265249450.2862223_549.2321248787.1066904_566.9769883405.7802587_539.0612568405.7906434_554.2375236385.2951781_553.8628871535.4758534_547.9609031566.2220321_563.0969812609.2194766_556.3737553610.2187437_421.4031174379.2356286_541.4022053568.3482506_552.2722616348.2128079_505.6274627593.1712687_573.165673580.4270074_538.5403093240.1235369_276.6635341340.1276175_538.2966273211.0931491_478.5848188366.1928865_503.3821565305.1580488_551.6394932609.3480503_505.0756919655.3829627_556.3653037785.1088081_575.4620377106.4926529_546.2773127637.3985254_551.3836228185.1071571_473.5735498594.3882447_556.4115825596.3855908_566.5157332636.4161639_559.2318222562.3230895_545.1713706639.3866175_558.8251824581.3844283_548.3080336362.9177512_72.85364214696.4260093_553.9520979621.5109236_588.2195489607.4600209_585.9751807623.4919804_589.0466361159.1397476_549.412635622.5174381_589.1139816632.8428729_539.3293312632.9184409_548.3506137599.4835892_564.8079395602.5038525_570.3805337670.4797563_570.217266680.5178335_560.6745559606.4872022_562.0963383606.5062619_538.7246859673.3999774_547.7002182609.4489725_537.1477237669.441021_543.5882308636.5465392_554.4735045697.526184_551.5247215581.4768624_569.2253114264.2418399_588.8124222281.2173752_556.2156347600.5168938_587.8397053149.6313502_569.165761616.5469902_559.4280795650.5184894_587.8002155597.4890311_588.8191629629.5442978_583.3752767619.4700892_576.6737595620.4727256_587.0254229629.5289329_563.3639563651.4313844_579.2187509652.4335742_579.7586791169.6294463_548.0966018300.2604655_550.1411397665.4147445_553.6560001610.4382429_555.8772697618.3670423_563.6234026612.4041342_545.335839619.4124858_567.4644631277.1101387_519.9301869323.115413_545.1679404569.3638152_536.9350977158.6390034_549.1128265167.1550548_539.7125968649.3527512_547.7921871618.4257421_554.9831998612.4521512_557.717354613.4545914_566.5057794254.2205462_567.9757822265.1795295_533.2658547316.2846294_560.9968862299.2591644_575.0158672321.2444504_556.2924556479.3744294_538.6932671376.307073_544.1825402389.2905096_556.0070184253.2540713_575.7536831537.4152751_552.4243182651.490657_557.7907811665.4829533_564.9980689706.567786_584.9973965282.2447738_543.8569935650.4305516_572.8403182605.4804618_574.4818767653.5062776_546.2067243604.5176602_567.6598499605.5041543_554.0969967641.9444922_545.8372212577.4704763_525.8585951105.7588331_548.0272467650.492301_568.2104525635.5415072_559.4849589655.5189273_557.9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−5 0 5
Value
Color Key
G1 G2 - Gn
Subject1 19 19 - 100
Subject2 10 40 - 90
- - - - -
SubjectN 10 40 - 50
M1 M2 - Mn
Subject1 199 19 - 100
Subject2 10 40 90
- - - -
SubjectN 50 30 - 20
G1 G2 - Gn
M1 0.4 0.9 - 0.3
M2 0.7 0.1 - 0.5
M3 0.1 0.6 0.8
Relevance networks
Workflow
Metabolomics data
(n subjects X p metabolites)
Transcriptomics data
(n subjects X q genes)
Associa(on matrix
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
Associa6on score
Genes
Targeted inves6ga6on
(e.g.: Arginine x Transcriptome)
(-) log10 pvalue
Pathway enrichment
2 2.2 2.4 2.6 2.8 3
Methionine and
cysteine
metabolism
Cholesterol
biosynthesis
Clustering
Genes
Metabolites
Univariate
• Pearson Correla(on
• MetabNet (Uppal2015)
Mul(variate
• PLS, CCA, sparse PLS
• mixOmics (Cao 2009)

11. X0750irrdd2.2.cdf
X0816irrdd2.2.cdf
X0877iddrd2.2.cdf
X0960irrdd2.2.cdf
X0750irrdd3.2.cdf
X1162irrdd3.2.cdf
X0986irrdd2.2.cdf
X0877irrdd3.2.cdf
X1162irrdd2.2.cdf
X0750cntd7.2.cdf
X0986cntd7.2.cdf
X0960cntd7.2.cdf
X1162cntd7.2.cdf
X0960cntd2.2.cdf
X1162cntd3.2.cdf
X0816cntd7.2.cdf
X0816cntd2.2.cdf
X0816cntd3.2.cdf
X0877cntd3.2.cdf
X0877cntd7.2.cdf
X0986cntd3.2.cdf
X0986cntd2.2.cdf
X0960cntd3.2.cdf
X0750cntd3.2.cdf
X0750cntd2.2.cdf
X0877cntd2.2.cdf
X1162cntd2.2.cdf
X0986irrdd3.2.cdf
X0816irrdd3.2.cdf
X0816irrdd7.2.cdf
X0877irrdd7.2.cdf
X0986irrdd7.2.cdf
X1162irrdd7.2.cdf
X0750irrdd7.2.cdf
X0960irrdd7.2.cdf
X0877cntd17.2.cdf
X1162cntd17.2.cdf
X0816cntd14.2.cdf
X0986cntd14.2.cdf
X0816cntd28.2.cdf
X0877cntd28.2.cdf
X1162cntd28.2.cdf
X0960cntd28.2.cdf
X0986cntd28.2.cdf
X0750cntd21.2.cdf
X1162cntd21.2.cdf
X0960cntd17.2.cdf
X0986cntd17.2.cdf
X0750cntd17.2.cdf
X0816cntd17.2.cdf
X0750cntd10.2.cdf
X0877cntd21.2.cdf
X0960cntd21.2.cdf
X0750cntd14.2.cdf
X0816cntd10.2.cdf
X0877cntd10.2.cdf
X0986cntd10.2.cdf
X1162cntd14.2.cdf
X0960cntd10.2.cdf
X1162cntd10.2.cdf
X0877cntd14.2.cdf
X0960cntd14.2.cdf
X0750cntd35.2.cdf
X0877cntd35.2.cdf
X0986cntd21.2.cdf
X0816cntd21.2.cdf
X0750cntd28.2.cdf
X0960cntd35.2.cdf
X1162cntd35.2.cdf
X0816cntd35.2.cdf
X0986cntd35.2.cdf
X0986irrd35.2.cdf
X0816irrd35.2.cdf
X1162irrd35.2.cdf
X0877irrd35.2.cdf
X0960irrd35.2.cdf
X0816irrd14.2.cdf
X0986irrd14.2.cdf
X0816ittd21.2.cdf
X0986irrd21.2.cdf
X0750irrd28.2.cdf
X0877irrd28.2.cdf
X0986irrd28.2.cdf
X0877irrd17.2.cdf
X0986irrd17.2.cdf
X0816irrd28.2.cdf
X0750irrd35.2.cdf
X0960irrd28.2.cdf
X1162irrd28.2.cdf
X0877irrd21.2.cdf
X0960irrd21.2.cdf
X1162irrd21.2.cdf
X0750irrd21.2.cdf
X0816irrd17.2.cdf
X1162irrd17.2.cdf
X0750irrd17.2.cdf
X0960irrd17.2.cdf
X0750irrd14.2.cdf
X0960irrd10.2.cdf
X0960irrd14.2.cdf
X1162irrd14.2.cdf
X0877irrd14.2.cdf
X0986irrd10.2.cdf
X0877irrd10.2.cdf
X1162irrd10.2.cdf
X0750irrd10.2.cdf
X0816irrd10.2.cdf
551.5859951_471.9759025529.4376461_539.8285224546.4243019_523.1556065547.4271663_526.2363159233.0689917_490.7579052535.4505804_532.1500798348.1773101_535.1897514354.2294185_536.2349615365.1953471_527.4346883471.2737825_555.4923569594.4653686_525.0570538228.1139203_465.8183376551.0849288_491.8148044431.3368909_562.3868326404.2809145_551.3156691547.4479183_529.9512275212.0897938_477.736422316.1656468_537.3225129307.1591566_531.3062747253.7276241_448.6706638506.4422163_499.4587144682.4089338_571.3186564455.5302829_469.1895487590.4153904_527.8768707323.185583_516.8517473354.2162392_537.3378765353.2029908_548.1090893373.1907353_541.0360446353.1573289_540.5154177298.1559911_546.9382199455.5937805_44.17214808320.2274219_555.2525695337.2592043_554.215962363.2738762_543.401315430.3330383_554.0649703446.3276835_553.0300436323.1633642_550.9715589432.313578_552.5961041283.1305615_525.3281571346.2293277_525.2951014289.1809268_548.5812503305.1758526_544.9037518369.1961826_540.6358688333.1976024_541.6896398351.2168739_520.87455303.1967781_543.4467123323.2219958_549.0190239406.8179962_540.9221163438.3435472_443.9449503353.1733614_537.7456659548.4598401_524.2762655361.2210255_552.6408999532.4645874_545.6798553533.4684048_546.0769454534.4725917_546.3351778313.0682688_493.6267954314.1886946_528.190614383.2735553_551.0826629365.2159934_534.1014981545.4322986_536.8188169549.4630213_543.4436076402.2908794_547.7217445528.4314123_553.2860452347.2423119_533.8969493339.1305178_531.9654868315.1961298_556.9598907347.2132825_548.7013793448.2502347_549.6484329329.2123517_530.1333469629.3886093_558.5096133630.3925047_559.364221348.2180326_525.0291541364.2136963_516.1065997360.2092777_538.6483324552.3134385_569.054043333.2266335_557.9049279336.2548713_553.7501974105.0676709_556.7167396363.3119304_583.2702098407.3380474_584.9716496329.1024272_535.0043574513.3237167_564.7458241541.4180794_585.5436013628.471463_583.0780646313.2174201_561.489423314.2210041_571.4016718316.2001448_547.7994493299.2015583_554.3473771311.2017295_538.4397432491.254069_557.2946268535.2876623_559.7423431536.2896206_563.4825832312.2012513_520.5068429630.3658231_561.996786533.4297917_553.5609892532.4455858_559.9500556533.4493758_552.2661325301.2175382_556.965358300.204322_541.9357178301.1808788_542.3492052343.2282279_538.3697426346.1844654_510.7241538353.1647166_521.695479104.4059677_562.2693329652.3926679_538.3595097319.1914464_546.0601276317.2033418_549.7560074319.2279067_554.8232913299.1287715_530.2906821285.1773133_523.7371816285.1820164_537.6685565433.2512959_548.3785162415.3048761_573.265249450.2862223_549.2321248787.1066904_566.9769883405.7802587_539.0612568405.7906434_554.2375236385.2951781_553.8628871535.4758534_547.9609031566.2220321_563.0969812609.2194766_556.3737553610.2187437_421.4031174379.2356286_541.4022053568.3482506_552.2722616348.2128079_505.6274627593.1712687_573.165673580.4270074_538.5403093240.1235369_276.6635341340.1276175_538.2966273211.0931491_478.5848188366.1928865_503.3821565305.1580488_551.6394932609.3480503_505.0756919655.3829627_556.3653037785.1088081_575.4620377106.4926529_546.2773127637.3985254_551.3836228185.1071571_473.5735498594.3882447_556.4115825596.3855908_566.5157332636.4161639_559.2318222562.3230895_545.1713706639.3866175_558.8251824581.3844283_548.3080336362.9177512_72.85364214696.4260093_553.9520979621.5109236_588.2195489607.4600209_585.9751807623.4919804_589.0466361159.1397476_549.412635622.5174381_589.1139816632.8428729_539.3293312632.9184409_548.3506137599.4835892_564.8079395602.5038525_570.3805337670.4797563_570.217266680.5178335_560.6745559606.4872022_562.0963383606.5062619_538.7246859673.3999774_547.7002182609.4489725_537.1477237669.441021_543.5882308636.5465392_554.4735045697.526184_551.5247215581.4768624_569.2253114264.2418399_588.8124222281.2173752_556.2156347600.5168938_587.8397053149.6313502_569.165761616.5469902_559.4280795650.5184894_587.8002155597.4890311_588.8191629629.5442978_583.3752767619.4700892_576.6737595620.4727256_587.0254229629.5289329_563.3639563651.4313844_579.2187509652.4335742_579.7586791169.6294463_548.0966018300.2604655_550.1411397665.4147445_553.6560001610.4382429_555.8772697618.3670423_563.6234026612.4041342_545.335839619.4124858_567.4644631277.1101387_519.9301869323.115413_545.1679404569.3638152_536.9350977158.6390034_549.1128265167.1550548_539.7125968649.3527512_547.7921871618.4257421_554.9831998612.4521512_557.717354613.4545914_566.5057794254.2205462_567.9757822265.1795295_533.2658547316.2846294_560.9968862299.2591644_575.0158672321.2444504_556.2924556479.3744294_538.6932671376.307073_544.1825402389.2905096_556.0070184253.2540713_575.7536831537.4152751_552.4243182651.490657_557.7907811665.4829533_564.9980689706.567786_584.9973965282.2447738_543.8569935650.4305516_572.8403182605.4804618_574.4818767653.5062776_546.2067243604.5176602_567.6598499605.5041543_554.0969967641.9444922_545.8372212577.4704763_525.8585951105.7588331_548.0272467650.492301_568.2104525635.5415072_559.4849589655.5189273_557.9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9_543.6806912126.3625688_552.3434156351.4421175_549.0735189488.8326192_554.7346832125.5528099_551.878674102.2253997_547.272390985.94128663_548.859660186.70025866_551.324386112.3030863_551.3134802134.3979716_549.9271324208.3209038_548.5451249260.4272927_548.3488236370.6144124_548.7596569187.7708371_533.2935842485.7288577_560.0602596349.6206031_539.819177699.5287192_511.9771877199.6839632_543.104557793.17536575_521.2370705325.327837_545.5500437438.9944861_539.314654297.51034036_522.9406677353.62927_541.1363778267.6457446_548.9696353466.3890355_562.6403935391.1842171_492.2880889420.3144177_532.4332384464.367479_305.1039439491.3874572_296.6305536256.1187295_181.4803187338.386929_546.9473348353.4906434_535.188477336.4443089_549.4880877385.4191338_538.4837321245.2227432_520.7017795314.1414433_520.153437798.3185302_532.5864604322.5652481_547.9137286181.158246_515.6547753344.7426304_521.0499971412.0755116_528.0586679263.5238442_551.6493063263.9830589_549.2916723490.870276_565.4666289329.8999903_59.72157486413.414247_485.1831589523.0270903_117.6200968329.9905618_120.508997331.9872453_122.73413186.9911755_482.6019609422.0890492_113.9474153249.0431519_486.2370062192.979825_491.452196165.9867164_497.1107771203.0044658_502.4767888289.0739376_502.2989443303.0893668_508.6798471162.9894455_508.407185166.9852896_478.3354479323.0580544_479.6284024408.3474272_583.4301896197.5586331_553.6175498565.243082_530.0170963139.2923952_554.0173574139.2947209_535.641420896.43160283_541.100560695.37530707_540.7606348188.8391828_540.2543666142.2169867_541.0755826222.8545158_542.125718192.5504976_540.2282397145.1814437_540.4626305141.5983783_541.1808672146.99859_541.0060272419.7209541_540.318356145.1369038_538.4526193137.0973052_541.5194704144.5994059_538.832334995.49226951_541.2334808175.9098196_540.4774537111.2033398_540.2888052112.3305363_540.760309795.25867631_541.1183825221.2044163_539.5605388114.365631_541.436486795.60948651_541.0676006112.6810091_541.1648675183.4538061_541.1966246112.6431062_541.0885907134.6655107_540.4921052179.0602279_537.5340346112.7070771_540.9009705115.7299491_540.8696097184.8291528_541.2578951188.4889416_541.3478834112.4803681_541.3175907111.0194442_541.5314928499.8593285_541.3599866184.3220716_540.9893703142.4165104_541.09023187.9282728_541.2587812109.242_541.2182118175.4504445_541.1402887184.2887271_541.0885418141.8562885_541.2517208142.4971854_541.2904838303.4899124_541.7160921141.8147378_541.1007423184.899641_541.0555891110.7040759_541.058484190.84402321_541.2259919115.5177241_541.10083109.0890252_541.1171007179.5385289_541.4479105212.9907994_167.4094644214.986324_514.4174391490.3388548_499.0265952296.899411_482.6459159200.9460848_505.1258227209.9933949_505.7950767309.9183156_482.5367116211.9934518_495.5066855308.9197962_501.3177222312.9158319_507.3554122318.9095314_538.6709653322.904534_502.6266582249.0060127_492.4533173263.0217332_480.359289194.9954437_531.2993118322.9156697_494.3881598334.3326442_573.9846405317.252732_557.5128375334.2818047_559.9986259631.3232155_559.9594873360.3757852_546.8031337444.337368_548.8606592380.2262221_509.9258186344.2420142_492.0830161291.1962622_516.5433879349.1937892_503.9262023259.1913203_549.7215572362.2545994_503.4179088330.3357462_535.5255334358.3697721_545.1908655359.3727681_546.4205352402.3958724_545.5652235228.2329301_585.5287901172.6130521_448.2184981527.0672073_504.3529426
−5 0 5
Value
Color Key
X0750irrdd2.2.cdf
X0816irrdd2.2.cdf
X0877iddrd2.2.cdf
X0960irrdd2.2.cdf
X0750irrdd3.2.cdf
X1162irrdd3.2.cdf
X0986irrdd2.2.cdf
X0877irrdd3.2.cdf
X1162irrdd2.2.cdf
X0750cntd7.2.cdf
X0986cntd7.2.cdf
X0960cntd7.2.cdf
X1162cntd7.2.cdf
X0960cntd2.2.cdf
X1162cntd3.2.cdf
X0816cntd7.2.cdf
X0816cntd2.2.cdf
X0816cntd3.2.cdf
X0877cntd3.2.cdf
X0877cntd7.2.cdf
X0986cntd3.2.cdf
X0986cntd2.2.cdf
X0960cntd3.2.cdf
X0750cntd3.2.cdf
X0750cntd2.2.cdf
X0877cntd2.2.cdf
X1162cntd2.2.cdf
X0986irrdd3.2.cdf
X0816irrdd3.2.cdf
X0816irrdd7.2.cdf
X0877irrdd7.2.cdf
X0986irrdd7.2.cdf
X1162irrdd7.2.cdf
X0750irrdd7.2.cdf
X0960irrdd7.2.cdf
X0877cntd17.2.cdf
X1162cntd17.2.cdf
X0816cntd14.2.cdf
X0986cntd14.2.cdf
X0816cntd28.2.cdf
X0877cntd28.2.cdf
X1162cntd28.2.cdf
X0960cntd28.2.cdf
X0986cntd28.2.cdf
X0750cntd21.2.cdf
X1162cntd21.2.cdf
X0960cntd17.2.cdf
X0986cntd17.2.cdf
X0750cntd17.2.cdf
X0816cntd17.2.cdf
X0750cntd10.2.cdf
X0877cntd21.2.cdf
X0960cntd21.2.cdf
X0750cntd14.2.cdf
X0816cntd10.2.cdf
X0877cntd10.2.cdf
X0986cntd10.2.cdf
X1162cntd14.2.cdf
X0960cntd10.2.cdf
X1162cntd10.2.cdf
X0877cntd14.2.cdf
X0960cntd14.2.cdf
X0750cntd35.2.cdf
X0877cntd35.2.cdf
X0986cntd21.2.cdf
X0816cntd21.2.cdf
X0750cntd28.2.cdf
X0960cntd35.2.cdf
X1162cntd35.2.cdf
X0816cntd35.2.cdf
X0986cntd35.2.cdf
X0986irrd35.2.cdf
X0816irrd35.2.cdf
X1162irrd35.2.cdf
X0877irrd35.2.cdf
X0960irrd35.2.cdf
X0816irrd14.2.cdf
X0986irrd14.2.cdf
X0816ittd21.2.cdf
X0986irrd21.2.cdf
X0750irrd28.2.cdf
X0877irrd28.2.cdf
X0986irrd28.2.cdf
X0877irrd17.2.cdf
X0986irrd17.2.cdf
X0816irrd28.2.cdf
X0750irrd35.2.cdf
X0960irrd28.2.cdf
X1162irrd28.2.cdf
X0877irrd21.2.cdf
X0960irrd21.2.cdf
X1162irrd21.2.cdf
X0750irrd21.2.cdf
X0816irrd17.2.cdf
X1162irrd17.2.cdf
X0750irrd17.2.cdf
X0960irrd17.2.cdf
X0750irrd14.2.cdf
X0960irrd10.2.cdf
X0960irrd14.2.cdf
X1162irrd14.2.cdf
X0877irrd14.2.cdf
X0986irrd10.2.cdf
X0877irrd10.2.cdf
X1162irrd10.2.cdf
X0750irrd10.2.cdf
X0816irrd10.2.cdf
551.5859951_471.9759025529.4376461_539.8285224546.4243019_523.1556065547.4271663_526.2363159233.0689917_490.7579052535.4505804_532.1500798348.1773101_535.1897514354.2294185_536.2349615365.1953471_527.4346883471.2737825_555.4923569594.4653686_525.0570538228.1139203_465.8183376551.0849288_491.8148044431.3368909_562.3868326404.2809145_551.3156691547.4479183_529.9512275212.0897938_477.736422316.1656468_537.3225129307.1591566_531.3062747253.7276241_448.6706638506.4422163_499.4587144682.4089338_571.3186564455.5302829_469.1895487590.4153904_527.8768707323.185583_516.8517473354.2162392_537.3378765353.2029908_548.1090893373.1907353_541.0360446353.1573289_540.5154177298.1559911_546.9382199455.5937805_44.17214808320.2274219_555.2525695337.2592043_554.215962363.2738762_543.401315430.3330383_554.0649703446.3276835_553.0300436323.1633642_550.9715589432.313578_552.5961041283.1305615_525.3281571346.2293277_525.2951014289.1809268_548.5812503305.1758526_544.9037518369.1961826_540.6358688333.1976024_541.6896398351.2168739_520.87455303.1967781_543.4467123323.2219958_549.0190239406.8179962_540.9221163438.3435472_443.9449503353.1733614_537.7456659548.4598401_524.2762655361.2210255_552.6408999532.4645874_545.6798553533.4684048_546.0769454534.4725917_546.3351778313.0682688_493.6267954314.1886946_528.190614383.2735553_551.0826629365.2159934_534.1014981545.4322986_536.8188169549.4630213_543.4436076402.2908794_547.7217445528.4314123_553.2860452347.2423119_533.8969493339.1305178_531.9654868315.1961298_556.9598907347.2132825_548.7013793448.2502347_549.6484329329.2123517_530.1333469629.3886093_558.5096133630.3925047_559.364221348.2180326_525.0291541364.2136963_516.1065997360.2092777_538.6483324552.3134385_569.054043333.2266335_557.9049279336.2548713_553.7501974105.0676709_556.7167396363.3119304_583.2702098407.3380474_584.9716496329.1024272_535.0043574513.3237167_564.7458241541.4180794_585.5436013628.471463_583.0780646313.2174201_561.489423314.2210041_571.4016718316.2001448_547.7994493299.2015583_554.3473771311.2017295_538.4397432491.254069_557.2946268535.2876623_559.7423431536.2896206_563.4825832312.2012513_520.5068429630.3658231_561.996786533.4297917_553.5609892532.4455858_559.9500556533.4493758_552.2661325301.2175382_556.965358300.204322_541.9357178301.1808788_542.3492052343.2282279_538.3697426346.1844654_510.7241538353.1647166_521.695479104.4059677_562.2693329652.3926679_538.3595097319.1914464_546.0601276317.2033418_549.7560074319.2279067_554.8232913299.1287715_530.2906821285.1773133_523.7371816285.1820164_537.6685565433.2512959_548.3785162415.3048761_573.265249450.2862223_549.2321248787.1066904_566.9769883405.7802587_539.0612568405.7906434_554.2375236385.2951781_553.8628871535.4758534_547.9609031566.2220321_563.0969812609.2194766_556.3737553610.2187437_421.4031174379.2356286_541.4022053568.3482506_552.2722616348.2128079_505.6274627593.1712687_573.165673580.4270074_538.5403093240.1235369_276.6635341340.1276175_538.2966273211.0931491_478.5848188366.1928865_503.3821565305.1580488_551.6394932609.3480503_505.0756919655.3829627_556.3653037785.1088081_575.4620377106.4926529_546.2773127637.3985254_551.3836228185.1071571_473.5735498594.3882447_556.4115825596.3855908_566.5157332636.4161639_559.2318222562.3230895_545.1713706639.3866175_558.8251824581.3844283_548.3080336362.9177512_72.85364214696.4260093_553.9520979621.5109236_588.2195489607.4600209_585.9751807623.4919804_589.0466361159.1397476_549.412635622.5174381_589.1139816632.8428729_539.3293312632.9184409_548.3506137599.4835892_564.8079395602.5038525_570.3805337670.4797563_570.217266680.5178335_560.6745559606.4872022_562.0963383606.5062619_538.7246859673.3999774_547.7002182609.4489725_537.1477237669.441021_543.5882308636.5465392_554.4735045697.526184_551.5247215581.4768624_569.2253114264.2418399_588.8124222281.2173752_556.2156347600.5168938_587.8397053149.6313502_569.165761616.5469902_559.4280795650.5184894_587.8002155597.4890311_588.8191629629.5442978_583.3752767619.4700892_576.6737595620.4727256_587.0254229629.5289329_563.3639563651.4313844_579.2187509652.4335742_579.7586791169.6294463_548.0966018300.2604655_550.1411397665.4147445_553.6560001610.4382429_555.8772697618.3670423_563.6234026612.4041342_545.335839619.4124858_567.4644631277.1101387_519.9301869323.115413_545.1679404569.3638152_536.9350977158.6390034_549.1128265167.1550548_539.7125968649.3527512_547.7921871618.4257421_554.9831998612.4521512_557.717354613.4545914_566.5057794254.2205462_567.9757822265.1795295_533.2658547316.2846294_560.9968862299.2591644_575.0158672321.2444504_556.2924556479.3744294_538.6932671376.307073_544.1825402389.2905096_556.0070184253.2540713_575.7536831537.4152751_552.4243182651.490657_557.7907811665.4829533_564.9980689706.567786_584.9973965282.2447738_543.8569935650.4305516_572.8403182605.4804618_574.4818767653.5062776_546.2067243604.5176602_567.6598499605.5041543_554.0969967641.9444922_545.8372212577.4704763_525.8585951105.7588331_548.0272467650.492301_568.2104525635.5415072_559.4849589655.5189273_557.9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−5 0 5
Value
Color Key
B1 B2 - Bn
Subject1 19 19 - 100
Subject2 10 40 - 90
- - - - -
SubjectN 10 40 - 50
M1 M2 - Mn
Subject1 199 19 - 100
Subject2 10 40 90
- - - -
SubjectN 50 30 - 20
B1 B2 - Bn
M1 0.4 0.9 - 0.3
M2 0.7 0.1 - 0.5
M3 0.1 0.6 0.8
Relevance networks
Metabolomics data
(n subjects X p metabolites)
Microbiome data
(n subjects X q bacterial species)
Associa(on matrix
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
Associa6on score
Microbes
Targeted inves6ga6on
(-) log10 pvalue
Pathway enrichment
2 2.2 2.4 2.6 2.8 3
Methionine and
cysteine
metabolism
Cholesterol
biosynthesis
Clustering
Bacterial
Species
Metabolites
Univariate
• Pearson Correla(on
• MetabNet (Uppal2015)
Mul(variate
• PLS, CCA, sparse PLS
• mixOmics (Cao 2009)

12. Relevance networks
• What is a network (or graph)?
– A set of nodes (ver(ces) and edges (links)
– Edges describe a rela(onship (e.g. correla(on) between the nodes
• What is a relevance network?
– Networks of highly-correlated biomedical/clinical en((es (Buhe 2001;
PNAS)
– Metabolomics x Proteomics, Transcriptomics x Proteomics,
Metabolomics x Microbiome, Metabolomics x Clinical variables/
phenotypes, etc.
– Generate a bipar(te graph network
using a associa(on threshold
(e.g. 0.5) to visualize posi(ve or
nega(ve associa(ons
Circles: microbial species
Rectangles: metabolome features

13. Methods for genera(ng relevance
networks
• Univariate
– Pairwise Pearson or Spearman correla(on between data from different
biomedical/clinical technologies (Buhe et al. 2009, Uppal et al. 2015)
– Sojware:
• MetabNet (Uppal 2015; R package for performing pairwise correla(on analysis and
genera(ng relevance networks)
– Applica(on: Integration of TCE exposure data and physiological markers with metabolomics
(Douglas I. Walker et al. submitted)
• Mul(variate
– Mul(variate regression techniques such as par(al least squares (PLS), sparse
par(al least squares regression (sPLS), mul(level sparse par(al least squares
(msPLS) regression, etc.
– Sojware:
• mixOmics (Cao et al. 2009, Liquet et al. 2012; R package for integra(on and variable
selec(on using mul(variate regression)
– Applica(ons:
• Transcriptome x Metabolome (Roede, Uppal et al. 2013)
• Microbiome x Metabolome (Cribbs, Uppal et al. 2016 in press)

14. Univariate methods

15. MetabNet (R package; Uppal 2015)
• Performs pairwise correla(on (Pearson or Spearman) or
par(al correla(on analysis to generate associa(on matrix
(p x q) and relevance network using the data measure on
same N
• Large number of possible associa(ons (p x q)
– E.g.: 2x10^8 possible associa(ons for 20,000 genes x 10,000
metabolic features
– Computa(onally intensive and hard to interpret results
• More suitable when number of variables in at least one
layer (p or q) is small
• Availability: Sojware and tutorial available on sourceforge
(hhps://sourceforge.net/projects/metabnet/)

16. Case Study 1: Using MetabNet for cross-platform paired
integrative analysis. Integration of TCE exposure data and
physiological markers with metabolomics
(Walker, Uppal et al. manuscript submitted)

17. Courtesy:
Douglas I. Walker
(manuscript submihed)
Urinary TCE exposure markers
Renal biomarkers
Immunological markers
Unidentifiable halogenated m/z, by isotopic pair
Identified metabolite
Unidentifiable m/z; pos mass defect
Unidentifiable m/z; neg mass defect
-0.7
0
0.6
Correlation coefficient

18. Mul(variate methods

19. Genera(ng relevance network using
sPLS or msPLS techniques (Cao 2009,
Liquet 2012)
• sparse par(al least squares (sPLS) regression or
mul(level par(al least squares (msPLS) method
• One-step procedure for variable selec(on as well as
integra(on
• Comparison of different mul(variate integra(on
techniques showed that sPLS generates (Cao 2009)
• Implemented in the R package mixOmics
• Generates associa(on matrix and allows visualiza(on
of associa(ons using bipar(te relevance networks
(Liquet 2012)

20. sPLS method
• sPLS is a variable selec(on and dimensionality reduc(on method that
allows integra(on of heterogeneous omics data from same set of samples
• Robust approxima(on of Pearson correla(on using regression and latent
(principal) variates
• Eg: transcriptome (matrix X) and metabolome (matrix Y) data
where,
matrix X is an n × p matrix that includes n samples and p metabolites
matrix Y is an n × q matrix that includes n samples and q genes
Objec(ve func(on
max cov(Xu,Yv)
where
u1, u2…uH and v1, v2…vH are the loading vectors
H is the number of PLS-DA dimensions
A Lasso based op(miza(on is used to select most relevant variables

21. mul(level sPLS method for experiments with
repeated measurements
If X is an (N x p) intensity matrix, where N is the number of samples and p is the
number of m/z features, then
1) Split-up varia(on:
Xw= Xs(mula(on + X(me + Xs(mula(on x (me +Xresidual
+ Xsubject x S(mula(on + Xsubject x (me
2) sparse PLS objec(ve func(on:
max cor(Y,Xu)var(Xu)
where
Y is the matrix indica(ng group of each sample
X is the split-up varia(on
u1, u2…uH are the loading vectors
H is the number of PLS-DA dimensions
A Lasso based op(miza(on is used to select most relevant variables

22. Case Study 2: Application of sPLS technique for cross-
platform paired integrative analysis. Integration of targeted
bile acids measurements and clinical variables (age, BMI,
etc.) with metabolomics

23. A.
B.
C.
Associa(on threshold: 0.4
Associa(on threshold: 0.5
Associa(on threshold: 0.6 D. Pathway analysis (only top two displayed)
Legend
Circles: targeted bile acid
Rectangles: metabolome features
3 3.05 3.1 3.15 3.2 3.25 3.3 3.35
Saturated fahy acids beta-
oxida(on
Bile acid biosynthesis
(-) log10 pvalue

24. Case Study 3: Application of sPLS technique for
integrative –omics. Microbiome-Metabolome Wide
Association Study of Lung BAL: Global integration of 5930
m/z features with 153 microbial species using sparse Partial
Least Squares regression

25. A.
B.
C.
Associa(on threshold: 0.3
Associa(on threshold: 0.4
Associa(on threshold: 0.7 D. Using only subset of metabolic features
also associated with HIV status (+ve or –ve)
Legend
Circles: microbial species
Rectangles: metabolome features
Nocardioidaceae
Staphylococcaceae
Caulobacteraceae Streptococcus

26. Integra(ng data from other
sources (e.g. PubMed)

27. Text mining tools for literature-based
relation discovery biomedical text
Zhiyong Lu, “PubMed and beyond: a survey of web tools for
searching biomedical literature”, Database Vol. 2011
Associa(on mining based on
co-occurrence

28. Associa(on mining algorithm for
construc(ng rela(on trees
Pointwise Mutual Information(t1, t2) = vi * log2 p(t1 and t2)
----------------
p(t1) p(t2)
where
vi is 1 if term t2 is present in the controlled vocabulary, 0 otherwise;
p(t1) is the probability of term 1 in the corpus,
p(t2) is the probability of term 2 in the corpus, and
p(t1 and t2) is the probability of co-occurrence of terms 1 and 2 in the corpus

29. • Pa(ent
medical
records
• Clinical notes
• Lab results,
etc.
A) Electronic
Health Records
(EHR)
C) Omics data
Metabolo
mics
GWAS Epigenomics
Transcriptomi
cs
Proteomi
cs
B) Scien(fic
literature
Cochrane
Library
PubMed
Pa(ent
(disease/symptoms)
Clinician
Summary
Other
“omes”
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9_543.6806912126.3625688_552.3434156351.4421175_549.0735189488.8326192_554.7346832125.5528099_551.878674102.2253997_547.272390985.94128663_548.859660186.70025866_551.324386112.3030863_551.3134802134.3979716_549.9271324208.3209038_548.5451249260.4272927_548.3488236370.6144124_548.7596569187.7708371_533.2935842485.7288577_560.0602596349.6206031_539.819177699.5287192_511.9771877199.6839632_543.104557793.17536575_521.2370705325.327837_545.5500437438.9944861_539.314654297.51034036_522.9406677353.62927_541.1363778267.6457446_548.9696353466.3890355_562.6403935391.1842171_492.2880889420.3144177_532.4332384464.367479_305.1039439491.3874572_296.6305536256.1187295_181.4803187338.386929_546.9473348353.4906434_535.188477336.4443089_549.4880877385.4191338_538.4837321245.2227432_520.7017795314.1414433_520.153437798.3185302_532.5864604322.5652481_547.9137286181.158246_515.6547753344.7426304_521.0499971412.0755116_528.0586679263.5238442_551.6493063263.9830589_549.2916723490.870276_565.4666289329.8999903_59.72157486413.414247_485.1831589523.0270903_117.6200968329.9905618_120.508997331.9872453_122.73413186.9911755_482.6019609422.0890492_113.9474153249.0431519_486.2370062192.979825_491.452196165.9867164_497.1107771203.0044658_502.4767888289.0739376_502.2989443303.0893668_508.6798471162.9894455_508.407185166.9852896_478.3354479323.0580544_479.6284024408.3474272_583.4301896197.5586331_553.6175498565.243082_530.0170963139.2923952_554.0173574139.2947209_535.641420896.43160283_541.100560695.37530707_540.7606348188.8391828_540.2543666142.2169867_541.0755826222.8545158_542.125718192.5504976_540.2282397145.1814437_540.4626305141.5983783_541.1808672146.99859_541.0060272419.7209541_540.318356145.1369038_538.4526193137.0973052_541.5194704144.5994059_538.832334995.49226951_541.2334808175.9098196_540.4774537111.2033398_540.2888052112.3305363_540.760309795.25867631_541.1183825221.2044163_539.5605388114.365631_541.436486795.60948651_541.0676006112.6810091_541.1648675183.4538061_541.1966246112.6431062_541.0885907134.6655107_540.4921052179.0602279_537.5340346112.7070771_540.9009705115.7299491_540.8696097184.8291528_541.2578951188.4889416_541.3478834112.4803681_541.3175907111.0194442_541.5314928499.8593285_541.3599866184.3220716_540.9893703142.4165104_541.09023187.9282728_541.2587812109.242_541.2182118175.4504445_541.1402887184.2887271_541.0885418141.8562885_541.2517208142.4971854_541.2904838303.4899124_541.7160921141.8147378_541.1007423184.899641_541.0555891110.7040759_541.058484190.84402321_541.2259919115.5177241_541.10083109.0890252_541.1171007179.5385289_541.4479105212.9907994_167.4094644214.986324_514.4174391490.3388548_499.0265952296.899411_482.6459159200.9460848_505.1258227209.9933949_505.7950767309.9183156_482.5367116211.9934518_495.5066855308.9197962_501.3177222312.9158319_507.3554122318.9095314_538.6709653322.904534_502.6266582249.0060127_492.4533173263.0217332_480.359289194.9954437_531.2993118322.9156697_494.3881598334.3326442_573.9846405317.252732_557.5128375334.2818047_559.9986259631.3232155_559.9594873360.3757852_546.8031337444.337368_548.8606592380.2262221_509.9258186344.2420142_492.0830161291.1962622_516.5433879349.1937892_503.9262023259.1913203_549.7215572362.2545994_503.4179088330.3357462_535.5255334358.3697721_545.1908655359.3727681_546.4205352402.3958724_545.5652235228.2329301_585.5287901172.6130521_448.2184981527.0672073_504.3529426
−5 0 5
Value
Color Key
X0750irrdd2.2.cdf
X0816irrdd2.2.cdf
X0877iddrd2.2.cdf
X0960irrdd2.2.cdf
X0750irrdd3.2.cdf
X1162irrdd3.2.cdf
X0986irrdd2.2.cdf
X0877irrdd3.2.cdf
X1162irrdd2.2.cdf
X0750cntd7.2.cdf
X0986cntd7.2.cdf
X0960cntd7.2.cdf
X1162cntd7.2.cdf
X0960cntd2.2.cdf
X1162cntd3.2.cdf
X0816cntd7.2.cdf
X0816cntd2.2.cdf
X0816cntd3.2.cdf
X0877cntd3.2.cdf
X0877cntd7.2.cdf
X0986cntd3.2.cdf
X0986cntd2.2.cdf
X0960cntd3.2.cdf
X0750cntd3.2.cdf
X0750cntd2.2.cdf
X0877cntd2.2.cdf
X1162cntd2.2.cdf
X0986irrdd3.2.cdf
X0816irrdd3.2.cdf
X0816irrdd7.2.cdf
X0877irrdd7.2.cdf
X0986irrdd7.2.cdf
X1162irrdd7.2.cdf
X0750irrdd7.2.cdf
X0960irrdd7.2.cdf
X0877cntd17.2.cdf
X1162cntd17.2.cdf
X0816cntd14.2.cdf
X0986cntd14.2.cdf
X0816cntd28.2.cdf
X0877cntd28.2.cdf
X1162cntd28.2.cdf
X0960cntd28.2.cdf
X0986cntd28.2.cdf
X0750cntd21.2.cdf
X1162cntd21.2.cdf
X0960cntd17.2.cdf
X0986cntd17.2.cdf
X0750cntd17.2.cdf
X0816cntd17.2.cdf
X0750cntd10.2.cdf
X0877cntd21.2.cdf
X0960cntd21.2.cdf
X0750cntd14.2.cdf
X0816cntd10.2.cdf
X0877cntd10.2.cdf
X0986cntd10.2.cdf
X1162cntd14.2.cdf
X0960cntd10.2.cdf
X1162cntd10.2.cdf
X0877cntd14.2.cdf
X0960cntd14.2.cdf
X0750cntd35.2.cdf
X0877cntd35.2.cdf
X0986cntd21.2.cdf
X0816cntd21.2.cdf
X0750cntd28.2.cdf
X0960cntd35.2.cdf
X1162cntd35.2.cdf
X0816cntd35.2.cdf
X0986cntd35.2.cdf
X0986irrd35.2.cdf
X0816irrd35.2.cdf
X1162irrd35.2.cdf
X0877irrd35.2.cdf
X0960irrd35.2.cdf
X0816irrd14.2.cdf
X0986irrd14.2.cdf
X0816ittd21.2.cdf
X0986irrd21.2.cdf
X0750irrd28.2.cdf
X0877irrd28.2.cdf
X0986irrd28.2.cdf
X0877irrd17.2.cdf
X0986irrd17.2.cdf
X0816irrd28.2.cdf
X0750irrd35.2.cdf
X0960irrd28.2.cdf
X1162irrd28.2.cdf
X0877irrd21.2.cdf
X0960irrd21.2.cdf
X1162irrd21.2.cdf
X0750irrd21.2.cdf
X0816irrd17.2.cdf
X1162irrd17.2.cdf
X0750irrd17.2.cdf
X0960irrd17.2.cdf
X0750irrd14.2.cdf
X0960irrd10.2.cdf
X0960irrd14.2.cdf
X1162irrd14.2.cdf
X0877irrd14.2.cdf
X0986irrd10.2.cdf
X0877irrd10.2.cdf
X1162irrd10.2.cdf
X0750irrd10.2.cdf
X0816irrd10.2.cdf
551.5859951_471.9759025529.4376461_539.8285224546.4243019_523.1556065547.4271663_526.2363159233.0689917_490.7579052535.4505804_532.1500798348.1773101_535.1897514354.2294185_536.2349615365.1953471_527.4346883471.2737825_555.4923569594.4653686_525.0570538228.1139203_465.8183376551.0849288_491.8148044431.3368909_562.3868326404.2809145_551.3156691547.4479183_529.9512275212.0897938_477.736422316.1656468_537.3225129307.1591566_531.3062747253.7276241_448.6706638506.4422163_499.4587144682.4089338_571.3186564455.5302829_469.1895487590.4153904_527.8768707323.185583_516.8517473354.2162392_537.3378765353.2029908_548.1090893373.1907353_541.0360446353.1573289_540.5154177298.1559911_546.9382199455.5937805_44.17214808320.2274219_555.2525695337.2592043_554.215962363.2738762_543.401315430.3330383_554.0649703446.3276835_553.0300436323.1633642_550.9715589432.313578_552.5961041283.1305615_525.3281571346.2293277_525.2951014289.1809268_548.5812503305.1758526_544.9037518369.1961826_540.6358688333.1976024_541.6896398351.2168739_520.87455303.1967781_543.4467123323.2219958_549.0190239406.8179962_540.9221163438.3435472_443.9449503353.1733614_537.7456659548.4598401_524.2762655361.2210255_552.6408999532.4645874_545.6798553533.4684048_546.0769454534.4725917_546.3351778313.0682688_493.6267954314.1886946_528.190614383.2735553_551.0826629365.2159934_534.1014981545.4322986_536.8188169549.4630213_543.4436076402.2908794_547.7217445528.4314123_553.2860452347.2423119_533.8969493339.1305178_531.9654868315.1961298_556.9598907347.2132825_548.7013793448.2502347_549.6484329329.2123517_530.1333469629.3886093_558.5096133630.3925047_559.364221348.2180326_525.0291541364.2136963_516.1065997360.2092777_538.6483324552.3134385_569.054043333.2266335_557.9049279336.2548713_553.7501974105.0676709_556.7167396363.3119304_583.2702098407.3380474_584.9716496329.1024272_535.0043574513.3237167_564.7458241541.4180794_585.5436013628.471463_583.0780646313.2174201_561.489423314.2210041_571.4016718316.2001448_547.7994493299.2015583_554.3473771311.2017295_538.4397432491.254069_557.2946268535.2876623_559.7423431536.2896206_563.4825832312.2012513_520.5068429630.3658231_561.996786533.4297917_553.5609892532.4455858_559.9500556533.4493758_552.2661325301.2175382_556.965358300.204322_541.9357178301.1808788_542.3492052343.2282279_538.3697426346.1844654_510.7241538353.1647166_521.695479104.4059677_562.2693329652.3926679_538.3595097319.1914464_546.0601276317.2033418_549.7560074319.2279067_554.8232913299.1287715_530.2906821285.1773133_523.7371816285.1820164_537.6685565433.2512959_548.3785162415.3048761_573.265249450.2862223_549.2321248787.1066904_566.9769883405.7802587_539.0612568405.7906434_554.2375236385.2951781_553.8628871535.4758534_547.9609031566.2220321_563.0969812609.2194766_556.3737553610.2187437_421.4031174379.2356286_541.4022053568.3482506_552.2722616348.2128079_505.6274627593.1712687_573.165673580.4270074_538.5403093240.1235369_276.6635341340.1276175_538.2966273211.0931491_478.5848188366.1928865_503.3821565305.1580488_551.6394932609.3480503_505.0756919655.3829627_556.3653037785.1088081_575.4620377106.4926529_546.2773127637.3985254_551.3836228185.1071571_473.5735498594.3882447_556.4115825596.3855908_566.5157332636.4161639_559.2318222562.3230895_545.1713706639.3866175_558.8251824581.3844283_548.3080336362.9177512_72.85364214696.4260093_553.9520979621.5109236_588.2195489607.4600209_585.9751807623.4919804_589.0466361159.1397476_549.412635622.5174381_589.1139816632.8428729_539.3293312632.9184409_548.3506137599.4835892_564.8079395602.5038525_570.3805337670.4797563_570.217266680.5178335_560.6745559606.4872022_562.0963383606.5062619_538.7246859673.3999774_547.7002182609.4489725_537.1477237669.441021_543.5882308636.5465392_554.4735045697.526184_551.5247215581.4768624_569.2253114264.2418399_588.8124222281.2173752_556.2156347600.5168938_587.8397053149.6313502_569.165761616.5469902_559.4280795650.5184894_587.8002155597.4890311_588.8191629629.5442978_583.3752767619.4700892_576.6737595620.4727256_587.0254229629.5289329_563.3639563651.4313844_579.2187509652.4335742_579.7586791169.6294463_548.0966018300.2604655_550.1411397665.4147445_553.6560001610.4382429_555.8772697618.3670423_563.6234026612.4041342_545.335839619.4124858_567.4644631277.1101387_519.9301869323.115413_545.1679404569.3638152_536.9350977158.6390034_549.1128265167.1550548_539.7125968649.3527512_547.7921871618.4257421_554.9831998612.4521512_557.717354613.4545914_566.5057794254.2205462_567.9757822265.1795295_533.2658547316.2846294_560.9968862299.2591644_575.0158672321.2444504_556.2924556479.3744294_538.6932671376.307073_544.1825402389.2905096_556.0070184253.2540713_575.7536831537.4152751_552.4243182651.490657_557.7907811665.4829533_564.9980689706.567786_584.9973965282.2447738_543.8569935650.4305516_572.8403182605.4804618_574.4818767653.5062776_546.2067243604.5176602_567.6598499605.5041543_554.0969967641.9444922_545.8372212577.4704763_525.8585951105.7588331_548.0272467650.492301_568.2104525635.5415072_559.4849589655.5189273_557.9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−5 0 5
Value
Color Key
Relevance networks Clustering
Genes
Metabolites
Clustering

30. Knowledge-Based Approaches of Data Integra6on
Sophia A. Banton, Shuzhao Li
Clinical Biomarkers Laboratory
Emory University School of Medicine

31. Introduc6on: biological networks
• Types of biological networks:
– Intra-cellular networks
1. Transcrip6onal regulatory networks
2. Metabolic networks
3. RNA networks
4. Protein-protein interac(on (PPI) networks
5. Cell signaling networks
– Other biological networks
• Neuronal synap(c connec(on networks
• Brain func(onal networks
• Ecological food webs
• Phylogene(c networks
• Correla(on networks (e.g., gene co-expression)
• Disease – “disease gene” associa(on networks
• Drug – “drug target” networks
31

32. Transcrip6onal regula6on networks and modules
• Model regula(on of gene expression
– Gene à mRNA à protein
• Nodes correspond to genes
• Directed edges correspond to interac(ons through which the products of one gene
affect those of another
32

33. Blood Transcrip6on Modules (BTMs)
Blood Transcription Modules (BTM) as a powerful new tool. High quality gene network was first inferred
from public transcriptomic data. Context specific subnetworks were derived by intersecting GO, cell types,
interactome and bibliome. Gene modules were extracted from these subnetworks by search algorithms that
take into account connection density and underlying conditions. KEGG, BioCarta, PID, Reactome and TF
targets were integrated as search seeds. These BTM modules can be used as alternative to pathways, and
often offer better sensitivities.
33
Li et al., 2014. Nature Immunology, 15:195

34. Metabolic networks
• Used for studying and modeling metabolism
– Biochemical reac(ons in cells that allow an organism to carry out essen(al life func(ons
34
Banton et al 2016. JAALAS -KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway mapping of marmoset plasma metabolites associated with a change
from the NE diet to the purified diet. The black dots represent metabolites in the pathways that were iden(fied by using Mummichog

35. Metabolic networks
• Metabolites
– Small molecules
– Macromolecules
• Metabolic pathways
– Series of successive
biochemical reac(ons
for a specific metabolic
func(on, e.g.,
glycolysis, or penicillin
synthesis, that convert
one metabolite into
another
35
Banton et al 2016. JAALAS. Metabolic network activity of plasma amino acid
concentrations affected by changes between the baseline, standard, and synthetic diets
fed to the common marmoset (Callithrix jacchus).

36. Metabolic model at Genome-scale: need for
bioinformatics tools

37. Mummichog: rewri6ng metabolomics workflow
Li et al. 2013. PLoS Computational Biology. 9:e10031323
A) In the work flow of untargeted metabolomics, the conventional approach requires the
metabolites to be identified before pathway/network analysis, while mummichog (blue arrow)
predicts functional activity bypassing metabolite identification. B) Each row of dots represent
possible matches of metabolites from one m/z feature, red the true metabolite, gray the false
matches. The conventional approach first requires the identification of metabolites before
mapping them to the metabolic network. C)mummichog maps all possible metabolite matches to
the network and looks for local enrichment, which reflects the true activity because the false
matches will distribute randomly.
37

38. Case Studies
1. Reanalysis of Snyderome using new tools
2. Galactosemia: GALT transcriptomics and metabolomics integra(on
3. Forma(on of memory CD8+ T cells
4. Integra(on of Metabolomics and Transcriptomics to evaluate the effect
pyrimethamine on plasma hemoglobin using Group LASSO
38

39. 39
Case Study 1: Reanalysis of Snyderome using new tools

40. Case Study 1: Mummichog interpreta6on of Snyder metabolome
40
1
2
-Log10P-value
Significant Metabolic Pathways during RSV
infection (P < 0.05). Porphyrin pathway
pinpoints the clinical phenotype of anemia.
Metabolic Network module corresponding to
Linoleate pathway.
Gene Pathways from Snyder
transcriptome are Consistent
with Metabolite Pathways

41. Case Study 1: Reanalysis of Snyderome using new tools
41
Name NES NOM p-val
Enriched in monocytes (II) (M11.0) 2.386807 0
Enriched in monocytes (I) (M4.15) 2.08188 0.005102
RIG-1 like receptor signaling (M68) 1.867297 0.00978
Complement Activation (I) (M112.0) 1.948812 0.011086
Enriched in monocytes (III) (M73) 1.932553 0.011905
Cell Activation (IL15, IL23, TNF) (M24) 1.89332 0.012136
Cell Cycle and Growth Arrest (M31) 1.892822 0.016908
Formyl peptide receptor mediated neutrophil response (M11.2) 1.920023 0.021028
RA, WNT, CSF receptors network (Monocyte) (M23) 1.842038 0.022321
Extracellular Matrix, Collagen (M210) 1.76067 0.025316
Signaling in T Cells (I) (M35.0) 1.743513 0.027972
Myeloid cell enriched receptors and transporters (M4.3) 1.942556 0.02849
Inflammatory response (M33) 1.636564 0.029056
Enriched in activated dendritic cells (II) (M165) 1.918629 0.02973
Viral sensing & immunity; irf2 targets network (I) (M111.0) 1.815972 0.03038
Blood Coagulation (M11.1) 1.855351 0.030691
TLR and Inflammatory Signaling (M16) 2.084829 0.031884
Lysosome (M209) 1.678483 0.032407
Innate Antiviral Response (M150) 1.679814 0.033413
The transcriptomics signature during RSV infection, using BTMs. Enrichment test was performed within GSEA
software, using BTMs as custom gene sets.

42. Case Study 2: Background
• Galactosemia is an autosomal recessive condi(on that affects an individual's ability to metabolize
galactose
• In Drosophila melanogaster dGALT is the presumed ortholog of the human GALT gene which converts
Galactose-1-phosphate to UDP-Galactose
• Genotypes:
– Ap2 is the imprecise excision of a p element in the dGALT gene and results in loss of dGaLT ac(vity (Knockout).
– C2 is the precise excision of the same p element and results in normal GALT ac(vity (Wild-type).
42
• Metabolomics
• Human Data
• 3 Cases and 6 controls
• Using 19,505 metabolite features
• Fly Data
• 9 Knockout and 9 Wild-Type
• Using 9,767 metabolite features
By LHcheM - Own work, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?
curid=25944168
• Transcriptomics
• 15-20 ug of Larva were used for each
RNA extraction and subsequent cDNA
synthesis.
• Dye hybridization and microarray were
performed using Nimblegen technology.
Transcriptome x Metabolome

43. Case Study 2: Transcriptome x Metabolome
Fly Genes
Ahcy89E
SamDC Substrate
and
Product
of
SamDC
Knockout Wild-type
Products
of
Ahcy89E
Substrate
and
Product
of
SamDC
KnockoutWild-type
Fly Metabolites
Human Metabolites

44. CD8+ T cells provide protection against intracellular bacterial, para-
sitic and viral infections, as well as cancers1,2. Following stimulation
with antigens, naive CD8+ T cells go through many rounds of prolif-
eration, giving rise to effector T cells, which eliminate infected cells.
Upon clearance of the antigens, most effector CD8+ T cells undergo
apoptosis, leaving only a small pool of cells to survive and differenti-
ate into memory cells3–5. During this naive-to-effector to memory-
differentiation process, T cells undergo cellular and metabolic
reprogramming to shift from anabolic processes and proliferation to
catabolic processes and contraction of cell populations to generate
memory. It is important to define the role of macroautophagy (called
‘autophagy’ here) during this process.
Autophagy is an evolutionarily conserved process that involves the
engulfment and delivery of cytosolic contents to the lysosome for
degradation6–10. This catabolic activity of autophagy is essential for
cellular homeostasis and has been suggested to be inversely correlated
with cell growth and proliferation11. In contrast to that paradigm,
it has been reported that autophagy is upregulated in proliferating
T cells9,12,13. Stimulation via the T cell antigen receptor (TCR) pro-
motes the activation and proliferation of T cells and also induces
signaling via the metabolic checkpoint kinase mTOR, which would
be expected to inhibit rather than induce autophagy8. Thus, questions
remain about why and how proliferating T cells upregulate autophagy
T cells after stimulation via the TCR, little is known about in vivo
autophagy activity in antigen-specific T cells during the course of the
differentiation of effector and memory T cells after viral infection.
The in vivo function of autophagy in antigen-specific T cells during
viral infection remains unclear but is important, as pharmacological
manipulation of autophagy is being considered as a treatment for
many human diseases14. Mice with a conditional null mutation result-
ing in selective deletion of the gene encoding either of the autophagy-
related molecules Atg5 or Atg7 during early T cell development
(through the use of Cre recombinase expressed from T cell–specific
gene Lck) have fewer mature peripheral T cells than their wild-type
counterparts have10,15. Similarly, chimeric mice reconstituted with
Atg5−/− fetal liver cells have fewer peripheral T cells than do chi-
meric mice reconstituted with wild-type fetal liver cells9. That study
also showed that Atg5-deficient T cells exhibit diminished prolifera-
tive capacity following in vitro stimulation via the TCR9. Although
such data indicate that autophagy has a key role in the development
and homeostasis of T cells, they shed less light on the function of
autophagy molecules in T cells responding to antigen because the cells
studied had developed in the absence of autophagy molecules such
as Atg5 or Atg7 and exhibited abnormalities in gene expression and
mitochondrial number and function10,15. Thus, a new approach using
phenotypically normal naive T cells is needed to study the function
Autophagy is essential for effector CD8+ T cell
survival and memory formation
Xiaojin Xu1,5, Koichi Araki1,5, Shuzhao Li2, Jin-Hwan Han1, Lilin Ye1, Wendy G Tan1, Bogumila T Konieczny1,
Monique W Bruinsma3, Jennifer Martinez4, Erika L Pearce3, Douglas R Green4, Dean P Jones2,
Herbert W Virgin3 & Rafi Ahmed1
The importance of autophagy in the generation of memory CD8+ T cells in vivo is not well defined. We report here that autophagy
was dynamically regulated in virus-specific CD8+ T cells during acute infection of mice with lymphocytic choriomeningitis
virus. In contrast to the current paradigm, autophagy decreased in activated proliferating effector CD8+ T cells and was then
upregulated when the cells stopped dividing just before the contraction phase. Consistent with those findings, deletion of the
gene encoding either of the autophagy-related molecules Atg5 or Atg7 had little to no effect on the proliferation and function of
effector cells, but these autophagy-deficient effector cells had survival defects that resulted in compromised formation of memory
T cells. Our studies define when autophagy is needed during effector and memory differentiation and warrant reexamination of
the relationship between T cell activation and autophagy.
A RT I C L E S
CD8+ T cells provide protection against intracellular bacterial, para-
sitic and viral infections, as well as cancers1,2. Following stimulation
with antigens, naive CD8+ T cells go through many rounds of prolif-
eration, giving rise to effector T cells, which eliminate infected cells.
Upon clearance of the antigens, most effector CD8+ T cells undergo
apoptosis, leaving only a small pool of cells to survive and differenti-
ate into memory cells3–5. During this naive-to-effector to memory-
differentiation process, T cells undergo cellular and metabolic
reprogramming to shift from anabolic processes and proliferation to
catabolic processes and contraction of cell populations to generate
memory. It is important to define the role of macroautophagy (called
‘autophagy’ here) during this process.
Autophagy is an evolutionarily conserved process that involves the
engulfment and delivery of cytosolic contents to the lysosome for
degradation6–10. This catabolic activity of autophagy is essential for
cellular homeostasis and has been suggested to be inversely correlated
with cell growth and proliferation11. In contrast to that paradigm,
T cells after stimulation via the TCR, little is known about in vivo
autophagy activity in antigen-specific T cells during the course of the
differentiation of effector and memory T cells after viral infection.
The in vivo function of autophagy in antigen-specific T cells during
viral infection remains unclear but is important, as pharmacological
manipulation of autophagy is being considered as a treatment for
many human diseases14. Mice with a conditional null mutation result-
ing in selective deletion of the gene encoding either of the autophagy-
related molecules Atg5 or Atg7 during early T cell development
(through the use of Cre recombinase expressed from T cell–specific
gene Lck) have fewer mature peripheral T cells than their wild-type
counterparts have10,15. Similarly, chimeric mice reconstituted with
Atg5−/− fetal liver cells have fewer peripheral T cells than do chi-
meric mice reconstituted with wild-type fetal liver cells9. That study
also showed that Atg5-deficient T cells exhibit diminished prolifera-
tive capacity following in vitro stimulation via the TCR9. Although
such data indicate that autophagy has a key role in the development
Autophagy is essential for effector CD8+ T cell
survival and memory formation
Xiaojin Xu1,5, Koichi Araki1,5, Shuzhao Li2, Jin-Hwan Han1, Lilin Ye1, Wendy G Tan1, Bogumila T Konieczny1,
Monique W Bruinsma3, Jennifer Martinez4, Erika L Pearce3, Douglas R Green4, Dean P Jones2,
Herbert W Virgin3 & Rafi Ahmed1
The importance of autophagy in the generation of memory CD8+ T cells in vivo is not well defined. We report here that autophagy
was dynamically regulated in virus-specific CD8+ T cells during acute infection of mice with lymphocytic choriomeningitis
virus. In contrast to the current paradigm, autophagy decreased in activated proliferating effector CD8+ T cells and was then
upregulated when the cells stopped dividing just before the contraction phase. Consistent with those findings, deletion of the
gene encoding either of the autophagy-related molecules Atg5 or Atg7 had little to no effect on the proliferation and function of
effector cells, but these autophagy-deficient effector cells had survival defects that resulted in compromised formation of memory
T cells. Our studies define when autophagy is needed during effector and memory differentiation and warrant reexamination of
the relationship between T cell activation and autophagy.
metabolomics transcriptomics
Case Study 3: Transcriptome x Metabolome

45. Enzymes associated with
significant metabolites
2.5.1.56, 2.7.1.91, 2.4.2.9, 2.4.2.8,
1.14.16.4, 1.14.16.5, 3.6.1.22,
2.4.2.1, 2.4.1.80, 3.1.4.35, 2.4.2.4,
2.4.2.7, 2.4.2.14, 2.4.2.11, 2.4.2.12,
3.5.4.17, 2.4.2.19, 1.1.1.94, 3.1.6.8,
3.1.6.1, 4.3.2.2, 1.14.14.1, 3.1.3.4,
3.1.3.5, 3.1.4.46, 2.4.1.141,
1.3.99.13, 3.6.1.5, 3.6.1.6, 3.6.1.9,
3.6.1.8, 2.1.1.1, 3.5.1.9, 2.7.1.1,
2.7.1.8, 3.1.1.4, 2.7.8.-, 3.2.1.18,
2.7.8.2, 2.7.8.5, 2.7.8.8, 1.1.99.4,
1.1.99.5, 2.7.1.74, 2.7.7.14, 3.6.1.29,
3.6.1.19, 3.6.1.17, 2.7.1.138,
2.4.1.47, 6.3.5.1, 6.3.5.3, 6.3.5.2,
6.2.1.3, 1.1.1.102, 4.1.3.3,
1.14.13.30, 3.2.2.1, 2.5.1.18,
3.5.1.23, 1.13.11.11, 2.6.1.7,
2.7.1.59, 4.1.2.13, 2.4.99.8, 2.4.99.9,
1.3.3.6, 3.1.3.10, 3.2.1.46, 3.2.1.45,
6.3.4.4, 2.2.1.1, 2.2.1.2, 6.3.4.1,
2.7.8.1, 2.7.1.20, 1.7.1.7, 2.4.2.22,
2.3.1.24, 2.7.8.11, 2.7.8.15, 3.5.4.3,
3.1.4.2, 3.5.4.6, 2.7.6.1, 2.6.1.16,
3.1.4.12, 3.1.4.17, 2.4.1.117, 1.2.3.1,
3.5.4.4, 1.4.3.2, 4.1.2.27, 3.1.4.3,
6.1.1.2, 4.2.1.17, 3.2.2.2, 3.1.2.2,
3.2.2.6, 3.2.2.5, 3.5.99.6, 3.2.2.8,
1.1.1.8, 3.7.1.3, 1.13.11.34
Gpd1l, Kdsr, Ado, Acox1, Gmpr2, Tkt, Alg5,
Alg13, Hprt, Nampt, Gsta4, Gstk1, Gstm1,
Gstm4, Gsto1, Gstp1, Gstp2, Gstt2, Hpgds,
Gfpt1, Adk, Nagk, Dck, Sphk1, Sphk2, Prps1,
Prps2, Cept1, Ept1, Cept1, Cdipt, Plb1, Acot2,
Lpin1, Lpin2, Pde1b, Pde2a, Pde3b, Pde4a,
Pde4d, Pde7a, Pde8a, Pde5a, Arsa, Gba2,
Galc, Bst1, Cd38, Asah1, Asah2, Ada, Ampd1,
Ampd2, Ampd3, Cant1, Enpp1, Itpa, Enpp4,
Aldoa, Aldoc, Sgpl1, Npl, Acsl1, Acsl3, Acsl4,
Acsl5
Gpda, Acox3, Oxla, Gmpr1, T23o, Lox5, Cp4f3,
Cp4fe, Cp19a, Cp1a1, Cp1a2, Cp1b1, Cp237,
Cp238, Cp239, Cp240, Cp254, Cp255, Cp270,
Cp2a4, Cp2a5, Cp2ac, Cp2b9, Cp2ba, Cp2bj,
Cp2ct, Cp2d9, Cp2da, Cp2db, Cp2dq, Cp2j5,
Cp2j6, Cp2s1, Cp2u1, Cp341, Cp3ab, Cp3ad,
Cp3ag, Cp3ap, Cp4b1, Cp4ca, Cp4x1, Cy250,
Tph1, Tph2, Alkmo, Nnmt, Tktl1, Tktl2, Taldo,
Cegt, Pnph, Typh, Apt, Nadc, Sia8a, Siat9,
Gsta1, Gsta2, Gsta3, Gstm2, Gstm5, Gstm6,
Gstm7, Gsto2, Gstt1, Gstt4, Maai, Mgst1,
Mgst3, Aadat, Aatm, Kat1, Kat3, Gfpt2, Hkdc1,
Hxk1, Hxk2, Hxk3, Cerk1, Pcy2, Chpt1, Pgps1,
Gpt, Hrsl3, Pa21b, Pa24a, Pa24b, Pa24d,
Pa24e, Pa24f, Pa2g5, Pa2ga, Pa2gc, Pa2gd,
Pa2ge, Pa2gf, Pa2gx, Pg12a, Plpl9, Aco15,
Acot1, Acot3, Acot4, Acot5, Baat, Bach, Them4,
Lpin3, Lpp1, Lpp2, Lpp3, Lppr2, Lppr3, Lppr4,
Ppc1a, Ppc1b, 5nt1a, 5nt1b, 5nt3a, 5nt3b,
5ntc, 5ntd, Ppap, Gpcp1, Asm, Nsma2, Nsma3,
Nsma, Pde10, Pde11, Pde1a, Pde1c, Pde3a,
Pde4c, Pde7b, Pde8b, Cncg, Cnrg, Pde10,
Pde11, Pde6a, Pde6b, Pde6c, Pde9a, Neur1,
Neur2, Neur3, Neur4, Glcm, Kfa, Acer1, Acer2,
Guad, Gnpi1, Gnpi2, Entp1, Entp8, Entp4,
Entp5, Enpp3, Ap4a, Nud12, Fhit, Kynu, Aldob,
Echa, Echm, Echp, Pur8, Sywc, Sywm, Acbg1,
Acbg2, Acsl6, S27a2, Pura1, Pura2, Nade,
Guaa, Pur4
Enzymes Genes
Metabolites

46. Enzyme genes significantly
enriched towards KO
Expression of genes
corresponding to related
enzymes are enriched for
KO cells, DNA microarray
data, GSEA (Gene Set
Enrichment Analysis).
Nominal p = 0,
FWER p = 0.024.

47. Case Study 4: Integra(on of Metabolomics and Transcriptomics to evaluate the effect
of subcura(ve doses of pyrimethamine on plasma hemoglobin in Rhesus macaques
using Group LASSO
47

48. Experimental Design
• Five macaques were each delivered a sub-cura(ve dose of pyrimethamine at Day 21, and 3-day
cura(ve doses commencing at Days 52 and 90, in each case immediately following peripheral blood
sampling. This results in two pre-drug, three post-drug, and two inter-drug treatments as indicated.
• Plasma samples were collected over the course of 100 days.
• These correspond to:
– Time Point 1 = Day 0 (Baseline Sampling Point)
– Time Point 2 = Day 21
– Time Point 3 = Day 27
– Time Point 4 = Day 52
– Time Point 5 = Day 59
– Time Point 6 = Day 90
– Time Point 7 = Day 98
48
Day: 0 20 27 52 59 90 98
Pre1 Pre2 Post3 Inter4 Post5 Inter6 Post7

49. Analysis Summary
• Ques6on 1: Are there genes and metabolites that are associated with hemoglobin
levels?
• Correla(on Analysis
– 305 metabolites are significantly correlated with Hgb levels
– 1074 genes are significantly correlated with Hgb levels
• Ques6on 2: Are there features that separate subjects based on drug exposure (pre
vs. inter vs. post)
• Differen(al Expression Analysis
– 1660 metabolites can separate the subjects into inter and post drug exposure groups
– 925 genes can separate the subjects into pre, inter and post drug exposure groups
• Conclusion
– The list of poten(al targets is cumbersome (informa(on overload)
49

50. Can a single test answer two ques6ons?
• Which features from both pla]orms are associated with drug exposure?
• Among those features, which of them are specifically associated with
hemoglobin levels?
50

51. Hybrid concatena6on and transforma6on based integra6on using
Group LASSO (Banton et al.)
51
GROUP
PATHWAY
LASSO
Standardized
Pathways in
INTEGRATED
MATRIX
PCA
Pathways
.Genes
Pathways
.Metabolites
Standardized PCA
Contributions of the method
• Allows integration of multiple omics data types
• The method is not platform specific or dependent
• The method retains functional information provided by pathways
• The method allows prediction of outcomes and thus can be used in the development of clinical biomarkers

52. The lasso estimator is defined as:
Where λ is the tuning parameter
Least Absolute Shrinkage and Selec6on Operator (LASSO)
52
A popular model selection and shrinkage estimation method (Tibshirani 1995).
∑=
+−=
p
j
jX
1
2
2
β
|)|(ˆ argmin βλββλ Y
Meier L, Geer Svd, Bühlmann P. 2008. The Group Lasso for Logistic Regression. Journal of the Royal Statistical Society Series B (Statistical Methodology) 70:53-71.
Extended from the lasso penalty, the group lasso estimator is:
)(minargˆ
1
2
2
2 ∑=
+−=
G
g
Ig
XY βλββ
β
λ
: the index set belonging to the th group of variables.
The penalty does the variable selection at the group level, belonging to the intermediate
between and type penalty.
It encourages that either or for all
gI
−1l −2l
0ˆ =gβ 0ˆ
, ≠jgβ },,1{ gdfj …∈
g

53. Integra6on with Group LASSO
Y Pathway1 Pathway 2 Pathway 3 … Pathway n
y PC1 PC2 PC3 PC1 PC2 PC1 PC2 … PC1
y PC1 PC2 PC3 PC1 PC2 PC1 PC2 … PC1
y PC1 PC2 PC3 PC1 PC2 PC1 PC2 … PC1
PC1 PC2 PC3 PC1 PC2 PC1 PC2 … PC1
y PC1 PC2 PC3 PC1 PC2 PC1 PC2 … PC1
y PC1 PC2 PC3 PC1 PC2 PC1 PC2 … PC1
y PC1 PC2 PC3 PC1 PC2 PC1 PC2 … PC1
… PC1 PC2 PC3 PC1 PC2 PC1 PC2 … PC1
Outcome
Model*
53
*Model built using cross-validation
Where Y is the hemoglobin level in each subject

54. Results*: Number of Targets is dras6cally reduced
54
Pathway
Number
of Genes
Number
of Metabolites
Ascorbate and aldarate metabolism (Vitamin C) 5 4
Glycerophospholipid metabolism 53 7
Linoleic acid metabolism 6 6
Cysteine and methionine metabolism 26 11
Porphyrin and chlorophyll metabolism 20 9
Retinol metabolism (Vitamin A) 16 6
Valine, leucine and isoleucine degradation 38 6
Nicotinate and nicotinamide metabolism 18 3
Total features 182 52
*Lambda min = 0.009
Lambda se = 0.1872

55. Proof of Concept: Correla(ons between Hgb and significant genes/metabolites selected by
Group Pathway Lasso
55
Pathway: Porphyrin and chlorophyll metabolism (Heme dysregula(on in first, second, and eighth steps of biosynthe(c
pathways)
Step 1: Heme biosynthesis (ALAS2 gene à aminolevulinic
acid synthase)
*Putative database match
Step 2: Heme biosynthesis (ALAD gene)
Step 8: Terminal step in Heme biosynthesis (FECH
gene)

56. Ritchie MD, Holzinger ER, Li R, Pendergrass SA, Kim D. 2015. Methods of integrating data to uncover genotype-phenotype interactions. Nat Rev
Genet 16:85-97.
Meta-dimensional analysis can be divided into three categories. a | Concatenation-based integration involves combining data sets
from different data types at the raw or processed data level before modelling and analysis. b | Transformation-based integration
involves performing mapping or data transformation of the underlying data sets before analysis, and the modelling approach is
applied at the level of transformed matrices. c | Model-based integration is the process of performing analysis on each data type
independently, followed by integration of the resultant models to generate knowledge about the trait of interest. miRNA, microRNA;
SNP, single-nucleotide polymorphism.
Summary: methods of omics integration
56
Caulobacteraceae
Streptococcus
GROUP PATHWAY
LASSO
Standardized
Pathways in
INTEGRATED
MATRIX
PCA
Pathways
.Genes
Standardized PCA
Pathways
.Metabolites

57. Acknowledgements
57
Emory University SOM
Dean P. Jones (Professor, Director of Clinical
Biomarkers Laboratory)
Shuzhao Li (Asst. Professor, Director of
Immunometabolomics)
Douglas I. Walker (TCE study)
Paul A. Dawson (Bile acids study)
Sushma K. Cribbs (HIV study)
Judy Fridovich-keil (GALT study)
MahPIC (Pyrimethamine study)
Members of Clinical Biomarkers Laboratory
Georgia State University, PHI
Ruiyan Luo

58. Clinical Biomarkers Laboratory

59. Ques(ons?