The Human Gut Microbiome in HIV infection - UPC

TheHumanGutMicrobiomeinHIVinfection

YolandaGuillénMontalbán,PhDMicrobialGenomics

AIDSResearchInstitute,IrsicaixaHospitalUniversitariGermansTriasiPujol

Thehumanmicrobiotaandmicrobiome

Thehumanmicrobiotaisthecollectionofallthemicroorganismslivinginassociationwiththehumanbody,includingeukaryotes,archaea,bacteriaandviruses

Thehumanmicrobiomereferstotheirgenomes

Thehumanmicrobiome

Delsucetal.2005NatRevGen

TheHumanMicrobiomeProject

2008Characterizationofthemicroorganismsassociatedtohealthyand

diseasedhumans

Samplesfromdifferentpartsofthehumanbody

Unprecedentedinformationaboutthecomplexityofhumanmicrobial

communities

3,000microbialgenomesequences

Graphic Sciencie. Perrin Ireland. www.wagsrevue.com/thewag/

Thehumanmicrobiome

Thehumanmicrobiome

“Alldiseasesbegininthegut”(Epidemias)Hippocrates

460–370BC

Gutmicrobiota

ThePhisiologyofdefecationTheexaminationofthestool(alvinedischarge)yieldsthemostinformationaboutthenature,temperamentandoverallquality

oftheinitialFirstDigestionoffoodanddrinkinthegastrointestinaltract

AntonVanLeeuwenhoek&RobertHooke1665-1683

Microscopy,firstdescriptionofbacteria

neous generation’, which was a popular concept at the time (and not decisively disprovenas a phenomenon until the mid-nineteenth century, by Pasteur).

IDENTITY OF HOOKE’S MOULD

In Micrographia, Hooke provided the first illustrations of microfungi (Scheme XII, Fig.1). From the diagram and the detailed description, microbiologists10 have concluded thathis specimen can be identified as the widely distributed microfungus Mucor. There aremany species of Mucor, frequently referred to as ‘bread moulds’, which occur commonly

Discovery of microorganisms by Hooke and Leeuwenhoek 191

Figure 1. Microscopic view of a ‘hairy mould’ colony described by Robert Hooke in 1665 (in Micrographia). Thisimage was the first published depiction of a microorganism. The reproductive structures (sporangia) are characteris-tic of the microfungus Mucor. Sporangia in different stages are identified by the letters A, B, C, and D. Hookeincluded a scale reference; the length of the bar under the diagram represents 1/32 inch. (Reproduced courtesy of TheLilly Library, Indiana University, Bloomington, Indiana.)

rsnr.royalsocietypublishing.orgDownloaded from

Leeuwenhoek’s microscope First published depiction of a microorganism (R. Hooke, Micrographia, 1665)

Thehumangutmicrobiota

“Organ”of~1014microorganisms~2kgresponsibleformultiplephysiologicalfunctions

Immunesystem

Nutrition&metabolism

• Mucosaldevelopment

• GALTmaduration• Maintainance

homeostasis• Regeneration

intestinalepithelium

Productionessentialmetabolites

Thehumanmicrobiome

Thehumangutenterotypes

Intestinalmicrobiotavariationisgenerallystratifiedinthreeclustersorenterotypes,

drivenbygenuscomposition

Prevotellasp. Bacteroidessp.

Ruminococcussp.

Thehumanmicrobiome

Arumugametal.2011Nature

EuropeansAmericans

AsiansN=33

Sanger

DanishN=85

Illumina

USN=145

Illumina

Bacteriathatdrivethedifferententerotypestendtobemutually

excluding

GutdysbiosisRe

covery?

Gutdamage

Ecosystemimbalance

Thehumanmicrobiome

Thepathogenesisofintestinaldisordersincludinginflammatoryboweldisease,irritableboweldisease(IBD)andcoeliacdisease,aswellasextra-intestinaldisorderssuchasallergy,asthmaor

obesity,isassociatedwithgutdysbiosis

HIVinfectionandgutdamage

ThegutmicrobiomeandHIV

Brenchleyetal.2004JExpMed

HIVinfectionandgutdamage

ThegutmicrobiomeandHIV

Brenchleyetal.2004JExpMed

HIV- HIV+

DepletionofCD4+Tcells

Lymphoidaggregates

Absenceoflymphoidaggregates

Influence of the gut microbiota on HIV-1 infection

Chronic infection

Exploringthegutmicrobialcommunities

HIV-1 specific gut dysbiosis?

RandomshotgunTargetgenesurvey(16SrRNA)

MicrobialGenomics

MetagenomicsDeterminetheabundanceandidentityofmicrobesinasample

SingletargetsareamplifiedusingPCRandthentheproductsare

sequenced

TotalDNAisisolatedfromsamplesandthensequenced

ClassicalGenomicsCultivableorganisms


Onlyarestrictedsetofmicrobialcommunitiesare

cultivable

Tringe&Rubin2005NatRevGen

1.049.116 codes

~400 different codes per sample


16SrRNAsequencing

~400differentcodespersample


16SrRNAsequencingGenuslevel

Bacteriaandarchaea

Randomshotgunsequencing


Specieslevel

Bacteria,archaea,viruses,eukaryotes


Genecontentandfunctionalprofile

TheMetaHIVProject


PartI

PartII

16SrRNAsequencing

TheMetaHIVProject

Noguera-Julianetal.2016eBioMedicine

TheBarcelonaMetaHIVProject

TheMetaHIVProject

Microbialdiversityandrichness

ResultsI

MSMindividualshadthehighestmicrobial

diversityandrichnesscomparedtoHTSandPWIDinbothcohorts

AfterstratifyingforsexualpreferenceHIV-1infectionconsistently

associatedwithsignificantreductioninmicrobialrichness

Microbialdiversityandrichness

ResultsI

Thelowestmicrobialrichnessisobservedinsubjectswithavirological-immunediscordantresponsetoART

Microbialenterotypesandriskgroup

ResultsI

MSMindividualstendtoclustertogetherintermsofgenuscomposition,

nonMSMhaveadifferentcomposition

MSMmicrobiotaofindividualsfromBcnandStkhasanenrichmentofPrevotellaandnonMSM

anenrichmentofBacteroides

Dietinfluence

ResultsI

Short term recall questionnaire

presence of at least 2 clusters of fecal microbiomes in both cities(Fig. 4c). Such clusters were enriched either in Bacteroides orPrevotella, and had a similar bacterial composition to the correspondingpreviously described enterotypes (Arumugam et al., 2011; Koren et al.,2013) (Supplementary Fig. 3). As expected from previous work on gutenterotypes, there were strong positive correlations between the genusBacteroides and Parabacteroides, Barnesiella, Alistipes and Odoribacter,as well as between Prevotella and Alloprevotella, Catenibacterium,Mitsuokella and Intestinimonas, among others (Fig. 3), highlighting thatdifferences between the groups extended beyond a single genus. The

genera correlating with Prevotella were negatively correlated withBacteroides and vice versa. Moreover, themicrobiomes of the Bacteroidesand Prevotella clusters showed remarkably different functional profiles(Supplementary Figs. 4 and 5), also in agreement with previousenterotype descriptions (Arumugam et al., 2011).

3.4. Factors Associated With the Fecal Microbiota Composition

Weexplored variables potentially influencing the composition of thefecal microbiomes, according to a univariate ADONIS test of ecological

Fig. 6. Limited effect of diet on the composition of the microbiome. a) Subjects belonging to the Prevotella cluster and men who had sex with men (MSM) had significantly higher totalenergy intake. Therefore, all subsequent nutritional analyses were normalized for this factor. b) Main associations between bacterial genera, normalized amounts of nutrients (left)and food portions (right), according to a Dirichlet multinomial regression model. Positive and negative associations are shown in red and blue, respectively. Line thickness isproportional to the strength of the association. c) Of all links identified by the Dirichlet approach, the only significant differences between groups after adjusting for multiplecomparisons (Benjamini–Hochberg FDR b 0.1) were increased consumption of meat in the cluster Bacteroides and increased intake of dietary water in MSM. d) Spearman correlationsbetween normalized amounts of nutrients and Bray–Curtis distance to the furthest subject in the opposite cluster. Negative correlations imply increased amounts of nutrient withshorter distance to each cluster. Therefore, values in red and blue represent increased and decreased amounts of nutrients within each cluster, respectively. Although, in general,the direction of the correlations was concordant with previous publications, note the small effect sizes (R2 below the color key). None of the comparisons were statistically significantafter correction for multiple comparisons (Benjamini–Hochberg FDR b 0.1); Permanova p = 0.20 for overall differences between clusters. e, f) Mean and 95% confidence intervals forthe differences between clusters in consumption of nutrients (e) and portions of food (f). Comparisons were significant if the 95 confidence interval did not cross 0 (dashed red line).

143M. Noguera-Julian et al. / EBioMedicine 5 (2016) 135–146

presence of at least 2 clusters of fecal microbiomes in both cities(Fig. 4c). Such clusters were enriched either in Bacteroides orPrevotella, and had a similar bacterial composition to the correspondingpreviously described enterotypes (Arumugam et al., 2011; Koren et al.,2013) (Supplementary Fig. 3). As expected from previous work on gutenterotypes, there were strong positive correlations between the genusBacteroides and Parabacteroides, Barnesiella, Alistipes and Odoribacter,as well as between Prevotella and Alloprevotella, Catenibacterium,Mitsuokella and Intestinimonas, among others (Fig. 3), highlighting thatdifferences between the groups extended beyond a single genus. The

genera correlating with Prevotella were negatively correlated withBacteroides and vice versa. Moreover, themicrobiomes of the Bacteroidesand Prevotella clusters showed remarkably different functional profiles(Supplementary Figs. 4 and 5), also in agreement with previousenterotype descriptions (Arumugam et al., 2011).

3.4. Factors Associated With the Fecal Microbiota Composition

Weexplored variables potentially influencing the composition of thefecal microbiomes, according to a univariate ADONIS test of ecological

Fig. 6. Limited effect of diet on the composition of the microbiome. a) Subjects belonging to the Prevotella cluster and men who had sex with men (MSM) had significantly higher totalenergy intake. Therefore, all subsequent nutritional analyses were normalized for this factor. b) Main associations between bacterial genera, normalized amounts of nutrients (left)and food portions (right), according to a Dirichlet multinomial regression model. Positive and negative associations are shown in red and blue, respectively. Line thickness isproportional to the strength of the association. c) Of all links identified by the Dirichlet approach, the only significant differences between groups after adjusting for multiplecomparisons (Benjamini–Hochberg FDR b 0.1) were increased consumption of meat in the cluster Bacteroides and increased intake of dietary water in MSM. d) Spearman correlationsbetween normalized amounts of nutrients and Bray–Curtis distance to the furthest subject in the opposite cluster. Negative correlations imply increased amounts of nutrient withshorter distance to each cluster. Therefore, values in red and blue represent increased and decreased amounts of nutrients within each cluster, respectively. Although, in general,the direction of the correlations was concordant with previous publications, note the small effect sizes (R2 below the color key). None of the comparisons were statistically significantafter correction for multiple comparisons (Benjamini–Hochberg FDR b 0.1); Permanova p = 0.20 for overall differences between clusters. e, f) Mean and 95% confidence intervals forthe differences between clusters in consumption of nutrients (e) and portions of food (f). Comparisons were significant if the 95 confidence interval did not cross 0 (dashed red line).

143M. Noguera-Julian et al. / EBioMedicine 5 (2016) 135–146

Long-termdietarypatternshavebeenlinkedtoalternativeenterotypestates,butlimitedeffectofdietonthecompositionofthemicrobiotawasfoundinoursetting

Take-homemessagePartI

ConclusionsI

• Factors related with sexual preference might also affect the gutmicrobiotacompositionbyunknownmechanisms

• HIV-1 infection is consistently associated with reduced bacterialrichness (hallmark of HIV-1 infection) independently of sexualorientation

• EarlyARTinitiationmighthelptopreservegutmicrobialrichness

Research Paper

Gut Microbiota Linked to Sexual Preference and HIV Infection

Marc Noguera-Julian a,b,c,1, Muntsa Rocafort a,c,1, Yolanda Guillén a,c, Javier Rivera a,b, Maria Casadellà a,c,Piotr Nowak d, Falk Hildebrand e, Georg Zeller e, Mariona Parera a, Rocío Bellido a, Cristina Rodríguez a,Jorge Carrillo a,c,g, Beatriz Mothe a,b,c,f, Josep Coll a,f, Isabel Bravo f, Carla Estany f, Cristina Herrero f, Jorge Saz h,Guillem Sirera f, Ariadna Torrela i, Jordi Navarro i, Manel Crespo i, Christian Brander a,b,c,j, Eugènia Negredo b,c,f,Julià Blanco a,b,c, Francisco Guarner k, Maria Luz Calle b, Peer Bork e,l,m, Anders Sönnerborg d,Bonaventura Clotet a,b,c,f, Roger Paredes a,b,c,f,⁎a IrsiCaixa AIDS Research Institute, Ctra de Canyet s/n, 08916 Badalona, Catalonia, Spainb Universitat de Vic-Universitat Central de Catalunya, C. Sagrada Família 7, 08500 Vic, Catalonia, Spainc Universitat Autònoma de Barcelona, 08193 Bellaterra, Catalonia, Spaind Department of Medicine, Unit of Infectious Diseases, Karolinska University Hospital, Karolinska Institutet, Huddinge 141, 86, Stockholm, Swedene Structural and Computational Biology, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germanyf HIV Unit & Lluita Contra la SIDA Foundation, Hospital Universitari Germans Trias i Pujol, Ctra de Canyet s/n, 08916 Badalona, Catalonia, Spaing ISGLOBAL, Carrer Rosselló, 132, 08036 Barcelona, Catalonia, Spainh BCN Checkpoint, Carrer del Comte Borrell, 164, 08015 Barcelona, Catalonia, Spaini Infectious Diseases Unit, Hospital Universitari Vall d'Hebrón, Passeig de la Vall d'Hebrón, 119–129, 08035 Barcelona, Catalonia, Spainj Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spaink Digestive Diseases Department, Vall d'Hebrón Institute of Research, Hospital Universitari Vall d'Hebrón, Passeig de la Vall d'Hebrón, 119–129, 08035 Barcelona, Catalonia, Spainl Max-Delbrück-Centre for Molecular Medicine, Robert-Rössle-Str. 10, 13092 Berlin, Germanym Molecular Medicine Partnership Unit, EMBL, Meyerhofstrasse 1, 69117 Heidelberg, Germany

a b s t r a c ta r t i c l e i n f o

Article history:Received 9 December 2015Received in revised form 12 January 2016Accepted 27 January 2016Available online 28 January 2016

The precise effects of HIV-1 on the gut microbiome are unclear. Initial cross-sectional studies providedcontradictory associations between microbial richness and HIV serostatus and suggested shifts from Bacteroidesto Prevotella predominance following HIV-1 infection, which have not been found in animal models or in studiesmatched for HIV-1 transmission groups. In two independent cohorts of HIV-1-infected subjects and HIV-1-negative controls in Barcelona (n=156) and Stockholm (n=84),menwho have sexwithmen (MSM) predom-inantly belonged to the Prevotella-rich enterotypewhereasmost non-MSMsubjectswere enriched in Bacteroides,independently of HIV-1 status, and with only a limited contribution of diet effects. Moreover, MSM had a signif-icantly richer and more diverse fecal microbiota than non-MSM individuals. After stratifying for sexual orienta-tion, there was no solid evidence of an HIV-specific dysbiosis. However, HIV-1 infection remained consistentlyassociated with reduced bacterial richness, the lowest bacterial richness being observed in subjects with avirological-immune discordant response to antiretroviral therapy. Our findings indicate that HIV gutmicrobiomestudies must control for HIV risk factors and suggest interventions on gut bacterial richness as possible novelavenues to improve HIV-1-associated immune dysfunction.

© 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license(http://creativecommons.org/licenses/by/4.0/).

Keywords:HIV-1MicrobiomeMicrobiota16S rDNAPrevotellaBacteroides

1. Introduction

The main clinical problems of people living with HIV (PLWH)in areas with adequate healthcare standards and continued antire-troviral therapy (ART) supply are increasingly related to prematureaging (Paiardini and Müller-Trutwin, 2013). That is, a precocious

development of type 2 diabetes, dislipidemia, cardiovascular diseases,osteoporosis and frailty syndrome. Such diseases have been related tostructural or metabolic perturbations in the gut microbiota of non-HIV-infected subjects (Claesson et al., 2012; Koeth et al., 2013; LeChatelier et al., 2013; Tang et al., 2013) whereas, in PLWH, have beenlinked to chronic inflammation, immune activation and endotoxemia(Brenchley et al., 2006; Douek, 2003; Sandler and Douek, 2012). Thusthere is considerable interest in understanding the role of the humangut microbiome in HIV pathogenesis and, in particular, its ability toperpetuate chronic inflammation and foster immune senescence. Thishas immediate clinical implications because, in theory, it might be

EBioMedicine 5 (2016) 135–146

⁎ Corresponding author at: HIV Unit and IrsiCaixa AIDS Research Institute, Ctra deCanyet s/n, 08916 Badalona, Catalonia, Spain.

E-mail address: [email protected] (R. Paredes).1 Contributed equally to this work.

http://dx.doi.org/10.1016/j.ebiom.2016.01.0322352-3964/© 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Contents lists available at ScienceDirect

EBioMedicine

j ourna l homepage: www.eb iomed ic ine .com

Research Paper

Gut Microbiota Linked to Sexual Preference and HIV Infection

Marc Noguera-Julian a,b,c,1, Muntsa Rocafort a,c,1, Yolanda Guillén a,c, Javier Rivera a,b, Maria Casadellà a,c,Piotr Nowak d, Falk Hildebrand e, Georg Zeller e, Mariona Parera a, Rocío Bellido a, Cristina Rodríguez a,Jorge Carrillo a,c,g, Beatriz Mothe a,b,c,f, Josep Coll a,f, Isabel Bravo f, Carla Estany f, Cristina Herrero f, Jorge Saz h,Guillem Sirera f, Ariadna Torrela i, Jordi Navarro i, Manel Crespo i, Christian Brander a,b,c,j, Eugènia Negredo b,c,f,Julià Blanco a,b,c, Francisco Guarner k, Maria Luz Calle b, Peer Bork e,l,m, Anders Sönnerborg d,Bonaventura Clotet a,b,c,f, Roger Paredes a,b,c,f,⁎a IrsiCaixa AIDS Research Institute, Ctra de Canyet s/n, 08916 Badalona, Catalonia, Spainb Universitat de Vic-Universitat Central de Catalunya, C. Sagrada Família 7, 08500 Vic, Catalonia, Spainc Universitat Autònoma de Barcelona, 08193 Bellaterra, Catalonia, Spaind Department of Medicine, Unit of Infectious Diseases, Karolinska University Hospital, Karolinska Institutet, Huddinge 141, 86, Stockholm, Swedene Structural and Computational Biology, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germanyf HIV Unit & Lluita Contra la SIDA Foundation, Hospital Universitari Germans Trias i Pujol, Ctra de Canyet s/n, 08916 Badalona, Catalonia, Spaing ISGLOBAL, Carrer Rosselló, 132, 08036 Barcelona, Catalonia, Spainh BCN Checkpoint, Carrer del Comte Borrell, 164, 08015 Barcelona, Catalonia, Spaini Infectious Diseases Unit, Hospital Universitari Vall d'Hebrón, Passeig de la Vall d'Hebrón, 119–129, 08035 Barcelona, Catalonia, Spainj Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spaink Digestive Diseases Department, Vall d'Hebrón Institute of Research, Hospital Universitari Vall d'Hebrón, Passeig de la Vall d'Hebrón, 119–129, 08035 Barcelona, Catalonia, Spainl Max-Delbrück-Centre for Molecular Medicine, Robert-Rössle-Str. 10, 13092 Berlin, Germanym Molecular Medicine Partnership Unit, EMBL, Meyerhofstrasse 1, 69117 Heidelberg, Germany

a b s t r a c ta r t i c l e i n f o

Article history:Received 9 December 2015Received in revised form 12 January 2016Accepted 27 January 2016Available online 28 January 2016

The precise effects of HIV-1 on the gut microbiome are unclear. Initial cross-sectional studies providedcontradictory associations between microbial richness and HIV serostatus and suggested shifts from Bacteroidesto Prevotella predominance following HIV-1 infection, which have not been found in animal models or in studiesmatched for HIV-1 transmission groups. In two independent cohorts of HIV-1-infected subjects and HIV-1-negative controls in Barcelona (n=156) and Stockholm (n=84),menwho have sexwithmen (MSM) predom-inantly belonged to the Prevotella-rich enterotypewhereasmost non-MSMsubjectswere enriched in Bacteroides,independently of HIV-1 status, and with only a limited contribution of diet effects. Moreover, MSM had a signif-icantly richer and more diverse fecal microbiota than non-MSM individuals. After stratifying for sexual orienta-tion, there was no solid evidence of an HIV-specific dysbiosis. However, HIV-1 infection remained consistentlyassociated with reduced bacterial richness, the lowest bacterial richness being observed in subjects with avirological-immune discordant response to antiretroviral therapy. Our findings indicate that HIV gutmicrobiomestudies must control for HIV risk factors and suggest interventions on gut bacterial richness as possible novelavenues to improve HIV-1-associated immune dysfunction.

© 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license(http://creativecommons.org/licenses/by/4.0/).

Keywords:HIV-1MicrobiomeMicrobiota16S rDNAPrevotellaBacteroides

1. Introduction

The main clinical problems of people living with HIV (PLWH)in areas with adequate healthcare standards and continued antire-troviral therapy (ART) supply are increasingly related to prematureaging (Paiardini and Müller-Trutwin, 2013). That is, a precocious

development of type 2 diabetes, dislipidemia, cardiovascular diseases,osteoporosis and frailty syndrome. Such diseases have been related tostructural or metabolic perturbations in the gut microbiota of non-HIV-infected subjects (Claesson et al., 2012; Koeth et al., 2013; LeChatelier et al., 2013; Tang et al., 2013) whereas, in PLWH, have beenlinked to chronic inflammation, immune activation and endotoxemia(Brenchley et al., 2006; Douek, 2003; Sandler and Douek, 2012). Thusthere is considerable interest in understanding the role of the humangut microbiome in HIV pathogenesis and, in particular, its ability toperpetuate chronic inflammation and foster immune senescence. Thishas immediate clinical implications because, in theory, it might be

EBioMedicine 5 (2016) 135–146

⁎ Corresponding author at: HIV Unit and IrsiCaixa AIDS Research Institute, Ctra deCanyet s/n, 08916 Badalona, Catalonia, Spain.

E-mail address: [email protected] (R. Paredes).1 Contributed equally to this work.

http://dx.doi.org/10.1016/j.ebiom.2016.01.0322352-3964/© 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Contents lists available at ScienceDirect

EBioMedicine

j ourna l homepage: www.eb iomed ic ine .com

TheMetaHIVProject


PartI

PartII

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N = 103 N = 53

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CD4+ T cells / mm3

Gene count rank • Downsampling 1M à 70,865 – 313,572 • Downsampling 10M à 163,558 – 904,589 • All sequences à 226,219 – 1,373,562

N = 103 N = 53

Threshold 621.808

GenerichnessinBcncohort

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HIV-1 negative



2p-value = 0.001

2p-value = 0.009

2p-value = 0.055



HGC

LGC


< 10

0

100 -

200

200 -

500

> 50

0

0

25

50

75

100

18

1

18

4

34

16

21

15

11

16

2p-value = 0.002

HIV-1

nega

tive

CD4+ T cells / mm3

LGC HGC

HIV + 92 37

HIV - 11 16

• Chi-square p < 0.001 • Other factors linked to LGC: HIV risk group, older age, female

gender, caucasian ethnicity, HIV phenotype (viremic controller and HIV-negative higher in HGC), Nadir CD4+, creatinin, FA, fibrinogen and platelets

LGC HGC

HIV + 46 31

HIV - 8 15

• Chi-square p < 0.055

Twopopulationsaccordingtogenerichness

ResultsII

0e+00

1e�06

2e�06

250000 500000 750000

0.00000

0.00025

0.00050

0.00075

0.00100

3500 4000 4500 5000 5500

0

25

50

75

100

late p

rese

nter

disco

rdan

t

conc

ordan

t

early

-trea

ted

ART naive

virem

ic co

ntroll

er

elite

contr

oller

HIV-1

nega

tive

Fre

quen

cy (

%)

0e+00

1e�06

2e�06

3e�06

250000 500000 750000


HIV-1 negative

HIV-1 positive

0e+00

1e�06

2e�06

3e�06

4e�06

Gene richness, MSM

400000 800000600000

2

9

3

15

11

42

5

8

7

8

6

5

3

5

16

11

a b

c d

eD

ensi

tyD

ensi

ty

LGC HGC

HIV-1 positive

HIV-1 negative



2p-value = 0.001

2p-value = 0.009

2p-value = 0.055



HGC

LGC


< 10

0

100 -

200

200 -

500

> 50

0

0

25

50

75

100

18

1

18

4

34

16

21

15

11

16

2p-value = 0.002

HIV-1

nega

tive

CD4+ T cells / mm3

Generichnesscorrelateswithimmunediscordance

ResultsII

0e+00

1e�06

2e�06

250000 500000 750000

0.00000

0.00025

0.00050

0.00075

0.00100

3500 4000 4500 5000 5500

0

25

50

75

100

late p

rese

nter

disco

rdan

t

conc

ordan

t

early

-trea

ted

ART naive

virem

ic co

ntroll

er

elite

contr

oller

HIV-1

nega

tive

Fre

quen

cy (

%)

0e+00

1e�06

2e�06

3e�06

250000 500000 750000


HIV-1 negative

HIV-1 positive

0e+00

1e�06

2e�06

3e�06

4e�06

Gene richness, MSM

400000 800000600000

2

9

3

15

11

42

5

8

7

8

6

5

3

5

16

11

a b

c d

e

Den

sity

Den

sity

LGC HGC

HIV-1 positive

HIV-1 negative



2p-value = 0.001

2p-value = 0.009

2p-value = 0.055



HGC

LGC


< 10

0

100 -

200

200 -

500

> 50

0

0

25

50

75

100

18

1

18

4

34

16

21

15

11

16

2p-value = 0.002

HIV-1

nega

tive

CD4+ T cells / mm3

Subjects with LGC were gradually more

abundant in lower nadir CD4+ T-cell count

categories

NadirCD4+TcellLowestCD4+Tcellcountsrecorded

Markerforimmunerecovery:lownadirCD4=poorimmunerecovery

ResultsII

Increased risk of being LGC when nadir CD4+ T cell were < 500

Dose-effectrelationshipbetweennadirCD4+T-cellcountsandmicrobialgenerichnessinmultivariateregressionmodel

ResultsII

Positive correlation between gene richness and intake of

monounsaturated fats, carotenoids, iron, fiber and

Vitamin A

Dirichlet multinomial regression Association between iron intake

and P. copri, E. eligens and Ruminococcus spp.

Diet questionnaire Moderate influence of diet on gene

richness

ResultsII

0 1 2 3 4 5

Sutterella wadsworthensisBacteroides stercoris

Parabacteroides merdaeLachnospiraceae bacterium 1 4 56FAA

Bacteroides thetaiotaomicronBacteroides clarusPrevotella copri

Ruminococcus bromiiEubacterium biformeCoprococcus comes

Ruminococcus flavefaciensOxalobacter formigenes

Butyrivibrio crossotusOlsenella unclassified

Roseburia hominisMethanosphaera stadtmanae

Eubacterium siraeumCollinsella aerofaciensRuminococcus obeumAlistipes senegalensis

Coprococcus catusEubacterium eligens

Dorea formicigeneransMethanobrevibacter unclassified

Methanobrevibacter smithiiSubdoligranulum unclassified

0.00

-0.5

0

0.50

-0.2

5

0.25

Subdoligranulum unclassifedMethanobrevibacter smithii

Methanobrevibacter unclassifiedCoprococcus catus

Methanosphaera stadtmanaeCoprococcus comesEubacterium biforme

Olsenella unclassifiedButyrivibrio crossotus

Parabacteroides unclassifiedBacteroides ovatusRoseburia hominis

Eubacterium siraeumPrevotella copri

Prevotella stercoreaCatenibacterium mitsuokai

Ruminococcus obeumEubacterium ramulusEubacterium eligens

Ruminococcus flavefaciensBrachyspira unclassified

Dorea formicigeneransSutterella wadsworthensis

Parabacteroides merdaeRuminococcus callidus

Faecalibacterium prausnitziiDorea longicatena

Phascolarctobacterium succinatutensRuminococcus bromii

Alistipes senegalensis

AUC

0.9170.8820.8420.823

0.704

0.7530.7710.7680.729

0.7020.7350.7100.7110.5540.7310.7420.7000.7170.6960.6960.682

genehiv

HGCHGCLGCLGC

Totalge

2e+4e+6e+8e+

0 0.5 1.0

1.0

0.5

Prevotella copri

Gene richness

200,000

400,000

600,000

800,000

NMDS1

NM

DS

2

-1.0 -0.5

0

-0.5

-1.0

Prevotella stercoreaMegasphaera unclassified

Dorea longicatenaDorea formicigeneransCoprococcus comes

Catenibacterium mitsuokaiEubacterium biforme

Subdoligranulum unclassifiedMethanobrevibacter smithii

Bacteroides ovatus

Bacteroides vulgatus

Coprobacillus unclassifiedLachnospiraceae bacterium 9 1 43BFAA

Bacteroides doreiLachnospiraceae bacterium 1 4 56FAA

Anaerotruncus colihominisParabacteroides merdae

Barnesiella intes�nihominisAlis�pes putredinisBacteroides uniformis

Odoribacter splanchnicusOscillibacter unclassified

Alis�pes onderdonkiiFlavonifractor plau�i

Parabacteroides unclassified

HGC HIV-

HGC HIV+

LGC HIV-

LGC HIV+

0.7190.6420.7080.6890.6880.7040.6810.6510.634

a

MSMno

nMSM

Spearman's rho

Mean Decrease Gini

b c

Ace

But

For

Pro

Lac

Suc

CH

4

Eth

Met

Gra

m

+++++

++++

+

++++---

---

-

-

--

v

v

SCFA

Acid Alcoho

l

Gas

* * * * * * * * *

MoredifferentmicrobialconfigurationsinsubjectswithLGCsthaninHGCsinnon-metricmultidimensionalscaling

ResultsII

0 1 2 3 4 5












0.00

-0.5

0

0.50

-0.2

5

0.25















AUC

0.9170.8820.8420.823

0.704

0.7530.7710.7680.729

0.7020.7350.7100.7110.5540.7310.7420.7000.7170.6960.6960.682

genehiv

HGCHGCLGCLGC

Totalge

2e+4e+6e+8e+

0 0.5 1.0

1.0

0.5

Prevotella copri

Gene richness

200,000

400,000

600,000

800,000

NMDS1

NM

DS

2

-1.0 -0.5

0

-0.5

-1.0

Prevotella stercoreaMegasphaera unclassified

Dorea longicatenaDorea formicigeneransCoprococcus comes

Catenibacterium mitsuokaiEubacterium biforme

Subdoligranulum unclassifiedMethanobrevibacter smithii

Bacteroides ovatus


Coprobacillus unclassifiedLachnospiraceae bacterium 9 1 43BFAA

Bacteroides doreiLachnospiraceae bacterium 1 4 56FAA

Anaerotruncus colihominisParabacteroides merdae

Barnesiella intes�nihominisAlis�pes putredinisBacteroides uniformis

Odoribacter splanchnicusOscillibacter unclassified

Alis�pes onderdonkiiFlavonifractor plau�i

Parabacteroides unclassified

HGC HIV-

HGC HIV+

LGC HIV-

LGC HIV+

0.7190.6420.7080.6890.6880.7040.6810.6510.634

a

MSMno

nMSM

Spearman's rho

Mean Decrease Gini

b c

Ace

But

For

Pro

Lac

Suc

CH

4

Eth

Met

Gra

m

+++++

++++

+

++++---

---

-

-

--

v

v

SCFA

Acid Alcoho

l

Gas

* * * * * * * * *

ResultsII

Anna Karenina principle

“Happy families are all alike; every unhappy family is unhappy in its own way” (Tolstoy)

Successful ecological risk assessments (HGC) are all alike; every unsuccessful ecological risk assessment (LGC) fails in its own way (Moore)

0 1 2 3 4 5












0.00

-0.5

0

0.50

-0.2

5

0.25















AUC

0.9170.8820.8420.823

0.704

0.7530.7710.7680.729

0.7020.7350.7100.7110.5540.7310.7420.7000.7170.6960.6960.682

NMDS1

NM

DS

2

-1.0 -0.5

0

-0.5

-1.0

0.7190.6420.7080.6890.6880.7040.6810.6510.634

a

MSMno

nMSM

Spearman's rho

Mean Decrease Gini

b c

Ace

But

For

Pro

Lac

Suc

CH

4

Eth

Met

Gra

m

+++++

++++

+

++++---

---

-

-

--

v

v

SCFA

Acid Alcoho

l

Gas

* * * * * * * * *

ResultsII

Microbialspeciesandgenerichness

MSM

nonM

SM

−0.4

0.0

0.4

0.8

ko00680 Methane metabolism

ko00072 Synthesis and degradation of ketone bodiesko00970 AminoacyltRNA biosynthesis

ko00620 Pyruvate metabolismko00906 Carotenoid biosynthesis

ko05120 Epithelial cell signaling in Helicobacter pylori infectionko03010 Ribosome

ko00071 Fatty acid degradationko00380 Tryptophan metabolism

ko03022 Basal transcription factors

ko05100 Bacterial invasion of epithelial cellsko00561 Glycerolipid metabolism

ko00260 Glycine, serine and threonine metabolism

ko00643 Styrene degradationko00240 Pyrimidine metabolism

ko00010 Glycolysis / Gluconeogenesis

ko03430 Mismatch repairko03420 Nucleotide excision repair

ko00230 Purine metabolism

ko02010 ABC transportersko00564 Glycerophospholipid metabolism

ko03060 Protein export

ko00622 Xylene degradation

ko03030 DNA replicationko03440 Homologous recombination

ko00450 Selenocompound metabolism

ko03410 Base excision repair

ko04122 Sulfur relay system

ko00624 Polycyclic aromatic hydrocarbon degradationko00650 Butanoate metabolism

ko00400 Phenylalanine, tyrosine and tryptophan biosynthesisko00785 Lipoic acid metabolism

ko00520 Amino sugar and nucleotide sugar metabolismko00281 Geraniol degradation

ko00780 Biotin metabolism

ko00311 Penicillin and cephalosporin biosynthesisko00500 Starch and sucrose metabolism

ko00540 Lipopolysaccharide biosynthesisko00511 Other glycan degradation

ko00051 Fructose and mannose metabolism

ko00052 Galactose metabolism

ko00040 Pentose and glucuronate interconversionsko00600 Sphingolipid metabolism

a

1e+05

2e+05

3e+05

Glucose-6-phosphate dehydrogenase

1e+05

2e+05

3e+05

20000

40000

6-phosphogluconate dehydrogenase Glutathione reductase

Glutathione peroxidase

1e+05

2e+05

3e+05Thioredoxin reductase

9e+05

6e+05

3e+05

Peroxiredoxin AhpC5e+05

4e+05

3e+05

2e+05

1e+05

Peroxiredoxin Q/BCP Atypical 2-Cys peroxiredoxin Catalase

Superoxide dismutase Fe-Mn Bacterioferritin

4e+05

3e+05

2e+05

1e+05

4e+05

3e+05

2e+05

1e+05

200000

150000

100000

50000

0 0 0

0 0

0 0

4e+05

3e+05

2e+05

1e+05

0

3e+05

2e+05

1e+05

0

Nadir CD4+ T cells / mm3

negative

HIV-1 infected

Spearman's rho

Rea

d co

unts

Rea

d co

unts

Rea

d co

unts

Rea

d co

unts

bP = 7.2e-05 P = 5.4e-04 P = 0.054

P = 0.171 P = 0.565 P = 1.4e-04

P = 0.085 P = 1.6e-05 P = 1.5e-04

P = 4.6e-04 P = 0.007

Methanogenesis Basal transcription Machinery (Archaea)

Production of butyrate Transport and metabolism of free fatty acids

Microbial growth, replication and protein production

Synthesis of sphingolipids

LPS biosynthesis (Gram -) Synthesis of biotin

Complex carbohydrates enzymes

Specialists

Generalists

43 metabolic pathways correlated with gene richness

ResultsII

Reactive Oxygen Species (ROS) production during the host inflammatory

response

Antioxidants, such as glutathione peroxidase and catalase could

catalyze the decomposition of ROS into non-toxic compounds such as

oxygen and water.

ResultsII

HIV,InflammationandOxidativeStress

Wittmann et al. 2012. Advances in Hematology

MSM

nonM

SM

−0.4

0.0

0.4

0.8





























1e+05

2e+05

3e+05


1e+05

2e+05

3e+05

20000

40000

6-phosphogluconate dehydrogenase Glutathione reductase Glutathione peroxidase

1e+05

2e+05

3e+05

Thioredoxin reductase

9e+05

6e+05

3e+05


4e+05

3e+05

2e+05

1e+05

Peroxiredoxin Q/BCP Atypical 2-Cys peroxiredoxin

Catalase Superoxide dismutase Fe-Mn Bacterioferritin Methionine sulfoxide reductase A

4e+05

3e+05

2e+05

1e+05

4e+05

3e+05

2e+05

1e+05

200000

150000

100000

50000

200000

150000

100000

50000

0 0 0

0 0

0

4e+05

3e+05

2e+05

1e+05

0

3e+05

2e+05

1e+05

0 0

Spearman's rho

KE

GG

cop

ies

KE

GG

cop

ies

KE

GG

cop

ies

G6PDH

6PGD

GR

GPx

AHPC

BCP

TPX

CAT

SOD2

MSRA

0 3 6Wilcoxon test statistic

Alistipes onderdonkii

Alistipes putredinis

Bacteroidales_noname ph8

Bacteroides caccae

Bacteroides clarus

Bacteroides coprocola

Bacteroides dorei

Bacteroides eggerthii

Bacteroides faecis

Bacteroides finegoldii

Bacteroides fragilis

Bacteroides massiliensis

Bacteroides ovatus

Bacteroides plebeius

Bacteroides salyersiae

Bacteroides stercoris

Bacteroides thetaiotaomicron

Bacteroides uniformis


Barnesiella intestinihominis

Odoribacter splanchnicus

Parabacteroides distasonis

Parabacteroides merdae

Prevotella copri

Prevotella stercorea

Acidaminococcus unclassified

Butyrivibrio crossotus

Catenibacterium mitsuokai

Clostridium L2_50

Dorea longicatena

Erysipelotrichaceae_noname biforme

Eubacterium eligens

Eubacterium rectale

Eubacterium siraeum

Faecalibacterium prausnitzii

Roseburia hominis

Roseburia intestinalis

Roseburia inulinivorans

Ruminococcus bromii

Streptococcus salivarius

Subdoligranulum unclassified

Escherichia coli

Haemophilus parainfluenzae

Sutterella wadsworthensis

C2likevirus C2unclassified

Other taxa(154)

LGC enriched KEGG enzymes

<100 100 � 200 200 � 500 >500

Nadir CD4+ T cells / mm3negative

P = 7.2e-05 P = 5.4e-04 P = 0.054 P = 0.171

P = 0.565 P = 1.4e-04 P = 0.085 P = 1.6e-05

P = 1.5e-04 P = 4.6e-04 P = 0.007 P = 0.109

Gene copies were significantly more abundant as nadir CD4 decreased

ResultsII

ROSMetabolismenzymesenrichedinLGC

MSM

nonM

SM

−0.4

0.0

0.4

0.8





























1e+05

2e+05

3e+05


1e+05

2e+05

3e+05

20000

40000


1e+05

2e+05

3e+05


9e+05

6e+05

3e+05


4e+05

3e+05

2e+05

1e+05



4e+05

3e+05

2e+05

1e+05

4e+05

3e+05

2e+05

1e+05

200000

150000

100000

50000

200000

150000

100000

50000

0 0 0

0 0

0

4e+05

3e+05

2e+05

1e+05

0

3e+05

2e+05

1e+05

0 0

Spearman's rho

KE

GG

cop

ies

KE

GG

cop

ies

KE

GG

cop

ies

G6PDH

6PGD

GR

GPx

AHPC

BCP

TPX

CAT

SOD2

MSRA

0 3 6Wilcoxon test statistic




Bacteroides caccae

Bacteroides clarus


Bacteroides dorei


Bacteroides faecis




Bacteroides ovatus











Prevotella copri





Clostridium L2_50

Dorea longicatena


Eubacterium eligens

Eubacterium rectale

Eubacterium siraeum


Roseburia hominis



Ruminococcus bromii



Escherichia coli




Other taxa(154)


<100 100 � 200 200 � 500 >500


P = 7.2e-05 P = 5.4e-04 P = 0.054 P = 0.171

P = 0.565 P = 1.4e-04 P = 0.085 P = 1.6e-05

P = 1.5e-04 P = 4.6e-04 P = 0.007 P = 0.109

MSM

nonM

SM

−0.4

0.0

0.4

0.8





























1e+05

2e+05

3e+05


1e+05

2e+05

3e+05

20000

40000


1e+05

2e+05

3e+05


9e+05

6e+05

3e+05


4e+05

3e+05

2e+05

1e+05



4e+05

3e+05

2e+05

1e+05

4e+05

3e+05

2e+05

1e+05

200000

150000

100000

50000

200000

150000

100000

50000

0 0 0

0 0

0

4e+05

3e+05

2e+05

1e+05

0

3e+05

2e+05

1e+05

0 0

Spearman's rho

KE

GG

cop

ies

KE

GG

cop

ies

KE

GG

cop

ies

G6PDH

6PGD

GR

GPx

AHPC

BCP

TPX

CAT

SOD2

MSRA

0 3 6K07304

K04564

K03781

K11065

K03564

K03386

K00432

K00383

K00033

K00036

0 6

Func

tion

Wilcoxon test statistic




Bacteroides caccae

Bacteroides clarus


Bacteroides dorei


Bacteroides faecis




Bacteroides ovatus











Prevotella copri





Clostridium L2_50

Dorea longicatena


Eubacterium eligens

Eubacterium rectale

Eubacterium siraeum


Roseburia hominis



Ruminococcus bromii



Escherichia coli




Other taxa(154)


<100 100 � 200 200 � 500 >500


P = 7.2e-05 P = 5.4e-04 P = 0.054 P = 0.171

P = 0.565 P = 1.4e-04 P = 0.085 P = 1.6e-05

P = 1.5e-04 P = 4.6e-04 P = 0.007 P = 0.109

ROSMetabolismenzymesenrichedinLGC

ResultsII

Increased butyrate and propionate levels in LGCs

ResultsII

Fecalshortchainfattyacid(SCFA)levels

Increase in butyrate production by acetate consuming butyrogenic bacteria like Roseburia spp. in LGCs

ResultsII

Bacterialvirulencefactorsandantimicrobialresistance

Microbial shifts associated with immune deficiency

implied increases in bacterial virulence factors

and changes in the gut microbial resistome.

Discussion

Hypothesis

• HIV-1-induced immune deficiency is strongly linked to reduced microbial richness and results in significant shifts in the composition and function of the gut microbiome.

• Gut microbiome shifts observed in HIV-1 are not necessarily unique to the infection, but instead, share important characteristics with those seen in other diseases featuring gut inflammation

ConclusionsII

Take-homemessagePartII

HIV-1eradication

Influence of the gut microbiota on HIV-1 eradication

HIV-1eradication

Williams et al. Curr Op HIV 2017

WhyshouldwecareaboutmicrobiotainHIVeradication?

1. Microbiota priming of CD4 T and B cell repertories. Cross reactivity of HIV-1 vaccine responses (HVTN 505)

2. “Good bacteria” help fight cancer in mice. Antibiotic consumption associated with poor response to immunotherapeutic PD-1 blockade. Oral supplementation of Akkermansia muciniphila restores tumor-specific CTL responses.

3. Epigenetic modulation of the reservoir and virus production. Butyrate is and HDAC inhibitor.

Matson et al. Science 2018

HIV-1eradication

Casesofpost-treatmentcontrolandcure

Deeks et al. Iat Med. International aids Society: Global Scientific Strategy Towards an HIV Cure 2016

HIV-1eradication

Casesofpost-treatmentcontrolandcure

Deeks et al. Iat Med. International aids Society: Global Scientific Strategy Towards an HIV Cure 2016

HIVhidesinreservoirsthatarenotsensitivetocurrenttherapies

HIV-1eradication

Whatisvirallatency?

1. Virus is able to persist by integrating its genome into the host cell DNA. It remains hidden from immune responses.

2. Virus is present but not active in a cell.

3. Reservoirs are cells where HIV is able to persist in the latent phase, even while on antiretroviral therapy (ART).

4. Cellular reservoirs are widely dispersed throughout the body.

Avettland-Fènoël et al. Clin Micr Rev 2016

HIV-1eradication

“Kickandkill”andgutmicrobiotaki

ck a

nd k

ill

HIV-1eradication

“Kickandkill”andgutmicrobiotaki

ck a

nd k

ill

HIV-1eradication

“Kickandkill”andgutmicrobiota

HIV-1eradication

“Kickandkill”andgutmicrobiota

1. Asses the influence of the gut microbiota in a “kick-&-kill” based strategy by characterizing:

a. Microbial compositionb. Fecal metabolitesc. Immune responsed. Viral reservoir

2. Assess the effect on the microbial composition through the assay of:

a. Romidepsin and vaccination.b. ART stop and restart.c. Potential probiotics for future interventions

targeting HIV eradication.

HIV-1eradication

The Human Gut Microbiome in HIV infection - UPC

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