Pursuing the holy grail of Precision Medicine based approaches for NASH

Arun J. Sanyal M.B.B.S., M.D.

Professor of Medicine, Physiology and Molecular Pathology

Virginia Commonwealth University School of Medicine

Conflicts of Interest

• Dr. Sanyal is President of Sanyal Biotechnologies

• Stock options for Genfit, Tiziana, Indalo, Durect, Exhalenz, Galmed

• Consultant- Gilead, Intercept*, Allergan*, Lilly, Novo Nordisk, Astra Zeneca-Medimmune*, Novartis, Pfizer, Genentech*, Merck, Bristol Myers*, Boehringer Ingelhiem*, Immuron*, Echosense, GE, OWL*, Birdrock, Tern, Sundise, RedX*, IFMO, Lipocine*, Innovate*, Zydus*, AMRA, Hemoshear,

• Grant support: Bristol Myers, Intercept, Gilead, Allergan, Merck, Echosense, Novartis, Boehringer Ingelhiem

* no financial remuneration in last 24 months

Diet-induced obesity

Arteries(Hypertension,CVD, CAD, PVD)

Heart(HFPEF)

Pancreas(T2DM)

Kidney(CKD)

NAFLD is part of a multi-system disease process

CAD, coronary artery disease; CKD, chronic kidney disease; CVD, cerebrovascular disease; CVS, cardiovascular system; HFPEF, heart failure with preserved ejection fraction; PVD, peripheral vascular disease; T2DM, type 2 diabetes mellitus.

Liver(NAFLD)

Metabolic stressSystemic inflammation and fibrosis

GeneticEpigenetic

Microbiome

LifestyleDiabetes

Age

Alcohol

Ethnicity

NAFLD is a multifactorial disease

Courtesy- Jelena Mann

Personalized management of NAFLD

Assessment of risk

Primary Prevention

Diagnosis and Prognosis

PersonalizedTherapeutic strategy

PersonalizedResponse-guided

therapy

• Family History• GWAS• Microbiome• Behavioral analysis

STRATIFY FOR RISK OFOBESITY, DIABETES,HEART DISEASE, NAFLDAND CIRRHOSIS

Genetics influences the development and progression of NASH

Biochimica et Biophysica Acta 1812 (2011) 1557–1566

Metabo-phenotyping may allow subsegmentation of the population

Individual BCP

𝑓𝑎𝑡

𝑓𝑎𝑡 + 𝑚𝑢𝑠𝑐𝑙𝑒

𝑤𝑒𝑖𝑔ℎ𝑡

𝑚𝑢𝑠𝑐𝑙𝑒

𝐿𝑖𝑣𝑒𝑟 𝑓𝑎𝑡

𝑉𝑖𝑠𝑐𝑒𝑟𝑎𝑙 𝑓𝑎𝑡

𝐴𝑏𝑑𝑜𝑚𝑖𝑛𝑎𝑙 𝑓𝑎𝑡

𝑀𝑢𝑠𝑐𝑙𝑒 𝑓𝑎𝑡

Linge et al. Obesity 2018. Body Composition Profiling in the UK Biobank Imaging Study. https://doi.org/10.1002/oby.22210

The microbiome is everywhere

Role of the Gut Microbiome in Obesity and Diabetes Mellitus

Barlow et al, Nutrition in Clinical Practice, Volume: 30, Issue: 6, Pages: 787-797, First

published: 09 October 2015, DOI: (10.1177/0884533615609896)

How the microbiome impacts NASH development

normal

OTU

NAFLD

• 30 OTU- NAS• 3 OTU- stage 3-4

normal

Transcriptomicprofile

Amino acidmetabolism glycolysis

Carbohydratedigestion

NASH

• 12 pathways altered with NASH

normal

Fecalmetabolome

Spermidinepolyamines

Indolepropionatephenyllactate

Sec Bile Acid SCFA

Oxidant stress

FXR signalingBacterial overgrowth

Gut barrierendotoxin

Endothelial dysfunctionOncogenesisMitochondrial health

Matsumoto et al, Nutrients. 2019 May 27;11(5). , Puri et al Hepatology. 2018 Feb;67(2):534-548, Sanyal et al, AASLD 2019Madeo et al, Science. 2018 Jan 26;359(6374)

Diet-microbial interactions can be used to leverage the gut endocrine functions

TGR5

GLP-1

Release

b

Blood glucose

Insulin

Release

IntestineL Cells

b

Blood glucose

Insulin

Release

α

GLP-1

CDCA > LCA > CA

Kumar et al, BBRC 2012Kumar et al, JBC, 2016

Veillonella improves exercise capacity by conversion of lactate to propionate in marathon runners

Schieman et al, Nat Med. 2019 Jun 24. doi: 10.1038/s41591-019-0485-4

Pathophysiological implications

13

Koob, Volkow, 2017

Binge/Intoxication Dopamine Opioid peptides Serotonin GABA Acetylcholine

Withdrawal/Negative Affect CRF Dynorphin Norepinephrine Hypocretin (Orexin) Substance P Dopamine Serotonin NPY Nociceptin Endocannabinoids Oxytocin

Preoccupation/Anticipation Dopamine Glutamate Hypocretin Serotonin CRF

Are subjects with NASH able to change their lifestyle? Data on their stages of change from URICA

Pre- Contem Action Main0

25

50

75Pre-contemplation

Contemplation

Action

Maintenance

contemplation plation tenance

score

s

The precontemplation scores were higher than those for other stagesOf change indicating that most subjects did not perceive that their Lifestyle (diet, exercise) contributed to their NASH

Stewart et al, Liver International 2016

The critical role of behavioral factors in the metabolic syndrome and NAFLD

GENETICS

ECONOMIC

SOCIAL

CULTURAL

POLITICAL

BEHAVIOR• healthy lifestyle choices• Compliance• Motivation• Ability to change behavior

DISEASEPREDISPOSITIONPHENOTYPEPROGRESSION

Potential primary interventions

• Personalized behavioral intervention:• based on stage of change

• based on social support systems

• based on cultural, social and economic considerations

• Diet:• calorie control for all

• specific interventions to modulate microbiome

• Activity and sleep:• shift workers at particular risk and may need special

approaches to avoid bad eating-activity choices

There remains an unmet need for fully integrated models of disease development

Personalized management of NAFLDAssessment of risk

Primary Prevention

Diagnosis and Prognosis

PersonalizedTherapeutic strategy

PersonalizedResponse-guided

therapy

• Non-invasive diagnostic tools• Selection of liver bx candidates• Identification of underlying mechanisms• Selection of best therapy

Some basic concepts in NASH

Disease Onset

cirrhosis

Disease Activity

Stag

e

Liver-related outcomes

SteatohepatitisDisease activity scores(NAS)

Fibrosis

Is there a more precise,Individualized way to evaluate the drivers of disease and targetthem for treatment and use themto assess treatment response?

Disease Predisposition

Genetics

• PNPLA3:• Conserved from potatoes to

humans- TG lipase• Disconnects NASH from insulin

resistance• Accounts for 12-20% of variability

in phenotype

• TM6SF2:• Impairs lipid transport• Related to more advanced disease

HOW IN THE WORLDDOES THIS LEAD TO NASH?

Disease initiators:• Adiposity• Drugs (amiodarone)• TPN• Jejuno-ileal bypass• Genetic disorders• Systemic inflammation

SNPs do not live in a vaccum: learning how SNPs work together in a stressed environment to drive phenotype

NAFL NASH 0 1 2 3

Chen et al, PLoS One. 2013; 8(7): e65982.

Key pathways associated with NASH• eIF recycling• Terpenoid synthesis• Cholesterol synthesis• Steroid biosynthesis

mRNA splicing pathway

DNA methylation signatures reflect aging in patients with nonalcoholic steatohepatitis

Loomba et al, JCI Insight. 2018 Jan 25;3(2).

The molecular signature is expected to reflect the point of disease reversal and mechanism of disease reversal

Die

t /

Bas

elin

e

8 wks 22 wks 52 wks

Sterol / Fatty AcidMetabolism

PPARα Signaling

Inflammation

ECM / Fibrosis

Proliferation / Tumor

NAFL NASH F0-2 NASH F3 + HCC

Concordance with humans with NAFL/early NASH- Transcriptome (GSEA, PCR)- Western blots- Immunohistochemistry

GSEA concordance withHumans with cirrhosisNASH HCC

Cazanave et al, Scientific Reports, Dec 2017

25

NASH vs Healthy

SLC1A2

TUBA8

TNIP1

DNAH11

SOD1

EIF3CL

CCS

DYNLT1

SLC29A1

AJUBA

RCC2

CREBZF

SMG5

CENPC

ATG4A

CALM2

CENPF

ERCC6LCENPV

RRP1B

WIPI1

FAF1

CEP57

CENPO

OPTN

KLHDC1

RBM7

DIABLO

TUBG2

TUBGCP4

ATP1B3

PEA15

GRIA3

INVS

MAPRE1

TUBA4A

SYNE2

DNAH5

TCTN2DNALI1

TMEM67

SUN2

B9D1

KIF1C

SLC16A4

EIF4EBP2

NPHP1

FERMT2

LASP1

RAN

CBLL1

CSTB

VASPNFATC3

RNF187

PHLPP1

IGSF5

SH2D3A

KCND3

TESK2

TJP2

CTNNA3LIMK1

KRT18

CFL2NECTIN1

PPIF

LPP

SNX27

MAP3K20

CMYA5

EPHB6

MERTK

LNX2

PDLIM5

ACTR10

KRT8

SENP2

KCNS2

KPNA2

IPO5

LRRTM3

IFT172

LAMC3

CFL1

LCP1

ITPR2

IRF1

CTSB

JUP

GNAT1

TFDP2

CDK14

CYCS

HMGA1

EFCAB6

HIST2H2AC

TOP2A

RASSF4

NRF1

PSMB8

SRSF11

CCNYL1

PSMB9

CCNG1

HIST1H1D

RRAGA

CDC27

ARL3

RBL2

RASSF1

ORC2

LHPP

PSMC3CCNF

PSMD14

GEMIN4

H2AFYPSME1

CBS

PSMC4

CES1

ATP5B

HIST1H4H

KLHL22

DAXX

PSMD1

PSMD10

PSME2

CKS2ATP6V0B

H1F0

PSENEN

CD74

LAMP1

GPHN

PACSIN3

OASL

PARD3

SRC

RRAS HLA-DRB1

CHAD

PPIAITPKC

RIPK2

FGFR3

IRF2

MRASPDGFRB

ITGA4

WNT2

IRF7

HLA-BCOL5A2

ITGA7

TRPV4

HLA-DQA1

OAS1

SMOC1

IP6K2

SYNJ1

MX2

IFITM1

IFI35

MAGI3

ITGA6IL32

SMARCE1

PTK2

HIPK3

CAPNS1

CCT8

LBR

MT-ND3

FRY

UBLCP1SEC61A1

PDCD11

EBNA1BP2

AEN

NOP14

FTSJ3

EIF5AL1

NSUN6

PWP2

DIEXF

USP9Y

NOP53

DNMT3B

UBR4

TPT1

RPS24

EIF1AY

RAB11FIP2

MPST

UQCRQ

SSR3

ASCC3

LUC7L2HNRNPA1

PRDX6

SNRNP70

TAT

PEPD

CNOT4

EIF5A

MAT1A

RNPS1

IMP3

HECTD1

SAP30L

MYO1B

MYO5B

PPP1R3E

UNC13C

EIF3E

AMOTL2

UNC119

RPL22

RPS4Y1

EIF4B

EIF5

FBXL4

SNRPD1

KLHL8

DEK

EED

AHCY

MAP1LC3A

PPP2R1A

BUB1

CASP7NDRG2

CAMK1D

CACNB2

TUBGCP3

CAMK2D

CREB3

PPP2CA

DDIT4

STRN3

ZYX

RPS6KA2RPS6KA1

ACTR1A

SIK1

PRKX

CNGA1

CDH15

AKAP9

DUSP5

LRRC7

RICTORMBP

CDH2PRKCE

FOXO3

SGK1SGK2

MAPK13CREB3L3

SPEN

PTPRO

EIF2B4

MAPK6

YWHAZ

RHEB

SGK3

MEF2A

YWHAH

DEPTOR

PTPA

CDK1 CALM1

STK3

PMF1

TBL1Y

UTRN CELF1

CNPY3

GABRE

PI4K2A IMPA1

INPP4A

FMR1

SCYL2

DTNA

CAPG

NMT2

SNAP25

COPZ2

SEC24C CLTC

NECAP2

BLNK

FZD4

SEC23A

RASAL2

MAGI1

FZD5

HLA-DRA

BRD7

TLR7

SH3KBP1

PKD2

CD72

CMTM3

SH3D19

DNAJC6

ARF3

CAPZB

WNT11

CLDN14

CCDC88A

TNFRSF14

TAGLN2

NECTIN3

RGS16

AP2S1

TTC21B

KCTD5

FXR1

WDR19

IFT81

IFT46

KCTD2

SGCB

VPS41

SUCO

GOSR2

DMD VAMP8

HIVEP1

CLDN12

CPEB3

YKT6

MET CYFIP2

COL3A1

IFI30

SH3PXD2A

PIK3R5

TCF7L2

ITPK1

PTCH1

TIAM1

RAC2

ARHGEF4

CD151

CTSD

ITCH

ARPC5

ACTA2

ARRDC3

TMEM173

ARRDC4

TMED9

IGSF8

MAVS

RNF125

INPP5B

ELMO2

ACTR2

ARHGAP31

ABI3

NUDT4

TNIK

RAPGEF6

IP6K3

DSG1

CD82

STAP2

MYL6CXCL9

RGS14CCL28

NEU4

FPR3

C3AR1

ACKR3

RHOBTB2

S100A4

TSPAN3

AGFG1

NEU1

RAP2A

GRK3

AKTIP

WHRN

DFNB59

CDH23

EHD4

MUC3A

ST3GAL1

ST6GALNAC6

SETBP1

SYBU

OGN

LRIG3

SLC17A5

POLR1D

BSN

INHBC

GPR182

INHBE

NOD2

MYO1C

CTSA

ARF6

FCGR2A

NME1

HLA-DPB1

GRTP1 MYH10

UBTF

ERC1

ACVR2B

ST3GAL6

CHD4

FUT2

ZFHX4

LSP1

RBMS1

RAMP1

OR13D1

MYOM1

CXCL10

FBXO8

PROC

PPARA

NFE2L2

NR5A2

LPL

NFKBIZ

GAPDH

LGALS1

SLC25A20

LHX9

XPA

TMTC1

CARHSP1

SCD5

CYP4A11

PDK2

ABCB4

GGT1

SCAND1

PLIN2PKM

PGAM1

ALDH5A1

GCLM

GSS

RNF111

PC

DCAF11

CITED2

NONORAD54L2PYGB

HK3

HPRT1

CSACSL4

PRODH

ENPP1

G6PC3

GRHPR

ACLY

ADSSL1

CPS1

RTN3

SLC28A2

AGL

RTN1

ASS1

ASPASCN9A XYLB

ENO3

DPP3

AHR

ME1

LGALS3BP

PCK1

AKR1B10

GCK

CNDP2

SLC23A2

NADKSLC23A1 FTCD

UCKL1

GCLC

GATM

GYS2

ALDH18A1

CPSF6

AMY2B

HOGA1

DAO

PRDM10

FN1

JUN

SOCS2

IL2RB

PRKCD

CALR

TRPM8

PRKCA

SNAI2

ADRB3

IGFALS

PTPN3

AGPAT2

FGB

PAPPA2

HTR7

FGG

LEPR

CBLC

NCF2

GNA12

THBS2

EPHA1

GPC4

ERBB2

IRS2

PLA2G6

MPLKIPFAT1

MTOR

SERPINF2

KLF15

MED13MED27

RORA

CEBPD

MED13L FABP4MED29

MACROD1

MTHFD1

CBFA2T3

POU2F1

ZFP1

PLIN1

DCAF6

GNMT

APH1B

PER3

ATP6V0D1

SKIL

RBBP6

FBXL3

FBXL7

FBXO2

TAP1

ZC3H6

UBIAD1

FBXO4

CCNI

PAWR

AR

FOXO1

ATF3

INSR

GCAT

SARDH

AGXT

ATP6V1A

FOLH1

ATP6V1E1

CDK6

PPARGC1A

NCOR1

CDKN1A

SNAPC4

HIST1H1B

GADD45A

TFPI

MPZL1

LARP1B

RALGAPA1

SIRPA

PABPC1L

LSM1

LSM5

KLHL13

YY2

HNRNPL

CEBPA

SHBG

SERPINC1

SLC16A7

ALOX5

F5

NFKB1

SIRT1

LTB4R2

CCR5

LPAR6PTGFR

TXLNG

RFX5

MMP15

PLD2

IFNGR2

GRK2

SORBS1

COL1A2

LYZ REN

MMP9

RAC3

KALRN

LEF1

DEPDC7

GNB1 CIITA

RHOG

RHOQ

MYH11

ARHGAP5GMIP

GHR

THY1

TMSB4X

HRG SERPINE1

GCGR

GNAS

MAPKAPK2

F2RL1

SMAD7

ITGAM

SDC1

IGFBP1DUSP1

IGF1

NANOG

PRKAG2CTGF

PLAUIGF2

ALB

MMRN1

EPAS1

ITIH4DGKD

IGFBP2

ANGPTL3

HSPA6

FADS2PTGS1

ABCB11

NAMPT

UBE2A

SLCO1A2

ANXA5

COTL1

UBE3A

ABCG2

DNAJC25

ABCC2

MRPS26

DNAJC19

DLEU1

TIMM17A

ANGPTL8

HSPA4LHSPA1A

ABCA8

DNAJC12

NAPEPLDDPYSL2

CHDH

BCAP31

MSN

FUT4

ITGAE

TFE3

ERVW-1

ACVR1C

MUL1

CASP1

UBE2N

PELI2

PELI1

IKZF1

MFN2

TSC22D1

NIPA1

DEFB1

PRKN

NFYB

SERPINF1

ICAM3

KYNU

AADAT

CSAD ELOVL5

HAAO

DLAT

ITLN1

POR

EEF1A2

HNMT

GALNT2

DLD

AOX1

HSD17B2

GSTM1

ACMSD

NFE2L3

ACAT1

ABCC5

GLYCTKAACS

BPGM

PDE11A

ALDH3A1

MCEE

ALDH6A1

DHRS9

AKR1A1

PCCA

ACADSB

MCC

MCCC1

GCDH

GPT2

AMDHD1

HAL

ALPL

AMMECR1

KDM8

GPT

ACSM5

ABAT

PPT1

SLC38A7

FADS1

CYP2C19

AKR1B1 MAFK

CYP1A1MAOA

MUT

GALM

Immune function

IGF regulation &

platelet activation

Phospholipids in phagocytosis, TP53

regulates metabolic genes, FGFR

signaling

FA, TAG metabolism

Intermediate,

glucose metabolism

Hoang et al, MS under review 2019

Critical networks that drive histological activity (NAFLD ACTIVITY SCORES)

PPI= protein-protein interactome

Hubs= central components of communitiesHoang et al, 2019, MS under review

Top networks related to severity of fibrosis

GPCRs

Hoang et al, 2019, MS under review

Top pathways linked to severity of histological activity and fibrosis stage

NAFLD activity score Fibrosis stage

1 Intrinsic pathway for Apoptosis Ephrin-signaling

2 Fc epsilon receptor signaling CDK Chk1/2

3 Regulation of PTEN TP53

4 Transcription of TP53 RHO GTPases

5 Vesicle-mediated transport EphB-forward signaling

6 TNFR2-noncanonical NFkB G2 cell cycle

7 T cell receptor signaling Fc gamma receptor signaling

8 Membrane trafficking Signaling by MET

9 Programmed cell death Chondroitin metabolism

10 MHC class II antigen processing TP53 mediated transcription

Based on Gene Set Variance Analysis (GSVA)

Development of a gene-NAS (gNAS) score that is associated with histological NAS

Hoang et al, AASLD 2018

Development of a gene-Fibrosis (g-fib) score

Hoang et al, AASLD 2018

Molecular subtypes with distinct reactomeclusters not distinguishable by histological NAS

Cluster 1: immune regulation, cluster 2: cell proliferation, cluster 3: ECM

Sample clusters with distinct reactome pathway clusters linked to fibrosis stage

Subtypes of NASH based on methionine metabolome

Mayo et al, Hepatol Commun. 2018 Jul; 2(7): 807–820.

End Treatment Response Biomarkers for vitamin E (results from PIVENS)

Metabolites* Vit.E responder vsVit.E non-responder

Fold change P-value†

Gamma-

glutamylleucine

0.82 0.02

Gamma-

glutamylvaline

0.80 0.03

Sphingosine 0.64 0.02

* ANCOVA model adjusting for baseline level of metabolites † Tukey’s post test for pairwise comparisons

Cheng et al, PlosONE September, 2012

Monocyte-derived hepatocyte like cells provide personalized assessment of drug-induced toxicity risk

Benesic et al, Clinical Gastroenterology and Hepatology 2018;16:1488–1494

Personalized management of NAFLDAssessment of risk

Primary Prevention

Diagnosis and Prognosis

PersonalizedTherapeutic strategy

PersonalizedResponse-guided

therapy

• Noninvasive assessment of outcomes• Selection of second line Rx• Optimization of outcomes by

combining behavioral, dietary and drug Rx

So where do we go from here?

New data bring new questions

• Do static analyses predict dynamic changes in disease state?

• How to leverage this to understand spontaneous waxing and waning of disease?

• Will molecular subtype targeted therapy enhance efficacy rates?

• Can we make a NASH-chip so that we can identify which pathway is relevant for which patient?

3-D in vitro culture systems with flow may be a tool in Precision Medicine

Hemodynamic Flow

Interstitial Flow

Transport

Inflow

HSTx NASH

System

Human Biopsies (Puri et al. (2007))

NAFL NASH

Log2 FC p-value Log2 FC

p-

value Log2 FC p-value

TOTAL 1.1 <0.01 1.6 <0.05 1.5 <0.05

SFA 1.1 <0.01 1.7 <0.05 1.4 <0.05

MUFA 2.0 <0.01 2.5 n.s. 2.1 <0.05

PUFA -0.1 0.70 0.5 <0.05 0.9 <0.05

n-6/n-3 ratio 0.5 <0.01 0.7 <0.05 0.5 <0.05

FFA 0.5 0.06 0.1 n.s. 0.1 n.s.

DAG 2.1 <0.01 1.4 <0.05 0.8 <0.05

TAG 2.4 <0.01 3.2 <0.05 2.9 <0.05

CE 0.6 0.01 0.2 n.s. 0.0 n.s.

PC -0.3 0.13 -0.4 <0.05 -0.3 n.s.

PE -0.4 0.08 -0.3 <0.05 0.0 n.s.

SM 0.2 0.37 -0.2 n.s. 0.4 n.s.

Dash et al, American Journal of Physiology, 2013

Hemoshear (HSTX) system

FFA + insulin + TNF

Feaver et al, JCI Insight. 2016 Dec 8;1(20):e90954

40

Exploring Combination Therapies

As an example, introducing multiple drugs in search of synergies opens the door

for potential combination therapies.

Pathway Response

-0,75

-0,50

-0,25

0,00

0,25

0,50

0,75

1,00

1,25

1,50

1,75

2,00

FXR Signaling

Cholesterol Biosynthesis

Gluconeogenesis

TGFB Signaling

Collagen Formation

Inflammation

NASH

HEALTHY

OCA

LY2109761

Hypothetical patient stratification

Impaired satiety mechanisms

Impaired thermogenesis

Periph adipogenesis/lipolysis

Adipose inflammation

Augmented DNL

Impaired TG formation

Inappropriate TG lipolysis

Active lipotoxic lipid synthesis

Liver inflammatory pathways

Impaired wound response

Augmented fibrogenesis

Pathway category: Pt A Pt B Pt CLikelihood of

contributing to NASH

phenotype

Low

High

PNPLA3?→

“lean NASH”

Courtesy- Brent Tetri

Stratification → targeted treatment“Personalized medicine”

Impaired satiety mechanisms

Impaired thermogenesis

Periph adipogenesis/lipolysis

Adipose inflammation

Augmented DNL

Impaired TG formation

Inappropriate TG lipolysis

Active lipotoxic lipid synthesis

Liver inflammatory pathways

Impaired wound response

Augmented fibrogenesis

Pathway category: Pt A Pt B Pt C

PNPLA3?→

“lean NASH”

ACC1, SCD inhib.

JNK inhib?

PPARγ ligands

PGC1α activators

Central modulation

?

CYP4 inhibitors

iPLA2 inhibitors

Hedgehog inhib

Galectin inhib?

DGAT2 activators

Courtesy- Brent Tetri

Huang Dee: Nai-Ching (2600 BC, First Medical Text)

Superior doctors prevent the diseaseMediocre doctors treat the disease before evident

Inferior doctors treat the full-blown disease

Everest View, Kala Patthar, Nepal